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Author SHA1 Message Date
Chris Bartholomew 4faef5b902 fix(webhooks): route webhook endpoints through tenant-aware engine methods
Webhook create/list/get/update/delete and list-deliveries endpoints in
the HTTP layer were calling pool.fetchrow/pool.fetch directly with
fq_table("webhooks"), bypassing the async-local schema context that
fq_table reads via get_current_schema(). Under deployments that set a
per-request target schema (multi-tenant routing), this caused webhooks
to be written to and read from the default schema while every other
operation on the same bank correctly resolved to the per-target
schema. Webhooks would land in the wrong schema; the fire path
(which uses the bank's resolved schema) would not see them and never
enqueued webhook_delivery operations -- silent failure, no errors.

Move the SQL into MemoryEngine methods that call _authenticate_tenant
first (matching the pattern used by retain/consolidate/mental-models),
so fq_table sees the same schema as the rest of the bank's data.

Add schema-isolation tests covering create/list/get/update/delete and
deliveries.
2026-05-02 10:25:47 -04:00
2307 changed files with 35758 additions and 293275 deletions
-1
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@@ -1,7 +1,6 @@
{
"$schema": "https://anthropic.com/claude-code/marketplace.schema.json",
"name": "hindsight",
"version": "0.7.2",
"description": "Official Hindsight integrations for Claude Code",
"owner": {
"name": "vectorize-io"
+3 -45
View File
@@ -73,11 +73,6 @@ results = await asyncio.gather(*tasks, return_exceptions=True)
results = await asyncio.gather(*tasks, return_exceptions=True)
```
### API Layer & Data Access
- **No direct database access in `api/http.py`** (or any API router). HTTP handlers must not build SQL, call `acquire_with_retry` / `conn.fetch` / `conn.fetchrow` / `conn.execute`, or reference `fq_table(...)`. All persistence and queries live in `MemoryEngine` (the engine layer). A handler parses/validates the request, calls an engine method, shapes the HTTP response, and maps domain results to status codes (e.g. a `None` return → 404).
- **Authentication/tenancy is enforced inside each engine method, not assumed by the handler.** Every engine method that touches bank-scoped data must authenticate via `request_context` — typically `await self._authenticate_tenant(request_context)` (often indirectly through `get_bank_profile(...)`) — so the correct tenant schema is resolved before any query runs. Handlers must thread `request_context` through to the engine method; never query a tenant-scoped table assuming the schema is already set.
- Engine methods return typed models (Pydantic/dataclass), not raw dicts (see Type Safety).
### Branch Hygiene
- **Always start new feature branches from `origin/main`** — rebase to ensure a clean base.
- **Only include commits relevant to the PR/branch/feature** — no unrelated changes. If the branch contains commits that don't belong, they must be removed before merging.
@@ -140,13 +135,6 @@ For each new or significantly changed function/endpoint/class:
Flag any new logic that lacks test coverage.
**LLM-behaviour changes need a real-LLM judge test, not MockLLM.** If the change alters how the model interprets a prompt — fact/observation extraction, `fact_type` (world/experience) classification, speaker attribution, instruction-following, prompt wording — there MUST be a test marked `pytest.mark.hs_llm_core` that runs the real pipeline and asserts via `tests.llm_judge.assert_meets_criteria` (not string/enum matching). Flag these as findings:
- A prompt/classification change verified only by MockLLM or string assertions (MockLLM echoes input — such tests pass spuriously). **Should fix.**
- A test that hard-asserts `fact_type == "world"/"experience"` (or other model-decided output) instead of judging it — non-deterministic, will flake across providers/runs. **Should fix** (move the classification check into the judge `criteria`; keep only genuinely deterministic structural asserts direct).
- Deterministic mechanics (prompt assembly, suppression/branching logic) that are covered *only* by a slow LLM test — these should also have fast non-LLM unit tests. **Note.**
See CLAUDE.md → Key Conventions → Testing for the full pattern.
### 7. Check API consistency
If any files in `hindsight-api-slim/hindsight_api/api/` were changed:
@@ -154,12 +142,6 @@ If any files in `hindsight-api-slim/hindsight_api/api/` were changed:
- Were the client SDKs regenerated? (`./scripts/generate-clients.sh`)
- Were the control plane proxy routes updated? (`hindsight-control-plane/src/app/api/`)
### 7b. Check API-layer data-access boundary
For each changed handler in `hindsight-api-slim/hindsight_api/api/` (e.g. `http.py`, `mcp.py`):
- **Flag any direct DB access in the handler** — `acquire_with_retry`, `conn.fetch` / `fetchrow` / `execute`, raw SQL strings, or `fq_table(...)`. These are a **must fix**: the query must be moved into a `MemoryEngine` method that returns a typed model, and the handler must call that method.
- **Verify authentication is enforced in the engine** — the handler must delegate to an engine method that authenticates via `request_context` (`_authenticate_tenant`, typically through `get_bank_profile`). A handler that reads/writes tenant-scoped data without an engine method enforcing auth is a **must fix** (tenant data could leak across schemas).
### 8. Check code comments
For each non-trivial change:
@@ -172,8 +154,7 @@ For each non-trivial change:
If any files in `hindsight-integrations/` were added or changed, verify:
- **Tests exist** — the integration must have tests that simulate/exercise the external framework (not just pure unit tests of helpers). Check for a `tests/` directory with meaningful test files.
- **CI job exists** — check `.github/workflows/test.yml` for a corresponding `test-<name>-integration` job. If missing, flag it.
- **Release process** — check that the integration name is in the `VALID_INTEGRATIONS` array in `scripts/release-integration.sh` AND in the `INTEGRATIONS` dict in `hindsight-dev/hindsight_dev/generate_changelog.py` (the changelog generator keeps its own list; a release fails at the changelog step if the name is missing there). If either is missing, flag it.
- **Docs gallery + sidebar entry** — the integration must have an entry in `hindsight-docs/src/data/integrations.json`. This file is the **single source of truth** that drives both the integrations gallery and the docs sidebar (the sidebar category is injected from it at render time across all docs versions). The entry needs an internal `/sdks/integrations/<slug>` `link` and a matching page at `hindsight-docs/docs-integrations/<slug>.md(x)`. The `hindsight-docs/scripts/check-integrations.mjs` build step enforces both directions — forward: every internal JSON entry has a doc page; reverse: every released tag (`integrations/<name>/vX.Y.Z`) appears in the JSON (private infra like `cloudflare-oauth-proxy` is in the script's `EXCLUDED` set). Flag any integration that is released (or being released) but missing from `integrations.json`, and any JSON entry without a doc page. Do **not** hand-edit `versioned_sidebars/*.json` to add integration links — they are positional placeholders filled from the JSON.
- **Release process** — check that the integration name is in the `VALID_INTEGRATIONS` array in `scripts/release-integration.sh`. If missing, flag it.
- **Code standards** — the integration code must follow all Python style rules (type hints, no raw dicts, no tuple returns, etc.).
### 10. Check MCP tool registration completeness
@@ -185,26 +166,7 @@ If any new MCP tools were added or existing tools renamed in `hindsight-api-slim
- **`MCP_TOOL_GROUPS`** in `hindsight-control-plane/src/components/bank-config-view.tsx` — must include the new tool in the appropriate group for the UI tool selector
- **Tool count assertions** in tests (e.g., `test_mcp_tools.py`) — must be updated to reflect the new count
### 11. Check backup/restore table coverage
If a migration adds a new PostgreSQL table (look for `CREATE TABLE` / `op.create_table` in `hindsight-api-slim/hindsight_api/alembic/versions/`):
- **`BACKUP_TABLES`** in `hindsight-api-slim/hindsight_api/admin/cli.py` — must include the new table, placed after any table it references via foreign key (parents before children). A missing entry is silent data loss: the table is never backed up, and restore's `TRUNCATE banks CASCADE` wipes any FK-to-banks child (e.g. `mental_models`, `directives`) on restore even though it was never saved.
- The guard test `test_backup_tables_covers_entire_schema` in `tests/test_admin_backup_restore.py` enforces this — flag it as a **must fix** if a new table is absent from `BACKUP_TABLES`.
- Oracle-only tables (e.g. `observation_sources`) are intentionally excluded — admin backup/restore is PostgreSQL-only.
### 11b. Check new config flags update the env template
If the diff adds a new configuration field (a new `ENV_*` / `HINDSIGHT_*` env var
in `hindsight-api-slim/hindsight_api/config.py`):
- **`.env.example`** (repo root) — must add the variable (commented if optional)
alongside the docs entry in `hindsight-docs/docs/developer/configuration.md`.
A flag added to `config.py` but absent from `.env.example` is a **should fix**.
- **`hindsight-embed/hindsight_embed/env.example`** — the bundled copy must stay
byte-identical to the repo-root `.env.example` (it seeds embed/profile configs).
The `test_bundled_template_matches_repo_root` sync test fails on drift; if the
root file changed without re-copying, flag it as a **must fix**.
