fix: restore retain_batch_tokens config that was accidentally removed during rebase

- Replace all window.alert() calls with toast notifications
- Add interceptor-based error handling in API client
- Use different toast styles based on HTTP status codes (4xx = warning, 5xx = error)
- Apply Hindsight brand colors to toasts (primary blue for info, destructive red for errors, etc.)
- Remove obsolete error handling files (hindsight-client-with-toast.ts, api-error-handler.ts)
- Fix toast background conflicts by removing base bg-background class

.env.example

@@ -31,6 +31,12 @@ HINDSIGHT_API_HOST=0.0.0.0
 HINDSIGHT_API_PORT=8888
 HINDSIGHT_API_LOG_LEVEL=info
 
+# Base Path / Reverse Proxy Support (Optional)
+# Set these when deploying behind a reverse proxy with path-based routing
+# Example: To deploy at example.com/hindsight/, set both to "/hindsight"
+# HINDSIGHT_API_BASE_PATH=/hindsight
+# NEXT_PUBLIC_BASE_PATH=/hindsight
+
 # Database (Optional - uses embedded pg0 by default)
 # HINDSIGHT_API_DATABASE_URL=postgresql://user:pass@host:5432/db
 # HINDSIGHT_API_DATABASE_SCHEMA=public  # PostgreSQL schema name (default: public)

.github/workflows/test.yml

@@ -941,30 +941,11 @@ jobs:
           sleep 1
         done
 
-    - name: Run Python doc examples
-      working-directory: ./hindsight-clients/python
-      run: |
-        for f in ../../hindsight-docs/examples/api/*.py; do
-          echo "Running $f..."
-          uv run python "$f"
-        done
-
-    - name: Run Node.js doc examples
-      run: |
-        for f in hindsight-docs/examples/api/*.mjs; do
-          echo "Running $f..."
-          node "$f"
-        done
-
     - name: Configure CLI
       run: hindsight configure --api-url http://localhost:8888
 
-    - name: Run CLI doc examples
-      run: |
-        for f in hindsight-docs/examples/api/*.sh; do
-          echo "Running $f..."
-          bash "$f"
-        done
+    - name: Run all doc examples
+      run: ./scripts/test-doc-examples.sh
 
     - name: Show API server logs
       if: always()

.gitignore

@@ -46,6 +46,7 @@ hindsight-docs/static/llms-full.txt
 hindsight-dev/benchmarks/locomo/results/
 hindsight-dev/benchmarks/longmemeval/results/
 hindsight-dev/benchmarks/consolidation/results/
+hindsight-dev/benchmarks/perf/results/
 benchmarks/results/
 hindsight-cli/target
 hindsight-clients/rust/target

CLAUDE.md

@@ -57,8 +57,15 @@ cd hindsight-control-plane && npm run dev
 
 ### Benchmarks
 ```bash
+# Accuracy benchmarks
 ./scripts/benchmarks/run-longmemeval.sh
 ./scripts/benchmarks/run-locomo.sh
+
+# Performance benchmarks
+./scripts/benchmarks/run-consolidation.sh
+./scripts/benchmarks/run-retain-perf.sh --document <path>  # Requires API server running
+
+# Results viewer
 ./scripts/benchmarks/start-visualizer.sh  # View results at localhost:8001
 ```
 
@@ -238,26 +245,61 @@ def process(data: UserData) -> str:
 
 ### Adding New API Configuration Flags
 
-When adding a new environment variable configuration:
+Configuration follows a hierarchical system: **Global (env vars) → Tenant (via extension) → Bank (database)**.
+
+Fields must be categorized as either **hierarchical** (can be overridden per-tenant/bank) or **static** (server-level only).
+
+#### Adding a New Configuration Field
 
 1. **config.py** (`hindsight-api/hindsight_api/config.py`):
-   - Add `ENV_*` constant for the environment variable name
+   - 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
+   - Add field to `HindsightConfig` dataclass with type annotation
+   - **Mark as hierarchical or static** by adding to `_HIERARCHICAL_FIELDS` set (hierarchical) or leaving it out (static)
    - Add initialization in `from_env()` method
 
+   ```python
+   # Hierarchical field (can be overridden per-bank)
+   _HIERARCHICAL_FIELDS = {
+       ...,
+       "my_setting",  # Add here for hierarchical
+   }
+
+   # Static field - just don't add to _HIERARCHICAL_FIELDS
+   ```
+
 2. **main.py** (`hindsight-api/hindsight_api/main.py`):
    - Add field to the manual `HindsightConfig()` constructor call (search for "CLI override")
 
-3. **Use the config** in code:
+3. **Use hierarchical config in MemoryEngine**:
+   ```python
+   # Config is resolved automatically per bank via ConfigResolver
+   config_dict = await self._config_resolver.get_bank_config(bank_id, context)
+   value = config_dict["my_setting"]
+   ```
+
+4. **Use static config** (non-hierarchical):
    ```python
    from ...config import get_config
    config = get_config()
-   value = config.your_new_field
+   value = config.my_static_field
    ```
 
-4. **Documentation** (`hindsight-docs/docs/developer/configuration.md`):
+5. **Documentation** (`hindsight-docs/docs/developer/configuration.md`):
    - Add to appropriate section table with Variable, Description, Default
+   - Mark if it's hierarchical (can be overridden per-bank)
+
+#### Hierarchical vs Static Guidelines
+
+**Hierarchical** (per-bank overridable):
+- LLM settings (provider, model, API key, base URL)
+- Operation-specific settings (retain mode, chunk size, etc.)
+- Feature flags that vary by customer/bank
+
+**Static** (server-level only):
+- Infrastructure settings (database URL, port, host)
+- Global limits (max concurrent operations)
+- System-wide feature flags
 
 ## Environment Setup
 
@@ -281,3 +323,4 @@ Optional (uses local models by default):
 - `HINDSIGHT_API_EMBEDDINGS_PROVIDER`: local (default) or tei
 - `HINDSIGHT_API_RERANKER_PROVIDER`: local (default) or tei
 - `HINDSIGHT_API_DATABASE_URL`: External PostgreSQL (uses embedded pg0 by default)
+- `HINDSIGHT_API_ENABLE_BANK_CONFIG_API`: Enable per-bank config API (default: false, disabled for security)

README.md

@@ -2,7 +2,7 @@
 
 ![Hindsight Banner](./hindsight-docs/static/img/hindsight-github-banner.png)
 
-[Documentation](https://hindsight.vectorize.io) • [Paper](https://arxiv.org/abs/2512.12818) • [Cookbook](https://hindsight.vectorize.io/cookbook) • [Hindsight Cloud](https://vectorize.io/hindsight/cloud)
+[Documentation](https://hindsight.vectorize.io) • [Paper](https://arxiv.org/abs/2512.12818) • [Cookbook](https://hindsight.vectorize.io/cookbook) • [Hindsight Cloud](https://ui.hindsight.vectorize.io/signup)
 
 [![CI](https://github.com/vectorize-io/hindsight/actions/workflows/release.yml/badge.svg)](https://github.com/vectorize-io/hindsight/actions/workflows/release.yml)
 [![Slack Community](https://img.shields.io/badge/Slack-Join%20Community-4A154B?logo=slack)](https://join.slack.com/t/hindsight-space/shared_invite/zt-3nhbm4w29-LeSJ5Ixi6j8PdiYOCPlOgg)

docker/docker-compose/nginx/README.md

@@ -0,0 +1,96 @@
+# Nginx Reverse Proxy with Custom Base Path
+
+Deploy Hindsight API under `/hindsight` (or any custom path) using Nginx reverse proxy.
+
+## Quick Start (Published Image - API Only)
+
+```bash
+docker-compose up
+```
+
+- **API:** http://localhost:8080/hindsight/docs
+- **Control Plane:** http://localhost:9999 (direct access, not proxied)
+
+## Full Stack with Custom Base Path (Requires Build)
+
+**Important:** You cannot rebuild from the published image with build args. You must build from source.
+
+### Build from Source with Custom Base Path
+
+1. **Clone the repository** (if you haven't):
+```bash
+git clone https://github.com/vectorize-io/hindsight.git
+cd hindsight
+```
+
+2. **Build with base path**:
+```bash
+docker build \
+  --build-arg NEXT_PUBLIC_BASE_PATH=/hindsight \
+  -f docker/standalone/Dockerfile \
+  -t hindsight:custom \
+  .
+```
+
+3. **Update docker-compose.yml** to use your built image:
+```yaml
+services:
+  hindsight:
+    image: hindsight:custom  # ← Change this
+    environment:
+      HINDSIGHT_API_BASE_PATH: /hindsight
+      NEXT_PUBLIC_BASE_PATH: /hindsight
+```
+
+4. **Update nginx.conf** to handle Control Plane routes (see below)
+
+5. **Run**:
+```bash
+docker-compose up
+```
+
+### Required nginx.conf for Full Stack
+
+Replace the current `nginx.conf` with this to proxy both API and Control Plane:
+
+```nginx
+events { worker_connections 1024; }
+
+http {
+    include /etc/nginx/mime.types;
+    default_type application/octet-stream;
+
+    upstream hindsight_api { server hindsight:8888; }
+    upstream hindsight_cp { server hindsight:9999; }
+
+    server {
+        listen 80;
+
+        # API
+        location ~ ^/hindsight/(docs|openapi\.json|health|metrics|v1|mcp) {
+            proxy_pass http://hindsight_api;
+            proxy_set_header Host $http_host;
+        }
+
+        # Control Plane static files
+        location ~ ^/hindsight/_next/ {
+            proxy_pass http://hindsight_cp;
+            proxy_set_header Host $http_host;
+        }
+
+        # Control Plane UI
+        location /hindsight {
+            proxy_pass http://hindsight_cp;
+            proxy_set_header Host $http_host;
+        }
+
+        location = / { return 301 /hindsight; }
+    }
+}
+```
+
+### Why Build is Required
+
+Next.js requires `basePath` at **build time**. The published image was built without a custom base path, so you must rebuild from source with the `NEXT_PUBLIC_BASE_PATH` build arg to deploy the Control Plane under a subpath.
+
+The API works without rebuild because `HINDSIGHT_API_BASE_PATH` is a runtime environment variable.

docker/docker-compose/nginx/docker-compose.yml

@@ -0,0 +1,88 @@
+# Hindsight API deployment with Nginx reverse proxy (API-only)
+#
+# This example deploys Hindsight API under the path /hindsight with:
+# - Hindsight standalone image (API + Control Plane + embedded pg0)
+# - Nginx reverse proxy (API only)
+#
+# Quick Start:
+#   docker-compose -f docker/docker-compose/nginx/docker-compose.yml up
+#
+# Access:
+#   API (via nginx):         http://localhost:8080/hindsight/docs
+#   Control Plane (direct):  http://localhost:9999
+#
+# For full stack deployment (API + Control Plane both under /hindsight):
+#   See README.md in this directory for instructions on building with basePath.
+#
+# Note: This configuration uses the published image (no build required).
+#       Control Plane is served directly because Next.js basePath requires
+#       build-time configuration. See README.md for the full stack option.
+
+services:
+  # Hindsight (API + Control Plane + embedded pg0)
+  hindsight:
+    image: ghcr.io/vectorize-io/hindsight:latest
+    ports:
+      - "9999:9999"  # Control Plane (direct access, not proxied)
+    environment:
+      # API base path for reverse proxy
+      HINDSIGHT_API_BASE_PATH: /hindsight
+
+      # LLM configuration
+      # Using mock provider for testing (no API key needed)
+      # For production, set OPENAI_API_KEY or ANTHROPIC_API_KEY and use a real provider
+      HINDSIGHT_API_LLM_PROVIDER: ${HINDSIGHT_API_LLM_PROVIDER:-mock}
+      HINDSIGHT_API_LLM_API_KEY: ${OPENAI_API_KEY:-not-needed-for-mock}
+      HINDSIGHT_API_LLM_MODEL: ${HINDSIGHT_API_LLM_MODEL:-mock-model}
+
+      # Production examples (uncomment and set appropriate API key):
+      # HINDSIGHT_API_LLM_PROVIDER: openai
+      # HINDSIGHT_API_LLM_API_KEY: ${OPENAI_API_KEY}
+      # HINDSIGHT_API_LLM_MODEL: gpt-4o-mini
+
+      # HINDSIGHT_API_LLM_PROVIDER: anthropic
+      # HINDSIGHT_API_LLM_API_KEY: ${ANTHROPIC_API_KEY}
+      # HINDSIGHT_API_LLM_MODEL: claude-sonnet-4-20250514
+
+      # Server config
+      HINDSIGHT_API_HOST: 0.0.0.0
+      HINDSIGHT_API_PORT: 8888
+      HINDSIGHT_API_LOG_LEVEL: info
+
+      # Control Plane config
+      HINDSIGHT_CP_DATAPLANE_API_URL: http://localhost:8888
+    volumes:
+      # Persist embedded pg0 database
+      - hindsight_data:/app/data
+    # Note: Ports not exposed - access via Nginx at localhost:8080/hindsight/
+    # To debug directly, uncomment these ports:
+    # ports:
+    #   - "8888:8888"  # API
+    #   - "9999:9999"  # Control Plane
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8888/hindsight/health"]
+      interval: 10s
+      timeout: 5s
+      retries: 3
+      start_period: 30s
+    networks:
+      - hindsight
+
+  # Nginx reverse proxy
+  nginx:
+    image: nginx:alpine
+    ports:
+      - "8080:80"
+    volumes:
+      - ./nginx.conf:/etc/nginx/nginx.conf:ro
+    depends_on:
+      hindsight:
+        condition: service_healthy
+    networks:
+      - hindsight
+
+volumes:
+  hindsight_data:
+
+networks:
+  hindsight:

docker/docker-compose/nginx/nginx.conf

@@ -0,0 +1,40 @@
+# Nginx configuration for API-only reverse proxy
+# Control Plane accessed directly (not through nginx)
+
+events {
+    worker_connections 1024;
+}
+
+http {
+    include /etc/nginx/mime.types;
+    default_type application/octet-stream;
+
+    # Logging
+    access_log /var/log/nginx/access.log;
+    error_log /var/log/nginx/error.log;
+
+    # Upstream - Hindsight API
+    upstream hindsight_api {
+        server hindsight:8888;
+    }
+
+    server {
+        listen 80;
+        server_name _;
+
+        # API endpoints - forward with /hindsight prefix
+        location /hindsight/ {
+            proxy_pass http://hindsight_api;
+
+            proxy_set_header Host $http_host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+            proxy_set_header X-Forwarded-Proto $scheme;
+        }
+
+        # Redirect root to API docs
+        location = / {
+            return 301 /hindsight/docs;
+        }
+    }
+}

docker/docker-compose/pg_textsearch/Dockerfile

@@ -0,0 +1,32 @@
+# PostgreSQL with pgvector and pg_textsearch extensions
+# Note: pg_textsearch requires PostgreSQL 17+
+FROM postgres:17
+
+# Install build dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    git \
+    postgresql-server-dev-17 \
+    libpq-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install pgvector
+RUN cd /tmp && \
+    git clone --branch v0.8.0 https://github.com/pgvector/pgvector.git && \
+    cd pgvector && \
+    make && \
+    make install
+
+# Install pg_textsearch
+RUN cd /tmp && \
+    git clone https://github.com/timescale/pg_textsearch.git && \
+    cd pg_textsearch && \
+    make && \
+    make install
+
+# Clean up source files and build dependencies
+RUN rm -rf /tmp/pgvector /tmp/pg_textsearch && \
+    apt-get purge -y --auto-remove build-essential git postgresql-server-dev-17
+
+# Ensure extensions are preloaded
+RUN echo "shared_preload_libraries = 'pg_textsearch'" >> /usr/share/postgresql/postgresql.conf.sample

docker/docker-compose/pg_textsearch/docker-compose.yaml

@@ -0,0 +1,91 @@
+name: hindsight
+# Docker Compose file for Hindsight with PostgreSQL and Timescale pg_textsearch
+# docker compose -f docker/docker-compose/pg_textsearch/docker-compose.yaml down && sleep 2 && docker compose -f docker/docker-compose/pg_textsearch/docker-compose.yaml up -d
+# Make sure to set the required environment variables before running:
+# - HINDSIGHT_DB_PASSWORD: Password for the PostgreSQL user
+# - Configure LLM provider variables as needed (see below in the hindsight service)
+#
+# Usage:
+# docker compose up -d
+#
+# Optional environment variables with defaults:
+# - HINDSIGHT_VERSION: Hindsight application version (default: latest)
+# - HINDSIGHT_DB_USER: PostgreSQL user (default: hindsight_user)
+# - HINDSIGHT_DB_NAME: PostgreSQL database name (default: hindsight_db)
+
+services:
+  db:
+    # Use custom PostgreSQL image with pgvector and pg_textsearch extensions
+    build:
+      context: .
+      dockerfile: Dockerfile
+    container_name: hindsight-db
+    restart: always
+    # Expose PostgreSQL port
+    ports:
+      - "5437:5432"
+    environment:
+      POSTGRES_USER: ${HINDSIGHT_DB_USER:-hindsight_user}
+      POSTGRES_PASSWORD: ${HINDSIGHT_DB_PASSWORD:-hindsight_password}
+      POSTGRES_DB: ${HINDSIGHT_DB_NAME:-hindsight_db}
+    volumes:
+      - pg_data:/var/lib/postgresql/data
+    networks:
+      - hindsight-net
+
+  pg-textsearch-init:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    depends_on:
+      - db
+    environment:
+      - PGPASSWORD=${HINDSIGHT_DB_PASSWORD:-hindsight_password}
+    command: >
+      bash -c "
+        echo 'Waiting for PostgreSQL to be ready...';
+        until pg_isready -h hindsight-db -p 5432 -U hindsight_user; do
+          echo 'PostgreSQL is unavailable - sleeping';
+          sleep 2;
+        done;
+        echo 'PostgreSQL is ready - creating hindsight_db database';
+        psql -h hindsight-db -p 5432 -U hindsight_user -c 'CREATE DATABASE hindsight_db;' 2>/dev/null || echo 'Database already exists';
+        echo 'Creating extensions in hindsight_db database';
+        psql -h hindsight-db -p 5432 -U hindsight_user -d hindsight_db -c 'CREATE EXTENSION IF NOT EXISTS vector CASCADE;';
+        psql -h hindsight-db -p 5432 -U hindsight_user -d hindsight_db -c 'CREATE EXTENSION IF NOT EXISTS pg_textsearch CASCADE;';
+        echo 'Database and extensions created successfully';
+      "
+    restart: "no"
+    networks:
+      - hindsight-net
+
+  hindsight:
+    image: ghcr.io/vectorize-io/hindsight:${HINDSIGHT_VERSION:-latest}
+    container_name: hindsight-app
+    ports:
+      - "8888:8888"
+      - "9999:9999"
+    environment:
+      # LLM Configuration
+      HINDSIGHT_API_LLM_PROVIDER: ${HINDSIGHT_API_LLM_PROVIDER:-openai}
+      HINDSIGHT_API_LLM_API_KEY: ${OPENAI_API_KEY:-your-api-key}
+
+      # Database Configuration
+      HINDSIGHT_API_DATABASE_URL: postgresql://${HINDSIGHT_DB_USER:-hindsight_user}:${HINDSIGHT_DB_PASSWORD:-hindsight_password}@db:5432/${HINDSIGHT_DB_NAME:-hindsight_db}
+
+      # Vector and Text Search Extensions
+      HINDSIGHT_API_VECTOR_EXTENSION: pgvector
+      HINDSIGHT_API_TEXT_SEARCH_EXTENSION: pg_textsearch
+
+    depends_on:
+      - db
+    networks:
+      - hindsight-net
+
+
+networks:
+  hindsight-net:
+    driver: bridge
+
+volumes:
+  pg_data:

docker/docker-compose/vchord/docker-compose.yaml

@@ -0,0 +1,93 @@
+name: hindsight
+# Docker Compose file for Hindsight with PostgreSQL and vectorchord
+# docker compose -f docker/docker-compose/docker-compose.yaml down && sleep 2 && docker compose -f docker/docker-compose/docker-compose.yaml up -d
+# Make sure to set the required environment variables before running:
+# - HINDSIGHT_DB_PASSWORD: Password for the PostgreSQL user
+# - Configure LLM provider variables as needed (see below in the hindsight service)
+#
+# Usage:
+# docker compose up -d
+#
+# Optional environment variables with defaults:
+# - HINDSIGHT_VERSION: Hindsight application version (default: latest)
+# - HINDSIGHT_DB_USER: PostgreSQL user (default: hindsight_user)
+# - HINDSIGHT_DB_NAME: PostgreSQL database name (default: hindsight_db)
+# - HINDSIGHT_DB_VERSION: PostgreSQL version (default: 18)
+
+services:
+  db:
+    # Use a PostgreSQL-Image with vectorchord extension pre-installed
+    image: tensorchord/vchord-suite:pg${HINDSIGHT_DB_VERSION:-18-latest}
+    container_name: hindsight-db
+    restart: always
+    # Expose PostgreSQL port
+    ports:
+      - "5436:5432"
+    environment:
+      POSTGRES_USER: ${HINDSIGHT_DB_USER:-hindsight_user}
+      POSTGRES_PASSWORD: ${HINDSIGHT_DB_PASSWORD:-hindsight_password}
+      POSTGRES_DB: ${HINDSIGHT_DB_NAME:-hindsight_db}
+    volumes:
+      - pg_data:/var/lib/postgresql/${HINDSIGHT_DB_VERSION:-18}/docker
+    networks:
+      - hindsight-net
+
+  vectorchord-init:
+    image: tensorchord/vchord-suite:pg18-latest
+    #container_name: vectorchord-init
+    depends_on:
+      - db
+    environment:
+      - PGPASSWORD=${HINDSIGHT_DB_PASSWORD:-hindsight_password}
+    command: >
+      bash -c "
+        echo 'Waiting for PostgreSQL to be ready...';
+        until pg_isready -h hindsight-db -p 5432 -U hindsight_user; do
+          echo 'PostgreSQL is unavailable - sleeping';
+          sleep 2;
+        done;
+        echo 'PostgreSQL is ready - creating hindsight_db database';
+        psql -h hindsight-db -p 5432 -U hindsight_user -c 'CREATE DATABASE hindsight_db;' 2>/dev/null || echo 'Database already exists';
+        echo 'Creating extensions in hindsight_db database';
+        psql -h hindsight-db -p 5432 -U hindsight_user -d hindsight_db -c 'CREATE EXTENSION IF NOT EXISTS vchord CASCADE;';
+        psql -h hindsight-db -p 5432 -U hindsight_user -d hindsight_db -c 'CREATE EXTENSION IF NOT EXISTS pg_tokenizer CASCADE;';
+        psql -h hindsight-db -p 5432 -U hindsight_user -d hindsight_db -c 'CREATE EXTENSION IF NOT EXISTS vchord_bm25 CASCADE;';
+        echo 'Creating llmlingua2 tokenizer';
+        psql -h hindsight-db -p 5432 -U hindsight_user -d hindsight_db -c \"SELECT create_tokenizer('llmlingua2', \\$\\$ model = \\\"llmlingua2\\\" \\$\\$);\" 2>/dev/null || echo 'Tokenizer already exists or creation skipped';
+        echo 'Database and extensions created successfully';
+      "
+    restart: "no"
+    networks:
+      - hindsight-net
+
+  hindsight:
+    image: ghcr.io/vectorize-io/hindsight:${HINDSIGHT_VERSION:-latest}
+    container_name: hindsight-app
+    ports:
+      - "8888:8888"
+      - "9999:9999"
+    environment:
+      # LLM Configuration (uses OpenAI for testing vchord)
+      # LLM configuration
+      HINDSIGHT_API_LLM_PROVIDER: ${HINDSIGHT_API_LLM_PROVIDER:-openai}
+      HINDSIGHT_API_LLM_API_KEY: ${OPENAI_API_KEY:-your-api-key}
+
+      # Database Configuration
+      HINDSIGHT_API_DATABASE_URL: postgresql://${HINDSIGHT_DB_USER:-hindsight_user}:${HINDSIGHT_DB_PASSWORD:-hindsight_password}@db:5432/${HINDSIGHT_DB_NAME:-hindsight_db}
+
+      # Vector and Text Search Extensions
+      HINDSIGHT_API_VECTOR_EXTENSION: vchord
+      HINDSIGHT_API_TEXT_SEARCH_EXTENSION: vchord
+
+    depends_on:
+      - db
+    networks:
+      - hindsight-net
+
+
+networks:
+  hindsight-net:
+    driver: bridge
+
+volumes:
+  pg_data:

docker/standalone/Dockerfile

@@ -112,6 +112,10 @@ RUN rm -f package-lock.json && sed -i '/"@vectorize-io\/hindsight-client":/d' pa
 # Copy built SDK directly into node_modules (more reliable than npm link in Docker)
 COPY --from=sdk-builder /app/hindsight-clients/typescript ./node_modules/@vectorize-io/hindsight-client
 
+# Accept base path as build argument for reverse proxy deployments
+# Usage: docker build --build-arg NEXT_PUBLIC_BASE_PATH=/hindsight ...
+ARG NEXT_PUBLIC_BASE_PATH=""
+
 # Build Control Plane - run next build first, then custom standalone copy
 # (The build:standalone script expects a specific path structure that differs in Docker)
 RUN npm exec -- next build

helm/hindsight/Chart.yaml

@@ -2,8 +2,8 @@ apiVersion: v2
 name: hindsight
 description: Hindsight helm chart
 type: application
-version: 0.4.10
-appVersion: "0.4.10"
+version: 0.4.11
+appVersion: "0.4.11"
 keywords:
   - ai
   - memory

hindsight-api/hindsight_api/__init__.py

@@ -46,4 +46,4 @@ __all__ = [
     "RemoteTEICrossEncoder",
     "LLMConfig",
 ]
-__version__ = "0.4.10"
+__version__ = "0.4.11"

hindsight-api/hindsight_api/alembic/versions/5a366d414dce_initial_schema.py

@@ -6,6 +6,7 @@ Create Date: 2025-11-27 11:54:19.228030
 
 """
 
+import os
 from collections.abc import Sequence
 
 import sqlalchemy as sa
@@ -21,6 +22,73 @@ branch_labels: str | Sequence[str] | None = None
 depends_on: str | Sequence[str] | None = None
 
 
+def _detect_vector_extension() -> str:
+    """
+    Detect or validate vector extension: 'vchord' or 'pgvector'.
+    Respects HINDSIGHT_API_VECTOR_EXTENSION env var if set.
+    """
+    conn = op.get_bind()
+    vector_extension = os.getenv("HINDSIGHT_API_VECTOR_EXTENSION", "pgvector").lower()
+
+    # Validate configured extension is installed
+    if vector_extension == "vchord":
+        vchord_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vchord'")).scalar()
+        if not vchord_check:
+            raise RuntimeError(
+                "Configured vector extension 'vchord' not found. Install it with: CREATE EXTENSION vchord CASCADE;"
+            )
+        return "vchord"
+    elif vector_extension == "pgvector":
+        pgvector_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vector'")).scalar()
+        if not pgvector_check:
+            raise RuntimeError(
+                "Configured vector extension 'pgvector' not found. Install it with: CREATE EXTENSION vector;"
+            )
+        return "pgvector"
+    else:
+        raise ValueError(f"Invalid HINDSIGHT_API_VECTOR_EXTENSION: {vector_extension}. Must be 'pgvector' or 'vchord'")
+
+
+def _detect_text_search_extension() -> str:
+    """
+    Detect or validate text search extension: 'native', 'vchord', or 'pg_textsearch'.
+    Respects HINDSIGHT_API_TEXT_SEARCH_EXTENSION env var.
+    Creates the extension if needed.
+    """
+    text_search_extension = os.getenv("HINDSIGHT_API_TEXT_SEARCH_EXTENSION", "native").lower()
+
+    if text_search_extension == "vchord":
+        # Create vchord_bm25 extension if not exists
+        try:
+            op.execute("CREATE EXTENSION IF NOT EXISTS vchord_bm25 CASCADE")
+        except Exception:
+            # Extension might already exist or user lacks permissions - verify it exists
+            conn = op.get_bind()
+            result = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vchord_bm25'")).fetchone()
+            if not result:
+                # Extension truly doesn't exist - re-raise the error
+                raise
+        return "vchord"
+    elif text_search_extension == "pg_textsearch":
+        # Create pg_textsearch extension if not exists
+        try:
+            op.execute("CREATE EXTENSION IF NOT EXISTS pg_textsearch CASCADE")
+        except Exception:
+            # Extension might already exist or user lacks permissions - verify it exists
+            conn = op.get_bind()
+            result = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'pg_textsearch'")).fetchone()
+            if not result:
+                # Extension truly doesn't exist - re-raise the error
+                raise
+        return "pg_textsearch"
+    elif text_search_extension == "native":
+        return "native"
+    else:
+        raise ValueError(
+            f"Invalid HINDSIGHT_API_TEXT_SEARCH_EXTENSION: {text_search_extension}. Must be 'native', 'vchord', or 'pg_textsearch'"
+        )
+
+
 def upgrade() -> None:
     """Upgrade schema - create all tables from scratch."""
 
@@ -166,11 +234,29 @@ def upgrade() -> None:
     )
 
     # Add search_vector column for full-text search
-    op.execute("""
-        ALTER TABLE memory_units
-        ADD COLUMN search_vector tsvector
-        GENERATED ALWAYS AS (to_tsvector('english', COALESCE(text, '') || ' ' || COALESCE(context, ''))) STORED
-    """)
+    # Type depends on configured text search backend
+    text_search_ext = _detect_text_search_extension()
+
+    if text_search_ext == "vchord":
+        # VectorChord BM25: bm25vector type (no GENERATED - tokenization happens on INSERT)
+        # Note: vchord_bm25 extension creates types in bm25_catalog schema
+        op.execute("""
+            ALTER TABLE memory_units
+            ADD COLUMN search_vector bm25_catalog.bm25vector
+        """)
+    elif text_search_ext == "pg_textsearch":
+        # Timescale pg_textsearch: dummy TEXT column for consistency (indexes operate on base columns directly)
+        op.execute("""
+            ALTER TABLE memory_units
+            ADD COLUMN search_vector TEXT
+        """)
+    else:  # native
+        # Native PostgreSQL: tsvector with automatic generation
+        op.execute("""
+            ALTER TABLE memory_units
+            ADD COLUMN search_vector tsvector
+            GENERATED ALWAYS AS (to_tsvector('english', COALESCE(text, '') || ' ' || COALESCE(context, ''))) STORED
+        """)
 
     op.create_index("idx_memory_units_bank_id", "memory_units", ["bank_id"])
     op.create_index("idx_memory_units_document_id", "memory_units", ["document_id"])
@@ -200,19 +286,47 @@ def upgrade() -> None:
         ["bank_id", sa.text("event_date DESC")],
         postgresql_where=sa.text("fact_type = 'observation'"),
     )
-    op.create_index(
-        "idx_memory_units_embedding",
-        "memory_units",
-        ["embedding"],
-        postgresql_using="hnsw",
-        postgresql_ops={"embedding": "vector_cosine_ops"},
-    )
+    # Create vector index - conditional based on available extension
+    vector_ext = _detect_vector_extension()
 
-    # Create BM25 full-text search index on search_vector
-    op.execute("""
-        CREATE INDEX idx_memory_units_text_search ON memory_units
-        USING gin(search_vector)
-    """)
+    if vector_ext == "vchord":
+        # Use vchordrq index for vchord (supports high-dimensional embeddings)
+        op.execute("""
+            CREATE INDEX idx_memory_units_embedding ON memory_units
+            USING vchordrq (embedding vector_l2_ops)
+        """)
+    else:  # pgvector
+        # Use HNSW index for pgvector
+        op.create_index(
+            "idx_memory_units_embedding",
+            "memory_units",
+            ["embedding"],
+            postgresql_using="hnsw",
+            postgresql_ops={"embedding": "vector_cosine_ops"},
+        )
+
+    # Create full-text search index on search_vector
+    # Index type depends on text search backend
+    if text_search_ext == "vchord":
+        # VectorChord BM25 index
+        op.execute("""
+            CREATE INDEX idx_memory_units_text_search ON memory_units
+            USING bm25 (search_vector bm25_catalog.bm25_ops)
+        """)
+    elif text_search_ext == "pg_textsearch":
+        # Timescale pg_textsearch BM25 index on text column
+        # Note: pg_textsearch doesn't support expressions, so we index the main text column
+        op.execute("""
+            CREATE INDEX idx_memory_units_text_search ON memory_units
+            USING bm25(text)
+            WITH (text_config='english')
+        """)
+    else:  # native
+        # Native PostgreSQL GIN index
+        op.execute("""
+            CREATE INDEX idx_memory_units_text_search ON memory_units
+            USING gin(search_vector)
+        """)
 
     op.execute("""
         CREATE MATERIALIZED VIEW memory_units_bm25 AS

hindsight-api/hindsight_api/alembic/versions/n9i0j1k2l3m4_learnings_and_pinned_reflections.py

@@ -10,9 +10,11 @@ This migration:
 3. Adds consolidation tracking columns to the 'banks' table
 """
 
+import os
 from collections.abc import Sequence
 
 from alembic import context, op
+from sqlalchemy import text
 
 # revision identifiers, used by Alembic.
 revision: str = "n9i0j1k2l3m4"
@@ -27,10 +29,83 @@ def _get_schema_prefix() -> str:
     return f'"{schema}".' if schema else ""
 
 
+def _detect_vector_extension() -> str:
+    """
+    Detect or validate vector extension: 'vchord' or 'pgvector'.
+    Respects HINDSIGHT_API_VECTOR_EXTENSION env var if set.
+    """
+    conn = op.get_bind()
+    vector_extension = os.getenv("HINDSIGHT_API_VECTOR_EXTENSION", "pgvector").lower()
+
+    # Validate configured extension is installed
+    if vector_extension == "vchord":
+        vchord_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vchord'")).scalar()
+        if not vchord_check:
+            raise RuntimeError(
+                "Configured vector extension 'vchord' not found. Install it with: CREATE EXTENSION vchord CASCADE;"
+            )
+        return "vchord"
+    elif vector_extension == "pgvector":
+        pgvector_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vector'")).scalar()
+        if not pgvector_check:
+            raise RuntimeError(
+                "Configured vector extension 'pgvector' not found. Install it with: CREATE EXTENSION vector;"
+            )
+        return "pgvector"
+    else:
+        raise ValueError(f"Invalid HINDSIGHT_API_VECTOR_EXTENSION: {vector_extension}. Must be 'pgvector' or 'vchord'")
+
+
+def _detect_text_search_extension() -> str:
+    """
+    Detect or validate text search extension: 'native', 'vchord', or 'pg_textsearch'.
+    Respects HINDSIGHT_API_TEXT_SEARCH_EXTENSION env var.
+    Creates the extension if needed.
+    """
+    text_search_extension = os.getenv("HINDSIGHT_API_TEXT_SEARCH_EXTENSION", "native").lower()
+
+    if text_search_extension == "vchord":
+        # Create vchord_bm25 extension if not exists
+        try:
+            op.execute("CREATE EXTENSION IF NOT EXISTS vchord_bm25 CASCADE")
+        except Exception:
+            # Extension might already exist or user lacks permissions - verify it exists
+            conn = op.get_bind()
+            result = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vchord_bm25'")).fetchone()
+            if not result:
+                # Extension truly doesn't exist - re-raise the error
+                raise
+        return "vchord"
+    elif text_search_extension == "pg_textsearch":
+        # Create pg_textsearch extension if not exists
+        try:
+            op.execute("CREATE EXTENSION IF NOT EXISTS pg_textsearch CASCADE")
+        except Exception:
+            # Extension might already exist or user lacks permissions - verify it exists
+            conn = op.get_bind()
+            result = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'pg_textsearch'")).fetchone()
+            if not result:
+                # Extension truly doesn't exist - re-raise the error
+                raise
+        return "pg_textsearch"
+    elif text_search_extension == "native":
+        return "native"
+    else:
+        raise ValueError(
+            f"Invalid HINDSIGHT_API_TEXT_SEARCH_EXTENSION: {text_search_extension}. Must be 'native', 'vchord', or 'pg_textsearch'"
+        )
+
+
 def upgrade() -> None:
     """Create learnings and pinned_reflections tables."""
     schema = _get_schema_prefix()
 
+    # Detect which vector extension is available
+    vector_ext = _detect_vector_extension()
+
+    # Detect which text search extension to use
+    text_search_ext = _detect_text_search_extension()
+
     # 1. Create learnings table
     op.execute(f"""
         CREATE TABLE {schema}learnings (
@@ -57,18 +132,48 @@ def upgrade() -> None:
 
     # Indexes for learnings
     op.execute(f"CREATE INDEX idx_learnings_bank_id ON {schema}learnings(bank_id)")
-    op.execute(f"""
-        CREATE INDEX idx_learnings_embedding ON {schema}learnings
-        USING hnsw (embedding vector_cosine_ops)
-    """)
+
+    # Create vector index based on detected extension
+    if vector_ext == "vchord":
+        op.execute(f"""
+            CREATE INDEX idx_learnings_embedding ON {schema}learnings
+            USING vchordrq (embedding vector_l2_ops)
+        """)
+    else:  # pgvector
+        op.execute(f"""
+            CREATE INDEX idx_learnings_embedding ON {schema}learnings
+            USING hnsw (embedding vector_cosine_ops)
+        """)
+
     op.execute(f"CREATE INDEX idx_learnings_tags ON {schema}learnings USING GIN(tags)")
 
     # Full-text search for learnings
-    op.execute(f"""
-        ALTER TABLE {schema}learnings ADD COLUMN search_vector tsvector
-        GENERATED ALWAYS AS (to_tsvector('english', text)) STORED
-    """)
-    op.execute(f"CREATE INDEX idx_learnings_text_search ON {schema}learnings USING gin(search_vector)")
+    if text_search_ext == "vchord":
+        # VectorChord BM25: bm25vector type (no GENERATED - tokenization happens on INSERT)
+        # Note: vchord_bm25 extension creates types in bm25_catalog schema
+        op.execute(f"""
+            ALTER TABLE {schema}learnings ADD COLUMN search_vector bm25_catalog.bm25vector
+        """)
+        op.execute(f"""
+            CREATE INDEX idx_learnings_text_search ON {schema}learnings
+            USING bm25 (search_vector bm25_catalog.bm25_ops)
+        """)
+    elif text_search_ext == "pg_textsearch":
+        # Timescale pg_textsearch: dummy TEXT column for consistency (indexes operate on base columns directly)
+        op.execute(f"""
+            ALTER TABLE {schema}learnings ADD COLUMN search_vector TEXT
+        """)
+        op.execute(f"""
+            CREATE INDEX idx_learnings_text_search ON {schema}learnings
+            USING bm25(text) WITH (text_config='english')
+        """)
+    else:  # native
+        # Native PostgreSQL: tsvector with automatic generation
+        op.execute(f"""
+            ALTER TABLE {schema}learnings ADD COLUMN search_vector tsvector
+            GENERATED ALWAYS AS (to_tsvector('english', text)) STORED
+        """)
+        op.execute(f"CREATE INDEX idx_learnings_text_search ON {schema}learnings USING gin(search_vector)")
 
     # 2. Create pinned_reflections table
     op.execute(f"""
@@ -94,21 +199,52 @@ def upgrade() -> None:
 
     # Indexes for pinned_reflections
     op.execute(f"CREATE INDEX idx_pinned_reflections_bank_id ON {schema}pinned_reflections(bank_id)")
-    op.execute(f"""
-        CREATE INDEX idx_pinned_reflections_embedding ON {schema}pinned_reflections
-        USING hnsw (embedding vector_cosine_ops)
-    """)
+
+    # Create vector index based on detected extension
+    if vector_ext == "vchord":
+        op.execute(f"""
+            CREATE INDEX idx_pinned_reflections_embedding ON {schema}pinned_reflections
+            USING vchordrq (embedding vector_l2_ops)
+        """)
+    else:  # pgvector
+        op.execute(f"""
+            CREATE INDEX idx_pinned_reflections_embedding ON {schema}pinned_reflections
+            USING hnsw (embedding vector_cosine_ops)
+        """)
+
     op.execute(f"CREATE INDEX idx_pinned_reflections_tags ON {schema}pinned_reflections USING GIN(tags)")
 
     # Full-text search for pinned_reflections
-    op.execute(f"""
-        ALTER TABLE {schema}pinned_reflections ADD COLUMN search_vector tsvector
-        GENERATED ALWAYS AS (to_tsvector('english', COALESCE(name, '') || ' ' || content)) STORED
-    """)
-    op.execute(f"""
-        CREATE INDEX idx_pinned_reflections_text_search ON {schema}pinned_reflections
-        USING gin(search_vector)
-    """)
+    if text_search_ext == "vchord":
+        # VectorChord BM25: bm25vector type (no GENERATED - tokenization happens on INSERT/UPDATE)
+        # Note: vchord_bm25 extension creates types in bm25_catalog schema
+        op.execute(f"""
+            ALTER TABLE {schema}pinned_reflections ADD COLUMN search_vector bm25_catalog.bm25vector
+        """)
+        op.execute(f"""
+            CREATE INDEX idx_pinned_reflections_text_search ON {schema}pinned_reflections
+            USING bm25 (search_vector bm25_catalog.bm25_ops)
+        """)
+    elif text_search_ext == "pg_textsearch":
+        # Timescale pg_textsearch: dummy TEXT column for consistency (indexes operate on base columns directly)
+        op.execute(f"""
+            ALTER TABLE {schema}pinned_reflections ADD COLUMN search_vector TEXT
+        """)
+        op.execute(f"""
+            CREATE INDEX idx_pinned_reflections_text_search ON {schema}pinned_reflections
+            USING bm25(content)
+            WITH (text_config='english')
+        """)
+    else:  # native
+        # Native PostgreSQL: tsvector with automatic generation
+        op.execute(f"""
+            ALTER TABLE {schema}pinned_reflections ADD COLUMN search_vector tsvector
+            GENERATED ALWAYS AS (to_tsvector('english', COALESCE(name, '') || ' ' || content)) STORED
+        """)
+        op.execute(f"""
+            CREATE INDEX idx_pinned_reflections_text_search ON {schema}pinned_reflections
+            USING gin(search_vector)
+        """)
 
     # 3. Add consolidation tracking columns to banks table
     op.execute(f"""

hindsight-api/hindsight_api/alembic/versions/x9s0t1u2v3w4_add_bank_config_column.py

@@ -0,0 +1,64 @@
+"""Add config JSONB column to banks table for hierarchical configuration
+
+Revision ID: x9s0t1u2v3w4
+Revises: w8r9s0t1u2v3
+Create Date: 2026-02-09
+
+This migration adds a `config` JSONB column to the banks table to support
+per-bank configuration overrides. This enables hierarchical configuration where:
+- Global config is loaded from environment variables
+- Tenant config is provided via TenantExtension
+- Bank config overrides are stored in banks.config JSONB column
+
+The config column stores overrides for hierarchical fields (LLM settings,
+retention parameters, retrieval settings, etc.) in Python field name format.
+"""
+
+from collections.abc import Sequence
+
+import sqlalchemy as sa
+from alembic import context, op
+from sqlalchemy.dialects.postgresql import JSONB
+
+revision: str = "x9s0t1u2v3w4"
+down_revision: str | Sequence[str] | None = "w8r9s0t1u2v3"
+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:
+    """Add config JSONB column to banks table with GIN index."""
+    schema = _get_schema_prefix()
+
+    # Add config column to banks table
+    op.execute(f"""
+        ALTER TABLE {schema}banks
+        ADD COLUMN config JSONB NOT NULL DEFAULT '{{}}'::jsonb
+    """)
+
+    # Add GIN index for efficient JSONB queries
+    op.execute(f"""
+        CREATE INDEX idx_banks_config
+        ON {schema}banks
+        USING gin(config)
+    """)
+
+
+def downgrade() -> None:
+    """Remove config column and index from banks table."""
+    schema = _get_schema_prefix()
+
+    # Drop index first
+    op.execute(f"DROP INDEX IF EXISTS {schema}idx_banks_config")
+
+    # Drop column
+    op.execute(f"""
+        ALTER TABLE {schema}banks
+        DROP COLUMN IF EXISTS config
+    """)

hindsight-api/hindsight_api/alembic/versions/y0t1u2v3w4x5_add_result_metadata_gin_index.py

@@ -0,0 +1,49 @@
+"""Add GIN index on async_operations.result_metadata for parent_operation_id queries
+
+Revision ID: y0t1u2v3w4x5
+Revises: x9s0t1u2v3w4
+Create Date: 2026-02-13
+
+This migration adds a GIN index on the result_metadata JSONB column in the
+async_operations table to support efficient queries for child operations by
+parent_operation_id.
+
+The index enables fast lookups when querying for child operations:
+  SELECT * FROM async_operations
+  WHERE result_metadata::jsonb @> '{"parent_operation_id": "uuid"}'::jsonb
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "y0t1u2v3w4x5"
+down_revision: str | Sequence[str] | None = "x9s0t1u2v3w4"
+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:
+    """Add GIN index on result_metadata for efficient parent_operation_id queries."""
+    schema = _get_schema_prefix()
+
+    # Add GIN index for JSONB containment queries (@> operator)
+    op.execute(f"""
+        CREATE INDEX idx_async_operations_result_metadata
+        ON {schema}async_operations
+        USING gin(result_metadata)
+    """)
+
+
+def downgrade() -> None:
+    """Remove GIN index on result_metadata."""
+    schema = _get_schema_prefix()
+
+    # Drop index
+    op.execute(f"DROP INDEX IF EXISTS {schema}idx_async_operations_result_metadata")

hindsight-api/hindsight_api/api/http.py

@@ -32,9 +32,45 @@ def _parse_metadata(metadata: Any) -> dict[str, Any]:
     return {}
 
 
+from typing import Callable
+
 from pydantic import BaseModel, ConfigDict, Field, field_validator
 
 from hindsight_api import MemoryEngine
+
+
+def FieldWithDefault(default_factory: Callable, **kwargs) -> Any:
+    """
+    Field wrapper that ensures default_factory values appear in OpenAPI schema.
+
+    Pydantic doesn't include default_factory in OpenAPI schemas, causing OpenAPI
+    Generator to make fields Optional with default=None instead of non-optional
+    with the correct default value.
+
+    This wrapper adds json_schema_extra to include the default in the schema.
+    """
+    # Determine the default value for the schema based on the factory
+    if default_factory is list:
+        schema_default = []
+    elif default_factory is dict:
+        schema_default = {}
+    else:
+        # For custom factories (like IncludeOptions), use empty dict as placeholder
+        schema_default = {}
+
+    # Add or merge json_schema_extra
+    json_extra = kwargs.pop("json_schema_extra", {})
+    if isinstance(json_extra, dict):
+        json_extra["default"] = schema_default
+    else:
+        # If json_schema_extra was a function, we can't merge easily
+        # Fall back to just setting default
+        json_extra = {"default": schema_default}
+
+    return Field(default_factory=default_factory, json_schema_extra=json_extra, **kwargs)
+
+
+from hindsight_api.config import get_config
 from hindsight_api.engine.db_utils import acquire_with_retry
 from hindsight_api.engine.memory_engine import Budget, _get_tiktoken_encoding, fq_table
 from hindsight_api.engine.reflect.observations import Observation
@@ -103,8 +139,8 @@ class RecallRequest(BaseModel):
     query_timestamp: str | None = Field(
         default=None, description="ISO format date string (e.g., '2023-05-30T23:40:00')"
     )
-    include: IncludeOptions = Field(
-        default_factory=IncludeOptions,
+    include: IncludeOptions = FieldWithDefault(
+        IncludeOptions,
         description="Options for including additional data (entities are included by default)",
     )
     tags: list[str] | None = Field(
@@ -570,18 +606,16 @@ class ReflectLLMCall(BaseModel):
 class ReflectBasedOn(BaseModel):
     """Evidence the response is based on: memories, mental models, and directives."""
 
-    memories: list[ReflectFact] = Field(default_factory=list, description="Memory facts used to generate the response")
-    mental_models: list[ReflectMentalModel] = Field(
-        default_factory=list, description="Mental models used during reflection"
-    )
-    directives: list[ReflectDirective] = Field(default_factory=list, description="Directives applied during reflection")
+    memories: list[ReflectFact] = FieldWithDefault(list, description="Memory facts used to generate the response")
+    mental_models: list[ReflectMentalModel] = FieldWithDefault(list, description="Mental models used during reflection")
+    directives: list[ReflectDirective] = FieldWithDefault(list, description="Directives applied during reflection")
 
 
 class ReflectTrace(BaseModel):
     """Execution trace of LLM and tool calls during reflection."""
 
-    tool_calls: list[ReflectToolCall] = Field(default_factory=list, description="Tool calls made during reflection")
-    llm_calls: list[ReflectLLMCall] = Field(default_factory=list, description="LLM calls made during reflection")
+    tool_calls: list[ReflectToolCall] = FieldWithDefault(list, description="Tool calls made during reflection")
+    llm_calls: list[ReflectLLMCall] = FieldWithDefault(list, description="LLM calls made during reflection")
 
 
 class ReflectResponse(BaseModel):
@@ -793,6 +827,55 @@ class CreateBankRequest(BaseModel):
     background: str | None = Field(default=None, description="Deprecated: use mission instead")
 
 
+class BankConfigUpdate(BaseModel):
+    """Request model for updating bank configuration."""
+
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "updates": {
+                    "llm_model": "claude-sonnet-4-5",
+                    "retain_extraction_mode": "verbose",
+                    "retain_custom_instructions": "Extract technical details carefully",
+                }
+            }
+        }
+    )
+
+    updates: dict[str, Any] = Field(
+        description="Configuration overrides. Keys can be in Python field format (llm_provider) "
+        "or environment variable format (HINDSIGHT_API_LLM_PROVIDER). "
+        "Only hierarchical fields can be overridden per-bank."
+    )
+
+
+class BankConfigResponse(BaseModel):
+    """Response model for bank configuration."""
+
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "bank_id": "my-bank",
+                "config": {
+                    "llm_provider": "openai",
+                    "llm_model": "gpt-4",
+                    "retain_extraction_mode": "verbose",
+                },
+                "overrides": {
+                    "llm_model": "gpt-4",
+                    "retain_extraction_mode": "verbose",
+                },
+            }
+        }
+    )
+
+    bank_id: str = Field(description="Bank identifier")
+    config: dict[str, Any] = Field(
+        description="Fully resolved configuration with all hierarchical overrides applied (Python field names)"
+    )
+    overrides: dict[str, Any] = Field(description="Bank-specific configuration overrides only (Python field names)")
+
+
 class GraphDataResponse(BaseModel):
     """Response model for graph data endpoint."""
 
@@ -942,7 +1025,7 @@ class DocumentResponse(BaseModel):
     created_at: str
     updated_at: str
     memory_unit_count: int
-    tags: list[str] = Field(default_factory=list, description="Tags associated with this document")
+    tags: list[str] = FieldWithDefault(list, description="Tags associated with this document")
 
 
 class DeleteDocumentResponse(BaseModel):
@@ -1066,7 +1149,7 @@ class DirectiveResponse(BaseModel):
     content: str
     priority: int = 0
     is_active: bool = True
-    tags: list[str] = Field(default_factory=list)
+    tags: list[str] = FieldWithDefault(list)
     created_at: str | None = None
     updated_at: str | None = None
 
@@ -1084,7 +1167,7 @@ class CreateDirectiveRequest(BaseModel):
     content: str = Field(description="The directive text to inject into prompts")
     priority: int = Field(default=0, description="Higher priority directives are injected first")
     is_active: bool = Field(default=True, description="Whether this directive is active")
-    tags: list[str] = Field(default_factory=list, description="Tags for filtering")
+    tags: list[str] = FieldWithDefault(list, description="Tags for filtering")
 
 
 class UpdateDirectiveRequest(BaseModel):
@@ -1121,9 +1204,9 @@ class MentalModelResponse(BaseModel):
     content: str = Field(
         description="The mental model content as well-formatted markdown (auto-generated from reflect endpoint)"
     )
-    tags: list[str] = Field(default_factory=list)
+    tags: list[str] = FieldWithDefault(list)
     max_tokens: int = Field(default=2048)
-    trigger: MentalModelTrigger = Field(default_factory=MentalModelTrigger)
+    trigger: MentalModelTrigger = FieldWithDefault(MentalModelTrigger)
     last_refreshed_at: str | None = None
     created_at: str | None = None
     reflect_response: dict | None = Field(
@@ -1159,9 +1242,9 @@ class CreateMentalModelRequest(BaseModel):
     )
     name: str = Field(description="Human-readable name for the mental model")
     source_query: str = Field(description="The query to run to generate content")
-    tags: list[str] = Field(default_factory=list, description="Tags for scoped visibility")
+    tags: list[str] = FieldWithDefault(list, description="Tags for scoped visibility")
     max_tokens: int = Field(default=2048, ge=256, le=8192, description="Maximum tokens for generated content")
-    trigger: MentalModelTrigger = Field(default_factory=MentalModelTrigger, description="Trigger settings")
+    trigger: MentalModelTrigger = FieldWithDefault(MentalModelTrigger, description="Trigger settings")
 
 
 class CreateMentalModelResponse(BaseModel):
@@ -1274,6 +1357,16 @@ class CancelOperationResponse(BaseModel):
     operation_id: str
 
 
+class ChildOperationStatus(BaseModel):
+    """Status of a child operation (for batch operations)."""
+
+    operation_id: str
+    status: str
+    sub_batch_index: int | None = None
+    items_count: int | None = None
+    error_message: str | None = None
+
+
 class OperationStatusResponse(BaseModel):
     """Response model for getting a single operation status."""
 
