fix(python-client): async=true was silently ignored on retain calls

The hand-written client wrapper passed `async_=retain_async` to
RetainRequest, but the generated Pydantic model uses `var_async` as the
Python field name (with `alias="async"`). The `async_` kwarg didn't
match either the field name or the alias, so Pydantic silently ignored
it — every retain call ran synchronously regardless of the flag.

This has been broken since the client was first introduced (6073ac4f),
not a regression.

Also adds unit tests that verify the async field serializes correctly
in the request JSON, preventing future regressions.

.claude/skills/code-review/SKILL.md

@@ -1,196 +0,0 @@
----
-name: code-review
-description: Review changed code against project standards. Checks for missing tests, dead code, type safety, lint issues, and coding conventions. Run after completing any implementation work.
-user_invocable: true
----
-
-# Code Review
-
-Review all changed code against the project's quality standards and coding conventions.
-
-## Code Standards
-
-Read and internalize these standards before writing code. The review steps below verify compliance.
-
-### Python Style
-- Python 3.11+, type hints required
-- Async throughout (asyncpg, async FastAPI)
-- Pydantic models for request/response
-- Ruff for linting (line-length 120)
-- No Python files at project root - maintain clean directory structure
-- **Never use multi-item tuple return values** — not even for internal/private functions. Always use a dataclass or Pydantic model. No exceptions, no "it's just two values" shortcuts. If a function returns more than one value, define a named type for it.
-
-### Type Safety with Pydantic Models
-**NEVER use raw `dict` types for structured data** — this applies to all code, including internal helpers and private functions. If the dict has known keys, it must be a dataclass or Pydantic model:
-- Use Pydantic `BaseModel` for all data structures passed between functions
-- Use `@dataclass` for lightweight internal data containers when Pydantic validation isn't needed
-- Add `@field_validator` for type coercion (e.g., ensuring datetimes are timezone-aware)
-- Avoid `dict.get()` patterns - use typed model attributes instead
-- Parse external data (JSON, API responses) into Pydantic models at the boundary
-- This catches type errors at parse time, not deep in business logic
-- The only acceptable `dict` usage is for truly dynamic/unknown keys (e.g., arbitrary metadata, JSON blobs with no fixed schema)
-
-```python
-# BAD - error-prone dict access
-def process(data: dict) -> str:
-    return data.get("name", "")  # No validation, silent failures
-
-# GOOD - typed and validated
-class UserData(BaseModel):
-    name: str
-    created_at: datetime
-
-    @field_validator("created_at", mode="before")
-    @classmethod
-    def ensure_tz_aware(cls, v):
-        if isinstance(v, str):
-            v = datetime.fromisoformat(v.replace("Z", "+00:00"))
-        if v.tzinfo is None:
-            return v.replace(tzinfo=timezone.utc)
-        return v
-
-def process(data: UserData) -> str:
-    return data.name  # Type-safe, validated at construction
-```
-
-### TypeScript Style
-- Next.js App Router for control plane
-- Tailwind CSS with shadcn/ui components
-
-### Code Comments
-- **Always comment non-trivial technical decisions** with the reasoning behind the choice. If someone would ask "why is it done this way?", there should be a comment.
-- **Keep comments up to date with history** — when changing an approach, update the comment to explain what was tried before and why it was changed. Comments serve as a tracker of previous implementations that likely had problems.
-- Don't comment obvious code — only where the "why" isn't self-evident from the code itself.
-
-```python
-# BAD - no context for future readers
-results = await asyncio.gather(*tasks, return_exceptions=True)
-
-# GOOD - explains the non-obvious choice
-# Use return_exceptions=True to avoid cancelling sibling tasks on failure.
-# Previously we used TaskGroup but it cancelled all tasks when one failed,
-# causing partial writes that left orphaned entity links (see #412).
-results = await asyncio.gather(*tasks, return_exceptions=True)
-```
-
-### Branch Hygiene
-- **Always start new feature branches from `origin/main`** — rebase to ensure a clean base.
-- **Only include commits relevant to the PR/branch/feature** — no unrelated changes. If the branch contains commits that don't belong, they must be removed before merging.
-
-### General Principles
-- Don't add features, refactor code, or make "improvements" beyond what was asked
-- Don't add unnecessary error handling for impossible scenarios
-- Don't create helpers or abstractions for one-time operations
-- No backwards-compatibility hacks (unused vars, re-exports, "removed" comments)
-- Three similar lines of code is better than a premature abstraction
-
-## Review Steps
-
-### 1. Check branch hygiene
-
-- Run `git log --oneline main..HEAD` to list all commits on the branch.
-- Verify every commit is relevant to the feature/PR. Flag any unrelated commits.
-- Check the branch is based on a recent `origin/main` (no stale base).
-
-### 2. Identify changed files
-
-Run `git diff --name-only HEAD` (unstaged) and `git diff --cached --name-only` (staged) to get all changed files. If there are no local changes, diff against the base branch using `git diff main...HEAD --name-only` and `git diff main...HEAD` to review all commits on the current branch.
-
-### 3. Run linters
-
-```bash
-./scripts/hooks/lint.sh
-```
-
-Report any failures. Do NOT fix them yourself — just report.
-
-### 4. Check for dead code
-
-For each changed Python file, check for:
-- Unused imports (Ruff should catch these, but verify)
-- Functions/methods/classes that were added but are never called from anywhere
-- Variables assigned but never read
-- Commented-out code blocks that should be removed
-
-For each changed TypeScript file, check for:
-- Unused imports
-- Unused variables or functions
-- Commented-out code
-
-### 5. Check type safety (Python)
-
-For each changed Python file, check for violations:
-- **No raw `dict` for structured data** — must use Pydantic model or dataclass, even for internal/private functions (only exception: truly dynamic/unknown keys)
-- **No multi-item tuple returns** — must use dataclass or Pydantic model, even for internal/private functions (no exceptions)
-- **Missing type hints** on function parameters and return types
-- **Missing `@field_validator`** for datetime fields that should be timezone-aware
-
-### 6. Check for missing tests
-
-For each new or significantly changed function/endpoint/class:
-- Check if there is a corresponding test addition or update
-- New API endpoints MUST have integration tests
-- New utility functions MUST have unit tests
-- Bug fixes SHOULD have a regression test
-
-Flag any new logic that lacks test coverage.
-
-### 7. Check API consistency
-
-If any files in `hindsight-api-slim/hindsight_api/api/` were changed:
-- Were the OpenAPI specs regenerated? (`./scripts/generate-openapi.sh`)
-- Were the client SDKs regenerated? (`./scripts/generate-clients.sh`)
-- Were the control plane proxy routes updated? (`hindsight-control-plane/src/app/api/`)
-
-### 8. Check code comments
-
-For each non-trivial change:
-- **New non-obvious logic** — is there a comment explaining the reasoning?
-- **Changed approach** — does the comment include what was done before and why it changed?
-- **Stale comments** — do existing comments near the changed code still accurately describe the behavior?
-
-### 9. Check integration completeness
-
-If any files in `hindsight-integrations/` were added or changed, verify:
-- **Tests exist** — the integration must have tests that simulate/exercise the external framework (not just pure unit tests of helpers). Check for a `tests/` directory with meaningful test files.
-- **CI job exists** — check `.github/workflows/test.yml` for a corresponding `test-<name>-integration` job. If missing, flag it.
-- **Release process** — check that the integration name is in the `VALID_INTEGRATIONS` array in `scripts/release-integration.sh`. If missing, flag it.
-- **Code standards** — the integration code must follow all Python style rules (type hints, no raw dicts, no tuple returns, etc.).
-
-### 10. Review against other coding standards
-
-Check the diff for violations of the standards listed above:
-- Python files at project root (not allowed)
-- Missing async patterns (should be async throughout)
-- Pydantic models for request/response
-- Line length > 120 chars
-- New features/code beyond what was asked (over-engineering)
-- Unnecessary error handling for impossible scenarios
-- Premature abstractions or speculative helpers
-- Backwards-compatibility hacks (unused vars, re-exports, "removed" comments)
-
-### 11. Report findings
-
-Present a clear summary organized by severity:
-
-**Must fix** — issues that will break CI or violate hard project rules:
-- Unrelated commits on the branch
-- Lint failures
-- Missing type hints on public functions
-- Raw dict usage for structured data (including internal code)
-- Multi-item tuple returns (including internal code)
-- Missing tests for new endpoints
-- New integration missing tests, CI job, or release-integration.sh entry
-
-**Should fix** — issues that hurt code quality:
-- Dead code / unused imports missed by linter
-- Missing tests for non-trivial utility functions
-- Over-engineering beyond the task scope
-
-**Note** — observations that may or may not need action:
-- API changes that might need client regeneration
-- Patterns that deviate from nearby code style
-
-For each finding, include the file path, line number, and a brief explanation.
-
-Do NOT auto-fix any issues. Report all findings and let the user decide what to address. If there are no findings, confirm the code looks good.

.env.example

@@ -2,7 +2,7 @@
 # Copy this file to .env and fill in your values
 
 # LLM Configuration (Required)
-# Supported providers: openai, groq, ollama, gemini, anthropic, lmstudio, vertexai, minimax, volcano
+# Supported providers: openai, groq, ollama, gemini, anthropic, lmstudio, vertexai, minimax
 HINDSIGHT_API_LLM_PROVIDER=openai
 HINDSIGHT_API_LLM_API_KEY=your-api-key-here
 HINDSIGHT_API_LLM_MODEL=gpt-4o-mini
@@ -44,7 +44,6 @@ HINDSIGHT_API_LOG_LEVEL=info
 
 # Database (Optional - uses embedded pg0 by default)
 # HINDSIGHT_API_DATABASE_URL=postgresql://user:pass@host:5432/db
-# HINDSIGHT_API_MIGRATION_DATABASE_URL=  # Direct PostgreSQL URL for migrations (bypasses PgBouncer). Falls back to DATABASE_URL.
 # HINDSIGHT_API_DATABASE_SCHEMA=public  # PostgreSQL schema name (default: public)
 
 # Vector Extension (Optional - uses pgvector by default)

.github/workflows/deploy-docs.yml

@@ -44,5 +44,5 @@ jobs:
     runs-on: ubuntu-latest
     needs: build
     steps:
-      - uses: actions/deploy-pages@v5
+      - uses: actions/deploy-pages@v4
         id: deployment

.github/workflows/release.yml

@@ -382,7 +382,7 @@ jobs:
     - uses: actions/checkout@v6
 
     - name: Install Helm
-      uses: azure/setup-helm@v5
+      uses: azure/setup-helm@v4
       with:
         version: 'latest'
 

.gitignore

@@ -50,8 +50,7 @@ hindsight-dev/benchmarks/perf/results/
 benchmarks/results/
 hindsight-cli/target
 hindsight-clients/rust/target
-.claude/*
-!.claude/skills/
+.claude
 whats-next.md
 TASK.md
 # Changelog is now tracked in hindsight-docs/src/pages/changelog.md

CLAUDE.md

@@ -11,15 +11,9 @@ Hindsight is an agent memory system that provides long-term memory for AI agents
 