### 12. Review against other coding standards
### 11. Review against other coding standards
Check the diff for violations of the standards listed above:
- Python files at project root (not allowed)
@@ -216,7 +178,7 @@ Check the diff for violations of the standards listed above:
- Premature abstractions or speculative helpers
- Backwards-compatibility hacks (unused vars, re-exports, "removed" comments)
### 13. Report findings
### 12. Report findings
Present a clear summary organized by severity:
@@ -227,11 +189,7 @@ Present a clear summary organized by severity:
- Raw dict usage for structured data (including internal code)
- Multi-item tuple returns (including internal code)
- Missing tests for new endpoints
- Direct DB access (raw SQL / `acquire_with_retry` / `fq_table`) in an `api/` handler instead of a `MemoryEngine` method
- Tenant-scoped data accessed without authentication enforced in the engine (`_authenticate_tenant` / `get_bank_profile`)
- New integration missing tests, CI job, or release-integration.sh entry
- Released/added integration missing from `hindsight-docs/src/data/integrations.json`, or a JSON entry with no `docs-integrations/<slug>` page (fails the docs build via `check-integrations.mjs`)
- New PostgreSQL table missing from `BACKUP_TABLES` in `admin/cli.py` (silent data loss on restore)
**Should fix** — issues that hurt code quality:
- Dead code / unused imports missed by linter
-116
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@@ -1,116 +0,0 @@
---
name: hs-release
description: Cut a core Hindsight release (vX.Y.Z) and open the changelog + blog PR. Use when asked to cut/start a release, bump the version, or publish a new Hindsight version.
user_invocable: true
---
# Hindsight Release
Cut a **core** Hindsight release and open the accompanying changelog/blog PR. This is for the core
product version (API, clients, CLI, control plane, Helm). **Integrations are versioned
independently** — use `scripts/release-integration.sh` for those, not this skill.
The release is **irreversible and outward-facing**: it tags a version and pushes it straight to
`main`, which triggers CI that publishes packages to PyPI / npm / Helm. Confirm the version number
and that the intended fixes are already merged to `main` before you start.
## Step 0 — Pre-flight
1. **Decide the base.** A release is cut from the latest `origin/main`, never from a feature
branch. `git fetch origin --tags` first. Confirm the "couple of fixes" the user means are
actually merged to `main` (`git log v<prev>..origin/main --oneline`).
2. **Find where `main` is checked out.** `main` is often already checked out in a sibling worktree
(`git worktree list`). You **cannot** check out `main` in a second worktree — run the release in
the worktree that already holds it. If that worktree is dirty with throwaway cruft
(`.next-*` tsconfig paths, screenshots), `git stash push -u`, fast-forward to `origin/main`,
run the release, then `git stash pop`.
3. **Pitfall:** never pipe the checkout in an `&&` chain like
`git checkout main 2>&1 | tail && git reset --hard ...` — the pipe's exit status is `tail`'s
(always 0), so a failed checkout won't stop the chain and the `reset` fires on the **wrong
branch**. Check out as its own command and verify `git branch --show-current` before resetting.
## Step 1 — Cut the release
Run from the worktree on a clean `main`:
```bash
./scripts/release.sh <version> # e.g. 0.8.1 (no leading v)
```
`release.sh` bumps the version in every component, regenerates the OpenAPI spec + all client SDKs,
updates docs versioning, commits `Release v<version>`, tags `v<version>`, and **pushes the commit
and tag directly to `main`**. The push triggers the `Release` GitHub Actions workflow that builds
and publishes the packages. It is **not** a PR.
Verify after: `gh run list --limit 5` should show the `Release v<version>` workflow running, and
`git ls-remote --tags origin v<version>` should return the tag.
## Step 2 — Changelog + blog PR (separate)
Done **after** the tag exists, as its own PR (precedent: v0.8.0 = #2053, v0.8.1 = #2080). Work on a
branch off the new `main`:
```bash
git checkout -b docs-changelog-<version> origin/main
```
Only spin up a separate worktree (`git worktree add ../hindsight-changelog-<version> -b
docs-changelog-<version> origin/main`) if you can't get a clean checkout otherwise — e.g. `main` is
held in another worktree and the current one has work you don't want to disturb.
**Branch naming:** use the `docs-` (hyphen) convention, e.g. `docs-changelog-0.8.1`. A remote
branch literally named `docs` exists, so any `docs/...` branch is rejected on push with
`directory file conflict`.
### Changelog
```bash
uv run --directory hindsight-dev generate-changelog <version>
```
LLM-summarizes the commits between the previous tag and `v<version>` and prepends an entry to
`hindsight-docs/src/pages/changelog/index.md`. Requires `OPENAI_API_KEY` (already in the repo
`.env`). It excludes `hindsight-integrations/` source, but new integrations whose commits also
touched docs will still appear — that matches precedent, leave them in the **changelog**.
### Blog post
Hand-write `hindsight-docs/blog/YYYY-MM-DD-version-X-Y-Z.md` (mirror an existing one; patch
releases are short — see `2026-06-02-version-0-7-2.md`). Guidance:
- **Explain user impact, not internals/mechanism.** Lead with what the user can now do and what to
set. Config/env-var names are fine (developer-facing), code symbols and internals are not.
- **Do not list integrations in the release blog.** The core blog covers core engine / API /
ops changes; each integration ships its own changelog. (Integrations may still appear in the
generated `changelog/index.md` — that's fine; just keep them out of the blog.)
- Call out an upgrade recommendation when there are operational/data-integrity fixes.
- Validate formatting: `npx prettier --check <blog file>`.
### Sync the docs skill
```bash
./scripts/generate-docs-skill.sh
```
Refreshes `skills/hindsight-docs/references/changelog/index.md`. It will also bump
`skills/hindsight-docs/references/openapi.json` by one version — `release.sh` regenerates the skill
*before* bumping OpenAPI, so the skill copy lags a version in the release commit; this step syncs
it. Expect a one-line `version` diff there; keep it.
### Commit, push, PR
```bash
git add -A
git commit --no-verify -m "docs: changelog and blog post for v<version>"
git push -u origin docs-changelog-<version>
gh pr create --base main --title "docs: changelog and blog post for v<version>" --body "..."
```
Expected files in the PR: the changelog entry, the new blog post, the regenerated skill changelog
mirror, and the skill `openapi.json` version sync.
## Cleanup
If you created a temporary worktree, remove it once the PR is up
(`git worktree remove ../hindsight-changelog-<version>`; the branch stays on origin). Restore any
stash you popped in Step 0.
+3 -127
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@@ -2,24 +2,11 @@
# Copy this file to .env and fill in your values
# LLM Configuration (Required)
# Supported providers: openai, groq, ollama, gemini, anthropic, lmstudio, vertexai, minimax, deepseek, zai, atlas, volcano