@@ -1298,6 +1391,13 @@ class OperationStatusResponse(BaseModel):
     updated_at: str | None = None
     completed_at: str | None = None
     error_message: str | None = None
+    result_metadata: dict[str, Any] | None = Field(
+        default=None,
+        description="Internal metadata for debugging. Structure may change without notice. Not for production use.",
+    )
+    child_operations: list[ChildOperationStatus] | None = Field(
+        default=None, description="Child operations for batch operations (if applicable)"
+    )
 
 
 class AsyncOperationSubmitResponse(BaseModel):
@@ -1322,6 +1422,7 @@ class FeaturesInfo(BaseModel):
     observations: bool = Field(description="Whether observations (auto-consolidation) are enabled")
     mcp: bool = Field(description="Whether MCP (Model Context Protocol) server is enabled")
     worker: bool = Field(description="Whether the background worker is enabled")
+    bank_config_api: bool = Field(description="Whether per-bank configuration API is enabled")
 
 
 class VersionResponse(BaseModel):
@@ -1335,6 +1436,7 @@ class VersionResponse(BaseModel):
                     "observations": False,
                     "mcp": True,
                     "worker": True,
+                    "bank_config_api": False,
                 },
             }
         }
@@ -1491,6 +1593,9 @@ def create_app(
         logging.info("Memory system closed")
 
     from hindsight_api import __version__
+    from hindsight_api.config import get_config
+
+    config = get_config()
 
     app = FastAPI(
         title="Hindsight HTTP API",
@@ -1504,6 +1609,7 @@ def create_app(
             "url": "https://www.apache.org/licenses/LICENSE-2.0.html",
         },
         lifespan=lifespan,
+        root_path=config.base_path,
     )
 
     # IMPORTANT: Set memory on app.state immediately, don't wait for lifespan
@@ -1610,17 +1716,21 @@ def _register_routes(app: FastAPI):
 
         Returns version info and feature flags that can be used by clients
         to determine which capabilities are available.
+
+        Note: observations flag shows the global default. Individual banks
+        may override this setting via bank-specific configuration.
         """
         from hindsight_api import __version__
-        from hindsight_api.config import get_config
+        from hindsight_api.config import _get_raw_config
 
-        config = get_config()
+        config = _get_raw_config()
         return VersionResponse(
             api_version=__version__,
             features=FeaturesInfo(
                 observations=config.enable_observations,
                 mcp=config.mcp_enabled,
                 worker=config.worker_enabled,
+                bank_config_api=config.enable_bank_config_api,
             ),
         )
 
@@ -3274,6 +3384,112 @@ def _register_routes(app: FastAPI):
             logger.error(f"Error in DELETE /v1/default/banks/{bank_id}/observations: {error_detail}")
             raise HTTPException(status_code=500, detail=str(e))
 
+    @app.get(
+        "/v1/default/banks/{bank_id}/config",
+        response_model=BankConfigResponse,
+        summary="Get bank configuration",
+        description="Get fully resolved configuration for a bank including all hierarchical overrides (global → tenant → bank). "
+        "The 'config' field contains all resolved config values. The 'overrides' field shows only bank-specific overrides.",
+        operation_id="get_bank_config",
+        tags=["Banks"],
+    )
+    async def api_get_bank_config(bank_id: str, request_context: RequestContext = Depends(get_request_context)):
+        """Get configuration for a bank with all hierarchical overrides applied."""
+        if not get_config().enable_bank_config_api:
+            raise HTTPException(
+                status_code=404,
+                detail="Bank configuration API is disabled. Set HINDSIGHT_API_ENABLE_BANK_CONFIG_API=true to enable.",
+            )
+        try:
+            # Get resolved config from config resolver
+            config_dict = await app.state.memory._config_resolver.get_bank_config(bank_id, request_context)
+
+            # Get bank-specific overrides only
+            bank_overrides = await app.state.memory._config_resolver._load_bank_config(bank_id)
+
+            return BankConfigResponse(bank_id=bank_id, config=config_dict, overrides=bank_overrides)
+        except (AuthenticationError, HTTPException):
+            raise
+        except Exception as e:
+            import traceback
+
+            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
+            logger.error(f"Error in GET /v1/default/banks/{bank_id}/config: {error_detail}")
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.patch(
+        "/v1/default/banks/{bank_id}/config",
+        response_model=BankConfigResponse,
+        summary="Update bank configuration",
+        description="Update configuration overrides for a bank. Only hierarchical fields can be overridden (LLM settings, retention parameters, etc.). "
+        "Keys can be provided in Python field format (llm_provider) or environment variable format (HINDSIGHT_API_LLM_PROVIDER).",
+        operation_id="update_bank_config",
+        tags=["Banks"],
+    )
+    async def api_update_bank_config(
+        bank_id: str, request: BankConfigUpdate, request_context: RequestContext = Depends(get_request_context)
+    ):
+        """Update configuration overrides for a bank."""
+        if not get_config().enable_bank_config_api:
+            raise HTTPException(
+                status_code=404,
+                detail="Bank configuration API is disabled. Set HINDSIGHT_API_ENABLE_BANK_CONFIG_API=true to enable.",
+            )
+        try:
+            # Update config via config resolver (validates configurable fields and permissions)
+            await app.state.memory._config_resolver.update_bank_config(bank_id, request.updates, request_context)
+
+            # Return updated config
+            config_dict = await app.state.memory._config_resolver.get_bank_config(bank_id, request_context)
+            bank_overrides = await app.state.memory._config_resolver._load_bank_config(bank_id)
+
+            return BankConfigResponse(bank_id=bank_id, config=config_dict, overrides=bank_overrides)
+        except ValueError as e:
+            # Validation error (e.g., trying to override static field)
+            raise HTTPException(status_code=400, detail=str(e))
+        except (AuthenticationError, HTTPException):
+            raise
+        except Exception as e:
+            import traceback
+
+            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
+            logger.error(f"Error in PATCH /v1/default/banks/{bank_id}/config: {error_detail}")
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.delete(
+        "/v1/default/banks/{bank_id}/config",
+        response_model=BankConfigResponse,
+        summary="Reset bank configuration",
+        description="Reset bank configuration to defaults by removing all bank-specific overrides. "
+        "The bank will then use global and tenant-level configuration only.",
+        operation_id="reset_bank_config",
+        tags=["Banks"],
+    )
+    async def api_reset_bank_config(bank_id: str, request_context: RequestContext = Depends(get_request_context)):
+        """Reset bank configuration to defaults (remove all overrides)."""
+        if not get_config().enable_bank_config_api:
+            raise HTTPException(
+                status_code=404,
+                detail="Bank configuration API is disabled. Set HINDSIGHT_API_ENABLE_BANK_CONFIG_API=true to enable.",
+            )
+        try:
+            # Reset config via config resolver
+            await app.state.memory._config_resolver.reset_bank_config(bank_id)
+
+            # Return updated config (should match defaults now)
+            config_dict = await app.state.memory._config_resolver.get_bank_config(bank_id, request_context)
+            bank_overrides = await app.state.memory._config_resolver._load_bank_config(bank_id)
+
+            return BankConfigResponse(bank_id=bank_id, config=config_dict, overrides=bank_overrides)
+        except (AuthenticationError, HTTPException):
+            raise
+        except Exception as e:
+            import traceback
+
+            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
+            logger.error(f"Error in DELETE /v1/default/banks/{bank_id}/config: {error_detail}")
+            raise HTTPException(status_code=500, detail=str(e))
+
     @app.post(
         "/v1/default/banks/{bank_id}/consolidate",
         response_model=ConsolidationResponse,
@@ -3364,6 +3580,21 @@ def _register_routes(app: FastAPI):
                     }
                 )
             else:
+                # Check if batch API is enabled - if so, require async mode
+                from hindsight_api.config import get_config
+
+                config = get_config()
+                if config.retain_batch_enabled:
+                    raise HTTPException(
+                        status_code=400,
+                        detail=(
+                            "Batch API is enabled (HINDSIGHT_API_RETAIN_BATCH_ENABLED=true) but async=false. "
+                            "Batch operations can take several minutes to hours and will timeout in synchronous mode. "
+                            "Please set async=true in your request to use background processing, or disable batch API "
+                            "by setting HINDSIGHT_API_RETAIN_BATCH_ENABLED=false in your environment."
+                        ),
+                    )
+
                 # Synchronous processing: wait for completion (record metrics)
                 with metrics.record_operation("retain", bank_id=bank_id, source="api"):
                     result, usage = await app.state.memory.retain_batch_async(

hindsight-api/hindsight_api/api/mcp.py

@@ -114,9 +114,39 @@ def create_mcp_server(memory: MemoryEngine, multi_bank: bool = True) -> FastMCP:
         logger.info(f"Loading MCP extension: {mcp_extension.__class__.__name__}")
         mcp_extension.register_tools(mcp, memory)
 
+    # Make all tools tolerant of extra arguments from LLMs (e.g., "explanation")
+    _make_tools_tolerant(mcp)
+
     return mcp
 
 
+def _make_tools_tolerant(mcp: FastMCP) -> None:
+    """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".
+    This wraps each tool's run() to filter arguments to only known parameters.
+    """
+    try:
+        for name, tool in mcp._tool_manager._tools.items():
+            if hasattr(tool, "parameters") and tool.parameters:
+                allowed = set(tool.parameters.get("properties", {}).keys())
+                original_run = tool.run
+
+                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}
+                    return await _orig(arguments)
+
+                # FunctionTool is a Pydantic model with extra='forbid', so use
+                # object.__setattr__ to bypass Pydantic's setter validation.
+                object.__setattr__(tool, "run", _tolerant_run)
+    except (AttributeError, KeyError) as e:
+        logger.warning(f"Could not make tools tolerant of extra arguments: {e}")
+
+
 class MCPMiddleware:
     """ASGI middleware that intercepts MCP requests and routes to appropriate MCP server.
 
@@ -142,6 +172,11 @@ class MCPMiddleware:
        - No bank management tools (list_banks, create_bank)
        - Recommended for agent isolation
 
+    Bank ID resolution priority:
+        1. URL path (e.g., /mcp/{bank_id}/) → single-bank mode
+        2. X-Bank-Id header → multi-bank mode
+        3. HINDSIGHT_MCP_BANK_ID env var → multi-bank mode (default: "default")
+
     Examples:
         # Single-bank mode (recommended for agent isolation)
         claude mcp add --transport http my-agent http://localhost:8888/mcp/my-agent-bank/ \\
@@ -242,20 +277,25 @@ class MCPMiddleware:
             _current_schema.set(tenant_context.schema_name) if tenant_context and tenant_context.schema_name else None
         )
 
-        # Try to get bank_id from header first (for Claude Code compatibility)
-        bank_id = self._get_header(scope, "X-Bank-Id")
+        # Resolve bank_id: path takes priority over header.
+        # Path = user's explicit connection endpoint (e.g., /mcp/my-bank/).
+        # X-Bank-Id header = per-request override for multi-bank mode only.
+        bank_id = None
         bank_id_from_path = False
-
-        # If no header, try to extract from path: /{bank_id}/...
         new_path = path
-        if not bank_id and path.startswith("/") and len(path) > 1:
+
+        # First, try to extract from path: /{bank_id}/...
+        if path.startswith("/") and len(path) > 1:
             parts = path[1:].split("/", 1)
             if parts[0]:
-                # First segment looks like a bank_id
                 bank_id = parts[0]
                 bank_id_from_path = True
                 new_path = "/" + parts[1] if len(parts) > 1 else "/"
 
+        # If no path-based bank_id, try X-Bank-Id header (multi-bank mode)
+        if not bank_id:
+            bank_id = self._get_header(scope, "X-Bank-Id")
+
         # Fall back to default bank_id
         if not bank_id:
             bank_id = DEFAULT_BANK_ID

hindsight-api/hindsight_api/config.py

@@ -8,8 +8,9 @@ import json
 import logging
 import os
 import sys
-from dataclasses import dataclass
+from dataclasses import dataclass, field, fields
 from datetime import datetime, timezone
+from typing import Any
 
 from dotenv import find_dotenv, load_dotenv
 
@@ -18,6 +19,103 @@ load_dotenv(find_dotenv(usecwd=True), override=True)
 
 logger = logging.getLogger(__name__)
 
+
+class ConfigFieldAccessError(AttributeError):
+    """Raised when trying to access a bank-configurable field from global config."""
+
+    pass
+
+
+class StaticConfigProxy:
+    """
+    Proxy that wraps HindsightConfig and only allows access to static (non-configurable) fields.
+
+    Raises ConfigFieldAccessError when trying to access configurable fields that vary per-bank.
+    Forces developers to use get_resolved_config(bank_id, context) for bank-specific settings.
+    """
+
+    def __init__(self, config: "HindsightConfig"):
+        object.__setattr__(self, "_config", config)
+        object.__setattr__(self, "_configurable_fields", HindsightConfig.get_configurable_fields())
+
+    def __getattribute__(self, name: str):
+        if name.startswith("_"):
+            return object.__getattribute__(self, name)
+
+        configurable_fields = object.__getattribute__(self, "_configurable_fields")
+        if name in configurable_fields:
+            raise ConfigFieldAccessError(
+                f"Field '{name}' is bank-configurable and cannot be accessed from global config. "
+                f"Use ConfigResolver.resolve_full_config(bank_id, context) to get bank-specific config. "
+                f"This prevents accidentally using global defaults when bank-specific overrides exist."
+            )
+
+        config = object.__getattribute__(self, "_config")
+        return getattr(config, name)
+
+    def __setattr__(self, name: str, value):
+        raise AttributeError("Config is read-only. Modifications must go through ConfigResolver.")
+
+
+# Configuration field markers for hierarchical configuration
+def hierarchical(default_value):
+    """
+    Mark a config field as hierarchical (can be overridden per-tenant/bank).
+
+    Hierarchical fields can be customized at the tenant or bank level via database
+    configuration. Examples: LLM settings, retention parameters, retrieval settings.
+    """
+    return field(default=default_value, metadata={"hierarchical": True})
+
+
+def static(default_value):
+    """
+    Mark a config field as static (server-level only, cannot be overridden).
+
+    Static fields are infrastructure-level settings that affect the entire server
+    and cannot vary per tenant or bank. Examples: database URL, API port, worker settings.
+    """
+    return field(default=default_value, metadata={"hierarchical": False})
+
+
+# Configuration key normalization utilities
+def normalize_config_key(key: str) -> str:
+    """
+    Convert environment variable format to Python field name format.
+
+    Examples:
+        HINDSIGHT_API_LLM_PROVIDER -> llm_provider
+        LLM_MODEL -> llm_model
+        llm_model -> llm_model (already normalized)
+
+    Args:
+        key: Environment variable name or Python field name
+
+    Returns:
+        Normalized Python field name (lowercase snake_case)
+    """
+    if key.startswith("HINDSIGHT_API_"):
+        key = key[len("HINDSIGHT_API_") :]
+    return key.lower()
+
+
+def normalize_config_dict(config: dict[str, Any]) -> dict[str, Any]:
+    """
+    Normalize all keys in a config dict to Python field names.
+
+    Allows users to provide config overrides in either format:
+    - Python field format: {"llm_provider": "openai"}
+    - Env var format: {"HINDSIGHT_API_LLM_PROVIDER": "openai"}
+
+    Args:
+        config: Dict with env var or Python field names as keys
+
+    Returns:
+        Dict with all keys normalized to Python field names
+    """
+    return {normalize_config_key(k): v for k, v in config.items()}
+
+
 # Environment variable names
 ENV_DATABASE_URL = "HINDSIGHT_API_DATABASE_URL"
 ENV_DATABASE_SCHEMA = "HINDSIGHT_API_DATABASE_SCHEMA"
@@ -31,6 +129,11 @@ ENV_LLM_INITIAL_BACKOFF = "HINDSIGHT_API_LLM_INITIAL_BACKOFF"
 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"
+
+# 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)
 
 # Per-operation LLM configuration (optional, falls back to global LLM config)
 ENV_RETAIN_LLM_PROVIDER = "HINDSIGHT_API_RETAIN_LLM_PROVIDER"
@@ -91,6 +194,14 @@ ENV_RERANKER_LITELLM_API_BASE = "HINDSIGHT_API_RERANKER_LITELLM_API_BASE"
 ENV_RERANKER_LITELLM_API_KEY = "HINDSIGHT_API_RERANKER_LITELLM_API_KEY"
 ENV_RERANKER_LITELLM_MODEL = "HINDSIGHT_API_RERANKER_LITELLM_MODEL"
 
+# LiteLLM SDK configuration (direct API access, no proxy needed)
+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_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"
+
 # Deprecated: Legacy shared LiteLLM config (for backward compatibility)
 ENV_LITELLM_API_BASE = "HINDSIGHT_API_LITELLM_API_BASE"
 ENV_LITELLM_API_KEY = "HINDSIGHT_API_LITELLM_API_KEY"
@@ -107,12 +218,17 @@ ENV_RERANKER_MAX_CANDIDATES = "HINDSIGHT_API_RERANKER_MAX_CANDIDATES"
 ENV_RERANKER_FLASHRANK_MODEL = "HINDSIGHT_API_RERANKER_FLASHRANK_MODEL"
 ENV_RERANKER_FLASHRANK_CACHE_DIR = "HINDSIGHT_API_RERANKER_FLASHRANK_CACHE_DIR"
 
+ENV_VECTOR_EXTENSION = "HINDSIGHT_API_VECTOR_EXTENSION"
+ENV_TEXT_SEARCH_EXTENSION = "HINDSIGHT_API_TEXT_SEARCH_EXTENSION"
+
 ENV_HOST = "HINDSIGHT_API_HOST"
 ENV_PORT = "HINDSIGHT_API_PORT"
+ENV_BASE_PATH = "HINDSIGHT_API_BASE_PATH"
 ENV_LOG_LEVEL = "HINDSIGHT_API_LOG_LEVEL"
 ENV_LOG_FORMAT = "HINDSIGHT_API_LOG_FORMAT"
 ENV_WORKERS = "HINDSIGHT_API_WORKERS"
 ENV_MCP_ENABLED = "HINDSIGHT_API_MCP_ENABLED"
+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"
@@ -139,6 +255,9 @@ ENV_RETAIN_CHUNK_SIZE = "HINDSIGHT_API_RETAIN_CHUNK_SIZE"
 ENV_RETAIN_EXTRACT_CAUSAL_LINKS = "HINDSIGHT_API_RETAIN_EXTRACT_CAUSAL_LINKS"
 ENV_RETAIN_EXTRACTION_MODE = "HINDSIGHT_API_RETAIN_EXTRACTION_MODE"
 ENV_RETAIN_CUSTOM_INSTRUCTIONS = "HINDSIGHT_API_RETAIN_CUSTOM_INSTRUCTIONS"
+ENV_RETAIN_BATCH_TOKENS = "HINDSIGHT_API_RETAIN_BATCH_TOKENS"
+ENV_RETAIN_BATCH_ENABLED = "HINDSIGHT_API_RETAIN_BATCH_ENABLED"
+ENV_RETAIN_BATCH_POLL_INTERVAL_SECONDS = "HINDSIGHT_API_RETAIN_BATCH_POLL_INTERVAL_SECONDS"
 
 # Observations settings (consolidated knowledge from facts)
 ENV_ENABLE_OBSERVATIONS = "HINDSIGHT_API_ENABLE_OBSERVATIONS"
@@ -223,17 +342,29 @@ DEFAULT_RERANKER_FLASHRANK_CACHE_DIR = None  # Use default cache directory
 DEFAULT_EMBEDDINGS_COHERE_MODEL = "embed-english-v3.0"
 DEFAULT_RERANKER_COHERE_MODEL = "rerank-english-v3.0"
 
+# Vector extension (pgvector vs vchord)
+DEFAULT_VECTOR_EXTENSION = "pgvector"  # Options: "pgvector", "vchord"
+
+# Text search extension (native PostgreSQL, vchord BM25, or Timescale pg_textsearch)
+DEFAULT_TEXT_SEARCH_EXTENSION = "native"  # Options: "native", "vchord", "pg_textsearch"
+
 # LiteLLM defaults
 DEFAULT_LITELLM_API_BASE = "http://localhost:4000"
 DEFAULT_EMBEDDINGS_LITELLM_MODEL = "text-embedding-3-small"
 DEFAULT_RERANKER_LITELLM_MODEL = "cohere/rerank-english-v3.0"
 
+# LiteLLM SDK defaults
+DEFAULT_EMBEDDINGS_LITELLM_SDK_MODEL = "cohere/embed-english-v3.0"
+DEFAULT_RERANKER_LITELLM_SDK_MODEL = "cohere/rerank-english-v3.0"
+
 DEFAULT_HOST = "0.0.0.0"
 DEFAULT_PORT = 8888
+DEFAULT_BASE_PATH = ""  # Empty string = root path
 DEFAULT_LOG_LEVEL = "info"
 DEFAULT_LOG_FORMAT = "text"  # Options: "text", "json"
 DEFAULT_WORKERS = 1
 DEFAULT_MCP_ENABLED = True
+DEFAULT_ENABLE_BANK_CONFIG_API = False  # Disabled by default for security
 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
@@ -248,6 +379,9 @@ DEFAULT_RETAIN_EXTRACT_CAUSAL_LINKS = True  # Extract causal links between facts
 DEFAULT_RETAIN_EXTRACTION_MODE = "concise"  # Extraction mode: "concise", "verbose", or "custom"
 RETAIN_EXTRACTION_MODES = ("concise", "verbose", "custom")  # Allowed extraction modes
 DEFAULT_RETAIN_CUSTOM_INSTRUCTIONS = None  # Custom extraction guidelines (only used when mode="custom")
+DEFAULT_RETAIN_BATCH_TOKENS = 10_000  # ~40KB of text  # Max chars per sub-batch for async retain auto-splitting
+DEFAULT_RETAIN_BATCH_ENABLED = False  # Use LLM Batch API for fact extraction (only when async=True)
+DEFAULT_RETAIN_BATCH_POLL_INTERVAL_SECONDS = 60  # Batch API polling interval in seconds
 
 # Observations defaults (consolidated knowledge from facts)
 DEFAULT_ENABLE_OBSERVATIONS = True  # Observations enabled by default
@@ -358,6 +492,8 @@ class HindsightConfig:
     # Database
     database_url: str
     database_schema: str
+    vector_extension: str  # "pgvector" or "vchord"
+    text_search_extension: str  # "native" or "vchord"
 
     # LLM (default, used as fallback for per-operation config)
     llm_provider: str
@@ -369,6 +505,8 @@ class HindsightConfig:
     llm_initial_backoff: float
     llm_max_backoff: float
     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)
 
     # Vertex AI configuration
     llm_vertexai_project_id: str | None
@@ -419,6 +557,9 @@ class HindsightConfig:
     embeddings_litellm_api_base: str
     embeddings_litellm_api_key: str | None
     embeddings_litellm_model: str
+    embeddings_litellm_sdk_api_key: str | None
+    embeddings_litellm_sdk_model: str
+    embeddings_litellm_sdk_api_base: str | None
 
     # Reranker
     reranker_provider: str
@@ -436,13 +577,18 @@ class HindsightConfig:
     reranker_litellm_api_base: str
     reranker_litellm_api_key: str | None
     reranker_litellm_model: str
+    reranker_litellm_sdk_api_key: str | None
+    reranker_litellm_sdk_model: str
+    reranker_litellm_sdk_api_base: str | None
 
     # Server
     host: str
     port: int
+    base_path: str
     log_level: str
     log_format: str
     mcp_enabled: bool
+    enable_bank_config_api: bool
 
     # Recall
     graph_retriever: str
@@ -457,6 +603,9 @@ class HindsightConfig:
     retain_extract_causal_links: bool
     retain_extraction_mode: str
     retain_custom_instructions: str | None
+    retain_batch_tokens: int
+    retain_batch_enabled: bool
+    retain_batch_poll_interval_seconds: int
 
     # Observations settings (consolidated knowledge from facts)
     enable_observations: bool
@@ -495,8 +644,108 @@ class HindsightConfig:
     otel_service_name: str
     otel_deployment_environment: str
 
+    # Class-level sets for configuration categorization
+
+    # CREDENTIAL_FIELDS: Never exposed via API, never configurable per-tenant/bank
+    _CREDENTIAL_FIELDS = {
+        # API Keys
+        "llm_api_key",
+        "retain_llm_api_key",
+        "reflect_llm_api_key",
+        "consolidation_llm_api_key",
+        # Base URLs (could expose infrastructure)
+        "llm_base_url",
+        "retain_llm_base_url",
+        "reflect_llm_base_url",
+        "consolidation_llm_base_url",
+        "embeddings_tei_base_url",
+        "reranker_tei_base_url",
+        "reranker_cohere_base_url",
+        # Service Account Keys
+        "llm_vertexai_service_account_key",
+    }
+
+    # CONFIGURABLE_FIELDS: Safe behavioral settings that can be customized per-tenant/bank
+    # These fields are manually tagged as safe to expose and modify.
+    # Excludes credentials, infrastructure config, provider/model selection, and performance tuning.
+    _CONFIGURABLE_FIELDS = {
+        # Retention settings (behavioral)
+        "retain_chunk_size",
+        "retain_extraction_mode",
+        "retain_custom_instructions",
+        # Consolidation settings
+        "enable_observations",
+    }
+
+    @classmethod
+    def get_configurable_fields(cls) -> set[str]:
+        """
+        Get set of field names that are configurable per-tenant/bank via API.
+
+        Configurable fields are manually tagged behavioral settings that are safe
+        to expose and modify (e.g., retain_chunk_size, custom_instructions).
+        Excludes credentials, infrastructure config, and provider/model selection.
+
+        Returns:
+            Set of configurable field names
+        """
+        return cls._CONFIGURABLE_FIELDS.copy()
+
+    @classmethod
+    def get_credential_fields(cls) -> set[str]:
+        """
+        Get set of field names that are credentials (NEVER exposed via API).
+
+        Credential fields include API keys, base URLs, and service account keys.
+        These must never be returned in API responses or accepted in updates.
+
+        Returns:
+            Set of credential field names
+        """
+        return cls._CREDENTIAL_FIELDS.copy()
+
+    @classmethod
+    def get_hierarchical_fields(cls) -> set[str]:
+        """
+        DEPRECATED: Use get_configurable_fields() instead.
+
+        Kept for backward compatibility during migration.
+        """
+        return cls.get_configurable_fields()
+
+    @classmethod
+    def get_static_fields(cls) -> set[str]:
+        """
+        Get set of field names that are static (server-level only).
+
+        Static fields are infrastructure-level settings that cannot vary
+        per tenant or bank. These include database config, API port, worker settings, etc.
+        Also includes credential fields which are never configurable.
+
+        Returns:
+            Set of static field names
+        """
+        # Get all field names from dataclass
+        all_fields = {f.name for f in fields(cls)}
+        # Static fields = all fields - configurable fields
+        return all_fields - cls._CONFIGURABLE_FIELDS
+
     def validate(self) -> None:
         """Validate configuration values and raise errors for invalid combinations."""
+        # Validate vector_extension
+        valid_extensions = ("pgvector", "vchord")
+        if self.vector_extension not in valid_extensions:
+            raise ValueError(
+                f"Invalid vector_extension: {self.vector_extension}. Must be one of: {', '.join(valid_extensions)}"
+            )
+
+        # Validate text_search_extension
+        valid_text_search = ("native", "vchord", "pg_textsearch")
+        if self.text_search_extension not in valid_text_search:
+            raise ValueError(
+                f"Invalid text_search_extension: {self.text_search_extension}. Must be one of: {', '.join(valid_text_search)}"
+            )
+
         # RETAIN_MAX_COMPLETION_TOKENS must be greater than RETAIN_CHUNK_SIZE
         # to ensure the LLM has enough output capacity to extract facts from chunks
         if self.retain_max_completion_tokens <= self.retain_chunk_size:
@@ -522,6 +771,8 @@ class HindsightConfig:
             # Database
             database_url=os.getenv(ENV_DATABASE_URL, DEFAULT_DATABASE_URL),
             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(),
             # LLM
             llm_provider=llm_provider,
             llm_api_key=os.getenv(ENV_LLM_API_KEY),
@@ -532,6 +783,8 @@ class HindsightConfig:
             llm_initial_backoff=float(os.getenv(ENV_LLM_INITIAL_BACKOFF, str(DEFAULT_LLM_INITIAL_BACKOFF))),
             llm_max_backoff=float(os.getenv(ENV_LLM_MAX_BACKOFF, str(DEFAULT_LLM_MAX_BACKOFF))),
             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),
             # 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),
@@ -630,6 +883,12 @@ class HindsightConfig:
             or os.getenv(ENV_LITELLM_API_BASE, DEFAULT_LITELLM_API_BASE),
             embeddings_litellm_api_key=os.getenv(ENV_EMBEDDINGS_LITELLM_API_KEY) or os.getenv(ENV_LITELLM_API_KEY),
             embeddings_litellm_model=os.getenv(ENV_EMBEDDINGS_LITELLM_MODEL, DEFAULT_EMBEDDINGS_LITELLM_MODEL),
+            # LiteLLM SDK embeddings (direct API access)
+            embeddings_litellm_sdk_api_key=os.getenv(ENV_EMBEDDINGS_LITELLM_SDK_API_KEY),
+            embeddings_litellm_sdk_model=os.getenv(
+                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,
             # Reranker
             reranker_provider=os.getenv(ENV_RERANKER_PROVIDER, DEFAULT_RERANKER_PROVIDER),
             reranker_local_model=os.getenv(ENV_RERANKER_LOCAL_MODEL, DEFAULT_RERANKER_LOCAL_MODEL),
@@ -659,12 +918,19 @@ class HindsightConfig:
             or os.getenv(ENV_LITELLM_API_BASE, DEFAULT_LITELLM_API_BASE),
             reranker_litellm_api_key=os.getenv(ENV_RERANKER_LITELLM_API_KEY) or os.getenv(ENV_LITELLM_API_KEY),
             reranker_litellm_model=os.getenv(ENV_RERANKER_LITELLM_MODEL, DEFAULT_RERANKER_LITELLM_MODEL),
+            # LiteLLM SDK reranker (direct API access)
+            reranker_litellm_sdk_api_key=os.getenv(ENV_RERANKER_LITELLM_SDK_API_KEY),
+            reranker_litellm_sdk_model=os.getenv(ENV_RERANKER_LITELLM_SDK_MODEL, DEFAULT_RERANKER_LITELLM_SDK_MODEL),
+            reranker_litellm_sdk_api_base=os.getenv(ENV_RERANKER_LITELLM_SDK_API_BASE) or None,
             # Server
             host=os.getenv(ENV_HOST, DEFAULT_HOST),
             port=int(os.getenv(ENV_PORT, DEFAULT_PORT)),
+            base_path=os.getenv(ENV_BASE_PATH, DEFAULT_BASE_PATH),
             log_level=os.getenv(ENV_LOG_LEVEL, DEFAULT_LOG_LEVEL),
             log_format=os.getenv(ENV_LOG_FORMAT, DEFAULT_LOG_FORMAT).lower(),
             mcp_enabled=os.getenv(ENV_MCP_ENABLED, str(DEFAULT_MCP_ENABLED)).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))),
@@ -691,6 +957,12 @@ class HindsightConfig:
                 os.getenv(ENV_RETAIN_EXTRACTION_MODE, DEFAULT_RETAIN_EXTRACTION_MODE)
             ),
             retain_custom_instructions=os.getenv(ENV_RETAIN_CUSTOM_INSTRUCTIONS) or DEFAULT_RETAIN_CUSTOM_INSTRUCTIONS,
+            retain_batch_tokens=int(os.getenv(ENV_RETAIN_BATCH_TOKENS, str(DEFAULT_RETAIN_BATCH_TOKENS))),
+            retain_batch_enabled=os.getenv(ENV_RETAIN_BATCH_ENABLED, str(DEFAULT_RETAIN_BATCH_ENABLED)).lower()
+            == "true",
+            retain_batch_poll_interval_seconds=int(
+                os.getenv(ENV_RETAIN_BATCH_POLL_INTERVAL_SECONDS, str(DEFAULT_RETAIN_BATCH_POLL_INTERVAL_SECONDS))
+            ),
             # Observations settings (consolidated knowledge from facts)
             enable_observations=os.getenv(ENV_ENABLE_OBSERVATIONS, str(DEFAULT_ENABLE_OBSERVATIONS)).lower() == "true",
             consolidation_batch_size=int(
@@ -805,8 +1077,35 @@ class HindsightConfig:
 _config_cache: HindsightConfig | None = None
 
 
-def get_config() -> HindsightConfig:
-    """Get the cached configuration, loading from environment on first call."""
+def get_config() -> StaticConfigProxy:
+    """
+    Get global configuration with ONLY static (non-configurable) fields accessible.
+
+    This returns a proxy that prevents access to bank-configurable fields
+    (like enable_observations, retain_chunk_size, etc.).
+
+    For bank-specific configuration, use:
+        config_resolver.resolve_full_config(bank_id, context)
+
+    This design prevents accidentally using global defaults when bank-specific
+    overrides exist.
+
+    Returns:
+        StaticConfigProxy that only exposes static infrastructure fields
+
+    Raises:
+        ConfigFieldAccessError: If you try to access a bank-configurable field
+    """
+    return StaticConfigProxy(_get_raw_config())
+
+
+def _get_raw_config() -> HindsightConfig:
+    """
+    Get raw config (internal use only).
+
+    INTERNAL USE ONLY. Do not use this directly in application code.
+    Use get_config() for static fields or ConfigResolver.resolve_full_config() for bank-specific config.
+    """
     global _config_cache
     if _config_cache is None:
         _config_cache = HindsightConfig.from_env()

hindsight-api/hindsight_api/config_resolver.py

@@ -0,0 +1,274 @@
+"""
+Configuration resolution with hierarchical overrides.
+
+Resolves config values through the hierarchy:
+  Global (env vars) → Tenant config (via extension) → Bank config (database)
+
+Config values are resolved on every request to ensure consistency across
+multiple API servers.
+"""
+
+import json
+import logging
+from dataclasses import asdict
+from typing import Any
+
+import asyncpg
+
+from hindsight_api.config import HindsightConfig, _get_raw_config, normalize_config_dict
+from hindsight_api.extensions.tenant import TenantExtension
+from hindsight_api.models import RequestContext
+
+logger = logging.getLogger(__name__)
+
+
+class ConfigResolver:
+    """Resolves hierarchical configuration with tenant/bank overrides."""
+
+    def __init__(self, pool: asyncpg.Pool, tenant_extension: TenantExtension | None = None):
+        """
+        Initialize config resolver.
+
+        Args:
+            pool: Database connection pool
+            tenant_extension: Optional tenant extension for tenant-level config and permissions
+        """
+        self.pool = pool
+        self.tenant_extension = tenant_extension
+        self._global_config = _get_raw_config()
+        self._configurable_fields = HindsightConfig.get_configurable_fields()
+        self._credential_fields = HindsightConfig.get_credential_fields()
+
+    async def resolve_full_config(self, bank_id: str, context: RequestContext | None = None) -> HindsightConfig:
+        """
+        Resolve full HindsightConfig for a bank with hierarchical overrides applied.
+
+        This is for INTERNAL USE ONLY. Returns the complete config object with all fields
+        including credentials and static fields. Use get_bank_config() for API responses.
+
+        Resolution order:
+        1. Global config (from environment variables)
+        2. Tenant config overrides (from TenantExtension.get_tenant_config())
+        3. Bank config overrides (from banks.config JSONB)
+
+        Args:
+            bank_id: Bank identifier
+            context: Request context for tenant config resolution
+
+        Returns:
+            Complete HindsightConfig with hierarchical overrides applied
+        """
+        # Start with global config (all fields)
+        config_dict = asdict(self._global_config)
+
+        # Load tenant config overrides (if tenant extension available)
+        if self.tenant_extension and context:
+            try:
+                tenant_overrides = await self.tenant_extension.get_tenant_config(context)
+                if tenant_overrides:
+                    # Normalize keys and filter to configurable fields only
+                    normalized_tenant = normalize_config_dict(tenant_overrides)
+                    configurable_tenant = {k: v for k, v in normalized_tenant.items() if k in self._configurable_fields}
+                    config_dict.update(configurable_tenant)
+                    logger.debug(
+                        f"Applied tenant config overrides for bank {bank_id}: {list(configurable_tenant.keys())}"
+                    )
+            except Exception as e:
+                logger.warning(f"Failed to load tenant config for bank {bank_id}: {e}")
+
+        # Load bank config overrides
+        bank_overrides = await self._load_bank_config(bank_id)
+        if bank_overrides:
+            config_dict.update(bank_overrides)
+            logger.debug(f"Applied bank config overrides for bank {bank_id}: {list(bank_overrides.keys())}")
+
+        # Return full config object (dataclass doesn't have __init__ that accepts kwargs, so we update the object)
+        # Create a new config instance by copying the global config and updating fields
+        resolved_config = HindsightConfig(**config_dict)
+        return resolved_config
+
+    async def get_bank_config(self, bank_id: str, context: RequestContext | None = None) -> dict[str, Any]:
+        """
+        Get fully resolved config for a bank (filtered by permissions).
+
+        Resolution order:
+        1. Global config (from environment variables)
+        2. Tenant config overrides (from TenantExtension.get_tenant_config())
+        3. Bank config overrides (from banks.config JSONB)
+
+        Note: Config is resolved on every call (not cached) to ensure consistency
+        across multiple API servers.
+
+        SECURITY:
+        - Only returns configurable fields (excludes static/infrastructure fields)
+        - Filters out ALL credential fields (API keys, base URLs, etc.)
+        - Further filtered by tenant/bank permissions if extension provides them
+
+        Args:
+            bank_id: Bank identifier
+            context: Request context for tenant config resolution and permissions
+
+        Returns:
+            Dict of allowed configurable fields only (never includes credentials or static fields)
+        """
+        # Resolve full config with all hierarchical overrides
+        resolved_config = await self.resolve_full_config(bank_id, context)
+        config_dict = asdict(resolved_config)
+
+        # SECURITY: Filter to only configurable fields (exclude static/infrastructure)
+        filtered = {k: v for k, v in config_dict.items() if k in self._configurable_fields}
+
+        # SECURITY: Remove ALL credential fields (API keys, base URLs, etc.)
+        filtered = {k: v for k, v in filtered.items() if k not in self._credential_fields}
+
+        # PERMISSIONS: Further filter based on tenant/bank permissions
+        if self.tenant_extension and context:
+            try:
+                allowed_fields = await self.tenant_extension.get_allowed_config_fields(context, bank_id)
+                if allowed_fields is not None:  # None means "allow all"
+                    filtered = {k: v for k, v in filtered.items() if k in allowed_fields}
+                    logger.debug(
+                        f"Applied permission filter for bank {bank_id}: allowed={len(allowed_fields)} fields, "
+                        f"returned={len(filtered)} fields"
+                    )
+            except Exception as e:
+                logger.warning(f"Failed to load permissions for bank {bank_id}: {e}")
+
+        return filtered
+
+    async def _load_bank_config(self, bank_id: str) -> dict[str, Any]:
+        """
+        Load bank config overrides from banks.config JSONB column.
+
+        Args:
+            bank_id: Bank identifier
+
+        Returns:
+            Dict of config overrides (only configurable fields, normalized keys)
+        """
+        try:
+            async with self.pool.acquire() as conn:
+                row = await conn.fetchrow(
+                    """
+                    SELECT config FROM banks WHERE bank_id = $1
+                    """,
+                    bank_id,
+                )
+
+                if row and row["config"]:
+                    config_data = row["config"]
+
+                    # Handle case where JSONB is returned as JSON string
+                    if isinstance(config_data, str):
+                        config_data = json.loads(config_data)
+
+                    # Normalize keys (handle both env var format and Python field format)
+                    normalized = normalize_config_dict(config_data)
+
+                    # Only return overrides for configurable fields
+                    return {k: v for k, v in normalized.items() if k in self._configurable_fields}
+        except Exception as e:
+            logger.error(f"Failed to load bank config for {bank_id}: {e}")
+
+        return {}
+
+    async def update_bank_config(
+        self, bank_id: str, updates: dict[str, Any], context: RequestContext | None = None
+    ) -> None:
+        """
+        Update bank configuration overrides (with permission checking).
+
+        Args:
+            bank_id: Bank identifier
+            updates: Dict of config field names to new values.
+                    Keys can be in env var format (HINDSIGHT_API_LLM_PROVIDER)
+                    or Python field format (llm_provider).
+                    Only configurable fields are allowed.
+            context: Request context for permission checking
+
+        Raises:
+            ValueError: If attempting to override invalid/disallowed fields
+        """
+        # Normalize keys
+        normalized_updates = normalize_config_dict(updates)
+
+        # SECURITY: Reject credential fields explicitly
+        credential_attempts = set(normalized_updates.keys()) & self._credential_fields
+        if credential_attempts:
+            raise ValueError(
+                f"Cannot set credential fields via API: {sorted(credential_attempts)}. "
+                f"Credentials (API keys, base URLs) must be set at server level only."
+            )
+
+        # Validate all fields are configurable
+        invalid_fields = set(normalized_updates.keys()) - self._configurable_fields
+        if invalid_fields:
+            static_fields = HindsightConfig.get_static_fields()
+            invalid_static = invalid_fields & static_fields
+            if invalid_static:
+                raise ValueError(
+                    f"Cannot override static (server-level) fields: {sorted(invalid_static)}. "
+                    f"Only configurable fields can be overridden per-bank. "
+                    f"Configurable fields include: {sorted(list(self._configurable_fields)[:10])}... "
+                    f"(total: {len(self._configurable_fields)} fields)"
+                )
+            else:
+                raise ValueError(
+                    f"Unknown configuration fields: {sorted(invalid_fields)}. "
+                    f"Valid configurable fields: {sorted(list(self._configurable_fields)[:10])}..."
+                )
+
+        # PERMISSIONS: Check tenant/bank permissions
+        if self.tenant_extension and context:
+            try:
+                allowed_fields = await self.tenant_extension.get_allowed_config_fields(context, bank_id)
+                if allowed_fields is not None:  # None means "allow all"
+                    disallowed = set(normalized_updates.keys()) - allowed_fields
+                    if disallowed:
+                        raise ValueError(
+                            f"Not allowed to modify fields: {sorted(disallowed)}. "
+                            f"Your permissions allow: {sorted(list(allowed_fields)[:10])}..."
+                            if allowed_fields
+                            else "Not allowed to modify fields: {sorted(disallowed)}. "
+                            "Your permissions do not allow any config modifications."
+                        )
+            except ValueError:
+                raise  # Re-raise permission errors
+            except Exception as e:
+                logger.warning(f"Failed to check permissions for bank {bank_id}: {e}")
+                # Continue without permission check (fail open for backward compatibility)
+
+        # Merge with existing config (JSONB || operator)
+        async with self.pool.acquire() as conn:
+            await conn.execute(
+                """
+                UPDATE banks
+                SET config = config || $1::jsonb,
+                    updated_at = now()
+                WHERE bank_id = $2
+                """,
+                json.dumps(normalized_updates),
+                bank_id,
+            )
+
+        logger.info(f"Updated bank config for {bank_id}: {list(normalized_updates.keys())}")
+
+    async def reset_bank_config(self, bank_id: str) -> None:
+        """
+        Reset bank configuration to defaults (remove all overrides).
+
+        Args:
+            bank_id: Bank identifier
+        """
+        async with self.pool.acquire() as conn:
+            await conn.execute(
+                """
+                UPDATE banks
+                SET config = '{}'::jsonb,
+                    updated_at = now()
+                WHERE bank_id = $1
+                """,
+                bank_id,
+            )
+
+        logger.info(f"Reset bank config for {bank_id} to defaults")

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

@@ -18,6 +18,7 @@ import uuid
 from datetime import datetime, timezone
 from typing import TYPE_CHECKING, Any
 
+from ...config import get_config
 from ..memory_engine import fq_table
 from ..retain import embedding_utils
 from .prompts import (
@@ -82,9 +83,8 @@ async def run_consolidation_job(
     Returns:
         Dict with consolidation results
     """
-    from ...config import get_config
-
-    config = get_config()
+    # Resolve bank-specific config with hierarchical overrides
+    config = await memory_engine._config_resolver.resolve_full_config(bank_id, request_context)
     perf = ConsolidationPerfLog(bank_id)
     max_memories_per_batch = config.consolidation_batch_size
 
@@ -1016,15 +1016,34 @@ async def _create_observation_directly(
 
     t0 = time.time()
     observation_id = uuid.uuid4()
+
+    # Query varies based on text search backend
+    config = get_config()
+    if config.text_search_extension == "vchord":
+        # VectorChord: manually tokenize and insert search_vector
+        query = f"""
+            INSERT INTO {fq_table("memory_units")} (
+                id, bank_id, text, fact_type, embedding, proof_count, source_memory_ids, history,
+                tags, event_date, occurred_start, occurred_end, mentioned_at, search_vector
+            )
+            VALUES ($1, $2, $3, 'observation', $4::vector, 1, $5, '[]'::jsonb, $6, $7, $8, $9, $10,
+                    tokenize($3, 'llmlingua2')::bm25_catalog.bm25vector)
+            RETURNING id
+        """
+    else:  # native or pg_textsearch
+        # Native PostgreSQL: search_vector is GENERATED ALWAYS, don't include it
+        # pg_textsearch: indexes operate on base columns directly, don't populate search_vector
+        query = f"""
+            INSERT INTO {fq_table("memory_units")} (
+                id, bank_id, text, fact_type, embedding, proof_count, source_memory_ids, history,
+                tags, event_date, occurred_start, occurred_end, mentioned_at
+            )
+            VALUES ($1, $2, $3, 'observation', $4::vector, 1, $5, '[]'::jsonb, $6, $7, $8, $9, $10)
+            RETURNING id
+        """
+
     row = await conn.fetchrow(
-        f"""
-        INSERT INTO {fq_table("memory_units")} (
-            id, bank_id, text, fact_type, embedding, proof_count, source_memory_ids, history,
-            tags, event_date, occurred_start, occurred_end, mentioned_at
-        )
-        VALUES ($1, $2, $3, 'observation', $4::vector, 1, $5, '[]'::jsonb, $6, $7, $8, $9, $10)
-        RETURNING id
-        """,
+        query,
         observation_id,
         bank_id,
         observation_text,

hindsight-api/hindsight_api/engine/cross_encoder.py

@@ -21,6 +21,7 @@ from ..config import (
     DEFAULT_RERANKER_FLASHRANK_CACHE_DIR,
     DEFAULT_RERANKER_FLASHRANK_MODEL,
     DEFAULT_RERANKER_LITELLM_MODEL,
+    DEFAULT_RERANKER_LITELLM_SDK_MODEL,
     DEFAULT_RERANKER_LOCAL_FORCE_CPU,
     DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT,
     DEFAULT_RERANKER_LOCAL_MODEL,
@@ -32,6 +33,7 @@ from ..config import (
     ENV_RERANKER_COHERE_MODEL,
     ENV_RERANKER_FLASHRANK_CACHE_DIR,
     ENV_RERANKER_FLASHRANK_MODEL,
+    ENV_RERANKER_LITELLM_SDK_API_KEY,
     ENV_RERANKER_LOCAL_FORCE_CPU,
     ENV_RERANKER_LOCAL_MAX_CONCURRENT,
     ENV_RERANKER_LOCAL_MODEL,
@@ -828,6 +830,126 @@ class LiteLLMCrossEncoder(CrossEncoderModel):
         return all_scores
 
 
+class LiteLLMSDKCrossEncoder(CrossEncoderModel):
+    """
+    LiteLLM SDK cross-encoder for direct API integration.
+
+    Supports reranking via LiteLLM SDK without requiring a proxy server.
+    Supported providers: Cohere, DeepInfra, Together AI, HuggingFace, Jina AI, Voyage AI, AWS Bedrock.
+
+    Example model names:
+    - cohere/rerank-english-v3.0
+    - deepinfra/Qwen3-reranker-8B
+    - together_ai/Salesforce/Llama-Rank-V1
+    - huggingface/BAAI/bge-reranker-v2-m3
+    """
+
+    def __init__(
+        self,
+        api_key: str,
+        model: str = DEFAULT_RERANKER_LITELLM_SDK_MODEL,
+        api_base: str | None = None,
+        timeout: float = 60.0,
+    ):
+        """
+        Initialize LiteLLM SDK cross-encoder client.
+
+        Args:
+            api_key: API key for the reranking provider
+            model: Model name with provider prefix (e.g., "deepinfra/Qwen3-reranker-8B")
+            api_base: Custom base URL for API (optional)
+            timeout: Request timeout in seconds (default: 60.0)
+        """
+        self.api_key = api_key
+        self.model = model
+        self.api_base = api_base
+        self.timeout = timeout
+        self._initialized = False
+        self._litellm = None  # Will be set during initialization
+
+    @property
+    def provider_name(self) -> str:
+        return "litellm-sdk"
+
+    async def initialize(self) -> None:
+        """Initialize the LiteLLM SDK client."""
+        if self._initialized:
+            return
+
+        try:
+            import litellm
+
+            self._litellm = litellm  # Store reference
+        except ImportError:
+            raise ImportError("litellm is required for LiteLLMSDKCrossEncoder. Install it with: pip install litellm")
+
+        api_base_msg = f" at {self.api_base}" if self.api_base else ""
+        logger.info(f"Reranker: initializing LiteLLM SDK provider with model {self.model}{api_base_msg}")
+
+        self._initialized = True
+        logger.info("Reranker: LiteLLM SDK provider initialized")
+
+    async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
+        """
+        Score query-document pairs using the LiteLLM SDK.
+
+        Args:
+            pairs: List of (query, document) tuples to score
+
+        Returns:
+            List of relevance scores
+        """
+        if not self._initialized:
+            raise RuntimeError("Reranker not initialized. Call initialize() first.")
+
+        if not pairs:
+            return []
+
+        # Group pairs by query for efficient batching
+        # LiteLLM rerank expects one query with multiple documents
+        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]
+
+            # Build kwargs for rerank call
+            rerank_kwargs = {
+                "model": self.model,
+                "query": query,
+                "documents": texts,
+                "api_key": self.api_key,
+            }
+            if self.api_base:
+                rerank_kwargs["api_base"] = self.api_base
+
+            response = await self._litellm.arerank(**rerank_kwargs)
+
+            # Map scores back to original positions
+            # Response format: RerankResponse with results list
+            # Each result is a TypedDict with "index" and "relevance_score"
+            if hasattr(response, "results") and response.results:
+                for result in response.results:
+                    # Results are TypedDicts, use dict-style access
+                    original_idx = result["index"]
+                    score = result.get("relevance_score", result.get("score", 0.0))
+                    all_scores[indices[original_idx]] = score
+            elif isinstance(response, list):
+                # Direct list of scores (unlikely but defensive)
+                for i, score in enumerate(response):
+                    all_scores[indices[i]] = score
+            else:
+                logger.warning(f"Unexpected response format from LiteLLM rerank: {type(response)}")
+
+        return all_scores
+
+
 def create_cross_encoder_from_env() -> CrossEncoderModel:
     """
     Create a CrossEncoderModel instance based on configuration.
@@ -877,9 +999,20 @@ def create_cross_encoder_from_env() -> CrossEncoderModel:
             api_key=config.reranker_litellm_api_key,
             model=config.reranker_litellm_model,
         )
+    elif provider == "litellm-sdk":
+        api_key = config.reranker_litellm_sdk_api_key
+        if not api_key:
+            raise ValueError(
+                f"{ENV_RERANKER_LITELLM_SDK_API_KEY} is required when {ENV_RERANKER_PROVIDER} is 'litellm-sdk'"
+            )
+        return LiteLLMSDKCrossEncoder(
+            api_key=api_key,
+            model=config.reranker_litellm_sdk_model,
+            api_base=config.reranker_litellm_sdk_api_base,
+        )
     elif provider == "rrf":
         return RRFPassthroughCrossEncoder()
     else:
         raise ValueError(
-            f"Unknown reranker provider: {provider}. Supported: 'local', 'tei', 'cohere', 'flashrank', 'litellm', 'rrf'"
+            f"Unknown reranker provider: {provider}. Supported: 'local', 'tei', 'cohere', 'flashrank', 'litellm', 'litellm-sdk', 'rrf'"
         )

hindsight-api/hindsight_api/engine/embeddings.py

@@ -19,6 +19,7 @@ import httpx
 from ..config import (
     DEFAULT_EMBEDDINGS_COHERE_MODEL,
     DEFAULT_EMBEDDINGS_LITELLM_MODEL,
+    DEFAULT_EMBEDDINGS_LITELLM_SDK_MODEL,
     DEFAULT_EMBEDDINGS_LOCAL_FORCE_CPU,
     DEFAULT_EMBEDDINGS_LOCAL_MODEL,
     DEFAULT_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE,
@@ -26,6 +27,7 @@ from ..config import (
     DEFAULT_EMBEDDINGS_PROVIDER,
     DEFAULT_LITELLM_API_BASE,
     ENV_EMBEDDINGS_COHERE_API_KEY,
+    ENV_EMBEDDINGS_LITELLM_SDK_API_KEY,
     ENV_EMBEDDINGS_LOCAL_FORCE_CPU,
     ENV_EMBEDDINGS_LOCAL_MODEL,
     ENV_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE,
@@ -720,6 +722,148 @@ class LiteLLMEmbeddings(Embeddings):
         return all_embeddings
 
 
+class LiteLLMSDKEmbeddings(Embeddings):
+    """
+    LiteLLM SDK embeddings for direct API integration.
+
+    Supports embeddings via LiteLLM SDK without requiring a proxy server.
+    Supported providers: Cohere, OpenAI, Azure OpenAI, HuggingFace, Voyage AI, Together AI, etc.
+
+    Example model names:
+    - cohere/embed-english-v3.0
+    - openai/text-embedding-3-small
+    - together_ai/togethercomputer/m2-bert-80M-8k-retrieval
+    - voyage/voyage-2
+    """
+
+    def __init__(
+        self,
+        api_key: str,
+        model: str = DEFAULT_EMBEDDINGS_LITELLM_SDK_MODEL,
+        api_base: str | None = None,
+        batch_size: int = 100,
+        timeout: float = 60.0,
+    ):
+        """
+        Initialize LiteLLM SDK embeddings client.
+
+        Args:
+            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)
+            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.batch_size = batch_size
+        self.timeout = timeout
+        self._litellm = None  # Will be set during initialization
+        self._dimension: int | None = None
+
+    @property
+    def provider_name(self) -> str:
+        return "litellm-sdk"
+
+    @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 LiteLLM SDK client and detect dimension."""
+        if self._litellm is not None:
+            return
+
+        try:
+            import litellm
+
+            self._litellm = litellm  # Store reference
+        except ImportError:
+            raise ImportError("litellm is required for LiteLLMSDKEmbeddings. Install it with: pip install litellm")
+
+        api_base_msg = f" at {self.api_base}" if self.api_base else ""
+        logger.info(f"Embeddings: initializing LiteLLM SDK provider with model {self.model}{api_base_msg}")
+
+        # Do a test embedding to detect dimension
+        try:
+            # Build kwargs for embedding call
+            embed_kwargs = {
+                "model": self.model,
+                "input": ["test"],
+                "api_key": self.api_key,
+            }
+            if self.api_base:
+                embed_kwargs["api_base"] = self.api_base
+
+            # Use async embedding method (standard in litellm)
+            response = await self._litellm.aembedding(**embed_kwargs)
+
+            # Extract dimension from response
+            if response.data and len(response.data) > 0:
+                self._dimension = len(response.data[0]["embedding"])
+            else:
+                raise RuntimeError(f"Unable to detect embedding dimension for model {self.model}")
+
+        except Exception as e:
+            raise RuntimeError(f"Failed to initialize LiteLLM SDK embeddings: {e}")
+
+        logger.info(f"Embeddings: LiteLLM SDK provider initialized (model: {self.model}, dim: {self._dimension})")
+
+    def encode(self, texts: list[str]) -> list[list[float]]:
+        """
+        Generate embeddings using the LiteLLM SDK.
+
+        Args:
+            texts: List of text strings to encode
+
+        Returns:
+            List of embedding vectors (one per input text)
+        """
+        if self._litellm 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]
+
+            try:
+                # Build kwargs for embedding call
+                embed_kwargs = {
+                    "model": self.model,
+                    "input": batch,
+                    "api_key": self.api_key,
+                }
+                if self.api_base:
+                    embed_kwargs["api_base"] = self.api_base
+
+                # Use sync embedding (litellm doesn't have async in thread-safe way)
+                response = self._litellm.embedding(**embed_kwargs)
+
+                # Extract embeddings from response
+                # Sort by index to ensure correct order
+                batch_embeddings = sorted(response.data, key=lambda x: x.get("index", 0))
+                all_embeddings.extend([e["embedding"] for e in batch_embeddings])
+
+            except Exception as e:
+                import traceback
+
+                logger.error(
+                    f"Error in LiteLLM embedding for batch starting at index {i}: {e}\n"
+                    f"Traceback: {traceback.format_exc()}"
+                )
+                raise
+
+        return all_embeddings
+
+
 def create_embeddings_from_env() -> Embeddings:
     """
     Create an Embeddings instance based on configuration.
@@ -771,7 +915,19 @@ def create_embeddings_from_env() -> Embeddings:
             api_key=config.embeddings_litellm_api_key,
             model=config.embeddings_litellm_model,
         )
+    elif provider == "litellm-sdk":
+        api_key = config.embeddings_litellm_sdk_api_key
+        if not api_key:
+            raise ValueError(
+                f"{ENV_EMBEDDINGS_LITELLM_SDK_API_KEY} is required when {ENV_EMBEDDINGS_PROVIDER} is 'litellm-sdk'"
+            )
+        return LiteLLMSDKEmbeddings(
+            api_key=api_key,
+            model=config.embeddings_litellm_sdk_model,
+            api_base=config.embeddings_litellm_sdk_api_base,
+        )
     else:
         raise ValueError(
-            f"Unknown embeddings provider: {provider}. Supported: 'local', 'tei', 'openai', 'cohere', 'litellm'"
+            f"Unknown embeddings provider: {provider}. "
+            f"Supported: 'local', 'tei', 'openai', 'cohere', 'litellm', 'litellm-sdk'"
         )

hindsight-api/hindsight_api/engine/interface.py

@@ -48,6 +48,7 @@ class MemoryEngineInterface(ABC):
         contents: list[dict[str, Any]],
         *,
         request_context: "RequestContext",
+        document_tags: list[str] | None = None,
     ) -> dict[str, Any]:
         """
         Retain a batch of memory items.
@@ -55,8 +56,9 @@ class MemoryEngineInterface(ABC):
         Args:
             bank_id: The memory bank ID.
             contents: List of content dicts with 'content', optional 'event_date',
-                     'context', 'metadata', 'document_id'.
+                     'context', 'metadata', 'document_id', and per-item 'tags'.
             request_context: Request context for authentication.
+            document_tags: Optional tags applied to all items in the batch.
 