 ## Development Commands
 
-### Local Development (API + UI)
-```bash
-# Start both API server and control plane UI
-./scripts/dev/start.sh
-```
-
 ### API Server (Python/FastAPI)
 ```bash
-# Start API server only (loads .env automatically)
+# Start API server (loads .env automatically)
 ./scripts/dev/start-api.sh
 
 # Run all tests (parallelized with pytest-xdist)
@@ -79,16 +73,17 @@ cd hindsight-control-plane && npm run dev
 
 ### Monorepo Structure
 - **hindsight-api-slim/**: Core FastAPI server with memory engine (Python, uv)
+- **hindsight/**: Embedded Python bundle (hindsight-all package)
 - **hindsight-control-plane/**: Admin UI (Next.js, npm)
 - **hindsight-cli/**: CLI tool (Rust, cargo, uses progenitor for API client)
 - **hindsight-clients/**: Generated SDK clients (Python, TypeScript, Rust)
 - **hindsight-docs/**: Docusaurus documentation site
-- **hindsight-integrations/**: Framework integrations (LiteLLM, CrewAI, LangGraph, Pydantic AI, AG2, Claude Code, etc.)
+- **hindsight-integrations/**: Framework integrations (LiteLLM, OpenAI)
 - **hindsight-dev/**: Development tools and benchmarks
 
 ### Core Engine (hindsight-api-slim/hindsight_api/engine/)
-- `memory_engine.py`: Main orchestrator for retain/recall/reflect operations
-- `llm_wrapper.py`: LLM abstraction supporting OpenAI, Anthropic, Gemini, VertexAI, Groq, MiniMax, Ollama, LM Studio, LiteLLM, Claude Code
+- `memory_engine.py`: Main orchestrator (~170KB) for retain/recall/reflect operations
+- `llm_wrapper.py`: LLM abstraction supporting OpenAI, Anthropic, Gemini, Groq, MiniMax, Ollama, LM Studio
 - `embeddings.py`: Embedding generation (local sentence-transformers or TEI)
 - `cross_encoder.py`: Reranking (local or TEI)
 - `entity_resolver.py`: Entity extraction and normalization
@@ -101,13 +96,13 @@ cd hindsight-control-plane && npm run dev
 
 **search/**: Multi-strategy retrieval
 - `retrieval.py`: Main retrieval orchestrator
-- `graph_retrieval.py`: Graph retrieval abstract base class
-- `link_expansion_retrieval.py`: Link expansion graph retrieval
+- `graph_retrieval.py`: Entity/relationship graph traversal
+- `mpfp_retrieval.py`: Multi-Path Fact Propagation retrieval
 - `fusion.py`: Reciprocal rank fusion for combining results
 - `reranking.py`: Cross-encoder reranking
 
 ### API Layer (hindsight-api-slim/hindsight_api/api/)
-- `http.py`: FastAPI HTTP routers for all REST endpoints
+- `http.py`: FastAPI HTTP routers (~80KB) for all REST endpoints
 - `mcp.py`: Model Context Protocol server implementation
 
 Main operations:
@@ -169,17 +164,11 @@ Key tables: `banks`, `memory_units`, `documents`, `entities`, `entity_links`
 ## Key Conventions
 
 ### Code Quality
-
-**Before writing code, read `.claude/skills/code-review/SKILL.md`** for the full coding standards (Python style, type safety, TypeScript style, general principles).
-
 **Always run the lint script after making Python or TypeScript/Node changes:**
 ```bash
 ./scripts/hooks/lint.sh
 ```
-
-**After completing any implementation work, run `/code-review`** to verify your changes against project standards (missing tests, dead code, type safety, etc.). Fix any "must fix" issues before considering the task done.
-
-**MANDATORY: Run `/code-review` before pushing code or creating a pull request.** Do not push or create a PR until all "must fix" issues are resolved.
+This runs the same checks as the pre-commit hook (Ruff for Python, ESLint/Prettier for TypeScript).
 
 ### Memory Banks
 - Each bank is an isolated memory store (like a "brain" for one user/agent)
@@ -211,16 +200,48 @@ When adding or modifying parameters in the dataplane API (hindsight-api), you mu
    - Update the client type definition in `lib/api.ts`
    - Update any UI components that need to use the new parameter
 
-### Adding New Integrations
+### Python Style
+- Python 3.11+, type hints required
+- Async throughout (asyncpg, async FastAPI)
+- Pydantic models for request/response
+- Ruff for linting (line-length 120)
+- No Python files at project root - maintain clean directory structure
+- **Never use multi-item tuple return values** - prefer dataclass or Pydantic model for structured returns
 
-Every new integration in `hindsight-integrations/` must satisfy all of the following before it can be merged:
+### Type Safety with Pydantic Models
+**NEVER use raw `dict` types for structured data.** Always use Pydantic models:
+- Use Pydantic `BaseModel` for all data structures passed between functions
+- Add `@field_validator` for type coercion (e.g., ensuring datetimes are timezone-aware)
+- Avoid `dict.get()` patterns - use typed model attributes instead
+- Parse external data (JSON, API responses) into Pydantic models at the boundary
+- This catches type errors at parse time, not deep in business logic
 
-1. **Tests are required** — tests must simulate or exercise the external system (mock the framework's interfaces and verify the integration actually calls Hindsight correctly). Pure unit tests of helper functions are not sufficient.
-2. **CI job** — add a test job in `.github/workflows/test.yml` following the existing pattern (e.g., `test-crewai-integration`). The job must build, install deps, and run `uv run pytest tests -v`. Also add the integration to `detect-changes` outputs so it only runs when its files change.
-3. **Release process** — add the integration name to the `VALID_INTEGRATIONS` array in `scripts/release-integration.sh` so it can be released via the standard release workflow.
-4. **Follow project code standards** — Python style, type safety, no raw dicts for structured data, no multi-item tuple returns (see `.claude/skills/code-review/SKILL.md`).
+```python
+# BAD - error-prone dict access
+def process(data: dict) -> str:
+    return data.get("name", "")  # No validation, silent failures
 
-If any of these are missing, the integration is incomplete and must not be pushed or merged.
+# GOOD - typed and validated
+class UserData(BaseModel):
+    name: str
+    created_at: datetime
+
+    @field_validator("created_at", mode="before")
+    @classmethod
+    def ensure_tz_aware(cls, v):
+        if isinstance(v, str):
+            v = datetime.fromisoformat(v.replace("Z", "+00:00"))
+        if v.tzinfo is None:
+            return v.replace(tzinfo=timezone.utc)
+        return v
+
+def process(data: UserData) -> str:
+    return data.name  # Type-safe, validated at construction
+```
+
+### TypeScript Style
+- Next.js App Router for control plane
+- Tailwind CSS with shadcn/ui components
 
 ### Adding New API Configuration Flags
 
@@ -234,17 +255,17 @@ Fields must be categorized as either **hierarchical** (can be overridden per-ten
    - Add `ENV_*` constant for the environment variable name (e.g., `ENV_MY_SETTING = "HINDSIGHT_API_MY_SETTING"`)
    - Add `DEFAULT_*` constant for the default value
    - Add field to `HindsightConfig` dataclass with type annotation
-   - **Mark as configurable** by adding to `_CONFIGURABLE_FIELDS` set if the field should be overridable per-tenant/bank via API
+   - **Mark as hierarchical or static** by adding to `_HIERARCHICAL_FIELDS` set (hierarchical) or leaving it out (static)
    - Add initialization in `from_env()` method
 
    ```python
-   # Configurable field (can be overridden per-tenant/bank via API)
-   _CONFIGURABLE_FIELDS = {
+   # Hierarchical field (can be overridden per-bank)
+   _HIERARCHICAL_FIELDS = {
        ...,
-       "my_setting",  # Add here for configurable
+       "my_setting",  # Add here for hierarchical
    }
 
-   # Static field - just don't add to _CONFIGURABLE_FIELDS
+   # Static field - just don't add to _HIERARCHICAL_FIELDS
    ```
 
 2. **main.py** (`hindsight-api-slim/hindsight_api/main.py`):

docker/standalone/start-all.sh

@@ -1,28 +1,6 @@
 #!/bin/bash
 set -e
 
-# =============================================================================
-# Embedded pg0 data integrity check (#675)
-#
-# When using embedded pg0, check if the data directory has existing PostgreSQL
-# data before starting. If the directory exists but appears empty/corrupt
-# (e.g., missing PG_VERSION file), log a warning. This helps diagnose data
-# loss scenarios where a container restart caused the data directory to be
-# wiped despite a volume mount being present.
-# =============================================================================
-PG0_DATA_DIR="${HOME}/.pg0"
-if [ -d "$PG0_DATA_DIR" ]; then
-    # Look for actual PostgreSQL data directories (pg0 creates subdirs per instance)
-    if compgen -G "$PG0_DATA_DIR"/*/PG_VERSION > /dev/null 2>&1; then
-        echo "✅ Existing pg0 data directory detected at $PG0_DATA_DIR"
-    elif [ "$(ls -A "$PG0_DATA_DIR" 2>/dev/null)" ]; then
-        echo "⚠️  WARNING: pg0 data directory exists at $PG0_DATA_DIR but no PG_VERSION found."
-        echo "   This may indicate data corruption or an incomplete previous shutdown."
-        echo "   If you see all migrations running from scratch after this, your data may have been lost."
-        echo "   See: https://github.com/vectorize-io/hindsight/issues/675"
-    fi
-fi
-
 # Service flags (default to true if not set)
 ENABLE_API="${HINDSIGHT_ENABLE_API:-true}"
 ENABLE_CP="${HINDSIGHT_ENABLE_CP:-true}"
@@ -93,63 +71,6 @@ if [ "${HINDSIGHT_WAIT_FOR_DEPS:-false}" = "true" ]; then
     done
 fi
 
-# =============================================================================
-# Graceful shutdown handler (#675)
-#
-# Docker sends SIGTERM on `docker stop`/`docker restart`. Without a trap, child
-# processes (hindsight-api + pg0, control-plane) are killed abruptly. For the
-# embedded pg0 database this can cause data loss when the data directory is on
-# a Docker volume that gets remounted after restart.
-#
-# The trap forwards SIGTERM to all tracked child PIDs so that:
-#   - hindsight-api receives the signal and can run its shutdown hooks
-#   - pg0 gets a clean PostgreSQL shutdown (checkpoint + WAL flush)
-#   - The control-plane Node.js process exits cleanly
-# =============================================================================
-# Guard against concurrent cleanup (e.g., child crash + SIGTERM arriving together)
-SHUTTING_DOWN=false
-
-cleanup() {
-    if $SHUTTING_DOWN; then return; fi
-    SHUTTING_DOWN=true
-
-    echo ""
-    echo "🛑 Received shutdown signal, stopping services gracefully..."
-    for pid in "${PIDS[@]}"; do
-        if kill -0 "$pid" 2>/dev/null; then
-            kill -TERM "$pid" 2>/dev/null
-        fi
-    done
-    # Give processes time to shut down cleanly (pg0 needs to flush WAL).
-    # NOTE: Docker's default stop_grace_period is 10s. If you use the default,
-    # either set stop_grace_period: 30s in your compose file / docker stop -t 30,
-    # or Docker will SIGKILL the container before this timeout expires.
-    local timeout=30
-    for ((i=1; i<=timeout; i++)); do
-        local all_stopped=true
-        for pid in "${PIDS[@]}"; do
-            if kill -0 "$pid" 2>/dev/null; then
-                all_stopped=false
-                break
-            fi
-        done
-        if $all_stopped; then
-            echo "✅ All services stopped cleanly"
-            exit 0
-        fi
-        sleep 1
-    done
-    # Force kill if still running after timeout
-    echo "⚠️  Timeout reached, forcing shutdown..."
-    for pid in "${PIDS[@]}"; do
-        if kill -0 "$pid" 2>/dev/null; then
-            kill -9 "$pid" 2>/dev/null
-        fi
-    done
-    exit 1
-}
-trap cleanup SIGTERM SIGINT
-
 # Track PIDs for wait
 PIDS=()
 
@@ -217,21 +138,8 @@ if [ ${#PIDS[@]} -eq 0 ]; then
     exit 1
 fi
 
-# Wait for any process to exit (use wait -n with trap-safe loop)
-while true; do
-    # wait -n returns when any child exits; it also returns on signal delivery
-    # (the trap handler will run and exit, so this loop is just for robustness).
-    # `&& true` prevents `set -e` from killing the script when wait -n returns
-    # non-zero (child exited with error or no backgrounded children remain).
-    wait -n && true
-    # Check if any tracked PID has exited
-    for pid in "${PIDS[@]}"; do
-        if ! kill -0 "$pid" 2>/dev/null; then
-            wait "$pid" 2>/dev/null
-            exit_code=$?
-            echo "⚠️  Service (PID $pid) exited with code $exit_code"
-            # Trigger cleanup for remaining services
-            cleanup
-        fi
-    done
-done
+# Wait for any process to exit
+wait -n
+
+# Exit with status of first exited process
+exit $?

helm/hindsight/Chart.yaml

@@ -2,8 +2,8 @@ apiVersion: v2
 name: hindsight
 description: Hindsight helm chart
 type: application
-version: 0.4.22
-appVersion: "0.4.22"
+version: 0.4.20
+appVersion: "0.4.20"
 keywords:
   - ai
   - memory

helm/hindsight/templates/api-deployment.yaml

@@ -95,27 +95,6 @@ spec:
           {{- toYaml .Values.api.readinessProbe | nindent 10 }}
         resources:
           {{- toYaml .Values.api.resources | nindent 10 }}
-        {{- if or .Values.api.persistence.modelCache.enabled .Values.api.extraVolumeMounts }}
-        volumeMounts:
-        {{- if .Values.api.persistence.modelCache.enabled }}
-        - name: model-cache
-          mountPath: /home/hindsight/.cache
-        {{- end }}
-        {{- with .Values.api.extraVolumeMounts }}
-        {{- toYaml . | nindent 8 }}
-        {{- end }}
-        {{- end }}
-      {{- if or .Values.api.persistence.modelCache.enabled .Values.api.extraVolumes }}
-      volumes:
-      {{- if .Values.api.persistence.modelCache.enabled }}
-      - name: model-cache
-        persistentVolumeClaim:
-          claimName: {{ include "hindsight.fullname" . }}-api-model-cache
-      {{- end }}
-      {{- with .Values.api.extraVolumes }}
-      {{- toYaml . | nindent 6 }}
-      {{- end }}
-      {{- end }}
       {{- with .Values.nodeSelector }}
       nodeSelector:
         {{- toYaml . | nindent 8 }}

helm/hindsight/templates/api-model-cache-pvc.yaml

@@ -1,21 +0,0 @@
-{{- if and .Values.api.enabled .Values.api.persistence.modelCache.enabled }}
-apiVersion: v1
-kind: PersistentVolumeClaim
-metadata:
-  name: {{ include "hindsight.fullname" . }}-api-model-cache
-  labels:
-    {{- include "hindsight.api.labels" . | nindent 4 }}
-  {{- with .Values.api.persistence.modelCache.annotations }}
-  annotations:
-    {{- toYaml . | nindent 4 }}
-  {{- end }}
-spec:
-  accessModes:
-    {{- toYaml .Values.api.persistence.modelCache.accessModes | nindent 4 }}
-  {{- if .Values.api.persistence.modelCache.storageClass }}
-  storageClassName: {{ .Values.api.persistence.modelCache.storageClass }}
-  {{- end }}
-  resources:
-    requests:
-      storage: {{ .Values.api.persistence.modelCache.size }}
-{{- end }}

helm/hindsight/templates/worker-statefulset.yaml

@@ -95,16 +95,6 @@ spec:
           {{- toYaml .Values.worker.readinessProbe | nindent 10 }}
         resources:
           {{- toYaml .Values.worker.resources | nindent 10 }}
-        {{- if or .Values.worker.persistence.modelCache.enabled .Values.worker.extraVolumeMounts }}
-        volumeMounts:
-        {{- if .Values.worker.persistence.modelCache.enabled }}
-        - name: model-cache
-          mountPath: /home/hindsight/.cache
-        {{- end }}
-        {{- with .Values.worker.extraVolumeMounts }}
-        {{- toYaml . | nindent 8 }}
-        {{- end }}
-        {{- end }}
       {{- with .Values.nodeSelector }}
       nodeSelector:
         {{- toYaml . | nindent 8 }}
@@ -117,26 +107,4 @@ spec:
       tolerations:
         {{- toYaml . | nindent 8 }}
       {{- end }}
-      {{- with .Values.worker.extraVolumes }}
-      volumes:
-      {{- toYaml . | nindent 6 }}
-      {{- end }}
-  {{- if .Values.worker.persistence.modelCache.enabled }}
-  volumeClaimTemplates:
-  - metadata:
-      name: model-cache
-      {{- with .Values.worker.persistence.modelCache.annotations }}
-      annotations:
-        {{- toYaml . | nindent 8 }}
-      {{- end }}
-    spec:
-      accessModes:
-        {{- toYaml .Values.worker.persistence.modelCache.accessModes | nindent 8 }}
-      {{- if .Values.worker.persistence.modelCache.storageClass }}
-      storageClassName: {{ .Values.worker.persistence.modelCache.storageClass }}
-      {{- end }}
-      resources:
-        requests:
-          storage: {{ .Values.worker.persistence.modelCache.size }}
-  {{- end }}
 {{- end }}

helm/hindsight/values.yaml

@@ -67,33 +67,6 @@ api:
   # Pod affinity/anti-affinity (overrides global affinity for this component)
   # affinity: {}
 
-  # Persistent volume for local model cache (reranker, embeddings)
-  # Models are downloaded to /home/hindsight/.cache on first use.
-  # Without persistence, models are re-downloaded on every pod restart.
-  persistence:
-    modelCache:
-      enabled: false
-      size: 5Gi
-      storageClass: ""
-      accessModes:
-        - ReadWriteOnce
-      annotations: {}
-
-  # Extra volume mounts for the api container
-  # e.g.
-  # extraVolumeMounts:
-  #   - name: my-volume
-  #     mountPath: /mnt/my-volume
-  extraVolumeMounts: []
-
-  # Extra volumes for the api pod
-  # e.g.
-  # extraVolumes:
-  #   - name: my-volume
-  #     configMap:
-  #       name: my-configmap
-  extraVolumes: []
-
   # Environment variables
   env:
     #HINDSIGHT_API_LLM_PROVIDER: "groq"
@@ -167,32 +140,6 @@ worker:
   # Pod affinity/anti-affinity (overrides global affinity for this component)
   # affinity: {}
 
-  # Persistent volume for local model cache (reranker, embeddings)
-  # Uses volumeClaimTemplates since worker is a StatefulSet.
-  persistence:
-    modelCache:
-      enabled: false
-      size: 5Gi
-      storageClass: ""
-      accessModes:
-        - ReadWriteOnce
-      annotations: {}
-
-  # Extra volume mounts for the worker container
-  # e.g.
-  # extraVolumeMounts:
-  #   - name: my-volume
-  #     mountPath: /mnt/my-volume
-  extraVolumeMounts: []
-
-  # Extra volumes for the worker pod
-  # e.g.
-  # extraVolumes:
-  #   - name: my-volume
-  #     configMap:
-  #       name: my-configmap
-  extraVolumes: []
-
   # Secret environment variables (inherited from api.secrets if not specified)
   secrets: {}
 

hindsight-all-slim/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "hindsight-all-slim"
-version = "0.4.22"
+version = "0.4.20"
 description = "Hindsight: Agent Memory That Works Like Human Memory - Slim All-in-One Bundle"
 readme = "README.md"
 requires-python = ">=3.11"

hindsight-all/hindsight/embedded.py

@@ -73,9 +73,6 @@ class HindsightEmbedded:
         database_url: Optional database URL override (default: profile-specific pg0)
         idle_timeout: Seconds before daemon auto-exits when idle (default: 300)
         log_level: Daemon log level (default: "info")
-        ui: Whether to start the control plane web UI alongside the daemon (default: False)
-        ui_port: Port for the UI. Defaults to daemon_port + 10000.
-        ui_hostname: Hostname to bind the UI to. Defaults to "0.0.0.0".
     """
 
     def __init__(
@@ -88,9 +85,6 @@ class HindsightEmbedded:
         database_url: Optional[str] = None,
         idle_timeout: int = 300,
         log_level: str = "info",
-        ui: bool = False,
-        ui_port: Optional[int] = None,
-        ui_hostname: str = "0.0.0.0",
     ):
         """
         Initialize the embedded client (daemon starts on first use).
@@ -104,9 +98,6 @@ class HindsightEmbedded:
             database_url: Optional database URL override
             idle_timeout: Seconds before daemon auto-exits when idle
             log_level: Daemon log level
-            ui: Whether to start the control plane web UI alongside the daemon
-            ui_port: Port for the UI (defaults to daemon_port + 10000)
-            ui_hostname: Hostname to bind the UI to (defaults to "0.0.0.0")
         """
         self.profile = profile
 
@@ -125,10 +116,6 @@ class HindsightEmbedded:
         if database_url:
             self.config["HINDSIGHT_EMBED_API_DATABASE_URL"] = database_url
 
-        self._ui = ui
-        self._ui_port = ui_port
-        self._ui_hostname = ui_hostname
-
         self._client: Optional[Hindsight] = None
         self._lock = threading.Lock()
         self._started = False
@@ -170,15 +157,6 @@ class HindsightEmbedded:
             self._started = True
             logger.info(f"Connected to daemon at {daemon_url}")
 
-            # Start UI if requested
-            if self._ui:
-                logger.info(f"Starting UI for profile '{self.profile}'...")
-                ui_started = self._manager.start_ui(
-                    self.profile, self._ui_port, self._ui_hostname
-                )
-                if not ui_started:
-                    logger.warning(f"Failed to start UI for profile '{self.profile}'")
-
     def _cleanup(self, stop_daemon_on_close: bool = False):
         """
         Cleanup client resources (idempotent).
@@ -198,11 +176,6 @@ class HindsightEmbedded:
                 self._client.close()
                 self._client = None
 
-            # Stop UI if it was started
-            if self._ui and self._started:
-                logger.info(f"Stopping UI for profile '{self.profile}'...")
-                self._manager.stop_ui(self.profile, self._ui_port)
-
             # Optionally stop daemon (daemon has idle timeout, so not required)
             if stop_daemon_on_close and self._started:
                 logger.info(f"Stopping daemon for profile '{self.profile}'...")
@@ -406,6 +379,43 @@ class HindsightEmbedded:
         """Check if the client is initialized."""
         return self._started and not self._closed and self._client is not None
 
+    def start_ui(self, ui_port: int | None = None, hostname: str = "0.0.0.0") -> bool:
+        """Start the control plane web UI.
+
+        The daemon is started automatically if not already running.
+
+        Args:
+            ui_port: Port for the UI. Defaults to daemon_port + 10000.
+            hostname: Hostname to bind to. Defaults to 0.0.0.0.
+
+        Returns:
+            True if UI started successfully.
+        """
+        self._ensure_started()
+        return self._manager.start_ui(self.profile, ui_port, hostname)
+
+    def stop_ui(self, ui_port: int | None = None) -> bool:
+        """Stop the control plane web UI.
+
+        Args:
+            ui_port: Port the UI is running on. Defaults to daemon_port + 10000.
+
+        Returns:
+            True if stopped successfully.
+        """
+        return self._manager.stop_ui(self.profile, ui_port)
+
+    def is_ui_running(self, ui_port: int | None = None) -> bool:
+        """Check if the control plane web UI is running.
+
+        Args:
+            ui_port: Port to check. Defaults to daemon_port + 10000.
+
+        Returns:
+            True if UI is running and responsive.
+        """
+        return self._manager.is_ui_running(self.profile, ui_port)
+
     @property
     def ui_url(self) -> str:
         """Get the UI URL for this profile."""

hindsight-all/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "hindsight-all"
-version = "0.4.22"
+version = "0.4.20"
 description = "Hindsight: Agent Memory That Works Like Human Memory - All-in-One Bundle"
 readme = "README.md"
 requires-python = ">=3.11"

hindsight-all/tests/test_embedded.py

@@ -15,8 +15,6 @@ import os
 import uuid
 
 import pytest
-import urllib.request
-import json
 
 from hindsight import HindsightEmbedded
 
@@ -25,20 +23,12 @@ from hindsight import HindsightEmbedded
 def llm_config():
     """Get LLM configuration from environment (session-scoped)."""
     # Try both naming conventions
-    provider = os.getenv("HINDSIGHT_API_LLM_PROVIDER") or os.getenv(
-        "HINDSIGHT_LLM_PROVIDER", "groq"
-    )
-    api_key = os.getenv("HINDSIGHT_API_LLM_API_KEY") or os.getenv(
-        "HINDSIGHT_LLM_API_KEY", ""
-    )
-    model = os.getenv("HINDSIGHT_API_LLM_MODEL") or os.getenv(
-        "HINDSIGHT_LLM_MODEL", "openai/gpt-oss-120b"
-    )
+    provider = os.getenv("HINDSIGHT_API_LLM_PROVIDER") or os.getenv("HINDSIGHT_LLM_PROVIDER", "groq")
+    api_key = os.getenv("HINDSIGHT_API_LLM_API_KEY") or os.getenv("HINDSIGHT_LLM_API_KEY", "")
+    model = os.getenv("HINDSIGHT_API_LLM_MODEL") or os.getenv("HINDSIGHT_LLM_MODEL", "openai/gpt-oss-120b")
 
     if not api_key:
-        pytest.skip(
-            "LLM API key not configured. Set HINDSIGHT_API_LLM_API_KEY or HINDSIGHT_LLM_API_KEY."
-        )
+        pytest.skip("LLM API key not configured. Set HINDSIGHT_API_LLM_API_KEY or HINDSIGHT_LLM_API_KEY.")
 
     return {
         "llm_provider": provider,
@@ -88,9 +78,7 @@ def test_embedded_context_manager(llm_config):
 
         # Recall memory
         recall_results = client.recall(bank_id=bank_id, query="context")
-        assert isinstance(recall_results.results, list), (
-            "Recall should return results list"
-        )
+        assert isinstance(recall_results.results, list), "Recall should return results list"
 
     # Server should be stopped after context exit
     # Note: We can't check client.is_running here as client is out of scope
@@ -117,9 +105,7 @@ def test_embedded_complete_workflow(llm_config):
         # Step 1: Create a memory bank
         print(f"\n1. Creating memory bank: {bank_id}")
         bank_response = client.create_bank(
-            bank_id=bank_id,
-            name="Test Assistant",
-            mission="Help with programming tasks",
+            bank_id=bank_id, name="Test Assistant", mission="Help with programming tasks"
         )
         assert bank_response.bank_id == bank_id
 
@@ -140,9 +126,7 @@ def test_embedded_complete_workflow(llm_config):
             items=[
                 {"content": "User works with pandas and numpy."},
                 {"content": "User likes matplotlib for visualization."},
-                {
-                    "content": "User is interested in machine learning with scikit-learn."
-                },
+                {"content": "User is interested in machine learning with scikit-learn."},
             ],
         )
         assert batch_response.success
@@ -150,9 +134,7 @@ def test_embedded_complete_workflow(llm_config):
 
         # Step 4: Recall memories
         print("\n4. Recalling memories...")
-        recall_response = client.recall(
-            bank_id=bank_id, query="What tools does the user prefer?", max_tokens=2000
-        )
+        recall_response = client.recall(bank_id=bank_id, query="What tools does the user prefer?", max_tokens=2000)
         assert isinstance(recall_response.results, list)
         assert len(recall_response.results) > 0
         print(f"   Found {len(recall_response.results)} relevant memories")
@@ -170,9 +152,7 @@ def test_embedded_complete_workflow(llm_config):
 
         # Verify answer mentions relevant tools
         answer_lower = reflect_response.text.lower()
-        assert any(
-            term in answer_lower for term in ["python", "pandas", "numpy", "data"]
-        )
+        assert any(term in answer_lower for term in ["python", "pandas", "numpy", "data"])
 
         # Step 6: List memories
         print("\n6. Listing memories...")
@@ -235,9 +215,7 @@ def test_embedded_method_proxying(llm_config):
         assert bank.bank_id == bank_id
 
         # Test mission setting
-        mission_response = client.set_mission(
-            bank_id=bank_id, mission="Test mission for proxying"
-        )
+        mission_response = client.set_mission(bank_id=bank_id, mission="Test mission for proxying")
         assert mission_response.bank_id == bank_id
 
         # Test retain
@@ -286,9 +264,7 @@ def test_embedded_multiple_banks(llm_config):
 
         # Create second bank and store data
         client.create_bank(bank_id=bank2_id, name="Bank 2")
-        client.retain(
-            bank_id=bank2_id, content="Bob uses JavaScript for web development"
-        )
+        client.retain(bank_id=bank2_id, content="Bob uses JavaScript for web development")
 
         # Recall from both banks
         results1 = client.recall(bank_id=bank1_id, query="programming language")
@@ -299,9 +275,9 @@ def test_embedded_multiple_banks(llm_config):
 
         # Verify banks are isolated (each should only see their own content)
         # This is a basic check - content isolation is tested more thoroughly in other tests
-        assert results1.results[0].text != results2.results[0].text or len(
-            results1.results
-        ) != len(results2.results)
+        assert results1.results[0].text != results2.results[0].text or len(results1.results) != len(
+            results2.results
+        )
 
     finally:
         client.close()
@@ -320,14 +296,10 @@ def test_embedded_profile_isolation(llm_config):
 
     try:
         # Store data in profile1
-        client1.retain(
-            bank_id=bank_id, content="User likes TypeScript for frontend development"
-        )
+        client1.retain(bank_id=bank_id, content="User likes TypeScript for frontend development")
 
         # Store different data in profile2
-        client2.retain(
-            bank_id=bank_id, content="User prefers Rust for systems programming"
-        )
+        client2.retain(bank_id=bank_id, content="User prefers Rust for systems programming")
 
         # Each profile should only see its own data
         results1 = client1.recall(bank_id=bank_id, query="programming preference")
@@ -362,42 +334,5 @@ def test_embedded_error_after_close(llm_config):
     assert not client.is_running
 
     # Trying to use it after close should raise an error
-    with pytest.raises(
-        RuntimeError, match="Cannot use HindsightEmbedded after it has been closed"
-    ):
+    with pytest.raises(RuntimeError, match="Cannot use HindsightEmbedded after it has been closed"):
         client.retain(bank_id=bank_id, content="This should fail")
-
-
-def test_embedded_ui_flag(llm_config):
-    """
-    Test that ui=True starts the control plane UI alongside the daemon,
-    and that the UI's health endpoint reports a connected dataplane.
-    """
-    profile = f"test_ui_{uuid.uuid4().hex[:8]}"
-    bank_id = f"bank_{uuid.uuid4().hex[:8]}"
-
-    client = HindsightEmbedded(profile=profile, log_level="info", ui=True, **llm_config)
-
-    try:
-        # First use triggers daemon + UI startup
-        result = client.retain(bank_id=bank_id, content="UI integration test content")
-        assert result.success, "Retain should succeed"
-        assert client.is_running, "Daemon should be running"
-
-        # Verify UI is reachable and reports connected dataplane
-        ui_url = client.ui_url
-        assert ui_url, "ui_url should be set"
-
-        health_url = f"{ui_url}/api/health"
-        with urllib.request.urlopen(health_url, timeout=10) as resp:
-            health = json.loads(resp.read().decode())
-
-        assert health["status"] == "ok", (
-            f"UI health status should be 'ok', got: {health['status']}"
-        )
-        assert health["dataplane"]["status"] == "connected", (
-            f"Dataplane should be connected, got: {health['dataplane']}"
-        )
-
-    finally:
-        client.close()

hindsight-api-slim/hindsight_api/__init__.py

@@ -46,4 +46,4 @@ __all__ = [
     "RemoteTEICrossEncoder",
     "LLMConfig",
 ]
-__version__ = "0.4.22"
+__version__ = "0.4.20"

hindsight-api-slim/hindsight_api/admin/cli.py

@@ -249,7 +249,7 @@ async def _run_migration(
         schemas = list(dict.fromkeys(schemas))
 
     for schema in schemas:
-        run_migrations(resolved_url, schema=schema, migration_database_url=config.migration_database_url)
+        run_migrations(resolved_url, schema=schema)
 
     if embedding_dimension is not None:
         for schema in schemas:

hindsight-api-slim/hindsight_api/alembic/versions/2eee35aa3cfc_case_insensitive_entities_trgm_index.py

@@ -1,45 +0,0 @@
-"""Recreate entities trigram index on LOWER(canonical_name) for case-insensitive matching
-
-The previous GIN trigram index on canonical_name was case-sensitive, causing
-"Alice" and "alice" to have different trigram sets. This recreates it on
-LOWER(canonical_name) so the % operator matches case-insensitively.
-
-Revision ID: d6e7f8a9b0c1
-Revises: c5d6e7f8a9b0
-Create Date: 2026-03-31
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "d6e7f8a9b0c1"
-down_revision: str | Sequence[str] | None = "c5d6e7f8a9b0"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _get_schema_prefix() -> str:
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def upgrade() -> None:
-    schema = _get_schema_prefix()
-    # Drop the old case-sensitive trigram index
-    op.execute("DROP INDEX IF EXISTS entities_canonical_name_trgm_idx")
-    # Create case-insensitive trigram index on LOWER(canonical_name)
-    op.execute(
-        f"CREATE INDEX IF NOT EXISTS entities_canonical_name_lower_trgm_idx "
-        f"ON {schema}entities USING GIN (LOWER(canonical_name) gin_trgm_ops)"
-    )
-
-
-def downgrade() -> None:
-    op.execute("DROP INDEX IF EXISTS entities_canonical_name_lower_trgm_idx")
-    schema = _get_schema_prefix()
-    # Restore original case-sensitive index
-    op.execute(
-        f"CREATE INDEX IF NOT EXISTS entities_canonical_name_trgm_idx "
-        f"ON {schema}entities USING GIN (canonical_name gin_trgm_ops)"
-    )

hindsight-api-slim/hindsight_api/alembic/versions/a4b5c6d7e8f9_fix_per_bank_vector_index_type.py

@@ -1,142 +0,0 @@
-"""Fix per-bank vector indexes to match configured extension
-
-Revision ID: a4b5c6d7e8f9
-Revises: d6e7f8a9b0c1
-Create Date: 2026-04-01
-
-Migration d5e6f7a8b9c0 hardcoded HNSW when creating per-bank partial vector
-indexes, ignoring HINDSIGHT_API_VECTOR_EXTENSION. Banks that existed when that
-migration ran got HNSW indexes even when pgvectorscale (DiskANN) or vchord
-was configured.
-
-This migration detects the mismatch and recreates the affected indexes with
-the correct type. Skipped entirely when the configured extension is pgvector
-(the default), since those indexes are already correct.
-"""
-
-import os
-from collections.abc import Sequence
-
-from alembic import context, op
-from sqlalchemy import text
-
-revision: str = "a4b5c6d7e8f9"
-down_revision: str | Sequence[str] | None = "d6e7f8a9b0c1"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-_FACT_TYPES: dict[str, str] = {
-    "world": "worl",
-    "experience": "expr",
-    "observation": "obsv",
-}
-
-
-def _get_schema_prefix() -> str:
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def _target_index_type() -> str | None:
-    """Return the target index type, or None if pgvector (no fix needed)."""
-    ext = os.getenv("HINDSIGHT_API_VECTOR_EXTENSION", "pgvector").lower()
-    if ext == "pgvectorscale":
-        return "diskann"
-    elif ext == "vchord":
-        return "vchordrq"
-    return None
-
-
-def _vector_index_using_clause() -> str:
-    """Return the USING clause based on the configured vector extension."""
-    ext = os.getenv("HINDSIGHT_API_VECTOR_EXTENSION", "pgvector").lower()
-    if ext == "pgvectorscale":
-        return "USING diskann (embedding vector_cosine_ops) WITH (num_neighbors = 50)"
-    elif ext == "vchord":
-        return "USING vchordrq (embedding vector_l2_ops)"
-    else:
-        return "USING hnsw (embedding vector_cosine_ops)"
-
-
-def upgrade() -> None:
-    target = _target_index_type()
-    if target is None:
-        # pgvector — indexes are already HNSW, nothing to fix
-        return
-
-    bind = op.get_bind()
-    schema_name = context.config.get_main_option("target_schema")
-    schema = _get_schema_prefix()
-    table_ref = f'"{schema_name}".memory_units' if schema_name else "memory_units"
-    banks_ref = f'"{schema_name}".banks' if schema_name else "banks"
-    using_clause = _vector_index_using_clause()
-    pg_schema = schema_name or "public"
-
-    rows = bind.execute(text(f"SELECT bank_id, internal_id FROM {banks_ref}")).fetchall()  # noqa: S608
-    for row in rows:
-        bank_id = row[0]
-        internal_id = str(row[1]).replace("-", "")[:16]
-        escaped_bank_id = bank_id.replace("'", "''")
-        for ft, ft_short in _FACT_TYPES.items():
-            idx_name = f"idx_mu_emb_{ft_short}_{internal_id}"
-
-            # Check if this index exists and what type it is
-            idx_info = bind.execute(
-                text("SELECT indexdef FROM pg_indexes WHERE schemaname = :schema AND indexname = :idx"),
-                {"schema": pg_schema, "idx": idx_name},
-            ).fetchone()
-
-            if idx_info is None:
-                # Index doesn't exist — create it with the correct type
-                bind.execute(
-                    text(
-                        f"CREATE INDEX IF NOT EXISTS {idx_name} "
-                        f"ON {table_ref} {using_clause} "
-                        f"WHERE fact_type = '{ft}' AND bank_id = '{escaped_bank_id}'"
-                    )
-                )
-                continue
-
-            indexdef = idx_info[0].lower()
-            if target in indexdef:
-                # Already the correct type
-                continue
-
-            # Wrong type — drop and recreate
-            bind.execute(text(f"DROP INDEX IF EXISTS {schema}{idx_name}"))
-            bind.execute(
-                text(
-                    f"CREATE INDEX IF NOT EXISTS {idx_name} "
-                    f"ON {table_ref} {using_clause} "
-                    f"WHERE fact_type = '{ft}' AND bank_id = '{escaped_bank_id}'"
-                )
-            )
-
-
-def downgrade() -> None:
-    # Downgrade recreates indexes as HNSW (the original hardcoded behavior)
-    target = _target_index_type()
-    if target is None:
-        return
-
-    bind = op.get_bind()
-    schema_name = context.config.get_main_option("target_schema")
-    schema = _get_schema_prefix()
-    table_ref = f'"{schema_name}".memory_units' if schema_name else "memory_units"
-    banks_ref = f'"{schema_name}".banks' if schema_name else "banks"
-
-    rows = bind.execute(text(f"SELECT bank_id, internal_id FROM {banks_ref}")).fetchall()  # noqa: S608
-    for row in rows:
-        bank_id = row[0]
-        internal_id = str(row[1]).replace("-", "")[:16]
-        escaped_bank_id = bank_id.replace("'", "''")
-        for ft, ft_short in _FACT_TYPES.items():
-            idx_name = f"idx_mu_emb_{ft_short}_{internal_id}"
-            bind.execute(text(f"DROP INDEX IF EXISTS {schema}{idx_name}"))
-            bind.execute(
-                text(
-                    f"CREATE INDEX IF NOT EXISTS {idx_name} "
-                    f"ON {table_ref} USING hnsw (embedding vector_cosine_ops) "
-                    f"WHERE fact_type = '{ft}' AND bank_id = '{escaped_bank_id}'"
-                )
-            )

hindsight-api-slim/hindsight_api/alembic/versions/b3c4d5e6f7a8_add_content_hash_to_chunks.py

@@ -1,32 +0,0 @@
-"""add content_hash to chunks table for delta retain
-
-Revision ID: b3c4d5e6f7a8
-Revises: a3b4c5d6e7f8
-Create Date: 2026-03-25
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "b3c4d5e6f7a8"
-down_revision: str | Sequence[str] | None = "a3b4c5d6e7f8"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _get_schema_prefix() -> str:
-    """Get schema prefix for table names (required for multi-tenant support)."""
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def upgrade() -> None:
-    schema = _get_schema_prefix()
-    # Add content_hash column to chunks table for delta comparison
-    op.execute(f"ALTER TABLE {schema}chunks ADD COLUMN IF NOT EXISTS content_hash TEXT")
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-    op.execute(f"ALTER TABLE {schema}chunks DROP COLUMN IF EXISTS content_hash")

hindsight-api-slim/hindsight_api/alembic/versions/c2d3e4f5g6h7_add_audit_log_table.py

@@ -1,61 +0,0 @@
-"""Add audit_log table for feature usage tracking.
-
-Merge migration that combines the two existing heads (a3b4c5d6e7f8 + c8e5f2a3b4d1).
-
-Stores raw request/response as JSONB for expandability without future migrations.
-The metadata JSONB column allows adding arbitrary fields in the future.
-
-Revision ID: c2d3e4f5g6h7
-Revises: a3b4c5d6e7f8, c8e5f2a3b4d1
-Create Date: 2026-03-26
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "c2d3e4f5g6h7"
-down_revision: str | Sequence[str] | None = ("a3b4c5d6e7f8", "c8e5f2a3b4d1")
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _get_schema_prefix() -> str:
-    """Get schema prefix for table names (required for multi-tenant support)."""
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def upgrade() -> None:
-    schema = _get_schema_prefix()
-
-    op.execute(
-        f"""
-        CREATE TABLE IF NOT EXISTS {schema}audit_log (
-            id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
-            action TEXT NOT NULL,
-            transport TEXT NOT NULL,
-            bank_id TEXT,
-            started_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
-            ended_at TIMESTAMPTZ,
-            request JSONB,
-            response JSONB,
-            metadata JSONB DEFAULT '{{}}'::jsonb
-        )
-        """
-    )
-
-    op.execute(
-        f"CREATE INDEX IF NOT EXISTS idx_audit_log_action_started ON {schema}audit_log (action, started_at DESC)"
-    )
-    op.execute(f"CREATE INDEX IF NOT EXISTS idx_audit_log_bank_started ON {schema}audit_log (bank_id, started_at DESC)")
-    op.execute(f"CREATE INDEX IF NOT EXISTS idx_audit_log_started ON {schema}audit_log (started_at DESC)")
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-
-    op.execute(f"DROP INDEX IF EXISTS {schema}idx_audit_log_started")
-    op.execute(f"DROP INDEX IF EXISTS {schema}idx_audit_log_bank_started")
-    op.execute(f"DROP INDEX IF EXISTS {schema}idx_audit_log_action_started")
-    op.execute(f"DROP TABLE IF EXISTS {schema}audit_log")

hindsight-api-slim/hindsight_api/alembic/versions/c5d6e7f8a9b0_add_bank_id_to_memory_links.py

@@ -1,48 +0,0 @@
-"""Add bank_id column to memory_links for direct filtering
-
-The stats endpoint JOINs memory_links to memory_units just to filter by
-bank_id.  With millions of links this takes 18+ seconds.  Adding bank_id
-directly to memory_links lets Postgres push the filter down before the JOIN.
-
-Revision ID: c5d6e7f8a9b0
-Revises: b3c4d5e6f7a8
-Create Date: 2026-03-26
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "c5d6e7f8a9b0"
-down_revision: str | Sequence[str] | None = "b3c4d5e6f7a8"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _get_schema_prefix() -> str:
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def upgrade() -> None:
-    schema = _get_schema_prefix()
-
-    # 1. Add nullable column
-    op.execute(f"ALTER TABLE {schema}memory_links ADD COLUMN IF NOT EXISTS bank_id TEXT")
-
-    # 2. Backfill from memory_units
-    op.execute(f"""
-        UPDATE {schema}memory_links ml
-        SET bank_id = mu.bank_id
-        FROM {schema}memory_units mu
-        WHERE ml.from_unit_id = mu.id
-          AND ml.bank_id IS NULL
-    """)
-
-    # 3. Set NOT NULL
-    op.execute(f"ALTER TABLE {schema}memory_links ALTER COLUMN bank_id SET NOT NULL")
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-    op.execute(f"ALTER TABLE {schema}memory_links DROP COLUMN IF EXISTS bank_id")

hindsight-api-slim/hindsight_api/alembic/versions/d5e6f7a8b9c0_add_bank_internal_id_and_per_bank_hnsw.py

@@ -1,4 +1,4 @@
-"""Add internal_id to banks and per-(bank, fact_type) partial vector indexes
+"""Add internal_id to banks and per-(bank, fact_type) partial HNSW indexes
 
 Revision ID: d5e6f7a8b9c0
 Revises: a3b4c5d6e7f8
@@ -6,20 +6,25 @@ Create Date: 2026-03-11
 
 This migration:
 1. Adds internal_id UUID column to banks (stable identifier for index naming)
-2. Drops the global vector index (competes with per-bank partial indexes)
-3. Creates per-(bank_id, fact_type) partial vector indexes for all existing banks
-   using the configured vector extension (HNSW for pgvector, DiskANN for
-   pgvectorscale, vchordrq for vchord).
-   (new banks get indexes created at bank-creation time via bank_utils.create_bank_vector_indexes)
+2. Drops the global HNSW index (competes with per-bank partial indexes)
+3. Creates per-(bank_id, fact_type) partial HNSW indexes for all existing banks
+   (new banks get indexes created at bank-creation time via bank_utils.create_bank_hnsw_indexes)
 
 Why per-(bank, fact_type) indexes:
 - fact_type-only partial indexes are never chosen by the planner when bank_id is in the WHERE
   clause, because the idx_memory_units_bank_id B-tree index always wins at planning time.
 - Per-(bank, fact_type) partial indexes have both predicates matching → planner selects them.
-- The global vector index competes for larger partitions (world, observation) and must be dropped.
+- The global HNSW index competes for larger partitions (world, observation) and must be dropped.
+
+For large deployments, create indexes CONCURRENTLY before running this migration:
+    SELECT internal_id, bank_id FROM banks;
+    -- for each bank and each fact_type in (world, experience, observation):
+    CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_mu_emb_{ft}_{uid16}
+        ON memory_units USING hnsw (embedding vector_cosine_ops)
+        WHERE fact_type = '{ft}' AND bank_id = '{bank_id}';
+    DROP INDEX CONCURRENTLY IF EXISTS idx_memory_units_embedding;
 """
 
-import os
 from collections.abc import Sequence
 
 from alembic import context, op
@@ -30,7 +35,7 @@ down_revision: str | Sequence[str] | None = "c3d4e5f6g7h8"
 branch_labels: str | Sequence[str] | None = None
 depends_on: str | Sequence[str] | None = None
 
-_FACT_TYPES: dict[str, str] = {
+_HNSW_FACT_TYPES: dict[str, str] = {
     "world": "worl",
     "experience": "expr",
     "observation": "obsv",
@@ -42,17 +47,6 @@ def _get_schema_prefix() -> str:
     return f'"{schema}".' if schema else ""
 
 
-def _vector_index_using_clause() -> str:
-    """Return the USING clause based on the configured vector extension."""
-    ext = os.getenv("HINDSIGHT_API_VECTOR_EXTENSION", "pgvector").lower()
-    if ext == "pgvectorscale":
-        return "USING diskann (embedding vector_cosine_ops) WITH (num_neighbors = 50)"
-    elif ext == "vchord":
-        return "USING vchordrq (embedding vector_l2_ops)"
-    else:
-        return "USING hnsw (embedding vector_cosine_ops)"
-
-
 def upgrade() -> None:
     schema = _get_schema_prefix()
 
@@ -62,35 +56,33 @@ def upgrade() -> None:
     )
     op.execute(f"ALTER TABLE {schema}banks ADD CONSTRAINT banks_internal_id_unique UNIQUE (internal_id)")
 
-    # 2. Drop any fact_type-only partial indexes that may exist from prior migrations
+    # 2. Drop any fact_type-only partial HNSW indexes that may exist from prior migrations
     #    (bank_id B-tree always wins over them when bank_id is in the WHERE clause)
     op.execute(f"DROP INDEX IF EXISTS {schema}idx_mu_emb_world")
     op.execute(f"DROP INDEX IF EXISTS {schema}idx_mu_emb_observation")
     op.execute(f"DROP INDEX IF EXISTS {schema}idx_mu_emb_experience")
 
-    # 4. Drop global vector index (competes with per-bank partial indexes)
+    # 4. Drop global HNSW index (competes with per-bank partial indexes)
     op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_embedding")
 
-    # 5. Create per-(bank, fact_type) partial vector indexes for all existing banks
-    #    using the configured extension (HNSW / DiskANN / vchordrq)
+    # 5. Create per-(bank, fact_type) partial HNSW indexes for all existing banks
     bind = op.get_bind()
     schema_name = context.config.get_main_option("target_schema")
     table_ref = f'"{schema_name}".memory_units' if schema_name else "memory_units"
     banks_ref = f'"{schema_name}".banks' if schema_name else "banks"
-    using_clause = _vector_index_using_clause()
 
     rows = bind.execute(text(f"SELECT bank_id, internal_id FROM {banks_ref}")).fetchall()  # noqa: S608
     for row in rows:
         bank_id = row[0]
         internal_id = str(row[1]).replace("-", "")[:16]
         escaped_bank_id = bank_id.replace("'", "''")
-        for ft, ft_short in _FACT_TYPES.items():
+        for ft, ft_short in _HNSW_FACT_TYPES.items():
             idx_name = f"idx_mu_emb_{ft_short}_{internal_id}"
             # Index name is schema-unqualified (indexes live in the schema of their table)
             bind.execute(
                 text(
                     f"CREATE INDEX IF NOT EXISTS {idx_name} "
-                    f"ON {table_ref} {using_clause} "
+                    f"ON {table_ref} USING hnsw (embedding vector_cosine_ops) "
                     f"WHERE fact_type = '{ft}' AND bank_id = '{escaped_bank_id}'"
                 )
             )

hindsight-api-slim/hindsight_api/alembic/versions/f1a2b3c4d5e6_add_memory_links_composite_index.py

@@ -5,7 +5,7 @@ Revises: e0a1b2c3d4e5
 Create Date: 2025-01-12
 
 Add composite index on memory_links (from_unit_id, link_type, weight DESC)
-to optimize graph traversal queries that need top-k edges per type.
+to optimize MPFP graph traversal queries that need top-k edges per type.
 """
 
 from collections.abc import Sequence
@@ -26,7 +26,7 @@ def _get_schema_prefix() -> str:
 
 
 def upgrade() -> None:
-    """Add composite index for efficient graph retrieval edge loading."""
+    """Add composite index for efficient MPFP edge loading."""
     schema = _get_schema_prefix()
     # Create composite index for efficient top-k per (from_node, link_type) queries
     # This enables LATERAL joins to use index-only scans with early termination

hindsight-api-slim/hindsight_api/alembic/versions/f6g7h8i9j0k1_chunk_fk_cascade_delete.py

@@ -24,28 +24,9 @@ def upgrade() -> None:
     the memory_units rows survived with chunk_id = NULL, leaving ghost records.
     Switching to CASCADE ensures they are removed together with their chunk.
     """
-    from alembic import context
-
-    schema = context.config.get_main_option("target_schema")
-    schema_prefix = f'"{schema}".' if schema else ""
-    # Use raw SQL with IF EXISTS so this is safe on schemas where the FK was
-    # already dropped or never existed under this name.
-    op.execute(f"ALTER TABLE {schema_prefix}memory_units DROP CONSTRAINT IF EXISTS memory_units_chunk_fkey")
-    # Use a DO block so the ADD is also idempotent: if the FK already exists (e.g.
-    # the schema was provisioned after the base migration already added it) the
-    # duplicate_object exception is swallowed rather than failing the migration.
-    op.execute(
-        f"""
-        DO $$ BEGIN
-            ALTER TABLE {schema_prefix}memory_units
-                ADD CONSTRAINT memory_units_chunk_fkey
-                FOREIGN KEY (chunk_id)
-                REFERENCES {schema_prefix}chunks (chunk_id)
-                ON DELETE CASCADE;
-        EXCEPTION
-            WHEN duplicate_object THEN NULL;
-        END $$;
-        """
+    op.drop_constraint("memory_units_chunk_fkey", "memory_units", type_="foreignkey")
+    op.create_foreign_key(
+        "memory_units_chunk_fkey", "memory_units", "chunks", ["chunk_id"], ["chunk_id"], ondelete="CASCADE"
     )
 
 

hindsight-api-slim/hindsight_api/alembic/versions/g2h3i4j5k6l7_remove_opinion_fact_type.py

@@ -1,83 +0,0 @@
-"""remove_opinion_fact_type
-
-Revision ID: g2h3i4j5k6l7
-Revises: f1a2b3c4d5e6
-Create Date: 2026-04-02
-
-Remove the deprecated 'opinion' fact type: drop opinion-specific indexes,
-update CHECK constraints, delete any remaining opinion rows, and drop the
-confidence_score column (was only used for opinions, always NULL otherwise).
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-# revision identifiers, used by Alembic.
-revision: str = "g2h3i4j5k6l7"
-down_revision: str | Sequence[str] | None = "f1a2b3c4d5e6"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _get_schema_prefix() -> str:
-    """Get schema prefix for table names (required for multi-tenant support)."""
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def upgrade() -> None:
-    schema = _get_schema_prefix()
-
-    # 1. Delete any remaining opinion rows
-    op.execute(f"DELETE FROM {schema}memory_units WHERE fact_type = 'opinion'")
-
-    # 2. Drop opinion-specific indexes
-    op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_opinion_confidence")
-    op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_opinion_date")
-
-    # 3. Drop confidence_score constraints and column (only used for opinions, always NULL otherwise)
-    op.execute(f"ALTER TABLE {schema}memory_units DROP CONSTRAINT IF EXISTS confidence_score_fact_type_check")
-    op.execute(f"ALTER TABLE {schema}memory_units DROP CONSTRAINT IF EXISTS memory_units_confidence_score_check")
-    op.execute(f"ALTER TABLE {schema}memory_units DROP COLUMN IF EXISTS confidence_score")
-
-    # 4. Replace fact_type CHECK constraint
-    op.execute(f"ALTER TABLE {schema}memory_units DROP CONSTRAINT IF EXISTS memory_units_fact_type_check")
-    op.execute(
-        f"ALTER TABLE {schema}memory_units ADD CONSTRAINT memory_units_fact_type_check "
-        f"CHECK (fact_type IN ('world', 'experience', 'observation'))"
-    )
-
-
-def downgrade() -> None:
-    schema = _get_schema_prefix()
-
-    # Restore confidence_score column
-    op.execute(f"ALTER TABLE {schema}memory_units ADD COLUMN IF NOT EXISTS confidence_score float")
-    op.execute(
-        f"ALTER TABLE {schema}memory_units ADD CONSTRAINT memory_units_confidence_score_check "
-        f"CHECK (confidence_score IS NULL OR (confidence_score >= 0.0 AND confidence_score <= 1.0))"
-    )
-    op.execute(
-        f"ALTER TABLE {schema}memory_units ADD CONSTRAINT confidence_score_fact_type_check "
-        f"CHECK ((fact_type = 'opinion' AND confidence_score IS NOT NULL) OR "
-        f"(fact_type = 'observation') OR "
-        f"(fact_type NOT IN ('opinion', 'observation') AND confidence_score IS NULL))"
-    )
-
-    # Restore original fact_type CHECK constraint (with opinion)
-    op.execute(f"ALTER TABLE {schema}memory_units DROP CONSTRAINT IF EXISTS memory_units_fact_type_check")
-    op.execute(
-        f"ALTER TABLE {schema}memory_units ADD CONSTRAINT memory_units_fact_type_check "
-        f"CHECK (fact_type IN ('world', 'experience', 'opinion', 'observation'))"
-    )
-
-    # Recreate opinion indexes
-    op.execute(
-        f"CREATE INDEX idx_memory_units_opinion_confidence ON {schema}memory_units "
-        f"(bank_id, confidence_score DESC) WHERE fact_type = 'opinion'"
-    )
-    op.execute(
-        f"CREATE INDEX idx_memory_units_opinion_date ON {schema}memory_units "
-        f"(bank_id, event_date DESC) WHERE fact_type = 'opinion'"
-    )

hindsight-api-slim/hindsight_api/api/mcp.py

@@ -12,9 +12,44 @@ from hindsight_api.config import _get_raw_config
 from hindsight_api.engine.memory_engine import _current_schema
 from hindsight_api.extensions import MCPExtension, load_extension
 from hindsight_api.extensions.tenant import AuthenticationError
-from hindsight_api.mcp_tools import _ALL_TOOLS, MCPToolsConfig, register_mcp_tools
+from hindsight_api.mcp_tools import MCPToolsConfig, register_mcp_tools
 from hindsight_api.models import RequestContext
 
+# All tools available in the system (explicit list — no wildcards)
+_ALL_TOOLS: frozenset[str] = frozenset(
+    {
+        "retain",
+        "recall",
+        "reflect",
+        "list_banks",
+        "create_bank",
+        "list_mental_models",
+        "get_mental_model",
+        "create_mental_model",
+        "update_mental_model",
+        "delete_mental_model",
+        "refresh_mental_model",
+        "list_directives",
+        "create_directive",
+        "delete_directive",
+        "list_memories",
+        "get_memory",
+        "delete_memory",
+        "list_documents",
+        "get_document",
+        "delete_document",
+        "list_operations",
+        "get_operation",
+        "cancel_operation",
+        "list_tags",
+        "get_bank",
+        "get_bank_stats",
+        "update_bank",
+        "delete_bank",
+        "clear_memories",
+    }
+)
+
 # Configure logging from HINDSIGHT_API_LOG_LEVEL environment variable
 _log_level_str = os.environ.get("HINDSIGHT_API_LOG_LEVEL", "info").lower()
 _log_level_map = {
@@ -156,65 +191,24 @@ def create_mcp_server(memory: MemoryEngine, multi_bank: bool = True) -> FastMCP:
     return mcp
 
 
-def _get_mcp_tools(mcp: FastMCP) -> dict:
-    """Get tool name→object mapping, compatible with FastMCP 2.x and 3.x."""
-    # FastMCP 2.x: _tool_manager._tools
-    if hasattr(mcp, "_tool_manager"):
-        return mcp._tool_manager._tools  # type: ignore[union-attr]
-    # FastMCP 3.x: _local_provider._components with "tool:" prefix
-    if hasattr(mcp, "_local_provider"):
-        return {
-            k.split(":")[1].split("@")[0]: v
-            for k, v in mcp._local_provider._components.items()  # type: ignore[union-attr]
-            if k.startswith("tool:")
-        }
-    msg = "Cannot locate tools on FastMCP instance"
-    raise AttributeError(msg)
-
-
 def _make_tools_tolerant(mcp: FastMCP) -> None:
-    """Wrap all tool run methods to strip unknown arguments and coerce string-encoded JSON.
+    """Wrap all tool run methods to strip unknown arguments before validation.
 
     LLMs frequently add extra fields like "explanation" or "reasoning" to tool calls.
     FastMCP's Pydantic TypeAdapter rejects these with "Unexpected keyword argument".
-
-    LLMs also frequently serialize list/dict arguments as JSON strings instead of native
-    types (e.g., tags='["a","b"]' instead of tags=["a","b"]). This auto-coerces them.
-
-    This wraps each tool's run() to apply both fixes before validation.
+    This wraps each tool's run() to filter arguments to only known parameters.
     """
     try:
-        tools = _get_mcp_tools(mcp)
-        for name, tool in tools.items():
+        for name, tool in mcp._tool_manager._tools.items():
             if hasattr(tool, "parameters") and tool.parameters:
-                properties = tool.parameters.get("properties", {})
-                allowed = set(properties.keys())
-
-                # Build sets of parameter names that expect array or object types.
-                # Handles both direct types {"type": "array"} and anyOf/oneOf unions
-                # like {"anyOf": [{"type": "array", ...}, {"type": "null"}]}.
-                array_params: set[str] = set()
-                object_params: set[str] = set()
-                for param_name, param_schema in properties.items():
-                    _collect_coercible_types(param_schema, param_name, array_params, object_params)
-
+                allowed = set(tool.parameters.get("properties", {}).keys())
                 original_run = tool.run
 
-                async def _tolerant_run(
-                    arguments,
-                    _allowed=allowed,
-                    _orig=original_run,
-                    _array_params=array_params,
-                    _object_params=object_params,
-                ):
+                async def _tolerant_run(arguments, _allowed=allowed, _orig=original_run):
                     extra_keys = set(arguments.keys()) - _allowed
                     if extra_keys:
                         logger.debug(f"Stripping unknown arguments from tool call: {extra_keys}")
                         arguments = {k: v for k, v in arguments.items() if k in _allowed}
-
-                    # Coerce string-encoded JSON for list/dict parameters
-                    arguments = _coerce_string_json(arguments, _array_params, _object_params)
-
                     return await _orig(arguments)
 
                 # FunctionTool is a Pydantic model with extra='forbid', so use
@@ -224,59 +218,6 @@ def _make_tools_tolerant(mcp: FastMCP) -> None:
         logger.warning(f"Could not make tools tolerant of extra arguments: {e}")
 
 
-def _collect_coercible_types(schema: dict, param_name: str, array_params: set[str], object_params: set[str]) -> None:
-    """Check a JSON Schema property and add param_name to array_params/object_params if applicable."""
-    # Direct type
-    schema_type = schema.get("type")
-    if schema_type == "array":
-        array_params.add(param_name)
-        return
-    if schema_type == "object":
-        object_params.add(param_name)
-        return
-
-    # anyOf / oneOf unions (e.g., list[str] | None → {"anyOf": [{"type": "array"}, {"type": "null"}]})
-    for variant in schema.get("anyOf", []) + schema.get("oneOf", []):
-        variant_type = variant.get("type")
-        if variant_type == "array":
-            array_params.add(param_name)
-            return
-        if variant_type == "object":
-            object_params.add(param_name)
-            return
-
-
-def _coerce_string_json(arguments: dict, array_params: set[str], object_params: set[str]) -> dict:
-    """Auto-coerce string-encoded JSON arrays/objects to native types.
-
-    LLM agents frequently serialize list and dict tool arguments as JSON strings.
-    This is backward-compatible: native arrays/objects pass through unchanged.
-    """
-    for param_name in array_params:
-        val = arguments.get(param_name)
-        if isinstance(val, str):
-            try:
-                parsed = json.loads(val)
-                if isinstance(parsed, list):
-                    arguments = {**arguments, param_name: parsed}
-                    logger.debug(f"Coerced string to list for parameter '{param_name}'")
-            except (json.JSONDecodeError, TypeError):
-                pass
-
-    for param_name in object_params:
-        val = arguments.get(param_name)
-        if isinstance(val, str):
-            try:
-                parsed = json.loads(val)
-                if isinstance(parsed, dict):
-                    arguments = {**arguments, param_name: parsed}
-                    logger.debug(f"Coerced string to dict for parameter '{param_name}'")
-            except (json.JSONDecodeError, TypeError):
-                pass
-
-    return arguments
-
-
 class MCPMiddleware:
     """ASGI middleware that intercepts MCP requests and routes to appropriate MCP server.
 
@@ -340,12 +281,10 @@ class MCPMiddleware:
             self.single_bank_server = single_bank_server
         else:
             # Create servers internally (for direct construction / tests)
-            global_config = _get_raw_config()
-            stateless = global_config.mcp_stateless
             self.multi_bank_server = create_mcp_server(memory, multi_bank=True)
-            self.multi_bank_app = self.multi_bank_server.http_app(path="/", stateless_http=stateless)
+            self.multi_bank_app = self.multi_bank_server.http_app(path="/", stateless_http=True)
             self.single_bank_server = create_mcp_server(memory, multi_bank=False)
-            self.single_bank_app = self.single_bank_server.http_app(path="/", stateless_http=stateless)
+            self.single_bank_app = self.single_bank_server.http_app(path="/", stateless_http=True)
 
     def _get_header(self, scope: dict, name: str) -> str | None:
         """Extract a header value from ASGI scope."""
@@ -368,17 +307,6 @@ class MCPMiddleware:
             await self.app(scope, receive, send)
             return
 
-        # Handle GET-before-POST gracefully (Claude Code v2.1.84+ sends GET probe before POST initialize).
-        # Without a valid Mcp-Session-Id, GET has no meaningful response — return 200 OK so
-        # the client proceeds to POST initialize instead of marking the server as failed.
-        method = scope.get("method", "")
-        if method == "GET":
-            session_id = self._get_header(scope, "Mcp-Session-Id")
-            if not session_id:
-                logger.debug("MCP GET without session ID (client probe) — returning 200 OK")
-                await self._send_ok(send)
-                return
-
         # Strip prefix from path
         path = path[len(self.prefix) :] or "/"
 
@@ -508,22 +436,6 @@ class MCPMiddleware:
             if schema_token is not None:
                 _current_schema.reset(schema_token)
 
-    async def _send_ok(self, send):
-        """Send a 200 OK response with empty body (used for GET probes without session)."""
-        await send(
-            {
-                "type": "http.response.start",
-                "status": 200,
-                "headers": [(b"content-type", b"application/json")],
-            }
-        )
-        await send(
-            {
-                "type": "http.response.body",
-                "body": b"{}",
-            }
-        )
-
     async def _send_error(self, send, status: int, message: str, extra_headers: dict[str, str] | None = None):
         """Send an error response."""
         body = json.dumps({"error": message}).encode()
@@ -554,14 +466,10 @@ def create_mcp_servers(memory: MemoryEngine):
     Returns:
         Tuple of (multi_bank_server, single_bank_server, multi_bank_app, single_bank_app)
     """
-    global_config = _get_raw_config()
-    stateless = global_config.mcp_stateless
-
     multi_bank_server = create_mcp_server(memory, multi_bank=True)
-    multi_bank_app = multi_bank_server.http_app(path="/", stateless_http=stateless)
+    multi_bank_app = multi_bank_server.http_app(path="/", stateless_http=True)
 
     single_bank_server = create_mcp_server(memory, multi_bank=False)
-    single_bank_app = single_bank_server.http_app(path="/", stateless_http=stateless)
+    single_bank_app = single_bank_server.http_app(path="/", stateless_http=True)
 
-    logger.info(f"MCP servers created (stateless_http={stateless})")
     return multi_bank_server, single_bank_server, multi_bank_app, single_bank_app

hindsight-api-slim/hindsight_api/config.py

@@ -118,7 +118,6 @@ def normalize_config_dict(config: dict[str, Any]) -> dict[str, Any]:
 
 # Environment variable names
 ENV_DATABASE_URL = "HINDSIGHT_API_DATABASE_URL"
-ENV_MIGRATION_DATABASE_URL = "HINDSIGHT_API_MIGRATION_DATABASE_URL"
 ENV_DATABASE_SCHEMA = "HINDSIGHT_API_DATABASE_SCHEMA"
 ENV_LLM_PROVIDER = "HINDSIGHT_API_LLM_PROVIDER"
 ENV_LLM_API_KEY = "HINDSIGHT_API_LLM_API_KEY"
@@ -131,12 +130,10 @@ ENV_LLM_MAX_BACKOFF = "HINDSIGHT_API_LLM_MAX_BACKOFF"
 ENV_LLM_TIMEOUT = "HINDSIGHT_API_LLM_TIMEOUT"
 ENV_LLM_GROQ_SERVICE_TIER = "HINDSIGHT_API_LLM_GROQ_SERVICE_TIER"
 ENV_LLM_OPENAI_SERVICE_TIER = "HINDSIGHT_API_LLM_OPENAI_SERVICE_TIER"
-ENV_LLM_EXTRA_BODY = "HINDSIGHT_API_LLM_EXTRA_BODY"
 
 # Defaults for service tiers
 DEFAULT_LLM_GROQ_SERVICE_TIER = "auto"  # "on_demand", "flex", or "auto"
 DEFAULT_LLM_OPENAI_SERVICE_TIER = None  # None (default) or "flex" (50% cheaper)
-DEFAULT_LLM_EXTRA_BODY = None  # None = no extra body params; JSON dict merged into OpenAI extra_body
 
 # Per-operation LLM configuration (optional, falls back to global LLM config)
 ENV_RETAIN_LLM_PROVIDER = "HINDSIGHT_API_RETAIN_LLM_PROVIDER"
@@ -178,14 +175,6 @@ ENV_EMBEDDINGS_OPENAI_API_KEY = "HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY"
 ENV_EMBEDDINGS_OPENAI_MODEL = "HINDSIGHT_API_EMBEDDINGS_OPENAI_MODEL"
 ENV_EMBEDDINGS_OPENAI_BASE_URL = "HINDSIGHT_API_EMBEDDINGS_OPENAI_BASE_URL"
 
-# Gemini/Vertex AI embeddings configuration
-ENV_EMBEDDINGS_GEMINI_API_KEY = "HINDSIGHT_API_EMBEDDINGS_GEMINI_API_KEY"
-ENV_EMBEDDINGS_GEMINI_MODEL = "HINDSIGHT_API_EMBEDDINGS_GEMINI_MODEL"
-ENV_EMBEDDINGS_GEMINI_OUTPUT_DIMENSIONALITY = "HINDSIGHT_API_EMBEDDINGS_GEMINI_OUTPUT_DIMENSIONALITY"
-ENV_EMBEDDINGS_VERTEXAI_PROJECT_ID = "HINDSIGHT_API_EMBEDDINGS_VERTEXAI_PROJECT_ID"
-ENV_EMBEDDINGS_VERTEXAI_REGION = "HINDSIGHT_API_EMBEDDINGS_VERTEXAI_REGION"
-ENV_EMBEDDINGS_VERTEXAI_SERVICE_ACCOUNT_KEY = "HINDSIGHT_API_EMBEDDINGS_VERTEXAI_SERVICE_ACCOUNT_KEY"
-
 # Cohere configuration (separate for embeddings and reranker)
 ENV_EMBEDDINGS_COHERE_API_KEY = "HINDSIGHT_API_EMBEDDINGS_COHERE_API_KEY"
 ENV_EMBEDDINGS_COHERE_MODEL = "HINDSIGHT_API_EMBEDDINGS_COHERE_MODEL"
@@ -210,7 +199,6 @@ ENV_RERANKER_LITELLM_MAX_TOKENS_PER_DOC = "HINDSIGHT_API_RERANKER_LITELLM_MAX_TO
 ENV_EMBEDDINGS_LITELLM_SDK_API_KEY = "HINDSIGHT_API_EMBEDDINGS_LITELLM_SDK_API_KEY"
 ENV_EMBEDDINGS_LITELLM_SDK_MODEL = "HINDSIGHT_API_EMBEDDINGS_LITELLM_SDK_MODEL"
 ENV_EMBEDDINGS_LITELLM_SDK_API_BASE = "HINDSIGHT_API_EMBEDDINGS_LITELLM_SDK_API_BASE"
-ENV_EMBEDDINGS_LITELLM_SDK_OUTPUT_DIMENSIONS = "HINDSIGHT_API_EMBEDDINGS_LITELLM_SDK_OUTPUT_DIMENSIONS"
 ENV_RERANKER_LITELLM_SDK_API_KEY = "HINDSIGHT_API_RERANKER_LITELLM_SDK_API_KEY"
 ENV_RERANKER_LITELLM_SDK_MODEL = "HINDSIGHT_API_RERANKER_LITELLM_SDK_MODEL"
 ENV_RERANKER_LITELLM_SDK_API_BASE = "HINDSIGHT_API_RERANKER_LITELLM_SDK_API_BASE"
@@ -237,12 +225,6 @@ ENV_RERANKER_FLASHRANK_CACHE_DIR = "HINDSIGHT_API_RERANKER_FLASHRANK_CACHE_DIR"
 # ZeroEntropy configuration (reranker only)
 ENV_RERANKER_ZEROENTROPY_API_KEY = "HINDSIGHT_API_RERANKER_ZEROENTROPY_API_KEY"
 ENV_RERANKER_ZEROENTROPY_MODEL = "HINDSIGHT_API_RERANKER_ZEROENTROPY_MODEL"
-ENV_RERANKER_ZEROENTROPY_BASE_URL = "HINDSIGHT_API_RERANKER_ZEROENTROPY_BASE_URL"
-
-# Google Discovery Engine reranker configuration
-ENV_RERANKER_GOOGLE_MODEL = "HINDSIGHT_API_RERANKER_GOOGLE_MODEL"
-ENV_RERANKER_GOOGLE_PROJECT_ID = "HINDSIGHT_API_RERANKER_GOOGLE_PROJECT_ID"
-ENV_RERANKER_GOOGLE_SERVICE_ACCOUNT_KEY = "HINDSIGHT_API_RERANKER_GOOGLE_SERVICE_ACCOUNT_KEY"
 
 ENV_VECTOR_EXTENSION = "HINDSIGHT_API_VECTOR_EXTENSION"
 ENV_TEXT_SEARCH_EXTENSION = "HINDSIGHT_API_TEXT_SEARCH_EXTENSION"
@@ -255,9 +237,9 @@ ENV_LOG_FORMAT = "HINDSIGHT_API_LOG_FORMAT"
 ENV_WORKERS = "HINDSIGHT_API_WORKERS"
 ENV_MCP_ENABLED = "HINDSIGHT_API_MCP_ENABLED"
 ENV_MCP_ENABLED_TOOLS = "HINDSIGHT_API_MCP_ENABLED_TOOLS"
-ENV_MCP_STATELESS = "HINDSIGHT_API_MCP_STATELESS"
 ENV_ENABLE_BANK_CONFIG_API = "HINDSIGHT_API_ENABLE_BANK_CONFIG_API"
 ENV_GRAPH_RETRIEVER = "HINDSIGHT_API_GRAPH_RETRIEVER"
+ENV_MPFP_TOP_K_NEIGHBORS = "HINDSIGHT_API_MPFP_TOP_K_NEIGHBORS"
 ENV_RECALL_MAX_CONCURRENT = "HINDSIGHT_API_RECALL_MAX_CONCURRENT"
 ENV_RECALL_CONNECTION_BUDGET = "HINDSIGHT_API_RECALL_CONNECTION_BUDGET"
 ENV_RECALL_MAX_QUERY_TOKENS = "HINDSIGHT_API_RECALL_MAX_QUERY_TOKENS"
@@ -269,7 +251,6 @@ ENV_OTEL_EXPORTER_OTLP_ENDPOINT = "HINDSIGHT_API_OTEL_EXPORTER_OTLP_ENDPOINT"
 ENV_OTEL_EXPORTER_OTLP_HEADERS = "HINDSIGHT_API_OTEL_EXPORTER_OTLP_HEADERS"
 ENV_OTEL_SERVICE_NAME = "HINDSIGHT_API_OTEL_SERVICE_NAME"
 ENV_OTEL_DEPLOYMENT_ENVIRONMENT = "HINDSIGHT_API_OTEL_DEPLOYMENT_ENVIRONMENT"
-ENV_METRICS_INCLUDE_BANK_ID = "HINDSIGHT_API_METRICS_INCLUDE_BANK_ID"
 
 # Vertex AI configuration
 ENV_LLM_VERTEXAI_PROJECT_ID = "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID"
@@ -291,7 +272,6 @@ ENV_RETAIN_BATCH_TOKENS = "HINDSIGHT_API_RETAIN_BATCH_TOKENS"
 ENV_RETAIN_ENTITY_LOOKUP = "HINDSIGHT_API_RETAIN_ENTITY_LOOKUP"
 ENV_RETAIN_BATCH_ENABLED = "HINDSIGHT_API_RETAIN_BATCH_ENABLED"
 ENV_RETAIN_BATCH_POLL_INTERVAL_SECONDS = "HINDSIGHT_API_RETAIN_BATCH_POLL_INTERVAL_SECONDS"
-ENV_RETAIN_CHUNK_BATCH_SIZE = "HINDSIGHT_API_RETAIN_CHUNK_BATCH_SIZE"
 
 # File storage configuration
 ENV_FILE_STORAGE_TYPE = "HINDSIGHT_API_FILE_STORAGE_TYPE"
@@ -324,7 +304,6 @@ ENV_CONSOLIDATION_SOURCE_FACTS_MAX_TOKENS_PER_OBSERVATION = (
     "HINDSIGHT_API_CONSOLIDATION_SOURCE_FACTS_MAX_TOKENS_PER_OBSERVATION"
 )
 ENV_OBSERVATIONS_MISSION = "HINDSIGHT_API_OBSERVATIONS_MISSION"
-ENV_MAX_OBSERVATIONS_PER_SCOPE = "HINDSIGHT_API_MAX_OBSERVATIONS_PER_SCOPE"
 ENV_ENABLE_OBSERVATION_HISTORY = "HINDSIGHT_API_ENABLE_OBSERVATION_HISTORY"
 ENV_ENABLE_MENTAL_MODEL_HISTORY = "HINDSIGHT_API_ENABLE_MENTAL_MODEL_HISTORY"
 
@@ -355,7 +334,6 @@ ENV_WORKER_MAX_RETRIES = "HINDSIGHT_API_WORKER_MAX_RETRIES"
 ENV_WORKER_HTTP_PORT = "HINDSIGHT_API_WORKER_HTTP_PORT"
 ENV_WORKER_MAX_SLOTS = "HINDSIGHT_API_WORKER_MAX_SLOTS"
 ENV_WORKER_CONSOLIDATION_MAX_SLOTS = "HINDSIGHT_API_WORKER_CONSOLIDATION_MAX_SLOTS"
-ENV_RETAIN_MAX_CONCURRENT = "HINDSIGHT_API_RETAIN_MAX_CONCURRENT"
 
 # Reflect agent settings
 ENV_REFLECT_MAX_ITERATIONS = "HINDSIGHT_API_REFLECT_MAX_ITERATIONS"
@@ -364,11 +342,6 @@ ENV_REFLECT_WALL_TIMEOUT = "HINDSIGHT_API_REFLECT_WALL_TIMEOUT"
 ENV_REFLECT_MISSION = "HINDSIGHT_API_REFLECT_MISSION"
 ENV_REFLECT_SOURCE_FACTS_MAX_TOKENS = "HINDSIGHT_API_REFLECT_SOURCE_FACTS_MAX_TOKENS"
 
-# Audit log settings
-ENV_AUDIT_LOG_ENABLED = "HINDSIGHT_API_AUDIT_LOG_ENABLED"
-ENV_AUDIT_LOG_ACTIONS = "HINDSIGHT_API_AUDIT_LOG_ACTIONS"
-ENV_AUDIT_LOG_RETENTION_DAYS = "HINDSIGHT_API_AUDIT_LOG_RETENTION_DAYS"
-
 # Disposition settings
 ENV_DISPOSITION_SKEPTICISM = "HINDSIGHT_API_DISPOSITION_SKEPTICISM"
 ENV_DISPOSITION_LITERALISM = "HINDSIGHT_API_DISPOSITION_LITERALISM"
@@ -395,7 +368,6 @@ PROVIDER_DEFAULT_MODELS = {
     "none": "none",
     "litellm": "gpt-4o-mini",
     "bedrock": "us.amazon.nova-2-lite-v1:0",
-    "volcano": "doubao-pro-32k",
 }
 DEFAULT_LLM_MODEL = "gpt-4o-mini"  # Fallback if provider not in table
 DEFAULT_LLM_MAX_CONCURRENT = 32
@@ -417,8 +389,6 @@ DEFAULT_EMBEDDINGS_LOCAL_MODEL = "BAAI/bge-small-en-v1.5"
 DEFAULT_EMBEDDINGS_LOCAL_FORCE_CPU = False  # Force CPU mode for local embeddings (avoids MPS/XPC issues on macOS)
 DEFAULT_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE = False  # Security: disabled by default, required for some models
 DEFAULT_EMBEDDINGS_OPENAI_MODEL = "text-embedding-3-small"
-DEFAULT_EMBEDDINGS_GEMINI_MODEL = "gemini-embedding-001"
-DEFAULT_EMBEDDINGS_GEMINI_OUTPUT_DIMENSIONALITY = 768
 DEFAULT_EMBEDDING_DIMENSION = 384
 
 DEFAULT_RERANKER_PROVIDER = "local"
@@ -442,8 +412,6 @@ DEFAULT_RERANKER_COHERE_MODEL = "rerank-english-v3.0"
 
 DEFAULT_RERANKER_ZEROENTROPY_MODEL = "zerank-2"
 
-DEFAULT_RERANKER_GOOGLE_MODEL = "semantic-ranker-default-004"
-
 # Vector extension (pgvector, vchord, or pgvectorscale)
 DEFAULT_VECTOR_EXTENSION = "pgvector"  # Options: "pgvector", "vchord", "pgvectorscale"
 
@@ -468,9 +436,9 @@ DEFAULT_LOG_FORMAT = "text"  # Options: "text", "json"
 DEFAULT_WORKERS = 1
 DEFAULT_MCP_ENABLED = True
 DEFAULT_MCP_ENABLED_TOOLS: list[str] | None = None  # None = all tools enabled
-DEFAULT_MCP_STATELESS = False  # False = stateful (supports SSE/GET); True = stateless (POST-only)
 DEFAULT_ENABLE_BANK_CONFIG_API = True
-DEFAULT_GRAPH_RETRIEVER = "link_expansion"
+DEFAULT_GRAPH_RETRIEVER = "link_expansion"  # Options: "link_expansion", "mpfp", "bfs"
+DEFAULT_MPFP_TOP_K_NEIGHBORS = 20  # Fan-out limit per node in MPFP graph traversal
 DEFAULT_RECALL_MAX_CONCURRENT = 32  # Max concurrent recall operations per worker
 DEFAULT_RECALL_CONNECTION_BUDGET = 4  # Max concurrent DB connections per recall operation
 DEFAULT_RECALL_MAX_QUERY_TOKENS = 500  # Maximum tokens allowed in recall query
@@ -486,9 +454,6 @@ DEFAULT_RETAIN_MISSION = None  # Declarative spec of what to retain (injected in
 DEFAULT_RETAIN_CUSTOM_INSTRUCTIONS = None  # Custom extraction guidelines (only used when mode="custom")
 DEFAULT_RETAIN_DEFAULT_STRATEGY = None  # Default strategy name (None = no strategy override)
 DEFAULT_RETAIN_STRATEGIES: dict | None = None  # Named retain strategies (dict of name → config overrides)
-DEFAULT_RETAIN_CHUNK_BATCH_SIZE = (
-    100  # Max chunks per streaming batch. Each chunk produces ~17 facts, so 100 chunks = ~1700 facts/batch.
-)
 DEFAULT_RETAIN_BATCH_TOKENS = 10_000  # ~40KB of text  # Max chars per sub-batch for async retain auto-splitting
 DEFAULT_RETAIN_ENTITY_LOOKUP = "trigram"  # "full" or "trigram"
 DEFAULT_RETAIN_BATCH_ENABLED = False  # Use LLM Batch API for fact extraction (only when async=True)
@@ -517,7 +482,6 @@ DEFAULT_CONSOLIDATION_SOURCE_FACTS_MAX_TOKENS_PER_OBSERVATION = (
     256  # Max tokens of source facts per observation in consolidation prompt (-1 = unlimited)
 )
 DEFAULT_OBSERVATIONS_MISSION = None  # Declarative spec of what observations are for this bank
-DEFAULT_MAX_OBSERVATIONS_PER_SCOPE = -1  # Max observations per tag scope (-1 = unlimited)
 
 # Database migrations
 DEFAULT_RUN_MIGRATIONS_ON_STARTUP = True
@@ -536,7 +500,6 @@ DEFAULT_WORKER_MAX_RETRIES = 3  # Max retries before marking task failed
 DEFAULT_WORKER_HTTP_PORT = 8889  # HTTP port for worker metrics/health
 DEFAULT_WORKER_MAX_SLOTS = 10  # Total concurrent tasks per worker
 DEFAULT_WORKER_CONSOLIDATION_MAX_SLOTS = 2  # Max concurrent consolidation tasks per worker
-DEFAULT_RETAIN_MAX_CONCURRENT = 4  # Max concurrent retain DB phases (HNSW reads + writes). Limits I/O contention.
 
 # Reflect agent settings
 DEFAULT_REFLECT_MAX_ITERATIONS = 10  # Max tool call iterations before forcing response
@@ -553,12 +516,6 @@ DEFAULT_DISPOSITION_EMPATHY = None
 DEFAULT_OTEL_TRACES_ENABLED = False  # Disabled by default for backward compatibility
 DEFAULT_OTEL_SERVICE_NAME = "hindsight-api"