# Supported providers: openai, groq, ollama, gemini, anthropic, lmstudio, vertexai, minimax, deepseek, volcano
HINDSIGHT_API_LLM_PROVIDER=openai
HINDSIGHT_API_LLM_API_KEY=your-api-key-here
HINDSIGHT_API_LLM_MODEL=gpt-4o-mini
HINDSIGHT_API_LLM_BASE_URL=https://api.openai.com/v1
# Reasoning effort for providers/models that support it. Examples: low, medium, high, xhigh.
# HINDSIGHT_API_LLM_REASONING_EFFORT=low
# Sampling temperature for internal LLM calls. Set a number in [0.0, 2.0], or `none`
# to omit the temperature parameter entirely (required for models that reject explicit
# temperatures, e.g. Azure gpt-5.5). The global override below applies to every operation;
# per-operation overrides (defaults: verification=0.0, retain=0.1, reflect=0.9,
# consolidation=0.0) take precedence.
# HINDSIGHT_API_LLM_TEMPERATURE=none
# HINDSIGHT_API_LLM_TEMPERATURE_VERIFICATION=0.0
# HINDSIGHT_API_LLM_TEMPERATURE_RETAIN=0.1
# HINDSIGHT_API_LLM_TEMPERATURE_REFLECT=0.9
# HINDSIGHT_API_LLM_TEMPERATURE_CONSOLIDATION=0.0
# Example: Anthropic Claude configuration
# HINDSIGHT_API_LLM_PROVIDER=anthropic
@@ -36,53 +23,23 @@ HINDSIGHT_API_LLM_BASE_URL=https://api.openai.com/v1
# Example: MiniMax configuration (1M context window)
# HINDSIGHT_API_LLM_PROVIDER=minimax
# HINDSIGHT_API_LLM_API_KEY=your-minimax-api-key
# HINDSIGHT_API_LLM_MODEL=MiniMax-M3 # or MiniMax-M2.7 for the previous generation
# HINDSIGHT_API_LLM_MODEL=MiniMax-M2.7
# Example: DeepSeek configuration (https://api.deepseek.com)
# HINDSIGHT_API_LLM_PROVIDER=deepseek
# HINDSIGHT_API_LLM_API_KEY=your-deepseek-api-key
# HINDSIGHT_API_LLM_MODEL=deepseek-v4-flash # or deepseek-v4-pro / deepseek-chat / deepseek-reasoner
# Example: z.ai configuration (Zhipu GLM series, https://z.ai)
# HINDSIGHT_API_LLM_PROVIDER=zai
# HINDSIGHT_API_LLM_API_KEY=your-zai-api-key
# HINDSIGHT_API_LLM_MODEL=glm-4.5-flash # or glm-4.5-air for the paid tier
# Example: Atlas Cloud configuration (OpenAI-compatible, https://www.atlascloud.ai)
# HINDSIGHT_API_LLM_PROVIDER=atlas
# HINDSIGHT_API_LLM_API_KEY=your-atlascloud-api-key
# HINDSIGHT_API_LLM_MODEL=deepseek-ai/deepseek-v4-pro # reasoning model; also Qwen / GLM / Kimi / MiniMax, etc.
# Example: LM Studio local configuration (Qwen 2.5 32B recommended)
# HINDSIGHT_API_LLM_PROVIDER=lmstudio
# HINDSIGHT_API_LLM_API_KEY=lmstudio
# HINDSIGHT_API_LLM_BASE_URL=http://localhost:1234/v1
# HINDSIGHT_API_LLM_MODEL=qwen2.5-32b-instruct
# Multi-LLM strategies: configure extra LLMs by index alongside the primary above,
# then pick a routing strategy. Unset = single primary LLM (default). Members are
# numbered from 1; indices must be contiguous. Each operation can override with a
# RETAIN_/REFLECT_/CONSOLIDATION_ prefix (e.g. HINDSIGHT_API_RETAIN_LLM_1_PROVIDER).
# HINDSIGHT_API_LLM_1_PROVIDER=groq
# HINDSIGHT_API_LLM_1_API_KEY=your-groq-api-key
# HINDSIGHT_API_LLM_1_MODEL=openai/gpt-oss-120b
# HINDSIGHT_API_LLM_2_PROVIDER=anthropic
# HINDSIGHT_API_LLM_2_API_KEY=your-anthropic-api-key
# Strategy JSON: {"mode": "failover"} or {"mode": "round-robin"}.
# Round-robin accepts optional positive-int "weights" (one per member, primary first).
# HINDSIGHT_API_LLM_STRATEGY={"mode": "failover"}
# API Configuration (Optional)
HINDSIGHT_API_HOST=0.0.0.0
HINDSIGHT_API_PORT=8888
HINDSIGHT_API_LOG_LEVEL=info
# Optional retain chunking override for structured logs/transcripts.
# Unset uses HINDSIGHT_API_RETAIN_CHUNK_SIZE as the structured-chunk limit.
# HINDSIGHT_API_RETAIN_STRUCTURED_CHUNK_SIZE=
# Dry-run extraction preview endpoint (POST /memories/dry-run-extract). Enabled by default; it makes
# a real LLM call but stores nothing. Set to false to remove the endpoint (returns 404).
# HINDSIGHT_API_ENABLE_DRY_RUN_EXTRACT=true
# Base Path / Reverse Proxy Support (Optional)
# Set these when deploying behind a reverse proxy with path-based routing
@@ -92,10 +49,8 @@ HINDSIGHT_API_LOG_LEVEL=info
# Database (Optional - uses embedded pg0 by default)
# HINDSIGHT_API_DATABASE_URL=postgresql://user:pass@host:5432/db
# HINDSIGHT_API_READ_DATABASE_URL= # Optional read-replica URL. When set, recall queries (semantic, BM25, graph, temporal) flow through a separate pool against this URL, offloading the primary. Typically points to a read-only endpoint (CNPG's <cluster>-ro service or Aurora reader endpoint).
# HINDSIGHT_API_MIGRATION_DATABASE_URL= # Direct PostgreSQL URL for migrations (bypasses PgBouncer). Falls back to DATABASE_URL.
# HINDSIGHT_API_DATABASE_SCHEMA=public # PostgreSQL schema name (default: public)
# HINDSIGHT_API_MIGRATION_CONCURRENCY=1 # Tenant schemas to migrate concurrently (PG only, each in its own process; per-schema work stays sequential). Each worker has ~1-2s startup cost + uses ~3 DB connections, so it only pays off with many schemas (tens+) or slow migrations; keep concurrency*3 <= spare max_connections. Default: 1 (sequential).
# Vector Extension (Optional - uses pgvector by default)
# Options: "pgvector" (default), "vchord", "pgvectorscale" (DiskANN)
@@ -103,71 +58,13 @@ HINDSIGHT_API_LOG_LEVEL=info
# For Azure PostgreSQL with DiskANN:
# HINDSIGHT_API_VECTOR_EXTENSION=pgvectorscale # Auto-detects pg_diskann on Azure
# Text Search Extension (Optional - uses native PostgreSQL full-text search by default)
# Backend options: "native" (default), "vchord", "pg_textsearch", "pgroonga", "pg_search"
# HINDSIGHT_API_TEXT_SEARCH_EXTENSION=native
# Native backend dictionary (only used by HINDSIGHT_API_TEXT_SEARCH_EXTENSION=native)
# HINDSIGHT_API_TEXT_SEARCH_EXTENSION_NATIVE_LANGUAGE=english
# ParadeDB pg_search tokenizer (only used when creating pg_search BM25 indexes).
# Empty uses ParadeDB's default tokenizer: unicode_words.
# Supported values: unicode_words, simple, whitespace, literal, literal_normalized,
# chinese_compatible, icu, jieba, source_code,
# chinese_lindera/lindera(chinese), japanese_lindera/lindera(japanese),
# korean_lindera/lindera(korean), ngram(min,max), edge_ngram(min,max)
# HINDSIGHT_API_TEXT_SEARCH_EXTENSION_PG_SEARCH_TOKENIZER=
# File Parser (Optional - uses markitdown by default)
# HINDSIGHT_API_FILE_PARSER=markitdown
# Enable image OCR for MarkItDown using an OpenAI-compatible OCR/vision endpoint.
# These OCR settings are independent from HINDSIGHT_API_LLM_* because MarkItDown
# uses the OpenAI SDK directly and requires Chat Completions image input support.
# When OCR is enabled, API_KEY, BASE_URL, and MODEL are required.
# HINDSIGHT_API_FILE_PARSER_MARKITDOWN_OCR_ENABLED=false
# HINDSIGHT_API_FILE_PARSER_MARKITDOWN_OCR_API_KEY=
# HINDSIGHT_API_FILE_PARSER_MARKITDOWN_OCR_BASE_URL=
# HINDSIGHT_API_FILE_PARSER_MARKITDOWN_OCR_MODEL=
# HINDSIGHT_API_FILE_PARSER_MARKITDOWN_OCR_PROMPT=
# Embeddings Configuration (Optional - uses local by default)
# Provider: "local" (default), "onnx", "tei", "openai", "cohere", "google", "openrouter", "zeroentropy", "litellm", or "litellm-sdk"