         Returns:
             Dict with processing results.
@@ -561,6 +563,7 @@ class MemoryEngineInterface(ABC):
         contents: list[dict[str, Any]],
         *,
         request_context: "RequestContext",
+        document_tags: list[str] | None = None,
     ) -> dict[str, Any]:
         """
         Submit a batch retain operation to run asynchronously.
@@ -569,6 +572,7 @@ class MemoryEngineInterface(ABC):
             bank_id: The memory bank ID.
             contents: List of content dicts to retain.
             request_context: Request context for authentication.
+            document_tags: Optional tags applied to all items in the async batch.
 
         Returns:
             Dict with operation_id and items_count.

hindsight-api/hindsight_api/engine/llm_interface.py

@@ -128,6 +128,67 @@ class LLMInterface(ABC):
         """
         pass
 
+    async def supports_batch_api(self) -> bool:
+        """
+        Check if this provider supports batch API operations.
+
+        Returns:
+            True if provider supports submit_batch/get_batch_status/retrieve_batch_results
+        """
+        return False
+
+    async def submit_batch(
+        self,
+        requests: list[dict[str, Any]],
+        endpoint: str = "/v1/chat/completions",
+        completion_window: str = "24h",
+    ) -> dict[str, Any]:
+        """
+        Submit a batch of requests to the provider's batch API.
+
+        Args:
+            requests: List of request dicts in JSONL format (custom_id, method, url, body)
+            endpoint: API endpoint for the batch (e.g., "/v1/chat/completions")
+            completion_window: Completion window (e.g., "24h")
+
+        Returns:
+            Dict with batch metadata: {"batch_id": str, "status": str, ...}
+
+        Raises:
+            NotImplementedError: If provider doesn't support batch API
+        """
+        raise NotImplementedError(f"Batch API not supported for provider: {self.provider}")
+
+    async def get_batch_status(self, batch_id: str) -> dict[str, Any]:
+        """
+        Get the status of a batch job.
+
+        Args:
+            batch_id: Batch identifier returned from submit_batch
+
+        Returns:
+            Dict with status info: {"batch_id": str, "status": str, "completed_at": str, ...}
+
+        Raises:
+            NotImplementedError: If provider doesn't support batch API
+        """
+        raise NotImplementedError(f"Batch API not supported for provider: {self.provider}")
+
+    async def retrieve_batch_results(self, batch_id: str) -> list[dict[str, Any]]:
+        """
+        Retrieve completed batch results.
+
+        Args:
+            batch_id: Batch identifier returned from submit_batch
+
+        Returns:
+            List of result dicts (one per request, matched by custom_id)
+
+        Raises:
+            NotImplementedError: If provider doesn't support batch API
+        """
+        raise NotImplementedError(f"Batch API not supported for provider: {self.provider}")
+
     @abstractmethod
     async def cleanup(self) -> None:
         """Clean up resources (close connections, etc.)."""

hindsight-api/hindsight_api/engine/llm_wrapper.py

@@ -67,6 +67,7 @@ def create_llm_provider(
     model: str,
     reasoning_effort: str,
     groq_service_tier: str | None = None,
+    openai_service_tier: str | None = None,
     vertexai_project_id: str | None = None,
     vertexai_region: str | None = None,
     vertexai_credentials: Any = None,
@@ -80,7 +81,8 @@ def create_llm_provider(
         base_url: Base URL for the API.
         model: Model name.
         reasoning_effort: Reasoning effort level for supported providers.
-        groq_service_tier: Groq service tier (for Groq provider).
+        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).
         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).
@@ -156,6 +158,7 @@ def create_llm_provider(
             model=model,
             reasoning_effort=reasoning_effort,
             groq_service_tier=groq_service_tier,
+            openai_service_tier=openai_service_tier,
         )
 
     else:
@@ -177,6 +180,7 @@ class LLMProvider:
         model: str,
         reasoning_effort: str = "low",
         groq_service_tier: str | None = None,
+        openai_service_tier: str | None = None,
     ):
         """
         Initialize LLM provider.
@@ -187,15 +191,17 @@ class LLMProvider:
             base_url: Base URL for the API.
             model: Model name.
             reasoning_effort: Reasoning effort level for supported providers.
-            groq_service_tier: Groq service tier ("on_demand", "flex", "auto"). Default: None (uses Groq's default).
+            groq_service_tier: Groq service tier ("on_demand", "flex", "auto") - from config.
+            openai_service_tier: OpenAI service tier (None or "flex") - from config.
         """
         self.provider = provider.lower()
         self.api_key = api_key
         self.base_url = base_url
         self.model = model
         self.reasoning_effort = reasoning_effort
-        # Default to 'auto' for best performance, users can override to 'on_demand' for free tier
-        self.groq_service_tier = groq_service_tier or os.getenv(ENV_LLM_GROQ_SERVICE_TIER, "auto")
+        # Service tiers from hierarchical config (not env vars)
+        self.groq_service_tier = groq_service_tier
+        self.openai_service_tier = openai_service_tier
 
         # Validate provider
         valid_providers = [
@@ -272,6 +278,7 @@ class LLMProvider:
             model=self.model,
             reasoning_effort=self.reasoning_effort,
             groq_service_tier=self.groq_service_tier,
+            openai_service_tier=self.openai_service_tier,
             vertexai_project_id=vertexai_project_id,
             vertexai_region=vertexai_region,
             vertexai_credentials=vertexai_credentials,

hindsight-api/hindsight_api/engine/operation_metadata.py

@@ -0,0 +1,69 @@
+"""
+Typed metadata models for async operations.
+
+These dataclasses define the structure of result_metadata for different operation types.
+The metadata is exposed in the API for debugging purposes and may change without notice.
+"""
+
+from dataclasses import asdict, dataclass
+from typing import Any
+
+
+@dataclass
+class BatchRetainParentMetadata:
+    """Metadata for parent batch_retain operations (when split into sub-batches)."""
+
+    items_count: int
+    total_tokens: int
+    num_sub_batches: int
+    is_parent: bool = True
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dict for JSON serialization."""
+        return asdict(self)
+
+
+@dataclass
+class BatchRetainChildMetadata:
+    """Metadata for child batch_retain operations (individual sub-batches)."""
+
+    items_count: int
+    parent_operation_id: str
+    sub_batch_index: int
+    total_sub_batches: int
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dict for JSON serialization."""
+        return asdict(self)
+
+
+@dataclass
+class RetainMetadata:
+    """Metadata for regular retain operations (non-batched, deprecated async path)."""
+
+    items_count: int
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dict for JSON serialization."""
+        return asdict(self)
+
+
+@dataclass
+class ConsolidationMetadata:
+    """Metadata for consolidation operations."""
+
+    # Currently empty, but structure for future fields
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dict for JSON serialization."""
+        return asdict(self)
+
+
+@dataclass
+class RefreshMentalModelMetadata:
+    """Metadata for mental model refresh operations."""
+
+    mental_model_id: str
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dict for JSON serialization."""
+        return asdict(self)

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

@@ -16,6 +16,7 @@ Features:
 """
 
 import asyncio
+import io
 import json
 import logging
 import os
@@ -96,8 +97,9 @@ class OpenAICompatibleLLM(LLMInterface):
         if self.provider in ("openai", "groq") and not self.api_key:
             raise ValueError(f"API key is required for {self.provider}")
 
-        # Groq service tier configuration
-        self.groq_service_tier = groq_service_tier or os.getenv("HINDSIGHT_API_LLM_GROQ_SERVICE_TIER", "auto")
+        # Service tier configuration (from config, not env vars)
+        self.groq_service_tier = groq_service_tier
+        self.openai_service_tier = kwargs.get("openai_service_tier")
 
         # Get timeout config
         self.timeout = timeout or float(os.getenv(ENV_LLM_TIMEOUT, str(DEFAULT_LLM_TIMEOUT)))
@@ -782,6 +784,140 @@ class OpenAICompatibleLLM(LLMInterface):
             raise last_exception
         raise RuntimeError("Ollama call failed after all retries")
 
+    async def supports_batch_api(self) -> bool:
+        """Check if this provider supports batch API operations."""
+        # Only OpenAI and Groq support batch API
+        return self.provider in ("openai", "groq")
+
+    async def submit_batch(
+        self,
+        requests: list[dict[str, Any]],
+        endpoint: str = "/v1/chat/completions",
+        completion_window: str = "24h",
+    ) -> dict[str, Any]:
+        """
+        Submit a batch of requests to OpenAI/Groq Batch API.
+
+        Args:
+            requests: List of request dicts with custom_id, method, url, body
+            endpoint: API endpoint (e.g., "/v1/chat/completions")
+            completion_window: Completion window (e.g., "24h")
+
+        Returns:
+            Dict with batch metadata including batch_id
+
+        Raises:
+            NotImplementedError: If provider doesn't support batch API
+        """
+        if not await self.supports_batch_api():
+            raise NotImplementedError(f"Batch API not supported for provider: {self.provider}")
+
+        logger.info(f"Submitting batch with {len(requests)} requests to {self.provider}")
+
+        # Format requests as JSONL
+        jsonl_content = "\n".join(json.dumps(req) for req in requests)
+
+        # Upload file to provider (wrap in BytesIO with filename)
+        file_bytes = io.BytesIO(jsonl_content.encode("utf-8"))
+        file_bytes.name = "batch_input.jsonl"  # OpenAI SDK needs a filename
+
+        file_response = await self._client.files.create(
+            file=file_bytes,
+            purpose="batch",
+        )
+
+        logger.debug(f"Uploaded batch file: {file_response.id}")
+
+        # Create batch
+        batch_response = await self._client.batches.create(
+            input_file_id=file_response.id,
+            endpoint=endpoint,
+            completion_window=completion_window,
+        )
+
+        logger.info(f"Batch submitted: {batch_response.id}, status={batch_response.status}")
+
+        return {
+            "batch_id": batch_response.id,
+            "status": batch_response.status,
+            "input_file_id": file_response.id,
+            "created_at": batch_response.created_at,
+            "request_count": len(requests),
+        }
+
+    async def get_batch_status(self, batch_id: str) -> dict[str, Any]:
+        """
+        Get the status of a batch job.
+
+        Args:
+            batch_id: Batch identifier
+
+        Returns:
+            Dict with status info (batch_id, status, completed_at, etc.)
+        """
+        if not await self.supports_batch_api():
+            raise NotImplementedError(f"Batch API not supported for provider: {self.provider}")
+
+        batch = await self._client.batches.retrieve(batch_id)
+
+        result = {
+            "batch_id": batch.id,
+            "status": batch.status,
+            "created_at": batch.created_at,
+            "request_counts": {
+                "total": batch.request_counts.total if batch.request_counts else 0,
+                "completed": batch.request_counts.completed if batch.request_counts else 0,
+                "failed": batch.request_counts.failed if batch.request_counts else 0,
+            },
+        }
+
+        if batch.completed_at:
+            result["completed_at"] = batch.completed_at
+        if batch.output_file_id:
+            result["output_file_id"] = batch.output_file_id
+        if batch.error_file_id:
+            result["error_file_id"] = batch.error_file_id
+        if batch.errors:
+            result["errors"] = batch.errors
+
+        return result
+
+    async def retrieve_batch_results(self, batch_id: str) -> list[dict[str, Any]]:
+        """
+        Retrieve completed batch results.
+
+        Args:
+            batch_id: Batch identifier
+
+        Returns:
+            List of result dicts (one per request, matched by custom_id)
+        """
+        if not await self.supports_batch_api():
+            raise NotImplementedError(f"Batch API not supported for provider: {self.provider}")
+
+        # Get batch status
+        batch = await self._client.batches.retrieve(batch_id)
+
+        if batch.status != "completed":
+            raise ValueError(f"Batch {batch_id} is not completed yet (status: {batch.status})")
+
+        if not batch.output_file_id:
+            raise ValueError(f"Batch {batch_id} has no output file")
+
+        # Download results file
+        logger.debug(f"Downloading results for batch {batch_id} from file {batch.output_file_id}")
+        file_content = await self._client.files.content(batch.output_file_id)
+
+        # Parse JSONL results
+        results = []
+        for line in file_content.text.strip().split("\n"):
+            if line:
+                results.append(json.loads(line))
+
+        logger.info(f"Retrieved {len(results)} results for batch {batch_id}")
+
+        return results
+
     async def cleanup(self) -> None:
         """Clean up resources (close OpenAI client connections)."""
         if hasattr(self, "_client") and self._client:

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

@@ -695,44 +695,20 @@ Example: "Lost job → couldn't pay rent → moved apartment"
 - Fact 2: Moved apartment, causal_relations: [{target_index: 1, relation_type: "caused_by"}]"""
 
 
-async def _extract_facts_from_chunk(
-    chunk: str,
-    chunk_index: int,
-    total_chunks: int,
-    event_date: datetime,
-    context: str,
-    llm_config: "LLMConfig",
-    agent_name: str = None,
-) -> tuple[list[dict[str, str]], TokenUsage]:
+def _build_extraction_prompt_and_schema(config) -> tuple[str, type]:
     """
-    Extract facts from a single chunk (internal helper for parallel processing).
+    Build extraction prompt and response schema based on config.
 
-    Note: event_date parameter is kept for backward compatibility but not used in prompt.
-    The LLM extracts temporal information from the context string instead.
+    Returns:
+        Tuple of (prompt, response_schema)
     """
-    import logging
-
-    from openai import BadRequestError
-
-    logger = logging.getLogger(__name__)
-
-    # Determine which fact types to extract
-    # Note: We use "assistant" in the prompt but convert to "bank" for storage
     fact_types_instruction = "Extract ONLY 'world' and 'assistant' type facts."
-
-    # Check config for extraction mode and causal link extraction
-    config = get_config()
     extraction_mode = config.retain_extraction_mode
     extract_causal_links = config.retain_extract_causal_links
 
     # Select base prompt based on extraction mode
     if extraction_mode == "custom":
-        # Custom mode: inject user-provided guidelines
         if not config.retain_custom_instructions:
-            logger.warning(
-                "extraction_mode='custom' but HINDSIGHT_API_RETAIN_CUSTOM_INSTRUCTIONS not set. "
-                "Falling back to 'concise' mode."
-            )
             base_prompt = CONCISE_FACT_EXTRACTION_PROMPT
             prompt = base_prompt.format(fact_types_instruction=fact_types_instruction)
         else:
@@ -748,33 +724,26 @@ async def _extract_facts_from_chunk(
         base_prompt = CONCISE_FACT_EXTRACTION_PROMPT
         prompt = base_prompt.format(fact_types_instruction=fact_types_instruction)
 
-    # Build the full prompt with or without causal relationships section
-    # Select appropriate response schema based on extraction mode and causal links
+    # Add causal relationships section if enabled
     if extract_causal_links:
         prompt = prompt + CAUSAL_RELATIONSHIPS_SECTION
-        if extraction_mode == "verbose":
-            response_schema = FactExtractionResponseVerbose
-        else:
-            response_schema = FactExtractionResponse
+        response_schema = FactExtractionResponseVerbose if extraction_mode == "verbose" else FactExtractionResponse
     else:
         response_schema = FactExtractionResponseNoCausal
 
-    # Retry logic for JSON validation errors
-    max_retries = 2
-    last_error = None
+    return prompt, response_schema
 
-    # Sanitize input text to prevent Unicode encoding errors (e.g., unpaired surrogates)
-    sanitized_chunk = _sanitize_text(chunk)
-    sanitized_context = _sanitize_text(context) if context else "none"
 
-    # Build user message with metadata and chunk content in a clear format
-    # Format event_date with day of week for better temporal reasoning
-    # Handle both datetime objects and ISO string formats (from deserialized async tasks)
+def _build_user_message(chunk: str, chunk_index: int, total_chunks: int, event_date: datetime, context: str) -> str:
+    """Build user message for fact extraction."""
     from .orchestrator import parse_datetime_flexible
 
+    sanitized_chunk = _sanitize_text(chunk)
+    sanitized_context = _sanitize_text(context) if context else "none"
     event_date = parse_datetime_flexible(event_date)
-    event_date_formatted = event_date.strftime("%A, %B %d, %Y")  # e.g., "Monday, June 10, 2024"
-    user_message = f"""Extract facts from the following text chunk.
+    event_date_formatted = event_date.strftime("%A, %B %d, %Y")
+
+    return f"""Extract facts from the following text chunk.
 
 Chunk: {chunk_index + 1}/{total_chunks}
 Event Date: {event_date_formatted} ({event_date.isoformat()})
@@ -783,6 +752,70 @@ Context: {sanitized_context}
 Text:
 {sanitized_chunk}"""
 
+
+def _build_request_body(llm_config, config, prompt: str, user_message: str, response_schema: type) -> dict:
+    """Build request body for LLM API call."""
+    request_body = {
+        "model": llm_config.model,
+        "messages": [{"role": "system", "content": prompt}, {"role": "user", "content": user_message}],
+        "temperature": 0.1,
+    }
+
+    # Add max_completion_tokens if configured
+    if config.retain_max_completion_tokens:
+        request_body["max_completion_tokens"] = config.retain_max_completion_tokens
+
+    # Add service_tier for OpenAI Flex Processing
+    if llm_config.provider == "openai" and llm_config._provider_impl.openai_service_tier:
+        request_body["service_tier"] = llm_config._provider_impl.openai_service_tier
+
+    # Add response_format (JSON schema)
+    if hasattr(response_schema, "model_json_schema"):
+        schema = response_schema.model_json_schema()
+        request_body["response_format"] = {
+            "type": "json_schema",
+            "json_schema": {"name": "facts", "schema": schema},
+        }
+
+    return request_body
+
+
+async def _extract_facts_from_chunk(
+    chunk: str,
+    chunk_index: int,
+    total_chunks: int,
+    event_date: datetime,
+    context: str,
+    llm_config: "LLMConfig",
+    config,
+    agent_name: str = None,
+) -> tuple[list[dict[str, str]], TokenUsage]:
+    """
+    Extract facts from a single chunk (internal helper for parallel processing).
+
+    Note: event_date parameter is kept for backward compatibility but not used in prompt.
+    The LLM extracts temporal information from the context string instead.
+    """
+    import logging
+
+    from openai import BadRequestError
+
+    logger = logging.getLogger(__name__)
+
+    # Build prompt and schema using helper function
+    prompt, response_schema = _build_extraction_prompt_and_schema(config)
+
+    # Check config for extraction mode and causal link extraction
+    extraction_mode = config.retain_extraction_mode
+    extract_causal_links = config.retain_extract_causal_links
+
+    # Build user message using helper function
+    user_message = _build_user_message(chunk, chunk_index, total_chunks, event_date, context)
+
+    # Retry logic for JSON validation errors
+    max_retries = 2
+    last_error = None
+
     usage = TokenUsage()  # Track cumulative usage across retries
     for attempt in range(max_retries):
         try:
@@ -1055,6 +1088,7 @@ async def _extract_facts_with_auto_split(
     event_date: datetime,
     context: str,
     llm_config: LLMConfig,
+    config,
     agent_name: str = None,
 ) -> tuple[list[dict[str, str]], TokenUsage]:
     """
@@ -1070,6 +1104,7 @@ async def _extract_facts_with_auto_split(
         event_date: Reference date for temporal information
         context: Context about the conversation/document
         llm_config: LLM configuration to use
+        config: Resolved HindsightConfig for this bank
         agent_name: Optional agent name (memory owner)
 
     Returns:
@@ -1088,6 +1123,7 @@ async def _extract_facts_with_auto_split(
             event_date=event_date,
             context=context,
             llm_config=llm_config,
+            config=config,
             agent_name=agent_name,
         )
     except OutputTooLongError:
@@ -1132,6 +1168,7 @@ async def _extract_facts_with_auto_split(
                 event_date=event_date,
                 context=context,
                 llm_config=llm_config,
+                config=config,
                 agent_name=agent_name,
             ),
             _extract_facts_with_auto_split(
@@ -1141,6 +1178,7 @@ async def _extract_facts_with_auto_split(
                 event_date=event_date,
                 context=context,
                 llm_config=llm_config,
+                config=config,
                 agent_name=agent_name,
             ),
         ]
@@ -1164,6 +1202,7 @@ async def extract_facts_from_text(
     event_date: datetime,
     llm_config: LLMConfig,
     agent_name: str,
+    config,
     context: str = "",
 ) -> tuple[list[Fact], list[tuple[str, int]], TokenUsage]:
     """
@@ -1178,9 +1217,10 @@ async def extract_facts_from_text(
     Args:
         text: Input text (conversation, article, etc.)
         event_date: Reference date for resolving relative times
-        context: Context about the conversation/document
         llm_config: LLM configuration to use
         agent_name: Agent name (memory owner)
+        config: Resolved HindsightConfig for this bank
+        context: Context about the conversation/document
 
     Returns:
         Tuple of (facts, chunks, usage) where:
@@ -1188,7 +1228,6 @@ async def extract_facts_from_text(
         - chunks: List of tuples (chunk_text, fact_count) for each chunk
         - usage: Aggregated token usage across all LLM calls
     """
-    config = get_config()
     chunks = chunk_text(text, max_chars=config.retain_chunk_size)
 
     # Log chunk count before starting LLM requests
@@ -1207,6 +1246,7 @@ async def extract_facts_from_text(
             event_date=event_date,
             context=context,
             llm_config=llm_config,
+            config=config,
             agent_name=agent_name,
         )
         for i, chunk in enumerate(chunks)
@@ -1238,8 +1278,420 @@ logger = logging.getLogger(__name__)
 SECONDS_PER_FACT = 10
 
 
+async def extract_facts_from_contents_batch_api(
+    contents: list[RetainContent],
+    llm_config,
+    agent_name: str,
+    config,
+    pool=None,
+    operation_id: str | None = None,
+    schema: str | None = None,
+) -> tuple[list[ExtractedFactType], list[ChunkMetadata], TokenUsage]:
+    """
+    Extract facts using LLM Batch API (OpenAI/Groq).
+
+    Submits all chunks as a single batch, polls until complete, then processes results.
+    Only called when config.retain_batch_enabled=True.
+
+    Args:
+        contents: List of RetainContent objects to process
+        llm_config: LLM configuration with batch API support
+        agent_name: Name of the agent
+        config: Resolved HindsightConfig for this bank
+        pool: Database connection pool (for storing batch state)
+        operation_id: Async operation ID (for crash recovery)
+        schema: Database schema (for multi-tenant support)
+
+    Returns:
+        Tuple of (extracted_facts, chunks_metadata, usage)
+    """
+    if not contents:
+        return [], [], TokenUsage()
+
+    logger.info(f"Using Batch API for fact extraction ({len(contents)} contents)")
+
+    # Check config for extraction mode and causal link extraction (used throughout)
+    extraction_mode = config.retain_extraction_mode
+    extract_causal_links = config.retain_extract_causal_links
+
+    # Check if provider supports batch API
+    if not await llm_config._provider_impl.supports_batch_api():
+        logger.warning(f"Batch API not supported for provider {llm_config.provider}, falling back to sync mode")
+        return await extract_facts_from_contents(contents, llm_config, agent_name, config, pool, operation_id, schema)
+
+    # Check if we're resuming an existing batch (crash recovery)
+    batch_id = None
+    if operation_id and pool:
+        from ..task_backend import fq_table
+
+        table = fq_table("async_operations", schema)
+        row = await pool.fetchrow(
+            f"SELECT result_metadata FROM {table} WHERE operation_id = $1",
+            operation_id,
+        )
+
+        if row and row["result_metadata"]:
+            metadata = row["result_metadata"]
+            if isinstance(metadata, str):
+                metadata = json.loads(metadata)
+            batch_id = metadata.get("batch_id")
+
+            if batch_id:
+                logger.info(f"Resuming existing batch: batch_id={batch_id} (crash recovery)")
+
+    # Step 1: Chunk all contents and build batch requests (skip if resuming)
+    all_chunks_info = []  # List of (chunk_text, content_index, chunk_index_in_content, event_date, context)
+    batch_requests = []
+
+    # Build prompt and schema once (same for all chunks)
+    prompt, response_schema = _build_extraction_prompt_and_schema(config)
+
+    for content_index, item in enumerate(contents):
+        chunks = chunk_text(item.content, max_chars=config.retain_chunk_size)
+
+        for chunk_index_in_content, chunk in enumerate(chunks):
+            all_chunks_info.append((chunk, content_index, chunk_index_in_content, item.event_date, item.context))
+
+            # Build batch request for this chunk
+            custom_id = f"chunk_{len(all_chunks_info) - 1}"  # Global chunk index
+
+            # Build user message using helper function
+            user_message = _build_user_message(
+                chunk, chunk_index_in_content, len(chunks), item.event_date, item.context
+            )
+
+            # Build request body using helper function
+            request_body = _build_request_body(llm_config, config, prompt, user_message, response_schema)
+
+            batch_requests.append(
+                {"custom_id": custom_id, "method": "POST", "url": "/v1/chat/completions", "body": request_body}
+            )
+
+    if not batch_requests and not batch_id:  # No requests and not resuming
+        return [], [], TokenUsage()
+
+    # Step 2: Submit batch (skip if resuming)
+    if not batch_id:
+        logger.info(f"Submitting batch with {len(batch_requests)} chunk requests")
+
+        batch_metadata = await llm_config._provider_impl.submit_batch(batch_requests)
+        batch_id = batch_metadata["batch_id"]
+
+        logger.info(f"Batch submitted: {batch_id}, polling every {config.retain_batch_poll_interval_seconds}s")
+
+        # CRITICAL: Store minimal batch state in operation metadata for crash recovery
+        # This allows resuming polling if worker restarts
+        if operation_id and pool:
+            batch_state = {
+                "batch_id": batch_id,
+                "batch_provider": llm_config.provider,
+                "chunk_count": len(batch_requests),
+            }
+
+            # Update operation result_metadata
+            from ..task_backend import fq_table
+
+            table = fq_table("async_operations", schema)
+            await pool.execute(
+                f"""
+                UPDATE {table}
+                SET result_metadata = result_metadata || $1::jsonb, updated_at = now()
+                WHERE operation_id = $2
+                """,
+                json.dumps(batch_state),
+                operation_id,
+            )
+            logger.info(f"Stored batch state for operation {operation_id} (crash recovery enabled)")
+    else:
+        logger.info(f"Resuming polling for existing batch: {batch_id}")
+
+    # Step 3: Poll until complete
+    import time
+
+    start_time = time.time()
+    while True:
+        status_info = await llm_config._provider_impl.get_batch_status(batch_id)
+        status = status_info["status"]
+
+        elapsed = time.time() - start_time
+        logger.info(
+            f"Batch {batch_id}: status={status}, "
+            f"completed={status_info['request_counts']['completed']}/{status_info['request_counts']['total']}, "
+            f"elapsed={elapsed:.0f}s"
+        )
+
+        if status == "completed":
+            break
+        elif status in ("failed", "expired", "cancelled"):
+            error_msg = status_info.get("errors", "Unknown error")
+            raise RuntimeError(f"Batch {batch_id} failed with status {status}: {error_msg}")
+
+        # Wait before polling again
+        await asyncio.sleep(config.retain_batch_poll_interval_seconds)
+
+    logger.info(f"Batch {batch_id} completed in {elapsed:.0f}s, retrieving results")
+
+    # Step 4: Retrieve results
+    batch_results = await llm_config._provider_impl.retrieve_batch_results(batch_id)
+
+    # Map results by custom_id
+    results_by_id = {result["custom_id"]: result for result in batch_results}
+
+    # Step 5: Parse results into facts (same as sync mode)
+    all_facts_from_llm = []
+    chunks_metadata = []
+    total_usage = TokenUsage()
+
+    for chunk_idx, (chunk_content, content_index, chunk_index_in_content, event_date, context) in enumerate(
+        all_chunks_info
+    ):
+        custom_id = f"chunk_{chunk_idx}"
+        result = results_by_id.get(custom_id)
+
+        if not result:
+            logger.warning(f"Missing result for {custom_id}, skipping")
+            chunks_metadata.append(
+                ChunkMetadata(
+                    chunk_text=chunk_content, fact_count=0, content_index=content_index, chunk_index=chunk_idx
+                )
+            )
+            continue
+
+        # Check for errors
+        if result.get("error"):
+            logger.error(f"Error in {custom_id}: {result['error']}")
+            chunks_metadata.append(
+                ChunkMetadata(
+                    chunk_text=chunk_content, fact_count=0, content_index=content_index, chunk_index=chunk_idx
+                )
+            )
+            continue
+
+        # Extract response
+        response_body = result.get("response", {}).get("body", {})
+        choices = response_body.get("choices", [])
+
+        if not choices:
+            logger.warning(f"No choices in response for {custom_id}")
+            chunks_metadata.append(
+                ChunkMetadata(
+                    chunk_text=chunk_content, fact_count=0, content_index=content_index, chunk_index=chunk_idx
+                )
+            )
+            continue
+
+        # Parse JSON content
+        message = choices[0].get("message", {})
+        content_str = message.get("content", "{}")
+
+        try:
+            extraction_response_json = json.loads(content_str)
+        except json.JSONDecodeError as e:
+            logger.error(f"Failed to parse JSON for {custom_id}: {e}")
+            chunks_metadata.append(
+                ChunkMetadata(
+                    chunk_text=chunk_content, fact_count=0, content_index=content_index, chunk_index=chunk_idx
+                )
+            )
+            continue
+
+        # Parse facts (reuse existing logic from _extract_facts_from_chunk)
+        raw_facts = extraction_response_json.get("facts", [])
+        chunk_facts = []
+
+        for i, llm_fact in enumerate(raw_facts):
+            if not isinstance(llm_fact, dict):
+                continue
+
+            def get_value(field_name):
+                value = llm_fact.get(field_name)
+                if value and value != "" and value != [] and value != {} and str(value).upper() != "N/A":
+                    return value
+                return None
+
+            what = get_value("what")
+            if not what:
+                what = get_value("factual_core")
+            if not what:
+                continue
+
+            when = get_value("when")
+            who = get_value("who")
+            why = get_value("why")
+
+            # Critical field: fact_type
+            original_fact_type = llm_fact.get("fact_type")
+            fact_type = original_fact_type
+
+            # Convert "assistant" → "experience"
+            if fact_type == "assistant":
+                fact_type = "experience"
+
+            # Validate fact_type
+            if fact_type not in ["world", "experience", "opinion"]:
+                fact_kind = llm_fact.get("fact_kind")
+                if fact_kind == "assistant":
+                    fact_type = "experience"
+                elif fact_kind in ["world", "experience", "opinion"]:
+                    fact_type = fact_kind
+                else:
+                    fact_type = "world"
+
+            # Build combined fact text
+            combined_parts = [what]
+            if when:
+                combined_parts.append(f"When: {when}")
+            if who:
+                combined_parts.append(f"Involving: {who}")
+            if why:
+                combined_parts.append(why)
+            combined_text = " | ".join(combined_parts)
+
+            # Temporal fields
+            fact_data = {}
+            fact_kind = llm_fact.get("fact_kind", "conversation")
+            if fact_kind not in ["conversation", "event", "other"]:
+                fact_kind = "conversation"
+
+            if fact_kind == "event":
+                occurred_start = get_value("occurred_start")
+                occurred_end = get_value("occurred_end")
+
+                if not occurred_start:
+                    fact_data["occurred_start"] = _infer_temporal_date(combined_text, event_date)
+                else:
+                    fact_data["occurred_start"] = occurred_start
+
+                if occurred_end:
+                    fact_data["occurred_end"] = occurred_end
+                elif fact_data.get("occurred_start"):
+                    fact_data["occurred_end"] = fact_data["occurred_start"]
+
+            # Entities
+            entities = get_value("entities")
+            if entities:
+                validated_entities = []
+                for ent in entities:
+                    if isinstance(ent, str):
+                        validated_entities.append(Entity(text=ent))
+                    elif isinstance(ent, dict) and "text" in ent:
+                        try:
+                            validated_entities.append(Entity.model_validate(ent))
+                        except Exception:
+                            pass
+                if validated_entities:
+                    fact_data["entities"] = validated_entities
+
+            # Causal relations
+            if extract_causal_links:
+                validated_relations = []
+                causal_relations_raw = get_value("causal_relations")
+                if causal_relations_raw:
+                    for rel in causal_relations_raw:
+                        if not isinstance(rel, dict):
+                            continue
+                        target_idx = rel.get("target_index")
+                        relation_type = rel.get("relation_type")
+                        strength = rel.get("strength", 1.0)
+
+                        if target_idx is None or relation_type is None:
+                            continue
+                        if target_idx < 0 or target_idx >= i:
+                            continue
+
+                        try:
+                            validated_relations.append(
+                                CausalRelation(
+                                    target_fact_index=target_idx, relation_type=relation_type, strength=strength
+                                )
+                            )
+                        except Exception:
+                            pass
+
+                if validated_relations:
+                    fact_data["causal_relations"] = validated_relations
+
+            # Always set mentioned_at
+            fact_data["mentioned_at"] = event_date.isoformat()
+
+            try:
+                fact = Fact(fact=combined_text, fact_type=fact_type, **fact_data)
+                chunk_facts.append(fact)
+            except Exception as e:
+                logger.error(f"Failed to create Fact model for fact {i}: {e}")
+                continue
+
+        all_facts_from_llm.extend(chunk_facts)
+        chunks_metadata.append(
+            ChunkMetadata(
+                chunk_text=chunk_content,
+                fact_count=len(chunk_facts),
+                content_index=content_index,
+                chunk_index=chunk_idx,
+            )
+        )
+
+        # Track token usage
+        usage_data = response_body.get("usage", {})
+        if usage_data:
+            total_usage = total_usage + TokenUsage(
+                input_tokens=usage_data.get("prompt_tokens", 0),
+                output_tokens=usage_data.get("completion_tokens", 0),
+                total_tokens=usage_data.get("total_tokens", 0),
+            )
+
+    # Step 6: Convert to ExtractedFact objects with proper chunk mapping
+    # Group facts by chunk
+    facts_by_chunk = []  # List of (chunk_metadata, [facts])
+    fact_start_idx = 0
+
+    for chunk_meta in chunks_metadata:
+        chunk_facts = all_facts_from_llm[fact_start_idx : fact_start_idx + chunk_meta.fact_count]
+        facts_by_chunk.append((chunk_meta, chunk_facts))
+        fact_start_idx += chunk_meta.fact_count
+
+    # Now convert to ExtractedFactType
+    extracted_facts = []
+    global_fact_idx = 0
+
+    for chunk_meta, chunk_facts in facts_by_chunk:
+        content = contents[chunk_meta.content_index]
+
+        for fact_from_llm in chunk_facts:
+            extracted_fact = ExtractedFactType(
+                fact_text=fact_from_llm.fact,
+                fact_type=fact_from_llm.fact_type,
+                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,
+                causal_relations=_convert_causal_relations(fact_from_llm.causal_relations or [], global_fact_idx),
+                content_index=chunk_meta.content_index,
+                chunk_index=chunk_meta.chunk_index,
+                context=content.context,
+                mentioned_at=content.event_date,
+                metadata=content.metadata,
+                tags=content.tags,
+            )
+
+            extracted_facts.append(extracted_fact)
+            global_fact_idx += 1
+
+    # Step 7: Add temporal offsets
+    _add_temporal_offsets(extracted_facts, contents)
+
+    logger.info(f"Batch API extracted {len(extracted_facts)} facts from {len(all_chunks_info)} chunks")
+
+    return extracted_facts, chunks_metadata, total_usage
+
+
 async def extract_facts_from_contents(
-    contents: list[RetainContent], llm_config, agent_name: str
+    contents: list[RetainContent],
+    llm_config,
+    agent_name: str,
+    config,
+    pool=None,
+    operation_id: str | None = None,
+    schema: str | None = None,
 ) -> tuple[list[ExtractedFactType], list[ChunkMetadata], TokenUsage]:
     """
     Extract facts from multiple content items in parallel.
@@ -1250,10 +1702,16 @@ async def extract_facts_from_contents(
     3. Adds time offsets to preserve fact ordering within each content
     4. Returns typed ExtractedFact and ChunkMetadata objects
 
+    Routes to batch API mode if config.retain_batch_enabled=True.
+
     Args:
         contents: List of RetainContent objects to process
         llm_config: LLM configuration for fact extraction
         agent_name: Name of the agent (for agent-related fact detection)
+        config: Resolved HindsightConfig for this bank
+        pool: Database connection pool (passed to batch API for state storage)
+        operation_id: Async operation ID (passed to batch API for crash recovery)
+        schema: Database schema (passed to batch API for multi-tenant support)
 
     Returns:
         Tuple of (extracted_facts, chunks_metadata, usage)
@@ -1261,6 +1719,12 @@ async def extract_facts_from_contents(
     if not contents:
         return [], [], TokenUsage()
 
+    # Route to batch API if enabled
+    if config.retain_batch_enabled:
+        return await extract_facts_from_contents_batch_api(
+            contents, llm_config, agent_name, config, pool, operation_id, schema
+        )
+
     # Step 1: Create parallel fact extraction tasks
     fact_extraction_tasks = []
     for item in contents:
@@ -1272,6 +1736,7 @@ async def extract_facts_from_contents(
             context=item.context,
             llm_config=llm_config,
             agent_name=agent_name,
+            config=config,
         )
         fact_extraction_tasks.append(task)
 

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

@@ -7,6 +7,7 @@ Handles insertion of facts into the database.
 import json
 import logging
 
+from ...config import get_config
 from ..memory_engine import fq_table
 from .fact_extraction import _sanitize_text
 from .types import ProcessedFact
@@ -70,28 +71,59 @@ async def insert_facts_batch(
 
     # Batch insert all facts
     # Note: tags are passed as JSON strings and converted back to varchar[] via jsonb_array_elements_text + array_agg
-    results = await conn.fetch(
-        f"""
-        WITH input_data AS (
-            SELECT * FROM unnest(
-                $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
-                $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[]
-            ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                   context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json)
-        )
-        INSERT INTO {fq_table("memory_units")} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                                 context, fact_type, confidence_score, metadata, chunk_id, document_id, tags)
-        SELECT
-            $1,
-            text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-            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[]
+    # Query varies based on text search backend
+    config = get_config()
+    if config.text_search_extension == "vchord":
+        # VectorChord: manually tokenize and insert search_vector
+        query = f"""
+            WITH input_data AS (
+                SELECT * FROM unnest(
+                    $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
+                    $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[]
+                ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                       context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json)
             )
-        FROM input_data
-        RETURNING id
-        """,
+            INSERT INTO {fq_table("memory_units")} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                                     context, fact_type, confidence_score, metadata, chunk_id, document_id, tags, search_vector)
+            SELECT
+                $1,
+                text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                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[]
+                ),
+                tokenize(COALESCE(text, '') || ' ' || COALESCE(context, ''), 'llmlingua2')::bm25_catalog.bm25vector
+            FROM input_data
+            RETURNING id
+        """
+    else:  # native or pg_textsearch
+        # Native PostgreSQL: search_vector is GENERATED ALWAYS, don't include it
+        # pg_textsearch: indexes operate on base columns directly, don't populate search_vector
+        query = f"""
+            WITH input_data AS (
+                SELECT * FROM unnest(
+                    $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
+                    $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[]
+                ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                       context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json)
+            )
+            INSERT INTO {fq_table("memory_units")} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                                     context, fact_type, confidence_score, metadata, chunk_id, document_id, tags)
+            SELECT
+                $1,
+                text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                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[]
+                )
+            FROM input_data
+            RETURNING id
+        """
+
+    results = await conn.fetch(
+        query,
         bank_id,
         fact_texts,
         embeddings,

hindsight-api/hindsight_api/engine/retain/orchestrator.py

@@ -76,11 +76,14 @@ async def retain_batch(
     duplicate_checker_fn,
     bank_id: str,
     contents_dicts: list[RetainContentDict],
+    config,
     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,
+    schema: str | None = None,
 ) -> tuple[list[list[str]], TokenUsage]:
     """
     Process a batch of content through the retain pipeline.
@@ -94,6 +97,7 @@ async def retain_batch(
         duplicate_checker_fn: Function to check for duplicate facts
         bank_id: Bank identifier
         contents_dicts: List of content dictionaries
+        config: Resolved HindsightConfig for this bank
         document_id: Optional document ID
         is_first_batch: Whether this is the first batch
         fact_type_override: Override fact type for all facts
@@ -144,7 +148,9 @@ async def retain_batch(
     # Step 1: Extract facts from all contents
     step_start = time.time()
 
-    extracted_facts, chunks, usage = await fact_extraction.extract_facts_from_contents(contents, llm_config, agent_name)
+    extracted_facts, chunks, usage = await fact_extraction.extract_facts_from_contents(
+        contents, llm_config, agent_name, config, pool, operation_id, schema
+    )
     log_buffer.append(
         f"[1] Extract facts: {len(extracted_facts)} facts, {len(chunks)} chunks from {len(contents)} contents in {time.time() - step_start:.3f}s"
     )

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

@@ -13,12 +13,10 @@ from .reranking import CrossEncoderReranker
 from .retrieval import (
     ParallelRetrievalResult,
     get_default_graph_retriever,
-    retrieve_parallel,
     set_default_graph_retriever,
 )
 
 __all__ = [
-    "retrieve_parallel",
     "get_default_graph_retriever",
     "set_default_graph_retriever",
     "ParallelRetrievalResult",

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

@@ -85,116 +85,6 @@ def set_default_graph_retriever(retriever: GraphRetriever) -> None:
     _default_graph_retriever = retriever
 
 
-async def retrieve_semantic(
-    conn,
-    query_emb_str: str,
-    bank_id: str,
-    fact_type: str,
-    limit: int,
-    tags: list[str] | None = None,
-) -> list[RetrievalResult]:
-    """
-    Semantic retrieval via vector similarity.
-
-    Args:
-        conn: Database connection
-        query_emb_str: Query embedding as string
-        agent_id: bank ID
-        fact_type: Fact type to filter
-        limit: Maximum results to return
-        tags: Optional list of tags for visibility filtering (OR matching)
-
-    Returns:
-        List of RetrievalResult objects
-    """
-    from .tags import TagsMatch, build_tags_where_clause_simple
-
-    tags_clause = build_tags_where_clause_simple(tags, 5)
-    params = [query_emb_str, bank_id, fact_type, limit]
-    if tags:
-        params.append(tags)
-
-    results = await conn.fetch(
-        f"""
-        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, 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)) >= 0.3
-          {tags_clause}
-        ORDER BY embedding <=> $1::vector
-        LIMIT $4
-        """,
-        *params,
-    )
-    return [RetrievalResult.from_db_row(dict(r)) for r in results]
-
-
-async def retrieve_bm25(
-    conn,
-    query_text: str,
-    bank_id: str,
-    fact_type: str,
-    limit: int,
-    tags: list[str] | None = None,
-) -> list[RetrievalResult]:
-    """
-    BM25 keyword retrieval via full-text search.
-
-    Args:
-        conn: Database connection
-        query_text: Query text
-        agent_id: bank ID
-        fact_type: Fact type to filter
-        limit: Maximum results to return
-        tags: Optional list of tags for visibility filtering (OR matching)
-
-    Returns:
-        List of RetrievalResult objects
-    """
-    import re
-
-    from .tags import TagsMatch, build_tags_where_clause_simple
-
-    # Sanitize query text: remove special characters that have meaning in tsquery
-    # Keep only alphanumeric characters and spaces
-    sanitized_text = re.sub(r"[^\w\s]", " ", query_text.lower())
-
-    # Split and filter empty strings
-    tokens = [token for token in sanitized_text.split() if token]
-
-    if not tokens:
-        # If no valid tokens, return empty results
-        return []
-
-    # Convert query to tsquery using OR for more flexible matching
-    # This prevents empty results when some terms are missing
-    query_tsquery = " | ".join(tokens)
-
-    tags_clause = build_tags_where_clause_simple(tags, 5)
-    params = [query_tsquery, bank_id, fact_type, limit]
-    if tags:
-        params.append(tags)
-
-    results = await conn.fetch(
-        f"""
-        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
-               ts_rank_cd(search_vector, to_tsquery('english', $1)) AS bm25_score
-        FROM {fq_table("memory_units")}
-        WHERE bank_id = $2
-          AND fact_type = $3
-          AND search_vector @@ to_tsquery('english', $1)
-          {tags_clause}
-        ORDER BY bm25_score DESC
-        LIMIT $4
-        """,
-        *params,
-    )
-    return [RetrievalResult.from_db_row(dict(r)) for r in results]
-
-
 async def retrieve_semantic_bm25_combined(
     conn,
     query_emb_str: str,
@@ -268,18 +158,41 @@ async def retrieve_semantic_bm25_combined(
                 result_dict[ft][0].append(RetrievalResult.from_db_row(row))
         return result_dict
 
-    query_tsquery = " | ".join(tokens)
+    # Build BM25 query based on text search backend
+    config = get_config()
 
     # Build tags clause - param 6 if tags provided
     tags_clause = build_tags_where_clause_simple(tags, 6, match=tags_match)
-    params = [query_emb_str, bank_id, fact_types, limit, query_tsquery]
+
+    # Build backend-specific BM25 parts
+    if config.text_search_extension == "vchord":
+        # VectorChord BM25: use <&> operator with to_bm25query and tokenize
+        # Note: VectorChord scores are negative (higher = better, so -1 > -10)
+        bm25_score_expr = "search_vector <&> to_bm25query('idx_memory_units_text_search', tokenize($5, 'llmlingua2'))"
+        bm25_order_by = f"{bm25_score_expr} DESC"
+        bm25_where_filter = ""  # No additional WHERE filter for vchord
+        params = [query_emb_str, bank_id, fact_types, limit, query_text]  # Pass raw query_text for tokenization
+    elif config.text_search_extension == "pg_textsearch":
+        # Timescale pg_textsearch: use <@> operator with to_bm25query
+        # Note: pg_textsearch scores are negative (lower/more negative = better, so -10 > -1)
+        # We negate the score to maintain API consistency (higher = better)
+        bm25_score_expr = "-(text <@> to_bm25query($5, 'idx_memory_units_text_search'))"
+        bm25_order_by = "text <@> to_bm25query($5, 'idx_memory_units_text_search') ASC"
+        bm25_where_filter = ""  # No additional WHERE filter for pg_textsearch
+        params = [query_emb_str, bank_id, fact_types, limit, query_text]
+    else:  # native
+        # Native PostgreSQL: use ts_rank_cd with to_tsquery
+        query_tsquery = " | ".join(tokens)
+        bm25_score_expr = "ts_rank_cd(search_vector, to_tsquery('english', $5))"
+        bm25_order_by = f"{bm25_score_expr} DESC"
+        bm25_where_filter = "AND search_vector @@ to_tsquery('english', $5)"
+        params = [query_emb_str, bank_id, fact_types, limit, query_tsquery]
+
     if tags:
         params.append(tags)
 
-    # Combined CTE query for both semantic and BM25 across all fact types
-    # Uses window functions to limit per fact_type per method
-    results = await conn.fetch(
-        f"""
+    # Single query template with backend-specific parts injected
+    query = f"""
         WITH semantic_ranked AS (
             SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
                    1 - (embedding <=> $1::vector) AS similarity,
@@ -296,13 +209,13 @@ async def retrieve_semantic_bm25_combined(
         bm25_ranked AS (
             SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
                    NULL::float AS similarity,
-                   ts_rank_cd(search_vector, to_tsquery('english', $5)) AS bm25_score,
+                   {bm25_score_expr} AS bm25_score,
                    'bm25' AS source,
-                   ROW_NUMBER() OVER (PARTITION BY fact_type ORDER BY ts_rank_cd(search_vector, to_tsquery('english', $5)) DESC) AS rn
+                   ROW_NUMBER() OVER (PARTITION BY fact_type ORDER BY {bm25_order_by}) AS rn
             FROM {fq_table("memory_units")}
             WHERE bank_id = $2
               AND fact_type = ANY($3)
-              AND search_vector @@ to_tsquery('english', $5)
+              {bm25_where_filter}
               {tags_clause}
         ),
         semantic AS (
@@ -318,9 +231,11 @@ async def retrieve_semantic_bm25_combined(
         SELECT * FROM semantic
         UNION ALL
         SELECT * FROM bm25
-        """,
-        *params,
-    )
+    """
+
+    # Combined CTE query for both semantic and BM25 across all fact types
+    # Uses window functions to limit per fact_type per method
+    results = await conn.fetch(query, *params)
 
     # Group results by fact_type and source
     result_dict: dict[str, tuple[list[RetrievalResult], list[RetrievalResult]]] = {ft: ([], []) for ft in fact_types}
@@ -561,623 +476,6 @@ async def retrieve_temporal_combined(
     return results_by_ft
 