 DEFAULT_OTEL_DEPLOYMENT_ENVIRONMENT = "development"
-DEFAULT_METRICS_INCLUDE_BANK_ID = False  # Disabled by default to avoid high-cardinality OTel metric growth
-
-# Audit log defaults
-DEFAULT_AUDIT_LOG_ENABLED = False  # Disabled by default
-DEFAULT_AUDIT_LOG_ACTIONS = ""  # Empty = audit all eligible actions
-DEFAULT_AUDIT_LOG_RETENTION_DAYS = -1  # -1 = keep forever
 
 # Default MCP tool descriptions (can be customized via env vars)
 DEFAULT_MCP_RETAIN_DESCRIPTION = """Store important information to long-term memory.
@@ -648,7 +605,6 @@ class HindsightConfig:
 
     # Database
     database_url: str
-    migration_database_url: str | None
     database_schema: str
     vector_extension: str  # "pgvector" or "vchord"
     text_search_extension: str  # "native" or "vchord"
@@ -665,9 +621,6 @@ class HindsightConfig:
     llm_timeout: float
     llm_groq_service_tier: str  # Groq: "on_demand", "flex", or "auto"
     llm_openai_service_tier: str | None  # OpenAI: None (default) or "flex" (50% cheaper)
-    llm_extra_body: (
-        dict | None
-    )  # Extra body params merged into OpenAI-compatible API calls (e.g. {"chat_template_kwargs": {"enable_thinking": true}})
 
     # Vertex AI configuration
     llm_vertexai_project_id: str | None
@@ -724,14 +677,6 @@ class HindsightConfig:
     embeddings_litellm_sdk_api_key: str | None
     embeddings_litellm_sdk_model: str
     embeddings_litellm_sdk_api_base: str | None
-    embeddings_litellm_sdk_output_dimensions: int | None
-    # Gemini/Vertex AI embeddings
-    embeddings_gemini_api_key: str | None
-    embeddings_gemini_model: str
-    embeddings_gemini_output_dimensionality: int | None
-    embeddings_vertexai_project_id: str | None
-    embeddings_vertexai_region: str | None
-    embeddings_vertexai_service_account_key: str | None
 
     # Reranker
     reranker_provider: str
@@ -758,10 +703,6 @@ class HindsightConfig:
     reranker_litellm_sdk_api_base: str | None
     reranker_zeroentropy_api_key: str | None
     reranker_zeroentropy_model: str
-    reranker_zeroentropy_base_url: str | None
-    reranker_google_model: str
-    reranker_google_project_id: str | None
-    reranker_google_service_account_key: str | None
 
     # Server
     host: str
@@ -771,11 +712,11 @@ class HindsightConfig:
     log_format: str
     mcp_enabled: bool
     mcp_enabled_tools: list[str] | None  # None = all tools; explicit list = allowlist
-    mcp_stateless: bool  # True = stateless HTTP (POST-only); False = stateful (supports GET/SSE)
     enable_bank_config_api: bool
 
     # Recall
     graph_retriever: str
+    mpfp_top_k_neighbors: int
     recall_max_concurrent: int
     recall_connection_budget: int
     recall_max_query_tokens: int
@@ -794,7 +735,6 @@ class HindsightConfig:
     retain_batch_enabled: bool
     retain_batch_poll_interval_seconds: int
     retain_entity_lookup: str  # "full" or "trigram"
-    retain_chunk_batch_size: int  # Max chunks per streaming batch (0 = disabled)
 
     # File storage (static - server-level only)
     file_storage_type: str  # "native" (PostgreSQL) or "s3" (S3-compatible)
@@ -827,7 +767,6 @@ class HindsightConfig:
     consolidation_source_facts_max_tokens: int
     consolidation_source_facts_max_tokens_per_observation: int
     observations_mission: str | None
-    max_observations_per_scope: int
 
     # Entity labels (controlled vocabulary of key:value classification labels extracted at retain time)
     # List of label group dicts: [{key, description, type, optional, values: [{value, description}]}]
@@ -866,7 +805,6 @@ class HindsightConfig:
     worker_http_port: int
     worker_max_slots: int
     worker_consolidation_max_slots: int
-    retain_max_concurrent: int
 
     # Reflect agent settings
     reflect_max_iterations: int
@@ -879,12 +817,6 @@ class HindsightConfig:
     otel_exporter_otlp_headers: str | None
     otel_service_name: str
     otel_deployment_environment: str
-    metrics_include_bank_id: bool
-
-    # Audit log configuration (static - server-level only)
-    audit_log_enabled: bool  # Master switch for audit logging
-    audit_log_actions: list[str]  # Allowlist of action types (empty = all)
-    audit_log_retention_days: int  # -1 = keep forever, >0 = delete after N days
 
     # Webhook configuration (static - server-level only, not per-bank)
     webhook_url: str | None  # Global webhook URL (None = disabled)
@@ -909,13 +841,8 @@ class HindsightConfig:
         "embeddings_tei_base_url",
         "reranker_tei_base_url",
         "reranker_cohere_base_url",
-        "reranker_zeroentropy_base_url",
         # Service Account Keys
         "llm_vertexai_service_account_key",
-        "embeddings_vertexai_service_account_key",
-        "reranker_google_service_account_key",
-        # Embeddings API keys
-        "embeddings_gemini_api_key",
         # File storage credentials
         "file_storage_s3_access_key_id",
         "file_storage_s3_secret_access_key",
@@ -938,7 +865,6 @@ class HindsightConfig:
         "retain_custom_instructions",
         "retain_default_strategy",
         "retain_strategies",
-        "retain_chunk_batch_size",
         # Entity labels (controlled vocabulary for entity classification)
         "entity_labels",
         "entities_allow_free_form",
@@ -948,7 +874,6 @@ class HindsightConfig:
         "consolidation_source_facts_max_tokens",
         "consolidation_source_facts_max_tokens_per_observation",
         "observations_mission",
-        "max_observations_per_scope",
         # Reflect settings
         "reflect_mission",
         "reflect_source_facts_max_tokens",
@@ -1068,7 +993,6 @@ class HindsightConfig:
         config = cls(
             # Database
             database_url=os.getenv(ENV_DATABASE_URL, DEFAULT_DATABASE_URL),
-            migration_database_url=os.getenv(ENV_MIGRATION_DATABASE_URL) or None,
             database_schema=os.getenv(ENV_DATABASE_SCHEMA, DEFAULT_DATABASE_SCHEMA),
             vector_extension=os.getenv(ENV_VECTOR_EXTENSION, DEFAULT_VECTOR_EXTENSION).lower(),
             text_search_extension=os.getenv(ENV_TEXT_SEARCH_EXTENSION, DEFAULT_TEXT_SEARCH_EXTENSION).lower(),
@@ -1084,7 +1008,6 @@ class HindsightConfig:
             llm_timeout=float(os.getenv(ENV_LLM_TIMEOUT, str(DEFAULT_LLM_TIMEOUT))),
             llm_groq_service_tier=os.getenv(ENV_LLM_GROQ_SERVICE_TIER, DEFAULT_LLM_GROQ_SERVICE_TIER),
             llm_openai_service_tier=os.getenv(ENV_LLM_OPENAI_SERVICE_TIER, DEFAULT_LLM_OPENAI_SERVICE_TIER),
-            llm_extra_body=json.loads(os.getenv(ENV_LLM_EXTRA_BODY, "null")),
             # Vertex AI
             llm_vertexai_project_id=os.getenv(ENV_LLM_VERTEXAI_PROJECT_ID) or DEFAULT_LLM_VERTEXAI_PROJECT_ID,
             llm_vertexai_region=os.getenv(ENV_LLM_VERTEXAI_REGION, DEFAULT_LLM_VERTEXAI_REGION),
@@ -1191,23 +1114,6 @@ class HindsightConfig:
                 ENV_EMBEDDINGS_LITELLM_SDK_MODEL, DEFAULT_EMBEDDINGS_LITELLM_SDK_MODEL
             ),
             embeddings_litellm_sdk_api_base=os.getenv(ENV_EMBEDDINGS_LITELLM_SDK_API_BASE) or None,
-            embeddings_litellm_sdk_output_dimensions=int(v)
-            if (v := os.getenv(ENV_EMBEDDINGS_LITELLM_SDK_OUTPUT_DIMENSIONS))
-            else None,
-            # Gemini/Vertex AI embeddings (with fallback to LLM keys)
-            embeddings_gemini_api_key=os.getenv(ENV_EMBEDDINGS_GEMINI_API_KEY) or os.getenv(ENV_LLM_API_KEY),
-            embeddings_gemini_model=os.getenv(ENV_EMBEDDINGS_GEMINI_MODEL, DEFAULT_EMBEDDINGS_GEMINI_MODEL),
-            embeddings_gemini_output_dimensionality=int(
-                os.getenv(
-                    ENV_EMBEDDINGS_GEMINI_OUTPUT_DIMENSIONALITY,
-                    str(DEFAULT_EMBEDDINGS_GEMINI_OUTPUT_DIMENSIONALITY),
-                )
-            ),
-            embeddings_vertexai_project_id=os.getenv(ENV_EMBEDDINGS_VERTEXAI_PROJECT_ID)
-            or os.getenv(ENV_LLM_VERTEXAI_PROJECT_ID),
-            embeddings_vertexai_region=os.getenv(ENV_EMBEDDINGS_VERTEXAI_REGION) or os.getenv(ENV_LLM_VERTEXAI_REGION),
-            embeddings_vertexai_service_account_key=os.getenv(ENV_EMBEDDINGS_VERTEXAI_SERVICE_ACCOUNT_KEY)
-            or os.getenv(ENV_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY),
             # Reranker
             reranker_provider=os.getenv(ENV_RERANKER_PROVIDER, DEFAULT_RERANKER_PROVIDER),
             reranker_local_model=os.getenv(ENV_RERANKER_LOCAL_MODEL, DEFAULT_RERANKER_LOCAL_MODEL),
@@ -1256,13 +1162,6 @@ class HindsightConfig:
             # ZeroEntropy reranker
             reranker_zeroentropy_api_key=os.getenv(ENV_RERANKER_ZEROENTROPY_API_KEY),
             reranker_zeroentropy_model=os.getenv(ENV_RERANKER_ZEROENTROPY_MODEL, DEFAULT_RERANKER_ZEROENTROPY_MODEL),
-            reranker_zeroentropy_base_url=os.getenv(ENV_RERANKER_ZEROENTROPY_BASE_URL) or None,
-            # Google Discovery Engine reranker (with fallback to LLM Vertex AI keys)
-            reranker_google_model=os.getenv(ENV_RERANKER_GOOGLE_MODEL, DEFAULT_RERANKER_GOOGLE_MODEL),
-            reranker_google_project_id=os.getenv(ENV_RERANKER_GOOGLE_PROJECT_ID)
-            or os.getenv(ENV_LLM_VERTEXAI_PROJECT_ID),
-            reranker_google_service_account_key=os.getenv(ENV_RERANKER_GOOGLE_SERVICE_ACCOUNT_KEY)
-            or os.getenv(ENV_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY),
             # Server
             host=os.getenv(ENV_HOST, DEFAULT_HOST),
             port=int(os.getenv(ENV_PORT, DEFAULT_PORT)),
@@ -1273,11 +1172,11 @@ class HindsightConfig:
             mcp_enabled_tools=[t.strip() for t in os.getenv(ENV_MCP_ENABLED_TOOLS).split(",") if t.strip()]
             if os.getenv(ENV_MCP_ENABLED_TOOLS)
             else DEFAULT_MCP_ENABLED_TOOLS,
-            mcp_stateless=os.getenv(ENV_MCP_STATELESS, str(DEFAULT_MCP_STATELESS)).lower() == "true",
             enable_bank_config_api=os.getenv(ENV_ENABLE_BANK_CONFIG_API, str(DEFAULT_ENABLE_BANK_CONFIG_API)).lower()
             == "true",
             # Recall
             graph_retriever=os.getenv(ENV_GRAPH_RETRIEVER, DEFAULT_GRAPH_RETRIEVER),
+            mpfp_top_k_neighbors=int(os.getenv(ENV_MPFP_TOP_K_NEIGHBORS, str(DEFAULT_MPFP_TOP_K_NEIGHBORS))),
             recall_max_concurrent=int(os.getenv(ENV_RECALL_MAX_CONCURRENT, str(DEFAULT_RECALL_MAX_CONCURRENT))),
             recall_connection_budget=int(
                 os.getenv(ENV_RECALL_CONNECTION_BUDGET, str(DEFAULT_RECALL_CONNECTION_BUDGET))
@@ -1312,7 +1211,6 @@ class HindsightConfig:
             retain_batch_poll_interval_seconds=int(
                 os.getenv(ENV_RETAIN_BATCH_POLL_INTERVAL_SECONDS, str(DEFAULT_RETAIN_BATCH_POLL_INTERVAL_SECONDS))
             ),
-            retain_chunk_batch_size=int(os.getenv(ENV_RETAIN_CHUNK_BATCH_SIZE, str(DEFAULT_RETAIN_CHUNK_BATCH_SIZE))),
             # File storage
             file_storage_type=os.getenv(ENV_FILE_STORAGE_TYPE, DEFAULT_FILE_STORAGE_TYPE),
             file_storage_s3_bucket=os.getenv(ENV_FILE_STORAGE_S3_BUCKET) or None,
@@ -1372,9 +1270,6 @@ class HindsightConfig:
                 )
             ),
             observations_mission=os.getenv(ENV_OBSERVATIONS_MISSION) or DEFAULT_OBSERVATIONS_MISSION,
-            max_observations_per_scope=int(
-                os.getenv(ENV_MAX_OBSERVATIONS_PER_SCOPE, str(DEFAULT_MAX_OBSERVATIONS_PER_SCOPE))
-            ),
             entity_labels=None,
             entities_allow_free_form=True,
             # Database migrations
@@ -1394,7 +1289,6 @@ class HindsightConfig:
             worker_consolidation_max_slots=int(
                 os.getenv(ENV_WORKER_CONSOLIDATION_MAX_SLOTS, str(DEFAULT_WORKER_CONSOLIDATION_MAX_SLOTS))
             ),
-            retain_max_concurrent=int(os.getenv(ENV_RETAIN_MAX_CONCURRENT, str(DEFAULT_RETAIN_MAX_CONCURRENT))),
             # Reflect agent settings
             reflect_max_iterations=int(os.getenv(ENV_REFLECT_MAX_ITERATIONS, str(DEFAULT_REFLECT_MAX_ITERATIONS))),
             reflect_max_context_tokens=int(
@@ -1422,16 +1316,6 @@ class HindsightConfig:
             otel_exporter_otlp_headers=os.getenv(ENV_OTEL_EXPORTER_OTLP_HEADERS) or None,
             otel_service_name=os.getenv(ENV_OTEL_SERVICE_NAME, DEFAULT_OTEL_SERVICE_NAME),
             otel_deployment_environment=os.getenv(ENV_OTEL_DEPLOYMENT_ENVIRONMENT, DEFAULT_OTEL_DEPLOYMENT_ENVIRONMENT),
-            metrics_include_bank_id=os.getenv(ENV_METRICS_INCLUDE_BANK_ID, str(DEFAULT_METRICS_INCLUDE_BANK_ID)).lower()
-            in ("true", "1", "yes"),
-            # Audit log configuration (static, server-level only)
-            audit_log_enabled=os.getenv(ENV_AUDIT_LOG_ENABLED, str(DEFAULT_AUDIT_LOG_ENABLED)).lower() == "true",
-            audit_log_actions=[
-                a.strip() for a in os.getenv(ENV_AUDIT_LOG_ACTIONS, DEFAULT_AUDIT_LOG_ACTIONS).split(",") if a.strip()
-            ],
-            audit_log_retention_days=int(
-                os.getenv(ENV_AUDIT_LOG_RETENTION_DAYS, str(DEFAULT_AUDIT_LOG_RETENTION_DAYS))
-            ),
             # Webhook configuration (static, server-level only)
             webhook_url=os.getenv(ENV_WEBHOOK_URL) or DEFAULT_WEBHOOK_URL,
             webhook_secret=os.getenv(ENV_WEBHOOK_SECRET) or DEFAULT_WEBHOOK_SECRET,
@@ -1501,14 +1385,9 @@ class HindsightConfig:
 
         root_logger.addHandler(handler)
 
-        # Silence noisy third-party loggers
-        logging.getLogger("google_genai.models").setLevel(logging.WARNING)
-
     def log_config(self) -> None:
         """Log the current configuration (without sensitive values)."""
         logger.info(f"Database: {self.database_url} (schema: {self.database_schema})")
-        if self.migration_database_url:
-            logger.info(f"Migration database: {self.migration_database_url}")
         logger.info(f"LLM: provider={self.llm_provider}, model={self.llm_model}")
         if self.retain_llm_provider or self.retain_llm_model:
             retain_provider = self.retain_llm_provider or self.llm_provider

hindsight-api-slim/hindsight_api/engine/audit.py

@@ -1,209 +0,0 @@
-"""Audit logging for feature usage tracking.
-
-Provides fire-and-forget audit logging of all mutating and core operations
-(retain, recall, reflect, bank CRUD, etc.) across HTTP, MCP, and system transports.
-"""
-
-from __future__ import annotations
-
-import asyncio
-import json
-import logging
-import uuid
-from collections.abc import Callable
-from contextlib import asynccontextmanager
-from dataclasses import dataclass, field
-from datetime import datetime, timezone
-from typing import Any
-
-import asyncpg
-
-from ..engine.db_utils import acquire_with_retry
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class AuditEntry:
-    """A single audit log entry."""
-
-    action: str
-    transport: str  # "http", "mcp", "system"
-    bank_id: str | None = None
-    started_at: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
-    ended_at: datetime | None = None
-    request: dict[str, Any] | None = None
-    response: dict[str, Any] | None = None
-    metadata: dict[str, Any] = field(default_factory=dict)
-
-
-def _json_default(obj: Any) -> str:
-    """JSON serializer for objects not serializable by default."""
-    if isinstance(obj, datetime):
-        return obj.isoformat()
-    if isinstance(obj, uuid.UUID):
-        return str(obj)
-    if isinstance(obj, bytes):
-        return "<bytes>"
-    if isinstance(obj, set):
-        return list(obj)
-    return str(obj)
-
-
-def _safe_json(data: Any) -> str | None:
-    """Serialize data to JSON string, returning None on failure."""
-    if data is None:
-        return None
-    try:
-        return json.dumps(data, default=_json_default)
-    except Exception:
-        logger.debug("Failed to serialize audit data", exc_info=True)
-        return None
-
-
-_SWEEP_INTERVAL_SECONDS = 3600  # Run retention sweep every hour
-
-
-class AuditLogger:
-    """Fire-and-forget audit log writer with optional retention sweep."""
-
-    def __init__(
-        self,
-        pool_getter: Callable[[], asyncpg.Pool | None],
-        schema_getter: Callable[[], str],
-        enabled: bool,
-        allowed_actions: list[str],
-        retention_days: int = -1,
-    ) -> None:
-        self._pool_getter = pool_getter
-        self._schema_getter = schema_getter
-        self._enabled = enabled
-        self._allowed_actions: frozenset[str] | None = frozenset(allowed_actions) if allowed_actions else None
-        self._retention_days = retention_days
-        self._sweep_task: asyncio.Task | None = None
-
-    def is_enabled(self, action: str) -> bool:
-        """Check if audit logging is enabled for this action."""
-        if not self._enabled:
-            return False
-        if self._allowed_actions is not None:
-            return action in self._allowed_actions
-        return True
-
-    def log_fire_and_forget(self, entry: AuditEntry) -> None:
-        """Schedule an audit write as a background task."""
-        if not self.is_enabled(entry.action):
-            return
-        try:
-            asyncio.create_task(self._safe_log(entry))
-        except RuntimeError:
-            # No running event loop (e.g. during shutdown)
-            logger.debug("Cannot schedule audit log write: no running event loop")
-
-    async def _safe_log(self, entry: AuditEntry) -> None:
-        """Write audit entry to DB. Errors are logged, never raised."""
-        pool = self._pool_getter()
-        if pool is None:
-            logger.debug("Audit log skipped: pool not available")
-            return
-        try:
-            schema = self._schema_getter()
-            table = f"{schema}.audit_log"
-            async with acquire_with_retry(pool, max_retries=1) as conn:
-                await conn.execute(
-                    f"""
-                    INSERT INTO {table}
-                        (id, action, transport, bank_id, started_at, ended_at, request, response, metadata)
-                    VALUES
-                        ($1, $2, $3, $4, $5, $6, $7::jsonb, $8::jsonb, $9::jsonb)
-                    """,
-                    uuid.uuid4(),
-                    entry.action,
-                    entry.transport,
-                    entry.bank_id,
-                    entry.started_at,
-                    entry.ended_at,
-                    _safe_json(entry.request),
-                    _safe_json(entry.response),
-                    _safe_json(entry.metadata) or "{}",
-                )
-        except Exception as e:
-            logger.warning(f"Audit log write failed for action={entry.action}: {e}")
-
-    def start_retention_sweep(self) -> None:
-        """Start the periodic retention sweep if retention is configured."""
-        if self._retention_days <= 0 or not self._enabled:
-            return
-        try:
-            self._sweep_task = asyncio.create_task(self._sweep_loop())
-        except RuntimeError:
-            logger.debug("Cannot start retention sweep: no running event loop")
-
-    async def stop_retention_sweep(self) -> None:
-        """Stop the periodic retention sweep."""
-        if self._sweep_task and not self._sweep_task.done():
-            self._sweep_task.cancel()
-            try:
-                await self._sweep_task
-            except asyncio.CancelledError:
-                pass
-            self._sweep_task = None
-
-    async def _sweep_loop(self) -> None:
-        """Periodically delete audit log entries older than retention_days."""
-        while True:
-            await self._run_sweep()
-            await asyncio.sleep(_SWEEP_INTERVAL_SECONDS)
-
-    async def _run_sweep(self) -> None:
-        """Delete expired audit log entries. Concurrent-safe via row-level deletes."""
-        pool = self._pool_getter()
-        if pool is None:
-            return
-        try:
-            schema = self._schema_getter()
-            table = f"{schema}.audit_log"
-            async with acquire_with_retry(pool, max_retries=1) as conn:
-                result = await conn.execute(
-                    f"DELETE FROM {table} WHERE started_at < NOW() - INTERVAL '{self._retention_days} days'"
-                )
-                if result and result != "DELETE 0":
-                    logger.info(f"Audit log retention sweep: {result}")
-        except Exception as e:
-            logger.warning(f"Audit log retention sweep failed: {e}")
-
-
-@asynccontextmanager
-async def audit_context(
-    audit_logger: AuditLogger | None,
-    action: str,
-    transport: str,
-    bank_id: str | None = None,
-    request: dict[str, Any] | None = None,
-    metadata: dict[str, Any] | None = None,
-):
-    """Async context manager that times the operation and writes audit on exit.
-
-    Usage:
-        async with audit_context(logger, "retain", "http", bank_id, request_dict) as entry:
-            result = await do_work()
-            entry.response = result_dict
-    """
-    if audit_logger is None or not audit_logger.is_enabled(action):
-        entry = AuditEntry(action=action, transport=transport, bank_id=bank_id)
-        yield entry
-        return
-
-    entry = AuditEntry(
-        action=action,
-        transport=transport,
-        bank_id=bank_id,
-        started_at=datetime.now(timezone.utc),
-        request=request,
-        metadata=metadata or {},
-    )
-    try:
-        yield entry
-    finally:
-        entry.ended_at = datetime.now(timezone.utc)
-        audit_logger.log_fire_and_forget(entry)

hindsight-api-slim/hindsight_api/engine/consolidation/consolidator.py

@@ -119,39 +119,6 @@ def _aggregate_source_fields(source_mems: list[dict[str, Any]], tags: list[str]
     )
 
 
-async def _count_observations_for_scope(
-    conn: "Connection",
-    bank_id: str,
-    tags: list[str],
-) -> int:
-    """Count existing observations matching the given tag scope.
-
-    Returns the count of observations whose tags contain all specified tags.
-    Observations with no tags are not counted (the limit does not apply to them).
-    """
-    return await conn.fetchval(
-        f"SELECT COUNT(*) FROM {fq_table('memory_units')} "
-        f"WHERE bank_id = $1 AND fact_type = 'observation' AND tags @> $2::varchar[]",
-        bank_id,
-        tags,
-    )
-
-
-def _build_response_model(max_creates: int | None = None) -> type[_ConsolidationBatchResponse]:
-    """Build a response model, optionally constraining max creates via JSON schema."""
-    if max_creates is None or max_creates < 0:
-        return _ConsolidationBatchResponse
-
-    from pydantic import Field as PydanticField
-
-    clamped = max(max_creates, 0)
-
-    class _ConstrainedConsolidationBatchResponse(_ConsolidationBatchResponse):
-        creates: list[_CreateAction] = PydanticField(default=[], max_length=clamped)
-
-    return _ConstrainedConsolidationBatchResponse
-
-
 class ConsolidationPerfLog:
     """Performance logging for consolidation operations."""
 
@@ -731,26 +698,6 @@ async def _process_memory_batch(
         if recall_result.source_facts:
             union_source_facts.update(recall_result.source_facts)
 
-    # Determine effective tag scope for observations.
-    # When obs_tags_override is set, use it; otherwise use the memory's own tags.
-    if obs_tags_override is not None:
-        fact_tags = obs_tags_override
-    else:
-        # All memories in the batch share the same tag set (enforced by batching)
-        fact_tags = memories[0].get("tags") or [] if memories else []
-
-    # 2b. Compute remaining observation slots for this scope (if limit configured)
-    max_obs = config.max_observations_per_scope if config is not None else -1
-    remaining_observation_slots: int | None = None
-    if max_obs > 0 and fact_tags:
-        current_count = await _count_observations_for_scope(conn, bank_id, fact_tags)
-        remaining_observation_slots = max(max_obs - current_count, 0)
-        if remaining_observation_slots == 0:
-            logger.info(
-                f"[CONSOLIDATION] bank={bank_id} scope={fact_tags} at observation limit "
-                f"({current_count}/{max_obs}), only updates/deletes allowed"
-            )
-
     # 3. Single LLM call
     t0 = time.time()
     llm_result = await _consolidate_batch_with_llm(
@@ -759,32 +706,46 @@ async def _process_memory_batch(
         union_observations=union_observations,
         union_source_facts=union_source_facts,
         config=config,
-        remaining_observation_slots=remaining_observation_slots,
-        max_observations_per_scope=max_obs,
     )
     if perf:
         perf.record_timing("llm", time.time() - t0)
         perf.record_llm_call(llm_result.obs_count, llm_result.prompt_chars)
 
-    # 4. Sequential execution of deletes / updates / creates
-    # Deletes run first to free observation slots before creates consume them.
+    # 4. Sequential execution of creates / updates / deletes
     # Track which memory indices participated so we can build per-memory results for stats
     per_memory_created: set[str] = set()
     per_memory_updated: set[str] = set()
 
+    # Determine effective tag scope for observations.
+    # When obs_tags_override is set, use it; otherwise use the memory's own tags.
+    if obs_tags_override is not None:
+        fact_tags = obs_tags_override
+    else:
+        # All memories in the batch share the same tag set (enforced by batching)
+        fact_tags = memories[0].get("tags") or [] if memories else []
+
     mem_by_id = {str(m["id"]): m for m in memories}
 
-    # Execute deletes first to free observation slots before creates consume them
-    deleted_count = 0
-    for delete in llm_result.deletes:
-        # Security: the observation must be present in the unioned recall
-        if not any(str(obs.id) == delete.observation_id for obs in union_observations):
-            logger.debug(
-                f"Batch consolidation: rejected delete — observation {delete.observation_id} not in unioned recall"
-            )
+    for create in llm_result.creates:
+        source_mems = [mem_by_id[fid] for fid in create.source_fact_ids if fid in mem_by_id]
+        if not source_mems:
             continue
-        await _execute_delete_action(conn=conn, bank_id=bank_id, observation_id=delete.observation_id)
-        deleted_count += 1
+        agg = _aggregate_source_fields(source_mems, tags=fact_tags)
+        await _execute_create_action(
+            conn=conn,
+            memory_engine=memory_engine,
+            bank_id=bank_id,
+            source_memory_ids=[m["id"] for m in source_mems],
+            text=create.text,
+            source_fact_tags=agg.tags,
+            event_date=agg.event_date,
+            occurred_start=agg.occurred_start,
+            occurred_end=agg.occurred_end,
+            mentioned_at=agg.mentioned_at,
+            perf=perf,
+        )
+        for m in source_mems:
+            per_memory_created.add(str(m["id"]))
 
     for update in llm_result.updates:
         source_mems = [mem_by_id[fid] for fid in update.source_fact_ids if fid in mem_by_id]
@@ -815,26 +776,16 @@ async def _process_memory_batch(
         for m in source_mems:
             per_memory_updated.add(str(m["id"]))
 
-    for create in llm_result.creates:
-        source_mems = [mem_by_id[fid] for fid in create.source_fact_ids if fid in mem_by_id]
-        if not source_mems:
+    deleted_count = 0
+    for delete in llm_result.deletes:
+        # Security: the observation must be present in the unioned recall
+        if not any(str(obs.id) == delete.observation_id for obs in union_observations):
+            logger.debug(
+                f"Batch consolidation: rejected delete — observation {delete.observation_id} not in unioned recall"
+            )
             continue
-        agg = _aggregate_source_fields(source_mems, tags=fact_tags)
-        await _execute_create_action(
-            conn=conn,
-            memory_engine=memory_engine,
-            bank_id=bank_id,
-            source_memory_ids=[m["id"] for m in source_mems],
-            text=create.text,
-            source_fact_tags=agg.tags,
-            event_date=agg.event_date,
-            occurred_start=agg.occurred_start,
-            occurred_end=agg.occurred_end,
-            mentioned_at=agg.mentioned_at,
-            perf=perf,
-        )
-        for m in source_mems:
-            per_memory_created.add(str(m["id"]))
+        await _execute_delete_action(conn=conn, bank_id=bank_id, observation_id=delete.observation_id)
+        deleted_count += 1
 
     # Build per-memory result dicts for the stats tracker in the outer loop
     results: list[dict[str, Any]] = []
@@ -1132,8 +1083,6 @@ async def _consolidate_batch_with_llm(
     union_observations: "list[MemoryFact]",
     union_source_facts: "dict[str, MemoryFact]",
     config: Any = None,
-    remaining_observation_slots: int | None = None,
-    max_observations_per_scope: int = -1,
 ) -> _BatchLLMResult:
     """Single LLM call for a batch of facts against a pooled set of observations."""
     if union_observations:
@@ -1157,51 +1106,24 @@ async def _consolidate_batch_with_llm(
 
     facts_lines = "\n".join(_fact_line(m) for m in memories)
 
-    # Build capacity note for the prompt when observation limit is configured
-    observation_capacity_note: str | None = None
-    if remaining_observation_slots is not None and max_observations_per_scope > 0:
-        if remaining_observation_slots == 0:
-            observation_capacity_note = (
-                f"OBSERVATION LIMIT REACHED ({max_observations_per_scope}/{max_observations_per_scope}). "
-                "Only UPDATE or DELETE existing observations. Do NOT create new ones — "
-                "merge new knowledge into existing observations via UPDATE."
-            )
-        elif remaining_observation_slots <= len(memories):
-            observation_capacity_note = (
-                f"This scope has {remaining_observation_slots} observation slot(s) remaining "
-                f"(out of {max_observations_per_scope}). Prefer UPDATE over CREATE when possible."
-            )
-
     observations_mission = config.observations_mission if config is not None else None
-    prompt_template = build_batch_consolidation_prompt(observations_mission, observation_capacity_note)
+    prompt_template = build_batch_consolidation_prompt(observations_mission)
     prompt = prompt_template.format(
         facts_text=facts_lines,
         observations_text=observations_text,
     )
 
-    # Use a constrained response model when observation limit is active
-    response_model = _build_response_model(max_creates=remaining_observation_slots)
-
     max_attempts = 3
     last_exc: Exception | None = None
     for attempt in range(1, max_attempts + 1):
         try:
             response: _ConsolidationBatchResponse = await llm_config.call(
                 messages=[{"role": "user", "content": prompt}],
-                response_format=response_model,
+                response_format=_ConsolidationBatchResponse,
                 scope="consolidation",
             )
-            # Defensive truncation: some LLM providers may not enforce JSON schema max_length
-            creates = response.creates
-            if remaining_observation_slots is not None and remaining_observation_slots >= 0:
-                if len(creates) > remaining_observation_slots:
-                    logger.info(
-                        f"[CONSOLIDATION] Truncating {len(creates)} creates to {remaining_observation_slots} "
-                        f"(max_observations_per_scope={max_observations_per_scope})"
-                    )
-                    creates = creates[:remaining_observation_slots]
             return _BatchLLMResult(
-                creates=creates,
+                creates=response.creates,
                 updates=response.updates,
                 deletes=response.deletes,
                 obs_count=len(union_observations),

hindsight-api-slim/hindsight_api/engine/consolidation/prompts.py

@@ -5,24 +5,10 @@ _DEFAULT_MISSION = "Track every detail: names, numbers, dates, places, and relat
 
 # Processing rules — always present regardless of mission
 _PROCESSING_RULES = """Processing rules (always apply):
-
-1. ONE OBSERVATION PER DISTINCT FACET: each observation tracks exactly one specific facet — a count ("has 3 items"), a named entity ("has a dog named Rex"), a relationship ("works at Google"), etc. Never merge different facets into one observation.
-
-2. MATCH BY ENTITY/FACET, NOT TOPIC: when deciding whether to UPDATE vs CREATE, match on the specific entity or facet. "Sold item X" updates only the X observation. "Now has 5 items" updates only the count observation. Do not update observations about different entities just because they share a general topic.
-
-3. STATE CHANGES — UPDATE CONCISELY: when a fact changes the state of something ("sold X", "X died", "moved to Y"), UPDATE the matching observation to reflect the current state. Include dates when available. Keep it concise — only information about THAT specific facet. Example: "User owned a dog named Rex who died on March 15, 2025". Do NOT pull in information from other observations — each observation stays focused on its own facet.
-
-4. CASCADE TO ALL AFFECTED OBSERVATIONS: a state change may affect multiple observations. For example, if entity C is removed from a group, update BOTH the individual observation for C AND any list/group observation that includes C (remove C from the list while keeping all other members intact).
-
-5. NO COMPUTATION: you do not have the full picture — never calculate, derive, or adjust numeric values. If the user says "I have 2 dogs" and then "I have a dog named Rex", do NOT update the count to 3 — you don't know if Rex is one of the 2 or a new one. If the user says "I sold X", do NOT decrement a count. Only update a count when the user explicitly states a new count. Synthesize and consolidate what was stated, but never do arithmetic or logical deductions.
-
-6. SAME FACET → UPDATE, NOT CREATE: a new count supersedes the old count — UPDATE the existing count observation, don't create a second one. If there's an existing observation for the same specific facet, always UPDATE it rather than creating a duplicate.
-
-7. PRESERVE HISTORY: observations that record significant events (sold, died, moved, changed) are important history — never DELETE them. Only delete an observation when it is restated identically or truly meaningless. Be very conservative with deletes.
-
-8. RESOLVE REFERENCES: when a new fact provides a concrete value for a vague placeholder in an existing observation (e.g., "home country" → "Sweden"), UPDATE to embed the resolved value.
-
-9. NEVER merge observations about different people or unrelated topics."""
+- REDUNDANT: same info worded differently → UPDATE the existing observation.
+- CONTRADICTION/UPDATE: capture both states with temporal markers ("used to X, now Y").
+- RESOLVE REFERENCES: when a new fact provides a concrete value resolving a vague placeholder in an existing observation (e.g. "home country", "hometown", "birthplace", "native language", "her ex", "that city"), UPDATE the observation to embed the resolved value explicitly. Example: new fact says "grandma in Sweden" + existing observation says "moved from her home country" → update to "home country is Sweden".
+- NEVER merge observations about different people or unrelated topics."""
 
 # Data section — format placeholders {facts_text} and {observations_text} are substituted at call time
 _BATCH_DATA_SECTION = """
@@ -40,8 +26,8 @@ Each observation includes:
 - source_memories: array of supporting facts with their text and dates
 
 Compare the facts against existing observations:
-- Same facet as an existing observation → UPDATE it (observation_id + source_fact_ids)
-- New facet with durable knowledge → CREATE a new observation (source_fact_ids)
+- Same topic as an existing observation → UPDATE it (observation_id + source_fact_ids)
+- New topic with durable knowledge → CREATE a new observation (source_fact_ids)
 - Cross-reference facts within the batch: a later fact may resolve a vague reference in an earlier one
 - Purely ephemeral facts → omit them unless the MISSION above explicitly targets such data (e.g. timestamped events, session state, screen content)"""
 
@@ -80,10 +66,7 @@ Rules:
 - Return {{"creates": [], "updates": [], "deletes": []}} if nothing durable is found."""
 
 
-def build_batch_consolidation_prompt(
-    observations_mission: str | None = None,
-    observation_capacity_note: str | None = None,
-) -> str:
+def build_batch_consolidation_prompt(observations_mission: str | None = None) -> str:
     """
     Build the consolidation prompt for batch mode (multiple facts per LLM call).
 
@@ -92,13 +75,9 @@ def build_batch_consolidation_prompt(
     """
     mission = observations_mission or _DEFAULT_MISSION
 
-    capacity_section = ""
-    if observation_capacity_note:
-        capacity_section = f"\n\n## CAPACITY CONSTRAINT\n{observation_capacity_note}"
-
     return (
         "You are a memory consolidation system. Synthesize facts into observations "
         "and merge with existing observations when appropriate.\n\n"
-        f"## MISSION\n{mission}{capacity_section}\n\n"
+        f"## MISSION\n{mission}\n\n"
         f"{_PROCESSING_RULES}" + _BATCH_DATA_SECTION + _BATCH_OUTPUT_FORMAT
     )

hindsight-api-slim/hindsight_api/engine/cross_encoder.py

@@ -20,7 +20,6 @@ from ..config import (
     DEFAULT_RERANKER_COHERE_MODEL,
     DEFAULT_RERANKER_FLASHRANK_CACHE_DIR,
     DEFAULT_RERANKER_FLASHRANK_MODEL,
-    DEFAULT_RERANKER_GOOGLE_MODEL,
     DEFAULT_RERANKER_LITELLM_MAX_TOKENS_PER_DOC,
     DEFAULT_RERANKER_LITELLM_MODEL,
     DEFAULT_RERANKER_LITELLM_SDK_MODEL,
@@ -37,7 +36,6 @@ from ..config import (
     ENV_RERANKER_COHERE_MODEL,
     ENV_RERANKER_FLASHRANK_CACHE_DIR,
     ENV_RERANKER_FLASHRANK_MODEL,
-    ENV_RERANKER_GOOGLE_PROJECT_ID,
     ENV_RERANKER_LITELLM_SDK_API_KEY,
     ENV_RERANKER_LOCAL_FORCE_CPU,
     ENV_RERANKER_LOCAL_MAX_CONCURRENT,
@@ -546,7 +544,6 @@ class CohereCrossEncoder(CrossEncoderModel):
         self.base_url = base_url
         self.timeout = timeout
         self._client = None
-        self._httpx_client: httpx.Client | None = None
 
     @property
     def provider_name(self) -> str:
@@ -554,32 +551,23 @@ class CohereCrossEncoder(CrossEncoderModel):
 
     async def initialize(self) -> None:
         """Initialize the Cohere client."""
-        if self._client is not None or self._httpx_client is not None:
+        if self._client is not None:
             return
 
+        try:
+            import cohere
+        except ImportError:
+            raise ImportError("cohere is required for CohereCrossEncoder. Install it with: pip install cohere")
+
         base_url_msg = f" at {self.base_url}" if self.base_url else ""
         logger.info(f"Reranker: initializing Cohere provider with model {self.model}{base_url_msg}")
 
+        # Build client kwargs, only including base_url if set (for Azure or custom endpoints)
+        client_kwargs = {"api_key": self.api_key, "timeout": self.timeout}
         if self.base_url:
-            # For custom endpoints (Azure AI Foundry), use httpx directly to avoid SDK path appending
-            # Azure endpoints already include the full path (e.g., /models/.../invoke)
-            self._httpx_client = httpx.Client(
-                timeout=self.timeout,
-                headers={
-                    "Authorization": f"Bearer {self.api_key}",
-                    "Content-Type": "application/json",
-                },
-            )
-            logger.info("Reranker: Cohere provider initialized (using httpx for custom endpoint)")
-        else:
-            # For native Cohere API, use the official SDK
-            try:
-                import cohere
-            except ImportError:
-                raise ImportError("cohere is required for CohereCrossEncoder. Install it with: pip install cohere")
-
-            self._client = cohere.Client(api_key=self.api_key, timeout=self.timeout)
-            logger.info("Reranker: Cohere provider initialized")
+            client_kwargs["base_url"] = self.base_url
+        self._client = cohere.Client(**client_kwargs)
+        logger.info("Reranker: Cohere provider initialized")
 
     async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
         """
@@ -591,7 +579,7 @@ class CohereCrossEncoder(CrossEncoderModel):
         Returns:
             List of relevance scores
         """
-        if self._client is None and self._httpx_client is None:
+        if self._client is None:
             raise RuntimeError("Reranker not initialized. Call initialize() first.")
 
         if not pairs:
@@ -617,40 +605,18 @@ class CohereCrossEncoder(CrossEncoderModel):
             texts = [text for _, text in indexed_texts]
             indices = [idx for idx, _ in indexed_texts]
 
-            if self._httpx_client:
-                # Direct HTTP request for custom endpoints (Azure AI Foundry)
-                response = self._httpx_client.post(
-                    self.base_url,
-                    json={
-                        "model": self.model,
-                        "query": query,
-                        "documents": texts,
-                        "return_documents": False,
-                    },
-                )
-                response.raise_for_status()
-                result = response.json()
+            response = self._client.rerank(
+                query=query,
+                documents=texts,
+                model=self.model,
+                return_documents=False,
+            )
 
-                # Map scores back to original positions
-                # Azure Cohere response format: {"results": [{"index": 0, "relevance_score": 0.9}, ...]}
-                for item in result.get("results", []):
-                    original_idx = item["index"]
-                    score = item["relevance_score"]
-                    all_scores[indices[original_idx]] = score
-            else:
-                # Native Cohere SDK for standard API
-                response = self._client.rerank(
-                    query=query,
-                    documents=texts,
-                    model=self.model,
-                    return_documents=False,
-                )
-
-                # Map scores back to original positions
-                for result in response.results:
-                    original_idx = result.index
-                    score = result.relevance_score
-                    all_scores[indices[original_idx]] = score
+            # Map scores back to original positions
+            for result in response.results:
+                original_idx = result.index
+                score = result.relevance_score
+                all_scores[indices[original_idx]] = score
 
         return all_scores
 
@@ -663,14 +629,12 @@ class ZeroEntropyCrossEncoder(CrossEncoderModel):
     See: https://docs.zeroentropy.dev/models
     """
 
-    DEFAULT_BASE_URL = "https://api.zeroentropy.dev"
-    RERANK_PATH = "/v1/models/rerank"
+    RERANK_URL = "https://api.zeroentropy.dev/v1/models/rerank"
 
     def __init__(
         self,
         api_key: str,
         model: str = DEFAULT_RERANKER_ZEROENTROPY_MODEL,
-        base_url: str | None = None,
         timeout: float = 60.0,
     ):
         """
@@ -679,13 +643,10 @@ class ZeroEntropyCrossEncoder(CrossEncoderModel):
         Args:
             api_key: ZeroEntropy API key
             model: ZeroEntropy rerank model name (default: zerank-2)
-            base_url: Custom base URL for ZeroEntropy-compatible API (e.g., mock server or proxy)
             timeout: Request timeout in seconds (default: 60.0)
         """
         self.api_key = api_key
         self.model = model
-        self.base_url = base_url.rstrip("/") if base_url else self.DEFAULT_BASE_URL
-        self.rerank_url = f"{self.base_url}{self.RERANK_PATH}"
         self.timeout = timeout
         self._async_client: httpx.AsyncClient | None = None
 
@@ -738,7 +699,7 @@ class ZeroEntropyCrossEncoder(CrossEncoderModel):
             indices = [idx for idx, _ in indexed_texts]
 
             response = await self._async_client.post(
-                self.rerank_url,
+                self.RERANK_URL,
                 json={
                     "model": self.model,
                     "query": query,
@@ -1268,164 +1229,6 @@ class JinaMLXCrossEncoder(CrossEncoderModel):
         return await loop.run_in_executor(None, self._predict_sync, pairs)
 
 
-class GoogleCrossEncoder(CrossEncoderModel):
-    """
-    Google Discovery Engine cross-encoder using the Ranking REST API.
-
-    Uses httpx + google-auth for lightweight REST calls (no gRPC/protobuf).
-    Supports ADC (Application Default Credentials) or service account key file.
-
-    Available models:
-    - semantic-ranker-default-004: Best quality, 1024 tokens/record (recommended)
-    - semantic-ranker-fast-004: Lower latency, 1024 tokens/record
-
-    Max 200 records per API request. Location is always "global".
-    """
-
-    MAX_RECORDS_PER_REQUEST = 200
-    API_BASE = "https://discoveryengine.googleapis.com/v1"
-    SCOPES = ["https://www.googleapis.com/auth/cloud-platform"]
-
-    def __init__(
-        self,
-        project_id: str,
-        model: str = DEFAULT_RERANKER_GOOGLE_MODEL,
-        service_account_key: str | None = None,
-        location: str = "global",
-        timeout: float = 60.0,
-    ):
-        """
-        Initialize Google Discovery Engine cross-encoder.
-
-        Args:
-            project_id: Google Cloud project ID
-            model: Ranking model name (default: semantic-ranker-default-004)
-            service_account_key: Path to service account JSON key file.
-                                If None, uses Application Default Credentials (ADC).
-            location: API location (default: "global")
-            timeout: Request timeout in seconds (default: 60.0)
-        """
-        self.project_id = project_id
-        self.model = model
-        self.service_account_key = service_account_key
-        self.location = location
-        self.timeout = timeout
-        self._credentials = None
-        self._client: httpx.Client | None = None
-        self._rank_url: str | None = None
-
-    @property
-    def provider_name(self) -> str:
-        return "google"
-
-    def _get_auth_headers(self) -> dict[str, str]:
-        """Get Authorization header with a fresh access token."""
-        import google.auth.transport.requests
-
-        if not self._credentials.valid:
-            self._credentials.refresh(google.auth.transport.requests.Request())
-        return {"Authorization": f"Bearer {self._credentials.token}"}
-
-    async def initialize(self) -> None:
-        """Initialize credentials and HTTP client."""
-        if self._client is not None:
-            return
-
-        auth_method = "ADC" if not self.service_account_key else "service_account"
-        logger.info(
-            f"Reranker: initializing Google Discovery Engine provider "
-            f"(project={self.project_id}, model={self.model}, auth={auth_method})"
-        )
-        if self.service_account_key:
-            try:
-                from google.oauth2 import service_account
-            except ImportError:
-                raise ImportError(
-                    "google-auth is required for GoogleCrossEncoder. Install it with: pip install google-auth"
-                )
-            self._credentials = service_account.Credentials.from_service_account_file(
-                self.service_account_key,
-                scopes=self.SCOPES,
-            )
-        else:
-            try:
-                import google.auth
-            except ImportError:
-                raise ImportError(
-                    "google-auth is required for GoogleCrossEncoder. Install it with: pip install google-auth"
-                )
-            self._credentials, _ = google.auth.default(scopes=self.SCOPES)
-
-        ranking_config = f"projects/{self.project_id}/locations/{self.location}/rankingConfigs/default_ranking_config"
-        self._rank_url = f"{self.API_BASE}/{ranking_config}:rank"
-        self._client = httpx.Client(timeout=self.timeout)
-
-        logger.info("Reranker: Google Discovery Engine provider initialized")
-
-    def _predict_sync(self, pairs: list[tuple[str, str]]) -> list[float]:
-        """Synchronous predict via REST API."""
-        if not pairs:
-            return []
-
-        # Group pairs by query
-        query_groups: dict[str, list[tuple[int, str]]] = {}
-        for idx, (query, text) in enumerate(pairs):
-            if query not in query_groups:
-                query_groups[query] = []
-            query_groups[query].append((idx, text))
-
-        all_scores = [0.0] * len(pairs)
-
-        for query, indexed_texts in query_groups.items():
-            texts = [text for _, text in indexed_texts]
-            indices = [idx for idx, _ in indexed_texts]
-
-            # Process in batches of MAX_RECORDS_PER_REQUEST
-            for batch_start in range(0, len(texts), self.MAX_RECORDS_PER_REQUEST):
-                batch_texts = texts[batch_start : batch_start + self.MAX_RECORDS_PER_REQUEST]
-                batch_indices = indices[batch_start : batch_start + self.MAX_RECORDS_PER_REQUEST]
-
-                records = [{"id": str(i), "content": text} for i, text in enumerate(batch_texts)]
-
-                response = self._client.post(
-                    self._rank_url,
-                    headers=self._get_auth_headers(),
-                    json={
-                        "model": self.model,
-                        "query": query,
-                        "records": records,
-                        "topN": len(records),
-                    },
-                )
-                response.raise_for_status()
-                result = response.json()
-
-                for record in result.get("records", []):
-                    local_idx = int(record["id"])
-                    all_scores[batch_indices[local_idx]] = record["score"]
-
-        return all_scores
-
-    async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
-        """
-        Score query-document pairs using Google Discovery Engine Ranking API.
-
-        Args:
-            pairs: List of (query, document) tuples to score
-
-        Returns:
-            List of relevance scores (0-1, higher = more relevant)
-        """
-        if self._client is None:
-            raise RuntimeError("Reranker not initialized. Call initialize() first.")
-
-        if not pairs:
-            return []
-
-        loop = asyncio.get_event_loop()
-        return await loop.run_in_executor(None, self._predict_sync, pairs)
-
-
 def create_cross_encoder_from_env() -> CrossEncoderModel:
     """
     Create a CrossEncoderModel instance based on configuration.
@@ -1501,23 +1304,11 @@ def create_cross_encoder_from_env() -> CrossEncoderModel:
             api_key=api_key,
             model=config.reranker_zeroentropy_model,
         )
-    elif provider == "google":
-        project_id = config.reranker_google_project_id
-        if not project_id:
-            raise ValueError(
-                f"{ENV_RERANKER_GOOGLE_PROJECT_ID} (or HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID) "
-                f"is required when {ENV_RERANKER_PROVIDER} is 'google'"
-            )
-        return GoogleCrossEncoder(
-            project_id=project_id,
-            model=config.reranker_google_model,
-            service_account_key=config.reranker_google_service_account_key,
-        )
     elif provider == "rrf":
         return RRFPassthroughCrossEncoder()
     elif provider == "jina-mlx":
         return JinaMLXCrossEncoder()
     else:
         raise ValueError(
-            f"Unknown reranker provider: {provider}. Supported: 'local', 'tei', 'cohere', 'zeroentropy', 'google', 'flashrank', 'litellm', 'litellm-sdk', 'rrf', 'jina-mlx'"
+            f"Unknown reranker provider: {provider}. Supported: 'local', 'tei', 'cohere', 'zeroentropy', 'flashrank', 'litellm', 'litellm-sdk', 'rrf', 'jina-mlx'"
         )

hindsight-api-slim/hindsight_api/engine/embeddings.py

@@ -13,13 +13,11 @@ import logging
 import os
 import warnings
 from abc import ABC, abstractmethod
-from urllib.parse import parse_qs, urlparse, urlunparse
 
 import httpx
 
 from ..config import (
     DEFAULT_EMBEDDINGS_COHERE_MODEL,
-    DEFAULT_EMBEDDINGS_GEMINI_MODEL,
     DEFAULT_EMBEDDINGS_LITELLM_MODEL,
     DEFAULT_EMBEDDINGS_LITELLM_SDK_MODEL,
     DEFAULT_EMBEDDINGS_LOCAL_FORCE_CPU,
@@ -29,7 +27,6 @@ from ..config import (
     DEFAULT_EMBEDDINGS_PROVIDER,
     DEFAULT_LITELLM_API_BASE,
     ENV_EMBEDDINGS_COHERE_API_KEY,
-    ENV_EMBEDDINGS_GEMINI_API_KEY,
     ENV_EMBEDDINGS_LITELLM_SDK_API_KEY,
     ENV_EMBEDDINGS_LOCAL_FORCE_CPU,
     ENV_EMBEDDINGS_LOCAL_MODEL,
@@ -429,19 +426,9 @@ class OpenAIEmbeddings(Embeddings):
         logger.info(f"Embeddings: initializing OpenAI provider with model {self.model}{base_url_msg}")
 
         # Build client kwargs, only including base_url if set (for Azure or custom endpoints)
-        # Parse query parameters from base_url (e.g. ?api-version=xxx for Azure OpenAI)
-        # and pass them as default_query so they're included in every request.
         client_kwargs = {"api_key": self.api_key, "max_retries": self.max_retries}
         if self.base_url:
-            parsed = urlparse(self.base_url)
-            if parsed.query:
-                clean_url = urlunparse(parsed._replace(query=""))
-                client_kwargs["base_url"] = clean_url
-                default_query = {k: v[0] for k, v in parse_qs(parsed.query).items()}
-                client_kwargs["default_query"] = default_query
-                self.base_url = clean_url
-            else:
-                client_kwargs["base_url"] = self.base_url
+            client_kwargs["base_url"] = self.base_url
         self._client = OpenAI(**client_kwargs)
 
         # Try to get dimension from known models, otherwise do a test embedding
@@ -754,7 +741,6 @@ class LiteLLMSDKEmbeddings(Embeddings):
         api_key: str,
         model: str = DEFAULT_EMBEDDINGS_LITELLM_SDK_MODEL,
         api_base: str | None = None,
-        output_dimensions: int | None = None,
         batch_size: int = 100,
         timeout: float = 60.0,
     ):
@@ -765,14 +751,12 @@ class LiteLLMSDKEmbeddings(Embeddings):
             api_key: API key for the embedding provider
             model: Model name with provider prefix (e.g., "cohere/embed-english-v3.0")
             api_base: Custom base URL for API (optional)
-            output_dimensions: Optional output embedding dimensions (provider-dependent)
             batch_size: Maximum batch size for embedding requests (default: 100)
             timeout: Request timeout in seconds (default: 60.0)
         """
         self.api_key = api_key
         self.model = model
         self.api_base = api_base
-        self.output_dimensions = output_dimensions
         self.batch_size = batch_size
         self.timeout = timeout
         self._litellm = None  # Will be set during initialization
@@ -814,8 +798,6 @@ class LiteLLMSDKEmbeddings(Embeddings):
             }
             if self.api_base:
                 embed_kwargs["api_base"] = self.api_base
-            if self.output_dimensions is not None:
-                embed_kwargs["dimensions"] = self.output_dimensions
 
             # Use async embedding method (standard in litellm)
             response = await self._litellm.aembedding(**embed_kwargs)
@@ -863,8 +845,6 @@ class LiteLLMSDKEmbeddings(Embeddings):
                 }
                 if self.api_base:
                     embed_kwargs["api_base"] = self.api_base
-                if self.output_dimensions is not None:
-                    embed_kwargs["dimensions"] = self.output_dimensions
 
                 # Use sync embedding (litellm doesn't have async in thread-safe way)
                 response = self._litellm.embedding(**embed_kwargs)
@@ -886,179 +866,6 @@ class LiteLLMSDKEmbeddings(Embeddings):
         return all_embeddings
 
 
-class GeminiEmbeddings(Embeddings):
-    """
-    Google embeddings via the google.genai SDK.
-
-    Supports both:
-    1. Gemini API (api.generativeai.google.com) with API key authentication
-    2. Vertex AI with service account or Application Default Credentials (ADC)
-
-    Uses the embed_content API: client.models.embed_content(model, contents)
-    """
-
-    def __init__(
-        self,
-        model: str = DEFAULT_EMBEDDINGS_GEMINI_MODEL,
-        api_key: str | None = None,
-        vertexai_project_id: str | None = None,
-        vertexai_region: str | None = None,
-        vertexai_service_account_key: str | None = None,
-        output_dimensionality: int | None = None,
-        batch_size: int = 100,
-    ):
-        self.model = model
-        self.api_key = api_key
-        self.vertexai_project_id = vertexai_project_id
-        self.vertexai_region = vertexai_region or "us-central1"
-        self.vertexai_service_account_key = vertexai_service_account_key
-        self.output_dimensionality = output_dimensionality
-        self.batch_size = batch_size
-        self._client = None
-        self._dimension: int | None = None
-        self._is_vertexai = vertexai_project_id is not None
-        self._embed_config = None  # EmbedContentConfig, built during initialize()
-
-    @property
-    def provider_name(self) -> str:
-        return "google"
-
-    @property
-    def dimension(self) -> int:
-        if self._dimension is None:
-            raise RuntimeError("Embeddings not initialized. Call initialize() first.")
-        return self._dimension
-
-    async def initialize(self) -> None:
-        """Initialize the Google genai client and detect embedding dimension."""
-        if self._client is not None:
-            return
-
-        from google import genai
-        from google.genai import types as genai_types
-
-        if self._is_vertexai:
-            self._init_vertexai(genai)
-        else:
-            self._init_gemini(genai)
-
-        # Build EmbedContentConfig if output_dimensionality is set
-        if self.output_dimensionality is not None:
-            self._embed_config = genai_types.EmbedContentConfig(
-                output_dimensionality=self.output_dimensionality,
-            )
-
-        # Detect dimension via a test embedding (respects output_dimensionality)
-        embed_kwargs = {"model": self.model, "contents": ["test"]}
-        if self._embed_config is not None:
-            embed_kwargs["config"] = self._embed_config
-
-        result = self._client.models.embed_content(**embed_kwargs)  # type: ignore[union-attr]
-        if result.embeddings and len(result.embeddings) > 0:
-            self._dimension = len(result.embeddings[0].values)
-
-        auth_mode = "vertex_ai" if self._is_vertexai else "api_key"
-        logger.info(
-            f"Embeddings: google provider initialized (auth: {auth_mode}, model: {self.model}, dim: {self._dimension})"
-        )
-
-    def _init_gemini(self, genai) -> None:
-        """Initialize Gemini API client with API key."""
-        if not self.api_key:
-            raise ValueError("Gemini embeddings provider requires an API key")
-
-        self._client = genai.Client(api_key=self.api_key)
-        logger.info(f"Embeddings: initializing Gemini provider with model {self.model}")
-
-    def _init_vertexai(self, genai) -> None:
-        """Initialize Vertex AI client with project, region, and credentials."""
-        if not self.vertexai_project_id:
-            raise ValueError(
-                "HINDSIGHT_API_EMBEDDINGS_VERTEXAI_PROJECT_ID (or HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID) "
-                "is required for Vertex AI embeddings provider."
-            )
-
-        auth_method = "ADC"
-        credentials = None
-
-        if self.vertexai_service_account_key:
-            try:
-                from google.oauth2 import service_account
-            except ImportError:
-                raise ImportError(
-                    "Vertex AI service account auth requires 'google-auth' package. "
-                    "Install with: pip install google-auth"
-                )
-            credentials = service_account.Credentials.from_service_account_file(
-                self.vertexai_service_account_key,
-                scopes=["https://www.googleapis.com/auth/cloud-platform"],
-            )
-            auth_method = "service_account"
-            logger.info(f"Embeddings: Vertex AI using service account key: {self.vertexai_service_account_key}")
-
-        # Strip google/ prefix from model name — native SDK uses bare names
-        if self.model.startswith("google/"):
-            self.model = self.model[len("google/") :]
-
-        client_kwargs = {
-            "vertexai": True,
-            "project": self.vertexai_project_id,
-            "location": self.vertexai_region,
-        }
-        if credentials is not None:
-            client_kwargs["credentials"] = credentials
-
-        self._client = genai.Client(**client_kwargs)
-        logger.info(
-            f"Embeddings: initializing Vertex AI provider "
-            f"(project={self.vertexai_project_id}, region={self.vertexai_region}, "
-            f"model={self.model}, auth={auth_method})"
-        )
-
-    def encode(self, texts: list[str]) -> list[list[float]]:
-        """
-        Generate embeddings using the Google genai SDK.
-
-        Args:
-            texts: List of text strings to encode
-
-        Returns:
-            List of embedding vectors
-        """
-        if self._client is None:
-            raise RuntimeError("Embeddings not initialized. Call initialize() first.")
-
-        if not texts:
-            return []
-
-        all_embeddings = []
-
-        # Process in batches
-        for i in range(0, len(texts), self.batch_size):
-            batch = texts[i : i + self.batch_size]
-
-            embed_kwargs = {"model": self.model, "contents": batch}
-            if self._embed_config is not None:
-                embed_kwargs["config"] = self._embed_config
-
-            result = self._client.models.embed_content(**embed_kwargs)
-
-            all_embeddings.extend([emb.values for emb in result.embeddings])
-
-        # L2-normalize when output_dimensionality is set — Gemini only returns
-        # normalized vectors at full 3072 dims; truncated dims need re-normalization
-        # for accurate cosine similarity.
-        if self.output_dimensionality is not None:
-            import numpy as np
-
-            arr = np.array(all_embeddings)
-            norms = np.linalg.norm(arr, axis=1, keepdims=True)
-            norms[norms == 0] = 1
-            all_embeddings = (arr / norms).tolist()
-
-        return all_embeddings
-
-
 def create_embeddings_from_env() -> Embeddings:
     """
     Create an Embeddings instance based on configuration.
@@ -1120,29 +927,9 @@ def create_embeddings_from_env() -> Embeddings:
             api_key=api_key,
             model=config.embeddings_litellm_sdk_model,
             api_base=config.embeddings_litellm_sdk_api_base,
-            output_dimensions=config.embeddings_litellm_sdk_output_dimensions,
-        )
-    elif provider == "google":
-        vertexai_project_id = config.embeddings_vertexai_project_id
-        if vertexai_project_id:
-            api_key = None  # Vertex AI uses ADC or service account
-        else:
-            api_key = config.embeddings_gemini_api_key
-            if not api_key:
-                raise ValueError(
-                    f"{ENV_EMBEDDINGS_GEMINI_API_KEY} or {ENV_LLM_API_KEY} is required "
-                    f"when {ENV_EMBEDDINGS_PROVIDER} is 'google' (set VERTEXAI_PROJECT_ID for Vertex AI auth instead)"
-                )
-        return GeminiEmbeddings(
-            model=config.embeddings_gemini_model,
-            api_key=api_key,
-            vertexai_project_id=vertexai_project_id,
-            vertexai_region=config.embeddings_vertexai_region,
-            vertexai_service_account_key=config.embeddings_vertexai_service_account_key,
-            output_dimensionality=config.embeddings_gemini_output_dimensionality,
         )
     else:
         raise ValueError(
             f"Unknown embeddings provider: {provider}. "
-            f"Supported: 'local', 'tei', 'openai', 'cohere', 'google', 'litellm', 'litellm-sdk'"
+            f"Supported: 'local', 'tei', 'openai', 'cohere', 'litellm', 'litellm-sdk'"
         )

hindsight-api-slim/hindsight_api/engine/entity_resolver.py

@@ -317,13 +317,8 @@ class EntityResolver:
         entity_texts = list(set(e["text"] for e in entities_data))
 
         # Fetch candidates for all unique entity texts in a single batched query.
-        # Uses the GIN trigram index on LOWER(canonical_name) for case-insensitive
-        # similarity lookup. Previous version also had LIKE '%...' substring fallbacks,
-        # but those forced full sequential scans of the entities table and caused
-        # TimeoutErrors on banks with 10k+ entities. Lowering the similarity threshold
-        # to 0.15 (from default 0.3) catches most substring relationships while
-        # staying fully index-based.
-        await conn.execute("SET pg_trgm.similarity_threshold = 0.15")
+        # The trigram % operator uses the GIN index; the substring conditions cover
+        # exact prefix/suffix matches that trigrams might miss at low similarity.
         rows = await conn.fetch(
             f"""
             SELECT DISTINCT ON (e.id)
@@ -332,13 +327,16 @@ class EntityResolver:
             FROM unnest($2::text[]) AS q(query_text)
             JOIN {fq_table("entities")} e ON (
                 e.bank_id = $1
-                AND LOWER(e.canonical_name) % LOWER(q.query_text)
+                AND (
+                    e.canonical_name % q.query_text
+                    OR LOWER(e.canonical_name) LIKE '%' || LOWER(q.query_text) || '%'
+                    OR LOWER(q.query_text) LIKE '%' || LOWER(e.canonical_name) || '%'
+                )
             )
             """,
             bank_id,
             entity_texts,
         )
-        await conn.execute("RESET pg_trgm.similarity_threshold")
 
         # Group candidates by query_text
         all_candidates: dict[str, list] = {t: [] for t in entity_texts}
@@ -810,19 +808,14 @@ class EntityResolver:
             return await self._link_units_to_entities_batch_impl(conn, unit_entity_pairs)
 
     async def _link_units_to_entities_batch_impl(self, conn, unit_entity_pairs: list[tuple[str, str]]):
-        # Sorted bulk insert to prevent deadlocks from inconsistent lock ordering
-        # across concurrent transactions on the unit_entities unique index.
-        sorted_pairs = sorted(unit_entity_pairs)
-        unit_ids = [p[0] for p in sorted_pairs]
-        entity_ids = [p[1] for p in sorted_pairs]
-        await conn.execute(
+        # Batch insert all unit-entity links
+        await conn.executemany(
             f"""
             INSERT INTO {fq_table("unit_entities")} (unit_id, entity_id)
-            SELECT u, e FROM unnest($1::uuid[], $2::uuid[]) AS t(u, e)
+            VALUES ($1, $2)
             ON CONFLICT DO NOTHING
             """,
-            unit_ids,
-            entity_ids,
+            unit_entity_pairs,
         )
 
         # Build map of unit -> entities for co-occurrence calculation

hindsight-api-slim/hindsight_api/engine/interface.py

@@ -240,7 +240,6 @@ class MemoryEngineInterface(ABC):
         bank_id: str,
         *,
         fact_type: str | None = None,
-        delete_bank_profile: bool = True,
         request_context: "RequestContext",
     ) -> dict[str, int]:
         """
@@ -249,8 +248,6 @@ class MemoryEngineInterface(ABC):
         Args:
             bank_id: The memory bank ID.
             fact_type: If specified, only delete memories of this type.
-            delete_bank_profile: If True, also delete the bank profile row itself.
-                If False, only delete memories/entities/documents but preserve the bank.
             request_context: Request context for authentication.
 
         Returns:

hindsight-api-slim/hindsight_api/engine/llm_wrapper.py

@@ -146,7 +146,6 @@ def create_llm_provider(
     reasoning_effort: str,
     groq_service_tier: str | None = None,
     openai_service_tier: str | None = None,
-    extra_body: dict[str, Any] | None = None,
     vertexai_project_id: str | None = None,
     vertexai_region: str | None = None,
     vertexai_credentials: Any = None,
@@ -163,7 +162,6 @@ def create_llm_provider(
         reasoning_effort: Reasoning effort level for supported providers.
         groq_service_tier: Groq service tier (for Groq provider) - "on_demand", "flex", or "auto".
         openai_service_tier: OpenAI service tier (for OpenAI provider) - None (default) or "flex" (50% cheaper).
-        extra_body: Extra body params merged into OpenAI-compatible API calls.
         vertexai_project_id: Vertex AI project ID (for VertexAI provider).
         vertexai_region: Vertex AI region (for VertexAI provider).
         vertexai_credentials: Vertex AI credentials object (for VertexAI provider).
@@ -263,7 +261,7 @@ def create_llm_provider(
             reasoning_effort=reasoning_effort,
         )
 
-    elif provider_lower in ("openai", "groq", "ollama", "lmstudio", "minimax", "volcano"):
+    elif provider_lower in ("openai", "groq", "ollama", "lmstudio", "minimax"):
         return OpenAICompatibleLLM(
             provider=provider,
             api_key=api_key,
@@ -272,7 +270,6 @@ def create_llm_provider(
             reasoning_effort=reasoning_effort,
             groq_service_tier=groq_service_tier,
             openai_service_tier=openai_service_tier,
-            extra_body=extra_body,
         )
 
     else:
@@ -296,7 +293,6 @@ class LLMProvider:
         groq_service_tier: str | None = None,
         openai_service_tier: str | None = None,
         gemini_safety_settings: list | None = None,
-        extra_body: dict[str, Any] | None = None,
     ):
         """
         Initialize LLM provider.
@@ -310,7 +306,6 @@ class LLMProvider:
             groq_service_tier: Groq service tier ("on_demand", "flex", "auto") - from config.
             openai_service_tier: OpenAI service tier (None or "flex") - from config.
             gemini_safety_settings: Safety settings for Gemini/VertexAI providers.
-            extra_body: Extra body params merged into OpenAI-compatible API calls.
         """
         self.provider = provider.lower()
         self.api_key = api_key
@@ -322,8 +317,6 @@ class LLMProvider:
         self.openai_service_tier = openai_service_tier
         # Gemini safety settings (instance default; can be overridden per-request via context var)
         self.gemini_safety_settings = gemini_safety_settings
-        # Extra body params for OpenAI-compatible providers (e.g. chat_template_kwargs)
-        self.extra_body = extra_body
 
         # Validate provider
         valid_providers = [
@@ -341,7 +334,6 @@ class LLMProvider:
             "minimax",
             "litellm",
             "bedrock",
-            "volcano",
         ]
         if self.provider not in valid_providers:
             raise ValueError(f"Invalid LLM provider: {self.provider}. Must be one of: {', '.join(valid_providers)}")
@@ -420,7 +412,6 @@ class LLMProvider:
             reasoning_effort=self.reasoning_effort,
             groq_service_tier=self.groq_service_tier,
             openai_service_tier=self.openai_service_tier,
-            extra_body=self.extra_body,
             vertexai_project_id=vertexai_project_id,
             vertexai_region=vertexai_region,
             vertexai_credentials=vertexai_credentials,
@@ -715,40 +706,65 @@ class LLMProvider:
         pass
 
     @classmethod
-    def from_env(cls) -> "LLMProvider":
-        """Create provider from environment variables using config.py constants."""
-        from ..config import (
-            DEFAULT_LLM_MODEL,
-            DEFAULT_LLM_PROVIDER,
-            ENV_LLM_API_KEY,
-            ENV_LLM_BASE_URL,
-            ENV_LLM_EXTRA_BODY,
-            ENV_LLM_MODEL,
-            ENV_LLM_PROVIDER,
-        )
-
-        provider = os.getenv(ENV_LLM_PROVIDER, DEFAULT_LLM_PROVIDER)
-        api_key = os.getenv(ENV_LLM_API_KEY, "")
+    def for_memory(cls) -> "LLMProvider":
+        """Create provider for memory operations from environment variables."""
+        provider = os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq")
+        api_key = os.getenv("HINDSIGHT_API_LLM_API_KEY", "")
 
+        # API key not needed for openai-codex (uses OAuth), claude-code (uses Keychain OAuth),
+        # ollama (local), vertexai (uses GCP service account credentials),
+        # or litellm (uses provider-specific auth, e.g. AWS credentials for Bedrock)
         if not api_key and not requires_api_key(provider):
             pass  # Provider handles its own auth
         elif not api_key:
             raise ValueError(
-                f"{ENV_LLM_API_KEY} environment variable is required (unless using openai-codex, claude-code, or litellm)"
+                "HINDSIGHT_API_LLM_API_KEY environment variable is required (unless using openai-codex, claude-code, or litellm)"
             )
 
-        base_url = os.getenv(ENV_LLM_BASE_URL, "")
-        model = os.getenv(ENV_LLM_MODEL, DEFAULT_LLM_MODEL)
-        extra_body = json.loads(os.getenv(ENV_LLM_EXTRA_BODY, "null"))
+        base_url = os.getenv("HINDSIGHT_API_LLM_BASE_URL", "")
+        model = os.getenv("HINDSIGHT_API_LLM_MODEL", "openai/gpt-oss-120b")
 
-        return cls(
-            provider=provider,
-            api_key=api_key,
-            base_url=base_url,
-            model=model,
-            reasoning_effort="low",
-            extra_body=extra_body,
-        )
+        return cls(provider=provider, api_key=api_key, base_url=base_url, model=model, reasoning_effort="low")
+
+    @classmethod
+    def for_answer_generation(cls) -> "LLMProvider":
+        """Create provider for answer generation. Falls back to memory config if not set."""
+        provider = os.getenv("HINDSIGHT_API_ANSWER_LLM_PROVIDER", os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq"))
+        api_key = os.getenv("HINDSIGHT_API_ANSWER_LLM_API_KEY", os.getenv("HINDSIGHT_API_LLM_API_KEY", ""))
+
+        # API key not needed for providers with their own auth mechanisms
+        if not api_key and not requires_api_key(provider):
+            pass  # Provider handles its own auth
+        elif not api_key:
+            raise ValueError(
+                "HINDSIGHT_API_LLM_API_KEY or HINDSIGHT_API_ANSWER_LLM_API_KEY environment variable is required "
+                "(unless using openai-codex, claude-code, or litellm)"
+            )
+
+        base_url = os.getenv("HINDSIGHT_API_ANSWER_LLM_BASE_URL", os.getenv("HINDSIGHT_API_LLM_BASE_URL", ""))
+        model = os.getenv("HINDSIGHT_API_ANSWER_LLM_MODEL", os.getenv("HINDSIGHT_API_LLM_MODEL", "openai/gpt-oss-120b"))
+
+        return cls(provider=provider, api_key=api_key, base_url=base_url, model=model, reasoning_effort="high")
+
+    @classmethod
+    def for_judge(cls) -> "LLMProvider":
+        """Create provider for judge/evaluator operations. Falls back to memory config if not set."""
+        provider = os.getenv("HINDSIGHT_API_JUDGE_LLM_PROVIDER", os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq"))
+        api_key = os.getenv("HINDSIGHT_API_JUDGE_LLM_API_KEY", os.getenv("HINDSIGHT_API_LLM_API_KEY", ""))
+
+        # API key not needed for providers with their own auth mechanisms
+        if not api_key and not requires_api_key(provider):
+            pass  # Provider handles its own auth
+        elif not api_key:
+            raise ValueError(
+                "HINDSIGHT_API_LLM_API_KEY or HINDSIGHT_API_JUDGE_LLM_API_KEY environment variable is required "
+                "(unless using openai-codex, claude-code, or litellm)"
+            )
+
+        base_url = os.getenv("HINDSIGHT_API_JUDGE_LLM_BASE_URL", os.getenv("HINDSIGHT_API_LLM_BASE_URL", ""))