# Provider: "local" (default) or "tei" (HuggingFace Text Embeddings Inference)
# HINDSIGHT_API_EMBEDDINGS_PROVIDER=local
# For local provider:
# HINDSIGHT_API_EMBEDDINGS_LOCAL_MODEL=BAAI/bge-small-en-v1.5
# For ONNX provider (local CPU embeddings without an Ollama/TEI sidecar):
# HINDSIGHT_API_EMBEDDINGS_PROVIDER=onnx
# HINDSIGHT_API_EMBEDDINGS_ONNX_MODEL_ID=intfloat/multilingual-e5-small
# HINDSIGHT_API_EMBEDDINGS_ONNX_FILE=onnx/model.onnx
# HINDSIGHT_API_EMBEDDINGS_ONNX_DIMENSIONS=384
# HINDSIGHT_API_EMBEDDINGS_ONNX_MAX_TOKENS=512
# HINDSIGHT_API_EMBEDDINGS_ONNX_POOLING=mean
# HINDSIGHT_API_EMBEDDINGS_ONNX_NORMALIZE=true
# HINDSIGHT_API_EMBEDDINGS_ONNX_QUERY_PREFIX="query: "
# HINDSIGHT_API_EMBEDDINGS_ONNX_PASSAGE_PREFIX="passage: "
# Optional for local model paths or pre-downloaded artifacts:
# HINDSIGHT_API_EMBEDDINGS_ONNX_MODEL_PATH=/models/multilingual-e5-small/onnx/model.onnx
# HINDSIGHT_API_EMBEDDINGS_ONNX_TOKENIZER_NAME_OR_PATH=/models/multilingual-e5-small
# Optional for China network / restricted HF access:
# HF_ENDPOINT=https://hf-mirror.com
# For TEI provider:
# HINDSIGHT_API_EMBEDDINGS_TEI_URL=http://localhost:8080
# For OpenAI-compatible embeddings:
# HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY=sk-xxxx
# HINDSIGHT_API_EMBEDDINGS_OPENAI_MODEL=text-embedding-3-small
# HINDSIGHT_API_EMBEDDINGS_OPENAI_BASE_URL=https://api.openai.com/v1
# For ZeroEntropy zembed-1:
# HINDSIGHT_API_EMBEDDINGS_PROVIDER=zeroentropy
# HINDSIGHT_API_EMBEDDINGS_ZEROENTROPY_API_KEY=ze-xxxx
# HINDSIGHT_API_EMBEDDINGS_ZEROENTROPY_MODEL=zembed-1
# HINDSIGHT_API_EMBEDDINGS_ZEROENTROPY_DIMENSIONS=1280
# HINDSIGHT_API_EMBEDDINGS_ZEROENTROPY_ENCODING_FORMAT=float
# HINDSIGHT_API_EMBEDDINGS_ZEROENTROPY_LATENCY=fast
#
# IMPORTANT: Embedding keys require provider-specific names:
# HINDSIGHT_API_EMBEDDINGS_{PROVIDER}_{PARAMETER}
# (for example, HINDSIGHT_API_EMBEDDINGS_OPENAI_MODEL).
#
# DeepSeek note: DeepSeek is supported for LLM calls, but not for embeddings.
# If using DeepSeek as LLM provider, keep embeddings on local/openai/cohere/google/etc.
# Reranker Configuration (Optional - uses local by default)
# Provider: "local" (default) or "tei" (HuggingFace Text Embeddings Inference)
@@ -191,24 +88,3 @@ HINDSIGHT_API_LOG_LEVEL=info
# Custom service name and environment (optional, defaults: hindsight-api, development)
# HINDSIGHT_API_OTEL_SERVICE_NAME=hindsight-production
# HINDSIGHT_API_OTEL_DEPLOYMENT_ENVIRONMENT=production
#
# Expose async-operation queue + consolidation-backlog gauges on /metrics.
# Runs periodic per-schema COUNT queries on a background task (disabled by default).
# HINDSIGHT_API_METRICS_BACKLOG_ENABLED=true
# -----------------------------------------------------------------------------
# Control Plane (Optional)
# -----------------------------------------------------------------------------
# Dataplane API URL - where the CP proxies requests to
# HINDSIGHT_CP_DATAPLANE_API_URL=http://localhost:8888
# Optional: Bearer token the CP sends as `Authorization: Bearer <key>` to the
# dataplane API. Required when the API service is auth-protected; omit for a
# public/unauthenticated API.
# HINDSIGHT_CP_DATAPLANE_API_KEY=your-dataplane-bearer-token
# Optional: Require a shared access key to view the Control Plane UI.
# When set, visitors see a login page and must enter the key before
# accessing the dashboard or any /api/* routes (except /api/health).
# HINDSIGHT_CP_ACCESS_KEY=your-shared-secret-key
+6
View File
@@ -0,0 +1,6 @@
version: 2
updates:
- package-ecosystem: "github-actions"
directory: "/"
schedule:
interval: "weekly"
-2
View File
@@ -22,8 +22,6 @@ jobs:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
with:
fetch-depth: 0 # fetch tags so check-released-integrations can see them
- uses: actions/setup-node@v6
with:
node-version: 20
+37 -137
View File
@@ -23,30 +23,16 @@ on:
- retain
- recall
- recall-with-observations
- recall-temporal
- consolidation
- graph-maintenance
default: ""
locomo_conversations:
description: "LoComo conversation IDs (space-separated). Blank = curated set (conv-26 conv-30 conv-43)."
type: string
default: ""
locomo_max_conversations:
description: "LoComo max conversations (0 = skip, blank = all)"
type: number
default: 0
locomo_skip:
description: "Skip LoComo job"
type: boolean
default: false
obs_skip:
description: "Skip observation-dedup benchmark job"
type: boolean
default: false
obs_dataset:
description: "Obs benchmark dataset substring (blank = English hermes transcript)."
type: string
default: ""
obs_fraction:
description: "Obs benchmark fraction (0-1] of each document to run."
type: string
default: "1.0"
ref:
description: "Git ref to test (branch, tag, or SHA). Defaults to main."
type: string
@@ -97,36 +83,46 @@ jobs:
run: |
cd hindsight-dev && uv sync --frozen --all-extras --index-strategy unsafe-best-match
- name: Run perf-test
- name: "Suite: retain"
if: inputs.suite == '' || inputs.suite == 'retain'
run: |
SUITE_ARG=""
if [ -n "${{ inputs.suite }}" ]; then
SUITE_ARG="--suite ${{ inputs.suite }}"
fi
./scripts/benchmarks/run-perf-test.sh \
--scale ${{ inputs.scale || 'large' }} \
$SUITE_ARG \
--output perf-results.json
--suite retain \
--output perf-results-retain.json
- name: "Suite: recall"
if: inputs.suite == '' || inputs.suite == 'recall'
run: |
./scripts/benchmarks/run-perf-test.sh \
--scale ${{ inputs.scale || 'large' }} \
--suite recall \
--output perf-results-recall.json
- name: "Suite: recall-with-observations"
if: inputs.suite == '' || inputs.suite == 'recall-with-observations'
run: |
./scripts/benchmarks/run-perf-test.sh \
--scale ${{ inputs.scale || 'large' }} \
--suite recall-with-observations \
--output perf-results-recall-with-observations.json
- name: "Suite: consolidation"
if: inputs.suite == '' || inputs.suite == 'consolidation'
run: |
./scripts/benchmarks/run-perf-test.sh \
--scale ${{ inputs.scale || 'large' }} \
--suite consolidation \
--output perf-results-consolidation.json
- name: Upload perf results
if: always()
uses: actions/upload-artifact@v7
with:
name: perf-results-${{ github.sha }}
path: hindsight-dev/perf-results.json
path: hindsight-dev/perf-results-*.json
retention-days: 90
# Publish enriched results (perf JSON + commit metadata) to the dashboard
# repo's gh-pages branch. The static site at
# https://vectorize-io.github.io/hindsight-continuous-performance-monitor/
# reads data/index.json + data/<run>.json and renders charts client-side.
- name: Publish to dashboard
if: github.event_name == 'schedule' || github.event_name == 'push' || github.event_name == 'workflow_dispatch'
env:
PERF_DASHBOARD_TOKEN: ${{ secrets.PERF_DASHBOARD_TOKEN }}
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: ./scripts/benchmarks/publish-perf-results.sh hindsight-dev/perf-results.json
locomo:
if: inputs.locomo_skip != true
runs-on: ubuntu-latest
@@ -183,20 +179,14 @@ jobs:
cd hindsight-dev && uv sync --frozen --all-extras --index-strategy unsafe-best-match
- name: Run LoComo benchmark
# Curated 3-conversation subset (best/middle/worst by accuracy on the
# last successful full run): conv-26 (best), conv-30 (middle), conv-43
# (worst). Excludes conv-44, the bank with the largest unconsolidated
# set that has been pushing scheduled runs over the per-bank
# _wait_for_consolidation timeout. Override via workflow_dispatch with
# the locomo_conversations input.
run: |
CONVERSATIONS="${{ inputs.locomo_conversations }}"
if [ -z "$CONVERSATIONS" ]; then
CONVERSATIONS="conv-26 conv-30 conv-43"
MAX_CONV_ARG=""
if [ "${{ inputs.locomo_max_conversations }}" != "0" ] && [ -n "${{ inputs.locomo_max_conversations }}" ]; then
MAX_CONV_ARG="--max-conversations ${{ inputs.locomo_max_conversations }}"
fi
uv run python hindsight-dev/benchmarks/locomo/locomo_benchmark.py \
--wait-consolidation \
--conversation $CONVERSATIONS
$MAX_CONV_ARG
- name: Upload LoComo results
if: always()
@@ -205,93 +195,3 @@ jobs:
name: locomo-results-${{ github.sha }}
path: hindsight-dev/benchmarks/locomo/results/
retention-days: 90
- name: Publish LoComo to dashboard
if: success() && (github.event_name == 'schedule' || github.event_name == 'push' || github.event_name == 'workflow_dispatch')
env:
PERF_DASHBOARD_TOKEN: ${{ secrets.PERF_DASHBOARD_TOKEN }}
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: ./scripts/benchmarks/publish-locomo-results.sh hindsight-dev/benchmarks/locomo/results/benchmark_results.json
obs:
# Observation-dedup quality benchmark: ingests a transcript, drains consolidation
# (serial SyncTaskBackend + embedded pg0 — no external DB / worker), and reports the
# near-duplicate observation rate. Real LLM via VertexAI, mirroring the LoComo job.
if: inputs.obs_skip != true
runs-on: ubuntu-latest
env:
HINDSIGHT_API_LLM_PROVIDER: vertexai
HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
HINDSIGHT_API_LLM_MODEL: google/gemini-2.5-flash-lite
HINDSIGHT_API_ENABLE_OBSERVATIONS: "true"
steps:
- uses: actions/checkout@v6
with:
ref: ${{ inputs.ref || github.ref }}
- name: Setup GCP credentials
run: |
printf '%s' '${{ secrets.GCP_VERTEXAI_CREDENTIALS }}' > /tmp/gcp-credentials.json
PROJECT_ID=$(jq -r '.project_id' /tmp/gcp-credentials.json)
echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV
- name: Install uv
uses: astral-sh/setup-uv@v7
with:
enable-cache: true
prune-cache: false
- name: Set up Python
uses: actions/setup-python@v6
with:
python-version-file: ".python-version"
- name: Cache HuggingFace models
uses: actions/cache@v5
with:
path: ~/.cache/huggingface
key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api-slim/pyproject.toml') }}
restore-keys: |
${{ runner.os }}-huggingface-
- name: Pre-download models
working-directory: ./hindsight-api-slim
run: |
uv run --frozen --all-extras --index-strategy unsafe-best-match python -c "
from sentence_transformers import SentenceTransformer
print('Downloading embedding model...')