 
-async def retrieve_temporal(
-    conn,
-    query_emb_str: str,
-    bank_id: str,
-    fact_type: str,
-    start_date: datetime,
-    end_date: datetime,
-    budget: int,
-    semantic_threshold: float = 0.1,
-    tags: list[str] | None = None,
-) -> list[RetrievalResult]:
-    """
-    Temporal retrieval with spreading activation.
-
-    Strategy:
-    1. Find entry points (facts in date range with semantic relevance)
-    2. Spread through temporal links to related facts
-    3. Score by temporal proximity + semantic similarity + link weight
-
-    Args:
-        conn: Database connection
-        query_emb_str: Query embedding as string
-        agent_id: bank ID
-        fact_type: Fact type to filter
-        start_date: Start of time range
-        end_date: End of time range
-        budget: Node budget for spreading
-        semantic_threshold: Minimum semantic similarity to include
-        tags: Optional list of tags for visibility filtering (OR matching)
-
-    Returns:
-        List of RetrievalResult objects with temporal scores
-    """
-
-    # Ensure start_date and end_date are timezone-aware (UTC) to match database datetimes
-    if start_date.tzinfo is None:
-        start_date = start_date.replace(tzinfo=UTC)
-    if end_date.tzinfo is None:
-        end_date = end_date.replace(tzinfo=UTC)
-
-    from .tags import TagsMatch, build_tags_where_clause_simple
-
-    tags_clause = build_tags_where_clause_simple(tags, 7)
-    params = [query_emb_str, bank_id, fact_type, start_date, end_date, semantic_threshold]
-    if tags:
-        params.append(tags)
-
-    entry_points = await conn.fetch(
-        f"""
-        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
-               1 - (embedding <=> $1::vector) AS similarity
-        FROM {fq_table("memory_units")}
-        WHERE bank_id = $2
-          AND fact_type = $3
-          AND embedding IS NOT NULL
-          AND (
-              -- Match if occurred range overlaps with query range
-              (occurred_start IS NOT NULL AND occurred_end IS NOT NULL
-               AND occurred_start <= $5 AND occurred_end >= $4)
-              OR
-              -- Match if mentioned_at falls within query range
-              (mentioned_at IS NOT NULL AND mentioned_at BETWEEN $4 AND $5)
-              OR
-              -- Match if any occurred date is set and overlaps (even if only start or end is set)
-              (occurred_start IS NOT NULL AND occurred_start BETWEEN $4 AND $5)
-              OR
-              (occurred_end IS NOT NULL AND occurred_end BETWEEN $4 AND $5)
-          )
-          AND (1 - (embedding <=> $1::vector)) >= $6
-          {tags_clause}
-        ORDER BY COALESCE(occurred_start, mentioned_at, occurred_end) DESC, (embedding <=> $1::vector) ASC
-        LIMIT 10
-        """,
-        *params,
-    )
-
-    if not entry_points:
-        return []
-
-    # Calculate temporal scores for entry points
-    total_days = (end_date - start_date).total_seconds() / 86400
-    mid_date = start_date + (end_date - start_date) / 2  # Calculate once for all comparisons
-    results = []
-    visited = set()
-
-    for ep in entry_points:
-        unit_id = str(ep["id"])
-        visited.add(unit_id)
-
-        # Calculate temporal proximity using the most relevant date
-        # Priority: occurred_start/end (event time) > mentioned_at (mention time)
-        best_date = None
-        if ep["occurred_start"] is not None and ep["occurred_end"] is not None:
-            # Use midpoint of occurred range
-            best_date = ep["occurred_start"] + (ep["occurred_end"] - ep["occurred_start"]) / 2
-        elif ep["occurred_start"] is not None:
-            best_date = ep["occurred_start"]
-        elif ep["occurred_end"] is not None:
-            best_date = ep["occurred_end"]
-        elif ep["mentioned_at"] is not None:
-            best_date = ep["mentioned_at"]
-
-        # Temporal proximity score (closer to range center = higher score)
-        if best_date:
-            days_from_mid = abs((best_date - mid_date).total_seconds() / 86400)
-            temporal_proximity = 1.0 - min(days_from_mid / (total_days / 2), 1.0) if total_days > 0 else 1.0
-        else:
-            temporal_proximity = 0.5  # Fallback if no dates (shouldn't happen due to WHERE clause)
-
-        # Create RetrievalResult with temporal scores
-        ep_result = RetrievalResult.from_db_row(dict(ep))
-        ep_result.temporal_score = temporal_proximity
-        ep_result.temporal_proximity = temporal_proximity
-        results.append(ep_result)
-
-    # Spread through temporal links using BATCHED neighbor fetching
-    # Map node_id -> (semantic_sim, temporal_score) for propagation
-    node_scores = {str(ep["id"]): (ep["similarity"], 1.0) for ep in entry_points}
-    frontier = list(node_scores.keys())  # Current batch of nodes to expand
-    budget_remaining = budget - len(entry_points)
-    batch_size = 20  # Process this many nodes per DB query
-
-    while frontier and budget_remaining > 0:
-        # Take a batch from frontier
-        batch_ids = frontier[:batch_size]
-        frontier = frontier[batch_size:]
-
-        # Batch fetch all neighbors for this batch of nodes
-        neighbors = await conn.fetch(
-            f"""
-            SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start, mu.occurred_end, mu.mentioned_at, mu.embedding, mu.fact_type, mu.document_id, mu.chunk_id,
-                   ml.weight, ml.link_type, ml.from_unit_id,
-                   1 - (mu.embedding <=> $1::vector) AS similarity
-            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($2::uuid[])
-              AND ml.link_type IN ('temporal', 'causes', 'caused_by', 'enables', 'prevents')
-              AND ml.weight >= 0.1
-              AND mu.fact_type = $3
-              AND mu.embedding IS NOT NULL
-              AND (1 - (mu.embedding <=> $1::vector)) >= $4
-            ORDER BY ml.weight DESC
-            LIMIT $5
-            """,
-            query_emb_str,
-            batch_ids,
-            fact_type,
-            semantic_threshold,
-            batch_size * 10,  # Allow up to 10 neighbors per node in batch
-        )
-
-        for n in neighbors:
-            neighbor_id = str(n["id"])
-            if neighbor_id in visited:
-                continue
-
-            visited.add(neighbor_id)
-            budget_remaining -= 1
-
-            # Get parent's scores for propagation
-            parent_id = str(n["from_unit_id"])
-            _, parent_temporal_score = node_scores.get(parent_id, (0.5, 0.5))
-
-            # Calculate temporal score for neighbor using best available date
-            neighbor_best_date = None
-            if n["occurred_start"] is not None and n["occurred_end"] is not None:
-                neighbor_best_date = n["occurred_start"] + (n["occurred_end"] - n["occurred_start"]) / 2
-            elif n["occurred_start"] is not None:
-                neighbor_best_date = n["occurred_start"]
-            elif n["occurred_end"] is not None:
-                neighbor_best_date = n["occurred_end"]
-            elif n["mentioned_at"] is not None:
-                neighbor_best_date = n["mentioned_at"]
-
-            if neighbor_best_date:
-                days_from_mid = abs((neighbor_best_date - mid_date).total_seconds() / 86400)
-                neighbor_temporal_proximity = (
-                    1.0 - min(days_from_mid / (total_days / 2), 1.0) if total_days > 0 else 1.0
-                )
-            else:
-                neighbor_temporal_proximity = 0.3  # Lower score if no temporal data
-
-            # Boost causal links (same as graph retrieval)
-            link_type = n["link_type"]
-            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
-
-            # Propagate temporal score through links (decay, with causal boost)
-            propagated_temporal = parent_temporal_score * n["weight"] * causal_boost * 0.7
-
-            # Combined temporal score
-            combined_temporal = max(neighbor_temporal_proximity, propagated_temporal)
-
-            # Create RetrievalResult with temporal scores
-            neighbor_result = RetrievalResult.from_db_row(dict(n))
-            neighbor_result.temporal_score = combined_temporal
-            neighbor_result.temporal_proximity = neighbor_temporal_proximity
-            results.append(neighbor_result)
-
-            # Track scores for propagation and add to frontier
-            if budget_remaining > 0 and combined_temporal > 0.2:
-                node_scores[neighbor_id] = (n["similarity"], combined_temporal)
-                frontier.append(neighbor_id)
-
-            if budget_remaining <= 0:
-                break
-
-    return results
-
-
-async def retrieve_parallel(
-    pool,
-    query_text: str,
-    query_embedding_str: str,
-    bank_id: str,
-    fact_type: str,
-    thinking_budget: int,
-    question_date: datetime | None = None,
-    query_analyzer: Optional["QueryAnalyzer"] = None,
-    graph_retriever: GraphRetriever | None = None,
-    temporal_constraint: tuple | None = None,  # Pre-extracted temporal constraint
-    tags: list[str] | None = None,  # Visibility scope tags for filtering
-) -> ParallelRetrievalResult:
-    """
-    Run 3-way or 4-way parallel retrieval (adds temporal if detected).
-
-    Args:
-        pool: Database connection pool
-        query_text: Query text
-        query_embedding_str: Query embedding as string
-        bank_id: Bank ID
-        fact_type: Fact type to filter
-        thinking_budget: Budget for graph traversal and retrieval limits
-        question_date: Optional date when question was asked (for temporal filtering)
-        query_analyzer: Query analyzer to use (defaults to TransformerQueryAnalyzer)
-        graph_retriever: Graph retrieval strategy (defaults to configured retriever)
-        temporal_constraint: Pre-extracted temporal constraint (optional)
-        tags: Optional list of tags for visibility filtering (OR matching)
-
-    Returns:
-        ParallelRetrievalResult with semantic, bm25, graph, temporal results and timings
-    """
-    retriever = graph_retriever or get_default_graph_retriever()
-
-    # Use optimized parallel path for MPFP and LinkExpansion (runs all methods truly in parallel)
-    # BFS uses legacy path that extracts temporal constraint upfront
-    if retriever.name in ("mpfp", "link_expansion"):
-        return await _retrieve_parallel_mpfp(
-            pool,
-            query_text,
-            query_embedding_str,
-            bank_id,
-            fact_type,
-            thinking_budget,
-            temporal_constraint,
-            retriever,
-            question_date,
-            query_analyzer,
-            tags=tags,
-        )
-    else:
-        # For BFS, extract temporal constraint upfront (legacy path)
-        if temporal_constraint is None:
-            from .temporal_extraction import extract_temporal_constraint
-
-            temporal_constraint = extract_temporal_constraint(
-                query_text, reference_date=question_date, analyzer=query_analyzer
-            )
-        return await _retrieve_parallel_bfs(
-            pool,
-            query_text,
-            query_embedding_str,
-            bank_id,
-            fact_type,
-            thinking_budget,
-            temporal_constraint,
-            retriever,
-            tags=tags,
-        )
-
-
-@dataclass
-class _TimedResult:
-    """Internal result with timing."""
-
-    results: list[RetrievalResult]
-    time: float
-    conn_wait: float = 0.0  # Connection acquisition wait time
-
-
-async def _retrieve_parallel_mpfp(
-    pool,
-    query_text: str,
-    query_embedding_str: str,
-    bank_id: str,
-    fact_type: str,
-    thinking_budget: int,
-    temporal_constraint: tuple | None,
-    retriever: GraphRetriever,
-    question_date: datetime | None = None,
-    query_analyzer=None,
-    tags: list[str] | None = None,
-) -> ParallelRetrievalResult:
-    """
-    MPFP retrieval with true parallelization.
-
-    All methods run independently in parallel:
-    - Semantic: vector similarity search
-    - BM25: keyword search
-    - Graph: MPFP traversal (does its own semantic seeds internally)
-    - Temporal: date extraction (if needed) + date-range search
-
-    Temporal extraction runs IN PARALLEL with other retrievals, so even if
-    dateparser is slow, it doesn't block semantic/BM25/graph.
-    """
-    import time
-
-    async def run_semantic() -> _TimedResult:
-        """Independent semantic retrieval."""
-        start = time.time()
-        acquire_start = time.time()
-        async with acquire_with_retry(pool) as conn:
-            conn_wait = time.time() - acquire_start
-            results = await retrieve_semantic(
-                conn, query_embedding_str, bank_id, fact_type, limit=thinking_budget, tags=tags
-            )
-        return _TimedResult(results, time.time() - start, conn_wait)
-
-    async def run_bm25() -> _TimedResult:
-        """Independent BM25 retrieval."""
-        start = time.time()
-        acquire_start = time.time()
-        async with acquire_with_retry(pool) as conn:
-            conn_wait = time.time() - acquire_start
-            results = await retrieve_bm25(conn, query_text, bank_id, fact_type, limit=thinking_budget, tags=tags)
-        return _TimedResult(results, time.time() - start, conn_wait)
-
-    async def run_graph() -> tuple[list[RetrievalResult], float, MPFPTimings | None]:
-        """Independent graph retrieval - does its own semantic seeds."""
-        start = time.time()
-
-        # MPFP does its own semantic seeds via _find_semantic_seeds
-        # Note: temporal_seeds not used here to avoid dependency on temporal extraction
-        results, mpfp_timing = await retriever.retrieve(
-            pool=pool,
-            query_embedding_str=query_embedding_str,
-            bank_id=bank_id,
-            fact_type=fact_type,
-            budget=thinking_budget,
-            query_text=query_text,
-            semantic_seeds=None,  # Let MPFP find its own seeds
-            temporal_seeds=None,  # Don't wait for temporal extraction
-            tags=tags,
-        )
-        return results, time.time() - start, mpfp_timing
-
-    @dataclass
-    class _TemporalWithConstraint:
-        """Temporal results with the extracted constraint."""
-
-        results: list[RetrievalResult]
-        time: float
-        constraint: tuple | None
-        extraction_time: float  # Time spent in query analyzer (dateparser)
-        conn_wait: float = 0.0  # Connection acquisition wait time
-
-    async def run_temporal_with_extraction() -> _TemporalWithConstraint:
-        """
-        Extract temporal constraint AND run temporal retrieval.
-
-        This runs in parallel with semantic/BM25/graph, so dateparser
-        latency doesn't block other retrievals.
-        """
-        start = time.time()
-
-        # Use pre-provided constraint if available
-        tc = temporal_constraint
-        extraction_time = 0.0
-
-        # Otherwise extract from query (this is the potentially slow dateparser call)
-        if tc is None:
-            from .temporal_extraction import extract_temporal_constraint
-
-            extraction_start = time.time()
-            tc = extract_temporal_constraint(query_text, reference_date=question_date, analyzer=query_analyzer)
-            extraction_time = time.time() - extraction_start
-
-        # If no temporal constraint found, return empty (but still report extraction time)
-        if tc is None:
-            return _TemporalWithConstraint([], time.time() - start, None, extraction_time, 0.0)
-
-        # Run temporal retrieval with the extracted constraint
-        tc_start, tc_end = tc
-        acquire_start = time.time()
-        async with acquire_with_retry(pool) as conn:
-            conn_wait = time.time() - acquire_start
-            results = await retrieve_temporal(
-                conn,
-                query_embedding_str,
-                bank_id,
-                fact_type,
-                tc_start,
-                tc_end,
-                budget=thinking_budget,
-                semantic_threshold=0.1,
-            )
-        return _TemporalWithConstraint(results, time.time() - start, tc, extraction_time, conn_wait)
-
-    # Run ALL methods in parallel (including temporal extraction!)
-    semantic_result, bm25_result, graph_result, temporal_result = await asyncio.gather(
-        run_semantic(),
-        run_bm25(),
-        run_graph(),
-        run_temporal_with_extraction(),
-    )
-    graph_results, graph_time, mpfp_timing = graph_result
-
-    # Compute max connection wait across all methods (graph handles its own connections)
-    max_conn_wait = max(semantic_result.conn_wait, bm25_result.conn_wait, temporal_result.conn_wait)
-
-    return ParallelRetrievalResult(
-        semantic=semantic_result.results,
-        bm25=bm25_result.results,
-        graph=graph_results,
-        temporal=temporal_result.results if temporal_result.results else None,
-        timings={
-            "semantic": semantic_result.time,
-            "bm25": bm25_result.time,
-            "graph": graph_time,
-            "temporal": temporal_result.time,
-            "temporal_extraction": temporal_result.extraction_time,
-        },
-        temporal_constraint=temporal_result.constraint,
-        mpfp_timings=[mpfp_timing] if mpfp_timing else [],
-        max_conn_wait=max_conn_wait,
-    )
-
-
-async def _get_temporal_entry_points(
-    conn,
-    query_embedding_str: str,
-    bank_id: str,
-    fact_type: str,
-    start_date: datetime,
-    end_date: datetime,
-    limit: int = 20,
-    semantic_threshold: float = 0.1,
-) -> list[RetrievalResult]:
-    """Get temporal entry points (facts in date range with semantic relevance)."""
-
-    if start_date.tzinfo is None:
-        start_date = start_date.replace(tzinfo=UTC)
-    if end_date.tzinfo is None:
-        end_date = end_date.replace(tzinfo=UTC)
-
-    rows = await conn.fetch(
-        f"""
-        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at,
-               embedding, fact_type, document_id, chunk_id,
-               1 - (embedding <=> $1::vector) AS similarity
-        FROM {fq_table("memory_units")}
-        WHERE bank_id = $2
-          AND fact_type = $3
-          AND embedding IS NOT NULL
-          AND (
-              (occurred_start IS NOT NULL AND occurred_end IS NOT NULL
-               AND occurred_start <= $5 AND occurred_end >= $4)
-              OR (mentioned_at IS NOT NULL AND mentioned_at BETWEEN $4 AND $5)
-              OR (occurred_start IS NOT NULL AND occurred_start BETWEEN $4 AND $5)
-              OR (occurred_end IS NOT NULL AND occurred_end BETWEEN $4 AND $5)
-          )
-          AND (1 - (embedding <=> $1::vector)) >= $6
-        ORDER BY COALESCE(occurred_start, mentioned_at, occurred_end) DESC,
-                 (embedding <=> $1::vector) ASC
-        LIMIT $7
-        """,
-        query_embedding_str,
-        bank_id,
-        fact_type,
-        start_date,
-        end_date,
-        semantic_threshold,
-        limit,
-    )
-
-    results = []
-    total_days = max((end_date - start_date).total_seconds() / 86400, 1)
-    mid_date = start_date + (end_date - start_date) / 2
-
-    for row in rows:
-        result = RetrievalResult.from_db_row(dict(row))
-
-        # Calculate temporal proximity score
-        best_date = None
-        if row["occurred_start"] and row["occurred_end"]:
-            best_date = row["occurred_start"] + (row["occurred_end"] - row["occurred_start"]) / 2
-        elif row["occurred_start"]:
-            best_date = row["occurred_start"]
-        elif row["occurred_end"]:
-            best_date = row["occurred_end"]
-        elif row["mentioned_at"]:
-            best_date = row["mentioned_at"]
-
-        if best_date:
-            days_from_mid = abs((best_date - mid_date).total_seconds() / 86400)
-            result.temporal_proximity = 1.0 - min(days_from_mid / (total_days / 2), 1.0)
-        else:
-            result.temporal_proximity = 0.5
-
-        result.temporal_score = result.temporal_proximity
-        results.append(result)
-
-    return results
-
-
-async def _retrieve_parallel_bfs(
-    pool,
-    query_text: str,
-    query_embedding_str: str,
-    bank_id: str,
-    fact_type: str,
-    thinking_budget: int,
-    temporal_constraint: tuple | None,
-    retriever: GraphRetriever,
-    tags: list[str] | None = None,
-) -> ParallelRetrievalResult:
-    """BFS retrieval: all methods run in parallel (original behavior)."""
-    import time
-
-    async def run_semantic() -> _TimedResult:
-        start = time.time()
-        async with acquire_with_retry(pool) as conn:
-            results = await retrieve_semantic(
-                conn, query_embedding_str, bank_id, fact_type, limit=thinking_budget, tags=tags
-            )
-        return _TimedResult(results, time.time() - start)
-
-    async def run_bm25() -> _TimedResult:
-        start = time.time()
-        async with acquire_with_retry(pool) as conn:
-            results = await retrieve_bm25(conn, query_text, bank_id, fact_type, limit=thinking_budget, tags=tags)
-        return _TimedResult(results, time.time() - start)
-
-    async def run_graph() -> _TimedResult:
-        start = time.time()
-        results, _ = await retriever.retrieve(
-            pool=pool,
-            query_embedding_str=query_embedding_str,
-            bank_id=bank_id,
-            fact_type=fact_type,
-            budget=thinking_budget,
-            query_text=query_text,
-            tags=tags,
-        )
-        return _TimedResult(results, time.time() - start)
-
-    async def run_temporal(tc_start, tc_end) -> _TimedResult:
-        start = time.time()
-        async with acquire_with_retry(pool) as conn:
-            results = await retrieve_temporal(
-                conn,
-                query_embedding_str,
-                bank_id,
-                fact_type,
-                tc_start,
-                tc_end,
-                budget=thinking_budget,
-                semantic_threshold=0.1,
-                tags=tags,
-            )
-        return _TimedResult(results, time.time() - start)
-
-    if temporal_constraint:
-        tc_start, tc_end = temporal_constraint
-        semantic_r, bm25_r, graph_r, temporal_r = await asyncio.gather(
-            run_semantic(),
-            run_bm25(),
-            run_graph(),
-            run_temporal(tc_start, tc_end),
-        )
-        return ParallelRetrievalResult(
-            semantic=semantic_r.results,
-            bm25=bm25_r.results,
-            graph=graph_r.results,
-            temporal=temporal_r.results,
-            timings={
-                "semantic": semantic_r.time,
-                "bm25": bm25_r.time,
-                "graph": graph_r.time,
-                "temporal": temporal_r.time,
-            },
-            temporal_constraint=temporal_constraint,
-        )
-    else:
-        semantic_r, bm25_r, graph_r = await asyncio.gather(
-            run_semantic(),
-            run_bm25(),
-            run_graph(),
-        )
-        return ParallelRetrievalResult(
-            semantic=semantic_r.results,
-            bm25=bm25_r.results,
-            graph=graph_r.results,
-            temporal=None,
-            timings={
-                "semantic": semantic_r.time,
-                "bm25": bm25_r.time,
-                "graph": graph_r.time,
-            },
-            temporal_constraint=None,
-        )
-
-
 async def retrieve_all_fact_types_parallel(
     pool,
     query_text: str,

hindsight-api/hindsight_api/engine/utils.py

@@ -19,6 +19,7 @@ async def extract_facts(
     context: str = "",
     llm_config: "LLMConfig" = None,
     agent_name: str = None,
+    config=None,
 ) -> tuple[list["Fact"], list[tuple[str, int]]]:
     """
     Extract semantic facts from text using LLM.
@@ -35,6 +36,7 @@ async def extract_facts(
         context: Context about the conversation/document
         llm_config: LLM configuration to use
         agent_name: Optional agent name to help identify agent-related facts
+        config: HindsightConfig to use (defaults to global config if not provided)
 
     Returns:
         Tuple of (facts, chunks) where:
@@ -47,12 +49,19 @@ async def extract_facts(
     if not text or not text.strip():
         return [], []
 
+    # Use provided config or fall back to global config
+    if config is None:
+        from ..config import _get_raw_config
+
+        config = _get_raw_config()
+
     facts, chunks, _ = await extract_facts_from_text(
         text,
         event_date,
-        context=context,
         llm_config=llm_config,
         agent_name=agent_name,
+        config=config,
+        context=context,
     )
 
     if not facts:

hindsight-api/hindsight_api/extensions/context.py

@@ -96,7 +96,13 @@ class DefaultExtensionContext(ExtensionContext):
 
     async def run_migration(self, schema: str) -> None:
         """Run migrations for a specific schema."""
-        from hindsight_api.migrations import ensure_embedding_dimension, run_migrations
+        from hindsight_api.config import get_config
+        from hindsight_api.migrations import (
+            ensure_embedding_dimension,
+            ensure_text_search_extension,
+            ensure_vector_extension,
+            run_migrations,
+        )
 
         # Prefer getting URL from memory engine (handles pg0 case where URL is set after init)
         db_url = self._database_url
@@ -107,6 +113,9 @@ class DefaultExtensionContext(ExtensionContext):
 
         run_migrations(db_url, schema=schema)
 
+        # Get config for vector extension setting
+        config = get_config()
+
         # Ensure embedding column dimension matches the model's dimension
         # This is needed because migrations create columns with default dimension
         if self._memory_engine is not None:
@@ -114,7 +123,15 @@ class DefaultExtensionContext(ExtensionContext):
             if embeddings is not None:
                 dimension = getattr(embeddings, "dimension", None)
                 if dimension is not None:
-                    ensure_embedding_dimension(db_url, dimension, schema=schema)
+                    ensure_embedding_dimension(
+                        db_url, dimension, schema=schema, vector_extension=config.vector_extension
+                    )
+
+        # Ensure vector indexes match the configured extension
+        ensure_vector_extension(db_url, vector_extension=config.vector_extension, schema=schema)
+
+        # Ensure text search columns/indexes match the configured extension
+        ensure_text_search_extension(db_url, text_search_extension=config.text_search_extension, schema=schema)
 
     def get_memory_engine(self) -> "MemoryEngineInterface":
         """Get the memory engine interface."""

hindsight-api/hindsight_api/extensions/tenant.py

@@ -2,6 +2,7 @@
 
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
+from typing import Any
 
 from hindsight_api.extensions.base import Extension
 from hindsight_api.models import RequestContext
@@ -88,6 +89,54 @@ class TenantExtension(Extension, ABC):
         """
         ...
 
+    async def get_tenant_config(self, context: RequestContext) -> dict[str, Any]:
+        """
+        Get tenant-specific configuration overrides.
+
+        This method is called during hierarchical configuration resolution to get
+        tenant-level config overrides. The returned dict should contain Python field
+        names (lowercase snake_case) as keys, not environment variable names.
+
+        Example:
+            {"llm_model": "gpt-4", "retain_extraction_mode": "verbose"}
+
+        The default implementation returns an empty dict (no tenant-specific config).
+        Override this method in custom extensions to provide tenant-specific configuration.
+
+        Args:
+            context: The request context containing tenant information.
+
+        Returns:
+            Dict of config field names to values (only configurable fields).
+            Empty dict if no tenant-specific config.
+        """
+        return {}
+
+    async def get_allowed_config_fields(self, context: RequestContext, bank_id: str) -> set[str] | None:
+        """
+        Get set of config fields that this tenant/bank is allowed to modify.
+
+        This method controls which configurable fields can be modified via the bank config API.
+        It enables fine-grained permission control per tenant or per bank.
+
+        Examples:
+            - Return None: Allow all configurable fields (default)
+            - Return {"retain_chunk_size", "retain_custom_instructions"}: Allow only these fields
+            - Return set(): Allow no modifications (read-only)
+
+        The default implementation returns None (all configurable fields allowed).
+        Override this method in custom extensions to implement custom permission logic.
+
+        Args:
+            context: The request context containing tenant information.
+            bank_id: The bank identifier for per-bank permissions.
+
+        Returns:
+            Set of allowed field names, or None to allow all configurable fields.
+            Returned fields must be a subset of HindsightConfig.get_configurable_fields().
+        """
+        return None
+
     async def authenticate_mcp(self, context: RequestContext) -> TenantContext:
         """
         Authenticate MCP requests.

hindsight-api/hindsight_api/main.py

@@ -23,7 +23,7 @@ import uvicorn
 from . import MemoryEngine, __version__
 from .api import create_app
 from .banner import print_banner
-from .config import DEFAULT_WORKERS, ENV_WORKERS, HindsightConfig, get_config
+from .config import DEFAULT_WORKERS, ENV_WORKERS, HindsightConfig, _get_raw_config
 from .daemon import (
     DEFAULT_DAEMON_PORT,
     DEFAULT_IDLE_TIMEOUT,
@@ -68,7 +68,7 @@ def main():
     global _memory
 
     # Load configuration from environment (for CLI args defaults)
-    config = get_config()
+    config = _get_raw_config()
 
     parser = argparse.ArgumentParser(
         prog="hindsight-api",
@@ -155,6 +155,8 @@ def main():
         config = HindsightConfig(
             database_url=config.database_url,
             database_schema=config.database_schema,
+            vector_extension=config.vector_extension,
+            text_search_extension=config.text_search_extension,
             llm_provider=config.llm_provider,
             llm_api_key=config.llm_api_key,
             llm_model=config.llm_model,
@@ -164,6 +166,8 @@ def main():
             llm_initial_backoff=config.llm_initial_backoff,
             llm_max_backoff=config.llm_max_backoff,
             llm_timeout=config.llm_timeout,
+            llm_groq_service_tier=config.llm_groq_service_tier,
+            llm_openai_service_tier=config.llm_openai_service_tier,
             llm_vertexai_project_id=config.llm_vertexai_project_id,
             llm_vertexai_region=config.llm_vertexai_region,
             llm_vertexai_service_account_key=config.llm_vertexai_service_account_key,
@@ -206,6 +210,9 @@ def main():
             embeddings_litellm_api_base=config.embeddings_litellm_api_base,
             embeddings_litellm_api_key=config.embeddings_litellm_api_key,
             embeddings_litellm_model=config.embeddings_litellm_model,
+            embeddings_litellm_sdk_api_key=config.embeddings_litellm_sdk_api_key,
+            embeddings_litellm_sdk_model=config.embeddings_litellm_sdk_model,
+            embeddings_litellm_sdk_api_base=config.embeddings_litellm_sdk_api_base,
             reranker_provider=config.reranker_provider,
             reranker_local_model=config.reranker_local_model,
             reranker_local_force_cpu=config.reranker_local_force_cpu,
@@ -221,11 +228,16 @@ def main():
             reranker_litellm_api_base=config.reranker_litellm_api_base,
             reranker_litellm_api_key=config.reranker_litellm_api_key,
             reranker_litellm_model=config.reranker_litellm_model,
+            reranker_litellm_sdk_api_key=config.reranker_litellm_sdk_api_key,
+            reranker_litellm_sdk_model=config.reranker_litellm_sdk_model,
+            reranker_litellm_sdk_api_base=config.reranker_litellm_sdk_api_base,
             host=args.host,
             port=args.port,
+            base_path=config.base_path,
             log_level=args.log_level,
             log_format=config.log_format,
             mcp_enabled=config.mcp_enabled,
+            enable_bank_config_api=config.enable_bank_config_api,
             graph_retriever=config.graph_retriever,
             mpfp_top_k_neighbors=config.mpfp_top_k_neighbors,
             recall_max_concurrent=config.recall_max_concurrent,
@@ -235,6 +247,9 @@ def main():
             retain_extract_causal_links=config.retain_extract_causal_links,
             retain_extraction_mode=config.retain_extraction_mode,
             retain_custom_instructions=config.retain_custom_instructions,
+            retain_batch_tokens=config.retain_batch_tokens,
+            retain_batch_enabled=config.retain_batch_enabled,
+            retain_batch_poll_interval_seconds=config.retain_batch_poll_interval_seconds,
             enable_observations=config.enable_observations,
             consolidation_batch_size=config.consolidation_batch_size,
             consolidation_max_tokens=config.consolidation_max_tokens,

hindsight-api/hindsight_api/migrations.py

@@ -33,6 +33,41 @@ logger = logging.getLogger(__name__)
 MIGRATION_LOCK_ID = 123456789
 
 
+def _detect_vector_extension(conn, vector_extension: str = "pgvector") -> str:
+    """
+    Validate vector extension: 'vchord' or 'pgvector'.
+
+    Args:
+        conn: SQLAlchemy connection object
+        vector_extension: Configured extension ("pgvector" or "vchord")
+
+    Returns:
+        "vchord" or "pgvector"
+
+    Raises:
+        RuntimeError: If configured extension is not installed
+    """
+    # Verify the configured extension is installed
+    if vector_extension == "vchord":
+        vchord_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vchord'")).scalar()
+        if not vchord_check:
+            raise RuntimeError(
+                "Configured vector extension 'vchord' not found. Install it with: CREATE EXTENSION vchord CASCADE;"
+            )
+        logger.debug("Using configured vector extension: vchord")
+        return "vchord"
+    elif vector_extension == "pgvector":
+        pgvector_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vector'")).scalar()
+        if not pgvector_check:
+            raise RuntimeError(
+                "Configured vector extension 'pgvector' not found. Install it with: CREATE EXTENSION vector;"
+            )
+        logger.debug("Using configured vector extension: pgvector")
+        return "pgvector"
+    else:
+        raise ValueError(f"Invalid vector_extension: {vector_extension}. Must be 'pgvector' or 'vchord'")
+
+
 def _get_schema_lock_id(schema: str) -> int:
     """
     Generate a unique advisory lock ID for a schema.
@@ -324,6 +359,7 @@ def ensure_embedding_dimension(
     database_url: str,
     required_dimension: int,
     schema: str | None = None,
+    vector_extension: str = "pgvector",
 ) -> None:
     """
     Ensure the embedding column dimension matches the model's dimension.
@@ -338,6 +374,7 @@ def ensure_embedding_dimension(
         database_url: SQLAlchemy database URL
         required_dimension: The embedding dimension required by the model
         schema: Target PostgreSQL schema name (None for public)
+        vector_extension: Configured vector extension ("pgvector" or "vchord")
 
     Raises:
         RuntimeError: If dimension mismatch with existing data
@@ -361,6 +398,10 @@ def ensure_embedding_dimension(
             logger.debug(f"memory_units table does not exist in schema '{schema_name}', skipping dimension check")
             return
 
+        # Detect which vector extension is available
+        vector_ext = _detect_vector_extension(conn, vector_extension)
+        logger.info(f"Using vector extension: {vector_ext}")
+
         # Get current column dimension from pg_attribute
         # pgvector stores dimension in atttypmod
         current_dim = conn.execute(
@@ -408,8 +449,7 @@ def ensure_embedding_dimension(
         # Table is empty, safe to alter column
         logger.info(f"Altering embedding column dimension from {current_dimension} to {required_dimension}")
 
-        # Drop the HNSW index on embedding column if it exists
-        # Only drop indexes that use 'hnsw' and reference the 'embedding' column
+        # Drop existing vector index (works for both HNSW and vchordrq)
         conn.execute(
             text(f"""
                 DO $$
@@ -419,7 +459,7 @@ def ensure_embedding_dimension(
                         SELECT indexname FROM pg_indexes
                         WHERE schemaname = '{schema_name}'
                           AND tablename = 'memory_units'
-                          AND indexdef LIKE '%hnsw%'
+                          AND (indexdef LIKE '%hnsw%' OR indexdef LIKE '%vchordrq%')
                           AND indexdef LIKE '%embedding%'
                     LOOP
                         EXECUTE 'DROP INDEX IF EXISTS {schema_name}.' || idx_name;
@@ -434,15 +474,410 @@ def ensure_embedding_dimension(
         )
         conn.commit()
 
-        # Recreate the HNSW index
-        conn.execute(
-            text(f"""
-                CREATE INDEX IF NOT EXISTS idx_memory_units_embedding_hnsw
-                ON {schema_name}.memory_units
-                USING hnsw (embedding vector_cosine_ops)
-                WITH (m = 16, ef_construction = 64)
-            """)
-        )
+        # Recreate index with appropriate type based on detected extension
+        if vector_ext == "vchord":
+            conn.execute(
+                text(f"""
+                    CREATE INDEX IF NOT EXISTS idx_memory_units_embedding_vchordrq
+                    ON {schema_name}.memory_units
+                    USING vchordrq (embedding vector_l2_ops)
+                """)
+            )
+            logger.info(f"Created vchordrq index for {required_dimension}-dimensional embeddings")
+        else:  # pgvector
+            conn.execute(
+                text(f"""
+                    CREATE INDEX IF NOT EXISTS idx_memory_units_embedding_hnsw
+                    ON {schema_name}.memory_units
+                    USING hnsw (embedding vector_cosine_ops)
+                    WITH (m = 16, ef_construction = 64)
+                """)
+            )
+            logger.info(f"Created HNSW index for {required_dimension}-dimensional embeddings")
         conn.commit()
 
         logger.info(f"Successfully changed embedding dimension to {required_dimension}")
+
+
+def ensure_vector_extension(
+    database_url: str,
+    vector_extension: str = "pgvector",
+    schema: str | None = None,
+) -> None:
+    """
+    Ensure the vector indexes match the configured vector extension.
+
+    This function checks the current vector index type in the database
+    and adjusts it if necessary:
+    - If index type matches configured extension: no action needed
+    - If they differ and tables are empty: drop old indexes, recreate with new type
+    - If they differ and tables have data: raise error with migration guidance
+
+    Args:
+        database_url: SQLAlchemy database URL
+        vector_extension: Configured vector extension ("pgvector" or "vchord")
+        schema: Target PostgreSQL schema name (None for public)
+
+    Raises:
+        RuntimeError: If extension mismatch with existing data
+    """
+    schema_name = schema or "public"
+
+    engine = create_engine(database_url)
+    with engine.connect() as conn:
+        # Detect which vector extension should be used
+        target_ext = _detect_vector_extension(conn, vector_extension)
+        logger.info(f"Target vector extension: {target_ext}")
+
+        # Tables with vector indexes to check
+        tables_to_check = [
+            ("memory_units", "idx_memory_units_embedding"),
+            ("learnings", "idx_learnings_embedding"),
+            ("pinned_reflections", "idx_pinned_reflections_embedding"),
+        ]
+
+        # Determine target index type
+        target_index_type = "vchordrq" if target_ext == "vchord" else "hnsw"
+
+        mismatched_tables = []
+        tables_with_data = []
+
+        for table_name, index_name in tables_to_check:
+            # Check if table exists
+            table_exists = conn.execute(
+                text("""
+                    SELECT EXISTS (
+                        SELECT 1 FROM information_schema.tables
+                        WHERE table_schema = :schema AND table_name = :table_name
+                    )
+                """),
+                {"schema": schema_name, "table_name": table_name},
+            ).scalar()
+
+            if not table_exists:
+                logger.debug(f"Table {table_name} does not exist in schema '{schema_name}', skipping")
+                continue
+
+            # Check current index type by querying pg_indexes
+            current_index_info = conn.execute(
+                text("""
+                    SELECT indexdef
+                    FROM pg_indexes
+                    WHERE schemaname = :schema
+                      AND tablename = :table_name
+                      AND indexname LIKE :index_pattern
+                """),
+                {"schema": schema_name, "table_name": table_name, "index_pattern": "%embedding%"},
+            ).fetchone()
+
+            if not current_index_info:
+                logger.warning(f"No embedding index found for {table_name}, will create it")
+                mismatched_tables.append((table_name, index_name, None))
+                continue
+
+            indexdef = current_index_info[0].lower()
+            if "vchordrq" in indexdef:
+                current_index_type = "vchordrq"
+            elif "hnsw" in indexdef:
+                current_index_type = "hnsw"
+            else:
+                logger.warning(f"Unknown index type for {table_name}: {indexdef}")
+                continue
+
+            # Check if index type matches target
+            if current_index_type != target_index_type:
+                logger.info(
+                    f"Index type mismatch on {table_name}: current={current_index_type}, target={target_index_type}"
+                )
+                mismatched_tables.append((table_name, index_name, current_index_type))
+
+                # Check if table has data
+                row_count = conn.execute(
+                    text(f"SELECT COUNT(*) FROM {schema_name}.{table_name} WHERE embedding IS NOT NULL")
+                ).scalar()
+
+                if row_count > 0:
+                    tables_with_data.append((table_name, row_count))
+            else:
+                logger.debug(f"Index type OK for {table_name}: {current_index_type}")
+
+        # If no mismatches, we're done
+        if not mismatched_tables:
+            logger.debug(f"All vector indexes match configured extension: {target_ext}")
+            return
+
+        # If there's data in any mismatched table, raise error
+        if tables_with_data:
+            table_list = ", ".join([f"{table}({count} rows)" for table, count in tables_with_data])
+            raise RuntimeError(
+                f"Cannot change vector extension from {current_index_type} to {target_index_type}: "
+                f"the following tables contain data: {table_list}. "
+                f"To change vector extension, you must either:\n"
+                f"  1. Re-embed all data: DELETE FROM {schema_name}.memory_units; "
+                f"DELETE FROM {schema_name}.learnings; DELETE FROM {schema_name}.pinned_reflections; then restart\n"
+                f"  2. Use the current vector extension (set HINDSIGHT_API_VECTOR_EXTENSION='{current_index_type.replace('vchordrq', 'vchord').replace('hnsw', 'pgvector')}')"
+            )
+
+        # Tables are empty, safe to recreate indexes
+        logger.info(f"Recreating vector indexes for {target_ext}")
+
+        for table_name, index_name, current_type in mismatched_tables:
+            # Drop existing index if it exists
+            if current_type:
+                logger.info(f"Dropping {current_type} index on {table_name}")
+                conn.execute(text(f"DROP INDEX IF EXISTS {schema_name}.{index_name}"))
+
+            # Create new index with appropriate type
+            if target_ext == "vchord":
+                logger.info(f"Creating vchordrq index on {table_name}")
+                conn.execute(
+                    text(f"""
+                        CREATE INDEX IF NOT EXISTS {index_name}
+                        ON {schema_name}.{table_name}
+                        USING vchordrq (embedding vector_l2_ops)
+                    """)
+                )
+            else:  # pgvector
+                logger.info(f"Creating HNSW index on {table_name}")
+                conn.execute(
+                    text(f"""
+                        CREATE INDEX IF NOT EXISTS {index_name}
+                        ON {schema_name}.{table_name}
+                        USING hnsw (embedding vector_cosine_ops)
+                        WITH (m = 16, ef_construction = 64)
+                    """)
+                )
+
+        conn.commit()
+        logger.info(f"Successfully migrated vector indexes to {target_ext}")
+
+
+def ensure_text_search_extension(
+    database_url: str,
+    text_search_extension: str = "native",
+    schema: str | None = None,
+) -> None:
+    """
+    Ensure the text search columns and indexes match the configured extension.
+
+    This function checks the current search_vector column type and index type
+    in the database and adjusts them if necessary:
+    - If they match configured extension: no action needed
+    - If they differ and tables are empty: drop old column/index, recreate with new type
+    - If they differ and tables have data: raise error with migration guidance
+
+    Args:
+        database_url: SQLAlchemy database URL
+        text_search_extension: Configured text search extension ("native" or "vchord")
+        schema: Target PostgreSQL schema name (None for public)
+
+    Raises:
+        RuntimeError: If extension mismatch with existing data
+    """
+    schema_name = schema or "public"
+
+    engine = create_engine(database_url)
+    with engine.connect() as conn:
+        # Tables with search_vector columns to check
+        tables_to_check = [
+            "memory_units",
+            "reflections",  # Renamed from pinned_reflections in p1k2l3m4n5o6 migration
+        ]
+
+        # Determine target column type and index type
+        if text_search_extension == "vchord":
+            target_column_type = "bm25vector"
+            target_index_type = "bm25"
+        elif text_search_extension == "pg_textsearch":
+            target_column_type = "text"
+            target_index_type = "bm25"
+        else:  # native
+            target_column_type = "tsvector"
+            target_index_type = "gin"
+
+        mismatched_tables = []
+        tables_with_data = []
+
+        for table_name in tables_to_check:
+            # Check if table exists
+            table_exists = conn.execute(
+                text("""
+                    SELECT EXISTS (
+                        SELECT 1 FROM information_schema.tables
+                        WHERE table_schema = :schema AND table_name = :table_name
+                    )
+                """),
+                {"schema": schema_name, "table_name": table_name},
+            ).scalar()
+
+            if not table_exists:
+                logger.debug(f"Table {table_name} does not exist in schema '{schema_name}', skipping")
+                continue
+
+            # Get current column type from information_schema
+            current_column_info = conn.execute(
+                text("""
+                    SELECT data_type, udt_name
+                    FROM information_schema.columns
+                    WHERE table_schema = :schema
+                      AND table_name = :table_name
+                      AND column_name = 'search_vector'
+                """),
+                {"schema": schema_name, "table_name": table_name},
+            ).fetchone()
+
+            if not current_column_info:
+                logger.warning(f"No search_vector column found for {table_name}, will create it")
+                mismatched_tables.append((table_name, None, None))
+                continue
+
+            # Check column type (udt_name contains the actual type: tsvector, bm25vector, etc.)
+            current_column_type = current_column_info[1]  # udt_name
+
+            # Get current index type
+            current_index_info = conn.execute(
+                text("""
+                    SELECT am.amname
+                    FROM pg_indexes pi
+                    JOIN pg_class c ON c.relname = pi.indexname
+                    JOIN pg_am am ON am.oid = c.relam
+                    WHERE pi.schemaname = :schema
+                      AND pi.tablename = :table_name
+                      AND pi.indexname LIKE '%text_search%'
+                """),
+                {"schema": schema_name, "table_name": table_name},
+            ).fetchone()
+
+            current_index_type = current_index_info[0] if current_index_info else None
+
+            # Check if column and index types match target
+            column_matches = current_column_type == target_column_type
+            index_matches = current_index_type == target_index_type if current_index_type else False
+
+            if not (column_matches and index_matches):
+                logger.info(
+                    f"Text search mismatch on {table_name}: "
+                    f"column={current_column_type} (want {target_column_type}), "
+                    f"index={current_index_type} (want {target_index_type})"
+                )
+                mismatched_tables.append((table_name, current_column_type, current_index_type))
+
+                # Check if table has data
+                row_count = conn.execute(text(f"SELECT COUNT(*) FROM {schema_name}.{table_name}")).scalar()
+
+                if row_count > 0:
+                    tables_with_data.append((table_name, row_count))
+            else:
+                logger.debug(f"Text search OK for {table_name}: {current_column_type}/{current_index_type}")
+
+        # If no mismatches, we're done
+        if not mismatched_tables:
+            logger.debug(f"All text search columns/indexes match configured extension: {text_search_extension}")
+            return
+
+        # If there's data in any mismatched table, raise error
+        if tables_with_data:
+            table_list = ", ".join([f"{table}({count} rows)" for table, count in tables_with_data])
+            # Detect current extension from column type
+            current_col_type = mismatched_tables[0][1]
+            if current_col_type == "tsvector":
+                current_ext = "native"
+            elif current_col_type == "bm25vector":
+                current_ext = "vchord"
+            elif current_col_type == "text":
+                current_ext = "pg_textsearch"
+            else:
+                current_ext = "unknown"
+            raise RuntimeError(
+                f"Cannot change text search extension from {current_ext} to {text_search_extension}: "
+                f"the following tables contain data: {table_list}. "
+                f"To change text search extension, you must either:\n"
+                f"  1. Clear all data: DELETE FROM {schema_name}.memory_units; "
+                f"DELETE FROM {schema_name}.reflections; then restart\n"
+                f"  2. Use the current text search extension (set HINDSIGHT_API_TEXT_SEARCH_EXTENSION='{current_ext}')"
+            )
+
+        # Tables are empty, safe to recreate columns/indexes
+        logger.info(f"Recreating text search columns/indexes for {text_search_extension}")
+
+        for table_name, current_col_type, current_idx_type in mismatched_tables:
+            # Drop existing index if it exists
+            if current_idx_type:
+                logger.info(f"Dropping {current_idx_type} index on {table_name}")
+                conn.execute(
+                    text(f"""
+                        DROP INDEX IF EXISTS {schema_name}.idx_{table_name.replace(".", "_")}_text_search
+                    """)
+                )
+
+            # Drop existing column if it exists
+            if current_col_type:
+                logger.info(f"Dropping {current_col_type} column on {table_name}")
+                conn.execute(text(f"ALTER TABLE {schema_name}.{table_name} DROP COLUMN IF EXISTS search_vector"))
+
+            # Create new column with appropriate type
+            if text_search_extension == "vchord":
+                logger.info(f"Creating bm25vector column on {table_name}")
+                # Note: vchord_bm25 extension creates types in bm25_catalog schema
+                conn.execute(
+                    text(f"ALTER TABLE {schema_name}.{table_name} ADD COLUMN search_vector bm25_catalog.bm25vector")
+                )
+
+                # Create BM25 index
+                logger.info(f"Creating BM25 index on {table_name}")
+                conn.execute(
+                    text(f"""
+                        CREATE INDEX idx_{table_name.replace(".", "_")}_text_search
+                        ON {schema_name}.{table_name}
+                        USING bm25 (search_vector bm25_catalog.bm25_ops)
+                    """)
+                )
+            elif text_search_extension == "pg_textsearch":
+                logger.info(f"Creating TEXT column on {table_name}")
+                # Dummy TEXT column for consistency (indexes operate on base columns)
+                conn.execute(text(f"ALTER TABLE {schema_name}.{table_name} ADD COLUMN search_vector TEXT"))
+
+                # Create BM25 index on expression
+                logger.info(f"Creating BM25 index on {table_name}")
+                # Different expression for each table
+                if table_name == "memory_units":
+                    index_expr = "(COALESCE(text, '') || ' ' || COALESCE(context, ''))"
+                else:  # reflections
+                    index_expr = "(COALESCE(name, '') || ' ' || content)"
+
+                conn.execute(
+                    text(f"""
+                        CREATE INDEX idx_{table_name.replace(".", "_")}_text_search
+                        ON {schema_name}.{table_name}
+                        USING bm25({index_expr})
+                        WITH (text_config='english')
+                    """)
+                )
+            else:  # native
+                logger.info(f"Creating tsvector column on {table_name}")
+                # Different GENERATED expression for each table
+                if table_name == "memory_units":
+                    generated_expr = "to_tsvector('english', COALESCE(text, '') || ' ' || COALESCE(context, ''))"
+                else:  # reflections
+                    generated_expr = "to_tsvector('english', COALESCE(name, '') || ' ' || content)"
+
+                conn.execute(
+                    text(f"""
+                        ALTER TABLE {schema_name}.{table_name}
+                        ADD COLUMN search_vector tsvector
+                        GENERATED ALWAYS AS ({generated_expr}) STORED
+                    """)
+                )
+
+                # Create GIN index
+                logger.info(f"Creating GIN index on {table_name}")
+                conn.execute(
+                    text(f"""
+                        CREATE INDEX idx_{table_name.replace(".", "_")}_text_search
+                        ON {schema_name}.{table_name}
+                        USING gin(search_vector)
+                    """)
+                )
+
+        conn.commit()
+        logger.info(f"Successfully migrated text search to {text_search_extension}")

hindsight-api/hindsight_api/worker/poller.py

@@ -401,6 +401,8 @@ class WorkerPoller:
         On startup, we reset any tasks stuck in 'processing' for this worker_id
         back to 'pending' so they can be picked up again.
 
+        Also recovers batch API operations that were in-flight.
+
         If tenant_extension is configured, recovers across all tenant schemas.
 
         Returns:
@@ -413,11 +415,16 @@ class WorkerPoller:
             try:
                 table = fq_table("async_operations", schema)
 
+                # First, recover batch API operations (before resetting worker tasks)
+                batch_count = await self._recover_batch_operations(schema)
+                total_count += batch_count
+
+                # Then reset normal worker tasks
                 result = await self._pool.execute(
                     f"""
                     UPDATE {table}
                     SET status = 'pending', worker_id = NULL, claimed_at = NULL, updated_at = now()
-                    WHERE status = 'processing' AND worker_id = $1
+                    WHERE status = 'processing' AND worker_id = $1 AND result_metadata->>'batch_id' IS NULL
                     """,
                     self._worker_id,
                 )
@@ -434,6 +441,80 @@ class WorkerPoller:
             logger.info(f"Worker {self._worker_id} recovered {total_count} stale tasks from previous run")
         return total_count
 
+    async def _recover_batch_operations(self, schema: str | None) -> int:
+        """
+        Recover batch API operations that were in-flight when worker crashed.
+
+        Finds operations with batch_id in metadata and re-submits them as tasks
+        so polling can resume.
+
+        Args:
+            schema: Database schema to recover from
+
+        Returns:
+            Number of batch operations recovered
+        """
+        table = fq_table("async_operations", schema)
+
+        try:
+            # Find operations with batch_id in metadata (batch API operations)
+            rows = await self._pool.fetch(
+                f"""
+                SELECT operation_id, task_payload, result_metadata
+                FROM {table}
+                WHERE status = 'processing'
+                  AND result_metadata ? 'batch_id'
+                  AND task_payload IS NOT NULL
+                """
+            )
+
+            if not rows:
+                return 0
+
+            recovered = 0
+            for row in rows:
+                operation_id = str(row["operation_id"])
+                task_payload = row["task_payload"]
+                result_metadata = row["result_metadata"]
+
+                # Parse metadata
+                if isinstance(result_metadata, str):
+                    result_metadata = json.loads(result_metadata)
+
+                batch_id = result_metadata.get("batch_id")
+                batch_provider = result_metadata.get("batch_provider", "openai")
+
+                logger.info(
+                    f"Recovering batch operation: operation_id={operation_id}, batch_id={batch_id}, provider={batch_provider}"
+                )
+
+                # Parse task_payload
+                if isinstance(task_payload, str):
+                    task_dict = json.loads(task_payload)
+                else:
+                    task_dict = task_payload
+
+                # Mark operation as ready for re-processing
+                # Reset to pending with task_payload intact so worker picks it up again
+                await self._pool.execute(
+                    f"""
+                    UPDATE {table}
+                    SET status = 'pending', worker_id = NULL, claimed_at = NULL, updated_at = now()
+                    WHERE operation_id = $1
+                    """,
+                    operation_id,
+                )
+
+                recovered += 1
+                logger.info(f"Batch operation {operation_id} reset to pending for re-processing")
+
+            return recovered
+
+        except Exception as e:
+            schema_display = f'"{schema}"' if schema else str(schema)
+            logger.error(f"Failed to recover batch operations for schema {schema_display}: {e}")
+            return 0
+
     async def run(self):
         """
         Main polling loop with fire-and-forget task execution.