+        model = os.getenv("HINDSIGHT_API_JUDGE_LLM_MODEL", os.getenv("HINDSIGHT_API_LLM_MODEL", "openai/gpt-oss-120b"))
+
+        return cls(provider=provider, api_key=api_key, base_url=base_url, model=model, reasoning_effort="high")
 
 
 class ConfiguredLLMProvider:

hindsight-api-slim/hindsight_api/engine/memory_engine.py

@@ -16,7 +16,6 @@ import logging
 import time
 import uuid
 from collections.abc import Awaitable, Callable
-from dataclasses import dataclass
 from datetime import UTC, datetime, timedelta, timezone
 from typing import TYPE_CHECKING, Any
 
@@ -29,7 +28,6 @@ from ..metrics import get_metrics_collector
 from ..tracing import create_operation_span
 from ..utils import mask_network_location
 from ..worker.exceptions import RetryTaskAt
-from .audit import AuditLogger, audit_context
 from .db_budget import budgeted_operation
 from .operation_metadata import (
     BatchRetainChildMetadata,
@@ -227,42 +225,6 @@ def _get_tiktoken_encoding():
     return _TIKTOKEN_ENCODING
 
 
-@dataclass(frozen=True)
-class RefreshTagFiltering:
-    """Resolved tag filtering parameters for mental model refresh."""
-
-    tags: list[str] | None
-    tags_match: TagsMatch
-    tag_groups: list[TagGroup] | None
-
-
-def _resolve_refresh_tag_filtering(
-    model_tags: list[str] | None,
-    trigger_data: dict[str, Any],
-) -> RefreshTagFiltering:
-    """Resolve tag filtering parameters for mental model refresh.
-
-    Takes raw trigger dict from DB (JSONB with no fixed schema guarantee)
-    and resolves the tag filtering to use during reflect.
-
-    Priority:
-    - If trigger has tag_groups, use those (overrides flat tags entirely)
-    - If trigger has tags_match, use model's tags with that match mode
-    - Otherwise default to all_strict when tags present (security isolation)
-    """
-    trigger_tag_groups = trigger_data.get("tag_groups")
-    if trigger_tag_groups is not None:
-        from pydantic import TypeAdapter
-
-        adapter = TypeAdapter(TagGroup)
-        parsed = [adapter.validate_python(tg) for tg in trigger_tag_groups]
-        return RefreshTagFiltering(tags=None, tags_match="any", tag_groups=parsed)
-
-    trigger_tags_match = trigger_data.get("tags_match")
-    tags_match: TagsMatch = trigger_tags_match if trigger_tags_match else ("all_strict" if model_tags else "any")
-    return RefreshTagFiltering(tags=model_tags, tags_match=tags_match, tag_groups=None)
-
-
 class MemoryEngine(MemoryEngineInterface):
     """
     Advanced memory system using temporal and semantic linking with PostgreSQL.
@@ -270,7 +232,7 @@ class MemoryEngine(MemoryEngineInterface):
     This class provides:
     - Embedding generation for semantic search
     - Entity, temporal, and semantic link creation
-    - Think operations for formulating answers with observations
+    - Think operations for formulating answers with opinions
     - bank profile and disposition management
     """
 
@@ -433,7 +395,6 @@ class MemoryEngine(MemoryEngineInterface):
             api_key=memory_llm_api_key,
             base_url=memory_llm_base_url,
             model=memory_llm_model,
-            extra_body=config.llm_extra_body,
         )
 
         # Store client and model for convenience (deprecated: use _llm_config.call() instead)
@@ -460,7 +421,6 @@ class MemoryEngine(MemoryEngineInterface):
             api_key=retain_api_key,
             base_url=retain_base_url,
             model=retain_model,
-            extra_body=config.llm_extra_body,
         )
 
         # Reflect LLM config - for think/observe operations (can use lighter models)
@@ -482,7 +442,6 @@ class MemoryEngine(MemoryEngineInterface):
             api_key=reflect_api_key,
             base_url=reflect_base_url,
             model=reflect_model,
-            extra_body=config.llm_extra_body,
         )
 
         # Consolidation LLM config - for mental model consolidation (can use efficient models)
@@ -504,7 +463,6 @@ class MemoryEngine(MemoryEngineInterface):
             api_key=consolidation_api_key,
             base_url=consolidation_base_url,
             model=consolidation_model,
-            extra_body=config.llm_extra_body,
         )
 
         # Initialize cross-encoder reranker (cached for performance)
@@ -518,25 +476,14 @@ class MemoryEngine(MemoryEngineInterface):
             schema_getter=get_current_schema,
         )
 
-        # Audit logger for feature usage tracking
-        config = get_config()
-        self._audit_logger = AuditLogger(
-            pool_getter=lambda: self._pool,
-            schema_getter=get_current_schema,
-            enabled=config.audit_log_enabled,
-            allowed_actions=config.audit_log_actions,
-            retention_days=config.audit_log_retention_days,
-        )
-
         # Backpressure mechanism: limit concurrent searches to prevent overwhelming the database
         # Configurable via HINDSIGHT_API_RECALL_MAX_CONCURRENT (default: 50)
         self._search_semaphore = asyncio.Semaphore(get_config().recall_max_concurrent)
 
-        # Backpressure for retain DB writes: limit concurrent transactions to prevent contention
-        # on entity/link tables. Acquired in the orchestrator *after* LLM extraction completes,
-        # so LLM calls run in full parallelism while only the DB-heavy phase is throttled.
-        # Configurable via HINDSIGHT_API_RETAIN_MAX_CONCURRENT (default: 4).
-        self._put_semaphore = asyncio.Semaphore(get_config().retain_max_concurrent)
+        # Backpressure for put operations: limit concurrent puts to prevent database contention
+        # Each put_batch holds a connection for the entire transaction, so we limit to 5
+        # concurrent puts to avoid connection pool exhaustion and reduce write contention
+        self._put_semaphore = asyncio.Semaphore(5)
 
         # initialize encoding eagerly to avoid delaying the first time
         _get_tiktoken_encoding()
@@ -551,11 +498,6 @@ class MemoryEngine(MemoryEngineInterface):
             tenant_extension = DefaultTenantExtension(config={})
         self._tenant_extension = tenant_extension
 
-    @property
-    def audit_logger(self) -> AuditLogger:
-        """The audit logger for feature usage tracking."""
-        return self._audit_logger
-
     @property
     def tenant_extension(self) -> "TenantExtension | None":
         """The configured tenant extension, if any."""
@@ -946,23 +888,26 @@ class MemoryEngine(MemoryEngineInterface):
 
         source_query = mental_model["source_query"]
 
+        # SECURITY: If the mental model has tags, pass them to reflect with "all_strict" matching
+        # to ensure it can only access other mental models/memories with the SAME tags.
+        # This prevents cross-tenant/cross-user information leakage by excluding untagged content.
+        tags = mental_model.get("tags")
+        tags_match = "all_strict" if tags else "any"
+
         # Read reflect options from trigger (if stored)
         trigger_data = mental_model.get("trigger") or {}
         fact_types = trigger_data.get("fact_types")
         exclude_mental_models = trigger_data.get("exclude_mental_models", False)
         stored_exclude_ids: list[str] = trigger_data.get("exclude_mental_model_ids") or []
 
-        tag_filtering = _resolve_refresh_tag_filtering(mental_model.get("tags"), trigger_data)
-
         # Run reflect to generate new content, excluding the mental model being refreshed
         # Always add self to excluded IDs to prevent circular reference
         reflect_result = await self.reflect_async(
             bank_id=bank_id,
             query=source_query,
             request_context=internal_context,
-            tags=tag_filtering.tags,
-            tags_match=tag_filtering.tags_match,
-            tag_groups=tag_filtering.tag_groups,
+            tags=tags,
+            tags_match=tags_match,
             fact_types=fact_types,
             exclude_mental_models=exclude_mental_models,
             exclude_mental_model_ids=list({*stored_exclude_ids, mental_model_id}),
@@ -1085,78 +1030,72 @@ class MemoryEngine(MemoryEngineInterface):
                 # Continue with processing if we can't check status
 
         consolidation_result: dict | None = None
-        bank_id = task_dict.get("bank_id")
-        async with audit_context(
-            self._audit_logger, task_type or "unknown", "system", bank_id, request=task_dict
-        ) as audit_entry:
-            try:
-                if task_type == "batch_retain":
-                    await self._handle_batch_retain(task_dict)
-                elif task_type == "file_convert_retain":
-                    await self._handle_file_convert_retain(task_dict)
-                elif task_type == "consolidation":
-                    consolidation_result = await self._handle_consolidation(task_dict)
-                elif task_type == "refresh_mental_model":
-                    await self._handle_refresh_mental_model(task_dict)
-                elif task_type == "webhook_delivery":
-                    await self._handle_webhook_delivery(task_dict)
+        try:
+            if task_type == "batch_retain":
+                await self._handle_batch_retain(task_dict)
+            elif task_type == "file_convert_retain":
+                await self._handle_file_convert_retain(task_dict)
+            elif task_type == "consolidation":
+                consolidation_result = await self._handle_consolidation(task_dict)
+            elif task_type == "refresh_mental_model":
+                await self._handle_refresh_mental_model(task_dict)
+            elif task_type == "webhook_delivery":
+                await self._handle_webhook_delivery(task_dict)
+            else:
+                logger.error(f"Unknown task type: {task_type}")
+                # Don't retry unknown task types
+                if operation_id:
+                    await self._delete_operation_record(operation_id)
+                return
+
+            # Task succeeded - mark operation as completed
+            # file_convert_retain marks itself as completed in a transaction, skip double-marking
+            if operation_id and task_type not in ("file_convert_retain",):
+                if task_type == "consolidation":
+                    # Atomically mark completed AND queue webhook delivery in one transaction
+                    await self._mark_operation_completed_and_fire_webhook(
+                        operation_id=operation_id,
+                        bank_id=task_dict.get("bank_id", ""),
+                        status="completed",
+                        result=consolidation_result,
+                        schema=schema,
+                    )
                 else:
-                    logger.error(f"Unknown task type: {task_type}")
-                    # Don't retry unknown task types
-                    if operation_id:
-                        await self._delete_operation_record(operation_id)
-                    return
+                    await self._mark_operation_completed(operation_id)
 
-                # Task succeeded - mark operation as completed
-                # file_convert_retain marks itself as completed in a transaction, skip double-marking
-                if operation_id and task_type not in ("file_convert_retain",):
-                    if task_type == "consolidation":
-                        # Atomically mark completed AND queue webhook delivery in one transaction
-                        await self._mark_operation_completed_and_fire_webhook(
-                            operation_id=operation_id,
-                            bank_id=task_dict.get("bank_id", ""),
-                            status="completed",
-                            result=consolidation_result,
-                            schema=schema,
-                        )
-                    else:
-                        await self._mark_operation_completed(operation_id)
+        except RetryTaskAt:
+            # Task-owned retry: let the poller handle scheduling
+            raise
+        except Exception as e:
+            logger.error(f"Task execution failed: {task_type}, error: {e}")
+            import traceback
 
-                audit_entry.response = {"status": "completed", "operation_id": operation_id}
+            error_traceback = traceback.format_exc()
+            traceback.print_exc()
 
-            except RetryTaskAt:
-                # Task-owned retry: let the poller handle scheduling
+            if task_type == "file_convert_retain":
+                # Non-retryable: mark as failed immediately.
+                # Conversion failures won't improve on retry (missing OCR, corrupted file, etc.)
+                logger.error(f"Not retrying task {task_type} (non-retryable), marking as failed")
+                if operation_id:
+                    await self._mark_operation_failed(operation_id, str(e), error_traceback)
+            else:
+                if task_type == "consolidation" and operation_id:
+                    # Fire failure webhook (non-transactional — operation not yet marked failed;
+                    # poller will mark it failed after this raise)
+                    await self._fire_consolidation_webhook(
+                        bank_id=task_dict.get("bank_id", ""),
+                        operation_id=operation_id,
+                        status="failed",
+                        result=None,
+                        error_message=str(e),
+                        schema=schema,
+                    )
+                # Retryable: use RetryTaskAt if under the retry limit, else re-raise (poller marks failed)
+                retry_count = task_dict.get("_retry_count", 0)
+                if retry_count < 3:
+                    raise RetryTaskAt(retry_at=datetime.now(UTC) + timedelta(seconds=60), message=str(e))
                 raise
-            except Exception as e:
-                logger.error(f"Task execution failed: {task_type}, error: {e}")
-                import traceback
-
-                error_traceback = traceback.format_exc()
-                traceback.print_exc()
-
-                if task_type == "file_convert_retain":
-                    # Non-retryable: mark as failed immediately.
-                    # Conversion failures won't improve on retry (missing OCR, corrupted file, etc.)
-                    logger.error(f"Not retrying task {task_type} (non-retryable), marking as failed")
-                    if operation_id:
-                        await self._mark_operation_failed(operation_id, str(e), error_traceback)
-                else:
-                    if task_type == "consolidation" and operation_id:
-                        # Fire failure webhook (non-transactional — operation not yet marked failed;
-                        # poller will mark it failed after this raise)
-                        await self._fire_consolidation_webhook(
-                            bank_id=task_dict.get("bank_id", ""),
-                            operation_id=operation_id,
-                            status="failed",
-                            result=None,
-                            error_message=str(e),
-                            schema=schema,
-                        )
-                    # Retryable: use RetryTaskAt if under the retry limit, else re-raise (poller marks failed)
-                    retry_count = task_dict.get("_retry_count", 0)
-                    if retry_count < 3:
-                        raise RetryTaskAt(retry_at=datetime.now(UTC) + timedelta(seconds=60), message=str(e))
-                    raise
 
     async def _fire_consolidation_webhook(
         self,
@@ -1720,16 +1659,18 @@ class MemoryEngine(MemoryEngineInterface):
             # Migrate all schemas from the tenant extension
             # The tenant extension is the single source of truth for which schemas exist
             logger.info("Running database migrations...")
-            config = get_config()
             tenants = await self._tenant_extension.list_tenants()
             if tenants:
                 logger.info(f"Running migrations on {len(tenants)} schema(s)...")
                 for tenant in tenants:
                     schema = tenant.schema
                     if schema:
-                        run_migrations(self.db_url, schema=schema, migration_database_url=config.migration_database_url)
+                        run_migrations(self.db_url, schema=schema)
                 logger.info("Schema migrations completed")
 
+                # Get config for vector extension setting
+                config = get_config()
+
                 # Ensure embedding column dimension matches the model's dimension
                 # This is done after migrations and after embeddings.initialize()
                 for tenant in tenants:
@@ -1850,9 +1791,6 @@ class MemoryEngine(MemoryEngineInterface):
         self._task_backend.set_executor(self.execute_task)
         await self._task_backend.initialize()
 
-        # Start audit log retention sweep (if configured)
-        self._audit_logger.start_retention_sweep()
-
         self._initialized = True
         logger.info("Memory system initialized (pool and task backend started)")
 
@@ -1905,9 +1843,6 @@ class MemoryEngine(MemoryEngineInterface):
         """Close the connection pool and shutdown background workers."""
         logger.info("close() started")
 
-        # Stop audit log retention sweep
-        await self._audit_logger.stop_retention_sweep()
-
         # Shutdown task backend
         await self._task_backend.shutdown()
 
@@ -2003,6 +1938,7 @@ class MemoryEngine(MemoryEngineInterface):
         event_date: datetime | None = None,
         document_id: str | None = None,
         fact_type_override: str | None = None,
+        confidence_score: float | None = None,
         *,
         request_context: "RequestContext",
     ) -> list[str]:
@@ -2018,6 +1954,7 @@ class MemoryEngine(MemoryEngineInterface):
             event_date: When the event occurred (defaults to now)
             document_id: Optional document ID for tracking (always upserts if document already exists)
             fact_type_override: Override fact type ('world', 'experience')
+            confidence_score: Confidence score (0.0 to 1.0)
             request_context: Request context for authentication.
 
         Returns:
@@ -2036,6 +1973,7 @@ class MemoryEngine(MemoryEngineInterface):
             contents=[content_dict],
             request_context=request_context,
             fact_type_override=fact_type_override,
+            confidence_score=confidence_score,
         )
 
         # Return the first (and only) list of unit IDs
@@ -2049,6 +1987,7 @@ class MemoryEngine(MemoryEngineInterface):
         request_context: "RequestContext",
         document_id: str | None = None,
         fact_type_override: str | None = None,
+        confidence_score: float | None = None,
         document_tags: list[str] | None = None,
         return_usage: bool = False,
         operation_id: str | None = None,
@@ -2074,6 +2013,7 @@ class MemoryEngine(MemoryEngineInterface):
             document_id: **DEPRECATED** - Use "document_id" key in each content dict instead.
                         Applies the same document_id to ALL content items that don't specify their own.
             fact_type_override: Override fact type for all facts ('world', 'experience')
+            confidence_score: Confidence score (0.0 to 1.0)
             return_usage: If True, returns tuple of (unit_ids, TokenUsage). Default False for backward compatibility.
 
         Returns:
@@ -2123,6 +2063,7 @@ class MemoryEngine(MemoryEngineInterface):
                 request_context=request_context,
                 document_id=document_id,
                 fact_type_override=fact_type_override,
+                confidence_score=confidence_score,
             )
             result = await self._validate_operation(self._operation_validator.validate_retain(ctx))
             if result and result.contents is not None:
@@ -2208,6 +2149,7 @@ class MemoryEngine(MemoryEngineInterface):
                     document_id=document_id,
                     is_first_batch=i == 1,  # Only upsert on first batch
                     fact_type_override=fact_type_override,
+                    confidence_score=confidence_score,
                     document_tags=document_tags,
                     operation_id=operation_id,
                     strategy=strategy,
@@ -2232,6 +2174,7 @@ class MemoryEngine(MemoryEngineInterface):
                 document_id=document_id,
                 is_first_batch=True,
                 fact_type_override=fact_type_override,
+                confidence_score=confidence_score,
                 document_tags=document_tags,
                 operation_id=operation_id,
                 strategy=strategy,
@@ -2248,6 +2191,7 @@ class MemoryEngine(MemoryEngineInterface):
                 request_context=request_context,
                 document_id=document_id,
                 fact_type_override=fact_type_override,
+                confidence_score=confidence_score,
                 unit_ids=result,
                 success=True,
                 error=None,
@@ -2282,6 +2226,7 @@ class MemoryEngine(MemoryEngineInterface):
         document_id: str | None = None,
         is_first_batch: bool = True,
         fact_type_override: str | None = None,
+        confidence_score: float | None = None,
         document_tags: list[str] | None = None,
         operation_id: str | None = None,
         outbox_callback: "Callable[[asyncpg.Connection], Awaitable[None]] | None" = None,
@@ -2302,51 +2247,54 @@ class MemoryEngine(MemoryEngineInterface):
             document_id: Optional document ID (always upserts if exists)
             is_first_batch: Whether this is the first batch (for chunked operations, only delete on first batch)
             fact_type_override: Override fact type for all facts
+            confidence_score: Confidence score for opinions
             document_tags: Tags applied to all items in this batch
 
         Returns:
             Tuple of (unit ID lists, token usage for fact extraction)
         """
-        # Use the new modular orchestrator
-        from .retain import orchestrator
+        # Backpressure: limit concurrent retains to prevent database contention
+        async with self._put_semaphore:
+            # Use the new modular orchestrator
+            from .retain import orchestrator
 
-        pool = await self._get_pool()
+            pool = await self._get_pool()
 
-        # Resolve bank-specific config for this operation
-        resolved_config = await self._config_resolver.resolve_full_config(bank_id, request_context)
+            # Resolve bank-specific config for this operation
+            resolved_config = await self._config_resolver.resolve_full_config(bank_id, request_context)
 
-        # Force chunks mode when LLM provider is "none" (no LLM available for fact extraction)
-        if self._llm_config.provider == "none":
-            resolved_config.retain_extraction_mode = "chunks"
-            resolved_config.enable_observations = False
+            # Force chunks mode when LLM provider is "none" (no LLM available for fact extraction)
+            if self._llm_config.provider == "none":
+                resolved_config.retain_extraction_mode = "chunks"
+                resolved_config.enable_observations = False
 
-        # Apply strategy overrides: explicit strategy > bank default strategy
-        from hindsight_api.config_resolver import apply_strategy
+            # Apply strategy overrides: explicit strategy > bank default strategy
+            from hindsight_api.config_resolver import apply_strategy
 
-        effective_strategy = strategy or resolved_config.retain_default_strategy
-        if effective_strategy:
-            resolved_config = apply_strategy(resolved_config, effective_strategy)
+            effective_strategy = strategy or resolved_config.retain_default_strategy
+            if effective_strategy:
+                resolved_config = apply_strategy(resolved_config, effective_strategy)
 
-        # Create parent span for retain operation
-        with create_operation_span("retain", bank_id):
-            return await orchestrator.retain_batch(
-                pool=pool,
-                embeddings_model=self.embeddings,
-                llm_config=self._retain_llm_config.with_config(resolved_config),
-                entity_resolver=self.entity_resolver,
-                format_date_fn=self._format_readable_date,
-                bank_id=bank_id,
-                contents_dicts=contents,
-                document_id=document_id,
-                is_first_batch=is_first_batch,
-                fact_type_override=fact_type_override,
-                document_tags=document_tags,
-                config=resolved_config,
-                operation_id=operation_id,
-                schema=_current_schema.get(),
-                outbox_callback=outbox_callback,
-                db_semaphore=self._put_semaphore,
-            )
+            # Create parent span for retain operation
+            with create_operation_span("retain", bank_id):
+                return await orchestrator.retain_batch(
+                    pool=pool,
+                    embeddings_model=self.embeddings,
+                    llm_config=self._retain_llm_config.with_config(resolved_config),
+                    entity_resolver=self.entity_resolver,
+                    format_date_fn=self._format_readable_date,
+                    bank_id=bank_id,
+                    contents_dicts=contents,
+                    document_id=document_id,
+                    is_first_batch=is_first_batch,
+                    fact_type_override=fact_type_override,
+                    confidence_score=confidence_score,
+                    document_tags=document_tags,
+                    config=resolved_config,
+                    operation_id=operation_id,
+                    schema=_current_schema.get(),
+                    outbox_callback=outbox_callback,
+                )
 
     def recall(
         self,
@@ -2366,7 +2314,7 @@ class MemoryEngine(MemoryEngineInterface):
         Args:
             bank_id: bank ID to recall for
             query: Recall query
-            fact_type: Required filter for fact type ('world' or 'experience')
+            fact_type: Required filter for fact type ('world', 'experience', or 'opinion')
             budget: Budget level for graph traversal (low=100, mid=300, high=600 units)
             max_tokens: Maximum tokens to return (counts only 'text' field, default 4096)
             enable_trace: If True, returns detailed trace object
@@ -2460,10 +2408,8 @@ class MemoryEngine(MemoryEngineInterface):
         if fact_type is None:
             fact_type = list(VALID_RECALL_FACT_TYPES)
 
-        # Filter out 'opinion' (removed fact type, silently ignore for backwards compat)
+        # Filter out 'opinion' early (deprecated, silently ignore)
         fact_type = [ft for ft in fact_type if ft != "opinion"]
-        if not fact_type:
-            return RecallResultModel(results=[], entities={}, chunks={})
 
         # Validate fact types
         invalid_types = set(fact_type) - VALID_RECALL_FACT_TYPES
@@ -2472,6 +2418,9 @@ class MemoryEngine(MemoryEngineInterface):
                 f"Invalid fact type(s): {', '.join(sorted(invalid_types))}. "
                 f"Must be one of: {', '.join(sorted(VALID_RECALL_FACT_TYPES))}"
             )
+        if not fact_type:
+            # All requested types were opinions - return empty result
+            return RecallResultModel(results=[], entities={}, chunks={})
 
         # Validate operation if validator is configured
         if self._operation_validator:
@@ -2819,7 +2768,7 @@ class MemoryEngine(MemoryEngineInterface):
                 "temporal": 0.0,
                 "temporal_extraction": 0.0,
             }
-            all_graph_timings = []
+            all_mpfp_timings = []
 
             detected_temporal_constraint = None
             max_conn_wait = multi_result.max_conn_wait
@@ -2881,25 +2830,25 @@ class MemoryEngine(MemoryEngineInterface):
             )
 
             # Log graph retriever timing breakdown if available
-            if all_graph_timings:
+            if all_mpfp_timings:
                 retriever_name = get_default_graph_retriever().name.upper()
-                graph_total = all_graph_timings[0]  # Take first fact type's timing as representative
-                graph_parts = [
-                    f"db_queries={graph_total.db_queries}",
-                    f"edge_load={graph_total.edge_load_time:.3f}s",
-                    f"edges={graph_total.edge_count}",
-                    f"patterns={graph_total.pattern_count}",
+                mpfp_total = all_mpfp_timings[0]  # Take first fact type's timing as representative
+                mpfp_parts = [
+                    f"db_queries={mpfp_total.db_queries}",
+                    f"edge_load={mpfp_total.edge_load_time:.3f}s",
+                    f"edges={mpfp_total.edge_count}",
+                    f"patterns={mpfp_total.pattern_count}",
                 ]
-                if graph_total.seeds_time > 0.01:
-                    graph_parts.append(f"seeds={graph_total.seeds_time:.3f}s")
-                if graph_total.fusion > 0.001:
-                    graph_parts.append(f"fusion={graph_total.fusion:.3f}s")
-                if graph_total.fetch > 0.001:
-                    graph_parts.append(f"fetch={graph_total.fetch:.3f}s")
-                log_buffer.append(f"      [{retriever_name}] {', '.join(graph_parts)}")
+                if mpfp_total.seeds_time > 0.01:
+                    mpfp_parts.append(f"seeds={mpfp_total.seeds_time:.3f}s")
+                if mpfp_total.fusion > 0.001:
+                    mpfp_parts.append(f"fusion={mpfp_total.fusion:.3f}s")
+                if mpfp_total.fetch > 0.001:
+                    mpfp_parts.append(f"fetch={mpfp_total.fetch:.3f}s")
+                log_buffer.append(f"      [{retriever_name}] {', '.join(mpfp_parts)}")
                 # Log detailed hop timing for debugging slow queries
-                if graph_total.hop_details:
-                    for hd in graph_total.hop_details:
+                if mpfp_total.hop_details:
+                    for hd in mpfp_total.hop_details:
                         log_buffer.append(
                             f"        hop{hd['hop']}: exec={hd.get('exec_time', 0) * 1000:.0f}ms, "
                             f"uncached={hd.get('uncached_after_filter', 0)}, "
@@ -3534,13 +3483,11 @@ class MemoryEngine(MemoryEngineInterface):
             doc = await conn.fetchrow(
                 f"""
                 SELECT d.id, d.bank_id, d.original_text, d.content_hash,
-                       d.created_at, d.updated_at, d.tags, d.retain_params,
-                       COUNT(mu.id) as unit_count
+                       d.created_at, d.updated_at, d.tags, COUNT(mu.id) as unit_count
                 FROM {fq_table("documents")} d
                 LEFT JOIN {fq_table("memory_units")} mu ON mu.document_id = d.id
                 WHERE d.id = $1 AND d.bank_id = $2
-                GROUP BY d.id, d.bank_id, d.original_text, d.content_hash,
-                         d.created_at, d.updated_at, d.tags, d.retain_params
+                GROUP BY d.id, d.bank_id, d.original_text, d.content_hash, d.created_at, d.updated_at, d.tags
                 """,
                 document_id,
                 bank_id,
@@ -3549,14 +3496,6 @@ class MemoryEngine(MemoryEngineInterface):
             if not doc:
                 return None
 
-            retain_params_raw = doc["retain_params"]
-            retain_params_parsed = (
-                json.loads(retain_params_raw) if isinstance(retain_params_raw, str) else retain_params_raw
-            )
-
-            # document_metadata is sourced from retain_params.metadata
-            document_metadata = retain_params_parsed.get("metadata") if retain_params_parsed else None
-
             return {
                 "id": doc["id"],
                 "bank_id": doc["bank_id"],
@@ -3566,8 +3505,6 @@ class MemoryEngine(MemoryEngineInterface):
                 "created_at": doc["created_at"].isoformat() if doc["created_at"] else None,
                 "updated_at": doc["updated_at"].isoformat() if doc["updated_at"] else None,
                 "tags": list(doc["tags"]) if doc["tags"] else [],
-                "document_metadata": document_metadata or None,
-                "retain_params": retain_params_parsed or None,
             }
 
     async def delete_document(
@@ -3625,10 +3562,7 @@ class MemoryEngine(MemoryEngineInterface):
                 }
 
         if invalidated_obs > 0:
-            try:
-                await self.submit_async_consolidation(bank_id=bank_id, request_context=request_context)
-            except Exception as e:
-                logger.warning(f"Failed to submit consolidation after document deletion for bank {bank_id}: {e}")
+            await self.submit_async_consolidation(bank_id=bank_id, request_context=request_context)
 
         return result
 
@@ -3762,10 +3696,7 @@ class MemoryEngine(MemoryEngineInterface):
                             )
 
         if invalidated_obs > 0:
-            try:
-                await self.submit_async_consolidation(bank_id=bank_id, request_context=request_context)
-            except Exception as e:
-                logger.warning(f"Failed to submit consolidation after document update for bank {bank_id}: {e}")
+            await self.submit_async_consolidation(bank_id=bank_id, request_context=request_context)
 
         return True
 
@@ -3827,14 +3758,7 @@ class MemoryEngine(MemoryEngineInterface):
                 }
 
         if bank_id_for_consolidation:
-            try:
-                await self.submit_async_consolidation(
-                    bank_id=bank_id_for_consolidation, request_context=request_context
-                )
-            except Exception as e:
-                logger.warning(
-                    f"Failed to submit consolidation after memory deletion for bank {bank_id_for_consolidation}: {e}"
-                )
+            await self.submit_async_consolidation(bank_id=bank_id_for_consolidation, request_context=request_context)
 
         return result
 
@@ -3843,7 +3767,6 @@ class MemoryEngine(MemoryEngineInterface):
         bank_id: str,
         fact_type: str | None = None,
         *,
-        delete_bank_profile: bool = True,
         request_context: "RequestContext",
     ) -> dict[str, int]:
         """
@@ -3859,7 +3782,7 @@ class MemoryEngine(MemoryEngineInterface):
 
         Args:
             bank_id: bank ID to delete
-            fact_type: Optional fact type filter (world, experience). If provided, only deletes memories of that type.
+            fact_type: Optional fact type filter (world, experience, opinion). If provided, only deletes memories of that type.
             request_context: Request context for authentication.
 
         Returns:
@@ -3930,35 +3853,31 @@ class MemoryEngine(MemoryEngineInterface):
                         # Delete entities (cascades to unit_entities, entity_cooccurrences, memory_links with entity_id)
                         await conn.execute(f"DELETE FROM {fq_table('entities')} WHERE bank_id = $1", bank_id)
 
+                        # Delete the bank profile and retrieve internal_id for HNSW index cleanup
+                        internal_id = await conn.fetchval(
+                            f"DELETE FROM {fq_table('banks')} WHERE bank_id = $1 RETURNING internal_id", bank_id
+                        )
+                        if internal_id:
+                            bank_internal_id = str(internal_id)
+
                         result = {
                             "memory_units_deleted": units_count,
                             "entities_deleted": entities_count,
                             "documents_deleted": documents_count,
+                            "bank_deleted": True,
                         }
 
-                        if delete_bank_profile:
-                            # Delete the bank profile and retrieve internal_id for HNSW index cleanup
-                            internal_id = await conn.fetchval(
-                                f"DELETE FROM {fq_table('banks')} WHERE bank_id = $1 RETURNING internal_id", bank_id
-                            )
-                            if internal_id:
-                                bank_internal_id = str(internal_id)
-                            result["bank_deleted"] = True
-
                 except Exception as e:
                     raise Exception(f"Failed to delete agent data: {str(e)}")
 
-            # Drop per-bank vector indexes AFTER the transaction commits to avoid
+            # Drop per-bank HNSW indexes AFTER the transaction commits to avoid
             # AccessExclusiveLock deadlocks with concurrent bank deletions.
             # (DROP INDEX on memory_units conflicts with RowExclusiveLock from DELETE inside tx)
             if bank_internal_id:
-                await bank_utils.drop_bank_vector_indexes(conn, bank_internal_id)
+                await bank_utils.drop_bank_hnsw_indexes(conn, bank_internal_id)
 
         if invalidated_obs > 0:
-            try:
-                await self.submit_async_consolidation(bank_id=bank_id, request_context=request_context)
-            except Exception as e:
-                logger.warning(f"Failed to submit consolidation after bank deletion for bank {bank_id}: {e}")
+            await self.submit_async_consolidation(bank_id=bank_id, request_context=request_context)
 
         return result
 
@@ -4181,7 +4100,7 @@ class MemoryEngine(MemoryEngineInterface):
 
         Args:
             bank_id: Filter by bank ID
-            fact_type: Filter by fact type (world, experience)
+            fact_type: Filter by fact type (world, experience, opinion)
             limit: Maximum number of items to return (default: 1000)
             q: Full-text search query (searches text and context fields)
             tags: Filter by tags
@@ -4267,27 +4186,23 @@ class MemoryEngine(MemoryEngineInterface):
                     source_memory_ids.extend(unit["source_memory_ids"])
             source_memory_ids = list(set(source_memory_ids))  # Deduplicate
 
-            # Fetch links where BOTH endpoints are in the visible set (or source memories)
-            # Cap at 10k edges — the UI can't usefully render more, and uncapped queries
-            # on highly-connected graphs (e.g. 1000 nodes with 500k+ edges) are too slow.
-            max_edges = 10000
+            # Fetch links involving both visible units AND source memories
             all_relevant_ids = unit_ids + source_memory_ids
             if all_relevant_ids:
                 links = await conn.fetch(
                     f"""
-                    SELECT ml.from_unit_id,
-                           ml.to_unit_id,
-                           ml.link_type,
-                           ml.weight,
-                           e.canonical_name as entity_name
+                    SELECT DISTINCT ON (LEAST(ml.from_unit_id, ml.to_unit_id), GREATEST(ml.from_unit_id, ml.to_unit_id), ml.link_type, COALESCE(ml.entity_id, '00000000-0000-0000-0000-000000000000'::uuid))
+                        ml.from_unit_id,
+                        ml.to_unit_id,
+                        ml.link_type,
+                        ml.weight,
+                        e.canonical_name as entity_name
                     FROM {fq_table("memory_links")} ml
                     LEFT JOIN {fq_table("entities")} e ON ml.entity_id = e.id
-                    WHERE ml.from_unit_id = ANY($1::uuid[]) AND ml.to_unit_id = ANY($1::uuid[])
-                    ORDER BY ml.weight DESC NULLS LAST
-                    LIMIT $2
+                    WHERE ml.from_unit_id = ANY($1::uuid[]) OR ml.to_unit_id = ANY($1::uuid[])
+                    ORDER BY LEAST(ml.from_unit_id, ml.to_unit_id), GREATEST(ml.from_unit_id, ml.to_unit_id), ml.link_type, COALESCE(ml.entity_id, '00000000-0000-0000-0000-000000000000'::uuid), ml.weight DESC
                 """,
                     all_relevant_ids,
-                    max_edges,
                 )
             else:
                 links = []
@@ -4348,23 +4263,13 @@ class MemoryEngine(MemoryEngineInterface):
                 link for link in links if link["from_unit_id"] in unit_id_set and link["to_unit_id"] in unit_id_set
             ]
 
-            # Get entity information — only for visible units
-            # Fetch entities for visible units AND their source memories
-            # (so observations can inherit entities from source memories)
-            entity_lookup_ids = unit_ids + source_memory_ids
-            if entity_lookup_ids:
-                unit_entities = await conn.fetch(
-                    f"""
-                    SELECT ue.unit_id, e.canonical_name
-                    FROM {fq_table("unit_entities")} ue
-                    JOIN {fq_table("entities")} e ON ue.entity_id = e.id
-                    WHERE ue.unit_id = ANY($1::uuid[])
-                    ORDER BY ue.unit_id
-                """,
-                    entity_lookup_ids,
-                )
-            else:
-                unit_entities = []
+            # Get entity information
+            unit_entities = await conn.fetch(f"""
+                SELECT ue.unit_id, e.canonical_name
+                FROM {fq_table("unit_entities")} ue
+                JOIN {fq_table("entities")} e ON ue.entity_id = e.id
+                ORDER BY ue.unit_id
+            """)
 
         # Build entity mapping
         entity_map = {}
@@ -4562,7 +4467,7 @@ class MemoryEngine(MemoryEngineInterface):
 
         Args:
             bank_id: Filter by bank ID
-            fact_type: Filter by fact type (world, experience)
+            fact_type: Filter by fact type (world, experience, opinion)
             search_query: Full-text search query (searches text and context fields)
             limit: Maximum number of results to return
             offset: Offset for pagination
@@ -5019,14 +4924,6 @@ class MemoryEngine(MemoryEngineInterface):
                 bank_id_val = row["bank_id"]
                 unit_count = count_map.get((doc_id, bank_id_val), 0)
 
-                retain_params_val = row["retain_params"]
-                retain_params_val = (
-                    json.loads(retain_params_val) if isinstance(retain_params_val, str) else retain_params_val
-                )
-
-                # document_metadata is sourced from retain_params.metadata
-                document_metadata = retain_params_val.get("metadata") if retain_params_val else None
-
                 items.append(
                     {
                         "id": doc_id,
@@ -5036,8 +4933,7 @@ class MemoryEngine(MemoryEngineInterface):
                         "updated_at": row["updated_at"].isoformat() if row["updated_at"] else "",
                         "text_length": row["text_length"] or 0,
                         "memory_unit_count": unit_count,
-                        "retain_params": retain_params_val or None,
-                        "document_metadata": document_metadata or None,
+                        "retain_params": row["retain_params"] if row["retain_params"] else None,
                         "tags": row["tags"] if row["tags"] else [],
                     }
                 )
@@ -5930,16 +5826,14 @@ class MemoryEngine(MemoryEngineInterface):
                 bank_id,
             )
 
-            # Link stats — filter on ml.bank_id (indexed) instead of joining through mu.bank_id.
-            # With the idx_memory_links_bank_link_type index this turns a full-table hash join
-            # into an indexed scan + PK lookups.  link_counts and link_counts_by_fact_type are
-            # derived in Python from the breakdown.
+            # Single query for all link stats — avoids triple join on memory_links (can be 21M+ rows).
+            # link_counts and link_counts_by_fact_type are derived in Python from the breakdown.
             link_breakdown_stats = await conn.fetch(
                 f"""
                 SELECT mu.fact_type, ml.link_type, COUNT(*) as count
                 FROM {fq_table("memory_links")} ml
                 JOIN {fq_table("memory_units")} mu ON ml.from_unit_id = mu.id
-                WHERE ml.bank_id = $1
+                WHERE mu.bank_id = $1
                 GROUP BY mu.fact_type, ml.link_type
                 """,
                 bank_id,
@@ -6361,7 +6255,6 @@ class MemoryEngine(MemoryEngineInterface):
         *,
         tags: list[str] | None = None,
         tags_match: str = "any",
-        detail: str = "full",
         limit: int = 100,
         offset: int = 0,
         request_context: "RequestContext",
@@ -6372,7 +6265,6 @@ class MemoryEngine(MemoryEngineInterface):
             bank_id: Bank identifier
             tags: Optional tags to filter by
             tags_match: How to match tags - 'any', 'all', or 'exact'
-            detail: Detail level - 'metadata', 'content', or 'full'
             limit: Maximum number of results
             offset: Offset for pagination
             request_context: Request context for authentication
@@ -6414,14 +6306,13 @@ class MemoryEngine(MemoryEngineInterface):
                 *params,
             )
 
-            return [self._row_to_mental_model(row, detail=detail) for row in rows]
+            return [self._row_to_mental_model(row) for row in rows]
 
     async def get_mental_model(
         self,
         bank_id: str,
         mental_model_id: str,
         *,
-        detail: str = "full",
         request_context: "RequestContext",
     ) -> dict[str, Any] | None:
         """Get a single pinned mental model by ID.
@@ -6429,7 +6320,6 @@ class MemoryEngine(MemoryEngineInterface):
         Args:
             bank_id: Bank identifier
             mental_model_id: Pinned mental model UUID
-            detail: Detail level - 'metadata', 'content', or 'full'
             request_context: Request context for authentication
 
         Returns:
@@ -6463,7 +6353,7 @@ class MemoryEngine(MemoryEngineInterface):
                 mental_model_id,
             )
 
-            result = self._row_to_mental_model(row, detail=detail) if row else None
+            result = self._row_to_mental_model(row) if row else None
 
         # Post-operation hook (usage recording)
         if result and self._operation_validator:
@@ -6638,23 +6528,26 @@ class MemoryEngine(MemoryEngineInterface):
 
         # Create parent span for mental model refresh operation
         with create_operation_span("mental_model_refresh", bank_id):
+            # SECURITY: If the mental model has tags, pass them to reflect with "all_strict" matching
+            # to ensure it can only access other mental models/memories with the SAME tags.
+            # This prevents cross-tenant/cross-user information leakage by excluding untagged content.
+            tags = mental_model.get("tags")
+            tags_match = "all_strict" if tags else "any"
+
             # Read reflect options from trigger (if stored)
             trigger_data = mental_model.get("trigger") or {}
             fact_types = trigger_data.get("fact_types")
             exclude_mental_models = trigger_data.get("exclude_mental_models", False)
             stored_exclude_ids: list[str] = trigger_data.get("exclude_mental_model_ids") or []
 
-            tag_filtering = _resolve_refresh_tag_filtering(mental_model.get("tags"), trigger_data)
-
             # Run reflect with the source query, excluding the mental model being refreshed
             # Skip creating a nested "hindsight.reflect" span since we already have "hindsight.mental_model_refresh"
             reflect_result = await self.reflect_async(
                 bank_id=bank_id,
                 query=mental_model["source_query"],
                 request_context=request_context,
-                tags=tag_filtering.tags,
-                tags_match=tag_filtering.tags_match,
-                tag_groups=tag_filtering.tag_groups,
+                tags=tags,
+                tags_match=tags_match,
                 fact_types=fact_types,
                 exclude_mental_models=exclude_mental_models,
                 exclude_mental_model_ids=list({*stored_exclude_ids, mental_model_id}),
@@ -6861,45 +6754,34 @@ class MemoryEngine(MemoryEngineInterface):
 
         return result == "DELETE 1"
 
-    def _row_to_mental_model(self, row, *, detail: str = "full") -> dict[str, Any]:
-        """Convert a database row to a mental model dict.
-
-        Args:
-            row: Database row
-            detail: Detail level - 'metadata', 'content', or 'full'
-        """
-        result: dict[str, Any] = {
-            "id": str(row["id"]),
-            "bank_id": row["bank_id"],
-            "name": row["name"],
-            "tags": row["tags"] or [],
-            "last_refreshed_at": row["last_refreshed_at"].isoformat() if row["last_refreshed_at"] else None,
-            "created_at": row["created_at"].isoformat() if row["created_at"] else None,
-        }
-        if detail == "metadata":
-            return result
-
+    def _row_to_mental_model(self, row) -> dict[str, Any]:
+        """Convert a database row to a mental model dict."""
+        reflect_response = row.get("reflect_response")
+        # Parse JSON string to dict if needed (asyncpg may return JSONB as string)
+        if isinstance(reflect_response, str):
+            try:
+                reflect_response = json.loads(reflect_response)
+            except json.JSONDecodeError:
+                reflect_response = None
         trigger = row.get("trigger")
         if isinstance(trigger, str):
             try:
                 trigger = json.loads(trigger)
             except json.JSONDecodeError:
                 trigger = None
-        result["source_query"] = row["source_query"]
-        result["content"] = row["content"]
-        result["max_tokens"] = row.get("max_tokens")
-        result["trigger"] = trigger
-
-        if detail == "full":
-            reflect_response = row.get("reflect_response")
-            if isinstance(reflect_response, str):
-                try:
-                    reflect_response = json.loads(reflect_response)
-                except json.JSONDecodeError:
-                    reflect_response = None
-            result["reflect_response"] = reflect_response
-
-        return result
+        return {
+            "id": str(row["id"]),
+            "bank_id": row["bank_id"],
+            "name": row["name"],
+            "source_query": row["source_query"],
+            "content": row["content"],
+            "tags": row["tags"] or [],
+            "max_tokens": row.get("max_tokens"),
+            "trigger": trigger,
+            "last_refreshed_at": row["last_refreshed_at"].isoformat() if row["last_refreshed_at"] else None,
+            "created_at": row["created_at"].isoformat() if row["created_at"] else None,
+            "reflect_response": reflect_response,
+        }
 
     # =========================================================================
     # Directives - Hard rules injected into prompts

hindsight-api-slim/hindsight_api/engine/providers/claude_code_llm.py

@@ -331,11 +331,7 @@ class ClaudeCodeLLM(LLMInterface):
             max_retries: Maximum retry attempts.
             initial_backoff: Initial backoff time in seconds.
             max_backoff: Maximum backoff time in seconds.
-            tool_choice: How to choose tools - "auto", "none", "required", or specific function dict.
-                - "auto": Model decides whether to call tools (default)
-                - "required": Model must call at least one tool
-                - "none": Model must not call any tools
-                - {"type": "function", "function": {"name": "..."}}: Force specific tool call
+            tool_choice: How to choose tools (not used by Claude Agent SDK).
 
         Returns:
             LLMToolCallResult with content and/or tool_calls.
@@ -414,57 +410,16 @@ class ClaudeCodeLLM(LLMInterface):
                 tool_call_id = msg.get("tool_call_id", "")
                 user_content += f"\n\n[Tool result for {tool_call_id}: {content}]"
 
-        # Handle tool_choice parameter to filter tools and adjust instructions
-        # The Claude Agent SDK doesn't have a native tool_choice parameter, so we
-        # enforce it via allowed_tools filtering and system prompt instructions.
-
         # Format tool names for SDK MCP servers: mcp__{server_name}__{tool_name}
         # This is required by the Claude Agent SDK for MCP server tools
         allowed_tool_names = [f"mcp__hindsight_tools__{name}" for name in tool_names]
-        mcp_servers_config = {"hindsight_tools": mcp_server} if sdk_tools else {}
-
-        # Process tool_choice
-        if isinstance(tool_choice, dict) and tool_choice.get("type") == "function":
-            # Force a specific tool: filter allowed_tools to only that tool and add instruction
-            forced_name = tool_choice.get("function", {}).get("name")
-            if forced_name:
-                # Filter to only the forced tool (with MCP prefix)
-                forced_tool_mcp_name = f"mcp__hindsight_tools__{forced_name}"
-                if forced_tool_mcp_name in allowed_tool_names:
-                    allowed_tool_names = [forced_tool_mcp_name]
-                    # Add strong instruction to system prompt
-                    force_instruction = (
-                        f"\n\nIMPORTANT: You MUST call the '{forced_name}' tool. Do not respond with text only."
-                    )
-                    system_prompt += force_instruction
-                    logger.debug(f"Claude Code: Forcing tool call to '{forced_name}'")
-                else:
-                    logger.warning(f"Claude Code: Forced tool '{forced_name}' not found in available tools")
-        elif tool_choice == "required":
-            # Must call at least one tool
-            tool_instruction = (
-                "\n\nIMPORTANT: You MUST call at least one of the available tools. Do not respond with text only."
-            )
-            system_prompt += tool_instruction
-            logger.debug("Claude Code: Tool call required")
-        elif tool_choice == "none":
-            # No tools should be called - disable all tools
-            allowed_tool_names = []
-            mcp_servers_config = {}
-            logger.debug("Claude Code: Tools disabled (tool_choice=none)")
-        # else: tool_choice == "auto" or unspecified - use default behavior (no changes needed)
 
         # Configure SDK options with MCP server
-        # tools=[] disables built-in CLI tools (Read, Write, Bash, ToolSearch, etc.)
-        # Without this, Claude Code CLI defers MCP tools when too many built-in tools
-        # are loaded, forcing Claude to use ToolSearch first — which wastes the max_turns
-        # budget and prevents direct MCP tool calls.
         options = ClaudeAgentOptions(
             system_prompt=system_prompt if system_prompt else None,
-            tools=[],  # Disable built-in tools so MCP tools load eagerly
-            max_turns=2,  # Allow tool call + tool result round-trip
-            mcp_servers=mcp_servers_config,
-            allowed_tools=allowed_tool_names,
+            max_turns=1,  # Single-turn for API-style interactions
+            mcp_servers={"hindsight_tools": mcp_server} if sdk_tools else {},
+            allowed_tools=allowed_tool_names if allowed_tool_names else [],
         )
 
         # Call Claude Agent SDK with retry logic

hindsight-api-slim/hindsight_api/engine/providers/codex_llm.py

@@ -126,32 +126,6 @@ class CodexLLM(LLMInterface):
         }
         return mapping.get(effort.lower(), "auto")
 
-    def _normalize_tool_choice(self, tool_choice: str | dict[str, Any]) -> str | dict[str, Any]:
-        """Normalize forced function tool choice for the Codex Responses API.
-
-        Older agent paths may still pass OpenAI chat-completions style named
-        tool choice payloads such as:
-
-            {"type": "function", "function": {"name": "recall"}}
-
-        Codex Responses expects the named function at the top level instead:
-
-            {"type": "function", "name": "recall"}
-        """
-        if not isinstance(tool_choice, dict):
-            return tool_choice
-        if str(tool_choice.get("type") or "").strip() != "function":
-            return tool_choice
-        function_payload = tool_choice.get("function")
-        if isinstance(function_payload, dict):
-            function_name = str(function_payload.get("name") or "").strip()
-            if function_name:
-                return {"type": "function", "name": function_name}
-        function_name = str(tool_choice.get("name") or "").strip()
-        if function_name:
-            return {"type": "function", "name": function_name}
-        return tool_choice
-
     async def verify_connection(self) -> None:
         """Verify Codex connection by making a simple test call."""
         try:
@@ -166,10 +140,6 @@ class CodexLLM(LLMInterface):
             )
             logger.info(f"Codex LLM verified: {self.model}")
         except Exception as e:
-            # 429 means quota exhausted, not a configuration error — warn but allow startup
-            if "429" in str(e) or "usage_limit_reached" in str(e):
-                logger.warning(f"Codex LLM quota exhausted for {self.model}, continuing startup: {e}")
-                return
             raise RuntimeError(f"Codex LLM connection verification failed for {self.model}: {e}") from e
 
     async def call(
@@ -293,27 +263,24 @@ class CodexLLM(LLMInterface):
                 )
 
                 # Record trace span
-                try:
-                    from hindsight_api.tracing import get_span_recorder
+                from hindsight_api.tracing import get_span_recorder
 
-                    # Estimate tokens for tracing
-                    estimated_input = sum(len(m.get("content", "")) for m in messages) // 4
-                    estimated_output = len(content) // 4
-                    span_recorder = get_span_recorder()
-                    span_recorder.record_llm_call(
-                        provider=self.provider,
-                        model=self.model,
-                        scope=scope,
-                        messages=messages,
-                        response_content=result if isinstance(result, str) else result.model_dump_json(),
-                        input_tokens=estimated_input,
-                        output_tokens=estimated_output,
-                        duration=duration,
-                        finish_reason=None,
-                        error=None,
-                    )
-                except Exception:
-                    pass  # logging failure must never affect the operation
+                # Estimate tokens for tracing
+                estimated_input = sum(len(m.get("content", "")) for m in messages) // 4
+                estimated_output = len(content) // 4
+                span_recorder = get_span_recorder()
+                span_recorder.record_llm_call(
+                    provider=self.provider,
+                    model=self.model,
+                    scope=scope,
+                    messages=messages,
+                    response_content=result if isinstance(result, str) else json.dumps(result),
+                    input_tokens=estimated_input,
+                    output_tokens=estimated_output,
+                    duration=duration,
+                    finish_reason=None,
+                    error=None,
+                )
 
                 if return_usage:
                     # Codex doesn't provide token counts, estimate based on content
@@ -455,7 +422,7 @@ class CodexLLM(LLMInterface):
             max_retries: Maximum retry attempts.
             initial_backoff: Initial backoff time in seconds.
             max_backoff: Maximum backoff time in seconds.
-            tool_choice: How to choose tools - "auto", "none", "required", or a specific function.
+            tool_choice: How to choose tools - "auto", "none", "required", or specific function.
 
         Returns:
             LLMToolCallResult with content and/or tool_calls.
@@ -512,7 +479,7 @@ class CodexLLM(LLMInterface):
             "instructions": system_instruction,
             "input": user_messages,
             "tools": codex_tools,
-            "tool_choice": self._normalize_tool_choice(tool_choice),
+            "tool_choice": tool_choice,
             "parallel_tool_calls": True,
             "reasoning": {"summary": reasoning_summary},
             "store": False,
@@ -559,31 +526,26 @@ class CodexLLM(LLMInterface):
             )
 
             # Record OpenTelemetry span
-            try:
-                from hindsight_api.tracing import get_span_recorder
+            from hindsight_api.tracing import get_span_recorder
 
-                span_recorder = get_span_recorder()
-                # Convert LLMToolCall objects to dicts for span recording
-                tool_calls_dict = (
-                    [{"id": tc.id, "name": tc.name, "arguments": tc.arguments} for tc in tool_calls]
-                    if tool_calls
-                    else None
-                )
-                span_recorder.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    messages=messages,
-                    response_content=content,
-                    input_tokens=0,  # Codex doesn't provide token counts
-                    output_tokens=0,
-                    duration=duration,
-                    finish_reason="tool_calls" if tool_calls else "stop",
-                    error=None,
-                    tool_calls=tool_calls_dict,
-                )
-            except Exception:
-                pass  # logging failure must never affect the operation
+            span_recorder = get_span_recorder()
+            # Convert LLMToolCall objects to dicts for span recording
+            tool_calls_dict = (
+                [{"id": tc.id, "name": tc.name, "arguments": tc.arguments} for tc in tool_calls] if tool_calls else None
+            )
+            span_recorder.record_llm_call(
+                provider=self.provider,
+                model=self.model,
+                scope=scope,
+                messages=messages,
+                response_content=content,
+                input_tokens=0,  # Codex doesn't provide token counts
+                output_tokens=0,
+                duration=duration,
+                finish_reason="tool_calls" if tool_calls else "stop",
+                error=None,
+                tool_calls=tool_calls_dict,
+            )
 
             return LLMToolCallResult(
                 content=content,

hindsight-api-slim/hindsight_api/engine/providers/gemini_llm.py

@@ -7,7 +7,6 @@ This provider supports both:
 """
 
 import asyncio
-import base64
 import json
 import logging
 import os
@@ -473,10 +472,9 @@ class GeminiLLM(LLMInterface):
                         fn_args = parse_llm_json(fn_args_str)
                         thought_signature = tc.get("thought_signature")
                         fc_kwargs: dict[str, Any] = {"name": fn_name, "args": fn_args}
-                        part_kwargs: dict[str, Any] = {"function_call": genai_types.FunctionCall(**fc_kwargs)}
                         if thought_signature:
-                            part_kwargs["thought_signature"] = base64.b64decode(thought_signature)
-                        parts.append(genai_types.Part(**part_kwargs))
+                            fc_kwargs["thought_signature"] = thought_signature
+                        parts.append(genai_types.Part(function_call=genai_types.FunctionCall(**fc_kwargs)))
                     gemini_contents.append(genai_types.Content(role="model", parts=parts))
                 else:
                     gemini_contents.append(genai_types.Content(role="model", parts=[genai_types.Part(text=content)]))
@@ -549,10 +547,7 @@ class GeminiLLM(LLMInterface):
                                 content = part.text
                             if hasattr(part, "function_call") and part.function_call:
                                 fc = part.function_call
-                                _raw_ts = getattr(part, "thought_signature", None)
-                                thought_signature = (
-                                    base64.b64encode(_raw_ts).decode("ascii") if isinstance(_raw_ts, bytes) else _raw_ts
-                                )
+                                thought_signature = getattr(fc, "thought_signature", None)
                                 tool_calls.append(
                                     LLMToolCall(
                                         id=f"gemini_{len(tool_calls)}",

hindsight-api-slim/hindsight_api/engine/providers/openai_compatible_llm.py

@@ -80,7 +80,6 @@ class OpenAICompatibleLLM(LLMInterface):
         reasoning_effort: str = "low",
         timeout: float | None = None,
         groq_service_tier: str | None = None,
-        extra_body: dict[str, Any] | None = None,
         **kwargs: Any,
     ):
         """
@@ -94,13 +93,12 @@ class OpenAICompatibleLLM(LLMInterface):
             reasoning_effort: Reasoning effort level for supported models ("low", "medium", "high").
             timeout: Request timeout in seconds (uses env var or 300s default).
             groq_service_tier: Groq service tier ("on_demand", "flex", "auto").
-            extra_body: Extra body params merged into every API call.
             **kwargs: Additional provider-specific parameters.
         """
         super().__init__(provider, api_key, base_url, model, reasoning_effort, **kwargs)
 
         # Validate provider
-        valid_providers = ["openai", "groq", "ollama", "lmstudio", "minimax", "volcano"]
+        valid_providers = ["openai", "groq", "ollama", "lmstudio", "minimax"]
         if self.provider not in valid_providers:
             raise ValueError(f"OpenAICompatibleLLM only supports: {', '.join(valid_providers)}. Got: {self.provider}")
 
@@ -126,8 +124,6 @@ class OpenAICompatibleLLM(LLMInterface):
         # Service tier configuration (from config, not env vars)
         self.groq_service_tier = groq_service_tier
         self.openai_service_tier = kwargs.get("openai_service_tier")
-        # User-configured extra body params (merged into every API call)
-        self._config_extra_body = extra_body or {}
 
         # Get timeout config
         self.timeout = timeout or float(os.getenv(ENV_LLM_TIMEOUT, str(DEFAULT_LLM_TIMEOUT)))
@@ -191,23 +187,6 @@ class OpenAICompatibleLLM(LLMInterface):
 
         return None
 
-    def _max_tokens_param_name(self) -> str:
-        """Return the correct parameter name for limiting response tokens.
-
-        Native OpenAI and Groq accept 'max_completion_tokens'. Mistral and other
-        OpenAI-compatible endpoints that haven't adopted the newer parameter name
-        require 'max_tokens'. Using a custom base_url with the openai provider
-        signals a third-party compatible API, so fall back to 'max_tokens'.
-        """
-        # Native OpenAI (no custom base URL) and Groq use max_completion_tokens
-        if self.provider == "groq":
-            return "max_completion_tokens"
-        if self.provider == "openai" and not self.base_url:
-            return "max_completion_tokens"
-        # openai with custom base_url, ollama, lmstudio, minimax, volcano —
-        # use the widely-supported max_tokens
-        return "max_tokens"
-
     async def call(
         self,
         messages: list[dict[str, str]],
@@ -280,7 +259,9 @@ class OpenAICompatibleLLM(LLMInterface):
             # For reasoning models, enforce minimum to ensure space for reasoning + output
             if is_reasoning_model and max_completion_tokens < 16000:
                 max_completion_tokens = 16000
-            call_params[self._max_tokens_param_name()] = max_completion_tokens
+            call_params["max_completion_tokens"] = max_completion_tokens
+
+        # Temperature - reasoning models don't support custom temperature
         if temperature is not None and not is_reasoning_model:
             # MiniMax requires temperature in (0.0, 1.0] — clamp accordingly
             if self.provider == "minimax":
@@ -292,17 +273,17 @@ class OpenAICompatibleLLM(LLMInterface):
             call_params["reasoning_effort"] = self.reasoning_effort
 
         # Provider-specific parameters
-        extra_body: dict[str, Any] = {**self._config_extra_body}
         if self.provider == "groq":
             call_params["seed"] = DEFAULT_LLM_SEED
+            extra_body: dict[str, Any] = {}
             # Add service_tier if configured
             if self.groq_service_tier:
                 extra_body["service_tier"] = self.groq_service_tier
             # Add reasoning parameters for reasoning models
             if is_reasoning_model:
                 extra_body["include_reasoning"] = False
-        if extra_body:
-            call_params["extra_body"] = extra_body
+            if extra_body:
+                call_params["extra_body"] = extra_body
 
         # Prepare response format ONCE before retry loop
         if response_format is not None:
@@ -335,8 +316,8 @@ class OpenAICompatibleLLM(LLMInterface):
                         first_msg = call_params["messages"][0]
                         if isinstance(first_msg, dict) and isinstance(first_msg.get("content"), str):
                             first_msg["content"] = schema_msg + "\n\n" + first_msg["content"]
-                if self.provider not in ("lmstudio", "ollama", "volcano"):
-                    # LM Studio, Ollama and Volcano don't support json_object response format reliably
+                if self.provider not in ("lmstudio", "ollama"):
+                    # LM Studio and Ollama don't support json_object response format reliably
                     call_params["response_format"] = {"type": "json_object"}
 
         last_exception = None
@@ -592,7 +573,7 @@ class OpenAICompatibleLLM(LLMInterface):
         }
 
         if max_completion_tokens is not None:
-            call_params[self._max_tokens_param_name()] = max_completion_tokens
+            call_params["max_completion_tokens"] = max_completion_tokens
         if temperature is not None:
             # MiniMax requires temperature in (0.0, 1.0] — clamp accordingly
             if self.provider == "minimax":
@@ -600,11 +581,8 @@ class OpenAICompatibleLLM(LLMInterface):
             call_params["temperature"] = temperature
 
         # Provider-specific parameters
-        extra_body: dict[str, Any] = {**self._config_extra_body}
         if self.provider == "groq":
             call_params["seed"] = DEFAULT_LLM_SEED
-        if extra_body:
-            call_params["extra_body"] = extra_body
 
         last_exception = None
 

hindsight-api-slim/hindsight_api/engine/query_analyzer.py

@@ -137,21 +137,7 @@ class DateparserQueryAnalyzer(QueryAnalyzer):
             "RETURN_AS_TIMEZONE_AWARE": False,
         }
 
-        # Wrap dateparser in a defensive try/except. dateparser has been
-        # observed to crash with internal errors (e.g., IndexError from
-        # locale.translate_search) on certain query inputs. A parser bug
-        # should not bring down the whole search/consolidation pipeline —
-        # treat any failure as "no temporal constraint found" so the caller
-        # can fall back to non-temporal retrieval.
-        try:
-            results = self._search_dates(query, settings=settings)
-        except Exception as e:
-            logger.warning(
-                "dateparser raised %s on query (treating as no temporal constraint): %s",
-                type(e).__name__,
-                e,
-            )
-            return QueryAnalysis(temporal_constraint=None)
+        results = self._search_dates(query, settings=settings)
 
         if not results:
             return QueryAnalysis(temporal_constraint=None)

hindsight-api-slim/hindsight_api/engine/response_models.py

@@ -10,6 +10,7 @@ from typing import Any
 
 from pydantic import BaseModel, ConfigDict, Field, field_validator
 
+# Valid fact types for recall operations (excludes 'opinion' which is deprecated)
 VALID_RECALL_FACT_TYPES = frozenset(["world", "experience", "observation"])
 
 

hindsight-api-slim/hindsight_api/engine/retain/bank_utils.py

@@ -10,47 +10,32 @@ from typing import TypedDict
 
 from pydantic import BaseModel, Field
 
-from ...config import get_config
 from ..db_utils import acquire_with_retry
 from ..memory_engine import fq_table, get_current_schema
 from ..response_models import DispositionTraits
 
 logger = logging.getLogger(__name__)
 
-# Fact types that get per-bank partial vector indexes, mapped to their 4-char index suffix.
-_BANK_INDEX_FACT_TYPES: dict[str, str] = {
+# Fact types that get per-bank partial HNSW indexes, mapped to their 4-char index suffix.
+_HNSW_FACT_TYPES: dict[str, str] = {
     "world": "worl",
     "experience": "expr",
     "observation": "obsv",
 }
 
 
-def _bank_index_name(ft: str, internal_id: str) -> str:
-    """Deterministic, schema-safe vector index name for a (bank, fact_type) pair.
+def _hnsw_index_name(ft: str, internal_id: str) -> str:
+    """Deterministic, schema-safe HNSW index name for a (bank, fact_type) pair.
 
     Uses the first 16 hex chars of internal_id (8 bytes of entropy) — unique
     enough in practice, fits comfortably within PostgreSQL's 63-char identifier limit.
     """
     uid = str(internal_id).replace("-", "")[:16]
-    return f"idx_mu_emb_{_BANK_INDEX_FACT_TYPES[ft]}_{uid}"
+    return f"idx_mu_emb_{_HNSW_FACT_TYPES[ft]}_{uid}"
 
 
-def _vector_index_clause() -> str:
-    """Return the USING clause for vector index creation based on the configured extension."""
-    ext = get_config().vector_extension
-    if ext == "pgvectorscale":
-        return "USING diskann (embedding vector_cosine_ops) WITH (num_neighbors = 50)"
-    elif ext == "vchord":
-        return "USING vchordrq (embedding vector_l2_ops)"
-    else:  # pgvector (default)
-        return "USING hnsw (embedding vector_cosine_ops)"
-
-
-async def create_bank_vector_indexes(conn, bank_id: str, internal_id: str) -> None:
-    """Create per-(bank, fact_type) partial vector indexes for a newly created bank.
-
-    Respects the HINDSIGHT_API_VECTOR_EXTENSION config to use the appropriate
-    index type (HNSW for pgvector, DiskANN for pgvectorscale, vchordrq for vchord).
+async def create_bank_hnsw_indexes(conn, bank_id: str, internal_id: str) -> None:
+    """Create per-(bank, fact_type) partial HNSW indexes for a newly created bank.
 
     Called immediately after the bank row is first inserted. Safe on empty banks
     (index build is instant). Idempotent via CREATE INDEX IF NOT EXISTS.
@@ -58,25 +43,24 @@ async def create_bank_vector_indexes(conn, bank_id: str, internal_id: str) -> No
     """
     table = fq_table("memory_units")
     escaped = bank_id.replace("'", "''")
-    using_clause = _vector_index_clause()
-    for ft in _BANK_INDEX_FACT_TYPES:
-        idx = _bank_index_name(ft, internal_id)
+    for ft in _HNSW_FACT_TYPES:
+        idx = _hnsw_index_name(ft, internal_id)
         await conn.execute(
             f"CREATE INDEX IF NOT EXISTS {idx} "
-            f"ON {table} {using_clause} "
+            f"ON {table} USING hnsw (embedding vector_cosine_ops) "
             f"WHERE fact_type = '{ft}' AND bank_id = '{escaped}'"
         )
 
 
-async def drop_bank_vector_indexes(conn, internal_id: str) -> None:
-    """Drop per-(bank, fact_type) partial vector indexes for a bank being deleted.
+async def drop_bank_hnsw_indexes(conn, internal_id: str) -> None:
+    """Drop per-(bank, fact_type) partial HNSW indexes for a bank being deleted.
 
     Called before the bank row is deleted so internal_id is still known.
     Idempotent via DROP INDEX IF EXISTS.
     """
     schema = get_current_schema()
-    for ft in _BANK_INDEX_FACT_TYPES:
-        idx = _bank_index_name(ft, internal_id)
+    for ft in _HNSW_FACT_TYPES:
+        idx = _hnsw_index_name(ft, internal_id)
         await conn.execute(f"DROP INDEX IF EXISTS {schema}.{idx}")
 
 
@@ -137,7 +121,7 @@ async def get_bank_profile(pool, bank_id: str) -> BankProfile:
 
         # Bank doesn't exist, create with defaults.
         # Generate internal_id here so we control the value and can use it
-        # immediately for vector index creation without a RETURNING round-trip.
+        # immediately for HNSW index creation without a RETURNING round-trip.
         internal_id = uuid.uuid4()
         inserted = await conn.fetchval(
             f"""
@@ -154,8 +138,8 @@ async def get_bank_profile(pool, bank_id: str) -> BankProfile:
         )
 
         if inserted:
-            # Fresh insert — create per-bank vector indexes (instant on empty bank)
-            await create_bank_vector_indexes(conn, bank_id, str(internal_id))
+            # Fresh insert — create per-bank HNSW indexes (instant on empty bank)
+            await create_bank_hnsw_indexes(conn, bank_id, str(internal_id))
 
         return BankProfile(name=bank_id, disposition=DispositionTraits(**DEFAULT_DISPOSITION), mission="")
 

hindsight-api-slim/hindsight_api/engine/retain/chunk_storage.py

@@ -4,9 +4,7 @@ Chunk storage for retain pipeline.
 Handles storage of document chunks in the database.
 """
 
-import hashlib
 import logging
-from dataclasses import dataclass
 
 from ..memory_engine import fq_table
 from .types import ChunkMetadata
@@ -14,61 +12,6 @@ from .types import ChunkMetadata
 logger = logging.getLogger(__name__)
 
 
-def compute_chunk_hash(chunk_text: str) -> str:
-    """Compute SHA256 hash of chunk text for delta comparison."""
-    return hashlib.sha256(chunk_text.encode()).hexdigest()
-
-
-@dataclass
-class ExistingChunk:
-    """Represents a chunk already stored in the database."""
-
-    chunk_id: str
-    chunk_index: int
-    content_hash: str | None
-
-
-async def load_existing_chunks(conn, bank_id: str, document_id: str) -> list[ExistingChunk]:
-    """
-    Load existing chunk metadata for a document.
-
-    Returns list of ExistingChunk with chunk_id, chunk_index, and content_hash.
-    """
-    rows = await conn.fetch(
-        f"""
-        SELECT chunk_id, chunk_index, content_hash
-        FROM {fq_table("chunks")}
-        WHERE document_id = $1 AND bank_id = $2
-        ORDER BY chunk_index
-        """,
-        document_id,
-        bank_id,
-    )
-    return [
-        ExistingChunk(
-            chunk_id=row["chunk_id"],
-            chunk_index=row["chunk_index"],
-            content_hash=row["content_hash"],
-        )
-        for row in rows
-    ]
-
-
-async def delete_chunks_by_ids(conn, chunk_ids: list[str]) -> None:
-    """
-    Delete specific chunks by their IDs.
-
-    This cascades to memory_units (via FK with CASCADE delete)
-    and their links.
-    """
-    if not chunk_ids:
-        return
-    await conn.execute(
-        f"DELETE FROM {fq_table('chunks')} WHERE chunk_id = ANY($1::text[])",
-        chunk_ids,
-    )
-
-
 async def store_chunks_batch(conn, bank_id: str, document_id: str, chunks: list[ChunkMetadata]) -> dict[int, str]:
     """
     Store document chunks in the database.
@@ -89,7 +32,6 @@ async def store_chunks_batch(conn, bank_id: str, document_id: str, chunks: list[
     chunk_ids = []
     chunk_texts = []
     chunk_indices = []
-    content_hashes = []
     chunk_id_map = {}
 
     for chunk in chunks:
@@ -97,21 +39,19 @@ async def store_chunks_batch(conn, bank_id: str, document_id: str, chunks: list[
         chunk_ids.append(chunk_id)
         chunk_texts.append(chunk.chunk_text)
         chunk_indices.append(chunk.chunk_index)
-        content_hashes.append(compute_chunk_hash(chunk.chunk_text))
         chunk_id_map[chunk.chunk_index] = chunk_id
 
     # Batch insert all chunks
     await conn.execute(
         f"""
-        INSERT INTO {fq_table("chunks")} (chunk_id, document_id, bank_id, chunk_text, chunk_index, content_hash)
-        SELECT * FROM unnest($1::text[], $2::text[], $3::text[], $4::text[], $5::integer[], $6::text[])
+        INSERT INTO {fq_table("chunks")} (chunk_id, document_id, bank_id, chunk_text, chunk_index)
+        SELECT * FROM unnest($1::text[], $2::text[], $3::text[], $4::text[], $5::integer[])
         """,
         chunk_ids,
         [document_id] * len(chunk_texts),
         [bank_id] * len(chunk_texts),
         chunk_texts,
         chunk_indices,
-        content_hashes,
     )
 
     return chunk_id_map

hindsight-api-slim/hindsight_api/engine/retain/entity_processing.py

@@ -12,27 +12,61 @@ from .types import EntityLink, ProcessedFact
 logger = logging.getLogger(__name__)
 
 
-def _prepare_facts_for_entity_processing(
+async def process_entities_batch(
+    entity_resolver,
+    conn,
+    bank_id: str,
+    unit_ids: list[str],
     facts: list[ProcessedFact],
-    user_entities_per_content: dict[int, list[dict]] | None = None,
-) -> tuple[list[str], list, list[list[dict]]]:
+    log_buffer: list[str] = None,
+    user_entities_per_content: dict[int, list[dict]] = None,
+    entity_labels: list | None = None,
+) -> list[EntityLink]:
     """
-    Extract fact texts, dates, and merged entity lists from ProcessedFact objects.
+    Process entities for all facts and create entity links.
+
+    This function:
+    1. Extracts entity mentions from fact texts
+    2. Merges user-provided entities with LLM-extracted entities
+    3. Resolves entity names to canonical entities
+    4. Creates entity records in the database
+    5. Returns entity links ready for insertion
+
+    Args:
+        entity_resolver: EntityResolver instance for entity resolution
+        conn: Database connection
+        bank_id: Bank identifier
+        unit_ids: List of unit IDs (same length as facts)
+        facts: List of ProcessedFact objects
+        log_buffer: Optional buffer for detailed logging
+        user_entities_per_content: Dict mapping content_index to list of user-provided entities
 
     Returns:
-        Tuple of (fact_texts, fact_dates, entities_per_fact)
+        List of EntityLink objects for batch insertion
     """
+    if not unit_ids or not facts:
+        return []
+
+    if len(unit_ids) != len(facts):
+        raise ValueError(f"Mismatch between unit_ids ({len(unit_ids)}) and facts ({len(facts)})")
+
     user_entities_per_content = user_entities_per_content or {}
 
+    # Extract data for link_utils function
     fact_texts = [fact.fact_text for fact in facts]
+    # Use occurred_start if available, otherwise use mentioned_at for entity timestamps
     fact_dates = [fact.occurred_start if fact.occurred_start is not None else fact.mentioned_at for fact in facts]
 
+    # Convert EntityRef objects to dict format and merge with user-provided entities
     entities_per_fact = []
     for fact in facts:
+        # Start with LLM-extracted entities
         llm_entities = [{"text": entity.name, "type": "CONCEPT"} for entity in (fact.entities or [])]
 
+        # Get user entities for this content (use content_index from fact)
         user_entities = user_entities_per_content.get(fact.content_index, [])
 
+        # Merge with case-insensitive deduplication
         seen_texts = {e["text"].lower() for e in llm_entities}
         for user_entity in user_entities:
             if user_entity["text"].lower() not in seen_texts:
@@ -46,48 +80,8 @@ def _prepare_facts_for_entity_processing(
 
         entities_per_fact.append(llm_entities)
 
-    return fact_texts, fact_dates, entities_per_fact
-
-
-async def resolve_entities(
-    entity_resolver,
-    conn,
-    bank_id: str,
-    unit_ids: list[str],
-    facts: list[ProcessedFact],
-    log_buffer: list[str] = None,
-    user_entities_per_content: dict[int, list[dict]] = None,
-    entity_labels: list | None = None,
-) -> tuple[list[str], list[tuple], dict[str, list[str]]]:
-    """
-    Phase 1: Resolve entity names to canonical IDs (read-heavy).
-
-    Should be called on a SEPARATE connection OUTSIDE the main write transaction
-    to avoid holding the transaction open during expensive trigram scans.
-
-    Args:
-        entity_resolver: EntityResolver instance
-        conn: Database connection (separate from the main write transaction)
-        bank_id: Bank identifier
-        unit_ids: Placeholder unit IDs (used only for grouping)
-        facts: List of ProcessedFact objects
-        log_buffer: Optional buffer for detailed logging
-        user_entities_per_content: Dict mapping content_index to user-provided entities
-        entity_labels: Optional entity label taxonomy
-
-    Returns:
-        Tuple of (resolved_entity_ids, entity_to_unit, unit_to_entity_ids)
-        to pass to build_entity_links().
-    """
-    if not unit_ids or not facts:
-        return [], [], {}
-
-    if len(unit_ids) != len(facts):
-        raise ValueError(f"Mismatch between unit_ids ({len(unit_ids)}) and facts ({len(facts)})")
-
-    fact_texts, fact_dates, entities_per_fact = _prepare_facts_for_entity_processing(facts, user_entities_per_content)
-
-    return await link_utils.resolve_entities_only(
+    # Use existing link_utils function for entity processing
+    entity_links = await link_utils.extract_entities_batch_optimized(
         entity_resolver,
         conn,
         bank_id,
@@ -96,67 +90,22 @@ async def resolve_entities(
         "",  # context (not used in current implementation)
         fact_dates,
         entities_per_fact,
-        log_buffer,
+        log_buffer,  # Pass log_buffer for detailed logging
         entity_labels=entity_labels,
     )
 
-
-async def build_entity_links(
-    entity_resolver,
-    conn,
-    bank_id: str,
-    unit_ids: list[str],
-    resolved_entity_ids: list[str],
-    entity_to_unit: list[tuple],
-    unit_to_entity_ids: dict[str, list[str]],
-    log_buffer: list[str] = None,
-    skip_unit_entities_insert: bool = False,
-) -> list[EntityLink]:
-    """
-    Build entity links for UI graph visualization.
-
-    Queries unit_entities to find shared entities between new and existing units,
-    then generates EntityLink objects. When called from Phase 3 (post-transaction),
-    set skip_unit_entities_insert=True since unit_entities were already inserted
-    in Phase 2.
-
-    Args:
-        entity_resolver: EntityResolver instance
-        conn: Database connection
-        bank_id: Bank identifier
-        unit_ids: Actual unit IDs (must already be inserted in the DB)
-        resolved_entity_ids: From resolve_entities()
-        entity_to_unit: From resolve_entities()
-        unit_to_entity_ids: From resolve_entities()
-        log_buffer: Optional buffer for detailed logging
-        skip_unit_entities_insert: Skip unit_entities INSERT (already done in Phase 2)
-
-    Returns:
-        List of EntityLink objects for batch insertion
-    """
-    return await link_utils.build_entity_links_from_resolved(
-        entity_resolver,
-        conn,
-        bank_id,
-        unit_ids,
-        resolved_entity_ids,
-        entity_to_unit,
-        unit_to_entity_ids,
-        log_buffer,
-        skip_unit_entities_insert=skip_unit_entities_insert,
-    )
+    return entity_links
 
 
-async def insert_entity_links_batch(conn, entity_links: list[EntityLink], bank_id: str) -> None:
+async def insert_entity_links_batch(conn, entity_links: list[EntityLink]) -> None:
     """
     Insert entity links in batch.
 
     Args:
         conn: Database connection
         entity_links: List of EntityLink objects
-        bank_id: Bank identifier (stored directly on memory_links for fast filtering)
     """
     if not entity_links:
         return
 
-    await link_utils.insert_entity_links_batch(conn, entity_links, bank_id)
+    await link_utils.insert_entity_links_batch(conn, entity_links)

hindsight-api-slim/hindsight_api/engine/retain/fact_extraction.py

@@ -87,7 +87,7 @@ class Fact(BaseModel):
 
     # Required fields
     fact: str = Field(description="Combined fact text: what | when | where | who | why")
-    fact_type: Literal["world", "experience"] = Field(description="Perspective: world/experience")
+    fact_type: Literal["world", "experience", "opinion"] = Field(description="Perspective: world/experience/opinion")
 
     # Optional temporal fields
     occurred_start: str | None = None
@@ -159,9 +159,7 @@ class ExtractedFact(BaseModel):
     fact_kind: str = Field(default="conversation", description="'event' or 'conversation'")
     occurred_start: str | None = Field(default=None, description="ISO timestamp for events")
     occurred_end: str | None = Field(default=None, description="ISO timestamp for event end")
-    fact_type: Literal["world", "assistant"] = Field(
-        description="'world' = objective/external facts. 'assistant' = first-person actions, experiences, or observations by the speaker."
-    )
+    fact_type: Literal["world", "assistant"] = Field(description="'world' or 'assistant'")
     entities: list[Entity] | None = Field(default=None, description="People, places, concepts")
     causal_relations: list[FactCausalRelation] | None = Field(
         default=None, description="Links to previous facts (target_index < this fact's index)"
@@ -263,7 +261,7 @@ class ExtractedFactVerbose(BaseModel):
     )
 
     fact_type: Literal["world", "assistant"] = Field(
-        description="'world' = objective/external facts about other people, events, general knowledge. 'assistant' = first-person actions, experiences, or observations by the speaker (e.g., 'I changed X', 'I discovered Y')."
+        description="'world' = about the user/others (background, experiences). 'assistant' = experience with the assistant."
     )
 
     entities: list[Entity] | None = Field(
@@ -354,9 +352,7 @@ class VerbatimExtractedFact(BaseModel):
     fact_kind: str = Field(default="conversation", description="'event' or 'conversation'")
     occurred_start: str | None = Field(default=None, description="ISO timestamp for events")
     occurred_end: str | None = Field(default=None, description="ISO timestamp for event end")
-    fact_type: Literal["world", "assistant"] = Field(
-        description="'world' = objective/external facts. 'assistant' = first-person actions, experiences, or observations by the speaker."
-    )
+    fact_type: Literal["world", "assistant"] = Field(description="'world' or 'assistant'")
     entities: list[Entity] | None = Field(default=None, description="People, places, concepts")
 
     @field_validator("entities", mode="before")
@@ -503,8 +499,8 @@ fact_kind:
 - "conversation": Ongoing state, preference, trait (no dates)
 
 fact_type:
-- "world": About other people, external events, general knowledge, objective facts
-- "assistant": First-person actions, experiences, or observations by the speaker/author (e.g., "I changed X", "I discovered Y", "I debugged Z"). Also includes interactions with the user (requests, recommendations). If the narrator describes something they did, tried, learned, or decided — use "assistant".
+- "world": About user's life, other people, external events
+- "assistant": Interactions with assistant (requests, recommendations)
 
 ══════════════════════════════════════════════════════════════════════════
 TEMPORAL HANDLING
@@ -620,7 +616,7 @@ VERBOSE_FACT_EXTRACTION_PROMPT = """Extract facts from text into structured form
 
 LANGUAGE: MANDATORY — Detect the language of the input text and produce ALL output in that EXACT same language. You are STRICTLY FORBIDDEN from translating or switching to any other language. Every single word of your output must be in the same language as the input. Do NOT output in a different language under any circumstance.
 
-{retain_mission_section}══════════════════════════════════════════════════════════════════════════
+══════════════════════════════════════════════════════════════════════════
 FACT FORMAT - ALL FIVE DIMENSIONS REQUIRED - MAXIMUM VERBOSITY
 ══════════════════════════════════════════════════════════════════════════
 
@@ -831,9 +827,7 @@ def _build_extraction_prompt_and_schema(config) -> tuple[str, type]:
                 custom_instructions=config.retain_custom_instructions,
             )
     elif extraction_mode == "verbose":
-        prompt = VERBOSE_FACT_EXTRACTION_PROMPT.format(
-            retain_mission_section=retain_mission_section,
-        )
+        prompt = VERBOSE_FACT_EXTRACTION_PROMPT
     elif extraction_mode == "verbatim":
         prompt = VERBATIM_FACT_EXTRACTION_PROMPT.format(
             retain_mission_section=retain_mission_section,
@@ -1055,7 +1049,7 @@ async def _extract_facts_from_chunk(
                     f"LLM response missing 'facts' field or returned empty list. "
                     f"Response: {extraction_response_json}. "
                     f"Input: "
-                    f"date: {event_date.isoformat() if event_date else 'unset'}, "
+                    f"date: {event_date.isoformat()}, "
                     f"context: {context if context else 'none'}, "
                     f"text: {chunk}"
                 )
@@ -1464,76 +1458,28 @@ async def extract_facts_from_text(
             f"chunk_size={config.retain_chunk_size:,}) - starting parallel LLM extraction"
         )
 
-    # Per-chunk retry wrapper: each chunk gets up to MAX_CHUNK_RETRIES attempts.
-    # This handles transient LLM failures (timeouts, rate limits, malformed responses)
-    # without discarding the entire batch. If a chunk still fails after all retries,
-    # the ENTIRE retain fails — we do not accept partial extraction.
-    MAX_CHUNK_RETRIES = 3
-    CHUNK_RETRY_BASE_DELAY = 2.0  # seconds, doubles each retry
-
-    async def _extract_chunk_with_retry(chunk: str, chunk_index: int) -> tuple:
-        """Extract facts from a single chunk with retries on failure."""
-        last_exception = None
-        for attempt in range(MAX_CHUNK_RETRIES):
-            try:
-                return await _extract_facts_with_auto_split(
-                    chunk=chunk,
-                    chunk_index=chunk_index,
-                    total_chunks=len(chunks),
-                    event_date=event_date,
-                    context=context,
-                    llm_config=llm_config,
-                    config=config,
-                    agent_name=agent_name,
-                    metadata=metadata,
-                )
-            except Exception as e:
-                last_exception = e
-                if attempt < MAX_CHUNK_RETRIES - 1:
-                    delay = CHUNK_RETRY_BASE_DELAY * (2**attempt)
-                    logger.warning(
-                        f"Chunk {chunk_index}/{len(chunks)} extraction failed "
-                        f"(attempt {attempt + 1}/{MAX_CHUNK_RETRIES}): "
-                        f"{type(e).__name__}. Retrying in {delay:.0f}s..."
-                    )
-                    await asyncio.sleep(delay)
-                else:
-                    logger.error(
-                        f"Chunk {chunk_index}/{len(chunks)} extraction failed after "
-                        f"{MAX_CHUNK_RETRIES} attempts: {type(e).__name__}: {e}"
-                    )
-        raise last_exception
-
-    tasks = [_extract_chunk_with_retry(chunk, i) for i, chunk in enumerate(chunks)]
-
-    # return_exceptions=True so we can collect all results even if some chunks
-    # exhausted their retries. We check for failures below and fail the retain
-    # if ANY chunk could not be extracted — partial extraction is not acceptable.
-    chunk_results = await asyncio.gather(*tasks, return_exceptions=True)
-
+    tasks = [
+        _extract_facts_with_auto_split(
+            chunk=chunk,
+            chunk_index=i,
+            total_chunks=len(chunks),
+            event_date=event_date,
+            context=context,
+            llm_config=llm_config,
+            config=config,
+            agent_name=agent_name,
+            metadata=metadata,
+        )
+        for i, chunk in enumerate(chunks)
+    ]
+    chunk_results = await asyncio.gather(*tasks)
     all_facts = []
     chunk_metadata = []  # [(chunk_text, fact_count), ...]
     total_usage = TokenUsage()
-    failed_chunks = []
-    for i, (chunk, result) in enumerate(zip(chunks, chunk_results)):
-        if isinstance(result, Exception):
-            failed_chunks.append((i, result))
-            continue
-        chunk_facts, chunk_usage = result
+    for chunk, (chunk_facts, chunk_usage) in zip(chunks, chunk_results):
         all_facts.extend(chunk_facts)
         chunk_metadata.append((chunk, len(chunk_facts)))
         total_usage = total_usage + chunk_usage
-
-    if failed_chunks:
-        # Fail the entire retain — partial extraction is not acceptable.
-        # All successfully extracted facts are discarded because the transaction
-        # hasn't committed yet. The worker poller will retry the entire task.
-        failed_summary = ", ".join(f"chunk {idx}: {type(err).__name__}" for idx, err in failed_chunks[:5])
-        raise RuntimeError(
-            f"Fact extraction failed: {len(failed_chunks)}/{len(chunks)} chunks failed "
-            f"after {MAX_CHUNK_RETRIES} retries each. First failures: {failed_summary}"
-        )
-
     return all_facts, chunk_metadata, total_usage
 
 
@@ -1967,7 +1913,7 @@ async def extract_facts_from_contents_batch_api(
         for fact_from_llm in chunk_facts:
             extracted_fact = ExtractedFactType(
                 fact_text=fact_from_llm.fact,
-                fact_type=fact_from_llm.fact_type,
+                fact_type="experience" if fact_from_llm.fact_type == "assistant" else "world",
                 entities=[e.text for e in (fact_from_llm.entities or [])],
                 occurred_start=_parse_datetime(fact_from_llm.occurred_start) if fact_from_llm.occurred_start else None,
                 occurred_end=_parse_datetime(fact_from_llm.occurred_end) if fact_from_llm.occurred_end else None,
@@ -2103,9 +2049,8 @@ async def extract_facts_from_contents(
         )
         fact_extraction_tasks.append(task)
 
-    # Step 2: Wait for all fact extractions to complete.
-    # Use return_exceptions=True so one content item failure doesn't discard the rest.
-    all_fact_results = await asyncio.gather(*fact_extraction_tasks, return_exceptions=True)
+    # Step 2: Wait for all fact extractions to complete
+    all_fact_results = await asyncio.gather(*fact_extraction_tasks)
 
     # Step 3: Flatten and convert to typed objects
     extracted_facts: list[ExtractedFactType] = []
@@ -2115,16 +2060,9 @@ async def extract_facts_from_contents(
     global_chunk_idx = 0
     global_fact_idx = 0
 
-    # Filter out failed content items
-    valid_results = []
-    for content, result in zip(contents, all_fact_results):
-        if isinstance(result, Exception):
-            logger.warning(f"Content extraction failed (skipping): {type(result).__name__}: {result}")
-            valid_results.append((content, ([], [], TokenUsage())))
-        else:
-            valid_results.append((content, result))
-
-    for content_index, (content, (facts_from_llm, chunks_from_llm, content_usage)) in enumerate(valid_results):
+    for content_index, (content, (facts_from_llm, chunks_from_llm, content_usage)) in enumerate(
+        zip(contents, all_fact_results)
+    ):
         total_usage = total_usage + content_usage
         chunk_start_idx = global_chunk_idx
 
@@ -2152,7 +2090,7 @@ async def extract_facts_from_contents(
                     # mentioned_at is always the event_date (when the conversation/document occurred)
                     extracted_fact = ExtractedFactType(
                         fact_text=fact_from_llm.fact,
-                        fact_type=fact_from_llm.fact_type,
+                        fact_type="experience" if fact_from_llm.fact_type == "assistant" else "world",
                         entities=[e.text for e in (fact_from_llm.entities or [])],
                         # occurred_start/end: from LLM only, leave None if not provided
                         occurred_start=_parse_datetime(fact_from_llm.occurred_start)

hindsight-api-slim/hindsight_api/engine/retain/fact_storage.py

@@ -10,7 +10,7 @@ import uuid
 
 from ...config import get_config
 from ..memory_engine import fq_table
-from .bank_utils import DEFAULT_DISPOSITION, create_bank_vector_indexes
+from .bank_utils import DEFAULT_DISPOSITION, create_bank_hnsw_indexes
 from .fact_extraction import _sanitize_text
 from .types import ProcessedFact
 
@@ -44,6 +44,7 @@ async def insert_facts_batch(
     mentioned_ats = []
     contexts = []
     fact_types = []
+    confidence_scores = []
     metadata_jsons = []
     chunk_ids = []
     document_ids = []
@@ -63,6 +64,8 @@ async def insert_facts_batch(
         mentioned_ats.append(fact.mentioned_at)
         contexts.append(_sanitize_text(fact.context))
         fact_types.append(fact.fact_type)
+        # confidence_score is only for opinion facts
+        confidence_scores.append(1.0 if fact.fact_type == "opinion" else None)
         metadata_jsons.append(json.dumps(fact.metadata))
         chunk_ids.append(fact.chunk_id)
         # Use per-fact document_id if available, otherwise fallback to batch-level document_id
@@ -100,18 +103,18 @@ async def insert_facts_batch(
             WITH input_data AS (
                 SELECT * FROM unnest(
                     $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
-                    $8::text[], $9::text[], $10::jsonb[], $11::text[], $12::text[], $13::jsonb[], $14::jsonb[], $15::text[]
+                    $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[], $15::jsonb[], $16::text[]
                 ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                       context, fact_type, metadata, chunk_id, document_id, tags_json,
+                       context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json,
                        observation_scopes_json, text_signals)
             )
             INSERT INTO {fq_table("memory_units")} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                                     context, fact_type, metadata, chunk_id, document_id, tags,
+                                     context, fact_type, confidence_score, metadata, chunk_id, document_id, tags,
                                      observation_scopes, text_signals, search_vector)
             SELECT
                 $1,
                 text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                context, fact_type, metadata, chunk_id, document_id,
+                context, fact_type, confidence_score, metadata, chunk_id, document_id,
                 COALESCE(
                     (SELECT array_agg(elem) FROM jsonb_array_elements_text(tags_json) AS elem),
                     '{{}}'::varchar[]
@@ -132,18 +135,18 @@ async def insert_facts_batch(
             WITH input_data AS (
                 SELECT * FROM unnest(
                     $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
-                    $8::text[], $9::text[], $10::jsonb[], $11::text[], $12::text[], $13::jsonb[], $14::jsonb[], $15::text[]
+                    $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[], $15::jsonb[], $16::text[]
                 ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                       context, fact_type, metadata, chunk_id, document_id, tags_json,
+                       context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json,
                        observation_scopes_json, text_signals)
             )
             INSERT INTO {fq_table("memory_units")} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                                     context, fact_type, metadata, chunk_id, document_id, tags,
+                                     context, fact_type, confidence_score, metadata, chunk_id, document_id, tags,
                                      observation_scopes, text_signals)
             SELECT
                 $1,
                 text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                context, fact_type, metadata, chunk_id, document_id,
+                context, fact_type, confidence_score, metadata, chunk_id, document_id,
                 COALESCE(
                     (SELECT array_agg(elem) FROM jsonb_array_elements_text(tags_json) AS elem),
                     '{{}}'::varchar[]
@@ -165,6 +168,7 @@ async def insert_facts_batch(
         mentioned_ats,
         contexts,
         fact_types,
+        confidence_scores,
         metadata_jsons,
         chunk_ids,
         document_ids,
@@ -203,8 +207,8 @@ async def ensure_bank_exists(conn, bank_id: str) -> None:
         internal_id,
     )
     if inserted:
-        # Fresh insert — create per-bank vector indexes
-        await create_bank_vector_indexes(conn, bank_id, str(internal_id))
+        # Fresh insert — create per-bank HNSW indexes
+        await create_bank_hnsw_indexes(conn, bank_id, str(internal_id))
 
 
 async def handle_document_tracking(
@@ -217,10 +221,7 @@ async def handle_document_tracking(
     document_tags: list[str] | None = None,
 ) -> None:
     """
-    Handle document tracking in the database (full-replace mode).
-
-    Deletes the existing document (cascading to all units and links) on the
-    first batch, then inserts the new document record.
+    Handle document tracking in the database.
 
     Args:
         conn: Database connection
@@ -237,58 +238,22 @@ async def handle_document_tracking(
     combined_content = _sanitize_text(combined_content) or ""
     content_hash = hashlib.sha256(combined_content.encode()).hexdigest()
 
-    # Delete old document first (cascades to units and links)
+    # Always delete old document first if it exists (cascades to units and links)
     # Only delete on the first batch to avoid deleting data we just inserted
     if is_first_batch:
         await conn.fetchval(
-            f"DELETE FROM {fq_table('documents')} WHERE id = $1 AND bank_id = $2 RETURNING id",
-            document_id,
-            bank_id,
+            f"DELETE FROM {fq_table('documents')} WHERE id = $1 AND bank_id = $2 RETURNING id", document_id, bank_id
         )
 
     # Insert document (or update if exists from concurrent operations)
-    await _upsert_document_row(conn, bank_id, document_id, combined_content, content_hash, retain_params, document_tags)
-
-
-async def upsert_document_metadata(
-    conn,
-    bank_id: str,
-    document_id: str,
-    combined_content: str,
-    retain_params: dict | None = None,
-    document_tags: list[str] | None = None,
-) -> None:
-    """
-    Update document metadata without deleting existing facts/chunks.
-
-    Used by delta retain: the document row is upserted but chunks and
-    memory_units are managed separately at the chunk level.
-    """
-    import hashlib
-
-    combined_content = _sanitize_text(combined_content) or ""
-    content_hash = hashlib.sha256(combined_content.encode()).hexdigest()
-
-    await _upsert_document_row(conn, bank_id, document_id, combined_content, content_hash, retain_params, document_tags)
-
-
-async def _upsert_document_row(
-    conn,
-    bank_id: str,
-    document_id: str,
-    combined_content: str,
-    content_hash: str,
-    retain_params: dict | None = None,
-    document_tags: list[str] | None = None,
-) -> None:
-    """Insert or update a document row."""
     await conn.execute(
         f"""
-        INSERT INTO {fq_table("documents")} (id, bank_id, original_text, content_hash, retain_params, tags)
-        VALUES ($1, $2, $3, $4, $5, $6)
+        INSERT INTO {fq_table("documents")} (id, bank_id, original_text, content_hash, metadata, retain_params, tags)
+        VALUES ($1, $2, $3, $4, $5, $6, $7)
         ON CONFLICT (id, bank_id) DO UPDATE
         SET original_text = EXCLUDED.original_text,
             content_hash = EXCLUDED.content_hash,
+            metadata = EXCLUDED.metadata,
             retain_params = EXCLUDED.retain_params,
             tags = EXCLUDED.tags,
             updated_at = NOW()
@@ -297,37 +262,7 @@ async def _upsert_document_row(
         bank_id,
         combined_content,
         content_hash,
+        json.dumps({}),  # Empty metadata dict
         json.dumps(retain_params) if retain_params else None,
         document_tags or [],
     )
-
-
-async def update_memory_units_tags(
-    conn,
-    bank_id: str,
-    document_id: str,
-    tags: list[str],
-) -> int:
-    """
-    Update tags on all memory_units belonging to a document.
-
-    Used during delta retain to propagate tag changes to unchanged facts.
-
-    Returns:
-        Number of memory units updated.
-    """
-    result = await conn.execute(
-        f"""
-        UPDATE {fq_table("memory_units")}
-        SET tags = $3, updated_at = NOW()
-        WHERE bank_id = $1 AND document_id = $2
-        """,
-        bank_id,
-        document_id,
-        tags or [],
-    )
-    # result is a status string like "UPDATE 5"
-    try:
-        return int(result.split()[-1])
-    except (ValueError, IndexError):
-        return 0

hindsight-api-slim/hindsight_api/engine/retain/link_creation.py

@@ -32,26 +32,17 @@ async def create_temporal_links_batch(conn, bank_id: str, unit_ids: list[str]) -
     return await link_utils.create_temporal_links_batch_per_fact(conn, bank_id, unit_ids, log_buffer=[])
 
 
-async def create_semantic_links_batch(
-    conn,
-    bank_id: str,
-    unit_ids: list[str],
-    embeddings: list[list[float]],
-    pre_computed_ann_links: list[tuple] | None = None,
-) -> int:
+async def create_semantic_links_batch(conn, bank_id: str, unit_ids: list[str], embeddings: list[list[float]]) -> int:
     """
     Create semantic links between facts.
 
     Links facts that are semantically similar based on embeddings.
-    When pre_computed_ann_links are provided (from Phase 1), they are used
-    instead of running ANN queries inside the transaction.
 
     Args:
         conn: Database connection
         bank_id: Bank identifier
         unit_ids: List of unit IDs to create links for
         embeddings: List of embedding vectors (same length as unit_ids)
-        pre_computed_ann_links: Pre-computed ANN results from Phase 1
 
     Returns:
         Number of semantic links created
@@ -62,12 +53,10 @@ async def create_semantic_links_batch(
     if len(unit_ids) != len(embeddings):
         raise ValueError(f"Mismatch between unit_ids ({len(unit_ids)}) and embeddings ({len(embeddings)})")
 
-    return await link_utils.create_semantic_links_batch(
-        conn, bank_id, unit_ids, embeddings, log_buffer=[], pre_computed_ann_links=pre_computed_ann_links
-    )
+    return await link_utils.create_semantic_links_batch(conn, bank_id, unit_ids, embeddings, log_buffer=[])
 
 
-async def create_causal_links_batch(conn, bank_id: str, unit_ids: list[str], facts: list[ProcessedFact]) -> int:
+async def create_causal_links_batch(conn, unit_ids: list[str], facts: list[ProcessedFact]) -> int:
     """
     Create causal links between facts.
 
@@ -105,6 +94,6 @@ async def create_causal_links_batch(conn, bank_id: str, unit_ids: list[str], fac
         else:
             causal_relations_per_fact.append([])
 
-    link_count = await link_utils.create_causal_links_batch(conn, bank_id, unit_ids, causal_relations_per_fact)
+    link_count = await link_utils.create_causal_links_batch(conn, unit_ids, causal_relations_per_fact)
 
     return link_count

hindsight-api-slim/hindsight_api/engine/retain/types.py

@@ -107,7 +107,7 @@ class ExtractedFact:
     """
 
     fact_text: str
-    fact_type: str  # "world", "experience", "observation"
+    fact_type: str  # "world", "experience", "opinion", "observation"
     entities: list[str] = field(default_factory=list)
     occurred_start: datetime | None = None
     occurred_end: datetime | None = None
@@ -221,45 +221,6 @@ class ProcessedFact:
         )
 
 
-@dataclass
-class Phase3Context:
-    """
-    Data passed from Phase 2 to Phase 3 for entity link building.
-
-    Contains the unit IDs and entity resolution data needed to build
-    entity links for UI graph visualization after the write transaction commits.
-    """
-
-    unit_ids: list[str] = field(default_factory=list)
-    resolved_entity_ids: list[str] = field(default_factory=list)
-    entity_to_unit: list[tuple] = field(default_factory=list)
-    unit_to_entity_ids: dict[str, list[str]] = field(default_factory=dict)
-
-
-@dataclass
-class EntityResolutionResult:
-    """
-    Result of Phase 1 entity resolution.
-
-    Contains resolved entity IDs and the mapping data needed to remap
-    placeholder unit IDs to real IDs after fact insertion in Phase 2.
-    """
-
-    resolved_entity_ids: list[str]
-    entity_to_unit: list[tuple]
-    unit_to_entity_ids: dict[str, list[str]]
-
-
-@dataclass
-class Phase1Result:
-    """
-    Full result of Phase 1 (entity resolution + optional semantic ANN).
-    """
-
-    entities: EntityResolutionResult
-    semantic_ann_links: list[tuple]
-
-
 @dataclass
 class EntityLink:
     """
@@ -287,6 +248,7 @@ class RetainBatch:
     contents: list[RetainContent]
     document_id: str | None = None
     fact_type_override: str | None = None
+    confidence_score: float | None = None
     document_tags: list[str] = field(default_factory=list)  # Tags applied to all items
 
     # Extracted data (populated during processing)

hindsight-api-slim/hindsight_api/engine/search/__init__.py

@@ -3,11 +3,12 @@ Search module for memory retrieval.
 
 Provides modular search architecture:
 - Retrieval: 4-way parallel (semantic + BM25 + graph + temporal)
-- Graph retrieval: Link expansion strategy
+- Graph retrieval: Pluggable strategies (BFS, PPR)
 - Reranking: Pluggable strategies (heuristic, cross-encoder)
 """
 
-from .graph_retrieval import GraphRetriever
+from .graph_retrieval import BFSGraphRetriever, GraphRetriever
+from .mpfp_retrieval import MPFPGraphRetriever
 from .reranking import CrossEncoderReranker
 from .retrieval import (
     ParallelRetrievalResult,
@@ -20,5 +21,7 @@ __all__ = [
     "set_default_graph_retriever",
     "ParallelRetrievalResult",
     "GraphRetriever",
+    "BFSGraphRetriever",
+    "MPFPGraphRetriever",
     "CrossEncoderReranker",
 ]

hindsight-api-slim/hindsight_api/engine/search/graph_retrieval.py

@@ -2,15 +2,17 @@
 Graph retrieval strategies for memory recall.
 
 This module provides an abstraction for graph-based memory retrieval,
-allowing different algorithms to be swapped without changing the rest
-of the recall pipeline.
+allowing different algorithms (BFS spreading activation, PPR, etc.) to be
+swapped without changing the rest of the recall pipeline.
 """
 
 import logging
 from abc import ABC, abstractmethod
 
-from .tags import TagGroup, TagsMatch
-from .types import GraphRetrievalTimings, RetrievalResult
+from ..db_utils import acquire_with_retry
+from ..memory_engine import fq_table
+from .tags import TagGroup, TagsMatch, filter_results_by_tag_groups, filter_results_by_tags
+from .types import MPFPTimings, RetrievalResult
 
 logger = logging.getLogger(__name__)
 
@@ -27,7 +29,7 @@ class GraphRetriever(ABC):
     @property
     @abstractmethod
     def name(self) -> str:
-        """Return identifier for this retrieval strategy (e.g., 'link_expansion')."""
+        """Return identifier for this retrieval strategy (e.g., 'bfs', 'mpfp')."""
         pass
 
     @abstractmethod
@@ -45,7 +47,7 @@ class GraphRetriever(ABC):
         tags: list[str] | None = None,  # Visibility scope tags for filtering
         tags_match: TagsMatch = "any",  # How to match tags: 'any' (OR) or 'all' (AND)
         tag_groups: list[TagGroup] | None = None,  # Compound boolean tag filter groups
-    ) -> tuple[list[RetrievalResult], GraphRetrievalTimings | None]:
+    ) -> tuple[list[RetrievalResult], MPFPTimings | None]:
         """
         Retrieve relevant facts via graph traversal.
 
@@ -53,15 +55,228 @@ class GraphRetriever(ABC):
             pool: Database connection pool
             query_embedding_str: Query embedding as string (for finding entry points)
             bank_id: Memory bank identifier
-            fact_type: Fact type to filter ('world', 'experience', 'observation')
+            fact_type: Fact type to filter ('world', 'experience', 'opinion', 'observation')
             budget: Maximum number of nodes to explore/return
             query_text: Original query text (optional, for some strategies)
             semantic_seeds: Pre-computed semantic entry points (from semantic retrieval)
             temporal_seeds: Pre-computed temporal entry points (from temporal retrieval)
-            adjacency: Pre-loaded typed adjacency graph (optional)
+            adjacency: Pre-loaded typed adjacency graph (optional, for MPFP)
             tags: Optional list of tags for visibility filtering (OR matching)
 
         Returns:
             Tuple of (List of RetrievalResult with activation scores, optional timing info)
         """
         pass
+
+
+class BFSGraphRetriever(GraphRetriever):
+    """
+    Graph retrieval using BFS-style spreading activation.
+
+    Starting from semantic entry points, spreads activation through
+    the memory graph (entity, temporal, causal links) using breadth-first
+    traversal with decaying activation.
+
+    This is the original Hindsight graph retrieval algorithm.
+    """
+
+    def __init__(
+        self,
+        entry_point_limit: int = 5,
+        entry_point_threshold: float = 0.5,
+        activation_decay: float = 0.8,
+        min_activation: float = 0.1,
+        batch_size: int = 20,
+    ):
+        """
+        Initialize BFS graph retriever.
+
+        Args:
+            entry_point_limit: Maximum number of entry points to start from
+            entry_point_threshold: Minimum semantic similarity for entry points
+            activation_decay: Decay factor per hop (activation *= decay)
+            min_activation: Minimum activation to continue spreading
+            batch_size: Number of nodes to process per batch (for neighbor fetching)
+        """
+        self.entry_point_limit = entry_point_limit
+        self.entry_point_threshold = entry_point_threshold
+        self.activation_decay = activation_decay
+        self.min_activation = min_activation
+        self.batch_size = batch_size
+
+    @property
+    def name(self) -> str:
+        return "bfs"
+
+    async def retrieve(
+        self,
+        pool,
+        query_embedding_str: str,
+        bank_id: str,
+        fact_type: str,
+        budget: int,
+        query_text: str | None = None,
+        semantic_seeds: list[RetrievalResult] | None = None,
+        temporal_seeds: list[RetrievalResult] | None = None,
+        adjacency=None,  # Not used by BFS
+        tags: list[str] | None = None,
+        tags_match: TagsMatch = "any",
+        tag_groups: list[TagGroup] | None = None,
+    ) -> tuple[list[RetrievalResult], MPFPTimings | None]:
+        """
+        Retrieve facts using BFS spreading activation.
+
+        Algorithm:
+        1. Find entry points (top semantic matches above threshold)
+        2. BFS traversal: visit neighbors, propagate decaying activation
+        3. Boost causal links (causes, enables, prevents)
+        4. Return visited nodes up to budget
+
+        Note: BFS finds its own entry points via embedding search.
+        The semantic_seeds, temporal_seeds, and adjacency parameters are accepted
+        for interface compatibility but not used.
+        """
+        async with acquire_with_retry(pool) as conn:
+            results = await self._retrieve_with_conn(
+                conn,
+                query_embedding_str,
+                bank_id,
+                fact_type,
+                budget,
+                tags=tags,
+                tags_match=tags_match,
+                tag_groups=tag_groups,
+            )
+            return results, None
+
+    async def _retrieve_with_conn(
+        self,
+        conn,
+        query_embedding_str: str,
+        bank_id: str,
+        fact_type: str,
+        budget: int,
+        tags: list[str] | None = None,
+        tags_match: TagsMatch = "any",
+        tag_groups: list[TagGroup] | None = None,
+    ) -> list[RetrievalResult]:
+        """Internal implementation with connection."""
+        from .tags import build_tag_groups_where_clause, build_tags_where_clause_simple
+
+        tags_clause = build_tags_where_clause_simple(tags, 6, match=tags_match)
+        tag_groups_param_start = 6 + (1 if tags else 0)
+        groups_clause, groups_params, _ = build_tag_groups_where_clause(tag_groups, tag_groups_param_start)
+        params = [query_embedding_str, bank_id, fact_type, self.entry_point_threshold, self.entry_point_limit]
+        if tags:
+            params.append(tags)
+        params.extend(groups_params)
+
+        # Step 1: Find entry points
+        entry_points = await conn.fetch(
+            f"""
+            SELECT id, text, context, event_date, occurred_start, occurred_end,
+                   mentioned_at, fact_type, document_id, chunk_id, tags,
+                   1 - (embedding <=> $1::vector) AS similarity
+            FROM {fq_table("memory_units")}
+            WHERE bank_id = $2
+              AND embedding IS NOT NULL
+              AND fact_type = $3
+              AND (1 - (embedding <=> $1::vector)) >= $4
+              {tags_clause}
+              {groups_clause}
+            ORDER BY embedding <=> $1::vector
+            LIMIT $5
+            """,
+            *params,
+        )
+
+        if not entry_points:
+            logger.debug(
+                f"[BFS] No entry points found for fact_type={fact_type} (tags={tags}, tags_match={tags_match})"
+            )
+            return []
+
+        logger.debug(
+            f"[BFS] Found {len(entry_points)} entry points for fact_type={fact_type} "
+            f"(tags={tags}, tags_match={tags_match})"
+        )
+
+        # Step 2: BFS spreading activation
+        visited = set()
+        results = []
+        queue = [(RetrievalResult.from_db_row(dict(r)), r["similarity"]) for r in entry_points]
+        budget_remaining = budget
+
+        while queue and budget_remaining > 0:
+            # Collect a batch of nodes to process
+            batch_nodes = []
+            batch_activations = {}
+
+            while queue and len(batch_nodes) < self.batch_size and budget_remaining > 0:
+                current, activation = queue.pop(0)
+                unit_id = current.id
+
+                if unit_id not in visited:
+                    visited.add(unit_id)
+                    budget_remaining -= 1
+                    current.activation = activation
+                    results.append(current)
+                    batch_nodes.append(current.id)
+                    batch_activations[unit_id] = activation
+
+            # Batch fetch neighbors
+            if batch_nodes and budget_remaining > 0:
+                max_neighbors = len(batch_nodes) * 20
+                neighbors = await conn.fetch(
+                    f"""
+                    SELECT mu.id, mu.text, mu.context, mu.occurred_start, mu.occurred_end,
+                           mu.mentioned_at, mu.fact_type,
+                           mu.document_id, mu.chunk_id, mu.tags, mu.metadata,
+                           ml.weight, ml.link_type, ml.from_unit_id
+                    FROM {fq_table("memory_links")} ml
+                    JOIN {fq_table("memory_units")} mu ON ml.to_unit_id = mu.id
+                    WHERE ml.from_unit_id = ANY($1::uuid[])
+                      AND ml.weight >= $2
+                      AND mu.fact_type = $3
+                    ORDER BY ml.weight DESC
+                    LIMIT $4
+                    """,
+                    batch_nodes,
+                    self.min_activation,
+                    fact_type,
+                    max_neighbors,
+                )
+
+                for n in neighbors:
+                    neighbor_id = str(n["id"])
+                    if neighbor_id not in visited:
+                        parent_id = str(n["from_unit_id"])
+                        parent_activation = batch_activations.get(parent_id, 0.5)
+
+                        # Boost causal links
+                        link_type = n["link_type"]
+                        base_weight = n["weight"]
+
+                        if link_type in ("causes", "caused_by"):
+                            causal_boost = 2.0
+                        elif link_type in ("enables", "prevents"):
+                            causal_boost = 1.5
+                        else:
+                            causal_boost = 1.0
+
+                        effective_weight = base_weight * causal_boost
+                        new_activation = parent_activation * effective_weight * self.activation_decay
+
+                        if new_activation > self.min_activation:
+                            neighbor_result = RetrievalResult.from_db_row(dict(n))
+                            queue.append((neighbor_result, new_activation))
+
+        # Apply tags filtering (BFS may traverse into memories that don't match tags criteria)
+        if tags:
+            results = filter_results_by_tags(results, tags, match=tags_match)
+
+        # Apply compound tag group filtering (post-traversal)
+        if tag_groups:
+            results = filter_results_by_tag_groups(results, tag_groups)
+
+        return results

hindsight-api-slim/hindsight_api/engine/search/link_expansion_retrieval.py

@@ -4,9 +4,9 @@ Link Expansion graph retrieval.
 Expands from semantic/temporal seeds through three parallel, first-class signals
 stored in memory_links:
 
-1. Entity links  — query-time self-join through unit_entities. Score = number of distinct
-                   shared entities between the seed set and each candidate, computed via
-                   COUNT(DISTINCT entity_id). More accurate than precomputed entity links.
+1. Entity links  — precomputed co-occurrence graph (created at retain time, bounded to
+                   MAX_LINKS_PER_ENTITY per entity). Score = number of distinct shared
+                   entities between the seed set and each candidate.
 2. Semantic links — precomputed kNN graph (each new fact linked to its top-5 most
                     similar existing facts at insert time, similarity >= 0.7). Checked
                     in both directions since the graph is not symmetric. Score = weight.
@@ -29,7 +29,7 @@ from ..db_utils import acquire_with_retry
 from ..memory_engine import fq_table
 from .graph_retrieval import GraphRetriever
 from .tags import TagGroup, TagsMatch, filter_results_by_tag_groups, filter_results_by_tags
-from .types import GraphRetrievalTimings, RetrievalResult
+from .types import MPFPTimings, RetrievalResult
 
 logger = logging.getLogger(__name__)
 
@@ -59,7 +59,7 @@ async def _find_semantic_seeds(
     rows = await conn.fetch(
         f"""
         SELECT id, text, context, event_date, occurred_start, occurred_end,
-               mentioned_at, fact_type, document_id, chunk_id, tags, proof_count,
+               mentioned_at, fact_type, document_id, chunk_id, tags,
                1 - (embedding <=> $1::vector) AS similarity
         FROM {fq_table("memory_units")}
         WHERE bank_id = $2
@@ -116,7 +116,7 @@ class LinkExpansionRetriever(GraphRetriever):
         tags: list[str] | None = None,
         tags_match: TagsMatch = "any",
         tag_groups: list[TagGroup] | None = None,
-    ) -> tuple[list[RetrievalResult], GraphRetrievalTimings | None]:
+    ) -> tuple[list[RetrievalResult], MPFPTimings | None]:
         """
         Retrieve facts by expanding links from seeds.
 
@@ -136,7 +136,7 @@ class LinkExpansionRetriever(GraphRetriever):
             Tuple of (results, timings)
         """
         start_time = time.time()
-        timings = GraphRetrievalTimings(fact_type=fact_type)
+        timings = MPFPTimings(fact_type=fact_type)
 
         async with acquire_with_retry(pool) as conn:
             # Find seeds if not provided
@@ -264,33 +264,29 @@ class LinkExpansionRetriever(GraphRetriever):
         """
         ml = fq_table("memory_links")
         mu = fq_table("memory_units")
-        ue = fq_table("unit_entities")
-
-        entity_cte = f"""
-            entity_expanded AS (
-                -- Entity co-occurrence via unit_entities self-join.
-                -- Finds units sharing entities with seeds at query time — more accurate
-                -- than precomputed entity links (no stale 50-neighbor cap).
-                -- Score = COUNT(DISTINCT shared entities), mapped to [0,1] via tanh.
-                SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
-                       mu.occurred_end, mu.mentioned_at,
-                       mu.fact_type, mu.document_id, mu.chunk_id, mu.tags, mu.proof_count,
-                       COUNT(DISTINCT ue_seed.entity_id)::float AS score,
-                       'entity'::text AS source
-                FROM {ue} ue_seed
-                JOIN {ue} ue_target ON ue_seed.entity_id = ue_target.entity_id
-                JOIN {mu} mu ON mu.id = ue_target.unit_id
-                WHERE ue_seed.unit_id = ANY($1::uuid[])
-                  AND ue_target.unit_id != ALL($1::uuid[])
+        all_rows = await conn.fetch(
+            f"""
+            WITH entity_expanded AS (
+                -- Entity co-occurrence: seeds → their precomputed entity-link neighbors.
+                -- Score = distinct shared entities (bounded at retain time to
+                -- MAX_LINKS_PER_ENTITY=50).  GROUP BY mu.id is sufficient because mu.id
+                -- is the primary key and functionally determines all other mu columns.
+                SELECT
+                    mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
+                    mu.occurred_end, mu.mentioned_at,
+                    mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
+                    COUNT(DISTINCT ml.entity_id)::float AS score,
+                    'entity'::text AS source
+                FROM {ml} ml
+                JOIN {mu} mu ON mu.id = ml.to_unit_id
+                WHERE ml.from_unit_id = ANY($1::uuid[])
+                  AND ml.link_type = 'entity'
                   AND mu.fact_type = $2
+                  AND mu.id != ALL($1::uuid[])
                 GROUP BY mu.id
                 ORDER BY score DESC
                 LIMIT $3
-            )"""
-
-        all_rows = await conn.fetch(
-            f"""
-            WITH {entity_cte},
+            ),
             semantic_expanded AS (
                 -- Semantic kNN: both outgoing (seeds → their kNN at insert time) and
                 -- incoming (facts inserted after seeds that found seeds as kNN).
@@ -298,14 +294,14 @@ class LinkExpansionRetriever(GraphRetriever):
                 SELECT
                     id, text, context, event_date, occurred_start,
                     occurred_end, mentioned_at,
-                    fact_type, document_id, chunk_id, tags, proof_count,
+                    fact_type, document_id, chunk_id, tags,
                     MAX(weight) AS score,
                     'semantic'::text AS source
                 FROM (
                     SELECT
                         mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
                         mu.occurred_end, mu.mentioned_at,
-                        mu.fact_type, mu.document_id, mu.chunk_id, mu.tags, mu.proof_count,
+                        mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
                         ml.weight
                     FROM {ml} ml
                     JOIN {mu} mu ON mu.id = ml.to_unit_id
@@ -317,7 +313,7 @@ class LinkExpansionRetriever(GraphRetriever):
                     SELECT
                         mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
                         mu.occurred_end, mu.mentioned_at,
-                        mu.fact_type, mu.document_id, mu.chunk_id, mu.tags, mu.proof_count,
+                        mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
                         ml.weight
                     FROM {ml} ml
                     JOIN {mu} mu ON mu.id = ml.from_unit_id
@@ -328,7 +324,7 @@ class LinkExpansionRetriever(GraphRetriever):
                 ) sem_raw
                 GROUP BY id, text, context, event_date, occurred_start,
                          occurred_end, mentioned_at,
-                         fact_type, document_id, chunk_id, tags, proof_count
+                         fact_type, document_id, chunk_id, tags
                 ORDER BY score DESC
                 LIMIT $3
             ),
@@ -339,7 +335,7 @@ class LinkExpansionRetriever(GraphRetriever):
                 SELECT DISTINCT ON (mu.id)
                     mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
                     mu.occurred_end, mu.mentioned_at,
-                    mu.fact_type, mu.document_id, mu.chunk_id, mu.tags, mu.proof_count,
+                    mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
                     ml.weight AS score,
                     'causal'::text AS source
                 FROM {ml} ml
@@ -401,19 +397,6 @@ class LinkExpansionRetriever(GraphRetriever):
                 f"{len(source_ids_found)} source_memory_ids found"
             )
 
-        ue = fq_table("unit_entities")
-
-        connected_sources_cte = f"""
-            connected_sources AS (
-                -- Find sources sharing entities with seed observation sources
-                -- via unit_entities self-join (query-time, no precomputed links needed).
-                SELECT DISTINCT ue_target.unit_id AS source_id
-                FROM seed_sources ss
-                JOIN {ue} ue_seed ON ue_seed.unit_id = ss.source_id
-                JOIN {ue} ue_target ON ue_seed.entity_id = ue_target.entity_id
-                WHERE ue_target.unit_id != ss.source_id
-            )"""
-
         entity_rows = await conn.fetch(
             f"""
             WITH seed_sources AS (
@@ -422,14 +405,22 @@ class LinkExpansionRetriever(GraphRetriever):
                 WHERE id = ANY($1::uuid[])
                   AND source_memory_ids IS NOT NULL
             ),
-            {connected_sources_cte},
+            connected_sources AS (
+                -- Mirror the non-observation entity expansion: follow pre-bounded entity
+                -- links in memory_links (capped to MAX_LINKS_PER_ENTITY=50 at retain time).
+                -- Score = number of distinct shared entities, same as the non-obs path.
+                SELECT DISTINCT ml.to_unit_id AS source_id
+                FROM seed_sources ss
+                JOIN {fq_table("memory_links")} ml ON ml.from_unit_id = ss.source_id
+                WHERE ml.link_type = 'entity'
+            ),
             connected_array AS (
                 SELECT array_agg(source_id) AS source_ids FROM connected_sources
             )
             SELECT
                 mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
                 mu.occurred_end, mu.mentioned_at,
-                mu.fact_type, mu.document_id, mu.chunk_id, mu.tags, mu.proof_count,
+                mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
                 (SELECT COUNT(DISTINCT s) FROM unnest(mu.source_memory_ids) s WHERE s = ANY(ca.source_ids))::float AS score
             FROM {fq_table("memory_units")} mu, connected_array ca
             WHERE mu.fact_type = 'observation'
@@ -453,13 +444,13 @@ class LinkExpansionRetriever(GraphRetriever):
                 SELECT
                     id, text, context, event_date, occurred_start,
                     occurred_end, mentioned_at,
-                    fact_type, document_id, chunk_id, tags, proof_count,
+                    fact_type, document_id, chunk_id, tags,
                     MAX(weight) AS score,
                     'semantic'::text AS source
                 FROM (
                     SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
                            mu.occurred_end, mu.mentioned_at, mu.fact_type, mu.document_id,
-                           mu.chunk_id, mu.tags, mu.proof_count, ml.weight
+                           mu.chunk_id, mu.tags, ml.weight
                     FROM {ml} ml JOIN {mu} mu ON mu.id = ml.to_unit_id
                     WHERE ml.from_unit_id = ANY($1::uuid[])
                       AND ml.link_type = 'semantic' AND mu.fact_type = 'observation'
@@ -467,21 +458,21 @@ class LinkExpansionRetriever(GraphRetriever):
                     UNION ALL
                     SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
                            mu.occurred_end, mu.mentioned_at, mu.fact_type, mu.document_id,
-                           mu.chunk_id, mu.tags, mu.proof_count, ml.weight
+                           mu.chunk_id, mu.tags, ml.weight
                     FROM {ml} ml JOIN {mu} mu ON mu.id = ml.from_unit_id
                     WHERE ml.to_unit_id = ANY($1::uuid[])
                       AND ml.link_type = 'semantic' AND mu.fact_type = 'observation'
                       AND mu.id != ALL($1::uuid[])
                 ) sem_raw
                 GROUP BY id, text, context, event_date, occurred_start, occurred_end,
-                         mentioned_at, fact_type, document_id, chunk_id, tags, proof_count
+                         mentioned_at, fact_type, document_id, chunk_id, tags
                 ORDER BY score DESC LIMIT $2
             ),
             causal_expanded AS (
                 SELECT DISTINCT ON (mu.id)
                     mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
                     mu.occurred_end, mu.mentioned_at, mu.fact_type, mu.document_id,
-                    mu.chunk_id, mu.tags, mu.proof_count, ml.weight AS score, 'causal'::text AS source
+                    mu.chunk_id, mu.tags, ml.weight AS score, 'causal'::text AS source
                 FROM {ml} ml JOIN {mu} mu ON ml.to_unit_id = mu.id
                 WHERE ml.from_unit_id = ANY($1::uuid[])
                   AND ml.link_type IN ('causes', 'caused_by', 'enables', 'prevents')

hindsight-api-slim/hindsight_api/engine/search/mpfp_retrieval.py

@@ -0,0 +1,702 @@
+"""
+Meta-Path Forward Push (MPFP) graph retrieval.
+
+A sublinear graph traversal algorithm for memory retrieval over heterogeneous
+graphs with multiple edge types (semantic, temporal, causal, entity).
+
+Combines meta-path patterns from HIN literature with Forward Push local
+propagation from Approximate PPR.
+
+Key properties:
+- Sublinear in graph size (threshold pruning bounds active nodes)
+- Lazy edge loading: only loads edges for frontier nodes, not entire graph
+- Predefined patterns capture different retrieval intents
+- All patterns run in parallel, results fused via RRF
+- No LLM in the loop during traversal
+"""
+
+import asyncio
+import logging
+from collections import defaultdict
+from dataclasses import dataclass, field
+
+from ..db_utils import acquire_with_retry
+from ..memory_engine import fq_table
+from .graph_retrieval import GraphRetriever
+from .tags import TagGroup, TagsMatch
+from .types import MPFPTimings, RetrievalResult
+
+logger = logging.getLogger(__name__)
+
+
+# -----------------------------------------------------------------------------
+# Data Classes
+# -----------------------------------------------------------------------------
+
+
+@dataclass
+class EdgeTarget:
+    """A neighbor node with its edge weight."""
+
+    node_id: str
+    weight: float
+
+
+@dataclass
+class EdgeCache:
+    """
+    Cache for lazily-loaded edges.
+
+    Grows per-hop as edges are loaded for frontier nodes.
+    Shared across patterns to avoid redundant loads.
+    Loads ALL edge types at once to minimize DB queries.
+    Thread-safe via asyncio lock to prevent redundant concurrent loads.
+    """
+
+    # edge_type -> from_node_id -> list of EdgeTarget
+    graphs: dict[str, dict[str, list[EdgeTarget]]] = field(default_factory=dict)
+    # Track which nodes have been fully loaded (all edge types)
+    _fully_loaded: set[str] = field(default_factory=set)
+    # Timing stats
+    db_queries: int = 0
+    edge_load_time: float = 0.0
+    # Detailed hop timing for debugging
+    hop_details: list[dict] = field(default_factory=list)
+    # Lock to prevent redundant concurrent loads
+    _lock: asyncio.Lock = field(default_factory=asyncio.Lock)
+
+    def get_neighbors(self, edge_type: str, node_id: str) -> list[EdgeTarget]:
+        """Get neighbors for a node via a specific edge type."""
+        return self.graphs.get(edge_type, {}).get(node_id, [])
+
+    def get_normalized_neighbors(self, edge_type: str, node_id: str, top_k: int) -> list[EdgeTarget]:
+        """Get top-k neighbors with weights normalized to sum to 1."""
+        neighbors = self.get_neighbors(edge_type, node_id)[:top_k]
+        if not neighbors:
+            return []
+
+        total = sum(n.weight for n in neighbors)
+        if total == 0:
+            return []
+
+        return [EdgeTarget(node_id=n.node_id, weight=n.weight / total) for n in neighbors]
+
+    def is_fully_loaded(self, node_id: str) -> bool:
+        """Check if all edges for this node have been loaded."""
+        return node_id in self._fully_loaded
+
+    def get_uncached(self, node_ids: list[str]) -> list[str]:
+        """Get node IDs that haven't been fully loaded yet."""
+        return [n for n in node_ids if not self.is_fully_loaded(n)]
+
+    def add_all_edges(self, edges_by_type: dict[str, dict[str, list[EdgeTarget]]], all_queried: list[str]):
+        """
+        Add loaded edges to the cache (all edge types at once).
+
+        Args:
+            edges_by_type: Dict mapping edge_type -> from_node_id -> list of EdgeTarget
+            all_queried: All node IDs that were queried (marks them as fully loaded)
+        """
+        for edge_type, edges in edges_by_type.items():
+            if edge_type not in self.graphs:
+                self.graphs[edge_type] = {}
+            for node_id, neighbors in edges.items():
+                self.graphs[edge_type][node_id] = neighbors
+
+        # Mark all queried nodes as fully loaded (even if they have no edges)
+        self._fully_loaded.update(all_queried)
+
+
+@dataclass
+class PatternResult:
+    """Result from a single pattern traversal."""
+
+    pattern: list[str]
+    scores: dict[str, float]  # node_id -> accumulated mass
+
+
+@dataclass
+class MPFPConfig:
+    """Configuration for MPFP algorithm."""
+
+    alpha: float = 0.15  # teleport/keep probability
+    threshold: float = 1e-6  # mass pruning threshold (lower = explore more)
+    top_k_neighbors: int = 20  # fan-out limit per node
+
+    # Patterns from semantic seeds
+    patterns_semantic: list[list[str]] = field(
+        default_factory=lambda: [
+            ["semantic", "semantic"],  # topic expansion
+            ["entity", "temporal"],  # entity timeline
+            ["semantic", "causes"],  # reasoning chains (forward)
+            ["semantic", "caused_by"],  # reasoning chains (backward)
+            ["entity", "semantic"],  # entity context
+        ]
+    )
+
+    # Patterns from temporal seeds
+    patterns_temporal: list[list[str]] = field(
+        default_factory=lambda: [
+            ["temporal", "semantic"],  # what was happening then
+            ["temporal", "entity"],  # who was involved then
+        ]
+    )
+
+
+@dataclass
+class SeedNode:
+    """An entry point node with its initial score."""
+
+    node_id: str
+    score: float  # initial mass (e.g., similarity score)
+
+
+# -----------------------------------------------------------------------------
+# Lazy Edge Loading
+# -----------------------------------------------------------------------------
+
+
+async def load_all_edges_for_frontier(
+    pool,
+    node_ids: list[str],
+    top_k_per_type: int = 20,
+) -> dict[str, dict[str, list[EdgeTarget]]]:
+    """
+    Load top-k edges per (node, edge_type) for frontier nodes.
+
+    Uses a LATERAL join to efficiently fetch only the top-k edges per type,
+    avoiding loading hundreds of entity edges when only 20 are needed.
+
+    Requires composite index: (from_unit_id, link_type, weight DESC)
+
+    Args:
+        pool: Database connection pool
+        node_ids: Frontier node IDs to load edges for
+        top_k_per_type: Max edges to load per (node, link_type) pair
+
+    Returns:
+        Dict mapping edge_type -> from_node_id -> list of EdgeTarget
+    """
+    if not node_ids:
+        return {}
+
+    async with acquire_with_retry(pool) as conn:
+        # Use LATERAL join to get top-k per (from_node, link_type)
+        # This leverages the composite index for efficient early termination
+        rows = await conn.fetch(
+            f"""
+            WITH frontier(node_id) AS (SELECT unnest($1::uuid[]))
+            SELECT f.node_id as from_unit_id, lt.link_type, edges.to_unit_id, edges.weight
+            FROM frontier f
+            CROSS JOIN (VALUES ('semantic'), ('temporal'), ('entity'), ('causes'), ('caused_by')) AS lt(link_type)
+            CROSS JOIN LATERAL (
+                SELECT ml.to_unit_id, ml.weight
+                FROM {fq_table("memory_links")} ml
+                WHERE ml.from_unit_id = f.node_id
+                  AND ml.link_type = lt.link_type
+                  AND ml.weight >= 0.1
+                ORDER BY ml.weight DESC
+                LIMIT $2
+            ) edges
+            """,
+            node_ids,
+            top_k_per_type,
+        )
+
+    # Group by edge_type -> from_node -> neighbors
+    result: dict[str, dict[str, list[EdgeTarget]]] = defaultdict(lambda: defaultdict(list))
+    for row in rows:
+        edge_type = row["link_type"]
+        from_id = str(row["from_unit_id"])
+        to_id = str(row["to_unit_id"])
+        weight = row["weight"]
+        result[edge_type][from_id].append(EdgeTarget(node_id=to_id, weight=weight))
+
+    # Convert nested defaultdicts to regular dicts
+    return {edge_type: dict(edges) for edge_type, edges in result.items()}
+
+
+# -----------------------------------------------------------------------------
+# Core Algorithm (Async with Lazy Loading)
+# -----------------------------------------------------------------------------
+
+
+@dataclass
+class PatternState:
+    """State for a pattern traversal between hops."""
+
+    pattern: list[str]
+    hop_index: int
+    scores: dict[str, float]
+    frontier: dict[str, float]
+
+
+def _init_pattern_state(seeds: list[SeedNode], pattern: list[str]) -> PatternState:
+    """Initialize pattern state from seeds."""
+    if not seeds:
+        return PatternState(pattern=pattern, hop_index=0, scores={}, frontier={})
+
+    total_seed_score = sum(s.score for s in seeds)
+    if total_seed_score == 0:
+        total_seed_score = len(seeds)
+
+    frontier = {s.node_id: s.score / total_seed_score for s in seeds}
+    return PatternState(pattern=pattern, hop_index=0, scores={}, frontier=frontier)
+
+
+def _execute_hop(state: PatternState, cache: EdgeCache, config: MPFPConfig) -> set[str]:
+    """
+    Execute ONE hop of traversal, return frontier nodes for next hop.
+
+    This is a pure function that uses cached edges (no DB access).
+    Returns set of uncached nodes needed for next hop.
+    """
+    if state.hop_index >= len(state.pattern):
+        return set()
+
+    edge_type = state.pattern[state.hop_index]
+
+    # Collect active nodes above threshold
+    active_nodes = [node_id for node_id, mass in state.frontier.items() if mass >= config.threshold]
+    if not active_nodes:
+        state.frontier = {}
+        return set()
+
+    # Propagate mass using cached edges
+    next_frontier: dict[str, float] = {}
+    uncached_for_next: set[str] = set()
+
+    for node_id, mass in state.frontier.items():
+        if mass < config.threshold:
+            continue
+
+        # Keep α portion for this node
+        state.scores[node_id] = state.scores.get(node_id, 0) + config.alpha * mass
+
+        # Push (1-α) to neighbors
+        push_mass = (1 - config.alpha) * mass
+        neighbors = cache.get_normalized_neighbors(edge_type, node_id, config.top_k_neighbors)
+
+        for neighbor in neighbors:
+            next_frontier[neighbor.node_id] = next_frontier.get(neighbor.node_id, 0) + push_mass * neighbor.weight
+            # Track if we'll need edges for this node in the next hop
+            if not cache.is_fully_loaded(neighbor.node_id):
+                uncached_for_next.add(neighbor.node_id)
+
+    state.frontier = next_frontier
+    state.hop_index += 1
+
+    return uncached_for_next
+
+
+def _finalize_pattern(state: PatternState, config: MPFPConfig) -> PatternResult:
+    """Finalize pattern by adding remaining frontier mass to scores."""
+    for node_id, mass in state.frontier.items():
+        if mass >= config.threshold:
+            state.scores[node_id] = state.scores.get(node_id, 0) + mass
+
+    return PatternResult(pattern=state.pattern, scores=state.scores)
+
+
+async def mpfp_traverse_hop_synchronized(
+    pool,
+    pattern_jobs: list[tuple[list[SeedNode], list[str]]],
+    config: MPFPConfig,
+    cache: EdgeCache,
+) -> list[PatternResult]:
+    """
+    Execute ALL patterns with hop-synchronized edge loading.
+
+    Instead of running each pattern independently (causing multiple DB queries),
+    this function:
+    1. Runs hop 1 for ALL patterns (using pre-warmed seed edges)
+    2. Collects ALL unique hop-2 frontier nodes across patterns
+    3. Pre-warms hop-2 edges in ONE query
+    4. Runs hop 2 for ALL patterns
+
+    This reduces DB queries from O(patterns * hops) to O(hops).
+
+    Args:
+        pool: Database connection pool
+        pattern_jobs: List of (seeds, pattern) tuples
+        config: Algorithm parameters
+        cache: Shared edge cache (should be pre-warmed with seed edges)
+
+    Returns:
+        List of PatternResult for each pattern
+    """
+    import time
+
+    # Initialize all pattern states
+    states = [_init_pattern_state(seeds, pattern) for seeds, pattern in pattern_jobs]
+
+    # Determine max hops (all patterns should be same length, but be safe)
+    max_hops = max((len(p) for _, p in pattern_jobs), default=0)
+
+    # Detailed timing for debugging
+    hop_times: list[dict] = []
+
+    # Execute hop-by-hop across ALL patterns
+    for hop in range(max_hops):
+        hop_start = time.time()
+        hop_timing = {"hop": hop, "patterns_executed": 0, "uncached_count": 0, "load_time": 0.0}
+
+        # Execute this hop for all patterns, collect uncached nodes for next hop
+        all_uncached: set[str] = set()
+        exec_start = time.time()
+        for state in states:
+            if state.hop_index < len(state.pattern):
+                uncached = _execute_hop(state, cache, config)
+                all_uncached.update(uncached)
+                hop_timing["patterns_executed"] += 1
+        hop_timing["exec_time"] = time.time() - exec_start
+
+        # Pre-warm edges for ALL uncached nodes before next hop
+        hop_timing["uncached_count"] = len(all_uncached)
+        if all_uncached:
+            uncached_list = list(all_uncached - cache._fully_loaded)
+            hop_timing["uncached_after_filter"] = len(uncached_list)
+            if uncached_list:
+                load_start = time.time()
+                edges_by_type = await load_all_edges_for_frontier(pool, uncached_list, config.top_k_neighbors)
+                hop_timing["load_time"] = time.time() - load_start
+                cache.edge_load_time += hop_timing["load_time"]
+                cache.db_queries += 1
+                cache.add_all_edges(edges_by_type, uncached_list)
+                hop_timing["edges_loaded"] = sum(
+                    len(neighbors) for edges in edges_by_type.values() for neighbors in edges.values()
+                )
+
+        hop_timing["total_time"] = time.time() - hop_start
+        hop_times.append(hop_timing)
+
+    # Store hop timing details in cache for logging
+    cache.hop_details = hop_times
+
+    # Finalize all patterns
+    return [_finalize_pattern(state, config) for state in states]
+
+
+async def mpfp_traverse_async(
+    pool,
+    seeds: list[SeedNode],
+    pattern: list[str],
+    config: MPFPConfig,
+    cache: EdgeCache,
+) -> PatternResult:
+    """