SentenceTransformer('BAAI/bge-small-en-v1.5')
print('Model downloaded successfully')
"
- name: Install hindsight-dev dependencies
run: |
cd hindsight-dev && uv sync --frozen --all-extras --index-strategy unsafe-best-match
- name: Run obs benchmark
# Default to the English hermes transcript at full fraction — a clean, deterministic
# consolidation-dedup signal (the Chinese variant adds a cross-lingual embedding
# confound). Override dataset/fraction via workflow_dispatch.
run: |
DATASET="${{ inputs.obs_dataset }}"
if [ -z "$DATASET" ]; then DATASET="hermes_session_2026-05-15_en"; fi
FRACTION="${{ inputs.obs_fraction }}"
if [ -z "$FRACTION" ]; then FRACTION="1.0"; fi
cd hindsight-dev
uv run python -m benchmarks.obs.obs_benchmark \
--dataset "$DATASET" --fraction "$FRACTION" --wipe-bank --output obs-results.json
- name: Upload obs results
if: always()
uses: actions/upload-artifact@v7
with:
name: obs-results-${{ github.sha }}
path: hindsight-dev/obs-results.json
retention-days: 90
- name: Publish obs to dashboard
if: success() && (github.event_name == 'schedule' || github.event_name == 'push' || github.event_name == 'workflow_dispatch')
env:
PERF_DASHBOARD_TOKEN: ${{ secrets.PERF_DASHBOARD_TOKEN }}
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: ./scripts/benchmarks/publish-obs-results.sh hindsight-dev/obs-results.json
+4 -78
View File
@@ -9,11 +9,7 @@ jobs:
publish:
runs-on: ubuntu-latest
permissions:
id-token: write # for PyPI trusted publishing + build-provenance attestations
attestations: write # for actions/attest-build-provenance (Obsidian assets)
# No `contents: write`: we never create releases in this repo. The Obsidian
# plugin's distribution release is pushed to its dedicated repo using
# OBSIDIAN_DIST_TOKEN (see the "Mirror Obsidian plugin" step below).
id-token: write # for PyPI trusted publishing
steps:
- uses: actions/checkout@v6
@@ -75,7 +71,7 @@ jobs:
if: steps.type.outputs.type == 'plugin'
run: |
echo "Plugin integration ${{ steps.info.outputs.integration }} v${{ steps.info.outputs.version }} — no package to publish."
echo "Users install via: claude plugin marketplace add vectorize-io/hindsight"
echo "Users install via: claude plugin marketplace add vectorize-io/hindsight --sparse hindsight-integrations"
# ── TypeScript integrations (ai-sdk, chat, openclaw) ────────────────────
@@ -116,86 +112,16 @@ jobs:
working-directory: ./hindsight-integrations/${{ steps.info.outputs.integration }}
run: npm run build
# Build-provenance attestations for the Obsidian release assets (community-store
# recommendation). Runs after the build so main.js exists. The assets are
# released in the dedicated repo while the build runs here, so users verify at
# owner scope: `gh attestation verify main.js --owner vectorize-io`.
- name: Attest Obsidian plugin build provenance
if: steps.type.outputs.type == 'typescript' && steps.info.outputs.integration == 'obsidian'
uses: actions/attest-build-provenance@v2
with:
subject-path: |
hindsight-integrations/obsidian/main.js
hindsight-integrations/obsidian/styles.css
# ── Obsidian plugin — mirror to its dedicated repo + cut the BRAT release ──
# We do NOT create a GitHub Release in this monorepo: per-integration
# releases pollute the repo's release list (it's for the core product) and
# steal the "Latest" badge, and BRAT / the community store read a repo's
# *latest* release — not a tag — so they can't target a tag in a monorepo.
#
# Instead this monorepo stays the source of truth, and on each obsidian
# release we mirror hindsight-integrations/obsidian/ → the *root* of
# github.com/vectorize-io/hindsight-obsidian (git subtree, history
# preserved) and cut the BRAT / community-store release *there*.
#
# Requires secret OBSIDIAN_DIST_TOKEN — a token with `contents: write` on
# vectorize-io/hindsight-obsidian (fine-grained PAT or app installation
# token). The dedicated repo is generated; never edit it directly.
- name: Mirror Obsidian plugin to its dedicated repo
if: steps.type.outputs.type == 'typescript' && steps.info.outputs.integration == 'obsidian'
env:
DIST_TOKEN: ${{ secrets.OBSIDIAN_DIST_TOKEN }}
run: |
set -euo pipefail
VERSION="${{ steps.info.outputs.version }}"
DIST_REPO="vectorize-io/hindsight-obsidian"
OBS_DIR="hindsight-integrations/obsidian"
# `git subtree split` needs full history; the default checkout is shallow.
git fetch --unshallow 2>/dev/null || true
git config user.name "github-actions[bot]"
git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
# The runner injects the default GITHUB_TOKEN as an http.extraheader via
# an *included* config file (/home/runner/work/_temp/git-credentials-*.config),
# so `git config --local --unset-all` can't remove it and it authenticates
# the push as github-actions[bot] (no access to the dedicated repo → 403).
# The documented way to drop an inherited extraheader is to RESET the list
# with an empty value: since command-line `-c` is read last, the empty
# value clears the accumulated headers (including the included one) at
# request-build time. The dist token then comes from the push URL → a
# single Authorization header.
git subtree split --prefix="$OBS_DIR" -b _obs_dist
git -c "http.https://github.com/.extraheader=" \
push "https://x-access-token:${DIST_TOKEN}@github.com/${DIST_REPO}.git" _obs_dist:main
# Cut the BRAT / community-store release. Bare version tag (e.g. 0.1.0)
# to match manifest.json — idempotent so re-runs just refresh the assets.
export GH_TOKEN="$DIST_TOKEN"
ASSETS="$OBS_DIR/main.js $OBS_DIR/manifest.json $OBS_DIR/styles.css"
NOTES="Hindsight for Obsidian v${VERSION}. Install via BRAT (add ${DIST_REPO}) or copy main.js/manifest.json/styles.css into <vault>/.obsidian/plugins/hindsight/."
if gh release view "$VERSION" --repo "$DIST_REPO" >/dev/null 2>&1; then
gh release upload "$VERSION" $ASSETS --repo "$DIST_REPO" --clobber
else
gh release create "$VERSION" $ASSETS --repo "$DIST_REPO" --title "$VERSION" --notes "$NOTES"
fi
- name: Publish TypeScript package to npm
if: steps.type.outputs.type == 'typescript'
working-directory: ./hindsight-integrations/${{ steps.info.outputs.integration }}
run: |
set +e
OUTPUT=$(npm publish --access public --provenance 2>&1)
OUTPUT=$(npm publish --access public 2>&1)
EXIT_CODE=$?
echo "$OUTPUT"
if [ $EXIT_CODE -ne 0 ]; then
# Treat "already published" as success so re-pointed-tag re-runs stay green.
# "cannot publish over" = the version exists. TLOG_CREATE_ENTRY_ERROR / 409
# "equivalent entry already exists in the transparency log" = the identical
# --provenance artifact was already logged on a prior run (Sigstore tlog is
# idempotent); the package is published, so this is benign.
if echo "$OUTPUT" | grep -qE "cannot publish over|TLOG_CREATE_ENTRY_ERROR|already exists in the transparency log"; then
if echo "$OUTPUT" | grep -q "cannot publish over"; then
echo "Package version already published, skipping..."
exit 0
fi
+2 -29
View File
@@ -266,7 +266,7 @@ jobs:
strategy:
matrix:
include:
- os: ubuntu-22.04
- os: ubuntu-latest
target: x86_64-unknown-linux-gnu
artifact_name: hindsight
asset_name: hindsight-linux-amd64
@@ -278,7 +278,7 @@ jobs:
target: aarch64-apple-darwin
artifact_name: hindsight
asset_name: hindsight-darwin-arm64
- os: ubuntu-22.04-arm
- os: ubuntu-24.04-arm
target: aarch64-unknown-linux-gnu
artifact_name: hindsight
asset_name: hindsight-linux-arm64
@@ -314,7 +314,6 @@ jobs:
permissions:
contents: read
packages: write
id-token: write
strategy:
matrix:
include:
@@ -411,7 +410,6 @@ jobs:
# Build multi-platform and push to release tags
- name: Build and push release images
id: build
uses: docker/build-push-action@v7
with:
context: .