hindsight-api/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "hindsight-api"
-version = "0.4.10"
+version = "0.4.11"
 description = "Hindsight: Agent Memory That Works Like Human Memory"
 readme = "README.md"
 requires-python = ">=3.11"
@@ -42,6 +42,7 @@ dependencies = [
     "typer>=0.9.0",
     "cohere>=5.0.0",
     "flashrank>=0.2.0",
+    "litellm>=1.0.0",
     # Local ML models for embeddings/reranking - can be excluded in Docker with INCLUDE_LOCAL_MODELS=false
     "sentence-transformers>=3.3.0",
     "transformers>=4.53.0", # Security fixes for ReDoS vulnerabilities

hindsight-api/tests/test_async_batch_retain.py

@@ -0,0 +1,423 @@
+"""Test async batch retain with smart batching and parent-child operations."""
+
+import asyncio
+import json
+import uuid
+
+import pytest
+
+from hindsight_api.extensions import RequestContext
+
+
+@pytest.mark.asyncio
+async def test_duplicate_document_ids_rejected_async(memory, request_context):
+    """Test that async retain rejects batches with duplicate document_ids."""
+    bank_id = "test_duplicate_async"
+    contents = [
+        {"content": "First item", "document_id": "doc1"},
+        {"content": "Second item", "document_id": "doc2"},
+        {"content": "Third item", "document_id": "doc1"},  # Duplicate!
+    ]
+
+    # Should raise ValueError due to duplicate document_ids
+    with pytest.raises(ValueError, match="duplicate document_ids.*doc1"):
+        await memory.submit_async_retain(
+            bank_id=bank_id,
+            contents=contents,
+            request_context=request_context,
+        )
+
+
+@pytest.mark.asyncio
+async def test_duplicate_document_ids_rejected_sync(memory, request_context):
+    """Test that sync retain also rejects batches with duplicate document_ids."""
+    bank_id = "test_duplicate_sync"
+    contents = [
+        {"content": "First item", "document_id": "doc1"},
+        {"content": "Second item", "document_id": "doc1"},  # Duplicate!
+    ]
+
+    # Should raise ValueError due to duplicate document_ids
+    with pytest.raises(ValueError, match="duplicate document_ids.*doc1"):
+        await memory.retain_batch_async(
+            bank_id=bank_id,
+            contents=contents,
+            request_context=request_context,
+        )
+
+
+@pytest.mark.asyncio
+async def test_small_async_batch_no_splitting(memory, request_context):
+    """Test that small async batches create parent with single child (simplified code path)."""
+    bank_id = "test_small_async"
+    contents = [{"content": "Alice works at Google", "document_id": f"doc{i}"} for i in range(5)]
+
+    # Calculate total chars (should be well under threshold)
+    total_chars = sum(len(item["content"]) for item in contents)
+    assert total_chars < 10_000, "Test batch should be small"
+
+    # Submit async retain
+    result = await memory.submit_async_retain(
+        bank_id=bank_id,
+        contents=contents,
+        request_context=request_context,
+    )
+
+    # Verify we got an operation_id back
+    assert "operation_id" in result
+    assert "items_count" in result
+    assert result["items_count"] == 5
+
+    operation_id = result["operation_id"]
+
+    # Wait for task to complete (SyncTaskBackend executes immediately)
+    await asyncio.sleep(0.1)
+
+    # Check operation status
+    status = await memory.get_operation_status(
+        bank_id=bank_id,
+        operation_id=operation_id,
+        request_context=request_context,
+    )
+
+    # Should be a parent operation with single child (simplified code path)
+    assert status["status"] == "completed"
+    assert status["operation_type"] == "batch_retain"
+    assert "child_operations" in status
+    assert status["result_metadata"]["num_sub_batches"] == 1  # Single sub-batch
+    assert len(status["child_operations"]) == 1
+    assert status["child_operations"][0]["status"] == "completed"
+
+
+@pytest.mark.asyncio
+async def test_large_async_batch_auto_splits(memory, request_context):
+    """Test that large async batches automatically split into sub-batches with parent operation."""
+    from hindsight_api.engine.memory_engine import count_tokens
+
+    bank_id = "test_large_async"
+
+    # Create a large batch that exceeds the threshold (10k tokens default)
+    # Repeating "A"s gets heavily compressed by tokenizer, use varied content
+    # Use ~22k chars per item = ~5.5k tokens per item, 2 items = ~11k tokens total (exceeds 10k)
+    large_content = "The quick brown fox jumps over the lazy dog. " * 500  # ~22k chars = ~5.5k tokens
+    contents = [{"content": large_content + f" item {i}", "document_id": f"doc{i}"} for i in range(2)]
+
+    # Calculate total tokens (should exceed threshold)
+    total_tokens = sum(count_tokens(item["content"]) for item in contents)
+    assert total_tokens > 10_000, "Test batch should exceed threshold"
+
+    # Submit async retain
+    result = await memory.submit_async_retain(
+        bank_id=bank_id,
+        contents=contents,
+        request_context=request_context,
+    )
+
+    # Verify we got an operation_id back
+    assert "operation_id" in result
+    assert "items_count" in result
+    assert result["items_count"] == 2
+
+    parent_operation_id = result["operation_id"]
+
+    # Wait for tasks to complete
+    await asyncio.sleep(0.5)
+
+    # Check parent operation status
+    parent_status = await memory.get_operation_status(
+        bank_id=bank_id,
+        operation_id=parent_operation_id,
+        request_context=request_context,
+    )
+
+    # Should be a parent operation with children
+    assert parent_status["operation_type"] == "batch_retain"
+    assert "child_operations" in parent_status
+    assert "num_sub_batches" in parent_status["result_metadata"]
+    assert parent_status["result_metadata"]["num_sub_batches"] >= 2  # Should split into at least 2 batches
+    assert parent_status["result_metadata"]["items_count"] == 2
+
+    # Verify child operations
+    child_ops = parent_status["child_operations"]
+    assert len(child_ops) >= 2, "Should have at least 2 child operations"
+
+    # All children should be completed (SyncTaskBackend executes immediately)
+    for child in child_ops:
+        assert child["status"] == "completed"
+        assert child["sub_batch_index"] is not None
+        assert child["items_count"] > 0
+
+    # Parent status should be aggregated as "completed"
+    assert parent_status["status"] == "completed"
+
+
+@pytest.mark.asyncio
+async def test_parent_operation_status_aggregation_pending(memory, request_context):
+    """Test that parent operation shows 'pending' when children are pending."""
+    bank_id = "test_parent_pending"
+    pool = await memory._get_pool()
+
+    # Manually create a parent operation
+    parent_id = uuid.uuid4()
+    async with pool.acquire() as conn:
+        await conn.execute(
+            """
+            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
+            VALUES ($1, $2, $3, $4, $5)
+            """,
+            parent_id,
+            bank_id,
+            "batch_retain",
+            json.dumps({"items_count": 20, "num_sub_batches": 2, "is_parent": True}),
+            "pending",
+        )
+
+        # Create 2 child operations - one completed, one pending
+        child1_id = uuid.uuid4()
+        await conn.execute(
+            """
+            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
+            VALUES ($1, $2, $3, $4, $5)
+            """,
+            child1_id,
+            bank_id,
+            "retain",
+            json.dumps(
+                {
+                    "items_count": 10,
+                    "parent_operation_id": str(parent_id),
+                    "sub_batch_index": 1,
+                    "total_sub_batches": 2,
+                }
+            ),
+            "completed",
+        )
+
+        child2_id = uuid.uuid4()
+        await conn.execute(
+            """
+            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
+            VALUES ($1, $2, $3, $4, $5)
+            """,
+            child2_id,
+            bank_id,
+            "retain",
+            json.dumps(
+                {
+                    "items_count": 10,
+                    "parent_operation_id": str(parent_id),
+                    "sub_batch_index": 2,
+                    "total_sub_batches": 2,
+                }
+            ),
+            "pending",
+        )
+
+    # Check parent status
+    parent_status = await memory.get_operation_status(
+        bank_id=bank_id,
+        operation_id=str(parent_id),
+        request_context=request_context,
+    )
+
+    # Parent should aggregate as "pending" since one child is still pending
+    assert parent_status["status"] == "pending"
+    assert len(parent_status["child_operations"]) == 2
+
+
+@pytest.mark.asyncio
+async def test_parent_operation_status_aggregation_failed(memory, request_context):
+    """Test that parent operation shows 'failed' when any child fails."""
+    bank_id = "test_parent_failed"
+    pool = await memory._get_pool()
+
+    # Manually create a parent operation
+    parent_id = uuid.uuid4()
+    async with pool.acquire() as conn:
+        await conn.execute(
+            """
+            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
+            VALUES ($1, $2, $3, $4, $5)
+            """,
+            parent_id,
+            bank_id,
+            "batch_retain",
+            json.dumps({"items_count": 20, "num_sub_batches": 2, "is_parent": True}),
+            "pending",
+        )
+
+        # Create 2 child operations - one completed, one failed
+        child1_id = uuid.uuid4()
+        await conn.execute(
+            """
+            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
+            VALUES ($1, $2, $3, $4, $5)
+            """,
+            child1_id,
+            bank_id,
+            "retain",
+            json.dumps(
+                {
+                    "items_count": 10,
+                    "parent_operation_id": str(parent_id),
+                    "sub_batch_index": 1,
+                    "total_sub_batches": 2,
+                }
+            ),
+            "completed",
+        )
+
+        child2_id = uuid.uuid4()
+        await conn.execute(
+            """
+            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status, error_message)
+            VALUES ($1, $2, $3, $4, $5, $6)
+            """,
+            child2_id,
+            bank_id,
+            "retain",
+            json.dumps(
+                {
+                    "items_count": 10,
+                    "parent_operation_id": str(parent_id),
+                    "sub_batch_index": 2,
+                    "total_sub_batches": 2,
+                }
+            ),
+            "failed",
+            "Test error",
+        )
+
+    # Check parent status
+    parent_status = await memory.get_operation_status(
+        bank_id=bank_id,
+        operation_id=str(parent_id),
+        request_context=request_context,
+    )
+
+    # Parent should aggregate as "failed" since one child failed
+    assert parent_status["status"] == "failed"
+    assert len(parent_status["child_operations"]) == 2
+
+    # Verify child with error is included
+    failed_child = [c for c in parent_status["child_operations"] if c["status"] == "failed"][0]
+    assert failed_child["error_message"] == "Test error"
+
+
+@pytest.mark.asyncio
+async def test_parent_operation_status_aggregation_completed(memory, request_context):
+    """Test that parent operation shows 'completed' when all children are completed."""
+    bank_id = "test_parent_completed"
+    pool = await memory._get_pool()
+
+    # Manually create a parent operation
+    parent_id = uuid.uuid4()
+    async with pool.acquire() as conn:
+        await conn.execute(
+            """
+            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
+            VALUES ($1, $2, $3, $4, $5)
+            """,
+            parent_id,
+            bank_id,
+            "batch_retain",
+            json.dumps({"items_count": 20, "num_sub_batches": 2, "is_parent": True}),
+            "pending",
+        )
+
+        # Create 2 child operations - both completed
+        child1_id = uuid.uuid4()
+        await conn.execute(
+            """
+            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
+            VALUES ($1, $2, $3, $4, $5)
+            """,
+            child1_id,
+            bank_id,
+            "retain",
+            json.dumps(
+                {
+                    "items_count": 10,
+                    "parent_operation_id": str(parent_id),
+                    "sub_batch_index": 1,
+                    "total_sub_batches": 2,
+                }
+            ),
+            "completed",
+        )
+
+        child2_id = uuid.uuid4()
+        await conn.execute(
+            """
+            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
+            VALUES ($1, $2, $3, $4, $5)
+            """,
+            child2_id,
+            bank_id,
+            "retain",
+            json.dumps(
+                {
+                    "items_count": 10,
+                    "parent_operation_id": str(parent_id),
+                    "sub_batch_index": 2,
+                    "total_sub_batches": 2,
+                }
+            ),
+            "completed",
+        )
+
+    # Check parent status
+    parent_status = await memory.get_operation_status(
+        bank_id=bank_id,
+        operation_id=str(parent_id),
+        request_context=request_context,
+    )
+
+    # Parent should aggregate as "completed" since all children are completed
+    assert parent_status["status"] == "completed"
+    assert len(parent_status["child_operations"]) == 2
+    assert all(c["status"] == "completed" for c in parent_status["child_operations"])
+
+
+@pytest.mark.asyncio
+async def test_config_retain_batch_tokens_respected(memory, request_context):
+    """Test that the retain_batch_tokens config setting is respected."""
+    from hindsight_api.config import get_config
+    from hindsight_api.engine.memory_engine import count_tokens
+
+    bank_id = "test_config_batch_tokens"
+    config = get_config()
+
+    # Check that config has the retain_batch_tokens setting
+    assert hasattr(config, "retain_batch_tokens")
+    assert config.retain_batch_tokens > 0
+
+    # Create a batch that's just under the threshold
+    # Use content that produces roughly half the token limit per item
+    content_size = config.retain_batch_tokens * 2  # chars (rough estimate: 1 token ~= 4 chars)
+    contents = [{"content": "A" * content_size, "document_id": f"doc{i}"} for i in range(2)]
+
+    total_tokens = sum(count_tokens(item["content"]) for item in contents)
+    # Should be equal to threshold (boundary case, no splitting since we use > not >=)
+    assert total_tokens <= config.retain_batch_tokens
+
+    # Submit - should NOT split
+    result = await memory.submit_async_retain(
+        bank_id=bank_id,
+        contents=contents,
+        request_context=request_context,
+    )
+
+    # Wait for completion
+    await asyncio.sleep(0.1)
+
+    # Check status - should be a parent with single child (even for small batches)
+    status = await memory.get_operation_status(
+        bank_id=bank_id,
+        operation_id=result["operation_id"],
+        request_context=request_context,
+    )
+
+    # Even small batches use parent-child pattern now (simpler code path)
+    assert "child_operations" in status
+    assert status["result_metadata"]["num_sub_batches"] == 1

hindsight-api/tests/test_async_retain_tags.py

@@ -0,0 +1,93 @@
+"""Unit tests for async retain tag propagation."""
+
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+from hindsight_api.engine.memory_engine import MemoryEngine
+from hindsight_api.models import RequestContext
+
+
+@pytest.mark.asyncio
+async def test_submit_async_retain_includes_document_tags_in_task_payload():
+    """submit_async_retain should include document_tags in queued task payload."""
+    engine = MemoryEngine.__new__(MemoryEngine)
+    engine._initialized = True
+    engine._authenticate_tenant = AsyncMock()
+    engine._submit_async_operation = AsyncMock(return_value={"operation_id": "op-1"})
+
+    # Mock the pool and connection for parent operation creation
+    mock_conn = AsyncMock()
+    mock_conn.execute = AsyncMock()
+    mock_conn.transaction = MagicMock()
+    mock_conn.transaction.return_value.__aenter__ = AsyncMock()
+    mock_conn.transaction.return_value.__aexit__ = AsyncMock()
+
+    mock_pool = AsyncMock()
+    mock_pool.acquire = AsyncMock(return_value=mock_conn)
+    mock_pool.release = AsyncMock()
+
+    engine._get_pool = AsyncMock(return_value=mock_pool)
+
+    request_context = RequestContext(tenant_id="tenant-a", api_key_id="key-a")
+    contents = [{"content": "Async retain payload test."}]
+    document_tags = ["scope:tools", "user:alice"]
+
+    result = await MemoryEngine.submit_async_retain(
+        engine,
+        bank_id="bank-1",
+        contents=contents,
+        document_tags=document_tags,
+        request_context=request_context,
+    )
+
+    # Check result structure
+    assert "operation_id" in result
+    assert "items_count" in result
+    assert result["items_count"] == 1
+
+    # Verify authentication was called
+    engine._authenticate_tenant.assert_awaited_once_with(request_context)
+
+    # Verify child operation was submitted
+    engine._submit_async_operation.assert_awaited_once()
+
+    # Verify child operation payload contains document_tags
+    kwargs = engine._submit_async_operation.await_args.kwargs
+    assert kwargs["bank_id"] == "bank-1"
+    assert kwargs["operation_type"] == "retain"
+    assert kwargs["task_type"] == "batch_retain"
+    assert kwargs["task_payload"]["contents"] == contents
+    assert kwargs["task_payload"]["document_tags"] == document_tags
+    assert kwargs["task_payload"]["_tenant_id"] == "tenant-a"
+    assert kwargs["task_payload"]["_api_key_id"] == "key-a"
+
+
+@pytest.mark.asyncio
+async def test_handle_batch_retain_forwards_document_tags_to_retain_batch_async():
+    """Worker handler should forward document_tags from task payload."""
+    engine = MemoryEngine.__new__(MemoryEngine)
+    engine._initialized = True
+    engine.retain_batch_async = AsyncMock(return_value={"items_count": 1})
+
+    task_dict = {
+        "bank_id": "bank-1",
+        "contents": [{"content": "Forward tags test."}],
+        "document_tags": ["scope:client"],
+        "_tenant_id": "tenant-a",
+        "_api_key_id": "key-a",
+    }
+
+    await MemoryEngine._handle_batch_retain(engine, task_dict)
+
+    engine.retain_batch_async.assert_awaited_once()
+    kwargs = engine.retain_batch_async.await_args.kwargs
+    assert kwargs["bank_id"] == "bank-1"
+    assert kwargs["contents"] == task_dict["contents"]
+    assert kwargs["document_tags"] == ["scope:client"]
+
+    request_context = kwargs["request_context"]
+    assert request_context.internal is True
+    assert request_context.user_initiated is True
+    assert request_context.tenant_id == "tenant-a"
+    assert request_context.api_key_id == "key-a"

hindsight-api/tests/test_base_path.py

@@ -0,0 +1,189 @@
+"""
+Integration test for API base path support.
+
+Tests that the API works correctly when deployed with a base path (e.g., /hindsight)
+for reverse proxy deployments.
+"""
+import os
+import pytest
+import pytest_asyncio
+import httpx
+from hindsight_api.api import create_app
+from hindsight_api.config import clear_config_cache
+
+
+@pytest_asyncio.fixture
+async def api_client_with_base_path(memory):
+    """Create an async test client for the FastAPI app with a base path."""
+    # Set base path in environment
+    base_path = "/hindsight"
+    os.environ["HINDSIGHT_API_BASE_PATH"] = base_path
+
+    # Clear config cache to force reload with new base_path
+    clear_config_cache()
+
+    # Memory is already initialized by the conftest fixture (with migrations)
+    app = create_app(memory, initialize_memory=False)
+
+    # Use base_url with base path
+    transport = httpx.ASGITransport(app=app)
+    async with httpx.AsyncClient(
+        transport=transport,
+        base_url=f"http://test{base_path}"
+    ) as client:
+        yield client
+
+    # Cleanup: unset base path
+    os.environ.pop("HINDSIGHT_API_BASE_PATH", None)
+    clear_config_cache()
+
+
+@pytest_asyncio.fixture
+async def api_client_without_base_path(memory):
+    """Create an async test client for the FastAPI app without a base path (root)."""
+    # Ensure no base path is set
+    os.environ.pop("HINDSIGHT_API_BASE_PATH", None)
+    clear_config_cache()
+
+    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.mark.asyncio
+async def test_base_path_health_endpoint(api_client_with_base_path):
+    """Test that health endpoint works with base path."""
+    # With base path set to /hindsight, health should be at /hindsight/health
+    # But since our client base_url is already http://test/hindsight, we request /health
+    response = await api_client_with_base_path.get("/health")
+    assert response.status_code == 200
+    data = response.json()
+    assert "status" in data
+    assert data["status"] in ["ok", "healthy"]  # Accept both formats
+
+
+@pytest.mark.asyncio
+async def test_base_path_banks_endpoint(api_client_with_base_path):
+    """Test that banks endpoint works with base path."""
+    response = await api_client_with_base_path.get("/v1/default/banks")
+    assert response.status_code == 200
+    data = response.json()
+    assert "banks" in data
+
+
+@pytest.mark.asyncio
+async def test_base_path_openapi_schema(api_client_with_base_path):
+    """Test that OpenAPI schema includes correct base path in servers."""
+    response = await api_client_with_base_path.get("/openapi.json")
+    assert response.status_code == 200
+    openapi_schema = response.json()
+
+    # Check that servers array includes base path
+    assert "servers" in openapi_schema
+    servers = openapi_schema["servers"]
+    assert len(servers) > 0
+    # FastAPI should set server URL to the root_path
+    assert servers[0]["url"] == "/hindsight"
+
+
+@pytest.mark.asyncio
+async def test_base_path_docs_redirect(api_client_with_base_path):
+    """Test that /docs redirects correctly with base path."""
+    # FastAPI docs endpoint should work
+    response = await api_client_with_base_path.get("/docs", follow_redirects=False)
+    # Should either return 200 (direct) or 307 (redirect to trailing slash)
+    assert response.status_code in [200, 307]
+
+
+@pytest.mark.asyncio
+async def test_base_path_metrics(api_client_with_base_path):
+    """Test that metrics endpoint works with base path."""
+    response = await api_client_with_base_path.get("/metrics")
+    assert response.status_code == 200
+    # Metrics should be in Prometheus format
+    assert "# HELP" in response.text or "# TYPE" in response.text
+
+
+@pytest.mark.asyncio
+async def test_base_path_full_workflow(api_client_with_base_path):
+    """
+    Test a full retain/recall workflow with base path.
+
+    This ensures that all memory operations work correctly when the API
+    is deployed with a base path.
+    """
+    bank_id = "test_base_path_bank"
+
+    # 1. Create/get bank
+    response = await api_client_with_base_path.get(f"/v1/default/banks/{bank_id}/profile")
+    assert response.status_code == 200
+
+    # 2. Store a memory
+    response = await api_client_with_base_path.post(
+        f"/v1/default/banks/{bank_id}/memories",
+        json={
+            "items": [
+                {
+                    "content": "The API supports base path deployment for reverse proxy use cases.",
+                    "context": "testing base path feature"
+                }
+            ]
+        }
+    )
+    assert response.status_code == 200
+    result = response.json()
+    assert result["success"] is True
+
+    # 3. Recall the memory
+    response = await api_client_with_base_path.post(
+        f"/v1/default/banks/{bank_id}/memories/recall",
+        json={
+            "query": "base path support"
+        }
+    )
+    assert response.status_code == 200
+    recall_result = response.json()
+    # API returns "results" not "memories"
+    assert "results" in recall_result
+    assert len(recall_result["results"]) > 0
+
+
+@pytest.mark.asyncio
+async def test_without_base_path_still_works(api_client_without_base_path):
+    """
+    Regression test: ensure default behavior (no base path) still works.
+
+    This test verifies that when HINDSIGHT_API_BASE_PATH is not set,
+    the API works at the root path as before.
+    """
+    # Health check at root
+    response = await api_client_without_base_path.get("/health")
+    assert response.status_code == 200
+
+    # Banks endpoint at root
+    response = await api_client_without_base_path.get("/v1/default/banks")
+    assert response.status_code == 200
+
+    # OpenAPI schema should have empty or "/" server path
+    response = await api_client_without_base_path.get("/openapi.json")
+    assert response.status_code == 200
+    openapi_schema = response.json()
+    servers = openapi_schema.get("servers", [])
+    if servers:
+        # Server URL should be empty string (root) or "/"
+        assert servers[0]["url"] in ["", "/"]
+
+
+@pytest.mark.skip(reason="MCP endpoint routing with base path needs investigation")
+@pytest.mark.asyncio
+async def test_base_path_mcp_endpoint(api_client_with_base_path):
+    """Test that MCP endpoint is accessible with base path."""
+    bank_id = "test_mcp_bank"
+
+    # MCP endpoint should be mounted at /mcp/{bank_id}/
+    # The MCP server uses a different protocol, so just check the root exists
+    response = await api_client_with_base_path.get(f"/mcp/{bank_id}/")
+    # MCP may return various status codes, but should not be 404 (not found)
+    # Accept 405 (method not allowed), 400 (bad request), etc.
+    assert response.status_code != 404, "MCP endpoint should exist"

hindsight-api/tests/test_batch_api.py

@@ -0,0 +1,508 @@
+"""
+Test OpenAI Batch API integration for retain fact extraction.
+
+Tests cover:
+- Normal batch API flow (submit, poll, complete)
+- Crash recovery (resume from existing batch_id)
+- Provider fallback (when batch API not supported)
+- Worker recovery on restart
+"""
+import pytest
+import asyncio
+import logging
+import json
+import uuid
+from datetime import datetime, timezone
+from unittest.mock import AsyncMock, MagicMock, patch
+from hindsight_api import RequestContext
+from hindsight_api.engine.retain.fact_extraction import (
+    extract_facts_from_contents_batch_api,
+    extract_facts_from_contents,
+    RetainContent,
+)
+from hindsight_api.config import HindsightConfig
+from hindsight_api.engine.llm_wrapper import create_llm_provider
+from hindsight_api.worker.poller import WorkerPoller
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture
+def mock_llm_config():
+    """Create a mock LLM config with batch API support."""
+    mock = MagicMock()
+    mock.provider = "openai"
+    mock.model = "gpt-4o-mini"
+    mock._provider_impl = AsyncMock()
+    return mock
+
+
+@pytest.fixture
+def test_contents():
+    """Create test content for fact extraction."""
+    return [
+        RetainContent(
+            content="Alice is a senior software engineer at TechCorp. She specializes in distributed systems.",
+            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
+            context="team overview",
+        ),
+        RetainContent(
+            content="Bob joined the team last month as a junior developer. He is learning React.",
+            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
+            context="team overview",
+        ),
+    ]
+
+
+@pytest.fixture
+def hindsight_config():
+    """Create test config with batch API enabled."""
+    config = HindsightConfig.from_env()
+    config.retain_batch_enabled = True
+    config.retain_batch_poll_interval_seconds = 1  # Fast polling for tests
+    config.retain_chunk_size = 4000
+    config.retain_extraction_mode = "concise"
+    config.retain_extract_causal_links = False
+    return config
+
+
+@pytest.mark.asyncio
+async def test_batch_api_normal_flow(mock_llm_config, test_contents, hindsight_config, memory, request_context):
+    """Test normal batch API flow: submit, poll, complete."""
+    bank_id = f"test_batch_{datetime.now(timezone.utc).timestamp()}"
+
+    try:
+        # Mock batch API responses
+        batch_id = "batch_test123"
+
+        # Mock supports_batch_api
+        mock_llm_config._provider_impl.supports_batch_api = AsyncMock(return_value=True)
+
+        # Mock submit_batch - returns batch metadata
+        mock_llm_config._provider_impl.submit_batch = AsyncMock(
+            return_value={
+                "batch_id": batch_id,
+                "status": "validating",
+                "request_counts": {"total": 2, "completed": 0, "failed": 0},
+            }
+        )
+
+        # Mock get_batch_status - simulate polling sequence
+        status_sequence = [
+            {"status": "in_progress", "request_counts": {"total": 2, "completed": 1, "failed": 0}},
+            {"status": "completed", "request_counts": {"total": 2, "completed": 2, "failed": 0}},
+        ]
+        mock_llm_config._provider_impl.get_batch_status = AsyncMock(side_effect=status_sequence)
+
+        # Mock retrieve_batch_results - returns fact extraction results
+        mock_results = [
+            {
+                "custom_id": "chunk_0",
+                "response": {
+                    "body": {
+                        "choices": [
+                            {
+                                "message": {
+                                    "content": json.dumps({
+                                        "facts": [
+                                            {
+                                                "what": "Alice is a senior software engineer at TechCorp",
+                                                "when": "present",
+                                                "where": "TechCorp",
+                                                "who": "Alice",
+                                                "why": "Professional background information",
+                                                "fact_type": "world",
+                                                "fact_kind": "conversation",
+                                            }
+                                        ]
+                                    })
+                                }
+                            }
+                        ],
+                        "usage": {"prompt_tokens": 100, "completion_tokens": 50, "total_tokens": 150},
+                    }
+                },
+            },
+            {
+                "custom_id": "chunk_1",
+                "response": {
+                    "body": {
+                        "choices": [
+                            {
+                                "message": {
+                                    "content": json.dumps({
+                                        "facts": [
+                                            {
+                                                "what": "Bob joined the team last month as a junior developer",
+                                                "when": "last month",
+                                                "where": "team",
+                                                "who": "Bob",
+                                                "why": "New team member information",
+                                                "fact_type": "world",
+                                                "fact_kind": "conversation",
+                                            }
+                                        ]
+                                    })
+                                }
+                            }
+                        ],
+                        "usage": {"prompt_tokens": 100, "completion_tokens": 50, "total_tokens": 150},
+                    }
+                },
+            },
+        ]
+        mock_llm_config._provider_impl.retrieve_batch_results = AsyncMock(return_value=mock_results)
+
+        # Call batch API extraction
+        facts, chunks, usage = await extract_facts_from_contents_batch_api(
+            contents=test_contents,
+            llm_config=mock_llm_config,
+            agent_name="test_agent",
+            config=hindsight_config,
+            pool=None,  # No DB pool for this test
+            operation_id=None,
+            schema=None,
+        )
+
+        # Verify results
+        assert len(facts) == 2, "Should extract 2 facts (one per chunk)"
+        # Facts are ExtractedFact objects with .fact_text field
+        assert "Alice" in facts[0].fact_text and "senior software engineer" in facts[0].fact_text
+        assert "Bob" in facts[1].fact_text and "junior developer" in facts[1].fact_text
+
+        # Verify chunks metadata
+        assert len(chunks) == 2, "Should have 2 chunks metadata"
+        assert chunks[0].fact_count == 1
+        assert chunks[1].fact_count == 1
+
+        # Verify token usage
+        assert usage.input_tokens == 200  # 100 per chunk
+        assert usage.output_tokens == 100  # 50 per chunk
+        assert usage.total_tokens == 300
+
+        # Verify API calls
+        mock_llm_config._provider_impl.submit_batch.assert_called_once()
+        assert mock_llm_config._provider_impl.get_batch_status.call_count == 2
+        mock_llm_config._provider_impl.retrieve_batch_results.assert_called_once_with(batch_id)
+
+        logger.info("✅ Normal batch API flow test passed")
+
+    finally:
+        # Cleanup
+        try:
+            await memory.delete_bank(bank_id, request_context=request_context)
+        except Exception:
+            pass
+
+
+@pytest.mark.asyncio
+async def test_batch_api_crash_recovery(mock_llm_config, test_contents, hindsight_config, memory, request_context):
+    """Test crash recovery: resume polling from existing batch_id."""
+    bank_id = f"test_crash_{datetime.now(timezone.utc).timestamp()}"
+    operation_id = str(uuid.uuid4())  # Must be UUID for async_operations table
+
+    try:
+        # Ensure bank exists
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        # Setup: Store batch_id in async_operations table (simulates partial execution)
+        batch_id = "batch_recovered_456"
+        pool = memory._pool
+        schema = request_context.tenant_id
+
+        from hindsight_api.engine.task_backend import fq_table
+        table = fq_table("async_operations", schema)
+
+        # Create operation with batch_id already stored
+        await pool.execute(
+            f"""
+            INSERT INTO {table} (operation_id, operation_type, bank_id, status, result_metadata)
+            VALUES ($1, 'retain', $2, 'processing', $3::jsonb)
+            """,
+            operation_id,
+            bank_id,
+            json.dumps({
+                "batch_id": batch_id,
+                "batch_provider": "openai",
+                "chunk_count": 2,
+            }),
+        )
+
+        # Mock batch API responses for resume scenario
+        mock_llm_config._provider_impl.supports_batch_api = AsyncMock(return_value=True)
+
+        # Mock get_batch_status - batch already in progress
+        mock_llm_config._provider_impl.get_batch_status = AsyncMock(
+            return_value={
+                "status": "completed",
+                "request_counts": {"total": 2, "completed": 2, "failed": 0},
+            }
+        )
+
+        # Mock retrieve_batch_results
+        mock_results = [
+            {
+                "custom_id": "chunk_0",
+                "response": {
+                    "body": {
+                        "choices": [
+                            {
+                                "message": {
+                                    "content": json.dumps({
+                                        "facts": [
+                                            {
+                                                "what": "Alice is a senior software engineer",
+                                                "when": "present",
+                                                "where": "TechCorp",
+                                                "who": "Alice",
+                                                "why": "Background",
+                                                "fact_type": "world",
+                                                "fact_kind": "conversation",
+                                            }
+                                        ]
+                                    })
+                                }
+                            }
+                        ],
+                        "usage": {"prompt_tokens": 100, "completion_tokens": 50, "total_tokens": 150},
+                    }
+                },
+            },
+            {
+                "custom_id": "chunk_1",
+                "response": {
+                    "body": {
+                        "choices": [
+                            {
+                                "message": {
+                                    "content": json.dumps({
+                                        "facts": [
+                                            {
+                                                "what": "Bob is a junior developer",
+                                                "when": "last month",
+                                                "where": "team",
+                                                "who": "Bob",
+                                                "why": "New member",
+                                                "fact_type": "world",
+                                                "fact_kind": "conversation",
+                                            }
+                                        ]
+                                    })
+                                }
+                            }
+                        ],
+                        "usage": {"prompt_tokens": 100, "completion_tokens": 50, "total_tokens": 150},
+                    }
+                },
+            },
+        ]
+        mock_llm_config._provider_impl.retrieve_batch_results = AsyncMock(return_value=mock_results)
+
+        # Call batch API extraction with operation_id (crash recovery scenario)
+        facts, chunks, usage = await extract_facts_from_contents_batch_api(
+            contents=test_contents,
+            llm_config=mock_llm_config,
+            agent_name="test_agent",
+            config=hindsight_config,
+            pool=pool,
+            operation_id=operation_id,  # Provides crash recovery context
+            schema=schema,
+        )
+
+        # Verify results
+        assert len(facts) == 2, "Should extract 2 facts after recovery"
+
+        # CRITICAL: Verify submit_batch was NOT called (because batch_id already exists)
+        mock_llm_config._provider_impl.submit_batch.assert_not_called()
+
+        # Verify get_batch_status WAS called (polling resumed)
+        mock_llm_config._provider_impl.get_batch_status.assert_called()
+
+        # Verify retrieve_batch_results was called with the recovered batch_id
+        mock_llm_config._provider_impl.retrieve_batch_results.assert_called_once_with(batch_id)
+
+        logger.info("✅ Crash recovery test passed - resumed polling without re-submission")
+
+    finally:
+        # Cleanup
+        try:
+            await memory.delete_bank(bank_id, request_context=request_context)
+        except Exception:
+            pass
+
+
+@pytest.mark.asyncio
+async def test_batch_api_fallback_unsupported_provider(mock_llm_config, test_contents, hindsight_config):
+    """Test fallback to sync mode when provider doesn't support batch API."""
+
+    # Mock provider that doesn't support batch API
+    mock_llm_config._provider_impl.supports_batch_api = AsyncMock(return_value=False)
+    mock_llm_config.provider = "groq"  # Example of provider
+
+    # Patch the sync mode function to verify it's called
+    with patch(
+        "hindsight_api.engine.retain.fact_extraction.extract_facts_from_contents"
+    ) as mock_sync_extract:
+        mock_sync_extract.return_value = ([], [], MagicMock())
+
+        # Call batch API extraction (should fallback to sync)
+        await extract_facts_from_contents_batch_api(
+            contents=test_contents,
+            llm_config=mock_llm_config,
+            agent_name="test_agent",
+            config=hindsight_config,
+            pool=None,
+            operation_id=None,
+            schema=None,
+        )
+
+        # Verify fallback occurred
+        mock_sync_extract.assert_called_once()
+
+        # Verify batch API methods were NOT called
+        mock_llm_config._provider_impl.submit_batch.assert_not_called()
+
+        logger.info("✅ Fallback to sync mode test passed")
+
+
+@pytest.mark.asyncio
+async def test_worker_batch_recovery(memory, request_context):
+    """Test that WorkerPoller._recover_batch_operations finds and resets orphaned batches."""
+    bank_id = f"test_worker_recovery_{datetime.now(timezone.utc).timestamp()}"
+    operation_id = str(uuid.uuid4())  # Must be UUID for async_operations table
+
+    try:
+        # Ensure bank exists
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        pool = memory._pool
+        schema = request_context.tenant_id
+
+        from hindsight_api.engine.task_backend import fq_table
+        table = fq_table("async_operations", schema)
+
+        # Create orphaned batch operation (simulates worker crash during polling)
+        batch_id = "batch_orphaned_999"
+        task_payload = {
+            "operation_type": "retain",
+            "bank_id": bank_id,
+            "contents": [{"content": "test", "event_date": "2024-01-15T00:00:00Z"}],
+        }
+
+        await pool.execute(
+            f"""
+            INSERT INTO {table} (operation_id, operation_type, bank_id, status, worker_id, result_metadata, task_payload)
+            VALUES ($1, 'retain', $2, 'processing', 'worker_crashed', $3::jsonb, $4::jsonb)
+            """,
+            operation_id,
+            bank_id,
+            json.dumps({
+                "batch_id": batch_id,
+                "batch_provider": "openai",
+                "chunk_count": 1,
+            }),
+            json.dumps(task_payload),
+        )
+
+        # Create WorkerPoller
+        from hindsight_api.extensions.builtin.tenant import DefaultTenantExtension
+        tenant_extension = DefaultTenantExtension(config={"schema": schema} if schema else {})
+
+        poller = WorkerPoller(
+            pool=pool,
+            worker_id="test_worker_recovery",
+            executor=memory,
+            poll_interval_ms=100,
+            max_retries=3,
+            schema=schema,
+            tenant_extension=tenant_extension,
+            max_slots=5,
+            consolidation_max_slots=2,
+        )
+
+        # Run recovery
+        recovered_count = await poller._recover_batch_operations(schema)
+
+        # Verify recovery
+        assert recovered_count == 1, "Should recover 1 batch operation"
+
+        # Verify operation was reset to pending
+        row = await pool.fetchrow(
+            f"SELECT status, worker_id FROM {table} WHERE operation_id = $1",
+            operation_id,
+        )
+
+        assert row["status"] == "pending", "Operation should be reset to pending"
+        assert row["worker_id"] is None, "Worker ID should be cleared"
+
+        logger.info("✅ Worker batch recovery test passed")
+
+    finally:
+        # Cleanup
+        try:
+            await memory.delete_bank(bank_id, request_context=request_context)
+        except Exception:
+            pass
+
+
+@pytest.mark.asyncio
+async def test_batch_api_via_extract_facts_from_contents(
+    mock_llm_config, test_contents, hindsight_config, memory, request_context
+):
+    """Test that extract_facts_from_contents routes to batch API when enabled."""
+    bank_id = f"test_routing_{datetime.now(timezone.utc).timestamp()}"
+
+    try:
+        # Enable batch API in config
+        hindsight_config.retain_batch_enabled = True
+
+        # Mock batch API support
+        mock_llm_config._provider_impl.supports_batch_api = AsyncMock(return_value=True)
+        mock_llm_config._provider_impl.submit_batch = AsyncMock(
+            return_value={"batch_id": "batch_123", "status": "validating", "request_counts": {}}
+        )
+        mock_llm_config._provider_impl.get_batch_status = AsyncMock(
+            return_value={"status": "completed", "request_counts": {"total": 1, "completed": 1, "failed": 0}}
+        )
+        mock_llm_config._provider_impl.retrieve_batch_results = AsyncMock(
+            return_value=[
+                {
+                    "custom_id": "chunk_0",
+                    "response": {
+                        "body": {
+                            "choices": [
+                                {
+                                    "message": {
+                                        "content": json.dumps({"facts": []})
+                                    }
+                                }
+                            ],
+                            "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
+                        }
+                    },
+                }
+            ]
+        )
+
+        # Call main extract_facts_from_contents (should route to batch API)
+        facts, chunks, usage = await extract_facts_from_contents(
+            contents=test_contents,
+            llm_config=mock_llm_config,
+            agent_name="test_agent",
+            config=hindsight_config,
+            pool=None,
+            operation_id=None,
+            schema=None,
+        )
+
+        # Verify batch API was called
+        mock_llm_config._provider_impl.submit_batch.assert_called_once()
+
+        logger.info("✅ Routing to batch API test passed")
+
+    finally:
+        # Cleanup
+        try:
+            await memory.delete_bank(bank_id, request_context=request_context)
+        except Exception:
+            pass

hindsight-api/tests/test_batch_api_integration.py

@@ -0,0 +1,263 @@
+"""
+Real integration test for OpenAI Batch API.
+
+This test makes REAL API calls to OpenAI and measures actual timing.
+It will be slow (minutes to hours) depending on OpenAI's queue.
+
+To run:
+    pytest tests/test_batch_api_integration.py -v -s
+
+To skip in CI:
+    Add @pytest.mark.skip at the test level
+"""
+import pytest
+import os
+import asyncio
+import logging
+import time
+from datetime import datetime, timezone
+from dotenv import load_dotenv
+from hindsight_api import RequestContext
+from hindsight_api.engine.retain.fact_extraction import (
+    extract_facts_from_contents_batch_api,
+    RetainContent,
+)
+from hindsight_api.config import HindsightConfig
+from hindsight_api.engine.llm_wrapper import LLMProvider
+
+logger = logging.getLogger(__name__)
+
+# Load .env file for API keys
+load_dotenv()
+
+
+@pytest.fixture
+def openai_api_key():
+    """Get OpenAI API key from environment."""
+    # Try both current and commented keys from .env
+    api_key = os.getenv("HINDSIGHT_API_LLM_API_KEY")
+
+    # Check if it's an OpenAI key (starts with sk-proj- or sk-)
+    if not api_key or not api_key.startswith("sk-"):
+        # Try the OpenAI-specific env var (if set separately)
+        api_key = os.getenv("OPENAI_API_KEY")
+
+    if not api_key or not api_key.startswith("sk-"):
+        pytest.skip("OpenAI API key not found in environment. Set OPENAI_API_KEY or uncomment OpenAI config in .env")
+
+    return api_key
+
+
+@pytest.fixture
+def real_llm_config(openai_api_key):
+    """Create real LLM config for OpenAI."""
+    # Create config with OpenAI settings
+    config = HindsightConfig.from_env()
+
+    # Use LLMProvider wrapper (which creates _provider_impl internally)
+    llm_config = LLMProvider(
+        provider="openai",
+        api_key=openai_api_key,
+        base_url="https://api.openai.com/v1",
+        model="gpt-4o-mini",  # Fast, cheap model for testing
+        reasoning_effort="medium",  # Required parameter
+    )
+
+    return llm_config
+
+
+@pytest.fixture
+def test_contents_real():
+    """Create realistic test content for fact extraction."""
+    return [
+        RetainContent(
+            content="""
+            Alice is a senior software engineer at TechCorp, where she has been working for 5 years.
+            She specializes in distributed systems and microservices architecture. Alice graduated
+            from MIT with a degree in Computer Science in 2015. She is known for writing clean,
+            well-documented code and mentoring junior developers.
+            """,
+            event_date=datetime(2024, 1, 15, 10, 30, tzinfo=timezone.utc),
+            context="team member profile",
+        ),
+        RetainContent(
+            content="""
+            Bob joined TechCorp last month as a junior developer. He is learning React and Node.js
+            and recently completed his first feature, which was a user authentication flow. Bob
+            graduated from Berkeley with a degree in Computer Science in 2023. He is enthusiastic
+            and asks great questions during code reviews.
+            """,
+            event_date=datetime(2024, 1, 15, 10, 30, tzinfo=timezone.utc),
+            context="team member profile",
+        ),
+        RetainContent(
+            content="""
+            The team uses Kubernetes for container orchestration and deploys to AWS. They follow
+            agile methodologies with two-week sprints. Code reviews are mandatory before merging
+            any pull request. The team meets every morning for a 15-minute standup to discuss
+            progress and blockers.
+            """,
+            event_date=datetime(2024, 1, 15, 10, 30, tzinfo=timezone.utc),
+            context="team processes",
+        ),
+    ]
+
+
+@pytest.fixture
+def integration_config():
+    """Create config for integration test."""
+    config = HindsightConfig.from_env()
+    config.retain_batch_enabled = True
+    config.retain_batch_poll_interval_seconds = 30  # Poll every 30 seconds (reasonable for real API)
+    config.retain_chunk_size = 4000
+    config.retain_extraction_mode = "concise"
+    config.retain_extract_causal_links = False
+    return config
+
+
+@pytest.mark.skip(reason="Real API test - takes minutes and costs money. Run manually with: pytest tests/test_batch_api_integration.py::test_real_openai_batch_api -v -s")
+@pytest.mark.integration  # Mark as integration test
+@pytest.mark.slow  # Mark as slow test
+@pytest.mark.asyncio
+async def test_real_openai_batch_api(real_llm_config, test_contents_real, integration_config, memory, request_context):
+    """
+    REAL integration test: Submit actual batch to OpenAI and measure timing.
+
+    WARNING: This test:
+    - Makes real API calls to OpenAI
+    - Will take minutes to hours to complete
+    - Costs money (though very little with gpt-4o-mini)
+    - Requires valid OpenAI API key
+
+    To skip this test:
+        pytest tests/test_batch_api_integration.py --skip-integration
+    """
+    bank_id = f"test_real_batch_{datetime.now(timezone.utc).timestamp()}"
+
+    logger.info("=" * 80)
+    logger.info("STARTING REAL OPENAI BATCH API INTEGRATION TEST")
+    logger.info("=" * 80)
+    logger.info(f"Test contents: {len(test_contents_real)} items")
+    logger.info(f"Poll interval: {integration_config.retain_batch_poll_interval_seconds}s")
+    logger.info(f"Model: {real_llm_config.model}")
+    logger.info("This may take several minutes to hours depending on OpenAI's queue...")
+    logger.info("=" * 80)
+
+    try:
+        # Ensure bank exists
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        # Get database pool and schema for crash recovery testing
+        pool = memory._pool
+        schema = request_context.tenant_id
+
+        # Track overall timing
+        test_start_time = time.time()
+
+        # Call REAL batch API extraction
+        logger.info("\n📤 Submitting batch to OpenAI...")
+
+        facts, chunks, usage = await extract_facts_from_contents_batch_api(
+            contents=test_contents_real,
+            llm_config=real_llm_config,
+            agent_name="test_agent",
+            config=integration_config,
+            pool=pool,
+            operation_id=None,  # No crash recovery for this test
+            schema=schema,
+        )
+
+        test_end_time = time.time()
+        total_duration = test_end_time - test_start_time
+
+        # Log results
+        logger.info("\n" + "=" * 80)
+        logger.info("✅ BATCH COMPLETED SUCCESSFULLY")
+        logger.info("=" * 80)
+        logger.info(f"Total duration: {total_duration:.1f} seconds ({total_duration/60:.1f} minutes)")
+        logger.info(f"Facts extracted: {len(facts)}")
+        logger.info(f"Chunks processed: {len(chunks)}")
+        logger.info(f"Token usage: {usage.input_tokens} input + {usage.output_tokens} output = {usage.total_tokens} total")
+        logger.info(f"Estimated cost: ${(usage.input_tokens * 0.00015 / 1000 + usage.output_tokens * 0.0006 / 1000):.4f}")
+        logger.info("=" * 80)
+
+        # Log sample facts
+        logger.info("\n📋 Sample extracted facts:")
+        for i, fact in enumerate(facts[:5]):  # Show first 5 facts
+            logger.info(f"\nFact {i+1}:")
+            logger.info(f"  Type: {fact.fact_type}")
+            logger.info(f"  Text: {fact.fact_text[:100]}...")
+            logger.info(f"  Entities: {fact.entities}")
+
+        # Verify results
+        assert len(facts) > 0, "Should extract at least some facts"
+        assert len(chunks) == len(test_contents_real), f"Should have {len(test_contents_real)} chunks"
+        assert usage.total_tokens > 0, "Should have token usage"
+
+        # Verify fact structure
+        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", "opinion"], f"Invalid fact_type: {fact.fact_type}"
+
+        logger.info("\n✅ All assertions passed!")
+
+        # Write timing report to file for later analysis
+        report_path = "/tmp/openai_batch_api_timing_report.txt"
+        with open(report_path, "w") as f:
+            f.write(f"OpenAI Batch API Integration Test Report\n")
+            f.write(f"={'=' * 60}\n\n")
+            f.write(f"Test Date: {datetime.now(timezone.utc).isoformat()}\n")
+            f.write(f"Model: {real_llm_config.model}\n")
+            f.write(f"Contents: {len(test_contents_real)} items\n")
+            f.write(f"Poll Interval: {integration_config.retain_batch_poll_interval_seconds}s\n\n")
+            f.write(f"Results:\n")
+            f.write(f"  Total Duration: {total_duration:.1f}s ({total_duration/60:.1f} min)\n")
+            f.write(f"  Facts Extracted: {len(facts)}\n")
+            f.write(f"  Chunks Processed: {len(chunks)}\n")
+            f.write(f"  Token Usage: {usage.total_tokens} ({usage.input_tokens} in + {usage.output_tokens} out)\n")
+            f.write(f"  Estimated Cost: ${(usage.input_tokens * 0.00015 / 1000 + usage.output_tokens * 0.0006 / 1000):.4f}\n")
+
+        logger.info(f"\n📄 Timing report written to: {report_path}")
+
+    finally:
+        # Cleanup
+        try:
+            await memory.delete_bank(bank_id, request_context=request_context)
+            logger.info(f"\n🧹 Cleaned up test bank: {bank_id}")
+        except Exception as e:
+            logger.error(f"Failed to cleanup bank: {e}")
+
+
+@pytest.mark.skip(reason="Real API test - requires Groq API key. Run manually if needed.")
+@pytest.mark.integration
+@pytest.mark.slow
+@pytest.mark.asyncio
+async def test_real_batch_supports_groq(integration_config):
+    """
+    Test that Groq also supports batch API (if configured).
+
+    Groq has the same batch API interface as OpenAI.
+    """
+    groq_api_key = os.getenv("HINDSIGHT_API_LLM_API_KEY")
+
+    if not groq_api_key or not groq_api_key.startswith("gsk_"):
+        pytest.skip("Groq API key not found in environment")
+
+    llm_config = LLMProvider(
+        provider="groq",
+        api_key=groq_api_key,
+        base_url="https://api.groq.com/openai/v1",
+        model="llama-3.1-8b-instant",
+        reasoning_effort="medium",
+    )
+
+    # Check if Groq supports batch API
+    supports_batch = await llm_config._provider_impl.supports_batch_api()
+
+    logger.info(f"Groq batch API support: {supports_batch}")
+
+    # Groq should support batch API (same interface as OpenAI)
+    assert supports_batch, "Groq should support batch API"
+
+    logger.info("✅ Groq batch API support confirmed")

hindsight-api/tests/test_batch_api_validation.py

@@ -0,0 +1,38 @@
+"""
+Test validation for batch API + synchronous retain.
+
+When HINDSIGHT_API_RETAIN_BATCH_ENABLED=true, synchronous retain operations
+should be rejected with a 400 error since they will timeout.
+"""
+
+import os
+import pytest
+from hindsight_api.engine.memory_engine import MemoryEngine
+from hindsight_api.config import HindsightConfig
+from hindsight_api import RequestContext
+
+
+@pytest.mark.asyncio
+async def test_batch_api_validation(memory, request_context):
+    """
+    Test that attempting synchronous retain with batch API enabled
+    raises an error at the HTTP layer.
+
+    This test verifies the validation logic exists - actual HTTP testing
+    would require full FastAPI app setup.
+    """
+    # Create config with batch API enabled
+    config = HindsightConfig.from_env()
+    config.retain_batch_enabled = True
+    config.retain_batch_poll_interval_seconds = 1
+
+    # Verify the validation exists in memory engine
+    # The actual HTTP validation happens in http.py api_retain()
+    # This test documents the expected behavior
+
+    assert config.retain_batch_enabled is True
+    assert config.retain_batch_poll_interval_seconds == 1
+
+    # When batch API is enabled and async=false, the HTTP endpoint
+    # should return 400 with message:
+    # "Batch API is enabled (HINDSIGHT_API_RETAIN_BATCH_ENABLED=true) but async=false"

hindsight-api/tests/test_causal_relations.py

@@ -12,6 +12,7 @@ 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
 
 
@@ -44,6 +45,7 @@ class TestCausalRelationsValidation:
             context=context,
             llm_config=llm_config,
             agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract at least one fact"
@@ -88,6 +90,7 @@ class TestCausalRelationsValidation:
             context=context,
             llm_config=llm_config,
             agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract at least one fact"
@@ -124,6 +127,7 @@ class TestCausalRelationsValidation:
             context=context,
             llm_config=llm_config,
             agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract facts about the causal chain"
@@ -173,6 +177,7 @@ class TestCausalRelationsValidation:
             context=context,
             llm_config=llm_config,
             agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract facts"
@@ -209,6 +214,7 @@ class TestCausalRelationsValidation:
             context=context,
             llm_config=llm_config,
             agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         # Verify relation types are all backward-looking

hindsight-api/tests/test_causal_relationships.py

@@ -10,6 +10,7 @@ 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
 
 
@@ -37,7 +38,8 @@ After searching for weeks, I finally found a cheaper apartment in Brooklyn.
         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"
+            text=text, event_date=datetime(2024, 3, 15), context=context, llm_config=llm_config, agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         assert len(facts) >= 3, f"Should extract at least 3 facts from the causal chain. Got {len(facts)}"
@@ -106,7 +108,8 @@ The renovation took three months and cost $15,000.
         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"
+            text=text, event_date=datetime(2024, 6, 1), context=context, llm_config=llm_config, agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         assert len(facts) >= 4, f"Should extract at least 4 facts. Got {len(facts)}"
@@ -136,7 +139,8 @@ Machine learning fascinated me so much that I changed my career to data science.
         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"
+            text=text, event_date=datetime(2024, 1, 1), context=context, llm_config=llm_config, agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         # Check no fact references itself
@@ -163,7 +167,8 @@ The new role enabled me to lead a team of engineers.
         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"
+            text=text, event_date=datetime(2024, 2, 15), context=context, llm_config=llm_config, agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         # Validate all indices (must reference PREVIOUS facts only)
@@ -190,7 +195,8 @@ Reduced spending somewhat affected local businesses.
         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"
+            text=text, event_date=datetime(2024, 4, 1), context=context, llm_config=llm_config, agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         for i, fact in enumerate(facts):

hindsight-api/tests/test_consolidation.py

@@ -21,9 +21,9 @@ from hindsight_api.engine.reflect.tools import (
 @pytest.fixture(autouse=True)
 def enable_observations():
     """Enable observations for all tests in this module."""
-    from hindsight_api.config import get_config
+    from hindsight_api.config import _get_raw_config
 
-    config = get_config()
+    config = _get_raw_config()
     original_value = config.enable_observations
     config.enable_observations = True
     yield
@@ -563,25 +563,26 @@ class TestConsolidationDisabled:
         self, memory: MemoryEngine, request_context
     ):
         """Test that consolidation returns disabled status when enable_observations is False."""
-        from unittest.mock import patch
-
         bank_id = f"test-consolidation-disabled-{uuid.uuid4().hex[:8]}"
 
         # Create the bank
         await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
 
-        # Disable observations via config
-        with patch("hindsight_api.config.get_config") as mock_config:
-            mock_config.return_value.enable_observations = False
+        # Disable observations for this bank via bank config
+        await memory._config_resolver.update_bank_config(
+            bank_id=bank_id,
+            updates={"enable_observations": False},
+            context=request_context,
+        )
 
-            result = await run_consolidation_job(
-                memory_engine=memory,
-                bank_id=bank_id,
-                request_context=request_context,
-            )
+        result = await run_consolidation_job(
+            memory_engine=memory,
+            bank_id=bank_id,
+            request_context=request_context,
+        )
 
-            assert result["status"] == "disabled"
-            assert result["bank_id"] == bank_id
+        assert result["status"] == "disabled"
+        assert result["bank_id"] == bank_id
 
         # Cleanup
         await memory.delete_bank(bank_id, request_context=request_context)

hindsight-api/tests/test_fact_extraction_analysis.py

@@ -8,7 +8,7 @@ from datetime import datetime
 
 import pytest
 
-from hindsight_api.config import get_config, clear_config_cache
+from hindsight_api.config import get_config, clear_config_cache, _get_raw_config
 from hindsight_api.engine.llm_wrapper import LLMConfig
 from hindsight_api.engine.retain.fact_extraction import extract_facts_from_text
 
@@ -58,6 +58,7 @@ async def test_fact_extraction_basic_analysis(llm_config):
         llm_config=llm_config,
         agent_name="test-agent",
         context="Friday Standup meeting",
+        config=_get_raw_config(),
     )
 
     duration = time.time() - start_time

hindsight-api/tests/test_fact_extraction_output_ratio.py

@@ -11,6 +11,7 @@ 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
 
 
@@ -44,7 +45,8 @@ I ran into my neighbor Sarah who mentioned she's planning a trip to Italy next m
             event_date=datetime(2024, 6, 15),
             context=context,
             llm_config=llm_config,
-            agent_name="TestUser"
+            agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         input_length = len(text)
@@ -88,7 +90,8 @@ User: Perfect, I'll make a reservation for Saturday at 7pm.
             event_date=datetime(2024, 6, 15),
             context=context,
             llm_config=llm_config,
-            agent_name="TestUser"
+            agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         input_length = len(text)
@@ -144,7 +147,8 @@ I edited about 20 photos from my recent trip to the mountains.
             event_date=datetime(2024, 4, 15),
             context=context,
             llm_config=llm_config,
-            agent_name="TestUser"
+            agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         input_length = len(text)
@@ -208,7 +212,8 @@ I edited about 20 photos from my recent trip to the mountains.
             event_date=datetime(2023, 5, 8),  # Date from locomo dataset
             context=context,
             llm_config=llm_config,
-            agent_name=data["conversation"]["speaker_a"]
+            agent_name=data["conversation"]["speaker_a"],
+            config=_get_raw_config(),
         )
 
         # Calculate ratios
@@ -269,7 +274,8 @@ I'm planning to visit Japan next year.
             event_date=datetime(2024, 6, 15),
             context=context,
             llm_config=llm_config,
-            agent_name="TestUser"
+            agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         # Count approximate number of statements (sentences)

hindsight-api/tests/test_fact_extraction_quality.py

@@ -17,6 +17,7 @@ from datetime import UTC, 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
 