+    Async Forward Push traversal with lazy edge loading.
+
+    NOTE: For better performance with multiple patterns, use mpfp_traverse_hop_synchronized().
+    This function is kept for single-pattern use cases.
+    """
+    if not seeds:
+        return PatternResult(pattern=pattern, scores={})
+
+    results = await mpfp_traverse_hop_synchronized(pool, [(seeds, pattern)], config, cache)
+    return results[0] if results else PatternResult(pattern=pattern, scores={})
+
+
+def rrf_fusion(
+    results: list[PatternResult],
+    k: int = 60,
+    top_k: int = 50,
+) -> list[tuple[str, float]]:
+    """
+    Reciprocal Rank Fusion to combine pattern results.
+
+    Args:
+        results: List of pattern results
+        k: RRF constant (higher = more uniform weighting)
+        top_k: Number of results to return
+
+    Returns:
+        List of (node_id, fused_score) tuples, sorted by score descending
+    """
+    fused: dict[str, float] = {}
+
+    for result in results:
+        if not result.scores:
+            continue
+
+        # Rank nodes by their score in this pattern
+        ranked = sorted(result.scores.keys(), key=lambda n: result.scores[n], reverse=True)
+
+        for rank, node_id in enumerate(ranked):
+            fused[node_id] = fused.get(node_id, 0) + 1.0 / (k + rank + 1)
+
+    # Sort by fused score and return top-k
+    sorted_results = sorted(fused.items(), key=lambda x: x[1], reverse=True)
+
+    return sorted_results[:top_k]
+
+
+# -----------------------------------------------------------------------------
+# Database Loading
+# -----------------------------------------------------------------------------
+
+
+async def fetch_memory_units_by_ids(
+    pool,
+    node_ids: list[str],
+    fact_type: str,
+) -> list[RetrievalResult]:
+    """Fetch full memory unit details for a list of node IDs."""
+    if not node_ids:
+        return []
+
+    async with acquire_with_retry(pool) as conn:
+        rows = await conn.fetch(
+            f"""
+            SELECT id, text, context, event_date, occurred_start, occurred_end,
+                   mentioned_at, fact_type, document_id, chunk_id, tags, metadata
+            FROM {fq_table("memory_units")}
+            WHERE id = ANY($1::uuid[])
+              AND fact_type = $2
+            """,
+            node_ids,
+            fact_type,
+        )
+
+    return [RetrievalResult.from_db_row(dict(r)) for r in rows]
+
+
+# -----------------------------------------------------------------------------
+# Graph Retriever Implementation
+# -----------------------------------------------------------------------------
+
+
+class MPFPGraphRetriever(GraphRetriever):
+    """
+    Graph retrieval using Meta-Path Forward Push with lazy edge loading.
+
+    Runs predefined patterns in parallel from semantic and temporal seeds,
+    loading edges on-demand per hop instead of loading entire graph upfront.
+    """
+
+    def __init__(self, config: MPFPConfig | None = None):
+        """
+        Initialize MPFP retriever.
+
+        Args:
+            config: Algorithm configuration (uses defaults if None)
+        """
+        if config is None:
+            # Read top_k_neighbors from global config
+            from ...config import get_config
+
+            global_config = get_config()
+            config = MPFPConfig(top_k_neighbors=global_config.mpfp_top_k_neighbors)
+        self.config = config
+
+    @property
+    def name(self) -> str:
+        return "mpfp"
+
+    async def retrieve(
+        self,
+        pool,
+        query_embedding_str: str,
+        bank_id: str,
+        fact_type: str,
+        budget: int,
+        query_text: str | None = None,
+        semantic_seeds: list[RetrievalResult] | None = None,
+        temporal_seeds: list[RetrievalResult] | None = None,
+        adjacency=None,  # Ignored - kept for interface compatibility
+        tags: list[str] | None = None,
+        tags_match: TagsMatch = "any",
+        tag_groups: list[TagGroup] | None = None,
+    ) -> tuple[list[RetrievalResult], MPFPTimings | None]:
+        """
+        Retrieve facts using MPFP algorithm with lazy edge loading.
+
+        Args:
+            pool: Database connection pool
+            query_embedding_str: Query embedding (used for fallback seed finding)
+            bank_id: Memory bank ID
+            fact_type: Fact type to filter
+            budget: Maximum results to return
+            query_text: Original query text (optional)
+            semantic_seeds: Pre-computed semantic entry points
+            temporal_seeds: Pre-computed temporal entry points
+            adjacency: Ignored (kept for interface compatibility)
+            tags: Optional list of tags for visibility filtering (OR matching)
+
+        Returns:
+            Tuple of (List of RetrievalResult with activation scores, MPFPTimings)
+        """
+        import time
+
+        timings = MPFPTimings(fact_type=fact_type)
+
+        # Convert seeds to SeedNode format
+        semantic_seed_nodes = self._convert_seeds(semantic_seeds, "similarity")
+        temporal_seed_nodes = self._convert_seeds(temporal_seeds, "temporal_score")
+
+        # If no semantic seeds provided, fall back to finding our own
+        if not semantic_seed_nodes:
+            seeds_start = time.time()
+            semantic_seed_nodes = await self._find_semantic_seeds(
+                pool,
+                query_embedding_str,
+                bank_id,
+                fact_type,
+                tags=tags,
+                tags_match=tags_match,
+                tag_groups=tag_groups,
+            )
+            timings.seeds_time = time.time() - seeds_start
+            logger.debug(
+                f"[MPFP] Found {len(semantic_seed_nodes)} semantic seeds for fact_type={fact_type} (tags={tags}, tags_match={tags_match})"
+            )
+
+        # Collect all pattern jobs
+        pattern_jobs = []
+
+        # Patterns from semantic seeds
+        for pattern in self.config.patterns_semantic:
+            if semantic_seed_nodes:
+                pattern_jobs.append((semantic_seed_nodes, pattern))
+
+        # Patterns from temporal seeds
+        for pattern in self.config.patterns_temporal:
+            if temporal_seed_nodes:
+                pattern_jobs.append((temporal_seed_nodes, pattern))
+
+        if not pattern_jobs:
+            logger.debug(
+                f"[MPFP] No pattern jobs (semantic_seeds={len(semantic_seed_nodes)}, temporal_seeds={len(temporal_seed_nodes)})"
+            )
+            return [], timings
+
+        timings.pattern_count = len(pattern_jobs)
+
+        # Shared edge cache across all patterns
+        cache = EdgeCache()
+
+        # Pre-warm cache with ALL seed node edges BEFORE running patterns
+        # This prevents redundant DB queries at hop 1
+        all_seed_ids = list({s.node_id for seeds, _ in pattern_jobs for s in seeds})
+        if all_seed_ids:
+            import time as time_module
+
+            prewarm_start = time_module.time()
+            edges_by_type = await load_all_edges_for_frontier(pool, all_seed_ids, self.config.top_k_neighbors)
+            cache.edge_load_time += time_module.time() - prewarm_start
+            cache.db_queries += 1
+            cache.add_all_edges(edges_by_type, all_seed_ids)
+
+        # Run all patterns with HOP-SYNCHRONIZED edge loading
+        # This batches hop-2 edge loads across ALL patterns into ONE query
+        # Reduces DB queries from O(patterns * hops) to O(hops)
+        step_start = time.time()
+        pattern_results = await mpfp_traverse_hop_synchronized(pool, pattern_jobs, self.config, cache)
+        timings.traverse = time.time() - step_start
+
+        # Record edge loading stats from cache
+        timings.edge_count = sum(len(neighbors) for g in cache.graphs.values() for neighbors in g.values())
+        timings.db_queries = cache.db_queries
+        timings.edge_load_time = cache.edge_load_time
+        timings.hop_details = cache.hop_details
+
+        # Fuse results
+        step_start = time.time()
+        fused = rrf_fusion(pattern_results, top_k=budget)
+        timings.fusion = time.time() - step_start
+
+        if not fused:
+            logger.debug(f"[MPFP] No fused results after RRF fusion (pattern_count={len(pattern_results)})")
+            return [], timings
+
+        # Get top result IDs
+        result_ids = [node_id for node_id, score in fused][:budget]
+
+        # Fetch full details
+        step_start = time.time()
+        results = await fetch_memory_units_by_ids(pool, result_ids, fact_type)
+        timings.fetch = time.time() - step_start
+
+        # Filter results by tags (graph traversal may have picked up unfiltered memories)
+        if tags:
+            from .tags import filter_results_by_tags
+
+            results = filter_results_by_tags(results, tags, match=tags_match)
+
+        # Apply compound tag group filtering (post-traversal)
+        if tag_groups:
+            from .tags import filter_results_by_tag_groups
+
+            results = filter_results_by_tag_groups(results, tag_groups)
+
+        timings.result_count = len(results)
+
+        # Add activation scores from fusion
+        score_map = {node_id: score for node_id, score in fused}
+        for result in results:
+            result.activation = score_map.get(result.id, 0.0)
+
+        # Sort by activation
+        results.sort(key=lambda r: r.activation or 0, reverse=True)
+
+        return results, timings
+
+    def _convert_seeds(
+        self,
+        seeds: list[RetrievalResult] | None,
+        score_attr: str,
+    ) -> list[SeedNode]:
+        """Convert RetrievalResult seeds to SeedNode format."""
+        if not seeds:
+            return []
+
+        result = []
+        for seed in seeds:
+            score = getattr(seed, score_attr, None)
+            if score is None:
+                score = seed.activation or seed.similarity or 1.0
+            result.append(SeedNode(node_id=seed.id, score=score))
+
+        return result
+
+    async def _find_semantic_seeds(
+        self,
+        pool,
+        query_embedding_str: str,
+        bank_id: str,
+        fact_type: str,
+        limit: int = 20,
+        threshold: float = 0.3,
+        tags: list[str] | None = None,
+        tags_match: TagsMatch = "any",
+        tag_groups: list[TagGroup] | None = None,
+    ) -> list[SeedNode]:
+        """Fallback: find semantic seeds via embedding search."""
+        from .tags import build_tag_groups_where_clause, build_tags_where_clause_simple
+
+        tags_clause = build_tags_where_clause_simple(tags, 6, match=tags_match)
+        tag_groups_param_start = 6 + (1 if tags else 0)
+        groups_clause, groups_params, _ = build_tag_groups_where_clause(tag_groups, tag_groups_param_start)
+        params = [query_embedding_str, bank_id, fact_type, threshold, limit]
+        if tags:
+            params.append(tags)
+        params.extend(groups_params)
+
+        async with acquire_with_retry(pool) as conn:
+            rows = await conn.fetch(
+                f"""
+                SELECT id, 1 - (embedding <=> $1::vector) AS similarity
+                FROM {fq_table("memory_units")}
+                WHERE bank_id = $2
+                  AND embedding IS NOT NULL
+                  AND fact_type = $3
+                  AND (1 - (embedding <=> $1::vector)) >= $4
+                  {tags_clause}
+                  {groups_clause}
+                ORDER BY embedding <=> $1::vector
+                LIMIT $5
+                """,
+                *params,
+            )
+
+        return [SeedNode(node_id=str(r["id"]), score=r["similarity"]) for r in rows]

hindsight-api-slim/hindsight_api/engine/search/reranking.py

@@ -2,7 +2,6 @@
 Cross-encoder neural reranking for search results.
 """
 
-import math
 from datetime import datetime, timezone
 
 from .types import MergedCandidate, ScoredResult
@@ -14,7 +13,6 @@ UTC = timezone.utc
 # so the max combined boost is (1 + alpha/2)^2 ≈ +21% and min is (1 - alpha/2)^2 ≈ -19%.
 _RECENCY_ALPHA: float = 0.2
 _TEMPORAL_ALPHA: float = 0.2
-_PROOF_COUNT_ALPHA: float = 0.1  # Conservative: max ±5% for evidence strength
 
 
 def apply_combined_scoring(
@@ -22,40 +20,28 @@ def apply_combined_scoring(
     now: datetime,
     recency_alpha: float = _RECENCY_ALPHA,
     temporal_alpha: float = _TEMPORAL_ALPHA,
-    proof_count_alpha: float = _PROOF_COUNT_ALPHA,
 ) -> None:
     """Apply combined scoring to a list of ScoredResults in-place.
 
-    Uses the cross-encoder score as the primary relevance signal, with recency,
-    temporal proximity, and proof count applied as multiplicative boosts. This
-    ensures the influence of these secondary signals is always proportional to
-    the base relevance score, regardless of the cross-encoder model's score
-    calibration.
+    Uses the cross-encoder score as the primary relevance signal, with recency
+    and temporal proximity applied as multiplicative boosts. This ensures the
+    influence of these secondary signals is always proportional to the base
+    relevance score, regardless of the cross-encoder model's score calibration.
 
     Formula::
 
-        recency_boost     = 1 + recency_alpha     * (recency     - 0.5)   # in [1-α/2, 1+α/2]
-        temporal_boost    = 1 + temporal_alpha    * (temporal    - 0.5)   # in [1-α/2, 1+α/2]
-        proof_count_boost = 1 + proof_count_alpha * (proof_norm  - 0.5)   # in [1-α/2, 1+α/2]
-        combined_score    = CE_normalized * recency_boost * temporal_boost * proof_count_boost
-
-    proof_norm maps proof_count using a smooth logarithmic curve centered at 0.5,
-    clamped to [0, 1]:
-      proof_count=1 → 0.5 + 0 = 0.5 (neutral multiplier)
-      proof_count=150 → clamped to 1.0 (max +5% boost)
+        recency_boost  = 1 + recency_alpha  * (recency  - 0.5)   # in [1-α/2, 1+α/2]
+        temporal_boost = 1 + temporal_alpha * (temporal - 0.5)   # in [1-α/2, 1+α/2]
+        combined_score = cross_encoder_score_normalized * recency_boost * temporal_boost
 
     Temporal proximity is treated as neutral (0.5) when not set by temporal retrieval,
     so temporal_boost collapses to 1.0 for non-temporal queries.
 
-    Proof count is treated as neutral (0.5) when not available (non-observation facts),
-    so proof_count_boost collapses to 1.0 for world/experience/opinion facts.
-
     Args:
         scored_results: Results from the cross-encoder reranker. Mutated in place.
         now: Current UTC datetime for recency calculation.
         recency_alpha: Max relative recency adjustment (default 0.2 → ±10%).
         temporal_alpha: Max relative temporal adjustment (default 0.2 → ±10%).
-        proof_count_alpha: Max relative proof count adjustment (default 0.1 → ±5%).
     """
     if now.tzinfo is None:
         now = now.replace(tzinfo=UTC)
@@ -73,23 +59,13 @@ def apply_combined_scoring(
         # Temporal proximity: meaningful only for temporal queries; neutral otherwise.
         sr.temporal = sr.retrieval.temporal_proximity if sr.retrieval.temporal_proximity is not None else 0.5
 
-        # Proof count: log-normalized evidence strength; neutral for non-observations.
-        proof_count = sr.retrieval.proof_count
-        if proof_count is not None and proof_count >= 1:
-            # Clamp to [0, 1] so extreme counts stay within documented ±5% range
-            proof_norm = min(1.0, max(0.0, 0.5 + (math.log(proof_count) / 10.0)))
-        else:
-            # Neutral baseline is precisely 0.5, ensuring neutral multiplier (1.0)
-            proof_norm = 0.5
-
         # RRF: kept at 0.0 for trace continuity but excluded from scoring.
         # RRF is batch-relative (min-max normalised) and redundant after reranking.
         sr.rrf_normalized = 0.0
 
         recency_boost = 1.0 + recency_alpha * (sr.recency - 0.5)
         temporal_boost = 1.0 + temporal_alpha * (sr.temporal - 0.5)
-        proof_count_boost = 1.0 + proof_count_alpha * (proof_norm - 0.5)
-        sr.combined_score = sr.cross_encoder_score_normalized * recency_boost * temporal_boost * proof_count_boost
+        sr.combined_score = sr.cross_encoder_score_normalized * recency_boost * temporal_boost
         sr.weight = sr.combined_score
 
 

hindsight-api-slim/hindsight_api/engine/search/retrieval.py

@@ -18,10 +18,11 @@ from typing import Optional
 from ...config import get_config
 from ..db_utils import acquire_with_retry
 from ..memory_engine import fq_table
-from .graph_retrieval import GraphRetriever
+from .graph_retrieval import BFSGraphRetriever, GraphRetriever
 from .link_expansion_retrieval import LinkExpansionRetriever
+from .mpfp_retrieval import MPFPGraphRetriever
 from .tags import TagGroup, TagsMatch, build_tag_groups_where_clause, build_tags_where_clause_simple
-from .types import GraphRetrievalTimings, RetrievalResult
+from .types import MPFPTimings, RetrievalResult
 
 logger = logging.getLogger(__name__)
 
@@ -45,9 +46,7 @@ class ParallelRetrievalResult:
     temporal: list[RetrievalResult] | None
     timings: dict[str, float] = field(default_factory=dict)
     temporal_constraint: tuple | None = None  # (start_date, end_date)
-    graph_timings: list[GraphRetrievalTimings] = field(
-        default_factory=list
-    )  # Graph retrieval sub-step timings per fact type
+    mpfp_timings: list[MPFPTimings] = field(default_factory=list)  # MPFP sub-step timings per fact type
     max_conn_wait: float = 0.0  # Maximum connection acquisition wait time across all methods
 
 
@@ -73,7 +72,15 @@ def get_default_graph_retriever() -> GraphRetriever:
     if _default_graph_retriever is None:
         config = get_config()
         retriever_type = config.graph_retriever.lower()
-        if retriever_type == "link_expansion":
+        if retriever_type == "mpfp":
+            _default_graph_retriever = MPFPGraphRetriever()
+            logger.info(
+                f"Using MPFP graph retriever (top_k_neighbors={_default_graph_retriever.config.top_k_neighbors})"
+            )
+        elif retriever_type == "bfs":
+            _default_graph_retriever = BFSGraphRetriever()
+            logger.info("Using BFS graph retriever")
+        elif retriever_type == "link_expansion":
             _default_graph_retriever = LinkExpansionRetriever()
             logger.info("Using LinkExpansion graph retriever")
         else:
@@ -141,7 +148,7 @@ async def retrieve_semantic_bm25_combined(
 
     cols = (
         "id, text, context, event_date, occurred_start, occurred_end, mentioned_at, "
-        "fact_type, document_id, chunk_id, tags, metadata, proof_count"
+        "fact_type, document_id, chunk_id, tags, metadata"
     )
     table = fq_table("memory_units")
 
@@ -336,7 +343,7 @@ async def retrieve_temporal_combined(
               {groups_clause}
         ),
         sim_ranked AS (
-            SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start, mu.occurred_end, mu.mentioned_at, mu.fact_type, mu.proof_count, mu.document_id, mu.chunk_id, mu.tags, mu.metadata,
+            SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start, mu.occurred_end, mu.mentioned_at, mu.fact_type, mu.document_id, mu.chunk_id, mu.tags, mu.metadata,
                    1 - (mu.embedding <=> $1::vector) AS similarity,
                    ROW_NUMBER() OVER (PARTITION BY mu.fact_type ORDER BY mu.embedding <=> $1::vector) AS sim_rn
             FROM date_ranked dr
@@ -344,7 +351,7 @@ async def retrieve_temporal_combined(
             WHERE dr.rn <= 50
               AND (1 - (mu.embedding <=> $1::vector)) >= $6
         )
-        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, proof_count, document_id, chunk_id, tags, metadata, similarity
+        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags, metadata, similarity
         FROM sim_ranked
         WHERE sim_rn <= 10
         """,
@@ -620,11 +627,9 @@ async def retrieve_all_fact_types_parallel(
     timings["temporal_combined"] = temporal_time
 
     # Step 3: Run graph retrieval for each fact type in parallel
-    async def run_graph_for_fact_type(
-        ft: str,
-    ) -> tuple[str, list[RetrievalResult], float, GraphRetrievalTimings | None]:
+    async def run_graph_for_fact_type(ft: str) -> tuple[str, list[RetrievalResult], float, MPFPTimings | None]:
         graph_start = time.time()
-        results, graph_timing = await retriever.retrieve(
+        results, mpfp_timing = await retriever.retrieve(
             pool=pool,
             query_embedding_str=query_embedding_str,
             bank_id=bank_id,
@@ -637,7 +642,7 @@ async def retrieve_all_fact_types_parallel(
             tags_match=tags_match,
             tag_groups=tag_groups,
         )
-        return ft, results, time.time() - graph_start, graph_timing
+        return ft, results, time.time() - graph_start, mpfp_timing
 
     # Run graph for all fact types in parallel
     graph_tasks = [run_graph_for_fact_type(ft) for ft in fact_types]
@@ -646,7 +651,7 @@ async def retrieve_all_fact_types_parallel(
     # Organize results by fact type
     results_by_fact_type: dict[str, ParallelRetrievalResult] = {}
     max_conn_wait = conn_wait  # Single connection for semantic+bm25+temporal
-    all_graph_timings: list[GraphRetrievalTimings] = []
+    all_mpfp_timings: list[MPFPTimings] = []
 
     for ft in fact_types:
         # Get semantic + bm25 results for this fact type
@@ -655,14 +660,14 @@ async def retrieve_all_fact_types_parallel(
         # Find graph results for this fact type
         graph_results = []
         graph_time = 0.0
-        graph_timing = None
+        mpfp_timing = None
         for gr in graph_results_list:
             if gr[0] == ft:
                 graph_results = gr[1]
                 graph_time = gr[2]
-                graph_timing = gr[3]
-                if graph_timing:
-                    all_graph_timings.append(graph_timing)
+                mpfp_timing = gr[3]
+                if mpfp_timing:
+                    all_mpfp_timings.append(mpfp_timing)
                 break
 
         # Get temporal results for this fact type from combined result
@@ -683,7 +688,7 @@ async def retrieve_all_fact_types_parallel(
                 "temporal_extraction": temporal_extraction_time,
             },
             temporal_constraint=temporal_constraint,
-            graph_timings=[graph_timing] if graph_timing else [],
+            mpfp_timings=[mpfp_timing] if mpfp_timing else [],
             max_conn_wait=max_conn_wait,
         )
 

hindsight-api-slim/hindsight_api/engine/search/think_utils.py

@@ -110,7 +110,11 @@ def build_think_prompt(
     context: str | None = None,
     entity_summaries_text: str | None = None,
 ) -> str:
-    """Build the think prompt for the LLM."""
+    """Build the think prompt for the LLM.
+
+    Note: opinion_facts_text parameter removed - opinions are now stored as mental models
+    and included via entity_summaries_text.
+    """
     disposition_desc = build_disposition_description(disposition)
 
     name_section = f"""

hindsight-api-slim/hindsight_api/engine/search/trace.py

@@ -131,7 +131,7 @@ class RetrievalResult(BaseModel):
     text: str = Field(description="Memory unit text content")
     context: str = Field(default="", description="Memory unit context")
     event_date: datetime | None = Field(default=None, description="When the memory occurred")
-    fact_type: str | None = Field(default=None, description="Fact type (world, experience)")
+    fact_type: str | None = Field(default=None, description="Fact type (world, experience, opinion)")
     score: float = Field(description="Score from this retrieval method")
     score_name: str = Field(description="Name of the score (e.g., 'similarity', 'bm25_score', 'activation')")
 
@@ -140,7 +140,9 @@ class RetrievalMethodResults(BaseModel):
     """Results from a single retrieval method."""
 
     method_name: Literal["semantic", "bm25", "graph", "temporal"] = Field(description="Name of retrieval method")
-    fact_type: str | None = Field(default=None, description="Fact type this retrieval was for (world, experience)")
+    fact_type: str | None = Field(
+        default=None, description="Fact type this retrieval was for (world, experience, opinion)"
+    )
     results: list[RetrievalResult] = Field(description="Retrieved results with ranks")
     duration_seconds: float = Field(description="Time taken for this retrieval")
     metadata: dict[str, Any] = Field(default_factory=dict, description="Method-specific metadata")

hindsight-api-slim/hindsight_api/engine/search/tracer.py

@@ -319,7 +319,7 @@ class SearchTracer:
             duration_seconds: Time taken for this retrieval
             score_field: Field name containing the score in data dict
             metadata: Optional metadata about this retrieval method
-            fact_type: Fact type this retrieval was for (world, experience)
+            fact_type: Fact type this retrieval was for (world, experience, opinion)
         """
         retrieval_results = []
         for rank, (doc_id, data) in enumerate(results, start=1):

hindsight-api-slim/hindsight_api/engine/search/types.py

@@ -11,8 +11,8 @@ from typing import Any
 
 
 @dataclass
-class GraphRetrievalTimings:
-    """Timing breakdown for a single graph retrieval call."""
+class MPFPTimings:
+    """Timing breakdown for a single MPFP retrieval call."""
 
     fact_type: str
     edge_count: int = 0  # Total edges loaded
@@ -48,7 +48,6 @@ class RetrievalResult:
     chunk_id: str | None = None
     tags: list[str] | None = None  # Visibility scope tags
     metadata: dict[str, str] | None = None  # User-provided metadata
-    proof_count: int | None = None  # Number of supporting memories (observations only)
 
     # Retrieval-specific scores (only one will be set depending on retrieval method)
     similarity: float | None = None  # Semantic retrieval
@@ -73,7 +72,6 @@ class RetrievalResult:
             chunk_id=row.get("chunk_id"),
             tags=row.get("tags"),
             metadata=row.get("metadata"),
-            proof_count=row.get("proof_count"),
             similarity=row.get("similarity"),
             bm25_score=row.get("bm25_score"),
             activation=row.get("activation"),

hindsight-api-slim/hindsight_api/engine/task_backend.py

@@ -82,16 +82,20 @@ class TaskBackend(ABC):
 
         Args:
             task_dict: Task dictionary to execute
-
-        Raises:
-            Exception: Re-raised from executor on failure.
         """
         if self._executor is None:
             task_type = task_dict.get("type", "unknown")
             logger.warning(f"No executor registered, skipping task {task_type}")
             return
 
-        await self._executor(task_dict)
+        try:
+            await self._executor(task_dict)
+        except Exception as e:
+            task_type = task_dict.get("type", "unknown")
+            logger.error(f"Error executing task {task_type}: {e}")
+            import traceback
+
+            traceback.print_exc()
 
 
 class SyncTaskBackend(TaskBackend):

hindsight-api-slim/hindsight_api/extensions/context.py

@@ -120,9 +120,7 @@ class DefaultExtensionContext(ExtensionContext):
         # CREATE INDEX CONCURRENTLY inside the migration waits for those transactions
         # forever — a deadlock.
         config = get_config()
-        await asyncio.to_thread(
-            run_migrations, db_url, schema=schema, migration_database_url=config.migration_database_url
-        )
+        await asyncio.to_thread(run_migrations, db_url, schema=schema)
 
         # Ensure embedding column dimension matches the model's dimension
         # This is needed because migrations create columns with default dimension

hindsight-api-slim/hindsight_api/extensions/operation_validator.py

@@ -96,6 +96,7 @@ class RetainContext:
     request_context: "RequestContext"
     document_id: str | None = None
     fact_type_override: str | None = None
+    confidence_score: float | None = None
 
 
 @dataclass
@@ -168,6 +169,7 @@ class RetainResult:
     request_context: "RequestContext"
     document_id: str | None
     fact_type_override: str | None
+    confidence_score: float | None
     # Result
     unit_ids: list[list[str]]  # List of unit IDs per content item
     success: bool = True
@@ -400,6 +402,7 @@ class OperationValidatorExtension(Extension, ABC):
                 - request_context: Request context with auth info
                 - document_id: Optional document ID
                 - fact_type_override: Optional fact type override
+                - confidence_score: Optional confidence score
 
         Returns:
             ValidationResult indicating whether the operation is allowed.
@@ -719,28 +722,3 @@ class OperationValidatorExtension(Extension, ABC):
             BankListResult with the filtered list of banks.
         """
         return BankListResult(banks=ctx.banks)
-
-    async def filter_mcp_tools(
-        self,
-        bank_id: str,
-        request_context: "RequestContext",
-        tools: frozenset[str],
-    ) -> frozenset[str]:
-        """
-        Filter MCP tools visible to this user on this bank.
-
-        Called during tools/list after bank-level mcp_enabled_tools filtering.
-        The input set is already narrowed by bank config — this method can only
-        remove tools, never add ones the bank config excluded.
-
-        Default: return all tools unchanged (no per-user filtering).
-
-        Args:
-            bank_id: Target bank ID (from URL path or header).
-            request_context: Authenticated context with tenant_id set.
-            tools: Tools remaining after bank config filtering.
-
-        Returns:
-            Subset of tools this user should see.
-        """
-        return tools

hindsight-api-slim/hindsight_api/mcp_tools.py

@@ -8,7 +8,7 @@ This module provides the core tool logic used by both:
 import json
 import logging
 from dataclasses import dataclass
-from datetime import datetime, timezone
+from datetime import datetime
 from typing import Any, Callable
 
 from fastmcp import FastMCP
@@ -18,48 +18,11 @@ from hindsight_api.config import (
     DEFAULT_MCP_RECALL_DESCRIPTION,
     DEFAULT_MCP_RETAIN_DESCRIPTION,
 )
-from hindsight_api.engine.audit import AuditEntry, AuditLogger
 from hindsight_api.engine.memory_engine import Budget
 from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES
 from hindsight_api.extensions import OperationValidationError
 from hindsight_api.models import RequestContext
 
-# All tools available in the system (explicit list — no wildcards).
-# Defined here (shared module) to avoid circular imports with api/mcp.py.
-_ALL_TOOLS: frozenset[str] = frozenset(
-    {
-        "retain",
-        "recall",
-        "reflect",
-        "list_banks",
-        "create_bank",
-        "list_mental_models",
-        "get_mental_model",
-        "create_mental_model",
-        "update_mental_model",
-        "delete_mental_model",
-        "refresh_mental_model",
-        "list_directives",
-        "create_directive",
-        "delete_directive",
-        "list_memories",
-        "get_memory",
-        "delete_memory",
-        "list_documents",
-        "get_document",
-        "delete_document",
-        "list_operations",
-        "get_operation",
-        "cancel_operation",
-        "list_tags",
-        "get_bank",
-        "get_bank_stats",
-        "update_bank",
-        "delete_bank",
-        "clear_memories",
-    }
-)
-
 logger = logging.getLogger(__name__)
 
 
@@ -326,7 +289,6 @@ def register_mcp_tools(
         _register_clear_memories(mcp, memory, config)
 
     _apply_bank_tool_filtering(mcp, memory, config)
-    _apply_audit_logging(mcp, memory, config)
 
 
 def _apply_bank_tool_filtering(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig) -> None:
@@ -341,29 +303,11 @@ def _apply_bank_tool_filtering(mcp: FastMCP, memory: MemoryEngine, config: MCPTo
         if not bank_id:
             return None
         request_context = _get_request_context(config)
-
-        # Layer 1: bank config filter (existing)
         bank_cfg = await memory._config_resolver.get_bank_config(bank_id, request_context)
-        bank_tools: list[str] | None = bank_cfg.get("mcp_enabled_tools")
-        enabled: set[str] | None = set(bank_tools) if bank_tools is not None else None
-
-        # Layer 2: operation validator filter
-        validator = memory._operation_validator
-        if validator is not None:
-            candidate = frozenset(enabled) if enabled is not None else _ALL_TOOLS
-            try:
-                filtered = await validator.filter_mcp_tools(bank_id, request_context, candidate)
-            except Exception:
-                logger.warning("filter_mcp_tools raised, returning unfiltered tools", exc_info=True)
-                return enabled
-            if filtered != candidate:
-                # Validator can only narrow, never expand beyond the bank config ceiling.
-                if bank_tools is not None:
-                    enabled = set(filtered) & set(bank_tools)
-                else:
-                    enabled = set(filtered)
-
-        return enabled
+        enabled: list[str] | None = bank_cfg.get("mcp_enabled_tools")
+        if enabled is None:
+            return None
+        return set(enabled)
 
     if hasattr(mcp, "list_tools"):
         # FastMCP 3.x: wrap list_tools() and get_tool() on the instance
@@ -417,112 +361,6 @@ def _apply_bank_tool_filtering(mcp: FastMCP, memory: MemoryEngine, config: MCPTo
         logger.warning("Could not apply bank tool filtering: unknown FastMCP version")
 
 
-_AUDITABLE_MCP_TOOLS: frozenset[str] = frozenset(
-    {
-        "retain",
-        "recall",
-        "reflect",
-        "create_bank",
-        "update_bank",
-        "delete_bank",
-        "clear_memories",
-        "create_mental_model",
-        "update_mental_model",
-        "delete_mental_model",
-        "refresh_mental_model",
-        "create_directive",
-        "delete_directive",
-        "delete_memory",
-        "delete_document",
-        "cancel_operation",
-    }
-)
-
-
-def _apply_audit_logging(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig) -> None:
-    """Wrap auditable MCP tool run methods with audit logging."""
-    audit_logger: AuditLogger = memory.audit_logger
-
-    def _wrap_tool_run(tool_name: str, original_run):
-        """Create an audited wrapper for a tool's run method."""
-
-        async def _audited_run(arguments, _name=tool_name, _orig=original_run):
-            if not audit_logger.is_enabled(_name):
-                return await _orig(arguments)
-
-            bank_id = None
-            if isinstance(arguments, dict):
-                bank_id = arguments.get("bank_id") or (config.bank_id_resolver() if config.bank_id_resolver else None)
-            elif hasattr(arguments, "get"):
-                bank_id = arguments.get("bank_id")
-
-            entry = AuditEntry(
-                action=_name,
-                transport="mcp",
-                bank_id=bank_id,
-                started_at=datetime.now(timezone.utc),
-                request=dict(arguments) if isinstance(arguments, dict) else {"raw": str(arguments)},
-            )
-
-            try:
-                result = await _orig(arguments)
-                if isinstance(result, dict):
-                    entry.response = result
-                elif isinstance(result, list):
-                    entry.response = {"items": result}
-                elif isinstance(result, str):
-                    entry.response = {"text": result}
-                return result
-            finally:
-                entry.ended_at = datetime.now(timezone.utc)
-                audit_logger.log_fire_and_forget(entry)
-
-        return _audited_run
-
-    if hasattr(mcp, "_tool_manager"):
-        # FastMCP 2.x
-        try:
-            for name, tool in mcp._tool_manager._tools.items():  # type: ignore[unresolved-attribute]  # FastMCP 2.x internal; guarded by hasattr
-                if name in _AUDITABLE_MCP_TOOLS:
-                    object.__setattr__(tool, "run", _wrap_tool_run(name, tool.run))
-        except (AttributeError, KeyError) as e:
-            logger.warning(f"Could not apply MCP audit logging (v2): {e}")
-    elif hasattr(mcp, "get_tool"):
-        # FastMCP 3.x: wrap call_tool
-        original_call_tool = getattr(mcp, "call_tool", None)
-        if original_call_tool:
-
-            async def _audited_call_tool(name, arguments=None, **kwargs):
-                if name not in _AUDITABLE_MCP_TOOLS or not audit_logger.is_enabled(name):
-                    return await original_call_tool(name, arguments, **kwargs)
-
-                bank_id = None
-                if isinstance(arguments, dict):
-                    bank_id = arguments.get("bank_id") or (
-                        config.bank_id_resolver() if config.bank_id_resolver else None
-                    )
-
-                entry = AuditEntry(
-                    action=name,
-                    transport="mcp",
-                    bank_id=bank_id,
-                    started_at=datetime.now(timezone.utc),
-                    request=dict(arguments) if isinstance(arguments, dict) else {},
-                )
-
-                try:
-                    result = await original_call_tool(name, arguments, **kwargs)
-                    entry.response = {"result": str(result)[:4096]}
-                    return result
-                finally:
-                    entry.ended_at = datetime.now(timezone.utc)
-                    audit_logger.log_fire_and_forget(entry)
-
-            object.__setattr__(mcp, "call_tool", _audited_call_tool)
-    else:
-        logger.warning("Could not apply MCP audit logging: unknown FastMCP version")
-
-
 def _register_retain(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig) -> None:
     """Register the retain tool."""
     description = config.retain_description or DEFAULT_MCP_RETAIN_DESCRIPTION
@@ -1002,7 +840,6 @@ def _register_list_mental_models(mcp: FastMCP, memory: MemoryEngine, config: MCP
         @mcp.tool()
         async def list_mental_models(
             tags: list[str] | None = None,
-            detail: str = "full",
             bank_id: str | None = None,
         ) -> str:
             """
@@ -1014,7 +851,6 @@ def _register_list_mental_models(mcp: FastMCP, memory: MemoryEngine, config: MCP
 
             Args:
                 tags: Optional tags to filter by (returns models matching any tag)
-                detail: Detail level - 'metadata' (names/tags only), 'content' (adds content/config), 'full' (includes reflect_response). Default: 'full'
                 bank_id: Optional bank to list from (defaults to session bank). Use for cross-bank operations.
             """
             try:
@@ -1025,7 +861,6 @@ def _register_list_mental_models(mcp: FastMCP, memory: MemoryEngine, config: MCP
                 models = await memory.list_mental_models(
                     bank_id=target_bank,
                     tags=tags,
-                    detail=detail,
                     request_context=_get_request_context(config),
                 )
                 return json.dumps({"items": models}, indent=2, default=str)
@@ -1041,7 +876,6 @@ def _register_list_mental_models(mcp: FastMCP, memory: MemoryEngine, config: MCP
         @mcp.tool()
         async def list_mental_models(
             tags: list[str] | None = None,
-            detail: str = "full",
         ) -> dict:
             """
             List mental models (pinned reflections) for this memory bank.
@@ -1052,7 +886,6 @@ def _register_list_mental_models(mcp: FastMCP, memory: MemoryEngine, config: MCP
 
             Args:
                 tags: Optional tags to filter by (returns models matching any tag)
-                detail: Detail level - 'metadata' (names/tags only), 'content' (adds content/config), 'full' (includes reflect_response). Default: 'full'
             """
             try:
                 target_bank = config.bank_id_resolver()
@@ -1062,7 +895,6 @@ def _register_list_mental_models(mcp: FastMCP, memory: MemoryEngine, config: MCP
                 models = await memory.list_mental_models(
                     bank_id=target_bank,
                     tags=tags,
-                    detail=detail,
                     request_context=_get_request_context(config),
                 )
                 return {"items": models}
@@ -1082,18 +914,16 @@ def _register_get_mental_model(mcp: FastMCP, memory: MemoryEngine, config: MCPTo
         @mcp.tool()
         async def get_mental_model(
             mental_model_id: str,
-            detail: str = "full",
             bank_id: str | None = None,
         ) -> str:
             """
             Get a specific mental model by ID.
 
-            Returns the mental model with the requested detail level. Use list_mental_models
-            first to discover available model IDs.
+            Returns the full mental model including its generated content, source query,
+            and metadata. Use list_mental_models first to discover available model IDs.
 
             Args:
                 mental_model_id: The ID of the mental model to retrieve
-                detail: Detail level - 'metadata' (names/tags only), 'content' (adds content/config), 'full' (includes reflect_response). Default: 'full'
                 bank_id: Optional bank (defaults to session bank). Use for cross-bank operations.
             """
             try:
@@ -1104,7 +934,6 @@ def _register_get_mental_model(mcp: FastMCP, memory: MemoryEngine, config: MCPTo
                 model = await memory.get_mental_model(
                     bank_id=target_bank,
                     mental_model_id=mental_model_id,
-                    detail=detail,
                     request_context=_get_request_context(config),
                 )
                 if model is None:
@@ -1122,17 +951,15 @@ def _register_get_mental_model(mcp: FastMCP, memory: MemoryEngine, config: MCPTo
         @mcp.tool()
         async def get_mental_model(
             mental_model_id: str,
-            detail: str = "full",
         ) -> dict:
             """
             Get a specific mental model by ID.
 
-            Returns the mental model with the requested detail level. Use list_mental_models
-            first to discover available model IDs.
+            Returns the full mental model including its generated content, source query,
+            and metadata. Use list_mental_models first to discover available model IDs.
 
             Args:
                 mental_model_id: The ID of the mental model to retrieve
-                detail: Detail level - 'metadata' (names/tags only), 'content' (adds content/config), 'full' (includes reflect_response). Default: 'full'
             """
             try:
                 target_bank = config.bank_id_resolver()
@@ -1142,7 +969,6 @@ def _register_get_mental_model(mcp: FastMCP, memory: MemoryEngine, config: MCPTo
                 model = await memory.get_mental_model(
                     bank_id=target_bank,
                     mental_model_id=mental_model_id,
-                    detail=detail,
                     request_context=_get_request_context(config),
                 )
                 if model is None:
@@ -2885,7 +2711,6 @@ def _register_clear_memories(mcp: FastMCP, memory: MemoryEngine, config: MCPTool
                 result = await memory.delete_bank(
                     target_bank,
                     fact_type=type,
-                    delete_bank_profile=False,
                     request_context=_get_request_context(config),
                 )
                 return json.dumps({"status": "cleared", "bank_id": target_bank, **result}, default=str)
@@ -2918,7 +2743,6 @@ def _register_clear_memories(mcp: FastMCP, memory: MemoryEngine, config: MCPTool
                 result = await memory.delete_bank(
                     target_bank,
                     fact_type=type,
-                    delete_bank_profile=False,
                     request_context=_get_request_context(config),
                 )
                 return {"status": "cleared", "bank_id": target_bank, **result}

hindsight-api-slim/hindsight_api/metrics.py

@@ -11,11 +11,14 @@ This module provides metrics for:
 - Database connection pool metrics
 """
 
-import importlib
 import logging
 import os
+import types
 
-_resource_mod = importlib.import_module("resource") if importlib.util.find_spec("resource") else None
+try:
+    import resource
+except ImportError:
+    resource: types.ModuleType | None = None  # Windows doesn't have resource module
 import threading
 import time
 from contextlib import contextmanager
@@ -252,9 +255,6 @@ class MetricsCollector(MetricsCollectorBase):
 
     def __init__(self):
         self.meter = get_meter()
-        from .config import get_config
-
-        self._include_bank_id = get_config().metrics_include_bank_id
 
         # Operation latency histogram (in seconds)
         # Records duration of retain, recall, reflect operations
@@ -335,11 +335,10 @@ class MetricsCollector(MetricsCollectorBase):
         start_time = time.time()
         attributes = {
             "operation": operation,
+            "bank_id": bank_id,
             "source": source,
             "tenant": _get_tenant(),
         }
-        if self._include_bank_id:
-            attributes["bank_id"] = bank_id
         if budget:
             attributes["budget"] = budget
         if max_tokens:
@@ -461,13 +460,13 @@ class MetricsCollector(MetricsCollectorBase):
 
     def _setup_process_metrics(self):
         """Set up observable gauges for process metrics."""
-        if _resource_mod is None:
+        if resource is None:
             return  # Skip process metrics on Windows
 
         def get_cpu_times(_options):
             """Get process CPU times."""
             try:
-                rusage = _resource_mod.getrusage(_resource_mod.RUSAGE_SELF)
+                rusage = resource.getrusage(resource.RUSAGE_SELF)
                 yield metrics.Observation(rusage.ru_utime, {"type": "user"})
                 yield metrics.Observation(rusage.ru_stime, {"type": "system"})
             except Exception:
@@ -476,7 +475,7 @@ class MetricsCollector(MetricsCollectorBase):
         def get_memory_usage(_options):
             """Get process memory usage in bytes."""
             try:
-                rusage = _resource_mod.getrusage(_resource_mod.RUSAGE_SELF)
+                rusage = resource.getrusage(resource.RUSAGE_SELF)
                 # ru_maxrss is in kilobytes on Linux, bytes on macOS
                 max_rss = rusage.ru_maxrss
                 if os.uname().sysname == "Linux":
@@ -494,7 +493,7 @@ class MetricsCollector(MetricsCollectorBase):
                     yield metrics.Observation(count)
                 else:
                     # Fallback: use resource limits
-                    soft, hard = _resource_mod.getrlimit(_resource_mod.RLIMIT_NOFILE)
+                    soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE)
                     yield metrics.Observation(soft, {"limit": "soft"})
             except Exception:
                 pass

hindsight-api-slim/hindsight_api/migrations.py

@@ -157,7 +157,7 @@ def _run_migrations_internal(database_url: str, script_location: str, schema: st
     # calls from different threads corrupt each other's context.
     try:
         with _alembic_lock:
-            command.upgrade(alembic_cfg, "heads")
+            command.upgrade(alembic_cfg, "head")
     except ResolutionError as e:
         # This happens during rolling deployments when a newer version of the code
         # has already run migrations, and this older replica doesn't have the new
@@ -176,7 +176,6 @@ def run_migrations(
     database_url: str,
     script_location: str | None = None,
     schema: str | None = None,
-    migration_database_url: str | None = None,
 ) -> None:
     """
     Run database migrations to the latest version using programmatic Alembic configuration.
@@ -214,14 +213,6 @@ def run_migrations(
             script_location="/path/to/copied/_alembic"
         )
     """
-    # Prefer a dedicated migration URL that bypasses connection poolers (e.g.
-    # PgBouncer in transaction mode).  Session-level advisory locks don't
-    # survive a PgBouncer transaction-mode cycle, so the distributed lock is
-    # ineffective when the app URL goes through a pooler.  Configure
-    # HINDSIGHT_API_MIGRATION_DATABASE_URL to the direct PostgreSQL endpoint
-    # (e.g. hindsight-pg-rw) to restore correct locking behaviour.
-    migration_url = migration_database_url or database_url
-
     try:
         # Determine script location
         if script_location is None:
@@ -258,7 +249,7 @@ def run_migrations(
         #   2. After acquiring the lock, COMMIT the transaction on the advisory-lock
         #      connection itself before running migrations.  pg_advisory_lock is
         #      session-level, so the lock survives the COMMIT.
-        engine = create_engine(migration_url)
+        engine = create_engine(database_url)
         with engine.connect() as conn:
             logger.debug(f"Acquiring migration advisory lock for schema '{schema_name}' (id={lock_id})...")
             while True:
@@ -403,7 +394,7 @@ def run_migrations(
                 conn.commit()
 
                 # Run migrations while holding the lock
-                _run_migrations_internal(migration_url, script_location, schema=schema)
+                _run_migrations_internal(database_url, script_location, schema=schema)
             finally:
                 # Explicitly release the lock (also released on connection close)
                 conn.execute(text(f"SELECT pg_advisory_unlock({lock_id})"))

hindsight-api-slim/hindsight_api/models.py

@@ -97,6 +97,7 @@ class MemoryUnit(Base):
     occurred_end: Mapped[datetime | None] = mapped_column(TIMESTAMP(timezone=True))  # When fact occurred (range end)
     mentioned_at: Mapped[datetime | None] = mapped_column(TIMESTAMP(timezone=True))  # When fact was mentioned
     fact_type: Mapped[str] = mapped_column(Text, nullable=False, server_default="world")
+    confidence_score: Mapped[float | None] = mapped_column(Float)
     unit_metadata: Mapped[dict] = mapped_column(
         "metadata", JSONB, server_default=sql_text("'{}'::jsonb")
     )  # User-defined metadata (str->str)
@@ -120,7 +121,14 @@ class MemoryUnit(Base):
             name="memory_units_document_fkey",
             ondelete="CASCADE",
         ),
-        CheckConstraint("fact_type IN ('world', 'experience', 'observation')"),
+        CheckConstraint("fact_type IN ('world', 'experience', 'opinion', 'observation')"),
+        CheckConstraint("confidence_score IS NULL OR (confidence_score >= 0.0 AND confidence_score <= 1.0)"),
+        CheckConstraint(
+            "(fact_type = 'opinion' AND confidence_score IS NOT NULL) OR "
+            "(fact_type = 'observation') OR "
+            "(fact_type NOT IN ('opinion', 'observation') AND confidence_score IS NULL)",
+            name="confidence_score_fact_type_check",
+        ),
         Index("idx_memory_units_bank_id", "bank_id"),
         Index("idx_memory_units_document_id", "document_id"),
         Index("idx_memory_units_event_date", "event_date", postgresql_ops={"event_date": "DESC"}),
@@ -134,6 +142,20 @@ class MemoryUnit(Base):
             "event_date",
             postgresql_ops={"event_date": "DESC"},
         ),
+        Index(
+            "idx_memory_units_opinion_confidence",
+            "bank_id",
+            "confidence_score",
+            postgresql_where=sql_text("fact_type = 'opinion'"),
+            postgresql_ops={"confidence_score": "DESC"},
+        ),
+        Index(
+            "idx_memory_units_opinion_date",
+            "bank_id",
+            "event_date",
+            postgresql_where=sql_text("fact_type = 'opinion'"),
+            postgresql_ops={"event_date": "DESC"},
+        ),
         Index(
             "idx_memory_units_observation_date",
             "bank_id",

hindsight-api-slim/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "hindsight-api-slim"
-version = "0.4.22"
+version = "0.4.20"
 description = "Hindsight: Agent Memory That Works Like Human Memory"
 readme = "README.md"
 requires-python = ">=3.11"
@@ -26,7 +26,7 @@ dependencies = [
     "tiktoken>=0.12.0",
     "httpx>=0.27.0",
     "PyJWT[crypto]>=2.8.0",
-    "fastmcp>=3.2.0", # SSRF/path traversal, OAuth confused deputy, command injection fixes
+    "fastmcp>=2.14.0", # CVE-2025-66416
     "python-dateutil>=2.8.0",
     "opentelemetry-api>=1.20.0",
     "opentelemetry-sdk>=1.20.0",
@@ -48,19 +48,17 @@ dependencies = [
     # Transitive dependency security fixes
     "pyasn1>=0.6.3", # DoS vulnerability fix
     "urllib3>=2.6.3", # Decompression-bomb safeguards bypass fix
-    "langchain-core>=1.2.22", # Path traversal in legacy load_prompt functions fix
+    "langchain-core>=1.2.11", # Serialization injection + SSRF vulnerability fix
     "langsmith>=0.6.3", # SSRF via tracing header injection fix
     "protobuf>=6.33.5", # JSON recursion depth bypass fix
     "pillow>=12.1.1", # Out-of-bounds write in PSD image loading fix
-    "cryptography>=46.0.6", # Incomplete DNS name constraint enforcement fix
+    "cryptography>=46.0.5", # Subgroup attack vulnerability fix
     "filelock>=3.20.1", # TOCTOU race condition fix
     "authlib>=1.6.9", # Account takeover/JWS header injection vulnerability fix
     "pyjwt>=2.12.0", # Accepts unknown crit header extensions fix
     "orjson>=3.11.6", # Unbounded recursion DoS fix
-    "python-multipart>=0.0.22", # Arbitrary file write via non-default configuration fix
     "tornado>=6.5.5", # DoS multipart/incomplete cookie validation fix
     "aiohttp>=3.13.3", # Multiple DoS vulnerabilities
-    "pygments>=2.20.0", # ReDoS via inefficient GUID regex fix
     "claude-agent-sdk>=0.1.27",
     "boto3>=1.42.74",
 ]
@@ -136,7 +134,6 @@ dev = [
     "pytest-asyncio>=1.3.0",
     "pytest-timeout>=2.4.0",
     "pytest-xdist>=3.8.0",
-    "pytest-rerunfailures>=15.0",
     "python-dotenv>=1.2.1",
     "filelock>=3.20.1",  # TOCTOU race condition fix
     "ruff>=0.8.0",

hindsight-api-slim/tests/conftest.py

@@ -17,7 +17,7 @@ from hindsight_api.pg0 import EmbeddedPostgres
 
 # Default pg0 instance configuration for tests
 DEFAULT_PG0_INSTANCE_NAME = "hindsight-test"
-DEFAULT_PG0_PORT = int(os.environ.get("HINDSIGHT_TEST_PG_PORT", "5556"))
+DEFAULT_PG0_PORT = 5556
 
 
 # Load environment variables from .env at the start of test session
@@ -126,7 +126,7 @@ def llm_config():
     Provide LLM configuration for tests.
     This can be used by tests that need to call LLM directly without memory system.
     """
-    return LLMConfig.from_env()
+    return LLMConfig.for_memory()
 
 
 @pytest.fixture(scope="session")

hindsight-api-slim/tests/test_admin_backup_restore.py

@@ -314,7 +314,7 @@ async def test_run_migration_without_schema_discovers_and_deduplicates_schemas(m
     async def fake_resolve_database_url(db_url: str) -> str:
         return f"resolved::{db_url}"
 
-    def fake_run_migrations(database_url: str, schema: str | None = None, **kwargs) -> None:
+    def fake_run_migrations(database_url: str, schema: str | None = None) -> None:
         calls["run_migrations"].append((database_url, schema))
 
     def fake_ensure_vector_extension(
@@ -376,7 +376,7 @@ async def test_run_migration_without_schema_runs_optional_post_migration_hooks(m
     async def fake_resolve_database_url(db_url: str) -> str:
         return f"resolved::{db_url}"
 
-    def fake_run_migrations(database_url: str, schema: str | None = None, **kwargs) -> None:
+    def fake_run_migrations(database_url: str, schema: str | None = None) -> None:
         calls["run_migrations"].append((database_url, schema))
 
     def fake_ensure_embedding_dimension(
@@ -453,7 +453,7 @@ async def test_run_migration_with_schema_only_runs_requested_schema(monkeypatch)
     async def fake_resolve_database_url(db_url: str) -> str:
         return f"resolved::{db_url}"
 
-    def fake_run_migrations(database_url: str, schema: str | None = None, **kwargs) -> None:
+    def fake_run_migrations(database_url: str, schema: str | None = None) -> None:
         calls["run_migrations"].append((database_url, schema))
 
     def fake_ensure_vector_extension(

hindsight-api-slim/tests/test_audit_log.py

@@ -1,449 +0,0 @@
-"""
-Tests for the audit log feature.
-
-Tests the audit log list, stats, filtering, and pagination endpoints.
-Verifies that audit entries are created for operations when audit logging is enabled.
-"""
-
-import asyncio
-
-import httpx
-import pytest
-import pytest_asyncio
-
-from hindsight_api.api import create_app
-from hindsight_api.config import get_config
-
-
-@pytest_asyncio.fixture
-async def audit_api_client(memory):
-    """Create a test client with audit logging enabled."""
-    # Enable audit logging on the memory engine's audit logger
-    memory._audit_logger._enabled = True
-    memory._audit_logger._allowed_actions = None  # All actions
-
-    app = create_app(memory, initialize_memory=False)
-    transport = httpx.ASGITransport(app=app)
-    async with httpx.AsyncClient(transport=transport, base_url="http://test") as client:
-        yield client
-
-
-@pytest.fixture
-def bank_id():
-    """Provide a unique bank ID for audit tests."""
-    from datetime import datetime
-
-    return f"audit_test_{datetime.now().timestamp()}"
-
-
-@pytest.mark.asyncio
-async def test_audit_log_list_empty(audit_api_client, bank_id):
-    """Test listing audit logs for a bank with no entries returns empty."""
-    # Create the bank first
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Audit Test Bank"},
-    )
-
-    # Small delay for fire-and-forget audit writes
-    await asyncio.sleep(0.5)
-
-    response = await audit_api_client.get(f"/v1/default/banks/{bank_id}/audit-logs")
-    assert response.status_code == 200
-    data = response.json()
-    assert data["bank_id"] == bank_id
-    assert "total" in data
-    assert "items" in data
-    assert "limit" in data
-    assert "offset" in data
-    assert isinstance(data["items"], list)
-
-
-@pytest.mark.asyncio
-async def test_audit_log_created_for_retain(audit_api_client, bank_id):
-    """Test that a retain operation creates an audit log entry."""
-    # Create bank
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Audit Test Bank"},
-    )
-
-    # Perform a retain
-    response = await audit_api_client.post(
-        f"/v1/default/banks/{bank_id}/memories",
-        json={
-            "items": [{"content": "Alice likes cats", "context": "preferences"}],
-        },
-    )
-    assert response.status_code == 200
-
-    # Wait for fire-and-forget audit writes
-    await asyncio.sleep(1.0)
-
-    # List audit logs - should have entries for create_bank and retain
-    response = await audit_api_client.get(f"/v1/default/banks/{bank_id}/audit-logs")
-    assert response.status_code == 200
-    data = response.json()
-    assert data["total"] >= 1
-
-    actions = [item["action"] for item in data["items"]]
-    assert "retain" in actions, f"Expected 'retain' in audit actions, got: {actions}"
-
-
-@pytest.mark.asyncio
-async def test_audit_log_entry_fields(audit_api_client, bank_id):
-    """Test that audit log entries have all expected fields."""
-    # Create bank + recall to generate entries
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Audit Test Bank"},
-    )
-
-    await audit_api_client.post(
-        f"/v1/default/banks/{bank_id}/memories/recall",
-        json={"query": "test query"},
-    )
-
-    await asyncio.sleep(1.0)
-
-    response = await audit_api_client.get(f"/v1/default/banks/{bank_id}/audit-logs")
-    assert response.status_code == 200
-    data = response.json()
-    assert data["total"] >= 1
-
-    # Check the recall entry has all fields
-    recall_entries = [item for item in data["items"] if item["action"] == "recall"]
-    assert len(recall_entries) >= 1, f"Expected recall entry, got actions: {[i['action'] for i in data['items']]}"
-
-    entry = recall_entries[0]
-    assert entry["id"] is not None
-    assert entry["action"] == "recall"
-    assert entry["transport"] == "http"
-    assert entry["bank_id"] == bank_id
-    assert entry["started_at"] is not None
-    assert entry["ended_at"] is not None
-    # Request should contain the recall parameters
-    assert entry["request"] is not None
-    assert "query" in entry["request"]
-    # Response should contain the recall results
-    assert entry["response"] is not None
-
-
-@pytest.mark.asyncio
-async def test_audit_log_filter_by_action(audit_api_client, bank_id):
-    """Test filtering audit logs by action type."""
-    # Create bank and do retain + recall
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Audit Test Bank"},
-    )
-    await audit_api_client.post(
-        f"/v1/default/banks/{bank_id}/memories",
-        json={"items": [{"content": "test content", "context": "test"}]},
-    )
-    await audit_api_client.post(
-        f"/v1/default/banks/{bank_id}/memories/recall",
-        json={"query": "test"},
-    )
-
-    await asyncio.sleep(1.0)
-
-    # Filter by retain only
-    response = await audit_api_client.get(
-        f"/v1/default/banks/{bank_id}/audit-logs",
-        params={"action": "retain"},
-    )
-    assert response.status_code == 200
-    data = response.json()
-    for item in data["items"]:
-        assert item["action"] == "retain"
-
-    # Filter by recall only
-    response = await audit_api_client.get(
-        f"/v1/default/banks/{bank_id}/audit-logs",
-        params={"action": "recall"},
-    )
-    assert response.status_code == 200
-    data = response.json()
-    for item in data["items"]:
-        assert item["action"] == "recall"
-
-
-@pytest.mark.asyncio
-async def test_audit_log_filter_by_transport(audit_api_client, bank_id):
-    """Test filtering audit logs by transport type."""
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Audit Test Bank"},
-    )
-
-    await asyncio.sleep(0.5)
-
-    # Filter by http transport
-    response = await audit_api_client.get(
-        f"/v1/default/banks/{bank_id}/audit-logs",
-        params={"transport": "http"},
-    )
-    assert response.status_code == 200
-    data = response.json()
-    for item in data["items"]:
-        assert item["transport"] == "http"
-
-    # Filter by mcp transport - should be empty (no MCP calls in this test)
-    response = await audit_api_client.get(
-        f"/v1/default/banks/{bank_id}/audit-logs",
-        params={"transport": "mcp"},
-    )
-    assert response.status_code == 200
-    data = response.json()
-    assert data["total"] == 0
-
-
-@pytest.mark.asyncio
-async def test_audit_log_filter_by_date_range(audit_api_client, bank_id):
-    """Test filtering audit logs by date range."""
-    from datetime import datetime, timedelta, timezone
-
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Audit Test Bank"},
-    )
-
-    await asyncio.sleep(0.5)
-
-    now = datetime.now(timezone.utc)
-
-    # Filter with start_date in the past - should include entries
-    past = (now - timedelta(hours=1)).isoformat()
-    response = await audit_api_client.get(
-        f"/v1/default/banks/{bank_id}/audit-logs",
-        params={"start_date": past},
-    )
-    assert response.status_code == 200
-    data = response.json()
-    assert data["total"] >= 1
-
-    # Filter with start_date in the future - should be empty
-    future = (now + timedelta(hours=1)).isoformat()
-    response = await audit_api_client.get(
-        f"/v1/default/banks/{bank_id}/audit-logs",
-        params={"start_date": future},
-    )
-    assert response.status_code == 200
-    data = response.json()
-    assert data["total"] == 0
-
-
-@pytest.mark.asyncio
-async def test_audit_log_pagination(audit_api_client, bank_id):
-    """Test audit log pagination with limit and offset."""
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Audit Test Bank"},
-    )
-
-    # Generate multiple audit entries
-    for i in range(5):
-        await audit_api_client.post(
-            f"/v1/default/banks/{bank_id}/memories/recall",
-            json={"query": f"test query {i}"},
-        )
-
-    await asyncio.sleep(1.5)
-
-    # Get first page
-    response = await audit_api_client.get(
-        f"/v1/default/banks/{bank_id}/audit-logs",
-        params={"limit": 2, "offset": 0},
-    )
-    assert response.status_code == 200
-    page1 = response.json()
-    assert len(page1["items"]) == 2
-    assert page1["limit"] == 2
-    assert page1["offset"] == 0
-    assert page1["total"] >= 5  # At least 5 recall + 1 create_bank
-
-    # Get second page
-    response = await audit_api_client.get(
-        f"/v1/default/banks/{bank_id}/audit-logs",
-        params={"limit": 2, "offset": 2},
-    )
-    assert response.status_code == 200
-    page2 = response.json()
-    assert len(page2["items"]) == 2
-    assert page2["offset"] == 2
-
-    # Entries should be different between pages
-    page1_ids = {item["id"] for item in page1["items"]}
-    page2_ids = {item["id"] for item in page2["items"]}
-    assert page1_ids.isdisjoint(page2_ids), "Pages should not overlap"
-
-
-@pytest.mark.asyncio
-async def test_audit_log_stats(audit_api_client, bank_id):
-    """Test the audit log stats endpoint returns correct structure."""
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Audit Test Bank"},
-    )
-
-    await audit_api_client.post(
-        f"/v1/default/banks/{bank_id}/memories/recall",
-        json={"query": "stats test"},
-    )
-
-    await asyncio.sleep(1.0)
-
-    # Get stats for last 24h
-    response = await audit_api_client.get(
-        f"/v1/default/banks/{bank_id}/audit-logs/stats",
-        params={"period": "1d"},
-    )
-    assert response.status_code == 200
-    data = response.json()
-    assert data["bank_id"] == bank_id
-    assert data["period"] == "1d"
-    assert data["trunc"] == "day"
-    assert "buckets" in data
-    assert isinstance(data["buckets"], list)
-
-    # Should have at least one bucket with our operations
-    assert len(data["buckets"]) >= 1
-    bucket = data["buckets"][0]
-    assert "time" in bucket
-    assert "actions" in bucket
-    assert "total" in bucket
-    assert bucket["total"] >= 1
-
-
-@pytest.mark.asyncio
-async def test_audit_log_stats_filter_by_action(audit_api_client, bank_id):
-    """Test stats endpoint filters by action."""
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Audit Test Bank"},
-    )
-
-    await audit_api_client.post(
-        f"/v1/default/banks/{bank_id}/memories/recall",
-        json={"query": "test"},
-    )
-
-    await asyncio.sleep(1.0)
-
-    # Stats filtered by recall
-    response = await audit_api_client.get(
-        f"/v1/default/banks/{bank_id}/audit-logs/stats",
-        params={"period": "1d", "action": "recall"},
-    )
-    assert response.status_code == 200
-    data = response.json()
-    for bucket in data["buckets"]:
-        # All actions in buckets should be "recall" only
-        for action_name in bucket["actions"]:
-            assert action_name == "recall"
-
-
-@pytest.mark.asyncio
-async def test_audit_log_stats_periods(audit_api_client, bank_id):
-    """Test stats endpoint supports different periods."""
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Audit Test Bank"},
-    )
-
-    await asyncio.sleep(0.5)
-
-    for period, expected_trunc in [("1d", "day"), ("7d", "day"), ("30d", "day")]:
-        response = await audit_api_client.get(
-            f"/v1/default/banks/{bank_id}/audit-logs/stats",
-            params={"period": period},
-        )
-        assert response.status_code == 200
-        data = response.json()
-        assert data["period"] == period
-        assert data["trunc"] == expected_trunc
-
-
-@pytest.mark.asyncio
-async def test_audit_log_disabled(memory):
-    """Test that no audit logs are created when audit logging is disabled."""
-    # Ensure audit logging is disabled
-    memory._audit_logger._enabled = False
-
-    app = create_app(memory, initialize_memory=False)
-    transport = httpx.ASGITransport(app=app)
-    async with httpx.AsyncClient(transport=transport, base_url="http://test") as client:
-        from datetime import datetime
-
-        bid = f"audit_disabled_test_{datetime.now().timestamp()}"
-
-        await client.put(f"/v1/default/banks/{bid}", json={"name": "No Audit"})
-        await client.post(
-            f"/v1/default/banks/{bid}/memories/recall",
-            json={"query": "test"},
-        )
-
-        await asyncio.sleep(0.5)
-
-        response = await client.get(f"/v1/default/banks/{bid}/audit-logs")
-        assert response.status_code == 200
-        data = response.json()
-        assert data["total"] == 0, "No audit entries should exist when audit logging is disabled"
-
-
-@pytest.mark.asyncio
-async def test_audit_log_action_allowlist(memory):
-    """Test that only allowed actions are audited when allowlist is set."""
-    memory._audit_logger._enabled = True
-    memory._audit_logger._allowed_actions = frozenset({"recall"})  # Only audit recall
-
-    app = create_app(memory, initialize_memory=False)
-    transport = httpx.ASGITransport(app=app)
-    async with httpx.AsyncClient(transport=transport, base_url="http://test") as client:
-        from datetime import datetime
-
-        bid = f"audit_allowlist_test_{datetime.now().timestamp()}"
-
-        # create_bank should NOT be audited
-        await client.put(f"/v1/default/banks/{bid}", json={"name": "Allowlist Test"})
-        # recall should be audited
-        await client.post(
-            f"/v1/default/banks/{bid}/memories/recall",
-            json={"query": "allowlist test"},
-        )
-
-        await asyncio.sleep(1.0)
-
-        response = await client.get(f"/v1/default/banks/{bid}/audit-logs")
-        assert response.status_code == 200
-        data = response.json()
-        actions = [item["action"] for item in data["items"]]
-        assert "recall" in actions, "recall should be audited"
-        assert "create_bank" not in actions, "create_bank should NOT be audited (not in allowlist)"
-
-
-@pytest.mark.asyncio
-async def test_audit_log_ordered_by_most_recent(audit_api_client, bank_id):
-    """Test that audit logs are returned ordered by most recent first."""
-    await audit_api_client.put(
-        f"/v1/default/banks/{bank_id}",
-        json={"name": "Order Test Bank"},
-    )
-
-    for i in range(3):
-        await audit_api_client.post(
-            f"/v1/default/banks/{bank_id}/memories/recall",
-            json={"query": f"order test {i}"},
-        )
-        await asyncio.sleep(0.2)  # Small gap between requests
-
-    await asyncio.sleep(1.0)
-
-    response = await audit_api_client.get(f"/v1/default/banks/{bank_id}/audit-logs")
-    assert response.status_code == 200
-    data = response.json()
-
-    # Check descending order by started_at
-    timestamps = [item["started_at"] for item in data["items"] if item["started_at"]]
-    assert timestamps == sorted(timestamps, reverse=True), "Audit logs should be ordered most recent first"

hindsight-api-slim/tests/test_bank_templates.py

@@ -1,600 +0,0 @@
-"""Integration tests for bank template import/export endpoints."""
-
-import pytest
-import pytest_asyncio
-import httpx
-from datetime import datetime
-from hindsight_api.api import create_app
-
-
-@pytest_asyncio.fixture
-async def api_client(memory):
-    """Create an async test client for the FastAPI app."""
-    app = create_app(memory, initialize_memory=False)
-    transport = httpx.ASGITransport(app=app)
-    async with httpx.AsyncClient(transport=transport, base_url="http://test") as client:
-        yield client
-
-
-@pytest.fixture
-def bank_id():
-    return f"template_test_{datetime.now().timestamp()}"
-
-
-@pytest.fixture
-def sample_template():
-    return {
-        "version": "1",
-        "bank": {
-            "reflect_mission": "Test mission for reflect",
-            "retain_mission": "Extract test data carefully",
-            "retain_extraction_mode": "verbose",
-            "disposition_empathy": 5,
-            "disposition_skepticism": 2,
-            "enable_observations": True,
-            "observations_mission": "Track test patterns",
-        },
-        "mental_models": [
-            {
-                "id": "test-model-one",
-                "name": "Test Model One",
-                "source_query": "What are the key patterns?",
-                "tags": ["test"],
-                "max_tokens": 1024,
-                "trigger": {"refresh_after_consolidation": True},
-            },
-            {
-                "id": "test-model-two",
-                "name": "Test Model Two",
-                "source_query": "What are the common issues?",
-            },
-        ],
-        "directives": [
-            {
-                "name": "Be concise",
-                "content": "Always respond concisely.",
-                "priority": 10,
-            },
-            {
-                "name": "Use examples",
-                "content": "Include examples when explaining concepts.",
-                "tags": ["style"],
-            },
-        ],
-    }
-
-
-class TestImportValidation:
-    """Test template manifest validation."""
-
-    @pytest.mark.asyncio
-    async def test_import_dry_run_valid(self, api_client, bank_id, sample_template):
-        """dry_run=true with a valid manifest returns what would happen."""
-        resp = await api_client.post(
-            f"/v1/default/banks/{bank_id}/import?dry_run=true",
-            json=sample_template,
-        )
-        assert resp.status_code == 200
-        data = resp.json()
-        assert data["dry_run"] is True
-        assert data["config_applied"] is True
-        assert set(data["mental_models_created"]) == {"test-model-one", "test-model-two"}
-        assert set(data["directives_created"]) == {"Be concise", "Use examples"}
-
-    @pytest.mark.asyncio
-    async def test_import_invalid_version(self, api_client, bank_id):
-        """Reject manifest with unsupported version."""
-        resp = await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={"version": "999"},
-        )
-        assert resp.status_code == 400
-
-    @pytest.mark.asyncio
-    async def test_import_invalid_extraction_mode(self, api_client, bank_id):
-        """Semantic validation catches bad extraction mode."""
-        resp = await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={
-                "version": "1",
-                "bank": {"retain_extraction_mode": "invalid_mode"},
-            },
-        )
-        assert resp.status_code == 400
-        assert "retain_extraction_mode" in resp.json()["detail"]
-
-    @pytest.mark.asyncio
-    async def test_import_custom_instructions_without_custom_mode(self, api_client, bank_id):
-        """Validate that custom_instructions requires extraction_mode=custom."""
-        resp = await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={
-                "version": "1",
-                "bank": {
-                    "retain_extraction_mode": "verbose",
-                    "retain_custom_instructions": "some custom prompt",
-                },
-            },
-        )
-        assert resp.status_code == 400
-        assert "retain_custom_instructions" in resp.json()["detail"]
-
-    @pytest.mark.asyncio
-    async def test_import_duplicate_mental_model_ids(self, api_client, bank_id):
-        """Reject manifest with duplicate mental model IDs."""
-        resp = await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={
-                "version": "1",
-                "mental_models": [
-                    {"id": "dup-id", "name": "First", "source_query": "q1"},
-                    {"id": "dup-id", "name": "Second", "source_query": "q2"},
-                ],
-            },
-        )
-        assert resp.status_code == 400
-
-    @pytest.mark.asyncio
-    async def test_import_duplicate_directive_names(self, api_client, bank_id):
-        """Reject manifest with duplicate directive names."""
-        resp = await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={