@@ -423,31 +421,6 @@ jobs:
tags: ${{ steps.meta.outputs.tags }}
labels: ${{ steps.meta.outputs.labels }}
- name: Install cosign
uses: sigstore/cosign-installer@v3
- name: Sign published images
env:
TAGS: ${{ steps.meta.outputs.tags }}
DIGEST: ${{ steps.build.outputs.digest }}
run: |
set -euo pipefail
refs=()
while IFS= read -r tag; do
[[ -z "${tag}" ]] && continue
refs+=("${tag}@${DIGEST}")
done <<< "${TAGS}"
cosign sign --yes "${refs[@]}"
- name: Verify signature on primary tag
env:
IMAGE: ghcr.io/${{ github.repository_owner }}/${{ matrix.image_name }}
DIGEST: ${{ steps.build.outputs.digest }}
run: |
cosign verify "${IMAGE}@${DIGEST}" \
--certificate-identity-regexp "^https://github\.com/${{ github.repository }}/\.github/workflows/release\.yml@.*" \
--certificate-oidc-issuer https://token.actions.githubusercontent.com
release-helm-chart:
runs-on: ubuntu-latest
permissions:
-71
View File
@@ -1,71 +0,0 @@
name: Sign published images
on:
workflow_dispatch:
inputs:
version:
description: 'Version to sign (without leading v, e.g. 0.6.0)'
required: true
type: string
default: '0.6.0'
permissions:
contents: read
packages: write
id-token: write
jobs:
sign:
name: Sign ${{ matrix.image }}:${{ inputs.version }}${{ matrix.suffix }}
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
include:
- { image: hindsight-api, suffix: '' }
- { image: hindsight-api, suffix: '-slim' }
- { image: hindsight-control-plane, suffix: '' }
- { image: hindsight, suffix: '' }
- { image: hindsight, suffix: '-slim' }
steps:
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v4
- name: Log in to GHCR
uses: docker/login-action@v4
with:
registry: ghcr.io
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Install cosign
uses: sigstore/cosign-installer@v3
- name: Resolve image digest
id: resolve
env:
IMAGE: ghcr.io/${{ github.repository_owner }}/${{ matrix.image }}
TAG: ${{ inputs.version }}${{ matrix.suffix }}
run: |
set -euo pipefail
DIGEST=$(docker buildx imagetools inspect "${IMAGE}:${TAG}" --format '{{json .Manifest.Digest}}' | tr -d '"')
if [[ -z "${DIGEST}" || "${DIGEST}" != sha256:* ]]; then
echo "Failed to resolve digest for ${IMAGE}:${TAG} (got: ${DIGEST})" >&2
exit 1
fi
echo "Resolved ${IMAGE}:${TAG} -> ${DIGEST}"
echo "ref=${IMAGE}@${DIGEST}" >> "$GITHUB_OUTPUT"
- name: Sign image
env:
REF: ${{ steps.resolve.outputs.ref }}
run: cosign sign --yes "${REF}"
- name: Verify signature
env:
REF: ${{ steps.resolve.outputs.ref }}
run: |
cosign verify "${REF}" \
--certificate-identity-regexp "^https://github\.com/${{ github.repository }}/\.github/workflows/sign-images\.yml@.*" \
--certificate-oidc-issuer https://token.actions.githubusercontent.com
+129 -1501
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-115
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@@ -1,115 +0,0 @@
name: Windows Smoke Test
# Daily smoke test that installs the API on Windows and runs the Python client
# integration tests against a live server. Windows is only exercised by the
# hindsight-embed jobs in test.yml on PRs; this catches Windows-specific
# regressions in the API server + client path (e.g. process spawning, console
# subsystem / ConPTY behaviour, see #1885) that the Linux client jobs miss.
on:
schedule:
# 06:00 UTC daily.
- cron: "0 6 * * *"
workflow_dispatch:
permissions:
contents: read
jobs:
windows-client-smoke:
# Don't run on forks: the job needs the org's Vertex AI credentials.
if: github.repository == 'vectorize-io/hindsight'
runs-on: windows-latest
timeout-minutes: 45
env:
HINDSIGHT_API_LLM_PROVIDER: vertexai
HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: ${{ github.workspace }}/gcp-credentials.json
HINDSIGHT_API_LLM_MODEL: google/gemini-2.5-flash-lite
HINDSIGHT_API_URL: http://localhost:8888
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
# Force UTF-8 I/O so the API/CLI's ✓/box-drawing output doesn't crash the
# default Windows cp1252 codec (matches test-embed-windows in test.yml).
PYTHONIOENCODING: utf-8
PYTHONUTF8: "1"
steps:
- uses: actions/checkout@v6
- name: Setup GCP credentials
shell: bash
run: |
printf '%s' '${{ secrets.GCP_VERTEXAI_CREDENTIALS }}' > gcp-credentials.json
PROJECT_ID=$(jq -r '.project_id' gcp-credentials.json)
echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> "$GITHUB_ENV"
- name: Install uv
uses: astral-sh/setup-uv@v7
with:
enable-cache: true
prune-cache: false
- name: Set up Python
uses: actions/setup-python@v6
with:
python-version-file: ".python-version"
- name: Install API dependencies (all extras - local-ml + embedded pg0)
working-directory: ./hindsight-api-slim
run: uv sync --frozen --all-extras --index-strategy unsafe-best-match
- name: Install Python client test dependencies
working-directory: ./hindsight-clients/python
run: uv sync --frozen --extra test --index-strategy unsafe-best-match
- name: Cache HuggingFace models
uses: actions/cache@v5
with:
path: ~/.cache/huggingface
key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api-slim/pyproject.toml') }}
restore-keys: |
${{ runner.os }}-huggingface-
# `uv run` re-syncs the project env to its default (no-extras) state before
# running, which drops sentence-transformers / pg0. Pass --all-extras on
# every `uv run` so the local-ml + embedded-db deps stay installed (this is
# the same reason hindsight-embed launches the daemon with `--extra all`).
- name: Pre-download models
working-directory: ./hindsight-api-slim
run: |
uv run --all-extras python -c "from sentence_transformers import SentenceTransformer, CrossEncoder; SentenceTransformer('BAAI/bge-small-en-v1.5'); CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2'); print('Models downloaded')"
# Start the server and run the client tests in a SINGLE step. On Windows
# runners a process backgrounded with `&` in one step is not reliably kept
# alive for later steps (unlike Linux, where it reparents to init), so the
# server must live in the same shell that runs pytest.
- name: Start API server and run Python client tests
shell: bash
run: |
# Config is read straight from the environment (job-level env + the
# PROJECT_ID exported to GITHUB_ENV above), so no .env file is needed.
# Embedded pg0 is the default when HINDSIGHT_API_DATABASE_URL is unset.
( cd hindsight-api-slim && uv run --all-extras hindsight-api --port 8888 ) > "$RUNNER_TEMP/api-server.log" 2>&1 &
server_pid=$!
echo "Waiting for API server to be ready (pid $server_pid)..."
# pg0 unpacks Postgres + runs initdb on first boot, which is slow on a
# cold Windows runner — give it a generous budget before failing.
ready=false
for i in $(seq 1 300); do
if curl -sf http://localhost:8888/health > /dev/null 2>&1; then
echo "API server is ready after ${i}s"
ready=true
break
fi
sleep 1
done
if [ "$ready" != true ]; then
echo "API server failed to start after 300s"
cat "$RUNNER_TEMP/api-server.log"
exit 1
fi
cd hindsight-clients/python && uv run --extra test pytest tests -v
- name: Show API server logs
if: always()
shell: bash
run: cat "$RUNNER_TEMP/api-server.log" || echo "No API server log found"
+1 -7
View File
@@ -6,7 +6,6 @@ dist/
wheels/
*.egg-info
.mcp.json
.playwright-mcp/
.osgrep
# Virtual environments
.venv
@@ -16,8 +15,6 @@ node_modules/
# Environment variables and local config
.env
.env.bak*
.env.*.bak
docker-compose.yml
docker-compose.override.yml
@@ -57,10 +54,7 @@ hindsight-clients/rust/target
!.claude/skills/
whats-next.md
TASK.md
# Parked / draft integrations that aren't ready to ship
hindsight-integrations/_drafts/
# Changelog is now tracked in hindsight-docs/src/pages/changelog.md
# CHANGELOG.md
blog-post*
.worktrees/
blog-post*
+5 -54
View File
@@ -216,46 +216,10 @@ migration file dispatches through `run_for_dialect`, which calls either
./scripts/hooks/lint.sh
```
Dead-code detection runs in CI (the `check-unused-code` job) at two levels:
- **Blocking:** unused imports (ruff `F401`) and variables (`F841`) — `lint.sh` auto-removes
them and `verify-generated-files` fails on any leftover diff; and **knip** for orphaned
control-plane files / unused (or unlisted) `package.json` dependencies.
- **Advisory:** whole unused Python functions (vulture) and unused control-plane *exports*
(the shadcn/ui surface is kept on purpose) — surfaced, not gated.
Run both locally with:
```bash
./scripts/hooks/check-unused.sh
```
**After completing any implementation work, run `/code-review`** to verify your changes against project standards (missing tests, dead code, type safety, etc.). Fix any "must fix" issues before considering the task done.