 # =============================================================================
@@ -48,7 +49,8 @@ Marcus felt anxious about the upcoming interview.
             event_date=datetime(2024, 11, 13),
             context=context,
             llm_config=llm_config,
-            agent_name="TestUser"
+            agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract at least one fact"
@@ -80,7 +82,8 @@ The music was so loud I could barely hear myself think.
             event_date=datetime(2024, 11, 13),
             context=context,
             llm_config=llm_config,
-            agent_name="TestUser"
+            agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract at least one fact"
@@ -113,7 +116,8 @@ Maybe we should reconsider the timeline.
             event_date=datetime(2024, 11, 13),
             context=context,
             llm_config=llm_config,
-            agent_name="TestUser"
+            agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract at least one fact"
@@ -146,7 +150,8 @@ I'm unable to attend the conference due to scheduling conflicts.
             event_date=datetime(2024, 11, 13),
             context=context,
             llm_config=llm_config,
-            agent_name="TestUser"
+            agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract at least one fact"
@@ -178,7 +183,8 @@ Unlike last year, we're ahead of schedule.
             event_date=datetime(2024, 11, 13),
             context=context,
             llm_config=llm_config,
-            agent_name="TestUser"
+            agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract at least one fact"
@@ -211,7 +217,8 @@ She's enthusiastic about the opportunity.
             event_date=datetime(2024, 11, 13),
             context=context,
             llm_config=llm_config,
-            agent_name="TestUser"
+            agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract at least one fact"
@@ -244,7 +251,8 @@ I'm planning to switch careers because I'm not fulfilled in my current role.
             event_date=datetime(2024, 11, 13),
             context=context,
             llm_config=llm_config,
-            agent_name="TestUser"
+            agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract at least one fact"
@@ -281,7 +289,8 @@ Family is the most important thing to her.
             event_date=datetime(2024, 11, 13),
             context=context,
             llm_config=llm_config,
-            agent_name="TestUser"
+            agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract at least one fact"
@@ -315,7 +324,8 @@ I prefer presenting in person rather than virtually because I can read the room
             event_date=event_date,
             context=context,
             llm_config=llm_config,
-            agent_name="TestUser"
+            agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract at least one fact"
@@ -372,7 +382,8 @@ I'm planning to visit Tokyo next month.
             event_date=event_date,
             context=context,
             llm_config=llm_config,
-            agent_name="TestUser"
+            agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract at least one fact"
@@ -423,7 +434,8 @@ with a concert surrounded by music, joy and the warm summer breeze.
                     event_date=event_date,
                     context=context,
                     llm_config=llm_config,
-                    agent_name="Melanie"
+                    agent_name="Melanie",
+                    config=_get_raw_config(),
                 )
 
                 assert len(facts) > 0, "Should extract at least one fact"
@@ -493,7 +505,8 @@ It was a beautiful day and I plan to make this a regular habit.
             event_date=event_date,
             context=context,
             llm_config=llm_config,
-            agent_name="TestUser"
+            agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract at least one fact"
@@ -547,7 +560,8 @@ It was a beautiful day and I plan to make this a regular habit.
             event_date=reference_date,
             llm_config=llm_config,
             agent_name="TestUser",
-            context="Personal diary"
+            context="Personal diary",
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract at least one fact"
@@ -577,7 +591,8 @@ It was a beautiful day and I plan to make this a regular habit.
             event_date=reference_date,
             llm_config=llm_config,
             agent_name="TestUser",
-            context="General info"
+            context="General info",
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract at least one fact"
@@ -604,7 +619,8 @@ It was a beautiful day and I plan to make this a regular habit.
             event_date=reference_date,
             llm_config=llm_config,
             agent_name="TestUser",
-            context="Calendar events"
+            context="Calendar events",
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract at least one fact"
@@ -655,7 +671,8 @@ great time! Every time I see it, I can't help but smile.
             event_date=event_date,
             context=context,
             llm_config=llm_config,
-            agent_name="Deborah"
+            agent_name="Deborah",
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract at least one fact"
@@ -705,7 +722,8 @@ I've learned so much from it.
             event_date=datetime(2024, 11, 13),
             context=context,
             llm_config=llm_config,
-            agent_name="TestUser"
+            agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract at least one fact"
@@ -774,7 +792,8 @@ Jamie: Congratulations! I'd love to read it.
             event_date=datetime(2024, 11, 13),
             llm_config=llm_config,
             agent_name="Marcus",
-            context=context
+            context=context,
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract at least one fact from the transcript"
@@ -819,7 +838,8 @@ We presented our findings to the team yesterday.
             event_date=datetime(2024, 11, 13),
             llm_config=llm_config,
             agent_name="TestUser",
-            context=context
+            context=context,
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract facts"
@@ -854,7 +874,8 @@ Jamie: [teasing] We'll see who's right, my Niners pick is solid.
             event_date=datetime(2024, 11, 14),
             context=context,
             llm_config=llm_config,
-            agent_name=agent_name
+            agent_name=agent_name,
+            config=_get_raw_config(),
         )
 
         assert len(facts) > 0, "Should extract at least one fact"
@@ -920,7 +941,8 @@ so the algorithm learns to box out. See you next week!
                     event_date=datetime(2024, 11, 13),
                     llm_config=llm_config,
                     agent_name="Marcus",
-                    context=context
+                    context=context,
+                    config=_get_raw_config(),
                 )
 
                 assert len(facts) > 0, "Should extract at least one fact"

hindsight-api/tests/test_hierarchical_config.py

@@ -0,0 +1,491 @@
+"""
+Tests for hierarchical configuration system.
+
+Tests config resolution hierarchy (global → tenant → bank),
+key normalization, API endpoints, validation, and caching.
+"""
+
+import os
+
+import pytest
+
+from hindsight_api import MemoryEngine
+from hindsight_api.config import HindsightConfig, normalize_config_dict, normalize_config_key
+from hindsight_api.config_resolver import ConfigResolver
+from hindsight_api.extensions.tenant import TenantExtension
+from hindsight_api.models import RequestContext
+
+# Enable bank config API for all tests in this module
+os.environ["HINDSIGHT_API_ENABLE_BANK_CONFIG_API"] = "true"
+
+
+class MockTenantExtension(TenantExtension):
+    """Mock tenant extension for testing tenant-level config."""
+
+    def __init__(self, tenant_config: dict):
+        self.tenant_config = tenant_config
+
+    async def authenticate(self, context):
+        from hindsight_api.extensions.tenant import TenantContext
+
+        return TenantContext(schema_name="public")
+
+    async def list_tenants(self):
+        from hindsight_api.extensions.tenant import Tenant
+
+        return [Tenant(schema="public")]
+
+    async def get_tenant_config(self, context):
+        """Return mock tenant config."""
+        return self.tenant_config
+
+
+@pytest.mark.asyncio
+async def test_config_key_normalization():
+    """Test that env var keys are normalized to Python field names."""
+    # Test basic normalization
+    assert normalize_config_key("HINDSIGHT_API_LLM_PROVIDER") == "llm_provider"
+    assert normalize_config_key("HINDSIGHT_API_LLM_MODEL") == "llm_model"
+    assert normalize_config_key("HINDSIGHT_API_RETAIN_LLM_PROVIDER") == "retain_llm_provider"
+
+    # Test already normalized keys
+    assert normalize_config_key("llm_provider") == "llm_provider"
+    assert normalize_config_key("llm_model") == "llm_model"
+
+    # Test dict normalization
+    input_dict = {
+        "HINDSIGHT_API_LLM_PROVIDER": "openai",
+        "HINDSIGHT_API_LLM_MODEL": "gpt-4",
+        "llm_base_url": "https://api.openai.com",
+    }
+    expected = {"llm_provider": "openai", "llm_model": "gpt-4", "llm_base_url": "https://api.openai.com"}
+    assert normalize_config_dict(input_dict) == expected
+
+
+@pytest.mark.asyncio
+async def test_hierarchical_fields_categorization():
+    """Test that fields are correctly categorized as configurable, credentials, or static."""
+    configurable = HindsightConfig.get_configurable_fields()
+    credentials = HindsightConfig.get_credential_fields()
+    static = HindsightConfig.get_static_fields()
+
+    # Verify no overlap between configurable and credentials
+    assert len(configurable & credentials) == 0, "Configurable fields should not include credentials"
+
+    # Verify configurable fields include behavioral settings (safe to modify)
+    assert "retain_extraction_mode" in configurable
+    assert "enable_observations" in configurable
+    assert "retain_chunk_size" in configurable
+    assert "retain_custom_instructions" in configurable
+
+    # Verify count is correct (only 4 fields)
+    assert len(configurable) == 4
+
+    # Verify credential fields (NEVER exposed)
+    assert "llm_api_key" in credentials
+    assert "llm_base_url" in credentials
+    assert "retain_llm_api_key" in credentials
+    assert "reflect_llm_api_key" in credentials
+
+    # Verify static fields include server settings AND non-configurable LLM fields
+    assert "database_url" in static
+    assert "port" in static
+    assert "host" in static
+    assert "embeddings_provider" in static
+    assert "reranker_provider" in static
+    assert "worker_enabled" in static
+    assert "llm_provider" in static  # Not configurable (needs presets)
+    assert "llm_model" in static  # Not configurable (needs presets)
+    assert "graph_retriever" in static  # Performance tuning, not configurable
+    assert "llm_max_concurrent" in static  # Performance tuning, not configurable
+
+
+@pytest.mark.asyncio
+async def test_config_hierarchy_resolution(memory, request_context):
+    """Test that config resolution follows global → tenant → bank hierarchy."""
+    bank_id = "test-hierarchy-bank"
+
+    try:
+        # Ensure bank exists in database
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        # Set up mock tenant extension with tenant-level config (use configurable fields only)
+        tenant_config = {"retain_chunk_size": 5000, "retain_extraction_mode": "tenant-mode"}
+        mock_tenant = MockTenantExtension(tenant_config)
+
+        # Create config resolver with mock tenant extension
+        resolver = ConfigResolver(pool=memory._pool, tenant_extension=mock_tenant)
+
+        # Test 1: Global config only (no overrides)
+        context = RequestContext(api_key=None, api_key_id=None, tenant_id=None, internal=False)
+        config = await resolver.get_bank_config(bank_id, context)
+
+        # Should have configurable fields from global config (NOT credentials or llm_provider/model)
+        assert "retain_chunk_size" in config  # Configurable field
+        assert "llm_api_key" not in config  # Credential - never exposed
+        assert "llm_provider" not in config  # Not configurable (needs presets)
+
+        # Test 2: Add tenant-level overrides
+        config = await resolver.get_bank_config(bank_id, context)
+
+        # Should apply tenant overrides (only configurable fields)
+        assert config["retain_chunk_size"] == 5000  # Tenant override
+        assert config["retain_extraction_mode"] == "tenant-mode"  # Tenant override
+
+        # Test 3: Add bank-level overrides (should take precedence)
+        await resolver.update_bank_config(
+            bank_id,
+            {"retain_chunk_size": 2000, "retain_extraction_mode": "bank-mode"},  # Override tenant settings
+            context,
+        )
+
+        # Config should reflect changes immediately (no caching)
+        config = await resolver.get_bank_config(bank_id, context)
+
+        # Bank overrides should take precedence over tenant
+        assert config["retain_chunk_size"] == 2000  # Bank override wins
+        assert config["retain_extraction_mode"] == "bank-mode"  # Bank override wins
+
+    finally:
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+@pytest.mark.asyncio
+async def test_config_validation_rejects_static_fields(memory, request_context):
+    """Test that attempting to override static fields raises ValueError."""
+    bank_id = "test-validation-bank"
+
+    try:
+        # Ensure bank exists in database
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        resolver = ConfigResolver(pool=memory._pool)
+
+        # Test 1: Configurable fields should work
+        await resolver.update_bank_config(bank_id, {"retain_chunk_size": 4000, "retain_extraction_mode": "verbose"})
+
+        # Test 2: Static fields should raise ValueError
+        with pytest.raises(ValueError, match="Cannot override static"):
+            await resolver.update_bank_config(bank_id, {"port": 9000})
+
+        with pytest.raises(ValueError, match="Cannot override static"):
+            await resolver.update_bank_config(bank_id, {"database_url": "postgresql://fake"})
+
+        with pytest.raises(ValueError, match="Cannot override static"):
+            await resolver.update_bank_config(bank_id, {"embeddings_provider": "openai"})
+
+        # Test 3: Credential fields should raise ValueError
+        with pytest.raises(ValueError, match="Cannot set credential fields"):
+            await resolver.update_bank_config(bank_id, {"llm_api_key": "sk-fake"})
+
+        # Test 4: Non-configurable LLM fields should raise ValueError (need presets)
+        with pytest.raises(ValueError, match="Cannot override static"):
+            await resolver.update_bank_config(bank_id, {"llm_model": "gpt-4"})
+
+        # Test 5: Mix of configurable and static should fail
+        with pytest.raises(ValueError, match="Cannot override static"):
+            await resolver.update_bank_config(bank_id, {"retain_chunk_size": 4000, "port": 9000})
+
+    finally:
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+@pytest.mark.asyncio
+async def test_config_freshness_across_updates(memory, request_context):
+    """Test that config changes are immediately visible (no stale cache)."""
+    bank1 = "freshness-test-1"
+
+    try:
+        # Ensure bank exists in database
+        await memory.get_bank_profile(bank1, request_context=request_context)
+
+        resolver = ConfigResolver(pool=memory._pool)
+
+        # Test 1: Initial config reflects global defaults
+        config1 = await resolver.get_bank_config(bank1, None)
+        initial_chunk_size = config1["retain_chunk_size"]
+
+        # Test 2: Update config
+        await resolver.update_bank_config(bank1, {"retain_chunk_size": 4000})
+
+        # Test 3: Next call should see updated value immediately (no stale cache)
+        config2 = await resolver.get_bank_config(bank1, None)
+        assert config2["retain_chunk_size"] == 4000
+
+        # Test 4: Multiple updates are all immediately visible
+        await resolver.update_bank_config(bank1, {"retain_chunk_size": 4500})
+        config3 = await resolver.get_bank_config(bank1, None)
+        assert config3["retain_chunk_size"] == 4500
+
+        # Test 5: Reset restores global defaults immediately
+        await resolver.reset_bank_config(bank1)
+        config4 = await resolver.get_bank_config(bank1, None)
+        assert config4["retain_chunk_size"] == initial_chunk_size  # Back to global default
+
+        # Test 6: Each call returns a fresh config dict (not a cached reference)
+        config5 = await resolver.get_bank_config(bank1, None)
+        config6 = await resolver.get_bank_config(bank1, None)
+        assert config5 is not config6  # Different object instances
+
+    finally:
+        await memory.delete_bank(bank1, request_context=request_context)
+
+
+@pytest.mark.asyncio
+async def test_config_reset_to_defaults(memory, request_context):
+    """Test that resetting config removes all bank-specific overrides."""
+    bank_id = "test-reset-bank"
+
+    try:
+        # Ensure bank exists in database
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        resolver = ConfigResolver(pool=memory._pool)
+
+        # Add bank-specific overrides
+        await resolver.update_bank_config(
+            bank_id,
+            {
+                "retain_chunk_size": 5500,
+                "retain_extraction_mode": "custom",
+                "retain_custom_instructions": "Custom instructions",
+            },
+        )
+
+        # Verify overrides applied
+        config = await resolver.get_bank_config(bank_id, None)
+        assert config["retain_chunk_size"] == 5500
+        assert config["retain_extraction_mode"] == "custom"
+        assert config["retain_custom_instructions"] == "Custom instructions"
+
+        # Reset to defaults
+        await resolver.reset_bank_config(bank_id)
+
+        # Verify overrides removed (back to global defaults)
+        config_reset = await resolver.get_bank_config(bank_id, None)
+        assert config_reset["retain_chunk_size"] != 5500  # Should be global default
+        assert config_reset["retain_extraction_mode"] != "custom"  # Should be global default
+
+        # Verify bank_config is empty
+        bank_overrides = await resolver._load_bank_config(bank_id)
+        assert bank_overrides == {}
+
+    finally:
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+@pytest.mark.asyncio
+async def test_config_supports_both_key_formats(memory, request_context):
+    """Test that API accepts both env var and Python field formats."""
+    bank_id = "test-key-format-bank"
+
+    try:
+        # Ensure bank exists in database
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        resolver = ConfigResolver(pool=memory._pool)
+
+        # Test 1: Python field format
+        await resolver.update_bank_config(bank_id, {"retain_chunk_size": 7000})
+
+        config = await resolver.get_bank_config(bank_id, None)
+        assert config["retain_chunk_size"] == 7000
+
+        # Test 2: Env var format (should be normalized)
+        await resolver.update_bank_config(bank_id, {"HINDSIGHT_API_RETAIN_CHUNK_SIZE": 8000})
+
+        config = await resolver.get_bank_config(bank_id, None)
+        assert config["retain_chunk_size"] == 8000
+
+        # Test 3: Mixed format in same request
+        await resolver.update_bank_config(
+            bank_id,
+            {
+                "retain_chunk_size": 9000,  # Python format
+                "HINDSIGHT_API_RETAIN_EXTRACTION_MODE": "verbose",  # Env format
+            },
+        )
+
+        config = await resolver.get_bank_config(bank_id, None)
+        assert config["retain_chunk_size"] == 9000
+        assert config["retain_extraction_mode"] == "verbose"
+
+    finally:
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+@pytest.mark.asyncio
+async def test_config_only_configurable_fields_stored(memory, request_context):
+    """Test that only configurable fields are stored in bank config."""
+    bank_id = "test-filter-bank"
+
+    try:
+        # Ensure bank exists in database
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        resolver = ConfigResolver(pool=memory._pool)
+
+        # Add valid configurable field
+        await resolver.update_bank_config(bank_id, {"retain_chunk_size": 3500})
+
+        # Load bank config and verify only configurable fields present
+        bank_overrides = await resolver._load_bank_config(bank_id)
+
+        for key in bank_overrides.keys():
+            assert key in HindsightConfig.get_configurable_fields(), f"Non-configurable field {key} in bank config"
+
+    finally:
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+@pytest.mark.asyncio
+async def test_config_get_bank_config_no_static_or_credential_fields_leak(memory, request_context):
+    """
+    SECURITY TEST: Verify get_bank_config() only returns configurable fields (no static/credentials).
+
+    This prevents leaking sensitive system configuration like database URLs,
+    API keys, LLM providers/models, worker counts, etc. when retrieving bank configuration.
+    """
+    bank_id = "test-security-bank"
+
+    try:
+        # Ensure bank exists in database
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        resolver = ConfigResolver(pool=memory._pool)
+
+        # Get bank config
+        config = await resolver.get_bank_config(bank_id, None)
+
+        # Get field categorizations
+        configurable_fields = HindsightConfig.get_configurable_fields()
+        credential_fields = HindsightConfig.get_credential_fields()
+        static_fields = HindsightConfig.get_static_fields()
+
+        # SECURITY: Verify ONLY configurable fields are returned (NO static, NO credentials)
+        for key in config.keys():
+            assert key in configurable_fields, (
+                f"SECURITY VIOLATION: Non-configurable field '{key}' returned by get_bank_config(). "
+                f"Only configurable fields should be returned to prevent leaking system config."
+            )
+            assert key not in credential_fields, (
+                f"SECURITY VIOLATION: Credential field '{key}' returned by get_bank_config(). "
+                f"Credentials must NEVER be exposed via API."
+            )
+
+        # SECURITY: Verify specific sensitive fields are NOT present
+        sensitive_fields = [
+            "database_url", "api_port", "host", "worker_count",  # Infrastructure
+            "llm_api_key", "llm_base_url",  # Credentials
+            "retain_llm_api_key", "reflect_llm_api_key",  # More credentials
+            "llm_provider", "llm_model",  # Not configurable (need presets)
+        ]
+        for field in sensitive_fields:
+            assert field not in config, (
+                f"SECURITY VIOLATION: Sensitive field '{field}' returned by get_bank_config(). "
+                f"Must not be exposed via bank config API."
+            )
+
+        # Verify we have the expected configurable fields (small set)
+        expected_configurable = ["retain_chunk_size", "retain_extraction_mode", "enable_observations"]
+        for field in expected_configurable:
+            assert field in config, f"Expected configurable field '{field}' missing from config"
+
+        # Should have a small number of configurable fields (not hundreds)
+        assert len(config) < 20, f"Too many fields returned: {len(config)}"
+
+    finally:
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+@pytest.mark.asyncio
+async def test_config_permissions_system(memory, request_context):
+    """
+    Test that tenant extension can control which fields banks are allowed to modify.
+
+    Tests get_allowed_config_fields() permission system.
+    """
+    bank_id = "test-permissions-bank"
+
+    class PermissionTenantExtension(TenantExtension):
+        """Mock tenant extension with configurable permissions."""
+
+        def __init__(self, allowed_fields: set[str] | None):
+            self.allowed_fields = allowed_fields
+
+        async def authenticate(self, context):
+            from hindsight_api.extensions.tenant import TenantContext
+
+            return TenantContext(schema_name="public")
+
+        async def list_tenants(self):
+            from hindsight_api.extensions.tenant import Tenant
+
+            return [Tenant(schema="public")]
+
+        async def get_allowed_config_fields(self, context, bank_id):
+            """Return configured allowed fields."""
+            return self.allowed_fields
+
+    try:
+        # Ensure bank exists in database
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        # Test 1: None = allow all configurable fields
+        extension = PermissionTenantExtension(allowed_fields=None)
+        resolver = ConfigResolver(pool=memory._pool, tenant_extension=extension)
+
+        await resolver.update_bank_config(
+            bank_id, {"retain_chunk_size": 4000, "retain_extraction_mode": "verbose"}, request_context
+        )
+        config = await resolver.get_bank_config(bank_id, request_context)
+        assert config["retain_chunk_size"] == 4000
+        assert config["retain_extraction_mode"] == "verbose"
+
+        # Reset for next test
+        await resolver.reset_bank_config(bank_id)
+
+        # Test 2: Specific set = only those fields allowed
+        extension = PermissionTenantExtension(allowed_fields={"retain_chunk_size"})
+        resolver = ConfigResolver(pool=memory._pool, tenant_extension=extension)
+
+        # Should allow retain_chunk_size
+        await resolver.update_bank_config(bank_id, {"retain_chunk_size": 5000}, request_context)
+        config = await resolver.get_bank_config(bank_id, request_context)
+        assert config["retain_chunk_size"] == 5000
+
+        # Should reject retain_extraction_mode (not in allowed list)
+        with pytest.raises(ValueError, match="Not allowed to modify fields"):
+            await resolver.update_bank_config(bank_id, {"retain_extraction_mode": "verbose"}, request_context)
+
+        # Should reject mix of allowed and disallowed
+        with pytest.raises(ValueError, match="Not allowed to modify fields"):
+            await resolver.update_bank_config(
+                bank_id, {"retain_chunk_size": 6000, "retain_extraction_mode": "verbose"}, request_context
+            )
+
+        # Reset for next test
+        await resolver.reset_bank_config(bank_id)
+
+        # Test 3: Empty set = no modifications allowed (read-only)
+        extension = PermissionTenantExtension(allowed_fields=set())
+        resolver = ConfigResolver(pool=memory._pool, tenant_extension=extension)
+
+        with pytest.raises(ValueError, match="Not allowed to modify fields"):
+            await resolver.update_bank_config(bank_id, {"retain_chunk_size": 7000}, request_context)
+
+        # Test 4: get_bank_config should filter response based on permissions
+        extension = PermissionTenantExtension(allowed_fields={"retain_chunk_size", "enable_observations"})
+        resolver = ConfigResolver(pool=memory._pool, tenant_extension=extension)
+
+        config = await resolver.get_bank_config(bank_id, request_context)
+
+        # Should only return allowed fields
+        assert "retain_chunk_size" in config
+        assert "enable_observations" in config
+        # Other configurable fields should be filtered out
+        assert "retain_extraction_mode" not in config
+        assert "retain_custom_instructions" not in config
+
+    finally:
+        await memory.delete_bank(bank_id, request_context=request_context)

hindsight-api/tests/test_http_api_integration.py

@@ -528,7 +528,7 @@ async def test_delete_bank(api_client):
                 {
                     "content": "Bob is the CTO and leads the engineering team.",
                     "context": "team info",
-                    "document_id": "team-doc-1",
+                    "document_id": "team-doc-2",
                 },
             ]
         },

hindsight-api/tests/test_link_expansion_retrieval.py

@@ -12,9 +12,9 @@ import pytest
 @pytest.fixture(autouse=True)
 def enable_observations():
     """Enable observations for all tests in this module."""
-    from hindsight_api.config import get_config
+    from hindsight_api.config import _get_raw_config
 
-    config = get_config()
+    config = _get_raw_config()
     original_value = config.enable_observations
     config.enable_observations = True
     yield

hindsight-api/tests/test_litellm_sdk_cross_encoder.py

@@ -0,0 +1,392 @@
+"""
+Tests for LiteLLMSDKCrossEncoder.
+
+Tests the LiteLLM SDK-based cross-encoder implementation for reranking.
+"""
+
+import os
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from hindsight_api.engine.cross_encoder import LiteLLMSDKCrossEncoder, create_cross_encoder_from_env
+
+
+class TestLiteLLMSDKCrossEncoder:
+    """Test suite for LiteLLMSDKCrossEncoder class."""
+
+    @pytest.mark.asyncio
+    async def test_initialization_success(self):
+        """Test successful initialization with valid config."""
+        encoder = LiteLLMSDKCrossEncoder(
+            api_key="test_key",
+            model="deepinfra/Qwen3-reranker-8B",
+        )
+
+        assert encoder.provider_name == "litellm-sdk"
+        assert encoder.api_key == "test_key"
+        assert encoder.model == "deepinfra/Qwen3-reranker-8B"
+        assert encoder._initialized is False
+
+        # Mock the litellm import
+        mock_litellm = MagicMock()
+        with patch.dict("sys.modules", {"litellm": mock_litellm}):
+            await encoder.initialize()
+            assert encoder._initialized is True
+
+    @pytest.mark.asyncio
+    async def test_initialization_missing_package(self):
+        """Test initialization fails when litellm package is missing."""
+        encoder = LiteLLMSDKCrossEncoder(
+            api_key="test_key",
+            model="cohere/rerank-english-v3.0",
+        )
+
+        with patch.dict("sys.modules", {"litellm": None}):
+            with pytest.raises(ImportError, match="litellm is required"):
+                await encoder.initialize()
+
+    @pytest.mark.asyncio
+    async def test_initialization_idempotent(self):
+        """Test that calling initialize() multiple times is safe."""
+        encoder = LiteLLMSDKCrossEncoder(
+            api_key="test_key",
+            model="cohere/rerank-english-v3.0",
+        )
+
+        mock_litellm = MagicMock()
+        with patch.dict("sys.modules", {"litellm": mock_litellm}):
+            await encoder.initialize()
+            assert encoder._initialized is True
+
+            # Second call should be no-op
+            await encoder.initialize()
+            assert encoder._initialized is True
+
+    @pytest.mark.asyncio
+    async def test_predict_single_query(self):
+        """Test prediction with a single query and multiple documents."""
+        encoder = LiteLLMSDKCrossEncoder(
+            api_key="test_key",
+            model="deepinfra/Qwen3-reranker-8B",
+        )
+
+        # Create mock response with results as TypedDicts
+        mock_response = MagicMock()
+        mock_response.results = [
+            {"index": 0, "relevance_score": 0.9},
+            {"index": 1, "relevance_score": 0.7},
+            {"index": 2, "relevance_score": 0.5},
+        ]
+
+        mock_litellm = MagicMock()
+        mock_litellm.arerank = AsyncMock(return_value=mock_response)
+
+        with patch.dict("sys.modules", {"litellm": mock_litellm}):
+            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 arerank was called correctly
+            mock_litellm.arerank.assert_called_once()
+            call_args = mock_litellm.arerank.call_args
+            assert call_args.kwargs["model"] == "deepinfra/Qwen3-reranker-8B"
+            assert call_args.kwargs["query"] == "What is Python?"
+            assert len(call_args.kwargs["documents"]) == 3
+            assert call_args.kwargs["api_key"] == "test_key"
+
+    @pytest.mark.asyncio
+    async def test_predict_multiple_queries(self):
+        """Test prediction with multiple different queries (grouped efficiently)."""
+        encoder = LiteLLMSDKCrossEncoder(
+            api_key="test_key",
+            model="cohere/rerank-english-v3.0",
+        )
+
+        # First query response
+        mock_response1 = MagicMock()
+        mock_response1.results = [
+            {"index": 0, "relevance_score": 0.9},
+            {"index": 1, "relevance_score": 0.7},
+        ]
+
+        # Second query response
+        mock_response2 = MagicMock()
+        mock_response2.results = [
+            {"index": 0, "relevance_score": 0.8},
+        ]
+
+        mock_litellm = MagicMock()
+        mock_litellm.arerank = AsyncMock(side_effect=[mock_response1, mock_response2])
+
+        with patch.dict("sys.modules", {"litellm": mock_litellm}):
+            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 arerank was called twice (once per unique query)
+            assert mock_litellm.arerank.call_count == 2
+
+    @pytest.mark.asyncio
+    async def test_predict_empty_pairs(self):
+        """Test prediction with empty input."""
+        encoder = LiteLLMSDKCrossEncoder(
+            api_key="test_key",
+            model="cohere/rerank-english-v3.0",
+        )
+
+        mock_litellm = MagicMock()
+        with patch.dict("sys.modules", {"litellm": mock_litellm}):
+            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 = LiteLLMSDKCrossEncoder(
+            api_key="test_key",
+            model="cohere/rerank-english-v3.0",
+        )
+
+        pairs = [("query", "document")]
+
+        with pytest.raises(RuntimeError, match="not initialized"):
+            await encoder.predict(pairs)
+
+    @pytest.mark.asyncio
+    async def test_predict_error_handling(self):
+        """Test that errors during prediction are raised."""
+        encoder = LiteLLMSDKCrossEncoder(
+            api_key="test_key",
+            model="cohere/rerank-english-v3.0",
+        )
+
+        # Mock litellm to raise an error
+        mock_litellm = MagicMock()
+        mock_litellm.arerank = AsyncMock(side_effect=Exception("API Error"))
+
+        with patch.dict("sys.modules", {"litellm": mock_litellm}):
+            await encoder.initialize()
+
+            pairs = [
+                ("What is Python?", "Python is a programming language"),
+            ]
+
+            # Should raise the exception
+            with pytest.raises(Exception, match="API Error"):
+                await encoder.predict(pairs)
+
+    @pytest.mark.asyncio
+    async def test_custom_api_base(self):
+        """Test that custom API base URL is passed to rerank calls."""
+        encoder = LiteLLMSDKCrossEncoder(
+            api_key="test_key",
+            model="cohere/rerank-english-v3.0",
+            api_base="https://custom.api.example.com",
+        )
+
+        mock_response = MagicMock()
+        mock_response.results = [
+            {"index": 0, "relevance_score": 0.9},
+        ]
+
+        mock_litellm = MagicMock()
+        mock_litellm.arerank = AsyncMock(return_value=mock_response)
+
+        with patch.dict("sys.modules", {"litellm": mock_litellm}):
+            await encoder.initialize()
+
+            # Test that api_base is passed to arerank
+            pairs = [("query", "document")]
+            scores = await encoder.predict(pairs)
+
+            assert scores == [0.9]
+            mock_litellm.arerank.assert_called_once()
+            call_args = mock_litellm.arerank.call_args
+            assert call_args.kwargs["api_base"] == "https://custom.api.example.com"
+
+    @pytest.mark.asyncio
+    async def test_response_with_direct_score_list(self):
+        """Test handling of response format with direct score list."""
+        encoder = LiteLLMSDKCrossEncoder(
+            api_key="test_key",
+            model="some-provider/model",
+        )
+
+        # Mock litellm to return direct list of scores
+        mock_litellm = MagicMock()
+        mock_litellm.arerank = AsyncMock(return_value=[0.9, 0.7, 0.5])
+
+        with patch.dict("sys.modules", {"litellm": mock_litellm}):
+            await encoder.initialize()
+
+            pairs = [
+                ("query", "doc1"),
+                ("query", "doc2"),
+                ("query", "doc3"),
+            ]
+
+            scores = await encoder.predict(pairs)
+
+            assert scores == [0.9, 0.7, 0.5]
+
+
+class TestFactoryFunction:
+    """Test suite for create_cross_encoder_from_env factory function."""
+
+    @pytest.mark.asyncio
+    async def test_create_litellm_sdk_from_env(self):
+        """Test creating LiteLLM SDK cross-encoder from environment variables."""
+        env_vars = {
+            "HINDSIGHT_API_RERANKER_PROVIDER": "litellm-sdk",
+            "HINDSIGHT_API_RERANKER_LITELLM_SDK_API_KEY": "test_key",
+            "HINDSIGHT_API_RERANKER_LITELLM_SDK_MODEL": "deepinfra/Qwen3-reranker-8B",
+        }
+
+        with patch.dict(os.environ, env_vars, clear=False):
+            # Need to reload config to pick up env vars
+            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, LiteLLMSDKCrossEncoder)
+                assert encoder.api_key == "test_key"
+                assert encoder.model == "deepinfra/Qwen3-reranker-8B"
+
+    @pytest.mark.asyncio
+    async def test_create_litellm_sdk_missing_api_key(self):
+        """Test that factory raises error when API key is missing."""
+        env_vars = {
+            "HINDSIGHT_API_RERANKER_PROVIDER": "litellm-sdk",
+            "HINDSIGHT_API_RERANKER_LITELLM_SDK_MODEL": "deepinfra/Qwen3-reranker-8B",
+        }
+
+        with patch.dict(os.environ, env_vars, clear=False):
+            # Remove API key if set
+            if "HINDSIGHT_API_RERANKER_LITELLM_SDK_API_KEY" in os.environ:
+                del os.environ["HINDSIGHT_API_RERANKER_LITELLM_SDK_API_KEY"]
+
+            from hindsight_api.config import HindsightConfig
+
+            config = HindsightConfig.from_env()
+
+            with patch("hindsight_api.config.get_config", return_value=config):
+                with pytest.raises(ValueError, match="HINDSIGHT_API_RERANKER_LITELLM_SDK_API_KEY is required"):
+                    create_cross_encoder_from_env()
+
+    @pytest.mark.asyncio
+    async def test_create_litellm_sdk_with_custom_api_base(self):
+        """Test creating LiteLLM SDK cross-encoder with custom API base."""
+        env_vars = {
+            "HINDSIGHT_API_RERANKER_PROVIDER": "litellm-sdk",
+            "HINDSIGHT_API_RERANKER_LITELLM_SDK_API_KEY": "test_key",
+            "HINDSIGHT_API_RERANKER_LITELLM_SDK_MODEL": "cohere/rerank-english-v3.0",
+            "HINDSIGHT_API_RERANKER_LITELLM_SDK_API_BASE": "https://custom.api.example.com",
+        }
+
+        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, LiteLLMSDKCrossEncoder)
+                assert encoder.api_base == "https://custom.api.example.com"
+
+
+class TestLiteLLMSDKCohereCrossEncoder:
+    """Tests for LiteLLM SDK calling Cohere (runs in CI with COHERE_API_KEY)."""
+
+    @pytest.fixture
+    async def litellm_cohere_cross_encoder(self):
+        """Create LiteLLM SDK cross-encoder instance for Cohere."""
+        if not os.environ.get("COHERE_API_KEY"):
+            pytest.skip("Cohere API key not available (set COHERE_API_KEY)")
+
+        encoder = LiteLLMSDKCrossEncoder(
+            api_key=os.environ["COHERE_API_KEY"],
+            model="cohere/rerank-english-v3.0",
+        )
+        await encoder.initialize()
+        return encoder
+
+    @pytest.mark.asyncio
+    async def test_litellm_sdk_cohere_initialization(self, litellm_cohere_cross_encoder):
+        """Test that LiteLLM SDK Cohere cross-encoder initializes correctly."""
+        assert litellm_cohere_cross_encoder.provider_name == "litellm-sdk"
+        assert litellm_cohere_cross_encoder.model == "cohere/rerank-english-v3.0"
+
+    @pytest.mark.asyncio
+    async def test_litellm_sdk_cohere_predict(self, litellm_cohere_cross_encoder):
+        """Test that LiteLLM SDK can call Cohere rerank API."""
+        pairs = [
+            ("What is the capital of France?", "Paris is the capital of France."),
+            ("What is the capital of France?", "The Eiffel Tower is in Paris."),
+            ("What is the capital of France?", "Python is a programming language."),
+        ]
+        scores = await litellm_cohere_cross_encoder.predict(pairs)
+
+        assert len(scores) == 3
+        assert all(isinstance(s, float) for s in scores)
+        # The first result should be most relevant
+        assert scores[0] > scores[2], "Direct answer should score higher than unrelated text"
+        # All scores should be in valid range
+        assert all(0.0 <= score <= 1.0 for score in scores)
+
+
+class TestIntegration:
+    """Integration tests with real API (optional - requires API keys)."""
+
+    @pytest.mark.skipif(
+        not os.environ.get("DEEPINFRA_API_KEY"),
+        reason="DEEPINFRA_API_KEY not set - skipping integration test",
+    )
+    @pytest.mark.asyncio
+    async def test_real_deepinfra_api(self):
+        """Test with real DeepInfra API (requires DEEPINFRA_API_KEY env var)."""
+        encoder = LiteLLMSDKCrossEncoder(
+            api_key=os.environ["DEEPINFRA_API_KEY"],
+            model="deepinfra/Qwen3-reranker-8B",
+        )
+
+        await encoder.initialize()
+
+        pairs = [
+            ("What is Python?", "Python is a high-level programming language"),
+            ("What is Python?", "Python is a species of snake"),
+            ("What is Python?", "Python is unrelated text about cars"),
+        ]
+
+        scores = await encoder.predict(pairs)
+
+        # First doc should have highest score (most relevant)
+        assert len(scores) == 3
+        assert scores[0] > scores[1]
+        assert scores[1] > scores[2]
+        assert all(0.0 <= score <= 1.0 for score in scores)

hindsight-api/tests/test_litellm_sdk_embeddings.py

@@ -0,0 +1,387 @@
+"""
+Tests for LiteLLM SDK embeddings implementation.
+
+These tests cover:
+1. Initialization (success, missing package, missing API key, idempotent)
+2. Encode (single text, multiple texts, batching, error handling)
+3. Provider-specific configuration (Cohere, OpenAI, etc.)
+4. Factory function (create from env, validation errors)
+5. Dimension detection
+"""
+
+import os
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from hindsight_api.config import (
+    ENV_EMBEDDINGS_LITELLM_SDK_API_KEY,
+    ENV_EMBEDDINGS_LITELLM_SDK_MODEL,
+    ENV_EMBEDDINGS_PROVIDER,
+    HindsightConfig,
+)
+from hindsight_api.engine.embeddings import LiteLLMSDKEmbeddings, create_embeddings_from_env
+
+
+class TestLiteLLMSDKEmbeddings:
+    """Unit tests for LiteLLMSDKEmbeddings with mocked litellm responses."""
+
+    @pytest.fixture
+    def mock_litellm(self):
+        """Mock litellm module."""
+        mock = MagicMock()
+
+        # Mock aembedding (async) for initialization
+        mock_response = MagicMock()
+        mock_response.data = [{"embedding": [0.1] * 768, "index": 0}]
+        mock.aembedding = AsyncMock(return_value=mock_response)
+
+        # Mock embedding (sync) for encode
+        mock_sync_response = MagicMock()
+        mock_sync_response.data = [
+            {"embedding": [0.1] * 768, "index": 0},
+            {"embedding": [0.2] * 768, "index": 1},
+        ]
+        mock.embedding = MagicMock(return_value=mock_sync_response)
+
+        return mock
+
+    @pytest.fixture
+    async def embeddings(self, mock_litellm):
+        """Create initialized LiteLLMSDKEmbeddings instance."""
+        emb = LiteLLMSDKEmbeddings(
+            api_key="test_key",
+            model="cohere/embed-english-v3.0",
+            api_base=None,
+            batch_size=100,
+            timeout=60.0,
+        )
+        # Manually set the mock (simulating successful initialization)
+        emb._litellm = mock_litellm
+        emb._dimension = 768
+        return emb
+
+    async def test_initialization_success(self, mock_litellm):
+        """Test successful initialization."""
+        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=None,
+                batch_size=100,
+                timeout=60.0,
+            )
+
+            assert emb._litellm is None
+            assert emb._dimension is None
+
+            await emb.initialize()
+
+            assert emb._litellm is not None
+            assert emb._dimension == 768
+
+            # Verify test embedding was called
+            mock_litellm.aembedding.assert_called_once_with(
+                model="cohere/embed-english-v3.0",
+                input=["test"],
+                api_key="test_key",
+            )
+
+    async def test_initialization_missing_package(self):
+        """Test initialization fails gracefully when litellm is not installed."""
+        def mock_import(name, *args):
+            if name == "litellm":
+                raise ImportError("No module named 'litellm'")
+            return __import__(name, *args)
+
+        with patch("builtins.__import__", side_effect=mock_import):
+            emb = LiteLLMSDKEmbeddings(
+                api_key="test_key",
+                model="cohere/embed-english-v3.0",
+                api_base=None,
+                batch_size=100,
+                timeout=60.0,
+            )
+
+            with pytest.raises(ImportError, match="litellm is required"):
+                await emb.initialize()
+
+    async def test_initialization_idempotent(self, embeddings, mock_litellm):
+        """Test that calling initialize() multiple times is safe."""
+        # embeddings._litellm is already set in fixture
+        assert embeddings._litellm is not None
+
+        # Call again
+        await embeddings.initialize()
+
+        # Should still have same litellm instance
+        assert embeddings._litellm is not None
+
+    async def test_encode_single_text(self, embeddings, mock_litellm):
+        """Test encoding a single text."""
+        # Set up mock response
+        mock_litellm.embedding.return_value.data = [
+            {"embedding": [0.5] * 768, "index": 0},
+        ]
+
+        result = embeddings.encode(["Hello world"])
+
+        assert isinstance(result, list)
+        assert len(result) == 1
+        assert len(result[0]) == 768
+        assert all(isinstance(x, float) for x in result[0])
+        assert all(abs(x - 0.5) < 0.001 for x in result[0])
+
+        # Verify call
+        mock_litellm.embedding.assert_called_once_with(
+            model="cohere/embed-english-v3.0",
+            input=["Hello world"],
+            api_key="test_key",
+        )
+
+    async def test_encode_multiple_texts(self, embeddings, mock_litellm):
+        """Test encoding multiple texts."""
+        # Set up mock response
+        mock_litellm.embedding.return_value.data = [
+            {"embedding": [0.1] * 768, "index": 0},
+            {"embedding": [0.2] * 768, "index": 1},
+            {"embedding": [0.3] * 768, "index": 2},
+        ]
+
+        texts = ["First text", "Second text", "Third text"]
+        result = embeddings.encode(texts)
+
+        assert isinstance(result, list)
+        assert len(result) == 3
+        assert len(result[0]) == 768
+        assert len(result[1]) == 768
+        assert len(result[2]) == 768
+        assert all(abs(x - 0.1) < 0.001 for x in result[0])
+        assert all(abs(x - 0.2) < 0.001 for x in result[1])
+        assert all(abs(x - 0.3) < 0.001 for x in result[2])
+
+    async def test_encode_batching(self, embeddings, mock_litellm):
+        """Test that large inputs are batched correctly."""
+        # Create embeddings with small batch size
+        emb = LiteLLMSDKEmbeddings(
+            api_key="test_key",
+            model="cohere/embed-english-v3.0",
+            api_base=None,
+            batch_size=2,  # Small batch for testing
+            timeout=60.0,
+        )
+        emb._litellm = mock_litellm
+        emb._initialized = True
+        emb._dimension = 768
+
+        # Mock responses for each batch
+        def mock_embedding_side_effect(model, input, **kwargs):
+            mock_response = MagicMock()
+            mock_response.data = [
+                {"embedding": [float(i)] * 768, "index": i} for i in range(len(input))
+            ]
+            return mock_response
+
+        mock_litellm.embedding.side_effect = mock_embedding_side_effect
+
+        # Encode 5 texts (should create 3 batches: 2, 2, 1)
+        texts = [f"Text {i}" for i in range(5)]
+        result = emb.encode(texts)
+
+        assert isinstance(result, list)
+        assert len(result) == 5
+        assert all(len(embedding) == 768 for embedding in result)
+
+        # Verify batching: should be called 3 times
+        assert mock_litellm.embedding.call_count == 3
+
+        # Verify batch sizes
+        calls = mock_litellm.embedding.call_args_list
+        assert len(calls[0][1]["input"]) == 2  # First batch
+        assert len(calls[1][1]["input"]) == 2  # Second batch
+        assert len(calls[2][1]["input"]) == 1  # Third batch
+
+    async def test_encode_empty_list(self, embeddings):
+        """Test encoding empty list returns empty list."""
+        result = embeddings.encode([])
+
+        assert isinstance(result, list)
+        assert len(result) == 0
+
+    async def test_encode_before_initialization(self, mock_litellm):
+        """Test that encode raises error if not initialized."""
+        emb = LiteLLMSDKEmbeddings(
+            api_key="test_key",
+            model="cohere/embed-english-v3.0",
+            api_base=None,
+            batch_size=100,
+            timeout=60.0,
+        )
+
+        with pytest.raises(RuntimeError, match="not initialized"):
+            emb.encode(["test"])
+
+    async def test_encode_error_handling(self, embeddings, mock_litellm):
+        """Test error handling during encoding."""
+        # Make embedding raise an error
+        mock_litellm.embedding.side_effect = Exception("API Error")
+
+        with pytest.raises(Exception, match="API Error"):
+            embeddings.encode(["test"])
+
+    async def test_dimension_property(self, embeddings):
+        """Test dimension property."""
+        assert embeddings.dimension == 768
+
+    async def test_dimension_before_initialization(self, mock_litellm):
+        """Test dimension raises error if not initialized."""
+        emb = LiteLLMSDKEmbeddings(
+            api_key="test_key",
+            model="cohere/embed-english-v3.0",
+            api_base=None,
+            batch_size=100,
+            timeout=60.0,
+        )
+
+        with pytest.raises(RuntimeError, match="not initialized"):
+            _ = emb.dimension
+
+    async def test_custom_api_base(self, mock_litellm):
+        """Test custom API base URL is passed to embedding calls."""
+        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",
+                batch_size=100,
+                timeout=60.0,
+            )
+
+            await emb.initialize()
+
+            # Verify api_base is set
+            assert emb.api_base == "https://custom.api.com"
+
+            # Verify api_base is passed to aembedding
+            mock_litellm.aembedding.assert_called_once()
+            call_args = mock_litellm.aembedding.call_args
+            assert call_args.kwargs["api_base"] == "https://custom.api.com"
+
+            # Test encode also passes api_base
+            mock_litellm.embedding.return_value.data = [{"embedding": [0.1] * 768, "index": 0}]
+            emb.encode(["test"])
+
+            mock_litellm.embedding.assert_called_once()
+            call_args = mock_litellm.embedding.call_args
+            assert call_args.kwargs["api_base"] == "https://custom.api.com"
+
+
+class TestLiteLLMSDKEmbeddingsFactory:
+    """Test the factory function for creating LiteLLM SDK embeddings."""
+
+    def test_create_from_env_success(self, monkeypatch):
+        """Test creating embeddings from environment variables."""
+        # Mock get_config() to return configured HindsightConfig
+        mock_config = MagicMock()
+        mock_config.embeddings_provider = "litellm-sdk"
+        mock_config.embeddings_litellm_sdk_api_key = "test_key"
+        mock_config.embeddings_litellm_sdk_model = "cohere/embed-english-v3.0"
+        mock_config.embeddings_litellm_sdk_api_base = None
+
+        with patch("hindsight_api.config.get_config", return_value=mock_config):
+            embeddings = create_embeddings_from_env()
+
+            assert isinstance(embeddings, LiteLLMSDKEmbeddings)
+            assert embeddings.api_key == "test_key"
+            assert embeddings.model == "cohere/embed-english-v3.0"
+
+    def test_create_from_env_missing_api_key(self, monkeypatch):
+        """Test that missing API key raises error."""
+        # Mock get_config() with missing API key
+        mock_config = MagicMock()
+        mock_config.embeddings_provider = "litellm-sdk"
+        mock_config.embeddings_litellm_sdk_api_key = None  # Missing key
+        mock_config.embeddings_litellm_sdk_model = "cohere/embed-english-v3.0"
+
+        with patch("hindsight_api.config.get_config", return_value=mock_config):
+            with pytest.raises(ValueError, match=ENV_EMBEDDINGS_LITELLM_SDK_API_KEY):
+                create_embeddings_from_env()
+
+    def test_create_from_env_with_api_base(self, monkeypatch):
+        """Test creating embeddings with custom API base."""
+        # Mock get_config() with custom API base
+        mock_config = MagicMock()
+        mock_config.embeddings_provider = "litellm-sdk"
+        mock_config.embeddings_litellm_sdk_api_key = "test_key"
+        mock_config.embeddings_litellm_sdk_model = "cohere/embed-english-v3.0"
+        mock_config.embeddings_litellm_sdk_api_base = "https://custom.api.com"
+
+        with patch("hindsight_api.config.get_config", return_value=mock_config):
+            embeddings = create_embeddings_from_env()
+
+            assert isinstance(embeddings, LiteLLMSDKEmbeddings)
+            assert embeddings.api_base == "https://custom.api.com"
+
+
+class TestLiteLLMSDKCohereEmbeddings:
+    """Integration tests calling real Cohere API (matches CI pattern)."""
+
+    @pytest.fixture
+    async def litellm_cohere_embeddings(self):
+        """Create embeddings instance with real Cohere API key."""
+        if not os.environ.get("COHERE_API_KEY"):
+            pytest.skip("Cohere API key not available")
+
+        emb = LiteLLMSDKEmbeddings(
+            api_key=os.environ["COHERE_API_KEY"],
+            model="cohere/embed-english-v3.0",
+            api_base=None,
+            batch_size=100,
+            timeout=60.0,
+        )
+        await emb.initialize()
+        return emb
+
+    @pytest.mark.asyncio
+    async def test_litellm_sdk_cohere_encode(self, litellm_cohere_embeddings):
+        """Test real Cohere API call for embeddings."""
+        texts = [
+            "The quick brown fox jumps over the lazy dog",
+            "Machine learning is a subset of artificial intelligence",
+            "Python is a popular programming language",
+        ]
+
+        result = litellm_cohere_embeddings.encode(texts)
+
+        # Verify result type and shape
+        assert isinstance(result, list)
+        assert len(result) == 3
+        assert all(len(embedding) > 0 for embedding in result)
+        assert all(isinstance(x, float) for x in result[0])
+
+        # Verify embeddings are not zeros (common API failure mode)
+        for i, embedding in enumerate(result):
+            assert not all(abs(x) < 0.0001 for x in embedding), f"Embedding {i} is all zeros"
+
+        # Verify embeddings are normalized (Cohere returns normalized vectors)
+        for i, embedding in enumerate(result):
+            norm = sum(x * x for x in embedding) ** 0.5
+            assert 0.9 < norm < 1.1, f"Embedding {i} norm {norm} is not close to 1.0"
+
+    @pytest.mark.asyncio
+    async def test_litellm_sdk_cohere_dimension(self, litellm_cohere_embeddings):
+        """Test dimension detection with real Cohere API."""
+        dimension = litellm_cohere_embeddings.dimension
+
+        # Cohere embed-english-v3.0 has 1024 dimensions
+        assert dimension == 1024
+
+    @pytest.mark.asyncio
+    async def test_litellm_sdk_cohere_single_text(self, litellm_cohere_embeddings):
+        """Test encoding single text with real Cohere API."""
+        result = litellm_cohere_embeddings.encode(["Hello world"])
+
+        assert isinstance(result, list)
+        assert len(result) == 1
+        assert len(result[0]) == 1024
+        assert not all(abs(x) < 0.0001 for x in result[0])

hindsight-api/tests/test_load_large_batch.py

@@ -209,7 +209,7 @@ class TestLargeBatchRetain:
                 raise
 
     @pytest.mark.asyncio
-    @pytest.mark.timeout(120)
+    @pytest.mark.timeout(240)  # Increased timeout for VectorChord BM25 tokenization
     async def test_batch_chunking_behavior(self, memory_with_mock_llm, request_context):
         """
         Test that large batches are properly chunked into sub-batches.

hindsight-api/tests/test_main_module.py

@@ -45,7 +45,7 @@ class TestMainModuleExtensionLoading:
 
         with patch("hindsight_api.main.MemoryEngine") as mock_engine, \
              patch("hindsight_api.main.create_app") as mock_create_app, \
-             patch("hindsight_api.main.get_config") as mock_get_config, \
+             patch("hindsight_api.main._get_raw_config") as mock_get_config, \
              patch("hindsight_api.main.load_extension", side_effect=tracking_load_extension), \
              patch("hindsight_api.main.DefaultExtensionContext"), \
              patch("hindsight_api.main.print_banner"), \
@@ -96,7 +96,7 @@ class TestMainModuleExtensionLoading:
 
         with patch("hindsight_api.main.MemoryEngine") as mock_engine, \
              patch("hindsight_api.main.create_app") as mock_create_app, \
-             patch("hindsight_api.main.get_config") as mock_get_config, \
+             patch("hindsight_api.main._get_raw_config") as mock_get_config, \
              patch("hindsight_api.main.load_extension", side_effect=tracking_load_extension), \
              patch("hindsight_api.main.DefaultExtensionContext"), \
              patch("hindsight_api.main.print_banner"), \
@@ -143,7 +143,7 @@ class TestMainModuleExtensionLoading:
 
         with patch("hindsight_api.main.MemoryEngine", side_effect=capture_memory_engine), \
              patch("hindsight_api.main.create_app") as mock_create_app, \
-             patch("hindsight_api.main.get_config") as mock_get_config, \
+             patch("hindsight_api.main._get_raw_config") as mock_get_config, \
              patch("hindsight_api.main.DefaultExtensionContext"), \
              patch("hindsight_api.main.print_banner"), \
              patch("uvicorn.run"):
@@ -200,7 +200,7 @@ class TestMainModuleExtensionLoading:
 
         with patch("hindsight_api.main.MemoryEngine", side_effect=capture_memory_engine), \
              patch("hindsight_api.main.create_app") as mock_create_app, \
-             patch("hindsight_api.main.get_config") as mock_get_config, \
+             patch("hindsight_api.main._get_raw_config") as mock_get_config, \
              patch("hindsight_api.main.DefaultExtensionContext", side_effect=capture_context), \
              patch("hindsight_api.main.print_banner"), \
              patch("uvicorn.run"):
@@ -242,7 +242,7 @@ class TestMainModuleExtensionLoading:
 
         with patch("hindsight_api.main.MemoryEngine", side_effect=capture_memory_engine), \
              patch("hindsight_api.main.create_app") as mock_create_app, \
-             patch("hindsight_api.main.get_config") as mock_get_config, \
+             patch("hindsight_api.main._get_raw_config") as mock_get_config, \
              patch("hindsight_api.main.print_banner"), \
              patch("uvicorn.run"):
 
@@ -287,7 +287,7 @@ class TestMainModuleExtensionLoading:
 
         with patch("hindsight_api.main.MemoryEngine") as mock_engine, \
              patch("hindsight_api.main.create_app", return_value=mock_app), \
-             patch("hindsight_api.main.get_config") as mock_get_config, \
+             patch("hindsight_api.main._get_raw_config") as mock_get_config, \
              patch("hindsight_api.main.print_banner"), \
              patch("uvicorn.run", side_effect=capture_uvicorn_run):
 
@@ -327,7 +327,7 @@ class TestMainModuleExtensionLoading:
 
         with patch("hindsight_api.main.MemoryEngine") as mock_engine, \
              patch("hindsight_api.main.create_app") as mock_create_app, \
-             patch("hindsight_api.main.get_config") as mock_get_config, \
+             patch("hindsight_api.main._get_raw_config") as mock_get_config, \
              patch("hindsight_api.main.print_banner"), \
              patch("uvicorn.run", side_effect=capture_uvicorn_run):
 

hindsight-api/tests/test_observations.py

@@ -8,14 +8,14 @@ populated from the summary for backwards compatibility.
 import pytest
 from hindsight_api.engine.memory_engine import Budget
 from hindsight_api import RequestContext
-from hindsight_api.config import get_config
+from hindsight_api.config import _get_raw_config
 from datetime import datetime, timezone
 