-                "version": "1",
-                "directives": [
-                    {"name": "Same Name", "content": "First"},
-                    {"name": "Same Name", "content": "Second"},
-                ],
-            },
-        )
-        assert resp.status_code == 400
-
-    @pytest.mark.asyncio
-    async def test_import_missing_mental_model_id(self, api_client, bank_id):
-        """Mental model without id is rejected."""
-        resp = await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={
-                "version": "1",
-                "mental_models": [
-                    {"name": "No ID Model", "source_query": "test query"},
-                ],
-            },
-        )
-        assert resp.status_code == 400
-
-    @pytest.mark.asyncio
-    async def test_import_invalid_mental_model_id_format(self, api_client, bank_id):
-        """Mental model with invalid ID format is rejected."""
-        resp = await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={
-                "version": "1",
-                "mental_models": [
-                    {"id": "UPPERCASE-NOT-ALLOWED", "name": "Bad", "source_query": "q"},
-                ],
-            },
-        )
-        assert resp.status_code == 400
-
-    @pytest.mark.asyncio
-    async def test_import_empty_manifest(self, api_client, bank_id):
-        """Import with no bank or mental_models is valid (no-op)."""
-        resp = await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={"version": "1"},
-        )
-        assert resp.status_code == 200
-        data = resp.json()
-        assert data["config_applied"] is False
-        assert data["mental_models_created"] == []
-        assert data["directives_created"] == []
-
-    @pytest.mark.asyncio
-    async def test_import_empty_mental_model_name(self, api_client, bank_id):
-        """Semantic validation catches empty mental model name."""
-        resp = await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={
-                "version": "1",
-                "mental_models": [
-                    {"id": "test-mm", "name": "  ", "source_query": "q"},
-                ],
-            },
-        )
-        assert resp.status_code == 400
-        assert "name" in resp.json()["detail"]
-
-    @pytest.mark.asyncio
-    async def test_import_empty_directive_content(self, api_client, bank_id):
-        """Semantic validation catches empty directive content."""
-        resp = await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={
-                "version": "1",
-                "directives": [
-                    {"name": "Bad Directive", "content": "  "},
-                ],
-            },
-        )
-        assert resp.status_code == 400
-        assert "content" in resp.json()["detail"]
-
-
-class TestImportApply:
-    """Test that import actually applies config, mental models, and directives."""
-
-    @pytest.mark.asyncio
-    async def test_import_applies_config(self, api_client, bank_id):
-        """Import with bank config applies config overrides on a new bank."""
-        resp = await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={
-                "version": "1",
-                "bank": {
-                    "reflect_mission": "Imported mission",
-                    "disposition_empathy": 4,
-                },
-            },
-        )
-        assert resp.status_code == 200
-        data = resp.json()
-        assert data["config_applied"] is True
-        assert data["dry_run"] is False
-
-        # Verify config was actually applied
-        config_resp = await api_client.get(f"/v1/default/banks/{bank_id}/config")
-        assert config_resp.status_code == 200
-        config = config_resp.json()
-        assert config["overrides"]["reflect_mission"] == "Imported mission"
-        assert config["overrides"]["disposition_empathy"] == 4
-
-    @pytest.mark.asyncio
-    async def test_import_into_existing_bank(self, api_client, bank_id):
-        """Import into an already-existing bank applies config and creates resources."""
-        # Pre-create the bank
-        await api_client.put(f"/v1/default/banks/{bank_id}", json={})
-
-        resp = await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={
-                "version": "1",
-                "bank": {"reflect_mission": "Existing bank mission"},
-                "mental_models": [
-                    {"id": "existing-bank-mm", "name": "MM", "source_query": "q"},
-                ],
-                "directives": [
-                    {"name": "Existing Bank Directive", "content": "Be helpful"},
-                ],
-            },
-        )
-        assert resp.status_code == 200
-        data = resp.json()
-        assert data["config_applied"] is True
-        assert "existing-bank-mm" in data["mental_models_created"]
-        assert "Existing Bank Directive" in data["directives_created"]
-
-        # Verify everything exists
-        config_resp = await api_client.get(f"/v1/default/banks/{bank_id}/config")
-        assert config_resp.json()["overrides"]["reflect_mission"] == "Existing bank mission"
-
-        mm_resp = await api_client.get(f"/v1/default/banks/{bank_id}/mental-models/existing-bank-mm")
-        assert mm_resp.status_code == 200
-
-        dir_resp = await api_client.get(f"/v1/default/banks/{bank_id}/directives")
-        assert dir_resp.status_code == 200
-        names = [d["name"] for d in dir_resp.json()["items"]]
-        assert "Existing Bank Directive" in names
-
-    @pytest.mark.asyncio
-    async def test_import_creates_mental_models(self, api_client, bank_id):
-        """Import creates mental models and returns operation IDs."""
-        resp = await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={
-                "version": "1",
-                "mental_models": [
-                    {
-                        "id": "import-mm-1",
-                        "name": "Imported Model",
-                        "source_query": "What patterns exist?",
-                        "tags": ["imported"],
-                    },
-                ],
-            },
-        )
-        assert resp.status_code == 200
-        data = resp.json()
-        assert "import-mm-1" in data["mental_models_created"]
-        assert len(data["operation_ids"]) == 1
-
-        # Verify mental model exists
-        mm_resp = await api_client.get(f"/v1/default/banks/{bank_id}/mental-models/import-mm-1")
-        assert mm_resp.status_code == 200
-        mm = mm_resp.json()
-        assert mm["name"] == "Imported Model"
-        assert mm["source_query"] == "What patterns exist?"
-        assert mm["tags"] == ["imported"]
-
-    @pytest.mark.asyncio
-    async def test_import_updates_existing_mental_models(self, api_client, bank_id):
-        """Re-importing updates existing mental models matched by ID."""
-        # First import
-        await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={
-                "version": "1",
-                "mental_models": [
-                    {
-                        "id": "reusable-mm",
-                        "name": "Original Name",
-                        "source_query": "Original query",
-                    },
-                ],
-            },
-        )
-
-        # Second import with same ID but different content
-        resp = await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={
-                "version": "1",
-                "mental_models": [
-                    {
-                        "id": "reusable-mm",
-                        "name": "Updated Name",
-                        "source_query": "Updated query",
-                    },
-                ],
-            },
-        )
-        assert resp.status_code == 200
-        data = resp.json()
-        assert "reusable-mm" in data["mental_models_updated"]
-        assert data["mental_models_created"] == []
-
-        # Verify update
-        mm_resp = await api_client.get(f"/v1/default/banks/{bank_id}/mental-models/reusable-mm")
-        assert mm_resp.status_code == 200
-        mm = mm_resp.json()
-        assert mm["name"] == "Updated Name"
-        assert mm["source_query"] == "Updated query"
-
-    @pytest.mark.asyncio
-    async def test_import_creates_directives(self, api_client, bank_id):
-        """Import creates directives."""
-        resp = await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={
-                "version": "1",
-                "directives": [
-                    {
-                        "name": "Test Directive",
-                        "content": "Always be helpful and precise.",
-                        "priority": 5,
-                        "tags": ["test"],
-                    },
-                ],
-            },
-        )
-        assert resp.status_code == 200
-        data = resp.json()
-        assert "Test Directive" in data["directives_created"]
-        assert data["directives_updated"] == []
-
-        # Verify directive exists
-        dir_resp = await api_client.get(f"/v1/default/banks/{bank_id}/directives")
-        assert dir_resp.status_code == 200
-        items = dir_resp.json()["items"]
-        assert len(items) == 1
-        assert items[0]["name"] == "Test Directive"
-        assert items[0]["content"] == "Always be helpful and precise."
-        assert items[0]["priority"] == 5
-        assert items[0]["tags"] == ["test"]
-
-    @pytest.mark.asyncio
-    async def test_import_updates_existing_directives(self, api_client, bank_id):
-        """Re-importing updates existing directives matched by name."""
-        # First import
-        await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={
-                "version": "1",
-                "directives": [
-                    {"name": "Reusable Directive", "content": "Original content", "priority": 1},
-                ],
-            },
-        )
-
-        # Second import with same name but different content
-        resp = await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={
-                "version": "1",
-                "directives": [
-                    {"name": "Reusable Directive", "content": "Updated content", "priority": 10},
-                ],
-            },
-        )
-        assert resp.status_code == 200
-        data = resp.json()
-        assert "Reusable Directive" in data["directives_updated"]
-        assert data["directives_created"] == []
-
-        # Verify update
-        dir_resp = await api_client.get(f"/v1/default/banks/{bank_id}/directives")
-        items = dir_resp.json()["items"]
-        directive = [d for d in items if d["name"] == "Reusable Directive"][0]
-        assert directive["content"] == "Updated content"
-        assert directive["priority"] == 10
-
-    @pytest.mark.asyncio
-    async def test_import_config_only(self, api_client, bank_id):
-        """Import with only bank config (no mental_models or directives) works."""
-        resp = await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={
-                "version": "1",
-                "bank": {"retain_extraction_mode": "verbose"},
-            },
-        )
-        assert resp.status_code == 200
-        data = resp.json()
-        assert data["config_applied"] is True
-        assert data["mental_models_created"] == []
-        assert data["directives_created"] == []
-        assert data["operation_ids"] == []
-
-    @pytest.mark.asyncio
-    async def test_import_mental_models_only(self, api_client, bank_id):
-        """Import with only mental_models works."""
-        resp = await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={
-                "version": "1",
-                "mental_models": [
-                    {"id": "mm-only", "name": "MM Only", "source_query": "test"},
-                ],
-            },
-        )
-        assert resp.status_code == 200
-        data = resp.json()
-        assert data["config_applied"] is False
-        assert "mm-only" in data["mental_models_created"]
-        assert data["directives_created"] == []
-
-    @pytest.mark.asyncio
-    async def test_import_directives_only(self, api_client, bank_id):
-        """Import with only directives works."""
-        resp = await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={
-                "version": "1",
-                "directives": [
-                    {"name": "Dir Only", "content": "test directive"},
-                ],
-            },
-        )
-        assert resp.status_code == 200
-        data = resp.json()
-        assert data["config_applied"] is False
-        assert data["mental_models_created"] == []
-        assert "Dir Only" in data["directives_created"]
-
-
-class TestExport:
-    """Test bank template export."""
-
-    @pytest.mark.asyncio
-    async def test_export_empty_bank(self, api_client, bank_id):
-        """Export a bank with no overrides returns minimal manifest."""
-        # Create bank
-        await api_client.put(f"/v1/default/banks/{bank_id}", json={})
-
-        resp = await api_client.get(f"/v1/default/banks/{bank_id}/export")
-        assert resp.status_code == 200
-        data = resp.json()
-        assert data["version"] == "1"
-        assert data["bank"] is None
-        assert data["mental_models"] is None
-        assert data["directives"] is None
-
-    @pytest.mark.asyncio
-    async def test_export_after_import(self, api_client, bank_id):
-        """Export after import returns the imported config, mental models, and directives."""
-        template = {
-            "version": "1",
-            "bank": {
-                "reflect_mission": "Roundtrip mission",
-                "disposition_empathy": 3,
-            },
-            "mental_models": [
-                {
-                    "id": "roundtrip-mm",
-                    "name": "Roundtrip Model",
-                    "source_query": "What happened?",
-                    "tags": ["roundtrip"],
-                    "max_tokens": 512,
-                },
-            ],
-            "directives": [
-                {
-                    "name": "Roundtrip Directive",
-                    "content": "Be thorough.",
-                    "priority": 3,
-                    "tags": ["roundtrip"],
-                },
-            ],
-        }
-
-        # Import
-        import_resp = await api_client.post(f"/v1/default/banks/{bank_id}/import", json=template)
-        assert import_resp.status_code == 200
-
-        # Export
-        resp = await api_client.get(f"/v1/default/banks/{bank_id}/export")
-        assert resp.status_code == 200
-        data = resp.json()
-
-        assert data["version"] == "1"
-        assert data["bank"]["reflect_mission"] == "Roundtrip mission"
-        assert data["bank"]["disposition_empathy"] == 3
-
-        assert len(data["mental_models"]) == 1
-        mm = data["mental_models"][0]
-        assert mm["id"] == "roundtrip-mm"
-        assert mm["name"] == "Roundtrip Model"
-        assert mm["source_query"] == "What happened?"
-        assert mm["tags"] == ["roundtrip"]
-        assert mm["max_tokens"] == 512
-
-        assert len(data["directives"]) == 1
-        d = data["directives"][0]
-        assert d["name"] == "Roundtrip Directive"
-        assert d["content"] == "Be thorough."
-        assert d["priority"] == 3
-        assert d["tags"] == ["roundtrip"]
-
-    @pytest.mark.asyncio
-    async def test_export_reimport_roundtrip(self, api_client, bank_id):
-        """Exported manifest can be re-imported into a new bank."""
-        # Set up source bank
-        await api_client.post(
-            f"/v1/default/banks/{bank_id}/import",
-            json={
-                "version": "1",
-                "bank": {"retain_mission": "Roundtrip test"},
-                "mental_models": [
-                    {"id": "rt-mm", "name": "RT Model", "source_query": "test query"},
-                ],
-                "directives": [
-                    {"name": "RT Directive", "content": "test directive"},
-                ],
-            },
-        )
-
-        # Export
-        export_resp = await api_client.get(f"/v1/default/banks/{bank_id}/export")
-        assert export_resp.status_code == 200
-        exported = export_resp.json()
-
-        # Import into a new bank
-        new_bank_id = f"{bank_id}_clone"
-        import_resp = await api_client.post(
-            f"/v1/default/banks/{new_bank_id}/import",
-            json=exported,
-        )
-        assert import_resp.status_code == 200
-        data = import_resp.json()
-        assert data["config_applied"] is True
-        assert "rt-mm" in data["mental_models_created"]
-        assert "RT Directive" in data["directives_created"]
-
-    @pytest.mark.asyncio
-    async def test_export_nonexistent_bank(self, api_client):
-        """Export from a nonexistent bank returns the bank with defaults (auto-created)."""
-        resp = await api_client.get("/v1/default/banks/nonexistent-export-test/export")
-        # get_bank_profile auto-creates, so this returns a valid empty manifest
-        assert resp.status_code == 200
-        data = resp.json()
-        assert data["version"] == "1"

hindsight-api-slim/tests/test_batch_api_integration.py

@@ -198,7 +198,7 @@ async def test_real_openai_batch_api(real_llm_config, test_contents_real, integr
         for fact in facts:
             assert hasattr(fact, "fact_text"), "Fact should have fact_text"
             assert hasattr(fact, "fact_type"), "Fact should have fact_type"
-            assert fact.fact_type in ["world", "experience"], f"Invalid fact_type: {fact.fact_type}"
+            assert fact.fact_type in ["world", "experience", "opinion"], f"Invalid fact_type: {fact.fact_type}"
 
         logger.info("\n✅ All assertions passed!")
 

hindsight-api-slim/tests/test_causal_relations.py

@@ -36,7 +36,7 @@ class TestCausalRelationsValidation:
         """
 
         context = "Personal life update"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
         event_date = datetime(2024, 3, 15)
 
         facts, _, usage = await extract_facts_from_text(
@@ -81,7 +81,7 @@ class TestCausalRelationsValidation:
         """
 
         context = "Project update"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
         event_date = datetime(2024, 6, 1)
 
         facts, _, _ = await extract_facts_from_text(
@@ -118,7 +118,7 @@ class TestCausalRelationsValidation:
         """
 
         context = "Personal achievement story"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
         event_date = datetime(2024, 7, 15)
 
         facts, _, _ = await extract_facts_from_text(
@@ -168,7 +168,7 @@ class TestCausalRelationsValidation:
         """
 
         context = "Business impact analysis"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
         event_date = datetime(2024, 4, 1)
 
         facts, _, usage = await extract_facts_from_text(
@@ -205,7 +205,7 @@ class TestCausalRelationsValidation:
         """
 
         context = "Career progression"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
         event_date = datetime(2024, 5, 1)
 
         facts, _, _ = await extract_facts_from_text(

hindsight-api-slim/tests/test_causal_relationships.py

@@ -35,7 +35,7 @@ After searching for weeks, I finally found a cheaper apartment in Brooklyn.
 """
 
         context = "Personal story about housing change"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=text, event_date=datetime(2024, 3, 15), context=context, llm_config=llm_config, agent_name="TestUser",
@@ -105,7 +105,7 @@ The renovation took three months and cost $15,000.
 """
 
         context = "Home repair story"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=text, event_date=datetime(2024, 6, 1), context=context, llm_config=llm_config, agent_name="TestUser",
@@ -136,7 +136,7 @@ Machine learning fascinated me so much that I changed my career to data science.
 """
 
         context = "Career change story"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=text, event_date=datetime(2024, 1, 1), context=context, llm_config=llm_config, agent_name="TestUser",
@@ -164,7 +164,7 @@ The new role enabled me to lead a team of engineers.
 """
 
         context = "Work promotion story"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=text, event_date=datetime(2024, 2, 15), context=context, llm_config=llm_config, agent_name="TestUser",
@@ -192,7 +192,7 @@ Reduced spending somewhat affected local businesses.
 """
 
         context = "Economic impact story"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=text, event_date=datetime(2024, 4, 1), context=context, llm_config=llm_config, agent_name="TestUser",

hindsight-api-slim/tests/test_codex_tool_choice.py

@@ -1,88 +0,0 @@
-"""
-Regression tests for Codex provider tool_choice normalization.
-
-The reflect agent forces tool selection via named tool_choice dicts on early iterations:
-  {"type": "function", "function": {"name": "recall"}}
-
-The Codex Responses API expects the function name at the top level instead:
-  {"type": "function", "name": "recall"}
-
-Without normalization, Codex rejects the request with:
-  400 Unknown parameter: 'tool_choice.function'
-"""
-
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-
-from hindsight_api.engine.providers.codex_llm import CodexLLM
-
-TOOLS = [
-    {
-        "type": "function",
-        "function": {
-            "name": "recall",
-            "description": "Recall semantic memories",
-            "parameters": {
-                "type": "object",
-                "properties": {"query": {"type": "string"}},
-                "required": ["query"],
-            },
-        },
-    }
-]
-
-
-def build_llm() -> CodexLLM:
-    with patch.object(CodexLLM, "_load_codex_auth", return_value=("token", "account")):
-        return CodexLLM(
-            provider="openai-codex",
-            api_key="ignored",
-            base_url="https://chatgpt.com/backend-api",
-            model="gpt-5.4-mini",
-        )
-
-
-@pytest.mark.asyncio
-async def test_codex_normalizes_legacy_named_tool_choice_shape():
-    llm = build_llm()
-    response = MagicMock()
-    response.status_code = 200
-    response.raise_for_status.return_value = None
-    with patch.object(llm._client, "post", new_callable=AsyncMock) as mock_post:
-        mock_post.return_value = response
-        with patch.object(llm, "_parse_sse_tool_stream", new_callable=AsyncMock) as mock_parse:
-            mock_parse.return_value = (None, [])
-            await llm.call_with_tools(
-                messages=[{"role": "user", "content": "recall the memory"}],
-                tools=TOOLS,
-                tool_choice={"type": "function", "function": {"name": "recall"}},
-                max_retries=0,
-            )
-        sent_payload = mock_post.call_args.kwargs["json"]
-
-    assert sent_payload["tool_choice"] == {"type": "function", "name": "recall"}
-
-
-@pytest.mark.asyncio
-async def test_codex_forced_tool_choice_still_yields_tool_calls():
-    llm = build_llm()
-    response = MagicMock()
-    response.status_code = 200
-    response.raise_for_status.return_value = None
-    tool_call = {"id": "call-1", "name": "recall", "arguments": {"query": "memory"}}
-    with patch.object(llm._client, "post", new_callable=AsyncMock) as mock_post:
-        mock_post.return_value = response
-        with patch.object(llm, "_parse_sse_tool_stream", new_callable=AsyncMock) as mock_parse:
-            mock_parse.return_value = (None, [tool_call])
-            result = await llm.call_with_tools(
-                messages=[{"role": "user", "content": "recall the memory"}],
-                tools=TOOLS,
-                tool_choice={"type": "function", "function": {"name": "recall"}},
-                max_retries=0,
-            )
-        sent_payload = mock_post.call_args.kwargs["json"]
-
-    assert len(result.tool_calls) == 1
-    assert result.tool_calls[0].name == "recall"
-    assert sent_payload["tool_choice"] == {"type": "function", "name": "recall"}

hindsight-api-slim/tests/test_cohere_cross_encoder.py

@@ -1,339 +0,0 @@
-"""
-Tests for CohereCrossEncoder.
-
-Tests the Cohere cross-encoder implementation, including Azure AI Foundry endpoint support.
-"""
-
-import os
-from unittest.mock import MagicMock, patch
-
-import httpx
-import pytest
-
-from hindsight_api.engine.cross_encoder import CohereCrossEncoder, create_cross_encoder_from_env
-
-
-class TestCohereCrossEncoder:
-    """Test suite for CohereCrossEncoder class."""
-
-    @pytest.mark.asyncio
-    async def test_initialization_native_cohere(self):
-        """Test successful initialization with native Cohere API (no base_url)."""
-        encoder = CohereCrossEncoder(
-            api_key="test_key",
-            model="rerank-english-v3.0",
-        )
-
-        assert encoder.provider_name == "cohere"
-        assert encoder.api_key == "test_key"
-        assert encoder.model == "rerank-english-v3.0"
-        assert encoder._client is None
-        assert encoder._httpx_client is None
-
-        # Mock the cohere import
-        mock_cohere = MagicMock()
-        mock_cohere.Client = MagicMock()
-        with patch.dict("sys.modules", {"cohere": mock_cohere}):
-            await encoder.initialize()
-            assert encoder._client is not None
-            assert encoder._httpx_client is None
-            mock_cohere.Client.assert_called_once_with(api_key="test_key", timeout=60.0)
-
-    @pytest.mark.asyncio
-    async def test_initialization_azure_endpoint(self):
-        """Test initialization with Azure AI Foundry endpoint (uses httpx)."""
-        encoder = CohereCrossEncoder(
-            api_key="test_key",
-            model="cohere-rerank-v3-english",
-            base_url="https://my-endpoint.inference.ai.azure.com/models/cohere-rerank-v3-english/invoke",
-        )
-
-        assert encoder.base_url == "https://my-endpoint.inference.ai.azure.com/models/cohere-rerank-v3-english/invoke"
-
-        await encoder.initialize()
-
-        assert encoder._httpx_client is not None
-        assert encoder._client is None
-        assert isinstance(encoder._httpx_client, httpx.Client)
-
-    @pytest.mark.asyncio
-    async def test_initialization_missing_package(self):
-        """Test initialization fails when cohere package is missing (native API)."""
-        encoder = CohereCrossEncoder(
-            api_key="test_key",
-            model="rerank-english-v3.0",
-        )
-
-        with patch.dict("sys.modules", {"cohere": None}):
-            with pytest.raises(ImportError, match="cohere is required"):
-                await encoder.initialize()
-
-    @pytest.mark.asyncio
-    async def test_initialization_idempotent(self):
-        """Test that calling initialize() multiple times is safe."""
-        encoder = CohereCrossEncoder(
-            api_key="test_key",
-            model="rerank-english-v3.0",
-        )
-
-        mock_cohere = MagicMock()
-        mock_cohere.Client = MagicMock()
-        with patch.dict("sys.modules", {"cohere": mock_cohere}):
-            await encoder.initialize()
-            assert encoder._client is not None
-
-            # Second call should be no-op
-            await encoder.initialize()
-            # Should only create client once
-            mock_cohere.Client.assert_called_once()
-
-    @pytest.mark.asyncio
-    async def test_predict_native_cohere_single_query(self):
-        """Test prediction with native Cohere SDK."""
-        encoder = CohereCrossEncoder(
-            api_key="test_key",
-            model="rerank-english-v3.0",
-        )
-
-        # Create mock Cohere response
-        mock_result_1 = MagicMock()
-        mock_result_1.index = 0
-        mock_result_1.relevance_score = 0.9
-
-        mock_result_2 = MagicMock()
-        mock_result_2.index = 1
-        mock_result_2.relevance_score = 0.7
-
-        mock_result_3 = MagicMock()
-        mock_result_3.index = 2
-        mock_result_3.relevance_score = 0.5
-
-        mock_response = MagicMock()
-        mock_response.results = [mock_result_1, mock_result_2, mock_result_3]
-
-        mock_cohere_client = MagicMock()
-        mock_cohere_client.rerank = MagicMock(return_value=mock_response)
-
-        mock_cohere = MagicMock()
-        mock_cohere.Client = MagicMock(return_value=mock_cohere_client)
-
-        with patch.dict("sys.modules", {"cohere": mock_cohere}):
-            await encoder.initialize()
-
-            pairs = [
-                ("What is Python?", "Python is a programming language"),
-                ("What is Python?", "Python is a snake"),
-                ("What is Python?", "Python is a British comedy group"),
-            ]
-
-            scores = await encoder.predict(pairs)
-
-            assert len(scores) == 3
-            assert scores == [0.9, 0.7, 0.5]
-
-            # Verify rerank was called correctly
-            mock_cohere_client.rerank.assert_called_once()
-            call_args = mock_cohere_client.rerank.call_args
-            assert call_args.kwargs["model"] == "rerank-english-v3.0"
-            assert call_args.kwargs["query"] == "What is Python?"
-            assert len(call_args.kwargs["documents"]) == 3
-            assert call_args.kwargs["return_documents"] is False
-
-    @pytest.mark.asyncio
-    async def test_predict_azure_endpoint_single_query(self):
-        """Test prediction with Azure AI Foundry endpoint (httpx direct call)."""
-        encoder = CohereCrossEncoder(
-            api_key="test_key",
-            model="cohere-rerank-v3-english",
-            base_url="https://my-endpoint.inference.ai.azure.com/models/cohere-rerank-v3-english/invoke",
-        )
-
-        await encoder.initialize()
-
-        # Mock httpx response
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "results": [
-                {"index": 0, "relevance_score": 0.9},
-                {"index": 1, "relevance_score": 0.7},
-                {"index": 2, "relevance_score": 0.5},
-            ]
-        }
-
-        encoder._httpx_client.post = MagicMock(return_value=mock_response)
-
-        pairs = [
-            ("What is Python?", "Python is a programming language"),
-            ("What is Python?", "Python is a snake"),
-            ("What is Python?", "Python is a British comedy group"),
-        ]
-
-        scores = await encoder.predict(pairs)
-
-        assert len(scores) == 3
-        assert scores == [0.9, 0.7, 0.5]
-
-        # Verify httpx.post was called with correct URL and payload
-        encoder._httpx_client.post.assert_called_once()
-        call_args = encoder._httpx_client.post.call_args
-        assert call_args[0][0] == "https://my-endpoint.inference.ai.azure.com/models/cohere-rerank-v3-english/invoke"
-        assert call_args.kwargs["json"]["model"] == "cohere-rerank-v3-english"
-        assert call_args.kwargs["json"]["query"] == "What is Python?"
-        assert len(call_args.kwargs["json"]["documents"]) == 3
-        assert call_args.kwargs["json"]["return_documents"] is False
-
-    @pytest.mark.asyncio
-    async def test_predict_multiple_queries(self):
-        """Test prediction with multiple different queries (grouped efficiently)."""
-        encoder = CohereCrossEncoder(
-            api_key="test_key",
-            model="rerank-english-v3.0",
-        )
-
-        # First query response
-        mock_result_1_1 = MagicMock()
-        mock_result_1_1.index = 0
-        mock_result_1_1.relevance_score = 0.9
-
-        mock_result_1_2 = MagicMock()
-        mock_result_1_2.index = 1
-        mock_result_1_2.relevance_score = 0.7
-
-        mock_response1 = MagicMock()
-        mock_response1.results = [mock_result_1_1, mock_result_1_2]
-
-        # Second query response
-        mock_result_2_1 = MagicMock()
-        mock_result_2_1.index = 0
-        mock_result_2_1.relevance_score = 0.8
-
-        mock_response2 = MagicMock()
-        mock_response2.results = [mock_result_2_1]
-
-        mock_cohere_client = MagicMock()
-        mock_cohere_client.rerank = MagicMock(side_effect=[mock_response1, mock_response2])
-
-        mock_cohere = MagicMock()
-        mock_cohere.Client = MagicMock(return_value=mock_cohere_client)
-
-        with patch.dict("sys.modules", {"cohere": mock_cohere}):
-            await encoder.initialize()
-
-            pairs = [
-                ("What is Python?", "Python is a programming language"),
-                ("What is Python?", "Python is a snake"),
-                ("What is Java?", "Java is a programming language"),
-            ]
-
-            scores = await encoder.predict(pairs)
-
-            assert len(scores) == 3
-            assert scores[0] == 0.9  # First query, first doc
-            assert scores[1] == 0.7  # First query, second doc
-            assert scores[2] == 0.8  # Second query, first doc
-
-            # Verify rerank was called twice (once per unique query)
-            assert mock_cohere_client.rerank.call_count == 2
-
-    @pytest.mark.asyncio
-    async def test_predict_empty_pairs(self):
-        """Test prediction with empty input."""
-        encoder = CohereCrossEncoder(
-            api_key="test_key",
-            model="rerank-english-v3.0",
-        )
-
-        mock_cohere = MagicMock()
-        mock_cohere.Client = MagicMock()
-        with patch.dict("sys.modules", {"cohere": mock_cohere}):
-            await encoder.initialize()
-            scores = await encoder.predict([])
-            assert scores == []
-
-    @pytest.mark.asyncio
-    async def test_predict_not_initialized(self):
-        """Test that predict fails if encoder not initialized."""
-        encoder = CohereCrossEncoder(
-            api_key="test_key",
-            model="rerank-english-v3.0",
-        )
-
-        pairs = [("query", "document")]
-
-        with pytest.raises(RuntimeError, match="not initialized"):
-            await encoder.predict(pairs)
-
-    @pytest.mark.asyncio
-    async def test_azure_endpoint_http_error(self):
-        """Test that HTTP errors from Azure endpoint are raised."""
-        encoder = CohereCrossEncoder(
-            api_key="test_key",
-            model="cohere-rerank-v3-english",
-            base_url="https://my-endpoint.inference.ai.azure.com/models/cohere-rerank-v3-english/invoke",
-        )
-
-        await encoder.initialize()
-
-        # Mock httpx to raise HTTP error
-        mock_response = MagicMock()
-        mock_response.raise_for_status.side_effect = httpx.HTTPStatusError(
-            "404 Not Found",
-            request=MagicMock(),
-            response=MagicMock(status_code=404),
-        )
-        encoder._httpx_client.post = MagicMock(return_value=mock_response)
-
-        pairs = [("What is Python?", "Python is a programming language")]
-
-        # Should raise the HTTP error
-        with pytest.raises(httpx.HTTPStatusError):
-            await encoder.predict(pairs)
-
-
-class TestFactoryFunction:
-    """Test suite for create_cross_encoder_from_env factory function."""
-
-    @pytest.mark.asyncio
-    async def test_create_cohere_from_env(self):
-        """Test creating Cohere cross-encoder from environment variables."""
-        env_vars = {
-            "HINDSIGHT_API_RERANKER_PROVIDER": "cohere",
-            "HINDSIGHT_API_RERANKER_COHERE_API_KEY": "test_key",
-            "HINDSIGHT_API_RERANKER_COHERE_MODEL": "rerank-english-v3.0",
-        }
-
-        with patch.dict(os.environ, env_vars, clear=False):
-            from hindsight_api.config import HindsightConfig
-
-            config = HindsightConfig.from_env()
-
-            with patch("hindsight_api.config.get_config", return_value=config):
-                encoder = create_cross_encoder_from_env()
-
-                assert isinstance(encoder, CohereCrossEncoder)
-                assert encoder.api_key == "test_key"
-                assert encoder.model == "rerank-english-v3.0"
-                assert encoder.base_url is None
-
-    @pytest.mark.asyncio
-    async def test_create_cohere_with_azure_base_url_from_env(self):
-        """Test creating Cohere cross-encoder with Azure base URL from environment."""
-        env_vars = {
-            "HINDSIGHT_API_RERANKER_PROVIDER": "cohere",
-            "HINDSIGHT_API_RERANKER_COHERE_API_KEY": "test_key",
-            "HINDSIGHT_API_RERANKER_COHERE_MODEL": "cohere-rerank-v3-english",
-            "HINDSIGHT_API_RERANKER_COHERE_BASE_URL": "https://my-endpoint.inference.ai.azure.com/models/cohere-rerank-v3-english/invoke",
-        }
-
-        with patch.dict(os.environ, env_vars, clear=False):
-            from hindsight_api.config import HindsightConfig
-
-            config = HindsightConfig.from_env()
-
-            with patch("hindsight_api.config.get_config", return_value=config):
-                encoder = create_cross_encoder_from_env()
-
-                assert isinstance(encoder, CohereCrossEncoder)
-                assert encoder.api_key == "test_key"
-                assert encoder.model == "cohere-rerank-v3-english"
-                assert encoder.base_url == "https://my-endpoint.inference.ai.azure.com/models/cohere-rerank-v3-english/invoke"

hindsight-api-slim/tests/test_delta_retain.py

@@ -1,842 +0,0 @@
-"""
-Tests for delta retain — upsert optimization that only re-processes changed chunks.
-"""
-
-import logging
-from datetime import datetime, timezone
-
-import pytest
-
-from hindsight_api import RequestContext
-from hindsight_api.engine.memory_engine import Budget
-
-logger = logging.getLogger(__name__)
-
-
-def _ts():
-    return datetime.now(timezone.utc).timestamp()
-
-
-# ============================================================
-# Core Delta Retain Tests
-# ============================================================
-
-
-@pytest.mark.asyncio
-async def test_delta_retain_unchanged_content_skips_llm(memory, request_context):
-    """
-    When upserting a document with identical content, no new facts should be
-    extracted (LLM is not called for unchanged chunks). The existing facts
-    should be preserved.
-    """
-    bank_id = f"test_delta_unchanged_{_ts()}"
-    document_id = "conversation-001"
-
-    try:
-        content = "Alice works at Google. Bob works at Microsoft."
-
-        # First retain — full processing
-        v1_units = await memory.retain_async(
-            bank_id=bank_id,
-            content=content,
-            context="team info",
-            document_id=document_id,
-            request_context=request_context,
-        )
-        assert len(v1_units) > 0, "v1 should create facts"
-
-        # Get v1 document state
-        doc_v1 = await memory.get_document(document_id, bank_id, request_context=request_context)
-        v1_unit_count = doc_v1["memory_unit_count"]
-
-        # Second retain — same content, should use delta path (no new facts)
-        v2_units = await memory.retain_async(
-            bank_id=bank_id,
-            content=content,
-            context="team info",
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        # No new units should be returned (nothing changed)
-        assert v2_units == [], "Delta retain with unchanged content should return empty unit list"
-
-        # Existing facts should still be there
-        doc_v2 = await memory.get_document(document_id, bank_id, request_context=request_context)
-        assert doc_v2["memory_unit_count"] == v1_unit_count, "Existing facts should be preserved"
-
-        # Verify recall still works
-        result = await memory.recall_async(
-            bank_id=bank_id,
-            query="Where does Alice work?",
-            budget=Budget.MID,
-            max_tokens=1000,
-            request_context=request_context,
-        )
-        assert len(result.results) > 0, "Should still recall facts after delta retain"
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-@pytest.mark.asyncio
-async def test_delta_retain_appended_content(memory, request_context):
-    """
-    When a conversation grows (new content appended), only new chunks should
-    be processed. Facts from unchanged chunks should be preserved.
-    """
-    bank_id = f"test_delta_append_{_ts()}"
-    document_id = "growing-conversation"
-
-    try:
-        # First version — short content (single chunk)
-        v1_content = "Alice is a software engineer at Google. She works on search infrastructure."
-
-        v1_units = await memory.retain_async(
-            bank_id=bank_id,
-            content=v1_content,
-            context="profile",
-            document_id=document_id,
-            request_context=request_context,
-        )
-        assert len(v1_units) > 0
-
-        # Get v1 facts via recall
-        v1_recall = await memory.recall_async(
-            bank_id=bank_id,
-            query="What does Alice do?",
-            budget=Budget.MID,
-            max_tokens=2000,
-            request_context=request_context,
-        )
-        v1_fact_texts = {r.text for r in v1_recall.results}
-
-        # Second version — original content + new content appended
-        # This should preserve facts from the first chunk and add new ones
-        v2_content = v1_content + "\n\nBob joined Google as a product manager in 2024. He previously worked at Meta on AR/VR products."
-
-        v2_units = await memory.retain_async(
-            bank_id=bank_id,
-            content=v2_content,
-            context="profile",
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        # Should have facts about Bob from the new content
-        v2_recall = await memory.recall_async(
-            bank_id=bank_id,
-            query="What does Bob do?",
-            budget=Budget.MID,
-            max_tokens=2000,
-            request_context=request_context,
-        )
-        bob_facts = [r for r in v2_recall.results if "bob" in r.text.lower()]
-        assert len(bob_facts) > 0, "Should have facts about Bob from appended content"
-
-        # Should still have facts about Alice from original content
-        alice_recall = await memory.recall_async(
-            bank_id=bank_id,
-            query="What does Alice do?",
-            budget=Budget.MID,
-            max_tokens=2000,
-            request_context=request_context,
-        )
-        assert len(alice_recall.results) > 0, "Should still have Alice facts from original content"
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-@pytest.mark.asyncio
-async def test_delta_retain_modified_chunk(memory, request_context):
-    """
-    When content in the middle changes, that chunk should be re-processed
-    while other chunks are preserved.
-    """
-    bank_id = f"test_delta_modified_{_ts()}"
-    document_id = "changing-doc"
-
-    try:
-        # v1: Alice works at Google
-        v1_content = "Alice works at Google as a senior engineer."
-        v1_units = await memory.retain_async(
-            bank_id=bank_id,
-            content=v1_content,
-            context="team",
-            document_id=document_id,
-            request_context=request_context,
-        )
-        assert len(v1_units) > 0
-
-        # v2: Alice works at Microsoft (changed)
-        v2_content = "Alice works at Microsoft as a principal engineer."
-        v2_units = await memory.retain_async(
-            bank_id=bank_id,
-            content=v2_content,
-            context="team",
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        # New facts should reflect the updated content
-        result = await memory.recall_async(
-            bank_id=bank_id,
-            query="Where does Alice work?",
-            budget=Budget.MID,
-            max_tokens=2000,
-            request_context=request_context,
-        )
-        all_texts = " ".join(r.text.lower() for r in result.results)
-        assert "microsoft" in all_texts, f"Should have updated fact about Microsoft, got: {all_texts}"
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-# ============================================================
-# Entity & Link Tests
-# ============================================================
-
-
-@pytest.mark.asyncio
-async def test_delta_retain_entities_preserved_for_unchanged_chunks(memory, request_context):
-    """
-    Entities linked to unchanged chunks should be preserved after delta retain.
-    """
-    bank_id = f"test_delta_entities_{_ts()}"
-    document_id = "entity-doc"
-
-    try:
-        v1_content = "Alice works at Google. She is a senior engineer in the Cloud division."
-        v1_units = await memory.retain_async(
-            bank_id=bank_id,
-            content=v1_content,
-            context="team",
-            document_id=document_id,
-            request_context=request_context,
-        )
-        assert len(v1_units) > 0
-
-        # Check entities exist
-        pool = await memory._get_pool()
-        async with pool.acquire() as conn:
-            v1_entities = await conn.fetch(
-                "SELECT canonical_name FROM entities WHERE bank_id = $1",
-                bank_id,
-            )
-        v1_entity_names = {e["canonical_name"].lower() for e in v1_entities}
-        assert len(v1_entity_names) > 0, "Should have entities after v1 retain"
-
-        # Upsert with same content — entities should persist
-        await memory.retain_async(
-            bank_id=bank_id,
-            content=v1_content,
-            context="team",
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        async with pool.acquire() as conn:
-            v2_entities = await conn.fetch(
-                "SELECT canonical_name FROM entities WHERE bank_id = $1",
-                bank_id,
-            )
-        v2_entity_names = {e["canonical_name"].lower() for e in v2_entities}
-
-        # All v1 entities should still exist
-        assert v1_entity_names.issubset(v2_entity_names), (
-            f"v1 entities {v1_entity_names} should be preserved, got {v2_entity_names}"
-        )
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-@pytest.mark.asyncio
-async def test_delta_retain_new_entities_created_for_new_chunks(memory, request_context):
-    """
-    New entities should be created for newly added chunks during delta retain.
-    """
-    bank_id = f"test_delta_new_entities_{_ts()}"
-    document_id = "entity-growth-doc"
-
-    try:
-        v1_content = "Alice works at Google."
-        await memory.retain_async(
-            bank_id=bank_id,
-            content=v1_content,
-            context="team",
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        pool = await memory._get_pool()
-        async with pool.acquire() as conn:
-            v1_entities = await conn.fetch(
-                "SELECT canonical_name FROM entities WHERE bank_id = $1",
-                bank_id,
-            )
-        v1_entity_names = {e["canonical_name"].lower() for e in v1_entities}
-
-        # Append content mentioning new entities
-        v2_content = v1_content + "\n\nBob joined Facebook. He works with Charlie on the Reality Labs project."
-        await memory.retain_async(
-            bank_id=bank_id,
-            content=v2_content,
-            context="team",
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        async with pool.acquire() as conn:
-            v2_entities = await conn.fetch(
-                "SELECT canonical_name FROM entities WHERE bank_id = $1",
-                bank_id,
-            )
-        v2_entity_names = {e["canonical_name"].lower() for e in v2_entities}
-
-        # Should have more entities after adding content with new people/orgs
-        assert len(v2_entity_names) > len(v1_entity_names), (
-            f"Should have more entities after append: v1={v1_entity_names}, v2={v2_entity_names}"
-        )
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-@pytest.mark.asyncio
-async def test_delta_retain_links_preserved_for_unchanged_chunks(memory, request_context):
-    """
-    Memory links (temporal, semantic, entity) for unchanged chunks should be preserved.
-    """
-    bank_id = f"test_delta_links_{_ts()}"
-    document_id = "links-doc"
-
-    try:
-        content = "Alice is a senior engineer at Google Cloud. She mentors junior engineers and reviews their code."
-        v1_units = await memory.retain_async(
-            bank_id=bank_id,
-            content=content,
-            context="team",
-            document_id=document_id,
-            request_context=request_context,
-        )
-        assert len(v1_units) > 0
-
-        # Count links after v1
-        pool = await memory._get_pool()
-        async with pool.acquire() as conn:
-            v1_link_count = await conn.fetchval(
-                """SELECT COUNT(*) FROM memory_links ml
-                   JOIN memory_units mu ON ml.from_unit_id = mu.id
-                   WHERE mu.bank_id = $1 AND mu.document_id = $2""",
-                bank_id,
-                document_id,
-            )
-
-        # Upsert with same content
-        await memory.retain_async(
-            bank_id=bank_id,
-            content=content,
-            context="team",
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        # Links should be preserved
-        async with pool.acquire() as conn:
-            v2_link_count = await conn.fetchval(
-                """SELECT COUNT(*) FROM memory_links ml
-                   JOIN memory_units mu ON ml.from_unit_id = mu.id
-                   WHERE mu.bank_id = $1 AND mu.document_id = $2""",
-                bank_id,
-                document_id,
-            )
-
-        assert v2_link_count == v1_link_count, (
-            f"Links should be preserved: v1={v1_link_count}, v2={v2_link_count}"
-        )
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-# ============================================================
-# Document Metadata & Tags Tests
-# ============================================================
-
-
-@pytest.mark.asyncio
-async def test_delta_retain_document_metadata_updated(memory, request_context):
-    """
-    Document metadata (retain_params, tags) should be updated even when
-    chunk content hasn't changed.
-    """
-    bank_id = f"test_delta_meta_{_ts()}"
-    document_id = "metadata-doc"
-
-    try:
-        content = "Alice works at Google."
-
-        # v1 with initial tags
-        await memory.retain_async(
-            bank_id=bank_id,
-            content=content,
-            context="initial context",
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        doc_v1 = await memory.get_document(document_id, bank_id, request_context=request_context)
-        assert doc_v1 is not None
-
-        # v2 with updated context (same content — triggers delta path)
-        await memory.retain_async(
-            bank_id=bank_id,
-            content=content,
-            context="updated context",
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        doc_v2 = await memory.get_document(document_id, bank_id, request_context=request_context)
-        assert doc_v2 is not None
-        assert doc_v2["updated_at"] >= doc_v1["updated_at"], "Document should have updated timestamp"
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-@pytest.mark.asyncio
-async def test_delta_retain_tags_propagated_to_existing_units(memory, request_context):
-    """
-    When tags change during an upsert with unchanged content, the new tags
-    should be propagated to all existing memory units.
-    """
-    bank_id = f"test_delta_tags_{_ts()}"
-    document_id = "tags-doc"
-
-    try:
-        content = "Alice works at Google."
-
-        # v1 with tag "team-a"
-        await memory.retain_batch_async(
-            bank_id=bank_id,
-            contents=[{
-                "content": content,
-                "document_id": document_id,
-                "tags": ["team-a"],
-            }],
-            request_context=request_context,
-        )
-
-        pool = await memory._get_pool()
-        async with pool.acquire() as conn:
-            v1_tags = await conn.fetch(
-                "SELECT tags FROM memory_units WHERE bank_id = $1 AND document_id = $2",
-                bank_id,
-                document_id,
-            )
-        assert all("team-a" in row["tags"] for row in v1_tags), "v1 units should have team-a tag"
-
-        # v2 with same content but different tags
-        await memory.retain_batch_async(
-            bank_id=bank_id,
-            contents=[{
-                "content": content,
-                "document_id": document_id,
-                "tags": ["team-b", "important"],
-            }],
-            request_context=request_context,
-        )
-
-        async with pool.acquire() as conn:
-            v2_tags = await conn.fetch(
-                "SELECT tags FROM memory_units WHERE bank_id = $1 AND document_id = $2",
-                bank_id,
-                document_id,
-            )
-        for row in v2_tags:
-            assert "team-b" in row["tags"], f"v2 units should have team-b tag, got {row['tags']}"
-            assert "important" in row["tags"], f"v2 units should have important tag, got {row['tags']}"
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-# ============================================================
-# Chunk Management Tests
-# ============================================================
-
-
-@pytest.mark.asyncio
-async def test_delta_retain_removed_chunks_delete_facts(memory, request_context):
-    """
-    When content is shortened (chunks removed), facts from the removed
-    chunks should be deleted.
-    """
-    bank_id = f"test_delta_removed_{_ts()}"
-    document_id = "shrinking-doc"
-
-    try:
-        # v1: longer content with facts about Alice and Bob
-        v1_content = (
-            "Alice is a senior engineer at Google Cloud. "
-            "She leads the infrastructure team and has been there for 5 years.\n\n"
-            "Bob is a product manager at Facebook Reality Labs. "
-            "He previously worked at Amazon on Alexa voice products."
-        )
-
-        v1_units = await memory.retain_async(
-            bank_id=bank_id,
-            content=v1_content,
-            context="profiles",
-            document_id=document_id,
-            request_context=request_context,
-        )
-        assert len(v1_units) > 0
-
-        doc_v1 = await memory.get_document(document_id, bank_id, request_context=request_context)
-        v1_count = doc_v1["memory_unit_count"]
-
-        # v2: Completely different content — all chunks change
-        v2_content = "Charlie works at Netflix as a data scientist."
-        v2_units = await memory.retain_async(
-            bank_id=bank_id,
-            content=v2_content,
-            context="profiles",
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        doc_v2 = await memory.get_document(document_id, bank_id, request_context=request_context)
-        assert doc_v2 is not None
-
-        # Should have facts about Charlie
-        result = await memory.recall_async(
-            bank_id=bank_id,
-            query="Who works at Netflix?",
-            budget=Budget.MID,
-            max_tokens=2000,
-            request_context=request_context,
-        )
-        all_texts = " ".join(r.text.lower() for r in result.results)
-        assert "charlie" in all_texts or "netflix" in all_texts, (
-            f"Should have facts about Charlie/Netflix after replacing content, got: {all_texts}"
-        )
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-@pytest.mark.asyncio
-async def test_delta_retain_chunks_have_content_hash(memory, request_context):
-    """
-    After retain, chunks should have content_hash populated.
-    """
-    bank_id = f"test_delta_hash_{_ts()}"
-    document_id = "hash-doc"
-
-    try:
-        content = "Alice works at Google as a software engineer."
-        await memory.retain_async(
-            bank_id=bank_id,
-            content=content,
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        pool = await memory._get_pool()
-        async with pool.acquire() as conn:
-            chunks = await conn.fetch(
-                "SELECT chunk_id, content_hash FROM chunks WHERE document_id = $1 AND bank_id = $2",
-                document_id,
-                bank_id,
-            )
-
-        assert len(chunks) > 0, "Should have stored chunks"
-        for chunk in chunks:
-            assert chunk["content_hash"] is not None, f"Chunk {chunk['chunk_id']} should have content_hash"
-            assert len(chunk["content_hash"]) == 64, "content_hash should be SHA256 hex (64 chars)"
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-# ============================================================
-# Backward Compatibility Tests
-# ============================================================
-
-
-@pytest.mark.asyncio
-async def test_retain_without_document_id_still_works(memory, request_context):
-    """
-    Retain without document_id should still work normally (no delta path).
-    """
-    bank_id = f"test_no_docid_{_ts()}"
-
-    try:
-        units = await memory.retain_async(
-            bank_id=bank_id,
-            content="Alice works at Google.",
-            context="test",
-            request_context=request_context,
-        )
-        assert len(units) > 0, "Should create facts without document_id"
-
-        result = await memory.recall_async(
-            bank_id=bank_id,
-            query="Where does Alice work?",
-            budget=Budget.MID,
-            max_tokens=1000,
-            request_context=request_context,
-        )
-        assert len(result.results) > 0
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-@pytest.mark.asyncio
-async def test_delta_retain_first_retain_full_path(memory, request_context):
-    """
-    First retain of a new document should use the full path (no delta possible).
-    """
-    bank_id = f"test_first_retain_{_ts()}"
-    document_id = "new-doc"
-
-    try:
-        units = await memory.retain_async(
-            bank_id=bank_id,
-            content="Alice works at Google.",
-            context="test",
-            document_id=document_id,
-            request_context=request_context,
-        )
-        assert len(units) > 0, "First retain should create facts via full path"
-
-        doc = await memory.get_document(document_id, bank_id, request_context=request_context)
-        assert doc is not None
-        assert doc["memory_unit_count"] > 0
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-# ============================================================
-# Edge Cases
-# ============================================================
-
-
-@pytest.mark.asyncio
-async def test_delta_retain_empty_to_content(memory, request_context):
-    """
-    Going from gibberish (zero facts) to real content should work.
-    """
-    bank_id = f"test_delta_empty_{_ts()}"
-    document_id = "empty-to-content"
-
-    try:
-        # v1: content that probably produces zero facts
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="!!!###$$$%%%",
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        doc_v1 = await memory.get_document(document_id, bank_id, request_context=request_context)
-        assert doc_v1 is not None
-
-        # v2: real content
-        v2_units = await memory.retain_async(
-            bank_id=bank_id,
-            content="Alice works at Google as a senior engineer.",
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        doc_v2 = await memory.get_document(document_id, bank_id, request_context=request_context)
-        assert doc_v2 is not None
-        assert doc_v2["memory_unit_count"] > 0 or len(v2_units) > 0, "Should have facts after updating with real content"
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-@pytest.mark.asyncio
-async def test_delta_retain_multiple_upserts(memory, request_context):
-    """
-    Multiple sequential upserts should work correctly, with delta optimization
-    kicking in after the first retain.
-    """
-    bank_id = f"test_delta_multi_{_ts()}"
-    document_id = "multi-upsert"
-
-    try:
-        # v1: initial
-        v1_content = "Alice works at Google."
-        await memory.retain_async(
-            bank_id=bank_id,
-            content=v1_content,
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        # v2: same content (delta: no changes)
-        await memory.retain_async(
-            bank_id=bank_id,
-            content=v1_content,
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        # v3: append
-        v3_content = v1_content + "\n\nBob works at Microsoft."
-        await memory.retain_async(
-            bank_id=bank_id,
-            content=v3_content,
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        # v4: same as v3 (delta: no changes again)
-        await memory.retain_async(
-            bank_id=bank_id,
-            content=v3_content,
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        # Final check: should have facts about both Alice and Bob
-        result = await memory.recall_async(
-            bank_id=bank_id,
-            query="Who works where?",
-            budget=Budget.MID,
-            max_tokens=2000,
-            request_context=request_context,
-        )
-        all_texts = " ".join(r.text.lower() for r in result.results)
-        assert "alice" in all_texts or "google" in all_texts, f"Should have Alice/Google facts, got: {all_texts}"
-
-        doc = await memory.get_document(document_id, bank_id, request_context=request_context)
-        assert doc is not None
-        assert doc["memory_unit_count"] > 0
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-@pytest.mark.asyncio
-async def test_delta_retain_with_user_entities(memory, request_context):
-    """
-    User-provided entities should work correctly with delta retain.
-    """
-    bank_id = f"test_delta_user_entities_{_ts()}"
-    document_id = "user-entity-doc"
-
-    try:
-        content = "The project is going well."
-
-        # v1 with user entities
-        await memory.retain_batch_async(
-            bank_id=bank_id,
-            contents=[{
-                "content": content,
-                "document_id": document_id,
-                "entities": [{"text": "Project Alpha", "type": "PROJECT"}],
-            }],
-            request_context=request_context,
-        )
-
-        pool = await memory._get_pool()
-        async with pool.acquire() as conn:
-            v1_entities = await conn.fetch(
-                "SELECT canonical_name FROM entities WHERE bank_id = $1",
-                bank_id,
-            )
-        v1_names = {e["canonical_name"].lower() for e in v1_entities}
-
-        # v2 with additional entity, same content
-        # Note: same content = delta path (no re-extraction)
-        # The user entities for NEW chunks only get processed
-        v2_content = content + "\n\nThe timeline is on track for Q2 delivery."
-        await memory.retain_batch_async(
-            bank_id=bank_id,
-            contents=[{
-                "content": v2_content,
-                "document_id": document_id,
-                "entities": [
-                    {"text": "Project Alpha", "type": "PROJECT"},
-                    {"text": "Q2 Deadline", "type": "MILESTONE"},
-                ],
-            }],
-            request_context=request_context,
-        )
-
-        # Should have entities from both v1 and v2
-        async with pool.acquire() as conn:
-            v2_entities = await conn.fetch(
-                "SELECT canonical_name FROM entities WHERE bank_id = $1",
-                bank_id,
-            )
-        v2_names = {e["canonical_name"].lower() for e in v2_entities}
-
-        # v1 entities should be preserved
-        assert v1_names.issubset(v2_names), f"v1 entities should be preserved: {v1_names} not in {v2_names}"
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-@pytest.mark.asyncio
-async def test_delta_retain_recall_with_chunks(memory, request_context):
-    """
-    After delta retain, recall with include_chunks should return correct chunk data.
-    """
-    bank_id = f"test_delta_recall_chunks_{_ts()}"
-    document_id = "recall-chunks-doc"
-
-    try:
-        content = "Alice is a senior engineer at Google Cloud. She designs distributed systems."
-        await memory.retain_async(
-            bank_id=bank_id,
-            content=content,
-            context="profile",
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        # Upsert with same content (delta: no changes)
-        await memory.retain_async(
-            bank_id=bank_id,
-            content=content,
-            context="profile",
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        # Recall with chunks
-        result = await memory.recall_async(
-            bank_id=bank_id,
-            query="What does Alice do?",
-            budget=Budget.MID,
-            max_tokens=2000,
-            include_chunks=True,
-            max_chunk_tokens=8192,
-            request_context=request_context,
-        )
-
-        assert len(result.results) > 0, "Should recall facts"
-
-        # Facts with chunk_ids should have corresponding chunks
-        facts_with_chunks = [r for r in result.results if r.chunk_id]
-        if facts_with_chunks and result.chunks:
-            for fact in facts_with_chunks:
-                assert fact.chunk_id in result.chunks, (
-                    f"Chunk {fact.chunk_id} should be in returned chunks"
-                )
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)

hindsight-api-slim/tests/test_document_tracking.py

@@ -141,70 +141,6 @@ async def test_memory_without_document(memory, request_context):
         await memory.delete_bank(bank_id, request_context=request_context)
 
 
-@pytest.mark.asyncio
-async def test_document_metadata_from_retain_params(memory, request_context):
-    """Test that document_metadata is returned from retain_params.metadata in both get and list."""
-    bank_id = f"test_doc_meta_{datetime.now(timezone.utc).timestamp()}"
-
-    try:
-        document_id = "doc-with-metadata"
-        metadata = {"source": "slack", "channel": "#general"}
-
-        await memory.retain_batch_async(
-            bank_id=bank_id,
-            contents=[{"content": "Alice works at Google.", "context": "Team meeting", "metadata": metadata}],
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        # get_document should include document_metadata
-        doc = await memory.get_document(document_id, bank_id, request_context=request_context)
-        assert doc is not None
-        assert doc["document_metadata"] == metadata
-        assert doc["retain_params"] is not None
-        assert doc["retain_params"]["metadata"] == metadata
-
-        # list_documents should also include document_metadata
-        docs_list = await memory.list_documents(
-            bank_id=bank_id, search_query=None, limit=100, offset=0, request_context=request_context
-        )
-        listed_doc = next(d for d in docs_list["items"] if d["id"] == document_id)
-        assert listed_doc["document_metadata"] == metadata
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-@pytest.mark.asyncio
-async def test_document_without_metadata(memory, request_context):
-    """Test that document_metadata is None when no metadata was provided during retain."""
-    bank_id = f"test_doc_no_meta_{datetime.now(timezone.utc).timestamp()}"
-
-    try:
-        document_id = "doc-no-metadata"
-
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="Bob works at Microsoft.",
-            context="Meeting",
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        doc = await memory.get_document(document_id, bank_id, request_context=request_context)
-        assert doc is not None
-        assert doc["document_metadata"] is None
-
-        docs_list = await memory.list_documents(
-            bank_id=bank_id, search_query=None, limit=100, offset=0, request_context=request_context
-        )
-        listed_doc = next(d for d in docs_list["items"] if d["id"] == document_id)
-        assert listed_doc["document_metadata"] is None
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
 @pytest.mark.asyncio
 async def test_document_persisted_with_zero_facts(memory, request_context):
     """

hindsight-api-slim/tests/test_experience_fact_type_passthrough.py

@@ -1,59 +0,0 @@
-"""
-Regression test for experience fact_type preservation.
-
-The LLM extraction layer normalizes raw "assistant" → "experience" early in parsing.
-The subsequent conversion to ExtractedFactType must pass through the already-normalized
-fact_type rather than re-checking for "assistant" (which would remap experience → world).
-
-See: https://github.com/vectorize-io/hindsight/pull/839
-"""
-
-from datetime import datetime, timezone
-from unittest.mock import AsyncMock, patch
-
-import pytest
-
-from hindsight_api.config import _get_raw_config
-from hindsight_api.engine.response_models import TokenUsage
-from hindsight_api.engine.retain.fact_extraction import (
-    Fact,
-    RetainContent,
-    extract_facts_from_contents,
-    extract_facts_from_contents_batch_api,
-)
-
-
-@pytest.mark.asyncio
-async def test_extract_facts_preserves_experience_type():
-    """
-    When extract_facts_from_text returns a Fact with fact_type="experience",
-    extract_facts_from_contents must preserve it (not remap to "world").
-    """
-    contents = [
-        RetainContent(
-            content="I fixed the failing tests after discovering they mocked the wrong interface.",
-            event_date=datetime(2026, 4, 1, tzinfo=timezone.utc),
-            context="assistant work log",
-        )
-    ]
-    extracted_fact = Fact(
-        fact="Fixed the failing tests after discovering they mocked the wrong interface.",
-        fact_type="experience",
-    )
-
-    with patch(
-        "hindsight_api.engine.retain.fact_extraction.extract_facts_from_text",
-        new=AsyncMock(return_value=([extracted_fact], [(contents[0].content, 1)], TokenUsage())),
-    ):
-        facts, _chunks, _usage = await extract_facts_from_contents(
-            contents=contents,
-            llm_config=None,
-            agent_name="TestAgent",
-            config=_get_raw_config(),
-        )
-
-    assert len(facts) == 1
-    assert facts[0].fact_type == "experience", (
-        f"Expected 'experience' but got '{facts[0].fact_type}' — "
-        f"the conversion layer is remapping the already-normalized fact_type"
-    )

hindsight-api-slim/tests/test_extensions.py

@@ -303,6 +303,7 @@ class TestOperationHooksParameters:
             contents=contents,
             document_id=document_id,
             fact_type_override="world",
+            confidence_score=0.9,
             request_context=ctx,
         )
 
@@ -316,6 +317,7 @@ class TestOperationHooksParameters:
         assert pre_ctx.contents[0]["content"] == contents[0]["content"]
         assert pre_ctx.document_id == document_id
         assert pre_ctx.fact_type_override == "world"
+        assert pre_ctx.confidence_score == 0.9
         assert pre_ctx.request_context == ctx
 
     @pytest.mark.asyncio
@@ -332,6 +334,7 @@ class TestOperationHooksParameters:
             contents=contents,
             document_id=document_id,
             fact_type_override="experience",
+            confidence_score=0.8,
             request_context=ctx,
         )
 
@@ -342,6 +345,7 @@ class TestOperationHooksParameters:
         assert post_result.bank_id == bank_id
         assert post_result.document_id == document_id
         assert post_result.fact_type_override == "experience"
+        assert post_result.confidence_score == 0.8
         assert post_result.request_context == ctx
 
         # Verify result data

hindsight-api-slim/tests/test_fact_extraction_agent_experience.py

@@ -1,131 +0,0 @@
-"""
-Test that first-person agent experiences are classified as 'experience' fact_type,
-not 'world'. This is critical for AI agent systems that store their own operational
-experiences (debugging, code changes, user interactions) separately from world knowledge.
-"""
-from datetime import datetime
-
-import pytest
-
-from hindsight_api import LLMConfig
-from hindsight_api.config import _get_raw_config
-from hindsight_api.engine.retain.fact_extraction import extract_facts_from_text
-
-
-class TestAgentExperienceClassification:
-    """Tests that first-person coding agent experiences get classified as 'experience'."""
-
-    @pytest.mark.asyncio
-    async def test_code_changes_classified_as_experience(self):
-        """First-person code change descriptions should be experience, not world."""
-        text = """
-I changed the return type of the `process_request` function from `dict` to `ResponseModel`.
-After that, I updated the three callers in `api/handlers.py` to destructure the new model fields.
-The type checker was happy after the change but I noticed one test was still using the old dict keys.
-"""
-        llm_config = LLMConfig.from_env()
-        facts, _, _ = await extract_facts_from_text(
-            text=text,
-            event_date=datetime(2025, 3, 28),
-            llm_config=llm_config,
-            agent_name="coding-agent",
-            context="agent work log",
-            config=_get_raw_config(),
-        )
-
-        assert len(facts) > 0, "Should extract at least one fact"
-        world_facts = [f for f in facts if f.fact_type == "world"]
-        experience_facts = [f for f in facts if f.fact_type == "experience"]
-        assert len(experience_facts) > len(world_facts), (
-            f"First-person code changes should be mostly 'experience', "
-            f"got {len(experience_facts)} experience vs {len(world_facts)} world. "
-            f"Facts: {[(f.fact, f.fact_type) for f in facts]}"
-        )
-
-    @pytest.mark.asyncio
-    async def test_debugging_session_classified_as_experience(self):
-        """First-person debugging narrative should be experience, not world."""
-        text = """
-The tests were failing with a ConnectionRefusedError on the Redis integration suite.
-I traced it to the connection pool not being initialized before the first test ran.
-I added a setup fixture that ensures the pool is warmed up, and all 47 tests pass now.
-"""
-        llm_config = LLMConfig.from_env()
-        facts, _, _ = await extract_facts_from_text(
-            text=text,
-            event_date=datetime(2025, 3, 28),
-            llm_config=llm_config,
-            agent_name="coding-agent",
-            context="agent work log",
-            config=_get_raw_config(),
-        )
-
-        assert len(facts) > 0, "Should extract at least one fact"
-        world_facts = [f for f in facts if f.fact_type == "world"]
-        experience_facts = [f for f in facts if f.fact_type == "experience"]
-        assert len(experience_facts) > len(world_facts), (
-            f"First-person debugging should be mostly 'experience', "
-            f"got {len(experience_facts)} experience vs {len(world_facts)} world. "
-            f"Facts: {[(f.fact, f.fact_type) for f in facts]}"
-        )
-
-    @pytest.mark.asyncio
-    async def test_user_interaction_classified_as_experience(self):
-        """Agent describing interactions with the user should be experience."""
-        text = """
-The user asked me to refactor the authentication middleware to support JWT tokens.
-I proposed splitting it into two modules: token_validation.py and session_management.py.
-The user approved my approach and I started with the token validation logic.
-I discovered that the existing tests were mocking the wrong interface, so I had to rewrite them first.
-"""
-        llm_config = LLMConfig.from_env()
-        facts, _, _ = await extract_facts_from_text(
-            text=text,
-            event_date=datetime(2025, 3, 28),
-            llm_config=llm_config,
-            agent_name="coding-agent",
-            context="agent work log",
-            config=_get_raw_config(),
-        )
-
-        assert len(facts) > 0, "Should extract at least one fact"
-        world_facts = [f for f in facts if f.fact_type == "world"]
-        experience_facts = [f for f in facts if f.fact_type == "experience"]
-        assert len(experience_facts) > len(world_facts), (
-            f"Agent-user interactions should be mostly 'experience', "