**MANDATORY: Run `/code-review` before pushing code or creating a pull request.** Do not push or create a PR until all "must fix" issues are resolved.
### Testing
Most tests are deterministic (MockLLM, pure functions) — assert directly.
**Tests that verify LLM behaviour use a real LLM + an LLM-as-judge.** When the thing under test is *how the model interprets a prompt* (classification, attribution, dimension preservation, instruction-following), MockLLM can't simulate it and exact string/enum asserts flake across providers and runs. Use this pattern instead:
1. Mark the test module `pytestmark = pytest.mark.hs_llm_core` (single-provider; CI runs it in the core-LLM job). Use `hs_llm_mat` only for provider-matrix acceptance tests.
2. Call the real pipeline (`LLMConfig.from_env()`, `_get_raw_config()`), e.g. `extract_facts_from_text(...)`.
3. Assert with the judge, not string matching:
```python
from tests.llm_judge import assert_meets_criteria
facts_summary = "\n".join(f"- [{f.fact_type}] {f.fact}" for f in facts)
await assert_meets_criteria(
response=facts_summary,
criteria="The first-person user statements are classified 'world' and attributed to the user, not the agent.",
context="What the input said and who was speaking.",
)
```
Rules of thumb:
- **Judge anything non-deterministic** — including `fact_type` classification and speaker attribution. Do NOT hard-assert `fact_type == "..."`; pass a `[fact_type] fact` summary to the judge instead. Structural facts that ARE deterministic (counts, presence of a field, that a substring was injected into a prompt) stay as direct asserts in fast unit tests.
- **Split the test surface**: cover the deterministic mechanics (prompt assembly, suppression logic) with fast non-LLM unit tests, and the model-following behaviour with one `hs_llm_core` judge test. (Example pair: `test_narrator_resolution.py` + `test_narrator_context_override.py`.)
- The judge model is independent of the test provider (defaults to Gemini); never judge with the same call you're testing.
### Memory Banks
- Each bank is an isolated memory store (like a "brain" for one user/agent)
- Banks have dispositions (skepticism, literalism, empathy traits 1-5) affecting reflect
@@ -327,10 +291,7 @@ Fields must be categorized as either **hierarchical** (can be overridden per-ten
```
2. **main.py** (`hindsight-api-slim/hindsight_api/main.py`):
- No change is needed for ordinary environment-backed config fields. The CLI starts from `_get_raw_config()`,
so new `HindsightConfig` fields are carried through automatically.
- If the new field should be overridable by a CLI flag, add the argparse option in `_parse_cli_args()` and include
that field in the `dataclasses.replace(config, ...)` call near the "CLI override" comment.
- Add field to the manual `HindsightConfig()` constructor call (search for "CLI override")
3. **Use hierarchical config in MemoryEngine**:
```python
@@ -350,16 +311,6 @@ Fields must be categorized as either **hierarchical** (can be overridden per-ten
- Add to appropriate section table with Variable, Description, Default
- Mark if it's hierarchical (can be overridden per-bank)
6. **Env template** (`.env.example`):
- Add the variable to the appropriate section, commented if optional, with a
short inline comment describing it (mirror the documentation entry).
- This file is the single source of truth for the env template:
`scripts/dev/setup.sh` copies it to `.env`, and `hindsight-embed` ships a
bundled copy (`hindsight-embed/hindsight_embed/env.example`) that seeds
embed/profile configs. After editing `.env.example`, re-copy it to the
embed package (`cp .env.example hindsight-embed/hindsight_embed/env.example`)
or the `test_bundled_template_matches_repo_root` sync test will fail.
#### Hierarchical vs Static Guidelines
**Hierarchical** (per-bank overridable):
@@ -376,7 +327,7 @@ Fields must be categorized as either **hierarchical** (can be overridden per-ten
```bash
cp .env.example .env
# Edit .env with the LLM provider/model and credentials for your setup
# Edit .env with LLM API key
# Python deps
uv sync --directory hindsight-api-slim/
@@ -385,10 +336,10 @@ uv sync --directory hindsight-api-slim/
npm install
```
Common LLM settings:
Required env vars:
- `HINDSIGHT_API_LLM_PROVIDER`: openai, anthropic, gemini, groq, minimax, ollama, lmstudio
- `HINDSIGHT_API_LLM_API_KEY`: API key for providers that require one
- `HINDSIGHT_API_LLM_MODEL`: Model name (defaults are provider-specific)
- `HINDSIGHT_API_LLM_API_KEY`: Your API key
- `HINDSIGHT_API_LLM_MODEL`: Model name (e.g., gpt-4o-mini, claude-sonnet-4-20250514)
Optional (uses local models by default):
- `HINDSIGHT_API_EMBEDDINGS_PROVIDER`: local (default) or tei
+2 -25
View File
@@ -9,36 +9,13 @@ Thanks for your interest in contributing to Hindsight!
git clone [email protected]:vectorize-io/hindsight.git
cd hindsight
```
2. Bootstrap your dev environment in one shot:
```bash
./scripts/dev/setup.sh
```
This is idempotent (safe to re-run) and gets you ready to develop, including
offline. It:
- installs the required toolchains if missing (uv/Python, Node/npm, Rust/cargo),
- creates `.env` from `.env.example` (remember to add your LLM API key),
- configures git hooks,
- installs all Python and Node workspace dependencies,
- pre-downloads the local ML models + tokenizer so the API runs offline,
- builds the TypeScript SDK and the Rust CLI.
Useful flags: `--skip-build` (deps only), `--skip-models` (skip ML model
download), `--with-docs` (also build the docs site), `--force` (rebuild
artifacts). Docker image builds are out of scope. Run
`./scripts/dev/setup.sh --help` for details.
### Manual setup
If you'd rather set things up by hand instead of running the script above:
1. Set up your environment:
2. Set up your environment:
```bash
cp .env.example .env
```
Edit the .env to add LLM API key and config as required
2. Install dependencies:
3. Install dependencies:
```bash
# Python dependencies
uv sync --directory hindsight-api/
+7 -21
View File
@@ -7,6 +7,7 @@
[![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)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![gitcgr](https://gitcgr.com/badge/vectorize-io/hindsight.svg)](https://gitcgr.com/vectorize-io/hindsight)
![PyPI - Downloads](https://img.shields.io/pypi/dm/hindsight-api?label=PyPI)
![NPM Downloads](https://img.shields.io/npm/dm/%40vectorize-io%2Fhindsight-client?logoColor=orange&label=NPM&color=blue&link=https%3A%2F%2Fwww.npmjs.com%2Fpackage%2F%40vectorize-io%2Fhindsight-client)
<br/>
@@ -29,7 +30,7 @@ It eliminates the shortcomings of alternative techniques such as RAG and knowled
Hindsight is the most accurate agent memory system ever tested according to benchmark performance. It has achieved state-of-the-art performance on the LongMemEval benchmark, widely used to assess memory system performance across a variety of conversational AI scenarios. The current reported performance of Hindsight and other agent memory solutions as of January 2026 is shown here:
![Overview](./hindsight-docs/static/img/hindsight-benchmarks.png)
![Overview](./hindsight-docs/static/img/hindsight-bench.jpg)
The benchmark performance data for Hindsight has been independently reproduced by research collaborators at the Virginia Tech [Sanghani Center for Artificial Intelligence and Data Analytics](https://sanghani.cs.vt.edu/) and The Washington Post. Other scores are self-reported by software vendors.
@@ -61,16 +62,16 @@ If you need more control over how and when your agent stores and recalls memorie
```bash
export OPENAI_API_KEY=sk-xxx
docker run -it --pull always --name hindsight --restart unless-stopped -p 8888:8888 -p 9999:9999 \
docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
-e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-v hindsight-data:/home/hindsight/.pg0 \
-v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
ghcr.io/vectorize-io/hindsight:latest
```
>API: http://localhost:8888
>UI: http://localhost:9999
You can modify the LLM provider by setting `HINDSIGHT_API_LLM_PROVIDER`. Valid options are `openai`, `anthropic`, `gemini`, `groq`, `ollama`, `lmstudio`, `minimax`, and `atlas` ([Atlas Cloud](https://www.atlascloud.ai/?utm_source=github&utm_medium=link&utm_campaign=hindsight)). The documentation provides more details on [supported models](https://hindsight.vectorize.io/developer/models).
You can modify the LLM provider by setting `HINDSIGHT_API_LLM_PROVIDER`. Valid options are `openai`, `anthropic`, `gemini`, `groq`, `ollama`, `lmstudio`, and `minimax`. The documentation provides more details on [supported models](https://hindsight.vectorize.io/developer/models).
@@ -142,8 +143,6 @@ main();
pip install hindsight-all -U
```
On Intel (x86_64) Macs, install `hindsight-all-slim` instead — see [Supported Platforms](#supported-platforms).