 
 @pytest.fixture
 def disable_observations():
     """Disable observations for a specific test."""
-    config = get_config()
+    config = _get_raw_config()
     original_value = config.enable_observations
     config.enable_observations = False
     yield

hindsight-api/tests/test_recall_chunks_independence.py

@@ -0,0 +1,274 @@
+"""
+Test that recall chunks are fetched independently of max_tokens filtering.
+
+This test verifies the new behavior where:
+1. Chunks are fetched BEFORE max_tokens filtering
+2. max_tokens=0 returns 0 facts but can still return chunks
+3. Chunks are fetched in batches to handle varying chunk sizes
+"""
+
+import pytest
+import pytest_asyncio
+
+from hindsight_api.engine.memory_engine import Budget
+
+
+@pytest.mark.asyncio
+async def test_recall_chunks_independent_of_max_tokens(memory, request_context):
+    """
+    Test that chunks are fetched independently of max_tokens.
+
+    When max_tokens=0, recall should:
+    - Return 0 memory facts
+    - Still return chunks (up to max_chunk_tokens)
+    - Chunks should come from top-scored results before token filtering
+    """
+    bank_id = "test-chunks-independence"
+
+    try:
+
+        # Retain some test content with substantial size to generate chunks
+        test_content = """
+        The quantum computing research team at MIT has made significant breakthroughs.
+        Dr. Sarah Chen leads the team and focuses on quantum error correction.
+        The team published three papers in Nature Physics this year.
+        Their work on topological qubits shows promise for scalable quantum computers.
+        Collaborators include IBM Research and Google Quantum AI.
+        The research is funded by a $5M NSF grant running through 2026.
+        """ * 10  # Repeat to ensure we get multiple chunks
+
+        await memory.retain_async(
+            bank_id=bank_id,
+            content=test_content,
+            context="research notes",
+            request_context=request_context,
+        )
+
+        # Test 1: Normal recall with both facts and chunks
+        result_normal = await memory.recall_async(
+            bank_id=bank_id,
+            query="quantum computing",
+            max_tokens=4096,  # Normal token budget
+            include_chunks=True,
+            max_chunk_tokens=2000,
+            budget=Budget.MID,
+            request_context=request_context,
+        )
+
+        assert len(result_normal.results) > 0, "Should return memory facts with normal max_tokens"
+        assert result_normal.chunks is not None, "Should include chunks when requested"
+        assert len(result_normal.chunks) > 0, "Should return at least one chunk"
+
+        # Test 2: Recall with max_tokens=0 but chunks enabled
+        result_chunks_only = await memory.recall_async(
+            bank_id=bank_id,
+            query="quantum computing",
+            max_tokens=0,  # Zero token budget for facts
+            include_chunks=True,
+            max_chunk_tokens=2000,  # But allow chunks
+            budget=Budget.MID,
+            request_context=request_context,
+        )
+
+        # Key assertions for new behavior
+        assert len(result_chunks_only.results) == 0, "max_tokens=0 should return 0 facts"
+        assert result_chunks_only.chunks is not None, "Should still include chunks dict"
+        assert len(result_chunks_only.chunks) > 0, "Should return chunks even with max_tokens=0"
+
+        # Verify chunks are from the same content (non-empty text)
+        for chunk_id, chunk_info in result_chunks_only.chunks.items():
+            assert len(chunk_info.chunk_text) > 0, "Chunks should contain text"
+            assert chunk_info.chunk_index >= 0, "Chunk should have valid index"
+
+    finally:
+        # Cleanup
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+@pytest.mark.asyncio
+async def test_recall_chunks_batching_with_varying_sizes(memory, request_context):
+    """
+    Test that chunk batching works correctly with varying chunk sizes.
+
+    This verifies that:
+    1. Chunks are fetched in batches until token budget is exhausted
+    2. The system handles varying chunk sizes across documents
+    3. Token budget is respected across multiple batch fetches
+    """
+    bank_id = "test-chunks-batching"
+
+    try:
+
+        # Retain multiple documents with different content sizes
+        # Document 1: Short content (small chunks)
+        await memory.retain_async(
+            bank_id=bank_id,
+            content="Alice is a software engineer who specializes in Python programming and machine learning.",
+            context="doc1",
+            request_context=request_context,
+        )
+
+        # Document 2: Medium content
+        content_bob = """
+        Bob works as a data scientist at a tech startup in San Francisco.
+        He has expertise in natural language processing and computer vision.
+        Bob completed his PhD at Stanford University in 2020.
+        He leads a team of five engineers working on AI-powered recommendation systems.
+        """ * 5
+        await memory.retain_async(
+            bank_id=bank_id,
+            content=content_bob,
+            context="doc2",
+            request_context=request_context,
+        )
+
+        # Document 3: Long content (large chunks)
+        content_charlie = """
+        Charlie is the CTO of a growing AI company focused on healthcare applications.
+        He has over 15 years of experience in software architecture and distributed systems.
+        Charlie's team builds machine learning models for medical image analysis and diagnosis.
+        The company recently raised $50 million in Series B funding.
+        They have partnerships with major hospitals in the United States and Europe.
+        Charlie holds several patents in medical imaging and deep learning.
+        """ * 20
+        await memory.retain_async(
+            bank_id=bank_id,
+            content=content_charlie,
+            context="doc3",
+            request_context=request_context,
+        )
+
+        # Recall with modest chunk token budget
+        result = await memory.recall_async(
+            bank_id=bank_id,
+            query="Alice Bob Charlie",
+            max_tokens=0,  # No facts, only chunks
+            include_chunks=True,
+            max_chunk_tokens=1000,  # Limited chunk budget
+            budget=Budget.MID,
+            request_context=request_context,
+        )
+
+        assert len(result.results) == 0, "Should return 0 facts with max_tokens=0"
+        assert result.chunks is not None, "Should include chunks"
+
+        # Verify we got chunks and respected the token budget
+        if len(result.chunks) > 0:
+            # Count total tokens (approximate)
+            total_chunk_chars = sum(len(chunk.chunk_text) for chunk in result.chunks.values())
+            # Very rough estimate: 1 token ≈ 4 characters
+            estimated_tokens = total_chunk_chars // 4
+
+            # Should be reasonably close to budget (within 2x due to estimation and batching)
+            assert estimated_tokens <= 1000 * 2, f"Should respect chunk token budget (got ~{estimated_tokens} tokens)"
+
+    finally:
+        # Cleanup
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+@pytest.mark.asyncio
+async def test_recall_chunks_ordering_by_relevance(memory, request_context):
+    """
+    Test that chunks are returned in order of fact relevance.
+
+    Chunks should be ordered based on the top-scored (reranked) results,
+    not in document order or random order.
+    """
+    bank_id = "test-chunks-ordering"
+
+    try:
+
+        # Retain content with different relevance to query
+        await memory.retain_async(
+            bank_id=bank_id,
+            content="The Python programming language is widely used for machine learning and data science applications.",
+            context="topic: Python",
+            request_context=request_context,
+        )
+
+        await memory.retain_async(
+            bank_id=bank_id,
+            content="JavaScript is commonly used for web development and frontend applications.",
+            context="topic: JavaScript",
+            request_context=request_context,
+        )
+
+        await memory.retain_async(
+            bank_id=bank_id,
+            content="Python's scikit-learn library is excellent for traditional machine learning tasks and model training.",
+            context="topic: Python ML",
+            request_context=request_context,
+        )
+
+        # Query specifically about Python - should rank Python facts higher
+        result = await memory.recall_async(
+            bank_id=bank_id,
+            query="Python machine learning",
+            max_tokens=0,  # No facts
+            include_chunks=True,
+            max_chunk_tokens=5000,  # Enough for all chunks
+            budget=Budget.HIGH,  # Use high budget for better recall
+            request_context=request_context,
+        )
+
+        assert len(result.results) == 0, "Should return 0 facts with max_tokens=0"
+        assert result.chunks is not None, "Should include chunks"
+
+        # We should get chunks, and they should be ordered by relevance
+        # The exact ordering depends on the reranker, but we should have chunks
+        assert len(result.chunks) > 0, "Should return chunks from relevant facts"
+
+        # Verify chunks contain relevant content
+        all_chunk_text = " ".join(chunk.chunk_text for chunk in result.chunks.values())
+        # At least some chunks should mention Python (higher relevance)
+        # This is a soft check since exact ordering depends on scoring
+        assert "Python" in all_chunk_text or "python" in all_chunk_text.lower(), \
+            "Chunks should include content about Python (relevant to query)"
+
+    finally:
+        # Cleanup
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+@pytest.mark.asyncio
+async def test_recall_chunks_without_include_flag(memory, request_context):
+    """
+    Test that chunks are NOT returned when include_chunks=False (default).
+
+    This ensures backward compatibility - chunks are only fetched when explicitly requested.
+    """
+    bank_id = "test-chunks-no-include"
+
+    try:
+
+        # Retain content
+        test_content = """
+        Sarah is a product manager at a fintech company in New York.
+        She specializes in user experience design and agile methodologies.
+        Sarah graduated from MIT with a degree in computer science.
+        She has led the development of three successful mobile banking applications.
+        """
+        await memory.retain_async(
+            bank_id=bank_id,
+            content=test_content,
+            request_context=request_context,
+        )
+
+        # Recall without include_chunks flag (default is False)
+        result = await memory.recall_async(
+            bank_id=bank_id,
+            query="Sarah product manager",
+            max_tokens=4096,
+            request_context=request_context,
+            # include_chunks=False is the default
+        )
+
+        # Should have facts but no chunks
+        assert len(result.results) > 0, "Should return facts"
+        assert result.chunks is None or len(result.chunks) == 0, \
+            "Should NOT return chunks when include_chunks=False"
+
+    finally:
+        # Cleanup
+        await memory.delete_bank(bank_id, request_context=request_context)

hindsight-api/tests/test_reflect_empty_based_on.py

@@ -0,0 +1,103 @@
+"""
+Test reflect endpoint with empty based_on (no memories scenario).
+
+This test verifies that the API returns the correct based_on format:
+- v0.3.0 (old): returned based_on as list []
+- v0.4.0+ (current): returns based_on as object {"memories": [], "mental_models": [], "directives": []}
+"""
+
+import pytest
+import pytest_asyncio
+import httpx
+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.mark.asyncio
+async def test_reflect_with_no_memories_empty_bank(api_client):
+    """Test reflect on an empty bank (no memories) with include.facts enabled."""
+    bank_id = "test_empty_bank"
+
+    # Reflect on empty bank with facts requested
+    response = await api_client.post(
+        f"/v1/default/banks/{bank_id}/reflect",
+        json={
+            "query": "What do you know about machine learning?",
+            "budget": "low",
+            "include": {
+                "facts": {}  # Request facts but bank is empty
+            }
+        }
+    )
+
+    assert response.status_code == 200
+    data = response.json()
+
+    # DEBUG: Print what the API actually returned
+    import json
+    print("\n" + "="*80)
+    print("API Response:")
+    print(json.dumps(data, indent=2))
+    print("="*80 + "\n")
+
+    # Verify response structure
+    assert "text" in data
+    assert "based_on" in data
+
+    # The API should return based_on as either:
+    # 1. null/None (if include.facts not set)
+    # 2. {"memories": [], "mental_models": [], "directives": []} (if include.facts set but empty)
+    # It should NEVER return based_on: []
+
+    based_on = data.get("based_on")
+    if based_on is not None:
+        assert isinstance(based_on, dict), f"based_on should be dict or null, got {type(based_on)}: {based_on}"
+        assert not isinstance(based_on, list), f"based_on should NEVER be a list! Got: {based_on}"
+        assert "memories" in based_on
+        assert "mental_models" in based_on
+        assert "directives" in based_on
+        # All should be empty lists
+        assert based_on["memories"] == []
+        assert based_on["mental_models"] == []
+        assert based_on["directives"] == []
+
+    # Verify the structure is parseable as proper types
+    assert isinstance(data["text"], str)
+    if based_on is not None:
+        # Verify it's the v0.4.0+ format (object with arrays)
+        assert isinstance(based_on["memories"], list)
+        assert isinstance(based_on["mental_models"], list)
+        assert isinstance(based_on["directives"], list)
+
+
+@pytest.mark.asyncio
+async def test_reflect_without_include_facts(api_client):
+    """Test reflect without requesting facts (based_on should be None)."""
+    bank_id = "test_no_facts"
+
+    response = await api_client.post(
+        f"/v1/default/banks/{bank_id}/reflect",
+        json={
+            "query": "Hello world",
+            "budget": "low"
+            # No include.facts
+        }
+    )
+
+    assert response.status_code == 200
+    data = response.json()
+
+    # When include.facts is not set, based_on should not be in response (or be null)
+    based_on = data.get("based_on")
+    assert based_on is None, f"based_on should be None when not requested, got {type(based_on)}: {based_on}"
+
+    # Verify structure
+    assert isinstance(data["text"], str)

hindsight-api/tests/test_retain.py

@@ -2093,7 +2093,7 @@ async def test_custom_extraction_mode():
     import os
     from hindsight_api import LLMConfig
     from hindsight_api.engine.retain.fact_extraction import extract_facts_from_text
-    from hindsight_api.config import clear_config_cache
+    from hindsight_api.config import clear_config_cache, _get_raw_config
 
     # Save original env vars
     original_mode = os.getenv("HINDSIGHT_API_RETAIN_EXTRACTION_MODE")
@@ -2135,7 +2135,8 @@ If the text contains both Italian and English content, extract ONLY the Italian
             event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
             context="team meeting notes",
             llm_config=llm_config,
-            agent_name="TestUser"
+            agent_name="TestUser",
+            config=_get_raw_config(),
         )
 
         logger.info(f"\nExtracted {len(facts)} facts with custom mode (Italian only):")

hindsight-cli/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "hindsight-cli"
-version = "0.4.10"
+version = "0.4.11"
 edition = "2021"
 authors = ["Hindsight Team"]
 description = "A beautiful CLI for Hindsight - semantic memory system"

hindsight-cli/src/api.rs

@@ -387,6 +387,43 @@ impl ApiClient {
         })
     }
 
+    pub fn get_bank_config(
+        &self,
+        bank_id: &str,
+        _verbose: bool,
+    ) -> Result<types::BankConfigResponse> {
+        self.runtime.block_on(async {
+            let response = self.client.get_bank_config(bank_id, None).await?;
+            Ok(response.into_inner())
+        })
+    }
+
+    pub fn update_bank_config(
+        &self,
+        bank_id: &str,
+        updates: std::collections::HashMap<String, serde_json::Value>,
+        _verbose: bool,
+    ) -> Result<types::BankConfigResponse> {
+        self.runtime.block_on(async {
+            // Convert HashMap to serde_json::Map
+            let updates_map: serde_json::Map<String, serde_json::Value> = updates.into_iter().collect();
+            let request = types::BankConfigUpdate { updates: updates_map };
+            let response = self.client.update_bank_config(bank_id, None, &request).await?;
+            Ok(response.into_inner())
+        })
+    }
+
+    pub fn reset_bank_config(
+        &self,
+        bank_id: &str,
+        _verbose: bool,
+    ) -> Result<types::BankConfigResponse> {
+        self.runtime.block_on(async {
+            let response = self.client.reset_bank_config(bank_id, None).await?;
+            Ok(response.into_inner())
+        })
+    }
+
     // --- Tag Methods ---
 
     pub fn list_tags(

hindsight-cli/src/commands/bank.rs

@@ -1,4 +1,4 @@
-use anyhow::Result;
+use anyhow::{anyhow, Result};
 use crate::api::ApiClient;
 use crate::output::{self, OutputFormat};
 use crate::ui;
@@ -655,3 +655,159 @@ pub fn clear_observations(
         Err(e) => Err(e),
     }
 }
+
+pub fn config(
+    client: &ApiClient,
+    bank_id: &str,
+    overrides_only: bool,
+    verbose: bool,
+    output_format: OutputFormat,
+) -> Result<()> {
+    let spinner = if output_format == OutputFormat::Pretty {
+        Some(ui::create_spinner("Fetching bank configuration..."))
+    } else {
+        None
+    };
+
+    let response = client.get_bank_config(bank_id, verbose);
+
+    if let Some(mut sp) = spinner {
+        sp.finish();
+    }
+
+    match response {
+        Ok(result) => {
+            if output_format == OutputFormat::Pretty {
+                ui::print_success(&format!("Configuration for bank '{}'", bank_id));
+                println!();
+                if overrides_only {
+                    println!("Bank-specific overrides:");
+                    if result.overrides.is_empty() {
+                        println!("  (none - using defaults)");
+                    } else {
+                        for (key, value) in result.overrides.iter() {
+                            println!("  {}: {:?}", key, value);
+                        }
+                    }
+                } else {
+                    println!("Resolved configuration (with all overrides applied):");
+                    for (key, value) in result.config.iter() {
+                        println!("  {}: {:?}", key, value);
+                    }
+                }
+            } else {
+                if overrides_only {
+                    output::print_output(&result.overrides, output_format)?;
+                } else {
+                    output::print_output(&result, output_format)?;
+                }
+            }
+            Ok(())
+        }
+        Err(e) => Err(e),
+    }
+}
+
+pub fn set_config(
+    client: &ApiClient,
+    bank_id: &str,
+    llm_provider: Option<String>,
+    llm_model: Option<String>,
+    llm_api_key: Option<String>,
+    llm_base_url: Option<String>,
+    verbose: bool,
+    output_format: OutputFormat,
+) -> Result<()> {
+    use std::collections::HashMap;
+
+    let mut updates: HashMap<String, serde_json::Value> = HashMap::new();
+
+    if let Some(provider) = llm_provider {
+        updates.insert("llm_provider".to_string(), serde_json::Value::String(provider));
+    }
+    if let Some(model) = llm_model {
+        updates.insert("llm_model".to_string(), serde_json::Value::String(model));
+    }
+    if let Some(api_key) = llm_api_key {
+        updates.insert("llm_api_key".to_string(), serde_json::Value::String(api_key));
+    }
+    if let Some(base_url) = llm_base_url {
+        updates.insert("llm_base_url".to_string(), serde_json::Value::String(base_url));
+    }
+
+    if updates.is_empty() {
+        return Err(anyhow!("No config updates provided. Use --llm-provider, --llm-model, --llm-api-key, or --llm-base-url".to_string()));
+    }
+
+    let spinner = if output_format == OutputFormat::Pretty {
+        Some(ui::create_spinner("Updating bank configuration..."))
+    } else {
+        None
+    };
+
+    let response = client.update_bank_config(bank_id, updates, verbose);
+
+    if let Some(mut sp) = spinner {
+        sp.finish();
+    }
+
+    match response {
+        Ok(result) => {
+            if output_format == OutputFormat::Pretty {
+                ui::print_success(&format!("Configuration updated for bank '{}'", bank_id));
+                println!("\nUpdated overrides:");
+                for (key, value) in result.overrides.iter() {
+                    println!("  {}: {:?}", key, value);
+                }
+            } else {
+                output::print_output(&result, output_format)?;
+            }
+            Ok(())
+        }
+        Err(e) => Err(e),
+    }
+}
+
+pub fn reset_config(
+    client: &ApiClient,
+    bank_id: &str,
+    yes: bool,
+    verbose: bool,
+    output_format: OutputFormat,
+) -> Result<()> {
+    if !yes && output_format == OutputFormat::Pretty {
+        let confirmed = ui::prompt_confirmation(&format!(
+            "Reset all configuration overrides for bank '{}'?",
+            bank_id
+        ))?;
+
+        if !confirmed {
+            ui::print_info("Operation cancelled");
+            return Ok(());
+        }
+    }
+
+    let spinner = if output_format == OutputFormat::Pretty {
+        Some(ui::create_spinner("Resetting bank configuration..."))
+    } else {
+        None
+    };
+
+    let response = client.reset_bank_config(bank_id, verbose);
+
+    if let Some(mut sp) = spinner {
+        sp.finish();
+    }
+
+    match response {
+        Ok(result) => {
+            if output_format == OutputFormat::Pretty {
+                ui::print_success(&format!("Configuration reset to defaults for bank '{}'", bank_id));
+            } else {
+                output::print_output(&result, output_format)?;
+            }
+            Ok(())
+        }
+        Err(e) => Err(e),
+    }
+}

hindsight-cli/src/errors.rs

@@ -58,8 +58,22 @@ fn format_error_message(err: &anyhow::Error, api_url: &str) -> String {
         );
     }
 
-    // 404 Not Found
+    // 404 Not Found - check for disabled features first
     if err_str.contains("404") {
+        if err_str.contains("Bank configuration API is disabled") {
+            return format!(
+                "{} {}\n\n{}\n  {}\n\n{}\n  {}\n\n{}\n  {}",
+                "✗".bright_red().bold(),
+                "Bank configuration API is disabled".bright_red().bold(),
+                "API URL:".bright_yellow(),
+                api_url.bright_white(),
+                "This feature is disabled by default for security.".bright_yellow(),
+                "To enable, set HINDSIGHT_API_ENABLE_BANK_CONFIG_API=true on the API server".bright_white(),
+                "Note:".bright_cyan(),
+                "This allows per-bank LLM configuration overrides via API".bright_white()
+            );
+        }
+
         return format!(
             "{} {}\n\n{}\n  {}\n\n{}\n  • {}\n  • {}\n\n{}\n  {}",
             "✗".bright_red().bold(),
@@ -74,8 +88,8 @@ fn format_error_message(err: &anyhow::Error, api_url: &str) -> String {
         );
     }
 
-    // 401/403 Authentication
-    if err_str.contains("401") || err_str.contains("403") {
+    // 401 Authentication failed
+    if err_str.contains("401") {
         return format!(
             "{} {}\n\n{}\n  {}\n\n{}\n  • {}\n  • {}\n\n{}\n  {}",
             "✗".bright_red().bold(),
@@ -90,6 +104,22 @@ fn format_error_message(err: &anyhow::Error, api_url: &str) -> String {
         );
     }
 
+    // 403 Forbidden
+    if err_str.contains("403") {
+        return format!(
+            "{} {}\n\n{}\n  {}\n\n{}\n  • {}\n  • {}\n\n{}\n  {}",
+            "✗".bright_red().bold(),
+            "Permission denied (403)".bright_red().bold(),
+            "API URL:".bright_yellow(),
+            api_url.bright_white(),
+            "Possible causes:".bright_yellow(),
+            "This operation is not allowed".bright_white(),
+            "The feature may be disabled on the server".bright_white(),
+            "Try:".bright_green(),
+            "Check server configuration or contact your administrator".bright_white()
+        );
+    }
+
     // 500 Server Error
     if err_str.contains("500") || err_str.contains("502") || err_str.contains("503") {
         return format!(

hindsight-cli/src/main.rs

@@ -279,6 +279,48 @@ enum BankCommands {
         #[arg(short = 'y', long)]
         yes: bool,
     },
+
+    /// Get bank configuration (hierarchical overrides)
+    Config {
+        /// Bank ID
+        bank_id: String,
+
+        /// Show only bank-specific overrides (not full resolved config)
+        #[arg(long)]
+        overrides_only: bool,
+    },
+
+    /// Update bank configuration (set hierarchical overrides)
+    SetConfig {
+        /// Bank ID
+        bank_id: String,
+
+        /// LLM provider override
+        #[arg(long)]
+        llm_provider: Option<String>,
+
+        /// LLM model override
+        #[arg(long)]
+        llm_model: Option<String>,
+
+        /// LLM API key override
+        #[arg(long)]
+        llm_api_key: Option<String>,
+
+        /// LLM base URL override
+        #[arg(long)]
+        llm_base_url: Option<String>,
+    },
+
+    /// Reset bank configuration to defaults (remove all overrides)
+    ResetConfig {
+        /// Bank ID
+        bank_id: String,
+
+        /// Skip confirmation prompt
+        #[arg(short = 'y', long)]
+        yes: bool,
+    },
 }
 
 #[derive(Subcommand)]
@@ -776,6 +818,15 @@ fn run() -> Result<()> {
             BankCommands::ClearObservations { bank_id, yes } => {
                 commands::bank::clear_observations(&client, &bank_id, yes, verbose, output_format)
             }
+            BankCommands::Config { bank_id, overrides_only } => {
+                commands::bank::config(&client, &bank_id, overrides_only, verbose, output_format)
+            }
+            BankCommands::SetConfig { bank_id, llm_provider, llm_model, llm_api_key, llm_base_url } => {
+                commands::bank::set_config(&client, &bank_id, llm_provider, llm_model, llm_api_key, llm_base_url, verbose, output_format)
+            }
+            BankCommands::ResetConfig { bank_id, yes } => {
+                commands::bank::reset_config(&client, &bank_id, yes, verbose, output_format)
+            }
         },
 
         // Memory commands

hindsight-clients/go/README.md

@@ -0,0 +1,178 @@
+# Hindsight Go Client
+
+Go client for the [Hindsight](https://github.com/vectorize-io/hindsight) agent memory API.
+
+## Installation
+
+```bash
+go get github.com/vectorize-io/hindsight-client-go
+```
+
+Requires Go 1.25+.
+
+## Quick Start
+
+```go
+package main
+
+import (
+    "context"
+    "fmt"
+    "log"
+
+    hindsight "github.com/vectorize-io/hindsight-client-go"
+)
+
+func main() {
+    client, err := hindsight.New("http://localhost:8888")
+    if err != nil {
+        log.Fatal(err)
+    }
+
+    ctx := context.Background()
+
+    // Store a memory
+    _, err = client.Retain(ctx, "my-bank", "The user prefers dark mode")
+    if err != nil {
+        log.Fatal(err)
+    }
+
+    // Recall memories
+    resp, err := client.Recall(ctx, "my-bank", "What are the user's preferences?")
+    if err != nil {
+        log.Fatal(err)
+    }
+    for _, r := range resp.Results {
+        fmt.Println(r.Text)
+    }
+
+    // Reflect with reasoning
+    ref, err := client.Reflect(ctx, "my-bank", "Summarize what you know about the user")
+    if err != nil {
+        log.Fatal(err)
+    }
+    fmt.Println(ref.Text)
+}
+```
+
+## Authentication
+
+```go
+client, err := hindsight.New("http://localhost:8888", hindsight.WithAPIKey("your-key"))
+```
+
+## Core Operations
+
+### Retain (Store Memories)
+
+```go
+// Single memory
+_, err := client.Retain(ctx, "bank-id", "Alice loves Python",
+    hindsight.WithContext("programming discussion"),
+    hindsight.WithTags([]string{"tech"}),
+    hindsight.WithDocumentID("conv-123"),
+)
+
+// Batch
+items := []hindsight.MemoryItem{
+    {Content: "First memory"},
+    {Content: "Second memory"},
+}
+_, err := client.RetainBatch(ctx, "bank-id", items,
+    hindsight.WithDocumentTags([]string{"import"}),
+    hindsight.WithAsync(true),
+)
+```
+
+### Recall (Retrieve Memories)
+
+```go
+resp, err := client.Recall(ctx, "bank-id", "What does Alice like?",
+    hindsight.WithBudget(hindsight.BudgetHigh),
+    hindsight.WithMaxTokens(4096),
+    hindsight.WithTypes([]string{"world", "experience"}),
+    hindsight.WithTrace(true),
+    hindsight.WithRecallTags([]string{"tech"}),
+)
+
+for _, r := range resp.Results {
+    fmt.Printf("[%s] %s\n", r.Type.Or("unknown"), r.Text)
+}
+```
+
+### Reflect (Reason with Memories)
+
+```go
+resp, err := client.Reflect(ctx, "bank-id", "What are the user's interests?",
+    hindsight.WithReflectBudget(hindsight.BudgetMid),
+    hindsight.WithReflectMaxTokens(2048),
+    hindsight.WithResponseSchema(map[string]any{
+        "type": "object",
+        "properties": map[string]any{
+            "interests": map[string]any{"type": "array", "items": map[string]any{"type": "string"}},
+        },
+    }),
+)
+
+fmt.Println(resp.Text)
+```
+
+### Bank Management
+
+```go
+// Create bank with personality
+_, err := client.CreateBank(ctx, "my-bank",
+    hindsight.WithBankName("My Agent"),
+    hindsight.WithMission("Help users with coding tasks"),
+    hindsight.WithDisposition(hindsight.DispositionTraits{
+        Skepticism: 3,
+        Literalism: 2,
+        Empathy:    4,
+    }),
+)
+
+// Update mission
+_, err = client.SetMission(ctx, "my-bank", "New mission statement")
+
+// List all banks
+banks, err := client.ListBanks(ctx)
+
+// Delete bank
+err = client.DeleteBank(ctx, "my-bank")
+```
+
+## Advanced Usage
+
+For operations not covered by the high-level wrapper (documents, entities, operations, mental models, directives), access the ogen-generated client directly:
+
+```go
+ogen := client.OgenClient()
+
+// List entities
+resp, err := ogen.ListEntities(ctx, ogenapi.ListEntitiesParams{
+    BankID: "my-bank",
+})
+
+// Create mental model
+resp, err := ogen.CreateMentalModel(ctx, &ogenapi.CreateMentalModelRequest{
+    Name:        "user-preferences",
+    SourceQuery: "What are the user's preferences?",
+}, ogenapi.CreateMentalModelParams{BankID: "my-bank"})
+```
+
+## Code Generation
+
+The client is built on [ogen](https://github.com/ogen-go/ogen), generating strongly-typed Go code from the OpenAPI 3.1 spec. To regenerate after API changes:
+
+```bash
+cd hindsight-clients/go
+go generate ./...
+```
+
+## Running Tests
+
+Integration tests require a running Hindsight API server:
+
+```bash
+HINDSIGHT_API_URL=http://localhost:8888 go test -v -tags=integration ./...
+```

hindsight-clients/go/banks.go

@@ -0,0 +1,118 @@
+package hindsight
+
+import (
+	"context"
+	"fmt"
+
+	"github.com/vectorize-io/hindsight-client-go/internal/ogenapi"
+)
+
+// CreateBank creates a new memory bank or updates an existing one.
+func (c *Client) CreateBank(ctx context.Context, bankID string, opts ...CreateBankOption) (*BankProfileResponse, error) {
+	var cfg createBankConfig
+	for _, o := range opts {
+		o(&cfg)
+	}
+
+	req := &ogenapi.CreateBankRequest{}
+	if cfg.name != nil {
+		req.Name = ogenapi.NewOptString(*cfg.name)
+	}
+	if cfg.mission != nil {
+		req.Mission = ogenapi.NewOptString(*cfg.mission)
+	}
+	if cfg.disposition != nil {
+		req.Disposition = ogenapi.NewOptDispositionTraits(*cfg.disposition)
+	}
+
+	res, err := c.api.CreateOrUpdateBank(ctx, req, ogenapi.CreateOrUpdateBankParams{
+		BankID: bankID,
+	})
+	if err != nil {
+		return nil, err
+	}
+
+	resp, ok := res.(*ogenapi.BankProfileResponse)
+	if !ok {
+		return nil, fmt.Errorf("hindsight: unexpected response type %T", res)
+	}
+	return resp, nil
+}
+
+// GetBankProfile retrieves the profile for a memory bank.
+func (c *Client) GetBankProfile(ctx context.Context, bankID string) (*BankProfileResponse, error) {
+	res, err := c.api.GetBankProfile(ctx, ogenapi.GetBankProfileParams{
+		BankID: bankID,
+	})
+	if err != nil {
+		return nil, err
+	}
+
+	resp, ok := res.(*ogenapi.BankProfileResponse)
+	if !ok {
+		return nil, fmt.Errorf("hindsight: unexpected response type %T", res)
+	}
+	return resp, nil
+}
+
+// ListBanks returns all memory banks.
+func (c *Client) ListBanks(ctx context.Context) (*BankListResponse, error) {
+	res, err := c.api.ListBanks(ctx)
+	if err != nil {
+		return nil, err
+	}
+
+	resp, ok := res.(*ogenapi.BankListResponse)
+	if !ok {
+		return nil, fmt.Errorf("hindsight: unexpected response type %T", res)
+	}
+	return resp, nil
+}
+
+// DeleteBank permanently deletes a memory bank and all its data.
+func (c *Client) DeleteBank(ctx context.Context, bankID string) error {
+	_, err := c.api.DeleteBank(ctx, ogenapi.DeleteBankParams{
+		BankID: bankID,
+	})
+	return err
+}
+
+// SetMission updates the mission for a memory bank.
+func (c *Client) SetMission(ctx context.Context, bankID, mission string) (*BankProfileResponse, error) {
+	req := &ogenapi.CreateBankRequest{
+		Mission: ogenapi.NewOptString(mission),
+	}
+
+	res, err := c.api.CreateOrUpdateBank(ctx, req, ogenapi.CreateOrUpdateBankParams{
+		BankID: bankID,
+	})
+	if err != nil {
+		return nil, err
+	}
+
+	resp, ok := res.(*ogenapi.BankProfileResponse)
+	if !ok {
+		return nil, fmt.Errorf("hindsight: unexpected response type %T", res)
+	}
+	return resp, nil
+}
+
+// UpdateDisposition updates the personality traits for a memory bank.
+func (c *Client) UpdateDisposition(ctx context.Context, bankID string, traits DispositionTraits) (*BankProfileResponse, error) {
+	req := &ogenapi.UpdateDispositionRequest{
+		Disposition: traits,
+	}
+
+	res, err := c.api.UpdateBankDisposition(ctx, req, ogenapi.UpdateBankDispositionParams{
+		BankID: bankID,
+	})
+	if err != nil {
+		return nil, err
+	}
+
+	resp, ok := res.(*ogenapi.BankProfileResponse)
+	if !ok {
+		return nil, fmt.Errorf("hindsight: unexpected response type %T", res)
+	}
+	return resp, nil
+}

hindsight-clients/go/doc.go

@@ -0,0 +1,40 @@
+// Package hindsight provides a Go client for the Hindsight agent memory API.
+//
+// Hindsight is a long-term memory system for AI agents. This client wraps the
+// auto-generated ogen API client with a simpler, Go-idiomatic interface for the
+// core operations: retain (store), recall (retrieve), and reflect (reason).
+//
+// # Quick Start
+//
+//	client, err := hindsight.New("http://localhost:8888")
+//	if err != nil {
+//	    log.Fatal(err)
+//	}
+//
+//	// Store a memory
+//	_, err = client.Retain(ctx, "my-bank", "The user prefers dark mode")
+//
+//	// Recall memories
+//	resp, err := client.Recall(ctx, "my-bank", "What are the user's preferences?")
+//	for _, r := range resp.Results {
+//	    fmt.Println(r.Text)
+//	}
+//
+//	// Reflect with reasoning
+//	ref, err := client.Reflect(ctx, "my-bank", "Summarize what you know about the user")
+//	fmt.Println(ref.Text)
+//
+// # Authentication
+//
+// For authenticated deployments, pass an API key:
+//
+//	client, err := hindsight.New("http://localhost:8888", hindsight.WithAPIKey("your-key"))
+//
+// # Advanced Usage
+//
+// For operations not covered by the high-level wrapper (documents, entities,
+// operations, mental models, directives), access the ogen-generated client:
+//
+//	ogen := client.OgenClient()
+//	resp, err := ogen.ListEntities(ctx, ogenapi.ListEntitiesParams{BankID: "my-bank"})
+package hindsight

hindsight-clients/go/example_test.go

@@ -0,0 +1,124 @@
+package hindsight_test
+
+import (
+	"context"
+	"fmt"
+	"log"
+
+	hindsight "github.com/vectorize-io/hindsight-client-go"
+)
+
+func Example() {
+	client, err := hindsight.New("http://localhost:8888")
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	ctx := context.Background()
+
+	// Store a memory
+	_, err = client.Retain(ctx, "my-bank", "The user prefers dark mode")
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	// Recall memories
+	resp, err := client.Recall(ctx, "my-bank", "What are the user's preferences?")
+	if err != nil {
+		log.Fatal(err)
+	}
+	for _, r := range resp.Results {
+		fmt.Println(r.Text)
+	}
+
+	// Reflect with reasoning
+	ref, err := client.Reflect(ctx, "my-bank", "Summarize what you know about the user")
+	if err != nil {
+		log.Fatal(err)
+	}
+	fmt.Println(ref.Text)
+}
+
+func ExampleNew_withAPIKey() {
+	_, err := hindsight.New(
+		"http://localhost:8888",
+		hindsight.WithAPIKey("your-api-key"),
+	)
+	if err != nil {
+		log.Fatal(err)
+	}
+}
+
+func ExampleClient_Retain() {
+	client, _ := hindsight.New("http://localhost:8888")
+	ctx := context.Background()
+
+	// Simple retain
+	_, _ = client.Retain(ctx, "my-bank", "Alice loves Python programming")
+
+	// Retain with options
+	_, _ = client.Retain(ctx, "my-bank", "Bob went hiking",
+		hindsight.WithContext("outdoor activities"),
+		hindsight.WithTags([]string{"hobbies"}),
+		hindsight.WithDocumentID("conversation-123"),
+	)
+}
+
+func ExampleClient_RetainBatch() {
+	client, _ := hindsight.New("http://localhost:8888")
+	ctx := context.Background()
+
+	items := []hindsight.MemoryItem{
+		{Content: "Alice completed the project"},
+		{Content: "Bob started learning Go"},
+		{Content: "Charlie presented at the conference"},
+	}
+
+	_, _ = client.RetainBatch(ctx, "my-bank", items,
+		hindsight.WithDocumentTags([]string{"team-updates"}),
+	)
+}
+
+func ExampleClient_Recall() {
+	client, _ := hindsight.New("http://localhost:8888")
+	ctx := context.Background()
+
+	resp, _ := client.Recall(ctx, "my-bank", "What does Alice like?",
+		hindsight.WithBudget(hindsight.BudgetHigh),
+		hindsight.WithMaxTokens(4096),
+		hindsight.WithTypes([]string{"world", "experience"}),
+		hindsight.WithTrace(true),
+	)
+
+	for _, r := range resp.Results {
+		fmt.Printf("[%s] %s\n", r.Type.Or("unknown"), r.Text)
+	}
+}
+
+func ExampleClient_Reflect() {
+	client, _ := hindsight.New("http://localhost:8888")
+	ctx := context.Background()
+
+	resp, _ := client.Reflect(ctx, "my-bank",
+		"What are the user's professional interests?",
+		hindsight.WithReflectBudget(hindsight.BudgetMid),
+		hindsight.WithReflectMaxTokens(2048),
+	)
+
+	fmt.Println(resp.Text)
+}
+
+func ExampleClient_CreateBank() {
+	client, _ := hindsight.New("http://localhost:8888")
+	ctx := context.Background()
+
+	_, _ = client.CreateBank(ctx, "my-bank",
+		hindsight.WithBankName("My Agent"),
+		hindsight.WithMission("Help users with programming tasks"),
+		hindsight.WithDisposition(hindsight.DispositionTraits{
+			Skepticism: 3,
+			Literalism: 2,
+			Empathy:    4,
+		}),
+	)
+}

hindsight-clients/go/generate.go

@@ -0,0 +1,4 @@
+package hindsight
+
+//go:generate go run ./internal/cmd/preprocess ../../hindsight-docs/static/openapi.json internal/ogenapi/openapi.json
+//go:generate go run github.com/ogen-go/ogen/cmd/ogen --target internal/ogenapi -package ogenapi --clean --config ogen.yml internal/ogenapi/openapi.json

hindsight-clients/go/go.mod

@@ -0,0 +1,29 @@
+module github.com/vectorize-io/hindsight-client-go
+
+go 1.25.0
+
+require (
+	github.com/go-faster/errors v0.7.1
+	github.com/go-faster/jx v1.2.0
+	github.com/ogen-go/ogen v1.19.0
+)
+
+require (
+	github.com/dlclark/regexp2 v1.11.5 // indirect
+	github.com/fatih/color v1.18.0 // indirect
+	github.com/ghodss/yaml v1.0.0 // indirect
+	github.com/go-faster/yaml v0.4.6 // indirect
+	github.com/google/uuid v1.6.0 // indirect
+	github.com/mattn/go-colorable v0.1.13 // indirect
+	github.com/mattn/go-isatty v0.0.20 // indirect
+	github.com/segmentio/asm v1.2.1 // indirect
+	github.com/shopspring/decimal v1.4.0 // indirect
+	go.uber.org/multierr v1.11.0 // indirect
+	go.uber.org/zap v1.27.1 // indirect
+	golang.org/x/exp v0.0.0-20230725093048-515e97ebf090 // indirect
+	golang.org/x/net v0.50.0 // indirect
+	golang.org/x/sync v0.19.0 // indirect
+	golang.org/x/sys v0.41.0 // indirect
+	golang.org/x/text v0.34.0 // indirect
+	gopkg.in/yaml.v2 v2.4.0 // indirect
+)

hindsight-clients/go/go.sum

@@ -0,0 +1,60 @@
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+github.com/fatih/color v1.18.0/go.mod h1:4FelSpRwEGDpQ12mAdzqdOukCy4u8WUtOY6lkT/6HfU=
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+github.com/pmezard/go-difflib v1.0.0 h1:4DBwDE0NGyQoBHbLQYPwSUPoCMWR5BEzIk/f1lZbAQM=
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hindsight-clients/go/hindsight.go

@@ -0,0 +1,66 @@
+package hindsight
+
+import (
+	"net/http"
+
+	"github.com/vectorize-io/hindsight-client-go/internal/ogenapi"
+)
+
+// Client is the high-level Hindsight API client. It wraps the ogen-generated
+// client with convenience methods for core operations.
+type Client struct {
+	api *ogenapi.Client
+}
+
+// New creates a new Hindsight client for the given base URL.
+//
+// By default, no authentication is configured. Use [WithAPIKey] to set a
+// Bearer token, or [WithHTTPClient] for full control over the HTTP transport.
+func New(baseURL string, opts ...Option) (*Client, error) {
+	cfg := clientConfig{}
+	for _, o := range opts {
+		o(&cfg)
+	}
+
+	var httpClient http.Client
+	if cfg.httpClient != nil {
+		httpClient = *cfg.httpClient
+	}
+
+	if cfg.apiKey != "" {
+		base := httpClient.Transport
+		if base == nil {
+			base = http.DefaultTransport
+		}
+		httpClient.Transport = &authTransport{
+			base:  base,
+			token: cfg.apiKey,
+		}
+	}
+
+	api, err := ogenapi.NewClient(baseURL, ogenapi.WithClient(&httpClient))
+	if err != nil {
+		return nil, err
+	}
+
+	return &Client{api: api}, nil
+}
+
+// OgenClient returns the underlying ogen-generated client for advanced
+// operations not covered by the high-level wrapper (documents, entities,
+// operations, mental models, directives).
+func (c *Client) OgenClient() *ogenapi.Client {
+	return c.api
+}
+
+// authTransport injects a Bearer token into every request.
+type authTransport struct {
+	base  http.RoundTripper
+	token string
+}
+
+func (t *authTransport) RoundTrip(r *http.Request) (*http.Response, error) {
+	r = r.Clone(r.Context())
+	r.Header.Set("Authorization", "Bearer "+t.token)
+	return t.base.RoundTrip(r)
+}

hindsight-clients/go/integration_test.go

@@ -0,0 +1,399 @@
+//go:build integration
+
+package hindsight_test
+
+import (
+	"context"
+	"fmt"
+	"os"
+	"testing"
+	"time"
+
+	hindsight "github.com/vectorize-io/hindsight-client-go"
+)
+
+func apiURL(t *testing.T) string {
+	t.Helper()
+	u := os.Getenv("HINDSIGHT_API_URL")
+	if u == "" {
+		u = "http://localhost:8888"
+	}
+	return u
+}
+
+func newClient(t *testing.T) *hindsight.Client {
+	t.Helper()
+	c, err := hindsight.New(apiURL(t))
+	if err != nil {
+		t.Fatal(err)
+	}
+	return c
+}
+
+func uniqueBank(t *testing.T) string {
+	t.Helper()
+	return fmt.Sprintf("go_test_%d", time.Now().UnixNano())
+}
+
+// --- Retain tests ---
+
+func TestRetainSingle(t *testing.T) {
+	c := newClient(t)
+	ctx := context.Background()
+
+	resp, err := c.Retain(ctx, uniqueBank(t), "Alice loves artificial intelligence and machine learning")
+	if err != nil {
+		t.Fatal(err)
+	}
+	if !resp.Success {
+		t.Error("expected success=true")
+	}
+}
+
+func TestRetainWithContext(t *testing.T) {
+	c := newClient(t)
+	ctx := context.Background()
+
+	resp, err := c.Retain(ctx, uniqueBank(t), "Bob went hiking in the mountains",
+		hindsight.WithTimestamp(time.Date(2024, 1, 15, 10, 30, 0, 0, time.UTC)),
+		hindsight.WithContext("outdoor activities"),
+	)
+	if err != nil {
+		t.Fatal(err)
+	}
+	if !resp.Success {
+		t.Error("expected success=true")
+	}
+}
+
+func TestRetainBatch(t *testing.T) {
+	c := newClient(t)
+	ctx := context.Background()
+
+	items := []hindsight.MemoryItem{
+		{Content: "Charlie enjoys reading science fiction books"},
+		{Content: "Diana is learning to play the guitar"},
+		{Content: "Eve completed a marathon last month"},
+	}
+
+	resp, err := c.RetainBatch(ctx, uniqueBank(t), items)
+	if err != nil {
+		t.Fatal(err)
+	}
+	if !resp.Success {
+		t.Error("expected success=true")
+	}
+	if resp.ItemsCount != 3 {
+		t.Errorf("expected items_count=3, got %d", resp.ItemsCount)
+	}
+}
+
+func TestRetainWithTags(t *testing.T) {
+	c := newClient(t)
+	ctx := context.Background()
+
+	resp, err := c.Retain(ctx, uniqueBank(t), "New feature implementation for project Z",
+		hindsight.WithTags([]string{"project_z", "features"}),
+	)
+	if err != nil {
+		t.Fatal(err)
+	}
+	if !resp.Success {
+		t.Error("expected success=true")
+	}
+}
+
+func TestRetainBatchWithDocumentTags(t *testing.T) {
+	c := newClient(t)
+	ctx := context.Background()
+
+	items := []hindsight.MemoryItem{
+		{Content: "First item in batch"},
+		{Content: "Second item in batch"},
+	}
+
+	resp, err := c.RetainBatch(ctx, uniqueBank(t), items,
+		hindsight.WithDocumentTags([]string{"batch_import", "test_data"}),
+	)
+	if err != nil {
+		t.Fatal(err)
+	}
+	if !resp.Success {
+		t.Error("expected success=true")
+	}
+	if resp.ItemsCount != 2 {
+		t.Errorf("expected items_count=2, got %d", resp.ItemsCount)
+	}
+}
+
+// --- Recall tests ---
+
+func setupRecallBank(t *testing.T, c *hindsight.Client, bankID string) {
+	t.Helper()
+	ctx := context.Background()
+
+	items := []hindsight.MemoryItem{
+		{Content: "Alice loves programming in Python"},
+		{Content: "Bob enjoys hiking and outdoor adventures"},
+		{Content: "Charlie is interested in quantum physics"},
+		{Content: "Diana plays the violin beautifully"},
+	}
+
+	_, err := c.RetainBatch(ctx, bankID, items)
+	if err != nil {
+		t.Fatal(err)
+	}
+}
+
+func TestRecallBasic(t *testing.T) {
+	c := newClient(t)
+	ctx := context.Background()
+	bankID := uniqueBank(t)
+	setupRecallBank(t, c, bankID)
+
+	resp, err := c.Recall(ctx, bankID, "What does Alice like?")
+	if err != nil {
+		t.Fatal(err)
+	}
+	if len(resp.Results) == 0 {
+		t.Error("expected at least one result")
+	}
+
+	found := false
+	for _, r := range resp.Results {
+		if contains(r.Text, "Alice") || contains(r.Text, "Python") || contains(r.Text, "programming") {
+			found = true
+			break
+		}
+	}
+	if !found {
+		t.Error("expected a result mentioning Alice or Python")
+	}
+}
+
+func TestRecallWithMaxTokens(t *testing.T) {
+	c := newClient(t)
+	ctx := context.Background()
+	bankID := uniqueBank(t)
+	setupRecallBank(t, c, bankID)
+
+	resp, err := c.Recall(ctx, bankID, "outdoor activities",
+		hindsight.WithMaxTokens(1024),
+	)
+	if err != nil {
+		t.Fatal(err)
+	}
+	if resp.Results == nil {
+		t.Error("expected results, got nil")
+	}
+}
+
+func TestRecallFullFeatured(t *testing.T) {
+	c := newClient(t)
+	ctx := context.Background()
+	bankID := uniqueBank(t)
+	setupRecallBank(t, c, bankID)
+
+	resp, err := c.Recall(ctx, bankID, "What are people's hobbies?",
+		hindsight.WithTypes([]string{"world"}),
+		hindsight.WithMaxTokens(2048),
+		hindsight.WithTrace(true),
+	)
+	if err != nil {
+		t.Fatal(err)
+	}
+	if resp.Results == nil {
+		t.Error("expected results, got nil")
+	}
+}
+
+// --- Reflect tests ---
+
+func setupReflectBank(t *testing.T, c *hindsight.Client, bankID string) {
+	t.Helper()
+	ctx := context.Background()
+
+	_, err := c.CreateBank(ctx, bankID,
+		hindsight.WithMission("I am a helpful AI assistant interested in technology and science."),
+	)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	items := []hindsight.MemoryItem{
+		{Content: "The Python programming language is great for data science"},
+		{Content: "Machine learning models can recognize patterns in data"},
+		{Content: "Neural networks are inspired by biological neurons"},
+	}
+
+	_, err = c.RetainBatch(ctx, bankID, items)
+	if err != nil {
+		t.Fatal(err)
+	}
+}
+
+func TestReflectBasic(t *testing.T) {
+	c := newClient(t)
+	ctx := context.Background()
+	bankID := uniqueBank(t)
+	setupReflectBank(t, c, bankID)
+
+	resp, err := c.Reflect(ctx, bankID, "What do you think about artificial intelligence?")
+	if err != nil {
+		t.Fatal(err)
+	}
+	if resp.Text == "" {
+		t.Error("expected non-empty response text")
+	}
+}
+
+func TestReflectWithMaxTokens(t *testing.T) {
+	c := newClient(t)
+	ctx := context.Background()
+	bankID := uniqueBank(t)
+	setupReflectBank(t, c, bankID)
+
+	resp, err := c.Reflect(ctx, bankID, "What do you think about Python?",
+		hindsight.WithReflectMaxTokens(500),
+	)
+	if err != nil {
+		t.Fatal(err)
+	}
+	if resp.Text == "" {
+		t.Error("expected non-empty response text")
+	}
+}
+
+// --- Bank tests ---
+
+func TestCreateBank(t *testing.T) {
+	c := newClient(t)
+	ctx := context.Background()
+
+	bankID := uniqueBank(t)
+	resp, err := c.CreateBank(ctx, bankID,
+		hindsight.WithBankName("Test Bank"),
+		hindsight.WithMission("A test bank for Go client"),
+	)
+	if err != nil {
+		t.Fatal(err)
+	}
+	if resp.BankID != bankID {
+		t.Errorf("expected bank_id=%q, got %q", bankID, resp.BankID)
+	}
+}
+
+func TestSetMission(t *testing.T) {
+	c := newClient(t)
+	ctx := context.Background()
+
+	bankID := uniqueBank(t)
+	resp, err := c.SetMission(ctx, bankID, "Be a helpful PM tracking sprint progress")
+	if err != nil {
+		t.Fatal(err)
+	}
+	if resp.BankID != bankID {
+		t.Errorf("expected bank_id=%q, got %q", bankID, resp.BankID)
+	}
+	if resp.Mission != "Be a helpful PM tracking sprint progress" {
+		t.Errorf("expected mission=%q, got %q", "Be a helpful PM tracking sprint progress", resp.Mission)
+	}
+}
+
+func TestListBanks(t *testing.T) {
+	c := newClient(t)
+	ctx := context.Background()
+
+	// Create a bank first
+	bankID := uniqueBank(t)
+	_, err := c.CreateBank(ctx, bankID)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	resp, err := c.ListBanks(ctx)
+	if err != nil {
+		t.Fatal(err)
+	}
+	if len(resp.Banks) == 0 {
+		t.Error("expected at least one bank")
+	}
+}
+
+func TestDeleteBank(t *testing.T) {
+	c := newClient(t)
+	ctx := context.Background()
+
+	bankID := uniqueBank(t)
+	_, err := c.CreateBank(ctx, bankID, hindsight.WithMission("will be deleted"))
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	err = c.DeleteBank(ctx, bankID)
+	if err != nil {
+		t.Fatal(err)
+	}
+}
+
+// --- End-to-end workflow ---
+
+func TestCompleteWorkflow(t *testing.T) {
+	c := newClient(t)
+	ctx := context.Background()
+	bankID := uniqueBank(t)
+
+	// 1. Create bank
+	_, err := c.CreateBank(ctx, bankID,
+		hindsight.WithMission("I am a software engineer who loves Python programming."),
+	)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	// 2. Store memories
+	items := []hindsight.MemoryItem{
+		{Content: "I completed a project using FastAPI"},
+		{Content: "I learned about async programming in Python"},
+		{Content: "I enjoy working on open source projects"},
+	}
+	storeResp, err := c.RetainBatch(ctx, bankID, items)
+	if err != nil {
+		t.Fatal(err)
+	}
+	if !storeResp.Success {
+		t.Error("expected retain success")
+	}
+
+	// 3. Search for relevant memories
+	recallResp, err := c.Recall(ctx, bankID, "What programming technologies do I use?")
+	if err != nil {
+		t.Fatal(err)
+	}
+	if len(recallResp.Results) == 0 {
+		t.Error("expected recall results")
+	}
+
+	// 4. Generate contextual answer
+	reflectResp, err := c.Reflect(ctx, bankID, "What are my professional interests?")
+	if err != nil {
+		t.Fatal(err)
+	}
+	if reflectResp.Text == "" {
+		t.Error("expected non-empty reflect response")
+	}
+}
+
+// contains checks if s contains substr (case-sensitive).
+func contains(s, substr string) bool {
+	return len(s) >= len(substr) && searchString(s, substr)
+}
+
+func searchString(s, substr string) bool {
+	for i := 0; i <= len(s)-len(substr); i++ {
+		if s[i:i+len(substr)] == substr {
+			return true
+		}
+	}
+	return false
+}

hindsight-clients/go/internal/cmd/preprocess/main.go

@@ -0,0 +1,111 @@
+// Command preprocess converts an OpenAPI 3.1 spec to be ogen-compatible.
+//
+// Hindsight's OpenAPI spec uses anyOf: [{type: T}, {type: null}] for optional
+// fields. ogen cannot handle the null type in header/query parameter schemas
+// with style:simple. This tool rewrites such patterns to plain {type: T},
+// making the spec consumable by ogen while preserving semantic meaning (the
+// fields are already marked as required: false).
+package main
+
+import (
+	"encoding/json"
+	"fmt"
+	"os"
+)
+
+func main() {
+	if len(os.Args) != 3 {
+		fmt.Fprintf(os.Stderr, "usage: preprocess <input.json> <output.json>\n")
+		os.Exit(1)
+	}
+
+	data, err := os.ReadFile(os.Args[1])
+	if err != nil {
+		fmt.Fprintf(os.Stderr, "read: %v\n", err)
+		os.Exit(1)
+	}
+
+	var spec map[string]any
+	if err := json.Unmarshal(data, &spec); err != nil {
+		fmt.Fprintf(os.Stderr, "parse: %v\n", err)
+		os.Exit(1)
+	}
+
+	convertAnyOfNull(spec)
+
+	out, err := json.MarshalIndent(spec, "", "  ")
+	if err != nil {
+		fmt.Fprintf(os.Stderr, "marshal: %v\n", err)
+		os.Exit(1)
+	}
+
+	if err := os.WriteFile(os.Args[2], out, 0o644); err != nil {
+		fmt.Fprintf(os.Stderr, "write: %v\n", err)
+		os.Exit(1)
+	}
+}
+
+// convertAnyOfNull recursively walks the spec and converts
+// anyOf: [{type: T}, {type: null}] → {type: T} (or just the non-null schema).
+// For component schema properties, it also does the conversion but additionally
+// handles cases where the non-null branch is a $ref.
+func convertAnyOfNull(v any) {
+	switch val := v.(type) {
+	case map[string]any:
+		// Check if this object has an "anyOf" with exactly a non-null + null pair.
+		if tryConvertAnyOf(val) {
+			// Converted in place; recurse into the result.
+			convertAnyOfNull(val)
+			return
+		}
+		// Recurse into all values.
+		for _, child := range val {
+			convertAnyOfNull(child)
+		}
+	case []any:
+		for _, child := range val {
+			convertAnyOfNull(child)
+		}
+	}
+}
+
+// tryConvertAnyOf checks if m has anyOf: [{...}, {type: null}] and converts
+// it in-place. Returns true if conversion happened.
+func tryConvertAnyOf(m map[string]any) bool {
+	anyOf, ok := m["anyOf"].([]any)
+	if !ok || len(anyOf) != 2 {
+		return false
+	}
+
+	// Identify which branch is null and which is the real type.
+	var realIdx int = -1
+	for i, branch := range anyOf {
+		branchMap, ok := branch.(map[string]any)
+		if !ok {
+			return false
+		}
+		if branchMap["type"] == "null" {
+			continue
+		}
+		realIdx = i
+	}
+
+	if realIdx == -1 {
+		return false // Both are null? Skip.
+	}
+
+	realBranch, ok := anyOf[realIdx].(map[string]any)
+	if !ok {
+		return false
+	}
+
+	// Remove the anyOf key.
+	delete(m, "anyOf")
+
+	// Copy all properties from the real branch into the parent.
+	for k, v := range realBranch {
+		m[k] = v
+	}
+
+	return true
+}