-            f"got {len(experience_facts)} experience vs {len(world_facts)} world. "
-            f"Facts: {[(f.fact, f.fact_type) for f in facts]}"
-        )
-
-    @pytest.mark.asyncio
-    async def test_mixed_agent_and_world_facts(self):
-        """Mix of agent experiences and world knowledge should be classified correctly."""
-        text = """
-Python 3.12 introduced a new type parameter syntax for generic classes.
-I migrated our codebase from the old TypeVar approach to the new syntax.
-The migration touched 23 files but was mostly mechanical.
-PEP 695 defines the new type statement that makes generics more readable.
-"""
-        llm_config = LLMConfig.from_env()
-        facts, _, _ = await extract_facts_from_text(
-            text=text,
-            event_date=datetime(2025, 3, 28),
-            llm_config=llm_config,
-            agent_name="coding-agent",
-            context="agent work log",
-            config=_get_raw_config(),
-        )
-
-        assert len(facts) > 0, "Should extract at least one fact"
-        world_facts = [f for f in facts if f.fact_type == "world"]
-        experience_facts = [f for f in facts if f.fact_type == "experience"]
-        # Should have both types - world facts about Python 3.12/PEP 695,
-        # experience facts about the migration work
-        assert len(world_facts) >= 1, (
-            f"Should have at least 1 world fact about Python 3.12/PEP 695. "
-            f"Facts: {[(f.fact, f.fact_type) for f in facts]}"
-        )
-        assert len(experience_facts) >= 1, (
-            f"Should have at least 1 experience fact about the migration. "
-            f"Facts: {[(f.fact, f.fact_type) for f in facts]}"
-        )

hindsight-api-slim/tests/test_fact_extraction_output_ratio.py

@@ -38,7 +38,7 @@ I ran into my neighbor Sarah who mentioned she's planning a trip to Italy next m
 """
 
         context = "Personal diary entry"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=text,
@@ -83,7 +83,7 @@ User: Perfect, I'll make a reservation for Saturday at 7pm.
 """
 
         context = "Restaurant recommendation conversation"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=text,
@@ -140,7 +140,7 @@ I edited about 20 photos from my recent trip to the mountains.
 """
 
         context = "Personal blog post"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=text,
@@ -205,7 +205,7 @@ I edited about 20 photos from my recent trip to the mountains.
         text = "\n".join([f"{turn['speaker']}: {turn['text']}" for turn in session])
 
         context = f"Conversation between {data['conversation']['speaker_a']} and {data['conversation']['speaker_b']}"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=text,
@@ -267,7 +267,7 @@ I'm planning to visit Japan next year.
 """
 
         context = "Personal info"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=text,

hindsight-api-slim/tests/test_fact_extraction_quality.py

@@ -42,7 +42,7 @@ Marcus felt anxious about the upcoming interview.
 """
 
         context = "Personal journal entry"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=text,
@@ -75,7 +75,7 @@ The music was so loud I could barely hear myself think.
 """
 
         context = "Personal experience"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=text,
@@ -109,7 +109,7 @@ Maybe we should reconsider the timeline.
 """
 
         context = "Team discussion"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=text,
@@ -143,7 +143,7 @@ I'm unable to attend the conference due to scheduling conflicts.
 """
 
         context = "Personal profile discussion"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=text,
@@ -176,7 +176,7 @@ Unlike last year, we're ahead of schedule.
 """
 
         context = "Project review"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=text,
@@ -210,7 +210,7 @@ She's enthusiastic about the opportunity.
 """
 
         context = "Team meeting"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=text,
@@ -244,7 +244,7 @@ I'm planning to switch careers because I'm not fulfilled in my current role.
 """
 
         context = "Personal goals discussion"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=text,
@@ -282,7 +282,7 @@ Family is the most important thing to her.
 """
 
         context = "Personal values discussion"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=text,
@@ -315,7 +315,7 @@ I prefer presenting in person rather than virtually because I can read the room
 """
 
         context = "Personal reflection"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         event_date = datetime(2024, 11, 13)
 
@@ -373,7 +373,7 @@ I'm planning to visit Tokyo next month.
 """
 
         context = "Personal conversation"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         event_date = datetime(2024, 11, 13)
 
@@ -421,7 +421,7 @@ with a concert surrounded by music, joy and the warm summer breeze.
 """
 
         context = "Conversation between Melanie and Caroline"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
         event_date = datetime(2023, 8, 14, 14, 24)
 
         last_error = None
@@ -496,7 +496,7 @@ It was a beautiful day and I plan to make this a regular habit.
 """
 
         context = "Personal diary"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         event_date = datetime(2024, 11, 13)
 
@@ -547,7 +547,7 @@ It was a beautiful day and I plan to make this a regular habit.
         """Test that relative dates are converted to absolute dates."""
 
         reference_date = datetime(2024, 3, 20, 14, 0, 0, tzinfo=UTC)
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         text = """
         Yesterday I went hiking in Yosemite.
@@ -582,7 +582,7 @@ It was a beautiful day and I plan to make this a regular habit.
         """Test that facts without temporal info are still extracted."""
 
         reference_date = datetime(2024, 3, 20, 14, 0, 0, tzinfo=UTC)
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         text = "Alice works at Google. She loves Python programming."
 
@@ -607,7 +607,7 @@ It was a beautiful day and I plan to make this a regular habit.
         """Test that absolute dates in text are preserved."""
 
         reference_date = datetime(2024, 3, 20, 14, 0, 0, tzinfo=UTC)
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         text = """
         On March 15, 2024, Alice joined Google.
@@ -662,7 +662,7 @@ great time! Every time I see it, I can't help but smile.
 """
 
         context = "Conversation between Deborah and Jolene"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         event_date = datetime(2023, 2, 23)
 
@@ -715,7 +715,7 @@ I've learned so much from it.
 """
 
         context = "Personal update"
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=text,
@@ -785,7 +785,7 @@ Jamie: Congratulations! I'd love to read it.
 
         context = "Podcast episode between you (Marcus) and Jamie discussing AI research"
 
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=transcript,
@@ -831,7 +831,7 @@ We presented our findings to the team yesterday.
 
         context = "Personal work log"
 
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=text,
@@ -867,7 +867,7 @@ Jamie: [teasing] We'll see who's right, my Niners pick is solid.
         context = "podcast episode on match prediction of week 10 - Marcus (you) and Jamie - 14 nov"
         agent_name = "Marcus"
 
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         facts, _, _ = await extract_facts_from_text(
             text=transcript,
@@ -929,7 +929,7 @@ so the algorithm learns to box out. See you next week!
 
         context = "Podcast episode between you (Marcus) and Jamie about AI"
 
-        llm_config = LLMConfig.from_env()
+        llm_config = LLMConfig.for_memory()
 
         max_retries = 3
         last_error = None

hindsight-api-slim/tests/test_fact_extraction_retry.py

@@ -139,76 +139,3 @@ async def test_retain_llm_max_retries_overrides_global():
     assert facts == []
     # Verify it retried exactly retain_llm_max_retries times
     assert llm_config.call.call_count == 5
-
-
-@pytest.mark.asyncio
-async def test_none_event_date_with_empty_facts_no_crash():
-    """
-    When event_date is None and the LLM returns an empty facts list,
-    the debug log should not crash with AttributeError on .isoformat().
-
-    Regression test for https://github.com/vectorize-io/hindsight/issues/874
-    """
-    from hindsight_api.engine.retain.fact_extraction import _extract_facts_from_chunk
-
-    config = _make_config(llm_max_retries=1)
-
-    # LLM returns a valid dict but with no facts — triggers the debug log path
-    llm_config = _make_llm_config(mock_response={"facts": []})
-
-    with patch(
-        "hindsight_api.engine.retain.fact_extraction._build_extraction_prompt_and_schema",
-        return_value=("system prompt", MagicMock()),
-    ):
-        facts, usage = await _extract_facts_from_chunk(
-            chunk="A plain text document with no timestamp.",
-            chunk_index=0,
-            total_chunks=1,
-            event_date=None,
-            context="",
-            llm_config=llm_config,
-            config=config,
-            agent_name="test-agent",
-        )
-
-    assert facts == []
-
-
-@pytest.mark.asyncio
-async def test_none_event_date_with_valid_facts_no_crash():
-    """
-    When event_date is None but the LLM returns valid facts,
-    extraction should succeed without errors.
-    """
-    from hindsight_api.engine.retain.fact_extraction import _extract_facts_from_chunk
-
-    config = _make_config(llm_max_retries=1)
-
-    llm_config = _make_llm_config(mock_response={
-        "facts": [
-            {
-                "what": "Alice visited Paris",
-                "when": "2023",
-                "who": "Alice",
-                "why": "vacation",
-            }
-        ]
-    })
-
-    with patch(
-        "hindsight_api.engine.retain.fact_extraction._build_extraction_prompt_and_schema",
-        return_value=("system prompt", MagicMock()),
-    ):
-        facts, usage = await _extract_facts_from_chunk(
-            chunk="Alice visited Paris in 2023.",
-            chunk_index=0,
-            total_chunks=1,
-            event_date=None,
-            context="",
-            llm_config=llm_config,
-            config=config,
-            agent_name="test-agent",
-        )
-
-    assert len(facts) == 1
-    assert "Alice visited Paris" in facts[0].fact

hindsight-api-slim/tests/test_gemini_embeddings.py

@@ -1,336 +0,0 @@
-"""
-Tests for Google embeddings implementation (Gemini API + Vertex AI).
-
-These tests cover:
-1. Initialization (Gemini API key, Vertex AI with ADC/service account)
-2. Dimension detection via test embedding
-3. Output dimensionality configuration
-4. Encode (single text, multiple texts, batching, empty list, uninitialized)
-5. Provider name and model name normalization
-6. Factory function (create from env, validation errors)
-"""
-
-from typing import Any
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from hindsight_api.config import (
-    ENV_EMBEDDINGS_GEMINI_API_KEY,
-    ENV_EMBEDDINGS_PROVIDER,
-    HindsightConfig,
-)
-from hindsight_api.engine.embeddings import GeminiEmbeddings, create_embeddings_from_env
-
-
-def _make_mock_embedding(values: list[float]) -> MagicMock:
-    emb = MagicMock()
-    emb.values = values
-    return emb
-
-
-def _make_mock_embed_result(embeddings_data: list[list[float]]) -> MagicMock:
-    result = MagicMock()
-    result.embeddings = [_make_mock_embedding(v) for v in embeddings_data]
-    return result
-
-
-def _make_mock_genai(embed_result: Any = None) -> MagicMock:
-    if embed_result is None:
-        embed_result = _make_mock_embed_result([[0.1] * 768])
-    mock_genai = MagicMock()
-    mock_client = MagicMock()
-    mock_client.models.embed_content = MagicMock(return_value=embed_result)
-    mock_genai.Client = MagicMock(return_value=mock_client)
-    return mock_genai
-
-
-def _make_mock_google_module(mock_genai: MagicMock) -> MagicMock:
-    mod = MagicMock()
-    mod.genai = mock_genai
-    mod.genai.types.EmbedContentConfig = MagicMock(side_effect=lambda **kw: MagicMock(**kw))
-    return mod
-
-
-def _patch_google_import(mock_genai: MagicMock):
-    original_import = __import__
-
-    def mock_import(name, *args, **kwargs):
-        if name == "google":
-            return _make_mock_google_module(mock_genai)
-        if name == "google.genai":
-            return mock_genai
-        return original_import(name, *args, **kwargs)
-
-    return patch("builtins.__import__", side_effect=mock_import)
-
-
-class TestGeminiEmbeddings:
-    """Unit tests for GeminiEmbeddings with mocked google.genai."""
-
-    async def test_initialization_api_key_success(self):
-        """Test successful Gemini API key initialization."""
-        mock_genai = _make_mock_genai()
-        emb = GeminiEmbeddings(model="gemini-embedding-001", api_key="test-key")
-
-        with _patch_google_import(mock_genai):
-            await emb.initialize()
-
-        assert emb._client is not None
-        assert emb.dimension == 768
-        assert emb.provider_name == "google"
-        assert emb._is_vertexai is False
-        mock_genai.Client.return_value.models.embed_content.assert_called_once()
-
-    async def test_initialization_vertexai_success(self):
-        """Test successful Vertex AI initialization."""
-        mock_genai = _make_mock_genai()
-        emb = GeminiEmbeddings(
-            model="gemini-embedding-001",
-            vertexai_project_id="test-project",
-            vertexai_region="us-central1",
-        )
-
-        with _patch_google_import(mock_genai):
-            await emb.initialize()
-
-        assert emb._client is not None
-        assert emb.dimension == 768
-        assert emb.provider_name == "google"
-        assert emb._is_vertexai is True
-        mock_genai.Client.assert_called_once_with(
-            vertexai=True,
-            project="test-project",
-            location="us-central1",
-        )
-
-    async def test_initialization_missing_api_key(self):
-        """Test that missing API key raises ValueError when no vertexai_project_id."""
-        mock_genai = _make_mock_genai()
-        emb = GeminiEmbeddings(model="gemini-embedding-001", api_key=None)
-
-        with _patch_google_import(mock_genai):
-            with pytest.raises(ValueError, match="requires an API key"):
-                await emb.initialize()
-
-    async def test_initialization_vertexai_missing_project_id(self):
-        """Test that Vertex AI mode requires project_id."""
-        mock_genai = _make_mock_genai()
-        emb = GeminiEmbeddings(model="gemini-embedding-001", vertexai_project_id="temp")
-        emb.vertexai_project_id = None  # Simulate misconfiguration
-
-        with _patch_google_import(mock_genai):
-            with pytest.raises(ValueError, match="is required for Vertex AI"):
-                await emb.initialize()
-
-    async def test_initialization_idempotent(self):
-        """Test that calling initialize() twice is a no-op."""
-        mock_genai = _make_mock_genai()
-        emb = GeminiEmbeddings(model="gemini-embedding-001", api_key="test-key")
-
-        with _patch_google_import(mock_genai):
-            await emb.initialize()
-            first_client = emb._client
-            await emb.initialize()
-            assert emb._client is first_client
-
-    async def test_dimension_detection_via_test_embedding(self):
-        """Test that dimension is detected via a test embedding call."""
-        test_embed = _make_mock_embed_result([[0.5] * 256])
-        mock_genai = _make_mock_genai(embed_result=test_embed)
-        emb = GeminiEmbeddings(model="some-new-model", api_key="test-key")
-
-        with _patch_google_import(mock_genai):
-            await emb.initialize()
-
-        assert emb.dimension == 256
-
-    async def test_output_dimensionality(self):
-        """Test that output_dimensionality is passed via EmbedContentConfig."""
-        test_embed = _make_mock_embed_result([[0.1] * 256])
-        mock_genai = _make_mock_genai(embed_result=test_embed)
-        emb = GeminiEmbeddings(model="gemini-embedding-001", api_key="test-key", output_dimensionality=256)
-
-        with _patch_google_import(mock_genai):
-            await emb.initialize()
-
-        assert emb.dimension == 256
-        assert emb._embed_config is not None
-        call_kwargs = mock_genai.Client.return_value.models.embed_content.call_args
-        assert "config" in call_kwargs.kwargs
-
-    async def test_no_output_dimensionality(self):
-        """Test that no EmbedContentConfig is built when output_dimensionality is None."""
-        mock_genai = _make_mock_genai()
-        emb = GeminiEmbeddings(model="gemini-embedding-001", api_key="test-key", output_dimensionality=None)
-
-        with _patch_google_import(mock_genai):
-            await emb.initialize()
-
-        assert emb._embed_config is None
-        call_kwargs = mock_genai.Client.return_value.models.embed_content.call_args
-        assert "config" not in call_kwargs.kwargs
-
-    def test_auto_detect_vertexai(self):
-        """Test that _is_vertexai is auto-detected from vertexai_project_id."""
-        assert GeminiEmbeddings(model="m", api_key="k")._is_vertexai is False
-        assert GeminiEmbeddings(model="m", vertexai_project_id="p")._is_vertexai is True
-
-    def test_encode_single_text(self):
-        emb = GeminiEmbeddings(model="gemini-embedding-001", api_key="test-key")
-        mock_client = MagicMock()
-        mock_client.models.embed_content = MagicMock(return_value=_make_mock_embed_result([[0.1, 0.2, 0.3]]))
-        emb._client = mock_client
-        emb._dimension = 3
-
-        assert emb.encode(["hello"]) == [[0.1, 0.2, 0.3]]
-
-    def test_encode_multiple_texts(self):
-        emb = GeminiEmbeddings(model="gemini-embedding-001", api_key="test-key")
-        mock_client = MagicMock()
-        mock_client.models.embed_content = MagicMock(
-            return_value=_make_mock_embed_result([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]])
-        )
-        emb._client = mock_client
-        emb._dimension = 2
-
-        result = emb.encode(["a", "b", "c"])
-        assert len(result) == 3
-        assert result[1] == [0.3, 0.4]
-
-    def test_encode_batching(self):
-        emb = GeminiEmbeddings(model="gemini-embedding-001", api_key="test-key", batch_size=2)
-        mock_client = MagicMock()
-        mock_client.models.embed_content = MagicMock(
-            side_effect=[_make_mock_embed_result([[0.1], [0.2]]), _make_mock_embed_result([[0.3]])]
-        )
-        emb._client = mock_client
-        emb._dimension = 1
-
-        assert emb.encode(["a", "b", "c"]) == [[0.1], [0.2], [0.3]]
-        assert mock_client.models.embed_content.call_count == 2
-
-    def test_encode_passes_config(self):
-        emb = GeminiEmbeddings(model="gemini-embedding-001", api_key="test-key")
-        mock_client = MagicMock()
-        mock_client.models.embed_content = MagicMock(return_value=_make_mock_embed_result([[0.1, 0.2]]))
-        emb._client = mock_client
-        emb._dimension = 2
-        emb._embed_config = MagicMock()
-
-        emb.encode(["hello"])
-        assert mock_client.models.embed_content.call_args.kwargs["config"] is emb._embed_config
-
-    def test_encode_empty_list(self):
-        emb = GeminiEmbeddings(model="gemini-embedding-001", api_key="test-key")
-        emb._client = MagicMock()
-        emb._dimension = 768
-        assert emb.encode([]) == []
-
-    def test_encode_before_initialization(self):
-        emb = GeminiEmbeddings(model="gemini-embedding-001", api_key="test-key")
-        with pytest.raises(RuntimeError, match="not initialized"):
-            emb.encode(["test"])
-
-    def test_dimension_before_initialization(self):
-        emb = GeminiEmbeddings(model="gemini-embedding-001", api_key="test-key")
-        with pytest.raises(RuntimeError, match="not initialized"):
-            _ = emb.dimension
-
-    def test_provider_name_always_google(self):
-        assert GeminiEmbeddings(model="m", api_key="k").provider_name == "google"
-        assert GeminiEmbeddings(model="m", vertexai_project_id="p").provider_name == "google"
-
-    def test_vertexai_strips_google_prefix(self):
-        mock_genai = _make_mock_genai()
-        emb = GeminiEmbeddings(model="google/gemini-embedding-001", vertexai_project_id="test-project")
-        emb._init_vertexai(mock_genai)
-        assert emb.model == "gemini-embedding-001"
-
-    def test_default_region(self):
-        emb = GeminiEmbeddings(model="m", vertexai_project_id="proj")
-        assert emb.vertexai_region == "us-central1"
-
-    def test_custom_region(self):
-        emb = GeminiEmbeddings(model="m", vertexai_project_id="proj", vertexai_region="europe-west1")
-        assert emb.vertexai_region == "europe-west1"
-
-
-class TestGeminiEmbeddingsFactory:
-    """Tests for create_embeddings_from_env() with 'google' provider."""
-
-    def _make_config(self, **overrides) -> HindsightConfig:
-        from dataclasses import fields
-
-        defaults = {}
-        for f in fields(HindsightConfig):
-            if f.type == "str":
-                defaults[f.name] = ""
-            elif f.type == "str | None":
-                defaults[f.name] = None
-            elif f.type == "int":
-                defaults[f.name] = 0
-            elif f.type == "int | None":
-                defaults[f.name] = None
-            elif f.type == "float":
-                defaults[f.name] = 0.0
-            elif f.type == "float | None":
-                defaults[f.name] = None
-            elif f.type == "bool":
-                defaults[f.name] = False
-            elif f.type == "list | None":
-                defaults[f.name] = None
-            else:
-                defaults[f.name] = None
-
-        defaults["embeddings_provider"] = "google"
-        defaults["embeddings_gemini_api_key"] = "test-key"
-        defaults["embeddings_gemini_model"] = "gemini-embedding-001"
-        defaults["embeddings_gemini_output_dimensionality"] = 768
-        defaults["embeddings_vertexai_project_id"] = None
-        defaults["embeddings_vertexai_region"] = None
-        defaults["embeddings_vertexai_service_account_key"] = None
-
-        defaults.update(overrides)
-        return HindsightConfig(**defaults)
-
-    def test_create_with_api_key(self):
-        config = self._make_config()
-        with patch("hindsight_api.config.get_config", return_value=config):
-            emb = create_embeddings_from_env()
-        assert isinstance(emb, GeminiEmbeddings)
-        assert emb.provider_name == "google"
-        assert emb.api_key == "test-key"
-        assert emb._is_vertexai is False
-
-    def test_create_with_vertexai(self):
-        config = self._make_config(
-            embeddings_gemini_api_key=None,
-            embeddings_vertexai_project_id="my-project",
-            embeddings_vertexai_region="us-east1",
-        )
-        with patch("hindsight_api.config.get_config", return_value=config):
-            emb = create_embeddings_from_env()
-        assert isinstance(emb, GeminiEmbeddings)
-        assert emb._is_vertexai is True
-        assert emb.api_key is None
-        assert emb.vertexai_project_id == "my-project"
-
-    def test_create_missing_all_credentials(self):
-        config = self._make_config(embeddings_gemini_api_key=None, embeddings_vertexai_project_id=None)
-        with patch("hindsight_api.config.get_config", return_value=config):
-            with pytest.raises(ValueError, match="is required"):
-                create_embeddings_from_env()
-
-    def test_vertexai_takes_priority(self):
-        config = self._make_config(embeddings_gemini_api_key="key", embeddings_vertexai_project_id="proj")
-        with patch("hindsight_api.config.get_config", return_value=config):
-            emb = create_embeddings_from_env()
-        assert emb._is_vertexai is True
-        assert emb.api_key is None
-
-    def test_create_with_custom_dimensionality(self):
-        config = self._make_config(embeddings_gemini_output_dimensionality=256)
-        with patch("hindsight_api.config.get_config", return_value=config):
-            emb = create_embeddings_from_env()
-        assert emb.output_dimensionality == 256

hindsight-api-slim/tests/test_google_cross_encoder.py

@@ -1,275 +0,0 @@
-"""
-Tests for Google Discovery Engine cross-encoder (Ranking REST API).
-
-These tests cover:
-1. Initialization (service account, ADC, missing project_id)
-2. Predict (single query, multiple queries, batching, empty pairs, uninitialized)
-3. Provider name
-4. Factory function (create from env, validation errors)
-"""
-
-from unittest.mock import MagicMock, patch
-
-import httpx
-import pytest
-
-from hindsight_api.config import (
-    ENV_RERANKER_GOOGLE_PROJECT_ID,
-    ENV_RERANKER_PROVIDER,
-    HindsightConfig,
-)
-from hindsight_api.engine.cross_encoder import GoogleCrossEncoder, create_cross_encoder_from_env
-
-
-def _make_rank_response(records: list[tuple[str, float]]) -> dict:
-    """Build a JSON response matching the Discovery Engine REST API format."""
-    return {"records": [{"id": rid, "score": score} for rid, score in records]}
-
-
-def _make_mock_httpx_client(responses: list[dict] | None = None) -> MagicMock:
-    """Create a mock httpx.Client that returns predefined responses."""
-    mock_client = MagicMock(spec=httpx.Client)
-    if responses:
-        side_effects = []
-        for resp_json in responses:
-            mock_resp = MagicMock(spec=httpx.Response)
-            mock_resp.json.return_value = resp_json
-            mock_resp.raise_for_status.return_value = None
-            side_effects.append(mock_resp)
-        mock_client.post.side_effect = side_effects
-    return mock_client
-
-
-def _make_mock_credentials() -> MagicMock:
-    """Create mock credentials with a valid token."""
-    creds = MagicMock()
-    creds.valid = True
-    creds.token = "mock-token"
-    return creds
-
-
-class TestGoogleCrossEncoder:
-    """Unit tests for GoogleCrossEncoder with mocked httpx + google-auth."""
-
-    async def test_initialization_adc_success(self):
-        """Test successful initialization with ADC (no service account key)."""
-        mock_creds = _make_mock_credentials()
-
-        encoder = GoogleCrossEncoder(project_id="test-project")
-
-        with patch("google.auth.default", return_value=(mock_creds, "test-project")):
-            await encoder.initialize()
-
-        assert encoder._client is not None
-        assert encoder._credentials is mock_creds
-        assert encoder.provider_name == "google"
-        assert "test-project" in encoder._rank_url
-
-    async def test_initialization_service_account(self):
-        """Test initialization with service account key."""
-        mock_creds = _make_mock_credentials()
-
-        encoder = GoogleCrossEncoder(
-            project_id="test-project",
-            service_account_key="/path/to/key.json",
-        )
-
-        with patch(
-            "google.oauth2.service_account.Credentials.from_service_account_file",
-            return_value=mock_creds,
-        ):
-            await encoder.initialize()
-
-        assert encoder._client is not None
-        assert encoder._credentials is mock_creds
-
-    async def test_initialization_idempotent(self):
-        """Test that calling initialize() twice is a no-op."""
-        mock_creds = _make_mock_credentials()
-        encoder = GoogleCrossEncoder(project_id="test-project")
-
-        with patch("google.auth.default", return_value=(mock_creds, "test-project")):
-            await encoder.initialize()
-            first_client = encoder._client
-            await encoder.initialize()
-            assert encoder._client is first_client
-
-    async def test_predict_single_query(self):
-        """Test prediction with a single query and multiple documents."""
-        mock_creds = _make_mock_credentials()
-        mock_client = _make_mock_httpx_client([
-            _make_rank_response([("1", 0.95), ("0", 0.30)]),
-        ])
-
-        encoder = GoogleCrossEncoder(project_id="test-project")
-        with patch("google.auth.default", return_value=(mock_creds, "p")):
-            await encoder.initialize()
-        encoder._client = mock_client
-
-        scores = await encoder.predict([
-            ("What is AI?", "AI is artificial intelligence"),
-            ("What is AI?", "The sky is blue"),
-        ])
-
-        assert len(scores) == 2
-        assert scores[0] == 0.30  # id="0" -> index 0
-        assert scores[1] == 0.95  # id="1" -> index 1
-        mock_client.post.assert_called_once()
-
-    async def test_predict_multiple_queries(self):
-        """Test prediction with multiple distinct queries."""
-        mock_creds = _make_mock_credentials()
-        mock_client = _make_mock_httpx_client([
-            _make_rank_response([("0", 0.9), ("1", 0.1)]),
-            _make_rank_response([("0", 0.8)]),
-        ])
-
-        encoder = GoogleCrossEncoder(project_id="test-project")
-        with patch("google.auth.default", return_value=(mock_creds, "p")):
-            await encoder.initialize()
-        encoder._client = mock_client
-
-        scores = await encoder.predict([
-            ("Query A", "Doc A1"),
-            ("Query A", "Doc A2"),
-            ("Query B", "Doc B1"),
-        ])
-
-        assert len(scores) == 3
-        assert scores[0] == 0.9
-        assert scores[1] == 0.1
-        assert scores[2] == 0.8
-        assert mock_client.post.call_count == 2
-
-    async def test_predict_empty_pairs(self):
-        """Test that empty pairs returns empty list."""
-        mock_creds = _make_mock_credentials()
-        encoder = GoogleCrossEncoder(project_id="test-project")
-
-        with patch("google.auth.default", return_value=(mock_creds, "p")):
-            await encoder.initialize()
-
-        scores = await encoder.predict([])
-        assert scores == []
-
-    async def test_predict_not_initialized(self):
-        """Test that predict raises if not initialized."""
-        encoder = GoogleCrossEncoder(project_id="test-project")
-        with pytest.raises(RuntimeError, match="not initialized"):
-            await encoder.predict([("q", "d")])
-
-    async def test_predict_batching(self):
-        """Test that >200 records are split into batches."""
-        mock_creds = _make_mock_credentials()
-        mock_client = _make_mock_httpx_client([
-            _make_rank_response([(str(i), 0.5) for i in range(200)]),
-            _make_rank_response([(str(i), 0.3) for i in range(50)]),
-        ])
-
-        encoder = GoogleCrossEncoder(project_id="test-project")
-        with patch("google.auth.default", return_value=(mock_creds, "p")):
-            await encoder.initialize()
-        encoder._client = mock_client
-
-        pairs = [("same query", f"doc {i}") for i in range(250)]
-        scores = await encoder.predict(pairs)
-
-        assert len(scores) == 250
-        assert mock_client.post.call_count == 2
-
-    async def test_auth_header_sent(self):
-        """Test that Authorization header is sent with requests."""
-        mock_creds = _make_mock_credentials()
-        mock_creds.token = "test-bearer-token"
-        mock_client = _make_mock_httpx_client([
-            _make_rank_response([("0", 0.9)]),
-        ])
-
-        encoder = GoogleCrossEncoder(project_id="test-project")
-        with patch("google.auth.default", return_value=(mock_creds, "p")):
-            await encoder.initialize()
-        encoder._client = mock_client
-
-        await encoder.predict([("q", "d")])
-
-        call_kwargs = mock_client.post.call_args
-        assert call_kwargs.kwargs["headers"]["Authorization"] == "Bearer test-bearer-token"
-
-    def test_provider_name(self):
-        assert GoogleCrossEncoder(project_id="p").provider_name == "google"
-
-    def test_default_model(self):
-        encoder = GoogleCrossEncoder(project_id="p")
-        assert encoder.model == "semantic-ranker-default-004"
-
-    def test_custom_model(self):
-        encoder = GoogleCrossEncoder(project_id="p", model="semantic-ranker-fast-004")
-        assert encoder.model == "semantic-ranker-fast-004"
-
-    def test_default_location(self):
-        encoder = GoogleCrossEncoder(project_id="p")
-        assert encoder.location == "global"
-
-
-class TestGoogleCrossEncoderFactory:
-    """Tests for create_cross_encoder_from_env() with 'google' provider."""
-
-    def _make_config(self, **overrides) -> HindsightConfig:
-        from dataclasses import fields
-
-        defaults = {}
-        for f in fields(HindsightConfig):
-            if f.type == "str":
-                defaults[f.name] = ""
-            elif f.type == "str | None":
-                defaults[f.name] = None
-            elif f.type == "int":
-                defaults[f.name] = 0
-            elif f.type == "int | None":
-                defaults[f.name] = None
-            elif f.type == "float":
-                defaults[f.name] = 0.0
-            elif f.type == "float | None":
-                defaults[f.name] = None
-            elif f.type == "bool":
-                defaults[f.name] = False
-            elif f.type == "list | None":
-                defaults[f.name] = None
-            else:
-                defaults[f.name] = None
-
-        defaults["reranker_provider"] = "google"
-        defaults["reranker_google_model"] = "semantic-ranker-default-004"
-        defaults["reranker_google_project_id"] = "test-project"
-        defaults["reranker_google_service_account_key"] = None
-
-        defaults.update(overrides)
-        return HindsightConfig(**defaults)
-
-    def test_create_with_project_id(self):
-        config = self._make_config()
-        with patch("hindsight_api.config.get_config", return_value=config):
-            encoder = create_cross_encoder_from_env()
-        assert isinstance(encoder, GoogleCrossEncoder)
-        assert encoder.provider_name == "google"
-        assert encoder.project_id == "test-project"
-        assert encoder.service_account_key is None
-
-    def test_create_with_service_account(self):
-        config = self._make_config(reranker_google_service_account_key="/path/to/key.json")
-        with patch("hindsight_api.config.get_config", return_value=config):
-            encoder = create_cross_encoder_from_env()
-        assert isinstance(encoder, GoogleCrossEncoder)
-        assert encoder.service_account_key == "/path/to/key.json"
-
-    def test_create_missing_project_id(self):
-        config = self._make_config(reranker_google_project_id=None)
-        with patch("hindsight_api.config.get_config", return_value=config):
-            with pytest.raises(ValueError, match="is required"):
-                create_cross_encoder_from_env()
-
-    def test_create_with_custom_model(self):
-        config = self._make_config(reranker_google_model="semantic-ranker-fast-004")
-        with patch("hindsight_api.config.get_config", return_value=config):
-            encoder = create_cross_encoder_from_env()
-        assert encoder.model == "semantic-ranker-fast-004"

hindsight-api-slim/tests/test_hierarchical_config.py

@@ -88,17 +88,8 @@ async def test_hierarchical_fields_categorization():
     assert "entities_allow_free_form" in configurable
     assert "entity_labels" in configurable
 
-    # Verify other configurable fields
-    assert "retain_default_strategy" in configurable
-    assert "retain_strategies" in configurable
-    assert "max_observations_per_scope" in configurable
-    assert "reflect_source_facts_max_tokens" in configurable
-    assert "llm_gemini_safety_settings" in configurable
-    assert "mcp_enabled_tools" in configurable
-    assert "retain_chunk_batch_size" in configurable
-
     # Verify count is correct
-    assert len(configurable) == 22
+    assert len(configurable) == 20
 
     # Verify credential fields (NEVER exposed)
     assert "llm_api_key" in credentials

hindsight-api-slim/tests/test_hnsw_indexes.py

@@ -1,10 +1,10 @@
 """
-Tests for per-bank vector index lifecycle and UNION ALL retrieval.
+Tests for per-bank HNSW index lifecycle and UNION ALL retrieval.
 
 Covers:
-- _bank_index_name deterministic naming
-- Per-bank vector indexes created on bank creation (retain_async / ensure_bank_exists)
-- Per-bank vector indexes dropped on bank deletion
+- _hnsw_index_name deterministic naming
+- Per-bank HNSW indexes created on bank creation (retain_async / ensure_bank_exists)
+- Per-bank HNSW indexes dropped on bank deletion
 - retrieve_semantic_bm25_combined groups results correctly by fact_type and source
 """
 import uuid
@@ -12,7 +12,7 @@ from datetime import datetime, timezone
 
 import pytest
 
-from hindsight_api.engine.retain.bank_utils import _BANK_INDEX_FACT_TYPES, _bank_index_name
+from hindsight_api.engine.retain.bank_utils import _HNSW_FACT_TYPES, _hnsw_index_name
 
 
 # ---------------------------------------------------------------------------
@@ -20,36 +20,36 @@ from hindsight_api.engine.retain.bank_utils import _BANK_INDEX_FACT_TYPES, _bank
 # ---------------------------------------------------------------------------
 
 
-class TestBankIndexName:
+class TestHnswIndexName:
     def test_deterministic(self):
         uid = "550e8400-e29b-41d4-a716-446655440000"
-        assert _bank_index_name("world", uid) == _bank_index_name("world", uid)
+        assert _hnsw_index_name("world", uid) == _hnsw_index_name("world", uid)
 
     def test_strips_dashes(self):
         uid = "550e8400-e29b-41d4-a716-446655440000"
-        name = _bank_index_name("world", uid)
+        name = _hnsw_index_name("world", uid)
         # uid16 should be hex chars only
         assert "-" not in name
 
     def test_uses_first_16_hex_chars(self):
         uid = "550e8400-e29b-41d4-a716-446655440000"
         uid16 = uid.replace("-", "")[:16]  # "550e8400e29b41d4"
-        assert name_ends_with(name=_bank_index_name("world", uid), suffix=uid16)
+        assert name_ends_with(name=_hnsw_index_name("world", uid), suffix=uid16)
 
     def test_suffix_per_fact_type(self):
         uid = "550e8400-e29b-41d4-a716-446655440000"
-        names = {ft: _bank_index_name(ft, uid) for ft in _BANK_INDEX_FACT_TYPES}
+        names = {ft: _hnsw_index_name(ft, uid) for ft in _HNSW_FACT_TYPES}
         # All three names must be distinct
         assert len(set(names.values())) == 3
 
     def test_all_fact_types_covered(self):
-        assert set(_BANK_INDEX_FACT_TYPES) == {"world", "experience", "observation"}
+        assert set(_HNSW_FACT_TYPES) == {"world", "experience", "observation"}
 
     def test_fits_pg_identifier_limit(self):
         # PostgreSQL max identifier length is 63 chars
         uid = "f" * 32  # simulated UUID without dashes
-        for ft in _BANK_INDEX_FACT_TYPES:
-            assert len(_bank_index_name(ft, uid)) <= 63
+        for ft in _HNSW_FACT_TYPES:
+            assert len(_hnsw_index_name(ft, uid)) <= 63
 
 
 def name_ends_with(name: str, suffix: str) -> bool:
@@ -61,7 +61,7 @@ def name_ends_with(name: str, suffix: str) -> bool:
 # ---------------------------------------------------------------------------
 
 
-async def _get_bank_vector_indexes(pool, bank_id: str) -> list[str]:
+async def _get_bank_hnsw_indexes(pool, bank_id: str) -> list[str]:
     """Return index names for memory_units that match the per-bank pattern."""
     async with pool.acquire() as conn:
         rows = await conn.fetch(
@@ -79,8 +79,8 @@ async def _get_bank_vector_indexes(pool, bank_id: str) -> list[str]:
 
 
 @pytest.mark.asyncio
-async def test_retain_creates_per_bank_vector_indexes(memory, request_context):
-    """retain_async on a new bank must create 3 per-(bank, fact_type) vector indexes."""
+async def test_retain_creates_per_bank_hnsw_indexes(memory, request_context):
+    """retain_async on a new bank must create 3 per-(bank, fact_type) HNSW indexes."""
     bank_id = f"test_hnsw_create_{uuid.uuid4().hex[:8]}"
     try:
         await memory.retain_async(
@@ -88,9 +88,9 @@ async def test_retain_creates_per_bank_vector_indexes(memory, request_context):
             content="Alice is a software engineer.",
             request_context=request_context,
         )
-        indexes = await _get_bank_vector_indexes(memory._pool, bank_id)
-        assert len(indexes) == 3, f"Expected 3 per-bank vector indexes, got: {indexes}"
-        for ft_short in _BANK_INDEX_FACT_TYPES.values():
+        indexes = await _get_bank_hnsw_indexes(memory._pool, bank_id)
+        assert len(indexes) == 3, f"Expected 3 per-bank HNSW indexes, got: {indexes}"
+        for ft_short in _HNSW_FACT_TYPES.values():
             assert any(ft_short in idx for idx in indexes), (
                 f"Missing index for fact_type short '{ft_short}' in {indexes}"
             )
@@ -99,8 +99,8 @@ async def test_retain_creates_per_bank_vector_indexes(memory, request_context):
 
 
 @pytest.mark.asyncio
-async def test_delete_bank_drops_vector_indexes(memory, request_context):
-    """delete_bank must drop all per-bank vector indexes."""
+async def test_delete_bank_drops_hnsw_indexes(memory, request_context):
+    """delete_bank must drop all per-bank HNSW indexes."""
     bank_id = f"test_hnsw_drop_{uuid.uuid4().hex[:8]}"
 
     await memory.retain_async(
@@ -109,12 +109,12 @@ async def test_delete_bank_drops_vector_indexes(memory, request_context):
         request_context=request_context,
     )
     # Verify indexes exist before deletion
-    indexes_before = await _get_bank_vector_indexes(memory._pool, bank_id)
+    indexes_before = await _get_bank_hnsw_indexes(memory._pool, bank_id)
     assert len(indexes_before) == 3
 
     await memory.delete_bank(bank_id, request_context=request_context)
 
-    indexes_after = await _get_bank_vector_indexes(memory._pool, bank_id)
+    indexes_after = await _get_bank_hnsw_indexes(memory._pool, bank_id)
     assert indexes_after == [], f"Indexes should be dropped after bank deletion, got: {indexes_after}"
 
 
@@ -133,7 +133,7 @@ async def test_retain_idempotent_bank_creation(memory, request_context):
             content="Carol joined the company in 2022.",
             request_context=request_context,
         )
-        indexes = await _get_bank_vector_indexes(memory._pool, bank_id)
+        indexes = await _get_bank_hnsw_indexes(memory._pool, bank_id)
         assert len(indexes) == 3
     finally:
         await memory.delete_bank(bank_id, request_context=request_context)

hindsight-api-slim/tests/test_horse_observations.py

@@ -1,353 +0,0 @@
-"""Test observation tracking for a sequence of horse-related memories.
-
-This test retains a series of facts about horses on a farm and inspects
-how observations track the evolving state over time, with full prompt debugging.
-"""
-
-import json
-import uuid
-from dataclasses import dataclass, field
-from typing import Any
-
-import pytest
-
-from hindsight_api.config import _get_raw_config
-from hindsight_api.engine.consolidation import consolidator as consolidator_mod
-from hindsight_api.engine.memory_engine import MemoryEngine
-
-
-@pytest.fixture(autouse=True)
-def enable_observations():
-    """Enable observations for all tests in this module."""
-    config = _get_raw_config()
-    original_value = config.enable_observations
-    config.enable_observations = True
-    yield
-    config.enable_observations = original_value
-
-
-@dataclass
-class _ActionLog:
-    text: str
-    source_fact_ids: list[str] = field(default_factory=list)
-    observation_id: str = ""
-
-
-@dataclass
-class _ConsolidationResponse:
-    creates: list[_ActionLog] = field(default_factory=list)
-    updates: list[_ActionLog] = field(default_factory=list)
-    deletes: list[_ActionLog] = field(default_factory=list)
-
-
-@dataclass
-class _ConsolidationDebugEntry:
-    facts: str
-    observations_text: str
-    response: _ConsolidationResponse
-
-
-# Store prompts/responses for debugging
-_debug_log: list[_ConsolidationDebugEntry] = []
-
-
-def _fact_line(m: dict[str, Any]) -> str:
-    text = f"[{m['id']}] {m['text']}"
-    temporal_parts = []
-    if m.get("occurred_start"):
-        temporal_parts.append(f"occurred_start={m['occurred_start']}")
-    if m.get("occurred_end"):
-        temporal_parts.append(f"occurred_end={m['occurred_end']}")
-    if m.get("mentioned_at"):
-        temporal_parts.append(f"mentioned_at={m['mentioned_at']}")
-    if temporal_parts:
-        text += f" ({', '.join(temporal_parts)})"
-    return text
-
-
-async def _instrumented_consolidate(
-    original_fn: Any,
-    *,
-    llm_config: Any,
-    memories: list[dict[str, Any]],
-    union_observations: Any,
-    union_source_facts: Any,
-    config: Any = None,
-    remaining_observation_slots: int | None = None,
-    max_observations_per_scope: int = -1,
-) -> Any:
-    """Wrapper that captures the prompt and response for debugging."""
-    if union_observations:
-        obs_list = consolidator_mod._build_observations_for_llm(union_observations, union_source_facts)
-        observations_text = json.dumps(obs_list, indent=2)
-    else:
-        observations_text = "[]"
-
-    facts_lines = "\n".join(_fact_line(m) for m in memories)
-
-    result = await original_fn(
-        llm_config=llm_config,
-        memories=memories,
-        union_observations=union_observations,
-        union_source_facts=union_source_facts,
-        config=config,
-        remaining_observation_slots=remaining_observation_slots,
-        max_observations_per_scope=max_observations_per_scope,
-    )
-
-    _debug_log.append(_ConsolidationDebugEntry(
-        facts=facts_lines,
-        observations_text=observations_text,
-        response=_ConsolidationResponse(
-            creates=[_ActionLog(text=c.text, source_fact_ids=c.source_fact_ids) for c in result.creates],
-            updates=[
-                _ActionLog(text=u.text, observation_id=u.observation_id, source_fact_ids=u.source_fact_ids)
-                for u in result.updates
-            ],
-            deletes=[_ActionLog(text="", observation_id=d.observation_id) for d in result.deletes],
-        ),
-    ))
-
-    return result
-
-
-def _print_consolidation_debug(entry: _ConsolidationDebugEntry, index: int) -> None:
-    """Print a single consolidation LLM call for debugging."""
-    print(f"\n  --- LLM Call #{index} ---")
-    print("  FACTS sent to LLM:")
-    for line in entry.facts.split("\n"):
-        print(f"    {line}")
-    print("\n  EXISTING OBSERVATIONS sent to LLM:")
-    obs_data = json.loads(entry.observations_text)
-    if obs_data:
-        for obs in obs_data:
-            src_summary = ""
-            if obs.get("source_memories"):
-                src_texts = [sm["text"] for sm in obs["source_memories"]]
-                src_summary = f" (sources: {src_texts})"
-            print(f"    [{obs['id'][:8]}..] proof={obs.get('proof_count', '?')}: {obs['text']}{src_summary}")
-    else:
-        print("    (none)")
-
-    resp = entry.response
-    print("\n  LLM RESPONSE:")
-    if resp.creates:
-        for c in resp.creates:
-            print(f"    CREATE: \"{c.text}\" (from facts: {[fid[:8] + '..' for fid in c.source_fact_ids]})")
-    if resp.updates:
-        for u in resp.updates:
-            print(
-                f"    UPDATE [{u.observation_id[:8]}..]: \"{u.text}\""
-                f" (from facts: {[fid[:8] + '..' for fid in u.source_fact_ids]})"
-            )
-    if resp.deletes:
-        for d in resp.deletes:
-            print(f"    DELETE [{d.observation_id[:8]}..]")
-    if not resp.creates and not resp.updates and not resp.deletes:
-        print("    (no actions)")
-
-
-def _parse_history(hist: Any) -> list[str]:
-    """Parse observation history from DB (may be list of dicts or JSON strings)."""
-    if not hist:
-        return []
-    parsed = hist if isinstance(hist, list) else json.loads(hist)
-    prev_texts = []
-    for h in parsed:
-        if isinstance(h, str):
-            h = json.loads(h)
-        prev_texts.append(h.get("previous_text", "?"))
-    return prev_texts
-
-
-@pytest.mark.asyncio
-@pytest.mark.flaky(reruns=2, reruns_delay=5)
-async def test_horse_farm_observation_history(memory: MemoryEngine, request_context: Any) -> None:
-    """Retain a sequence of horse facts and inspect how observations evolve."""
-    bank_id = f"test-horses-{uuid.uuid4().hex[:8]}"
-
-    await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
-
-    messages = [
-        "I have a farm.",
-        "I have 2 horses.",
-        "I have a horse named Daisy.",
-        "I have a horse named Buttercup.",
-        "I sold Buttercup.",
-        "I now have 1 horse.",
-        "I have 5 horses on my farm.",
-        "I have a horse named Midnight.",
-        "I have horses named Midnight and Shadow.",
-        "I have horses named Shadow and Twister.",
-        "I am sad to report that Shadow has died.",
-    ]
-
-    # Monkey-patch to intercept consolidation LLM calls
-    _original_consolidate = consolidator_mod._consolidate_batch_with_llm
-
-    async def _patched(**kwargs: Any) -> Any:
-        return await _instrumented_consolidate(_original_consolidate, **kwargs)
-
-    consolidator_mod._consolidate_batch_with_llm = _patched
-    _debug_log.clear()
-
-    try:
-        for i, content in enumerate(messages):
-            print(f"\n{'='*80}")
-            print(f"RETAIN #{i+1}: {content}")
-            print(f"{'='*80}")
-
-            log_start = len(_debug_log)
-
-            await memory.retain_async(
-                bank_id=bank_id,
-                content=content,
-                request_context=request_context,
-            )
-            await memory.wait_for_background_tasks()
-
-            for j, entry in enumerate(_debug_log[log_start:]):
-                _print_consolidation_debug(entry, j + 1)
-
-            # Dump current observations
-            pool = await memory._get_pool()
-            async with pool.acquire() as conn:
-                observations = await conn.fetch(
-                    """
-                    SELECT id, text, proof_count, source_memory_ids, history
-                    FROM memory_units
-                    WHERE bank_id = $1 AND fact_type = 'observation'
-                    ORDER BY created_at
-                    """,
-                    bank_id,
-                )
-
-                print(f"\n  CURRENT OBSERVATIONS ({len(observations)}):")
-                for obs in observations:
-                    prev_texts = _parse_history(obs["history"])
-                    hist_str = f" (was: {' -> '.join(prev_texts)})" if prev_texts else ""
-                    print(f"    [{str(obs['id'])[:8]}..] proof={obs['proof_count']}: {obs['text']}{hist_str}")
-    finally:
-        consolidator_mod._consolidate_batch_with_llm = _original_consolidate
-
-    # Final summary
-    print(f"\n{'='*80}")
-    print("FINAL STATE")
-    print(f"{'='*80}")
-    pool = await memory._get_pool()
-    async with pool.acquire() as conn:
-        observations = await conn.fetch(
-            """
-            SELECT id, text, proof_count, source_memory_ids, history
-            FROM memory_units
-            WHERE bank_id = $1 AND fact_type = 'observation'
-            ORDER BY created_at
-            """,
-            bank_id,
-        )
-        print(f"\nFinal observations ({len(observations)}):")
-        for obs in observations:
-            prev_texts = _parse_history(obs["history"])
-            if prev_texts:
-                chain = prev_texts + [obs["text"]]
-                print(f"  - [proof={obs['proof_count']}] {obs['text']}")
-                print(f"    evolution: {' -> '.join(chain)}")
-            else:
-                print(f"  - [proof={obs['proof_count']}] {obs['text']}")
-
-    # Create a mental model to synthesize the observations
-    print(f"\n{'='*80}")
-    print("MENTAL MODEL")
-    print(f"{'='*80}")
-
-    # Patch reflect _execute_tool to log tool inputs/outputs
-    from hindsight_api.engine.reflect import agent as reflect_agent_mod
-
-    _original_execute = reflect_agent_mod._execute_tool
-
-    async def _logging_execute(tool_name: str, args: dict[str, Any], *a: Any, **kw: Any) -> dict[str, Any]:
-        result = await _original_execute(tool_name, args, *a, **kw)
-        normalized = reflect_agent_mod._normalize_tool_name(tool_name)
-        print(f"\n  [REFLECT TOOL] {normalized}(args={args})")
-        if isinstance(result, dict):
-            if "observations" in result:
-                print(f"    Observations returned ({result.get('count', '?')}, freshness={result.get('freshness', '?')}):")
-                for obs in result.get("observations", []):
-                    print(f"      - [proof={obs.get('proof_count', '?')}] {obs.get('text', '?')}")
-            if "memories" in result:
-                print(f"    Memories returned ({result.get('count', '?')}):")
-                for mem in result.get("memories", []):
-                    chunk = mem.get("chunk_text", "")
-                    chunk_preview = f" | chunk: {chunk[:80]}..." if chunk else ""
-                    print(f"      - [{mem.get('fact_type', '?')}] {mem.get('text', '?')}{chunk_preview}")
-            if "mental_models" in result:
-                print(f"    Mental models returned ({result.get('count', '?')}):")
-                for mm_item in result.get("mental_models", []):
-                    print(f"      - {mm_item.get('name', '?')}: {str(mm_item.get('content', '?'))[:120]}")
-            if "error" in result:
-                print(f"    ERROR: {result['error']}")
-        return result
-
-    reflect_agent_mod._execute_tool = _logging_execute
-
-    source_query = (
-        "Produce a structured summary of all animals on the farm. Include:\n"
-        "1. A chronological timeline of events (acquisitions, sales, deaths) with dates\n"
-        "2. The list of all known horse names and their current status (alive, sold, died)\n"
-        "3. The current number of horses on the farm, accounting for all events\n"
-        "Reason step by step from the facts. If a horse died or was sold, subtract from the count."
-    )
-
-    try:
-        mm = await memory.create_mental_model(
-            bank_id=bank_id,
-            name="Farm Animals",
-            source_query=source_query,
-            content="(initial — awaiting refresh)",
-            request_context=request_context,
-        )
-
-        refreshed = await memory.refresh_mental_model(
-            bank_id=bank_id,
-            mental_model_id=mm["id"],
-            request_context=request_context,
-        )
-        content = refreshed["content"]
-    finally:
-        reflect_agent_mod._execute_tool = _original_execute
-    print(f"\nMental model content:\n{content}")
-
-    reflect_resp = refreshed.get("reflect_response")
-    if reflect_resp and isinstance(reflect_resp, str) and reflect_resp.strip():
-        try:
-            reflect_resp = json.loads(reflect_resp)
-        except json.JSONDecodeError:
-            reflect_resp = None
-    if isinstance(reflect_resp, dict):
-        based_on = reflect_resp.get("based_on", [])
-        if based_on:
-            print("\nBased on:")
-            for item in based_on:
-                if isinstance(item, str):
-                    try:
-                        item = json.loads(item)
-                    except json.JSONDecodeError:
-                        continue
-                print(f"  - [{item.get('fact_type', '?')}] {item.get('text', '?')}")
-
-    # Verify the mental model captures key facts
-    content_lower = content.lower()
-
-    for name in ["daisy", "buttercup", "midnight", "shadow", "twister"]:
-        assert name in content_lower, f"Mental model should mention {name}. Got:\n{content}"
-
-    assert "sold" in content_lower or "sale" in content_lower, (
-        f"Mental model should mention Buttercup was sold. Got:\n{content}"
-    )
-
-    assert "died" in content_lower or "passed" in content_lower or "death" in content_lower, (
-        f"Mental model should mention Shadow's death. Got:\n{content}"
-    )
-
-    # Cleanup
-    await memory.delete_bank(bank_id, request_context=request_context)

hindsight-api-slim/tests/test_http_api_integration.py

@@ -584,87 +584,6 @@ async def test_delete_bank(api_client):
     await api_client.delete(f"/v1/default/banks/{test_bank_id}")
 
 
-@pytest.mark.asyncio
-async def test_delete_bank_nonexistent(api_client):
-    """Test deleting a bank that doesn't exist returns success with zero counts."""
-    fake_bank_id = f"nonexistent_bank_{datetime.now().timestamp()}"
-
-    response = await api_client.delete(f"/v1/default/banks/{fake_bank_id}")
-    assert response.status_code == 200
-    result = response.json()
-    assert result["success"] is True
-    assert result["deleted_count"] == 0
-
-
-@pytest.mark.asyncio
-async def test_clear_memories_preserves_bank(api_client):
-    """Test that clearing memories preserves the bank profile.
-
-    Workflow:
-    1. Create a bank with memories
-    2. Clear all memories via DELETE /memories
-    3. Verify the bank still exists with its profile intact
-    4. Verify all memories are gone
-    """
-    test_bank_id = f"clear_memories_test_{datetime.now().timestamp()}"
-
-    try:
-        # 1. Create bank with memories
-        response = await api_client.post(
-            f"/v1/default/banks/{test_bank_id}/memories",
-            json={
-                "items": [
-                    {"content": "Alice is a software engineer.", "context": "team info"},
-                    {"content": "Bob works on infrastructure.", "context": "team info"},
-                ]
-            },
-        )
-        assert response.status_code == 200
-
-        # Verify bank exists and has data
-        response = await api_client.get(f"/v1/default/banks/{test_bank_id}/stats")
-        assert response.status_code == 200
-        assert response.json()["total_nodes"] > 0
-
-        response = await api_client.get("/v1/default/banks")
-        assert response.status_code == 200
-        bank_ids = [b["bank_id"] for b in response.json()["banks"]]
-        assert test_bank_id in bank_ids
-
-        # 2. Clear all memories
-        response = await api_client.delete(f"/v1/default/banks/{test_bank_id}/memories")
-        assert response.status_code == 200
-        assert response.json()["success"] is True
-
-        # 3. Bank should still exist in the list
-        response = await api_client.get("/v1/default/banks")
-        assert response.status_code == 200
-        bank_ids = [b["bank_id"] for b in response.json()["banks"]]
-        assert test_bank_id in bank_ids, "Bank should still exist after clearing memories"
-
-        # Profile should still be accessible
-        response = await api_client.get(f"/v1/default/banks/{test_bank_id}/profile")
-        assert response.status_code == 200
-
-        # 4. Memories should be gone
-        response = await api_client.get(f"/v1/default/banks/{test_bank_id}/stats")
-        assert response.status_code == 200
-        assert response.json()["total_nodes"] == 0
-
-    finally:
-        await api_client.delete(f"/v1/default/banks/{test_bank_id}")
-
-
-@pytest.mark.asyncio
-async def test_clear_memories_nonexistent_bank(api_client):
-    """Test clearing memories for a bank that doesn't exist returns success."""
-    fake_bank_id = f"nonexistent_clear_{datetime.now().timestamp()}"
-
-    response = await api_client.delete(f"/v1/default/banks/{fake_bank_id}/memories")
-    assert response.status_code == 200
-    assert response.json()["success"] is True
-
-
 @pytest.mark.asyncio
 async def test_async_retain(api_client):
     """Test asynchronous retain functionality.
@@ -1310,90 +1229,3 @@ async def test_retain_with_timestamp_async_complete_processing(api_client, test_
     assert response.status_code == 200
     items = response.json()["items"]
     assert len(items) > 0, "Should have stored memories after async processing"
-
-
-@pytest.mark.asyncio
-async def test_http_recall_preserves_metadata(api_client, test_bank_id):
-    """
-    Regression test for #797: HTTP recall must return metadata stored during retain.
-
-    The engine correctly preserves metadata, but _fact_to_result in http.py was
-    missing the metadata= kwarg, causing the HTTP endpoint to always return null.
-    """
-    metadata = {"source": "slack", "channel": "engineering", "importance": "high"}
-
-    # Retain with metadata
-    response = await api_client.post(
-        f"/v1/default/banks/{test_bank_id}/memories",
-        json={
-            "items": [
-                {
-                    "content": "The product launch is scheduled for March 1st.",
-                    "metadata": metadata,
-                }
-            ]
-        },
-    )
-    assert response.status_code == 200
-
-    # Recall via HTTP
-    response = await api_client.post(
-        f"/v1/default/banks/{test_bank_id}/memories/recall",
-        json={"query": "When is the product launch?", "budget": "low"},
-    )
-    assert response.status_code == 200
-    results = response.json()["results"]
-    assert len(results) > 0, "Should recall at least one fact"
-
-    # Find a result that has our metadata (LLM may extract multiple facts)
-    facts_with_metadata = [r for r in results if r.get("metadata")]
-    assert len(facts_with_metadata) > 0, "At least one fact must have metadata (regression #797)"
-    fact = facts_with_metadata[0]
-    assert fact["metadata"]["source"] == "slack"
-    assert fact["metadata"]["channel"] == "engineering"
-    assert fact["metadata"]["importance"] == "high"
-
-
-@pytest.mark.asyncio
-async def test_unknown_params_not_rejected(api_client):
-    """Unknown query params and body fields should not cause a rejection (no 400).
-
-    The server should return 200 with an X-Ignored-Params header listing the
-    unknown parameters instead of rejecting the request. This ensures forward
-    compatibility when a newer client talks to an older server.
-    """
-    test_bank_id = f"unknown_params_test_{datetime.now().timestamp()}"
-
-    # Ensure bank exists
-    await api_client.get(f"/v1/default/banks/{test_bank_id}/profile")
-
-    # Unknown query params on GET endpoint
-    response = await api_client.get(
-        f"/v1/default/banks/{test_bank_id}/memories/list",
-        params={"limit": 1, "tag": "source:slack", "created_after": "2026-01-01"},
-    )
-    assert response.status_code == 200
-    assert "X-Ignored-Params" in response.headers
-    ignored = response.headers["X-Ignored-Params"]
-    assert "tag" in ignored
-    assert "created_after" in ignored
-
-    # Unknown body fields on POST endpoint
-    response = await api_client.post(
-        f"/v1/default/banks/{test_bank_id}/memories",
-        json={
-            "items": [{"content": "test memory", "context": "test"}],
-            "unknown_future_field": True,
-        },
-    )
-    assert response.status_code == 200
-    assert "X-Ignored-Params" in response.headers
-    assert "unknown_future_field" in response.headers["X-Ignored-Params"]
-
-    # Known params only — no header
-    response = await api_client.get(
-        f"/v1/default/banks/{test_bank_id}/memories/list",
-        params={"limit": 1, "type": "world"},
-    )
-    assert response.status_code == 200
-    assert "X-Ignored-Params" not in response.headers

hindsight-api-slim/tests/test_link_utils.py

@@ -1,15 +1,11 @@
-"""Tests for link_utils datetime handling, temporal link computation, and semantic link splitting."""
-import numpy as np
+"""Tests for link_utils datetime handling and temporal link computation."""
 import pytest
 from datetime import datetime, timezone, timedelta
 
 from hindsight_api.engine.retain.link_utils import (
     _normalize_datetime,
-    _cap_links_per_unit,
     compute_temporal_links,
     compute_temporal_query_bounds,
-    compute_semantic_links_within_batch,
-    MAX_TEMPORAL_LINKS_PER_UNIT,
 )
 
 
@@ -258,133 +254,3 @@ class TestComputeTemporalLinks:
 
         assert len(links) == 1
         assert links[0][3] >= 0.3
-
-
-class TestCapLinksPerUnit:
-    """Tests for the _cap_links_per_unit helper function."""
-
-    def test_empty_links(self):
-        assert _cap_links_per_unit([]) == []
-
-    def test_under_cap_unchanged(self):
-        links = [
-            ("unit_a", "unit_x", "temporal", 0.9, None),
-            ("unit_a", "unit_y", "temporal", 0.8, None),
-        ]
-        result = _cap_links_per_unit(links, max_per_unit=5)
-        assert len(result) == 2
-
-    def test_caps_to_max_per_unit(self):
-        # Create 30 links from the same unit with descending weights
-        links = [("unit_a", f"unit_{i}", "temporal", 1.0 - i * 0.01, None) for i in range(30)]
-        result = _cap_links_per_unit(links, max_per_unit=10)
-        assert len(result) == 10
-        # Should keep the highest-weight links
-        weights = [lnk[3] for lnk in result]
-        assert weights == sorted(weights, reverse=True)
-        assert weights[0] == 1.0  # Highest weight kept
-
-    def test_caps_independently_per_unit(self):
-        links_a = [("unit_a", f"target_{i}", "temporal", 0.9 - i * 0.01, None) for i in range(10)]
-        links_b = [("unit_b", f"target_{i}", "temporal", 0.8 - i * 0.01, None) for i in range(10)]
-        result = _cap_links_per_unit(links_a + links_b, max_per_unit=5)
-        # 5 from unit_a + 5 from unit_b
-        assert len(result) == 10
-        from_a = [lnk for lnk in result if lnk[0] == "unit_a"]
-        from_b = [lnk for lnk in result if lnk[0] == "unit_b"]
-        assert len(from_a) == 5
-        assert len(from_b) == 5
-
-    def test_default_max_is_temporal_constant(self):
-        links = [("unit_a", f"target_{i}", "temporal", 1.0 - i * 0.01, None) for i in range(50)]
-        result = _cap_links_per_unit(links)
-        assert len(result) == MAX_TEMPORAL_LINKS_PER_UNIT
-
-    def test_preserves_tuple_structure(self):
-        links = [("from_id", "to_id", "temporal", 0.95, "entity_id")]
-        result = _cap_links_per_unit(links, max_per_unit=5)
-        assert result[0] == ("from_id", "to_id", "temporal", 0.95, "entity_id")
-
-
-class TestComputeSemanticLinksWithinBatch:
-    """Tests for compute_semantic_links_within_batch.
-
-    This function computes semantic links between units in the same batch
-    using numpy dot product (no DB access). It runs in Phase 2 (write
-    transaction) while the expensive ANN search against existing units runs
-    in Phase 1 on a separate connection to avoid TimeoutErrors from HNSW
-    index contention under concurrent load.
-    """
-
-    def test_empty_returns_empty(self):
-        assert compute_semantic_links_within_batch([], []) == []
-
-    def test_single_unit_returns_empty(self):
-        emb = [np.random.randn(384).tolist()]
-        assert compute_semantic_links_within_batch(["u1"], emb) == []
-
-    def test_identical_embeddings_produce_links(self):
-        """Two identical embeddings should have similarity=1.0 (above 0.7 threshold)."""
-        emb = [0.1] * 384
-        links = compute_semantic_links_within_batch(["u1", "u2"], [emb, emb])
-        assert len(links) == 2  # bidirectional: u1→u2, u2→u1
-        from_ids = {lnk[0] for lnk in links}
-        to_ids = {lnk[1] for lnk in links}
-        assert from_ids == {"u1", "u2"}
-        assert to_ids == {"u1", "u2"}
-        for lnk in links:
-            assert lnk[2] == "semantic"
-            assert lnk[3] >= 0.99  # near-1.0 similarity
-            assert lnk[4] is None  # no entity_id
-
-    def test_orthogonal_embeddings_no_links(self):
-        """Orthogonal embeddings should have similarity=0 (below 0.7 threshold)."""
-        emb1 = [1.0] + [0.0] * 383
-        emb2 = [0.0] + [1.0] + [0.0] * 382
-        links = compute_semantic_links_within_batch(["u1", "u2"], [emb1, emb2])
-        assert len(links) == 0
-
-    def test_respects_threshold(self):
-        """Links below threshold should be excluded."""
-        emb1 = np.random.randn(384).tolist()
-        # Create a slightly similar embedding (add noise)
-        emb2 = [x + np.random.randn() * 0.5 for x in emb1]
-        # Normalize both
-        norm1 = np.linalg.norm(emb1)
-        norm2 = np.linalg.norm(emb2)
-        emb1 = [x / norm1 for x in emb1]
-        emb2 = [x / norm2 for x in emb2]
-
-        links_low = compute_semantic_links_within_batch(["u1", "u2"], [emb1, emb2], threshold=0.0)
-        links_high = compute_semantic_links_within_batch(["u1", "u2"], [emb1, emb2], threshold=0.99)
-        # Low threshold should have more links than high threshold
-        assert len(links_low) >= len(links_high)
-
-    def test_top_k_limits_per_unit(self):
-        """Each unit should link to at most top_k other units."""
-        n = 10
-        # Create similar embeddings (all close to the same vector)
-        base = np.random.randn(384)
-        base = base / np.linalg.norm(base)
-        embs = [(base + np.random.randn(384) * 0.01).tolist() for _ in range(n)]
-        unit_ids = [f"u{i}" for i in range(n)]
-
-        links = compute_semantic_links_within_batch(unit_ids, embs, top_k=3, threshold=0.5)
-        # Each unit should have at most 3 outgoing links
-        from collections import Counter
-        from_counts = Counter(lnk[0] for lnk in links)
-        for count in from_counts.values():
-            assert count <= 3
-
-    def test_link_tuple_structure(self):
-        """Verify the tuple format matches what _bulk_insert_links expects."""
-        emb = [0.1] * 384
-        links = compute_semantic_links_within_batch(["u1", "u2"], [emb, emb])
-        for lnk in links:
-            assert len(lnk) == 5
-            from_id, to_id, link_type, weight, entity_id = lnk
-            assert isinstance(from_id, str)
-            assert isinstance(to_id, str)
-            assert link_type == "semantic"
-            assert 0.0 <= weight <= 1.0
-            assert entity_id is None

hindsight-api-slim/tests/test_litellm_sdk_embeddings.py

@@ -277,99 +277,6 @@ class TestLiteLLMSDKEmbeddings:
             call_args = mock_litellm.embedding.call_args
             assert call_args.kwargs["api_base"] == "https://custom.api.com"
 
-    async def test_output_dimensions_passed_when_set(self, mock_litellm):
-        """Test output dimensions are passed to LiteLLM when configured."""
-        with patch(
-            "builtins.__import__",
-            side_effect=lambda name, *args: mock_litellm if name == "litellm" else __import__(name, *args),
-        ):
-            emb = LiteLLMSDKEmbeddings(
-                api_key="test_key",
-                model="cohere/embed-english-v3.0",
-                output_dimensions=768,
-            )
-            await emb.initialize()
-
-            init_call_args = mock_litellm.aembedding.call_args
-            assert init_call_args.kwargs["dimensions"] == 768
-
-            mock_litellm.embedding.return_value.data = [{"embedding": [0.1] * 768, "index": 0}]
-            emb.encode(["test"])
-
-            encode_call_args = mock_litellm.embedding.call_args
-            assert encode_call_args.kwargs["dimensions"] == 768
-
-    async def test_output_dimensions_omitted_when_unset(self, mock_litellm):
-        """Test output dimensions are omitted when not configured."""
-        with patch(
-            "builtins.__import__",
-            side_effect=lambda name, *args: mock_litellm if name == "litellm" else __import__(name, *args),
-        ):
-            emb = LiteLLMSDKEmbeddings(
-                api_key="test_key",
-                model="cohere/embed-english-v3.0",
-            )
-            await emb.initialize()
-
-            init_call_args = mock_litellm.aembedding.call_args
-            assert "dimensions" not in init_call_args.kwargs
-
-            mock_litellm.embedding.return_value.data = [{"embedding": [0.1] * 768, "index": 0}]
-            emb.encode(["test"])
-
-            encode_call_args = mock_litellm.embedding.call_args
-            assert "dimensions" not in encode_call_args.kwargs
-
-    async def test_output_dimensions_and_api_base_passed_when_both_set(self, mock_litellm):
-        """Test both dimensions and api_base are forwarded when configured together."""
-        with patch(
-            "builtins.__import__",
-            side_effect=lambda name, *args: mock_litellm if name == "litellm" else __import__(name, *args),
-        ):
-            emb = LiteLLMSDKEmbeddings(
-                api_key="test_key",
-                model="cohere/embed-english-v3.0",
-                api_base="https://custom.api.com",
-                output_dimensions=768,
-            )
-            await emb.initialize()
-
-            init_call_args = mock_litellm.aembedding.call_args
-            assert init_call_args.kwargs["api_base"] == "https://custom.api.com"
-            assert init_call_args.kwargs["dimensions"] == 768
-
-            mock_litellm.embedding.return_value.data = [{"embedding": [0.1] * 768, "index": 0}]
-            emb.encode(["test"])
-
-            encode_call_args = mock_litellm.embedding.call_args
-            assert encode_call_args.kwargs["api_base"] == "https://custom.api.com"
-            assert encode_call_args.kwargs["dimensions"] == 768
-
-    async def test_openai_invalid_output_dimensions_raises(self, mock_litellm):
-        """Invalid dimensions fail during initialize() (probe call), not per HTTP request.
-
-        MemoryEngine runs this at app lifespan startup; the process typically fails to become
-        ready rather than returning a JSON error for a single API call. The RuntimeError
-        message should still chain the underlying provider/LiteLLM detail for logs.
-        """
-        mock_litellm.aembedding.side_effect = Exception("invalid dimensions for model")
-
-        with patch(
-            "builtins.__import__",
-            side_effect=lambda name, *args: mock_litellm if name == "litellm" else __import__(name, *args),
-        ):
-            emb = LiteLLMSDKEmbeddings(
-                api_key="test_key",
-                model="openai/text-embedding-3-small",
-                output_dimensions=9999,
-            )
-
-            with pytest.raises(
-                RuntimeError,
-                match="Failed to initialize LiteLLM SDK embeddings:.*invalid dimensions for model",
-            ):
-                await emb.initialize()
-
 
 class TestLiteLLMSDKEmbeddingsFactory:
     """Test the factory function for creating LiteLLM SDK embeddings."""
@@ -417,21 +324,6 @@ class TestLiteLLMSDKEmbeddingsFactory:
             assert isinstance(embeddings, LiteLLMSDKEmbeddings)
             assert embeddings.api_base == "https://custom.api.com"
 
-    def test_create_from_env_with_output_dimensions(self, monkeypatch):
-        """Test creating embeddings with configured output dimensions."""
-        mock_config = MagicMock()
-        mock_config.embeddings_provider = "litellm-sdk"
-        mock_config.embeddings_litellm_sdk_api_key = "test_key"
-        mock_config.embeddings_litellm_sdk_model = "gemini/gemini-embedding-2-preview"
-        mock_config.embeddings_litellm_sdk_api_base = None
-        mock_config.embeddings_litellm_sdk_output_dimensions = 768
-
-        with patch("hindsight_api.config.get_config", return_value=mock_config):
-            embeddings = create_embeddings_from_env()
-
-            assert isinstance(embeddings, LiteLLMSDKEmbeddings)
-            assert embeddings.output_dimensions == 768
-
 
 class TestLiteLLMSDKCohereEmbeddings:
     """Integration tests calling real Cohere API (matches CI pattern)."""

hindsight-api-slim/tests/test_llm_provider.py

@@ -284,7 +284,7 @@ async def test_llm_provider_memory_operations(provider: str, model: str):
     # Verify facts have required fields
     for fact in facts:
         assert fact.fact, f"{provider}/{model} fact missing text"
-        assert fact.fact_type in ["world", "experience"], f"{provider}/{model} invalid fact_type: {fact.fact_type}"
+        assert fact.fact_type in ["world", "experience", "opinion"], f"{provider}/{model} invalid fact_type: {fact.fact_type}"
 
     # Test 2: Reflect (actual reflect function)
     response = await reflect(