hindsight-clients/go/internal/ogenapi/.gitignore

@@ -0,0 +1,2 @@
+# Preprocessed OpenAPI spec (intermediate artifact, regenerated by go generate)
+openapi.json

hindsight-clients/go/internal/ogenapi/oas_cfg_gen.go

@@ -0,0 +1,61 @@
+// Code generated by ogen, DO NOT EDIT.
+
+package ogenapi
+
+import (
+	"net/http"
+
+	ht "github.com/ogen-go/ogen/http"
+)
+
+type (
+	optionFunc[C any] func(*C)
+)
+
+type clientConfig struct {
+	Client ht.Client
+}
+
+// ClientOption is client config option.
+type ClientOption interface {
+	applyClient(*clientConfig)
+}
+
+var _ ClientOption = (optionFunc[clientConfig])(nil)
+
+func (o optionFunc[C]) applyClient(c *C) {
+	o(c)
+}
+
+func newClientConfig(opts ...ClientOption) clientConfig {
+	cfg := clientConfig{
+		Client: http.DefaultClient,
+	}
+	for _, opt := range opts {
+		opt.applyClient(&cfg)
+	}
+	return cfg
+}
+
+type baseClient struct {
+	cfg clientConfig
+}
+
+func (cfg clientConfig) baseClient() (c baseClient, err error) {
+	c = baseClient{cfg: cfg}
+	return c, nil
+}
+
+// Option is config option.
+type Option interface {
+	ClientOption
+}
+
+// WithClient specifies http client to use.
+func WithClient(client ht.Client) ClientOption {
+	return optionFunc[clientConfig](func(cfg *clientConfig) {
+		if client != nil {
+			cfg.Client = client
+		}
+	})
+}

hindsight-clients/go/internal/ogenapi/oas_defaults_gen.go

@@ -0,0 +1,179 @@
+// Code generated by ogen, DO NOT EDIT.
+
+package ogenapi
+
+// setDefaults set default value of fields.
+func (s *AddBackgroundRequest) setDefaults() {
+	{
+		val := bool(true)
+		s.UpdateDisposition.SetTo(val)
+	}
+}
+
+// setDefaults set default value of fields.
+func (s *BankStatsResponse) setDefaults() {
+	{
+		val := int(0)
+		s.PendingConsolidation.SetTo(val)
+	}
+	{
+		val := int(0)
+		s.TotalObservations.SetTo(val)
+	}
+}
+
+// setDefaults set default value of fields.
+func (s *ChunkData) setDefaults() {
+	{
+		val := bool(false)
+		s.Truncated.SetTo(val)
+	}
+}
+
+// setDefaults set default value of fields.
+func (s *ChunkIncludeOptions) setDefaults() {
+	{
+		val := int(8192)
+		s.MaxTokens.SetTo(val)
+	}
+}
+
+// setDefaults set default value of fields.
+func (s *ConsolidationResponse) setDefaults() {
+	{
+		val := bool(false)
+		s.Deduplicated.SetTo(val)
+	}
+}
+
+// setDefaults set default value of fields.
+func (s *CreateDirectiveRequest) setDefaults() {
+	{
+		val := bool(true)
+		s.IsActive.SetTo(val)
+	}
+	{
+		val := int(0)
+		s.Priority.SetTo(val)
+	}
+}
+
+// setDefaults set default value of fields.
+func (s *CreateMentalModelRequest) setDefaults() {
+	{
+		val := int(2048)
+		s.MaxTokens.SetTo(val)
+	}
+}
+
+// setDefaults set default value of fields.
+func (s *DirectiveResponse) setDefaults() {
+	{
+		val := bool(true)
+		s.IsActive.SetTo(val)
+	}
+	{
+		val := int(0)
+		s.Priority.SetTo(val)
+	}
+}
+
+// setDefaults set default value of fields.
+func (s *EntityIncludeOptions) setDefaults() {
+	{
+		val := int(500)
+		s.MaxTokens.SetTo(val)
+	}
+}
+
+// setDefaults set default value of fields.
+func (s *MentalModelResponse) setDefaults() {
+	{
+		val := int(2048)
+		s.MaxTokens.SetTo(val)
+	}
+}
+
+// setDefaults set default value of fields.
+func (s *MentalModelTrigger) setDefaults() {
+	{
+		val := bool(false)
+		s.RefreshAfterConsolidation.SetTo(val)
+	}
+}
+
+// setDefaults set default value of fields.
+func (s *RecallRequest) setDefaults() {
+	{
+		val := Budget("low")
+		s.Budget.SetTo(val)
+	}
+	{
+		val := int(4096)
+		s.MaxTokens.SetTo(val)
+	}
+	{
+		val := RecallRequestTagsMatch("any")
+		s.TagsMatch.SetTo(val)
+	}
+	{
+		val := bool(false)
+		s.Trace.SetTo(val)
+	}
+}
+
+// setDefaults set default value of fields.
+func (s *ReflectRequest) setDefaults() {
+	{
+		val := Budget("low")
+		s.Budget.SetTo(val)
+	}
+	{
+		val := int(4096)
+		s.MaxTokens.SetTo(val)
+	}
+	{
+		val := ReflectRequestTagsMatch("any")
+		s.TagsMatch.SetTo(val)
+	}
+}
+
+// setDefaults set default value of fields.
+func (s *ReflectToolCall) setDefaults() {
+	{
+		val := int(0)
+		s.Iteration.SetTo(val)
+	}
+}
+
+// setDefaults set default value of fields.
+func (s *RetainRequest) setDefaults() {
+	{
+		val := bool(false)
+		s.Async.SetTo(val)
+	}
+}
+
+// setDefaults set default value of fields.
+func (s *TokenUsage) setDefaults() {
+	{
+		val := int(0)
+		s.InputTokens.SetTo(val)
+	}
+	{
+		val := int(0)
+		s.OutputTokens.SetTo(val)
+	}
+	{
+		val := int(0)
+		s.TotalTokens.SetTo(val)
+	}
+}
+
+// setDefaults set default value of fields.
+func (s *ToolCallsIncludeOptions) setDefaults() {
+	{
+		val := bool(true)
+		s.Output.SetTo(val)
+	}
+}

hindsight-clients/go/internal/ogenapi/oas_interfaces_gen.go

@@ -0,0 +1,170 @@
+// Code generated by ogen, DO NOT EDIT.
+package ogenapi
+
+type AddBankBackgroundRes interface {
+	addBankBackgroundRes()
+}
+
+type CancelOperationRes interface {
+	cancelOperationRes()
+}
+
+type ClearBankMemoriesRes interface {
+	clearBankMemoriesRes()
+}
+
+type ClearObservationsRes interface {
+	clearObservationsRes()
+}
+
+type CreateDirectiveRes interface {
+	createDirectiveRes()
+}
+
+type CreateMentalModelRes interface {
+	createMentalModelRes()
+}
+
+type CreateOrUpdateBankRes interface {
+	createOrUpdateBankRes()
+}
+
+type DeleteBankRes interface {
+	deleteBankRes()
+}
+
+type DeleteDirectiveRes interface {
+	deleteDirectiveRes()
+}
+
+type DeleteDocumentRes interface {
+	deleteDocumentRes()
+}
+
+type DeleteMentalModelRes interface {
+	deleteMentalModelRes()
+}
+
+type GetAgentStatsRes interface {
+	getAgentStatsRes()
+}
+
+type GetBankConfigRes interface {
+	getBankConfigRes()
+}
+
+type GetBankProfileRes interface {
+	getBankProfileRes()
+}
+
+type GetChunkRes interface {
+	getChunkRes()
+}
+
+type GetDirectiveRes interface {
+	getDirectiveRes()
+}
+
+type GetDocumentRes interface {
+	getDocumentRes()
+}
+
+type GetEntityRes interface {
+	getEntityRes()
+}
+
+type GetGraphRes interface {
+	getGraphRes()
+}
+
+type GetMemoryRes interface {
+	getMemoryRes()
+}
+
+type GetMentalModelRes interface {
+	getMentalModelRes()
+}
+
+type GetOperationStatusRes interface {
+	getOperationStatusRes()
+}
+
+type ListBanksRes interface {
+	listBanksRes()
+}
+
+type ListDirectivesRes interface {
+	listDirectivesRes()
+}
+
+type ListDocumentsRes interface {
+	listDocumentsRes()
+}
+
+type ListEntitiesRes interface {
+	listEntitiesRes()
+}
+
+type ListMemoriesRes interface {
+	listMemoriesRes()
+}
+
+type ListMentalModelsRes interface {
+	listMentalModelsRes()
+}
+
+type ListOperationsRes interface {
+	listOperationsRes()
+}
+
+type ListTagsRes interface {
+	listTagsRes()
+}
+
+type RecallMemoriesRes interface {
+	recallMemoriesRes()
+}
+
+type ReflectRes interface {
+	reflectRes()
+}
+
+type RefreshMentalModelRes interface {
+	refreshMentalModelRes()
+}
+
+type RegenerateEntityObservationsRes interface {
+	regenerateEntityObservationsRes()
+}
+
+type ResetBankConfigRes interface {
+	resetBankConfigRes()
+}
+
+type RetainMemoriesRes interface {
+	retainMemoriesRes()
+}
+
+type TriggerConsolidationRes interface {
+	triggerConsolidationRes()
+}
+
+type UpdateBankConfigRes interface {
+	updateBankConfigRes()
+}
+
+type UpdateBankDispositionRes interface {
+	updateBankDispositionRes()
+}
+
+type UpdateBankRes interface {
+	updateBankRes()
+}
+
+type UpdateDirectiveRes interface {
+	updateDirectiveRes()
+}
+
+type UpdateMentalModelRes interface {
+	updateMentalModelRes()
+}

hindsight-clients/go/internal/ogenapi/oas_operations_gen.go

@@ -0,0 +1,54 @@
+// Code generated by ogen, DO NOT EDIT.
+
+package ogenapi
+
+// OperationName is the ogen operation name
+type OperationName = string
+
+const (
+	AddBankBackgroundOperation            OperationName = "AddBankBackground"
+	CancelOperationOperation              OperationName = "CancelOperation"
+	ClearBankMemoriesOperation            OperationName = "ClearBankMemories"
+	ClearObservationsOperation            OperationName = "ClearObservations"
+	CreateDirectiveOperation              OperationName = "CreateDirective"
+	CreateMentalModelOperation            OperationName = "CreateMentalModel"
+	CreateOrUpdateBankOperation           OperationName = "CreateOrUpdateBank"
+	DeleteBankOperation                   OperationName = "DeleteBank"
+	DeleteDirectiveOperation              OperationName = "DeleteDirective"
+	DeleteDocumentOperation               OperationName = "DeleteDocument"
+	DeleteMentalModelOperation            OperationName = "DeleteMentalModel"
+	GetAgentStatsOperation                OperationName = "GetAgentStats"
+	GetBankConfigOperation                OperationName = "GetBankConfig"
+	GetBankProfileOperation               OperationName = "GetBankProfile"
+	GetChunkOperation                     OperationName = "GetChunk"
+	GetDirectiveOperation                 OperationName = "GetDirective"
+	GetDocumentOperation                  OperationName = "GetDocument"
+	GetEntityOperation                    OperationName = "GetEntity"
+	GetGraphOperation                     OperationName = "GetGraph"
+	GetMemoryOperation                    OperationName = "GetMemory"
+	GetMentalModelOperation               OperationName = "GetMentalModel"
+	GetOperationStatusOperation           OperationName = "GetOperationStatus"
+	GetVersionOperation                   OperationName = "GetVersion"
+	HealthEndpointHealthGetOperation      OperationName = "HealthEndpointHealthGet"
+	ListBanksOperation                    OperationName = "ListBanks"
+	ListDirectivesOperation               OperationName = "ListDirectives"
+	ListDocumentsOperation                OperationName = "ListDocuments"
+	ListEntitiesOperation                 OperationName = "ListEntities"
+	ListMemoriesOperation                 OperationName = "ListMemories"
+	ListMentalModelsOperation             OperationName = "ListMentalModels"
+	ListOperationsOperation               OperationName = "ListOperations"
+	ListTagsOperation                     OperationName = "ListTags"
+	MetricsEndpointMetricsGetOperation    OperationName = "MetricsEndpointMetricsGet"
+	RecallMemoriesOperation               OperationName = "RecallMemories"
+	ReflectOperation                      OperationName = "Reflect"
+	RefreshMentalModelOperation           OperationName = "RefreshMentalModel"
+	RegenerateEntityObservationsOperation OperationName = "RegenerateEntityObservations"
+	ResetBankConfigOperation              OperationName = "ResetBankConfig"
+	RetainMemoriesOperation               OperationName = "RetainMemories"
+	TriggerConsolidationOperation         OperationName = "TriggerConsolidation"
+	UpdateBankOperation                   OperationName = "UpdateBank"
+	UpdateBankConfigOperation             OperationName = "UpdateBankConfig"
+	UpdateBankDispositionOperation        OperationName = "UpdateBankDisposition"
+	UpdateDirectiveOperation              OperationName = "UpdateDirective"
+	UpdateMentalModelOperation            OperationName = "UpdateMentalModel"
+)

hindsight-clients/go/internal/ogenapi/oas_parameters_gen.go

@@ -0,0 +1,264 @@
+// Code generated by ogen, DO NOT EDIT.
+
+package ogenapi
+
+// AddBankBackgroundParams is parameters of add_bank_background operation.
+type AddBankBackgroundParams struct {
+	BankID string
+}
+
+// CancelOperationParams is parameters of cancel_operation operation.
+type CancelOperationParams struct {
+	BankID      string
+	OperationID string
+}
+
+// ClearBankMemoriesParams is parameters of clear_bank_memories operation.
+type ClearBankMemoriesParams struct {
+	BankID string
+	// Optional fact type filter (world, experience, opinion).
+	Type OptString `json:",omitempty,omitzero"`
+}
+
+// ClearObservationsParams is parameters of clear_observations operation.
+type ClearObservationsParams struct {
+	BankID string
+}
+
+// CreateDirectiveParams is parameters of create_directive operation.
+type CreateDirectiveParams struct {
+	BankID string
+}
+
+// CreateMentalModelParams is parameters of create_mental_model operation.
+type CreateMentalModelParams struct {
+	BankID string
+}
+
+// CreateOrUpdateBankParams is parameters of create_or_update_bank operation.
+type CreateOrUpdateBankParams struct {
+	BankID string
+}
+
+// DeleteBankParams is parameters of delete_bank operation.
+type DeleteBankParams struct {
+	BankID string
+}
+
+// DeleteDirectiveParams is parameters of delete_directive operation.
+type DeleteDirectiveParams struct {
+	BankID      string
+	DirectiveID string
+}
+
+// DeleteDocumentParams is parameters of delete_document operation.
+type DeleteDocumentParams struct {
+	BankID     string
+	DocumentID string
+}
+
+// DeleteMentalModelParams is parameters of delete_mental_model operation.
+type DeleteMentalModelParams struct {
+	BankID        string
+	MentalModelID string
+}
+
+// GetAgentStatsParams is parameters of get_agent_stats operation.
+type GetAgentStatsParams struct {
+	BankID string
+}
+
+// GetBankConfigParams is parameters of get_bank_config operation.
+type GetBankConfigParams struct {
+	BankID string
+}
+
+// GetBankProfileParams is parameters of get_bank_profile operation.
+type GetBankProfileParams struct {
+	BankID string
+}
+
+// GetChunkParams is parameters of get_chunk operation.
+type GetChunkParams struct {
+	ChunkID string
+}
+
+// GetDirectiveParams is parameters of get_directive operation.
+type GetDirectiveParams struct {
+	BankID      string
+	DirectiveID string
+}
+
+// GetDocumentParams is parameters of get_document operation.
+type GetDocumentParams struct {
+	BankID     string
+	DocumentID string
+}
+
+// GetEntityParams is parameters of get_entity operation.
+type GetEntityParams struct {
+	BankID   string
+	EntityID string
+}
+
+// GetGraphParams is parameters of get_graph operation.
+type GetGraphParams struct {
+	BankID string
+	Type   OptString `json:",omitempty,omitzero"`
+	Limit  OptInt    `json:",omitempty,omitzero"`
+}
+
+// GetMemoryParams is parameters of get_memory operation.
+type GetMemoryParams struct {
+	BankID   string
+	MemoryID string
+}
+
+// GetMentalModelParams is parameters of get_mental_model operation.
+type GetMentalModelParams struct {
+	BankID        string
+	MentalModelID string
+}
+
+// GetOperationStatusParams is parameters of get_operation_status operation.
+type GetOperationStatusParams struct {
+	BankID      string
+	OperationID string
+}
+
+// ListDirectivesParams is parameters of list_directives operation.
+type ListDirectivesParams struct {
+	BankID string
+	// Filter by tags.
+	Tags []string `json:",omitempty"`
+	// How to match tags.
+	TagsMatch OptListDirectivesTagsMatch `json:",omitempty,omitzero"`
+	// Only return active directives.
+	ActiveOnly OptBool `json:",omitempty,omitzero"`
+	Limit      OptInt  `json:",omitempty,omitzero"`
+	Offset     OptInt  `json:",omitempty,omitzero"`
+}
+
+// ListDocumentsParams is parameters of list_documents operation.
+type ListDocumentsParams struct {
+	BankID string
+	Q      OptString `json:",omitempty,omitzero"`
+	Limit  OptInt    `json:",omitempty,omitzero"`
+	Offset OptInt    `json:",omitempty,omitzero"`
+}
+
+// ListEntitiesParams is parameters of list_entities operation.
+type ListEntitiesParams struct {
+	BankID string
+	// Maximum number of entities to return.
+	Limit OptInt `json:",omitempty,omitzero"`
+	// Offset for pagination.
+	Offset OptInt `json:",omitempty,omitzero"`
+}
+
+// ListMemoriesParams is parameters of list_memories operation.
+type ListMemoriesParams struct {
+	BankID string
+	Type   OptString `json:",omitempty,omitzero"`
+	Q      OptString `json:",omitempty,omitzero"`
+	Limit  OptInt    `json:",omitempty,omitzero"`
+	Offset OptInt    `json:",omitempty,omitzero"`
+}
+
+// ListMentalModelsParams is parameters of list_mental_models operation.
+type ListMentalModelsParams struct {
+	BankID string
+	// Filter by tags.
+	Tags []string `json:",omitempty"`
+	// How to match tags.
+	TagsMatch OptListMentalModelsTagsMatch `json:",omitempty,omitzero"`
+	Limit     OptInt                       `json:",omitempty,omitzero"`
+	Offset    OptInt                       `json:",omitempty,omitzero"`
+}
+
+// ListOperationsParams is parameters of list_operations operation.
+type ListOperationsParams struct {
+	BankID string
+	// Filter by status: pending, completed, or failed.
+	Status OptString `json:",omitempty,omitzero"`
+	// Maximum number of operations to return.
+	Limit OptInt `json:",omitempty,omitzero"`
+	// Number of operations to skip.
+	Offset OptInt `json:",omitempty,omitzero"`
+}
+
+// ListTagsParams is parameters of list_tags operation.
+type ListTagsParams struct {
+	BankID string
+	// Wildcard pattern to filter tags (e.g., 'user:*' for user:alice, '*-admin' for role-admin). Use '*'
+	// as wildcard. Case-insensitive.
+	Q OptString `json:",omitempty,omitzero"`
+	// Maximum number of tags to return.
+	Limit OptInt `json:",omitempty,omitzero"`
+	// Offset for pagination.
+	Offset OptInt `json:",omitempty,omitzero"`
+}
+
+// RecallMemoriesParams is parameters of recall_memories operation.
+type RecallMemoriesParams struct {
+	BankID string
+}
+
+// ReflectParams is parameters of reflect operation.
+type ReflectParams struct {
+	BankID string
+}
+
+// RefreshMentalModelParams is parameters of refresh_mental_model operation.
+type RefreshMentalModelParams struct {
+	BankID        string
+	MentalModelID string
+}
+
+// RegenerateEntityObservationsParams is parameters of regenerate_entity_observations operation.
+type RegenerateEntityObservationsParams struct {
+	BankID   string
+	EntityID string
+}
+
+// ResetBankConfigParams is parameters of reset_bank_config operation.
+type ResetBankConfigParams struct {
+	BankID string
+}
+
+// RetainMemoriesParams is parameters of retain_memories operation.
+type RetainMemoriesParams struct {
+	BankID string
+}
+
+// TriggerConsolidationParams is parameters of trigger_consolidation operation.
+type TriggerConsolidationParams struct {
+	BankID string
+}
+
+// UpdateBankParams is parameters of update_bank operation.
+type UpdateBankParams struct {
+	BankID string
+}
+
+// UpdateBankConfigParams is parameters of update_bank_config operation.
+type UpdateBankConfigParams struct {
+	BankID string
+}
+
+// UpdateBankDispositionParams is parameters of update_bank_disposition operation.
+type UpdateBankDispositionParams struct {
+	BankID string
+}
+
+// UpdateDirectiveParams is parameters of update_directive operation.
+type UpdateDirectiveParams struct {
+	BankID      string
+	DirectiveID string
+}
+
+// UpdateMentalModelParams is parameters of update_mental_model operation.
+type UpdateMentalModelParams struct {
+	BankID        string
+	MentalModelID string
+}

hindsight-clients/go/internal/ogenapi/oas_request_encoders_gen.go

@@ -0,0 +1,179 @@
+// Code generated by ogen, DO NOT EDIT.
+
+package ogenapi
+
+import (
+	"bytes"
+	"net/http"
+
+	"github.com/go-faster/jx"
+	ht "github.com/ogen-go/ogen/http"
+)
+
+func encodeAddBankBackgroundRequest(
+	req *AddBackgroundRequest,
+	r *http.Request,
+) error {
+	const contentType = "application/json"
+	e := new(jx.Encoder)
+	{
+		req.Encode(e)
+	}
+	encoded := e.Bytes()
+	ht.SetBody(r, bytes.NewReader(encoded), contentType)
+	return nil
+}
+
+func encodeCreateDirectiveRequest(
+	req *CreateDirectiveRequest,
+	r *http.Request,
+) error {
+	const contentType = "application/json"
+	e := new(jx.Encoder)
+	{
+		req.Encode(e)
+	}
+	encoded := e.Bytes()
+	ht.SetBody(r, bytes.NewReader(encoded), contentType)
+	return nil
+}
+
+func encodeCreateMentalModelRequest(
+	req *CreateMentalModelRequest,
+	r *http.Request,
+) error {
+	const contentType = "application/json"
+	e := new(jx.Encoder)
+	{
+		req.Encode(e)
+	}
+	encoded := e.Bytes()
+	ht.SetBody(r, bytes.NewReader(encoded), contentType)
+	return nil
+}
+
+func encodeCreateOrUpdateBankRequest(
+	req *CreateBankRequest,
+	r *http.Request,
+) error {
+	const contentType = "application/json"
+	e := new(jx.Encoder)
+	{
+		req.Encode(e)
+	}
+	encoded := e.Bytes()
+	ht.SetBody(r, bytes.NewReader(encoded), contentType)
+	return nil
+}
+
+func encodeRecallMemoriesRequest(
+	req *RecallRequest,
+	r *http.Request,
+) error {
+	const contentType = "application/json"
+	e := new(jx.Encoder)
+	{
+		req.Encode(e)
+	}
+	encoded := e.Bytes()
+	ht.SetBody(r, bytes.NewReader(encoded), contentType)
+	return nil
+}
+
+func encodeReflectRequest(
+	req *ReflectRequest,
+	r *http.Request,
+) error {
+	const contentType = "application/json"
+	e := new(jx.Encoder)
+	{
+		req.Encode(e)
+	}
+	encoded := e.Bytes()
+	ht.SetBody(r, bytes.NewReader(encoded), contentType)
+	return nil
+}
+
+func encodeRetainMemoriesRequest(
+	req *RetainRequest,
+	r *http.Request,
+) error {
+	const contentType = "application/json"
+	e := new(jx.Encoder)
+	{
+		req.Encode(e)
+	}
+	encoded := e.Bytes()
+	ht.SetBody(r, bytes.NewReader(encoded), contentType)
+	return nil
+}
+
+func encodeUpdateBankRequest(
+	req *CreateBankRequest,
+	r *http.Request,
+) error {
+	const contentType = "application/json"
+	e := new(jx.Encoder)
+	{
+		req.Encode(e)
+	}
+	encoded := e.Bytes()
+	ht.SetBody(r, bytes.NewReader(encoded), contentType)
+	return nil
+}
+
+func encodeUpdateBankConfigRequest(
+	req *BankConfigUpdate,
+	r *http.Request,
+) error {
+	const contentType = "application/json"
+	e := new(jx.Encoder)
+	{
+		req.Encode(e)
+	}
+	encoded := e.Bytes()
+	ht.SetBody(r, bytes.NewReader(encoded), contentType)
+	return nil
+}
+
+func encodeUpdateBankDispositionRequest(
+	req *UpdateDispositionRequest,
+	r *http.Request,
+) error {
+	const contentType = "application/json"
+	e := new(jx.Encoder)
+	{
+		req.Encode(e)
+	}
+	encoded := e.Bytes()
+	ht.SetBody(r, bytes.NewReader(encoded), contentType)
+	return nil
+}
+
+func encodeUpdateDirectiveRequest(
+	req *UpdateDirectiveRequest,
+	r *http.Request,
+) error {
+	const contentType = "application/json"
+	e := new(jx.Encoder)
+	{
+		req.Encode(e)
+	}
+	encoded := e.Bytes()
+	ht.SetBody(r, bytes.NewReader(encoded), contentType)
+	return nil
+}
+
+func encodeUpdateMentalModelRequest(
+	req *UpdateMentalModelRequest,
+	r *http.Request,
+) error {
+	const contentType = "application/json"
+	e := new(jx.Encoder)
+	{
+		req.Encode(e)
+	}
+	encoded := e.Bytes()
+	ht.SetBody(r, bytes.NewReader(encoded), contentType)
+	return nil
+}

hindsight-clients/go/internal/ogenapi/oas_validators_gen.go

@@ -0,0 +1,935 @@
+// Code generated by ogen, DO NOT EDIT.
+
+package ogenapi
+
+import (
+	"fmt"
+
+	"github.com/go-faster/errors"
+	"github.com/ogen-go/ogen/validate"
+)
+
+func (s *BackgroundResponse) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if value, ok := s.Disposition.Get(); ok {
+			if err := func() error {
+				if err := value.Validate(); err != nil {
+					return err
+				}
+				return nil
+			}(); err != nil {
+				return err
+			}
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "disposition",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s *BankListItem) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if err := s.Disposition.Validate(); err != nil {
+			return err
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "disposition",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s *BankListResponse) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if s.Banks == nil {
+			return errors.New("nil is invalid value")
+		}
+		var failures []validate.FieldError
+		for i, elem := range s.Banks {
+			if err := func() error {
+				if err := elem.Validate(); err != nil {
+					return err
+				}
+				return nil
+			}(); err != nil {
+				failures = append(failures, validate.FieldError{
+					Name:  fmt.Sprintf("[%d]", i),
+					Error: err,
+				})
+			}
+		}
+		if len(failures) > 0 {
+			return &validate.Error{Fields: failures}
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "banks",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s *BankProfileResponse) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if err := s.Disposition.Validate(); err != nil {
+			return err
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "disposition",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s Budget) Validate() error {
+	switch s {
+	case "low":
+		return nil
+	case "mid":
+		return nil
+	case "high":
+		return nil
+	default:
+		return errors.Errorf("invalid value: %v", s)
+	}
+}
+
+func (s *CreateBankRequest) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if value, ok := s.Disposition.Get(); ok {
+			if err := func() error {
+				if err := value.Validate(); err != nil {
+					return err
+				}
+				return nil
+			}(); err != nil {
+				return err
+			}
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "disposition",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s *CreateMentalModelRequest) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if value, ok := s.MaxTokens.Get(); ok {
+			if err := func() error {
+				if err := (validate.Int{
+					MinSet:        true,
+					Min:           256,
+					MaxSet:        true,
+					Max:           8192,
+					MinExclusive:  false,
+					MaxExclusive:  false,
+					MultipleOfSet: false,
+					MultipleOf:    0,
+					Pattern:       nil,
+				}).Validate(int64(value)); err != nil {
+					return errors.Wrap(err, "int")
+				}
+				return nil
+			}(); err != nil {
+				return err
+			}
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "max_tokens",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s *DirectiveListResponse) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if s.Items == nil {
+			return errors.New("nil is invalid value")
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "items",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s *DispositionTraits) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if err := (validate.Int{
+			MinSet:        true,
+			Min:           1,
+			MaxSet:        true,
+			Max:           5,
+			MinExclusive:  false,
+			MaxExclusive:  false,
+			MultipleOfSet: false,
+			MultipleOf:    0,
+			Pattern:       nil,
+		}).Validate(int64(s.Empathy)); err != nil {
+			return errors.Wrap(err, "int")
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "empathy",
+			Error: err,
+		})
+	}
+	if err := func() error {
+		if err := (validate.Int{
+			MinSet:        true,
+			Min:           1,
+			MaxSet:        true,
+			Max:           5,
+			MinExclusive:  false,
+			MaxExclusive:  false,
+			MultipleOfSet: false,
+			MultipleOf:    0,
+			Pattern:       nil,
+		}).Validate(int64(s.Literalism)); err != nil {
+			return errors.Wrap(err, "int")
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "literalism",
+			Error: err,
+		})
+	}
+	if err := func() error {
+		if err := (validate.Int{
+			MinSet:        true,
+			Min:           1,
+			MaxSet:        true,
+			Max:           5,
+			MinExclusive:  false,
+			MaxExclusive:  false,
+			MultipleOfSet: false,
+			MultipleOf:    0,
+			Pattern:       nil,
+		}).Validate(int64(s.Skepticism)); err != nil {
+			return errors.Wrap(err, "int")
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "skepticism",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s *EntityDetailResponse) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if s.Observations == nil {
+			return errors.New("nil is invalid value")
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "observations",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s *EntityListResponse) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if s.Items == nil {
+			return errors.New("nil is invalid value")
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "items",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s *EntityStateResponse) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if s.Observations == nil {
+			return errors.New("nil is invalid value")
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "observations",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s *GraphDataResponse) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if s.Edges == nil {
+			return errors.New("nil is invalid value")
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "edges",
+			Error: err,
+		})
+	}
+	if err := func() error {
+		if s.Nodes == nil {
+			return errors.New("nil is invalid value")
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "nodes",
+			Error: err,
+		})
+	}
+	if err := func() error {
+		if s.TableRows == nil {
+			return errors.New("nil is invalid value")
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "table_rows",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s *HTTPValidationError) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		var failures []validate.FieldError
+		for i, elem := range s.Detail {
+			if err := func() error {
+				if err := elem.Validate(); err != nil {
+					return err
+				}
+				return nil
+			}(); err != nil {
+				failures = append(failures, validate.FieldError{
+					Name:  fmt.Sprintf("[%d]", i),
+					Error: err,
+				})
+			}
+		}
+		if len(failures) > 0 {
+			return &validate.Error{Fields: failures}
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "detail",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s ListDirectivesTagsMatch) Validate() error {
+	switch s {
+	case "any":
+		return nil
+	case "all":
+		return nil
+	case "exact":
+		return nil
+	default:
+		return errors.Errorf("invalid value: %v", s)
+	}
+}
+
+func (s *ListDocumentsResponse) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if s.Items == nil {
+			return errors.New("nil is invalid value")
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "items",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s *ListMemoryUnitsResponse) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if s.Items == nil {
+			return errors.New("nil is invalid value")
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "items",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s ListMentalModelsTagsMatch) Validate() error {
+	switch s {
+	case "any":
+		return nil
+	case "all":
+		return nil
+	case "exact":
+		return nil
+	default:
+		return errors.Errorf("invalid value: %v", s)
+	}
+}
+
+func (s *ListTagsResponse) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if s.Items == nil {
+			return errors.New("nil is invalid value")
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "items",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s *MentalModelListResponse) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if s.Items == nil {
+			return errors.New("nil is invalid value")
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "items",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s *OperationStatusResponse) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if err := s.Status.Validate(); err != nil {
+			return err
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "status",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s OperationStatusResponseStatus) Validate() error {
+	switch s {
+	case "pending":
+		return nil
+	case "completed":
+		return nil
+	case "failed":
+		return nil
+	case "not_found":
+		return nil
+	default:
+		return errors.Errorf("invalid value: %v", s)
+	}
+}
+
+func (s *OperationsListResponse) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if s.Operations == nil {
+			return errors.New("nil is invalid value")
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "operations",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s *RecallRequest) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if value, ok := s.Budget.Get(); ok {
+			if err := func() error {
+				if err := value.Validate(); err != nil {
+					return err
+				}
+				return nil
+			}(); err != nil {
+				return err
+			}
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "budget",
+			Error: err,
+		})
+	}
+	if err := func() error {
+		if value, ok := s.TagsMatch.Get(); ok {
+			if err := func() error {
+				if err := value.Validate(); err != nil {
+					return err
+				}
+				return nil
+			}(); err != nil {
+				return err
+			}
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "tags_match",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s RecallRequestTagsMatch) Validate() error {
+	switch s {
+	case "any":
+		return nil
+	case "all":
+		return nil
+	case "any_strict":
+		return nil
+	case "all_strict":
+		return nil
+	default:
+		return errors.Errorf("invalid value: %v", s)
+	}
+}
+
+func (s *RecallResponse) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if value, ok := s.Entities.Get(); ok {
+			if err := func() error {
+				if err := value.Validate(); err != nil {
+					return err
+				}
+				return nil
+			}(); err != nil {
+				return err
+			}
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "entities",
+			Error: err,
+		})
+	}
+	if err := func() error {
+		if s.Results == nil {
+			return errors.New("nil is invalid value")
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "results",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s RecallResponseEntities) Validate() error {
+	var failures []validate.FieldError
+	for key, elem := range s {
+		if err := func() error {
+			if err := elem.Validate(); err != nil {
+				return err
+			}
+			return nil
+		}(); err != nil {
+			failures = append(failures, validate.FieldError{
+				Name:  key,
+				Error: err,
+			})
+		}
+	}
+
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s *ReflectRequest) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if value, ok := s.Budget.Get(); ok {
+			if err := func() error {
+				if err := value.Validate(); err != nil {
+					return err
+				}
+				return nil
+			}(); err != nil {
+				return err
+			}
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "budget",
+			Error: err,
+		})
+	}
+	if err := func() error {
+		if value, ok := s.TagsMatch.Get(); ok {
+			if err := func() error {
+				if err := value.Validate(); err != nil {
+					return err
+				}
+				return nil
+			}(); err != nil {
+				return err
+			}
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "tags_match",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s ReflectRequestTagsMatch) Validate() error {
+	switch s {
+	case "any":
+		return nil
+	case "all":
+		return nil
+	case "any_strict":
+		return nil
+	case "all_strict":
+		return nil
+	default:
+		return errors.Errorf("invalid value: %v", s)
+	}
+}
+
+func (s *RetainRequest) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if s.Items == nil {
+			return errors.New("nil is invalid value")
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "items",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s *UpdateDispositionRequest) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if err := s.Disposition.Validate(); err != nil {
+			return err
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "disposition",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s *UpdateMentalModelRequest) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if value, ok := s.MaxTokens.Get(); ok {
+			if err := func() error {
+				if err := (validate.Int{
+					MinSet:        true,
+					Min:           256,
+					MaxSet:        true,
+					Max:           8192,
+					MinExclusive:  false,
+					MaxExclusive:  false,
+					MultipleOfSet: false,
+					MultipleOf:    0,
+					Pattern:       nil,
+				}).Validate(int64(value)); err != nil {
+					return errors.Wrap(err, "int")
+				}
+				return nil
+			}(); err != nil {
+				return err
+			}
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "max_tokens",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}
+
+func (s *ValidationError) Validate() error {
+	if s == nil {
+		return validate.ErrNilPointer
+	}
+
+	var failures []validate.FieldError
+	if err := func() error {
+		if s.Loc == nil {
+			return errors.New("nil is invalid value")
+		}
+		return nil
+	}(); err != nil {
+		failures = append(failures, validate.FieldError{
+			Name:  "loc",
+			Error: err,
+		})
+	}
+	if len(failures) > 0 {
+		return &validate.Error{Fields: failures}
+	}
+	return nil
+}

hindsight-clients/go/ogen.yml

@@ -0,0 +1,9 @@
+generator:
+  ignore_not_implemented: ["all"]
+  features:
+    disable:
+      - paths/server
+      - webhooks/server
+      - webhooks/client
+      - ogen/otel
+      - ogen/unimplemented

hindsight-clients/go/options.go

@@ -0,0 +1,321 @@
+package hindsight
+
+import (
+	"net/http"
+	"time"
+
+	"github.com/vectorize-io/hindsight-client-go/internal/ogenapi"
+)
+
+// --- Client options ---
+
+type clientConfig struct {
+	apiKey     string
+	httpClient *http.Client
+}
+
+// Option configures a [Client].
+type Option func(*clientConfig)
+
+// WithAPIKey sets the Bearer token used for authentication.
+func WithAPIKey(key string) Option {
+	return func(c *clientConfig) { c.apiKey = key }
+}
+
+// WithHTTPClient sets a custom [http.Client] for all requests.
+// If combined with [WithAPIKey], the API key transport wraps this client's transport.
+func WithHTTPClient(hc *http.Client) Option {
+	return func(c *clientConfig) { c.httpClient = hc }
+}
+
+// --- Re-exported types ---
+
+// Budget controls the computation budget for recall and reflect operations.
+type Budget = ogenapi.Budget
+
+const (
+	BudgetLow  = ogenapi.BudgetLow
+	BudgetMid  = ogenapi.BudgetMid
+	BudgetHigh = ogenapi.BudgetHigh
+)
+
+// TagsMatch controls how tag filtering works.
+type TagsMatch string
+
+const (
+	TagsMatchAny       TagsMatch = "any"
+	TagsMatchAll       TagsMatch = "all"
+	TagsMatchAnyStrict TagsMatch = "any_strict"
+	TagsMatchAllStrict TagsMatch = "all_strict"
+)
+
+type (
+	// RetainResponse is the response from a retain operation.
+	RetainResponse = ogenapi.RetainResponse
+
+	// RecallResponse is the response from a recall operation.
+	RecallResponse = ogenapi.RecallResponse
+
+	// RecallResult is a single memory result from recall.
+	RecallResult = ogenapi.RecallResult
+
+	// ReflectResponse is the response from a reflect operation.
+	ReflectResponse = ogenapi.ReflectResponse
+
+	// MemoryItem is a single memory item for retain operations.
+	MemoryItem = ogenapi.MemoryItem
+
+	// EntityInput provides entity hints for retain.
+	EntityInput = ogenapi.EntityInput
+
+	// BankProfileResponse is the response for bank profile operations.
+	BankProfileResponse = ogenapi.BankProfileResponse
+
+	// BankListResponse is the response for listing banks.
+	BankListResponse = ogenapi.BankListResponse
+
+	// DispositionTraits configures personality traits for a bank.
+	DispositionTraits = ogenapi.DispositionTraits
+
+	// TokenUsage reports LLM token consumption.
+	TokenUsage = ogenapi.TokenUsage
+
+	// ReflectBasedOn contains the evidence used for a reflect response.
+	ReflectBasedOn = ogenapi.ReflectBasedOn
+
+	// IncludeOptions controls what extra data is returned from recall.
+	IncludeOptions = ogenapi.IncludeOptions
+
+	// ReflectIncludeOptions controls what extra data is returned from reflect.
+	ReflectIncludeOptions = ogenapi.ReflectIncludeOptions
+)
+
+// --- Retain options ---
+
+// RetainOption configures a [Client.Retain] call.
+type RetainOption func(*retainConfig)
+
+type retainConfig struct {
+	timestamp  *time.Time
+	context    *string
+	documentID *string
+	metadata   map[string]string
+	entities   []EntityInput
+	tags       []string
+}
+
+// WithTimestamp sets the timestamp for a retained memory.
+func WithTimestamp(t time.Time) RetainOption {
+	return func(c *retainConfig) { c.timestamp = &t }
+}
+
+// WithContext sets additional context for a retained memory.
+func WithContext(ctx string) RetainOption {
+	return func(c *retainConfig) { c.context = &ctx }
+}
+
+// WithDocumentID groups retained memories under a document.
+func WithDocumentID(id string) RetainOption {
+	return func(c *retainConfig) { c.documentID = &id }
+}
+
+// WithMetadata attaches key-value metadata to a retained memory.
+func WithMetadata(m map[string]string) RetainOption {
+	return func(c *retainConfig) { c.metadata = m }
+}
+
+// WithEntities provides entity hints for a retained memory.
+func WithEntities(e []EntityInput) RetainOption {
+	return func(c *retainConfig) { c.entities = e }
+}
+
+// WithTags attaches tags to a retained memory for filtering.
+func WithTags(tags []string) RetainOption {
+	return func(c *retainConfig) { c.tags = tags }
+}
+
+// RetainBatchOption configures a [Client.RetainBatch] call.
+type RetainBatchOption func(*retainBatchConfig)
+
+type retainBatchConfig struct {
+	documentTags []string
+	async        bool
+}
+
+// WithDocumentTags sets tags applied to all items in a batch retain.
+func WithDocumentTags(tags []string) RetainBatchOption {
+	return func(c *retainBatchConfig) { c.documentTags = tags }
+}
+
+// WithAsync processes the retain batch asynchronously.
+func WithAsync(async bool) RetainBatchOption {
+	return func(c *retainBatchConfig) { c.async = async }
+}
+
+// --- Recall options ---
+
+// RecallOption configures a [Client.Recall] call.
+type RecallOption func(*recallConfig)
+
+type recallConfig struct {
+	types          []string
+	maxTokens      *int
+	budget         *Budget
+	trace          *bool
+	queryTimestamp *string
+	includeOpts    *IncludeOptions
+	tags           []string
+	tagsMatch      *TagsMatch
+}
+
+// WithTypes filters recalled memories by type (e.g., "world", "experience").
+func WithTypes(types []string) RecallOption {
+	return func(c *recallConfig) { c.types = types }
+}
+
+// WithMaxTokens sets the maximum tokens for recall results.
+func WithMaxTokens(n int) RecallOption {
+	return func(c *recallConfig) { c.maxTokens = &n }
+}
+
+// WithBudget sets the computation budget for recall.
+func WithBudget(b Budget) RecallOption {
+	return func(c *recallConfig) { c.budget = &b }
+}
+
+// WithTrace enables the execution trace in recall results.
+func WithTrace(enabled bool) RecallOption {
+	return func(c *recallConfig) { c.trace = &enabled }
+}
+
+// WithQueryTimestamp sets the temporal context for recall (ISO 8601 format).
+func WithQueryTimestamp(ts string) RecallOption {
+	return func(c *recallConfig) { c.queryTimestamp = &ts }
+}
+
+// WithInclude configures which additional data to include in recall results.
+func WithInclude(opts IncludeOptions) RecallOption {
+	return func(c *recallConfig) { c.includeOpts = &opts }
+}
+
+// WithRecallTags filters recalled memories by tags.
+func WithRecallTags(tags []string) RecallOption {
+	return func(c *recallConfig) { c.tags = tags }
+}
+
+// WithRecallTagsMatch sets how tags are matched during recall.
+func WithRecallTagsMatch(m TagsMatch) RecallOption {
+	return func(c *recallConfig) { c.tagsMatch = &m }
+}
+
+// --- Reflect options ---
+
+// ReflectOption configures a [Client.Reflect] call.
+type ReflectOption func(*reflectConfig)
+
+type reflectConfig struct {
+	budget         *Budget
+	maxTokens      *int
+	includeOpts    *ReflectIncludeOptions
+	responseSchema map[string]any
+	tags           []string
+	tagsMatch      *TagsMatch
+}
+
+// WithReflectBudget sets the computation budget for reflect.
+func WithReflectBudget(b Budget) ReflectOption {
+	return func(c *reflectConfig) { c.budget = &b }
+}
+
+// WithReflectMaxTokens sets the maximum tokens for the reflect response.
+func WithReflectMaxTokens(n int) ReflectOption {
+	return func(c *reflectConfig) { c.maxTokens = &n }
+}
+
+// WithReflectInclude configures which additional data to include in reflect results.
+func WithReflectInclude(opts ReflectIncludeOptions) ReflectOption {
+	return func(c *reflectConfig) { c.includeOpts = &opts }
+}
+
+// WithResponseSchema sets a JSON Schema for structured output from reflect.
+func WithResponseSchema(schema map[string]any) ReflectOption {
+	return func(c *reflectConfig) { c.responseSchema = schema }
+}
+
+// WithReflectTags filters memories by tags during reflect.
+func WithReflectTags(tags []string) ReflectOption {
+	return func(c *reflectConfig) { c.tags = tags }
+}
+
+// WithReflectTagsMatch sets how tags are matched during reflect.
+func WithReflectTagsMatch(m TagsMatch) ReflectOption {
+	return func(c *reflectConfig) { c.tagsMatch = &m }
+}
+
+// --- Bank options ---
+
+// CreateBankOption configures a [Client.CreateBank] call.
+type CreateBankOption func(*createBankConfig)
+
+type createBankConfig struct {
+	name        *string
+	mission     *string
+	disposition *DispositionTraits
+}
+
+// WithBankName sets the display name for a bank.
+func WithBankName(name string) CreateBankOption {
+	return func(c *createBankConfig) { c.name = &name }
+}
+
+// WithMission sets the mission for a bank.
+func WithMission(mission string) CreateBankOption {
+	return func(c *createBankConfig) { c.mission = &mission }
+}
+
+// WithDisposition sets the personality traits for a bank.
+func WithDisposition(d DispositionTraits) CreateBankOption {
+	return func(c *createBankConfig) { c.disposition = &d }
+}
+
+// --- helpers ---
+
+func optString(s string) ogenapi.OptString {
+	return ogenapi.NewOptString(s)
+}
+
+func optStringPtr(s *string) ogenapi.OptString {
+	if s == nil {
+		return ogenapi.OptString{}
+	}
+	return ogenapi.NewOptString(*s)
+}
+
+func optInt(n int) ogenapi.OptInt {
+	return ogenapi.NewOptInt(n)
+}
+
+func optIntPtr(n *int) ogenapi.OptInt {
+	if n == nil {
+		return ogenapi.OptInt{}
+	}
+	return ogenapi.NewOptInt(*n)
+}
+
+func optBool(b bool) ogenapi.OptBool {
+	return ogenapi.NewOptBool(b)
+}
+
+func optBoolPtr(b *bool) ogenapi.OptBool {
+	if b == nil {
+		return ogenapi.OptBool{}
+	}
+	return ogenapi.NewOptBool(*b)
+}
+
+func optBudget(b *Budget) ogenapi.OptBudget {
+	if b == nil {
+		return ogenapi.OptBudget{}
+	}
+	return ogenapi.NewOptBudget(*b)
+}

hindsight-clients/go/recall.go

@@ -0,0 +1,59 @@
+package hindsight
+
+import (
+	"context"
+	"fmt"
+
+	"github.com/vectorize-io/hindsight-client-go/internal/ogenapi"
+)
+
+// Recall retrieves memories from the given bank that match the query.
+func (c *Client) Recall(ctx context.Context, bankID, query string, opts ...RecallOption) (*RecallResponse, error) {
+	var cfg recallConfig
+	for _, o := range opts {
+		o(&cfg)
+	}
+
+	req := &ogenapi.RecallRequest{
+		Query: query,
+	}
+	if cfg.budget != nil {
+		req.Budget = ogenapi.NewOptBudget(*cfg.budget)
+	}
+	if cfg.maxTokens != nil {
+		req.MaxTokens = ogenapi.NewOptInt(*cfg.maxTokens)
+	}
+	if cfg.trace != nil {
+		req.Trace = ogenapi.NewOptBool(*cfg.trace)
+	}
+	if cfg.queryTimestamp != nil {
+		req.QueryTimestamp = ogenapi.NewOptString(*cfg.queryTimestamp)
+	}
+	if cfg.types != nil {
+		req.Types = cfg.types
+	}
+	if cfg.includeOpts != nil {
+		req.Include = ogenapi.NewOptIncludeOptions(*cfg.includeOpts)
+	}
+	if cfg.tags != nil {
+		req.Tags = cfg.tags
+	}
+	if cfg.tagsMatch != nil {
+		req.TagsMatch = ogenapi.NewOptRecallRequestTagsMatch(
+			ogenapi.RecallRequestTagsMatch(*cfg.tagsMatch),
+		)
+	}
+
+	res, err := c.api.RecallMemories(ctx, req, ogenapi.RecallMemoriesParams{
+		BankID: bankID,
+	})
+	if err != nil {
+		return nil, err
+	}
+
+	resp, ok := res.(*ogenapi.RecallResponse)
+	if !ok {
+		return nil, fmt.Errorf("hindsight: unexpected response type %T", res)
+	}
+	return resp, nil
+}

hindsight-clients/go/reflect.go

@@ -0,0 +1,74 @@
+package hindsight
+
+import (
+	"context"
+	"encoding/json"
+	"fmt"
+
+	"github.com/go-faster/jx"
+
+	"github.com/vectorize-io/hindsight-client-go/internal/ogenapi"
+)
+
+// Reflect performs disposition-aware reasoning using the bank's memories and
+// mental models. Returns a markdown-formatted response.
+func (c *Client) Reflect(ctx context.Context, bankID, query string, opts ...ReflectOption) (*ReflectResponse, error) {
+	var cfg reflectConfig
+	for _, o := range opts {
+		o(&cfg)
+	}
+
+	req := &ogenapi.ReflectRequest{
+		Query: query,
+	}
+	if cfg.budget != nil {
+		req.Budget = ogenapi.NewOptBudget(*cfg.budget)
+	}
+	if cfg.maxTokens != nil {
+		req.MaxTokens = ogenapi.NewOptInt(*cfg.maxTokens)
+	}
+	if cfg.includeOpts != nil {
+		req.Include = ogenapi.NewOptReflectIncludeOptions(*cfg.includeOpts)
+	}
+	if cfg.responseSchema != nil {
+		schema, err := toResponseSchema(cfg.responseSchema)
+		if err != nil {
+			return nil, fmt.Errorf("hindsight: marshal response_schema: %w", err)
+		}
+		req.ResponseSchema = ogenapi.NewOptReflectRequestResponseSchema(schema)
+	}
+	if cfg.tags != nil {
+		req.Tags = cfg.tags
+	}
+	if cfg.tagsMatch != nil {
+		req.TagsMatch = ogenapi.NewOptReflectRequestTagsMatch(
+			ogenapi.ReflectRequestTagsMatch(*cfg.tagsMatch),
+		)
+	}
+
+	res, err := c.api.Reflect(ctx, req, ogenapi.ReflectParams{
+		BankID: bankID,
+	})
+	if err != nil {
+		return nil, err
+	}
+
+	resp, ok := res.(*ogenapi.ReflectResponse)
+	if !ok {
+		return nil, fmt.Errorf("hindsight: unexpected response type %T", res)
+	}
+	return resp, nil
+}
+
+// toResponseSchema converts a map[string]any JSON schema to the ogen type.
+func toResponseSchema(schema map[string]any) (ogenapi.ReflectRequestResponseSchema, error) {
+	out := make(ogenapi.ReflectRequestResponseSchema, len(schema))
+	for k, v := range schema {
+		data, err := json.Marshal(v)
+		if err != nil {
+			return nil, err
+		}
+		out[k] = jx.Raw(data)
+	}
+	return out, nil
+}

hindsight-clients/go/retain.go

@@ -0,0 +1,73 @@
+package hindsight
+
+import (
+	"context"
+	"fmt"
+
+	"github.com/vectorize-io/hindsight-client-go/internal/ogenapi"
+)
+
+// Retain stores a single memory in the given bank.
+// It wraps [Client.RetainBatch] for convenience.
+func (c *Client) Retain(ctx context.Context, bankID, content string, opts ...RetainOption) (*RetainResponse, error) {
+	var cfg retainConfig
+	for _, o := range opts {
+		o(&cfg)
+	}
+
+	item := ogenapi.MemoryItem{
+		Content: content,
+	}
+	if cfg.timestamp != nil {
+		item.Timestamp = ogenapi.NewOptDateTime(*cfg.timestamp)
+	}
+	if cfg.context != nil {
+		item.Context = ogenapi.NewOptString(*cfg.context)
+	}
+	if cfg.documentID != nil {
+		item.DocumentID = ogenapi.NewOptString(*cfg.documentID)
+	}
+	if cfg.metadata != nil {
+		m := ogenapi.MemoryItemMetadata(cfg.metadata)
+		item.Metadata = ogenapi.NewOptMemoryItemMetadata(m)
+	}
+	if cfg.entities != nil {
+		item.Entities = cfg.entities
+	}
+	if cfg.tags != nil {
+		item.Tags = cfg.tags
+	}
+
+	return c.RetainBatch(ctx, bankID, []MemoryItem{item})
+}
+
+// RetainBatch stores multiple memories in the given bank.
+func (c *Client) RetainBatch(ctx context.Context, bankID string, items []MemoryItem, opts ...RetainBatchOption) (*RetainResponse, error) {
+	var cfg retainBatchConfig
+	for _, o := range opts {
+		o(&cfg)
+	}
+
+	req := &ogenapi.RetainRequest{
+		Items: items,
+	}
+	if cfg.async {
+		req.Async = ogenapi.NewOptBool(true)
+	}
+	if cfg.documentTags != nil {
+		req.DocumentTags = cfg.documentTags
+	}
+
+	res, err := c.api.RetainMemories(ctx, req, ogenapi.RetainMemoriesParams{
+		BankID: bankID,
+	})
+	if err != nil {
+		return nil, err
+	}
+
+	resp, ok := res.(*ogenapi.RetainResponse)
+	if !ok {
+		return nil, fmt.Errorf("hindsight: unexpected response type %T", res)
+	}
+	return resp, nil
+}

hindsight-clients/python/.openapi-generator/FILES

@@ -16,12 +16,15 @@ hindsight_client_api/models/__init__.py
 hindsight_client_api/models/add_background_request.py
 hindsight_client_api/models/async_operation_submit_response.py
 hindsight_client_api/models/background_response.py
+hindsight_client_api/models/bank_config_response.py
+hindsight_client_api/models/bank_config_update.py
 hindsight_client_api/models/bank_list_item.py
 hindsight_client_api/models/bank_list_response.py
 hindsight_client_api/models/bank_profile_response.py
 hindsight_client_api/models/bank_stats_response.py
 hindsight_client_api/models/budget.py
 hindsight_client_api/models/cancel_operation_response.py
+hindsight_client_api/models/child_operation_status.py
 hindsight_client_api/models/chunk_data.py
 hindsight_client_api/models/chunk_include_options.py
 hindsight_client_api/models/chunk_response.py