```python
import os
from hindsight import HindsightServer, HindsightClient
@@ -250,7 +249,7 @@ Recall performs 4 retrieval strategies in parallel:
- Graph: Entity/temporal/causal links
- Temporal: Time range filtering
![Recall Operation](hindsight-docs/static/img/recall-operation.webp)
![Retain Operation](hindsight-docs/static/img/recall-operation.webp)
The individual results from the retrievals are merged, then ordered by relevance using reciprocal rank fusion and a cross-encoder reranking model.
@@ -276,7 +275,7 @@ client = Hindsight(base_url="http://localhost:8888")
client.reflect(bank_id="my-bank", query="What should I know about Alice?")
```
![Reflect Operation](hindsight-docs/static/img/reflect-operation.webp)
![Retain Operation](hindsight-docs/static/img/reflect-operation.webp)
---
@@ -301,19 +300,6 @@ client.reflect(bank_id="my-bank", query="What should I know about Alice?")
[![Star History Chart](https://api.star-history.com/svg?repos=vectorize-io/hindsight&type=date&legend=top-left)](https://www.star-history.com/#vectorize-io/hindsight&type=date&legend=top-left)
---
## Supported Platforms
| Platform | Docker | Bare Metal (pip) | Embedded DB (pg0) |
|----------|--------|------------------|--------------------|
| **Linux** (x86_64, ARM64) | ✅ | ✅ | ✅ |
| **macOS** (Apple Silicon / arm64) | ✅ | ✅ | ✅ |
| **macOS** (Intel / x86_64) | ✅ | ⚠️ | ✅ |
| **Windows** (x86_64) | ✅ | ✅ | ✅ |
⚠️ Intel Macs: use `hindsight-all-slim` — see the [installation guide](https://hindsight.vectorize.io/developer/installation#supported-platforms) for details.
---
## Contributing
See [CONTRIBUTING.md](./CONTRIBUTING.md).
Generated
+2 -33
View File
@@ -42,16 +42,6 @@
},
"workspace": {
"members": {
"hindsight-all-npm": {
"packageJson": {
"dependencies": [
"npm:@types/node@22",
"npm:tsup@^8.5.1",
"npm:typescript@^5.7.0",
"npm:vitest@^4.1.2"
]
}
},
"hindsight-clients/typescript": {
"packageJson": {
"dependencies": [
@@ -68,7 +58,6 @@
"hindsight-control-plane": {
"packageJson": {
"dependencies": [
"npm:@chenglou/pretext@^0.0.3",
"npm:@eslint/eslintrc@^3.3.3",
"npm:@eslint/js@^9.39.2",
"npm:@radix-ui/react-alert-dialog@^1.1.15",
@@ -77,6 +66,7 @@
"npm:@radix-ui/react-dropdown-menu@^2.1.16",
"npm:@radix-ui/react-label@^2.1.8",
"npm:@radix-ui/react-popover@^1.1.15",
"npm:@radix-ui/react-radio-group@^1.3.8",
"npm:@radix-ui/react-select@^2.2.6",
"npm:@radix-ui/react-slider@^1.3.6",
"npm:@radix-ui/react-slot@^1.2.4",
@@ -101,12 +91,11 @@
"npm:eslint@^9.39.1",
"npm:[email protected]",
"npm:next-themes@~0.4.6",
"npm:next@^16.1.7",
"npm:next@^16.1.6",
"npm:postcss@^8.5.6",
"npm:prettier@^3.7.4",
"npm:react-chrono@^2.9.1",
"npm:react-dom@^19.2.0",
"npm:react-is@^19.2.4",
"npm:react-markdown@^10.1.0",
"npm:react18-json-view@~0.2.9",
"npm:react@^19.2.0",
@@ -144,26 +133,6 @@
"npm:typescript@~5.6.2"
]
}
},
"hindsight-tools/hindsight-agent-sdk": {
"packageJson": {
"dependencies": [
"npm:@vectorize-io/hindsight-client@~0.5.6",
"npm:typescript@^5.4.0",
"npm:vitest@^4.1.2"
]
}
},
"hindsight-tools/self-driving-agents": {
"packageJson": {
"dependencies": [
"npm:@clack/prompts@^1.2.0",
"npm:@vectorize-io/hindsight-client@~0.5.6",
"npm:picocolors@^1.1.0",
"npm:typescript@^5.4.0",
"npm:vitest@^4.1.2"
]
}
}
}
}
@@ -1,90 +0,0 @@
name: hindsight
# Docker Compose file for Hindsight with AlloyDB Omni and ScaNN
# Uses Google's free AlloyDB Omni container image: https://hub.docker.com/r/google/alloydbomni
#
# Usage:
# docker compose -f docker/docker-compose/alloydb/docker-compose.yaml up -d
#
# Make sure to set the required environment variables before running:
# - HINDSIGHT_DB_PASSWORD: password for the AlloyDB Omni/PostgreSQL user
# - Configure LLM provider variables as needed (see the hindsight service below)
#
# Optional environment variables with defaults:
# - HINDSIGHT_VERSION: Hindsight application version (default: latest)
# - HINDSIGHT_DB_VERSION: AlloyDB Omni image tag (default: 17)
# - HINDSIGHT_DB_USER: database user (default: hindsight_user)
# - HINDSIGHT_DB_NAME: database name (default: hindsight_db)
services:
db:
image: google/alloydbomni:${HINDSIGHT_DB_VERSION:-17}
container_name: hindsight-db-alloydb
restart: always
ports:
- "5438:5432"
environment:
POSTGRES_USER: ${HINDSIGHT_DB_USER:-hindsight_user}
POSTGRES_PASSWORD: ${HINDSIGHT_DB_PASSWORD:-hindsight_password}
POSTGRES_DB: ${HINDSIGHT_DB_NAME:-hindsight_db}
volumes:
- alloydb_data:/var/lib/postgresql/data
networks:
- hindsight-net
alloydb-init:
image: google/alloydbomni:${HINDSIGHT_DB_VERSION:-17}
depends_on:
- db
environment:
- PGPASSWORD=${HINDSIGHT_DB_PASSWORD:-hindsight_password}
command:
- bash
- -c
- |
echo 'Waiting for AlloyDB Omni to be ready...'
until pg_isready -h hindsight-db-alloydb -p 5432 -U ${HINDSIGHT_DB_USER:-hindsight_user}; do
echo 'AlloyDB Omni is unavailable - sleeping'
sleep 2
done
echo 'AlloyDB Omni is ready - creating ${HINDSIGHT_DB_NAME:-hindsight_db} database'
psql -h hindsight-db-alloydb -p 5432 -U ${HINDSIGHT_DB_USER:-hindsight_user} -c 'CREATE DATABASE ${HINDSIGHT_DB_NAME:-hindsight_db};' 2>/dev/null || echo 'Database already exists'
echo 'Creating vector and alloydb_scann extensions'
psql -h hindsight-db-alloydb -p 5432 -U ${HINDSIGHT_DB_USER:-hindsight_user} -d ${HINDSIGHT_DB_NAME:-hindsight_db} -c 'CREATE EXTENSION IF NOT EXISTS vector;'
psql -h hindsight-db-alloydb -p 5432 -U ${HINDSIGHT_DB_USER:-hindsight_user} -d ${HINDSIGHT_DB_NAME:-hindsight_db} -c 'CREATE EXTENSION IF NOT EXISTS alloydb_scann CASCADE;'
echo 'Database and extensions created successfully'
restart: "no"
networks:
- hindsight-net
hindsight:
image: ghcr.io/vectorize-io/hindsight:${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: scann
HINDSIGHT_API_TEXT_SEARCH_EXTENSION: native
depends_on:
db:
condition: service_started
alloydb-init:
condition: service_completed_successfully
networks:
- hindsight-net
networks:
hindsight-net:
driver: bridge
volumes:
alloydb_data:
-113
View File
@@ -1,113 +0,0 @@
# Hindsight with Claude Code (Claude Pro/Max subscription)
Run Hindsight inside Docker using the `claude-code` LLM provider, backed by
your host machine's Claude Pro or Max subscription credentials.
The standalone Hindsight Docker image ships `claude-agent-sdk` but does **not**
bundle the host `claude` CLI binary or any Claude credentials. This Compose
file bind-mounts the host's CLI install and credentials into the container so
the `claude-code` provider works without an API key.
## When to use this
- You have an active Claude Pro or Max subscription and want to use it for
Hindsight without paying separate Anthropic API costs.
- You want a one-command `docker compose up` instead of a long `docker run`
invocation with many flags.
- You are running on **Linux/amd64** — macOS Docker Desktop and Windows host
paths differ and are not yet covered (please open an issue if you'd like to
contribute a verified recipe for either).
> **Personal-use only.** Anthropic's
> [Agent SDK documentation](https://docs.claude.com/en/api/agent-sdk/overview)
> states that third-party developers should not offer claude.ai login or rate
> limits for their products. Hindsight does **not** perform any login on your
> behalf — it uses credentials you've already authenticated via
> `claude auth login`. In January 2026, Anthropic
> [enforced restrictions](https://paddo.dev/blog/anthropic-walled-garden-crackdown/)
> against tools that spoofed the Claude Code client identity; Hindsight uses
> the official Claude Agent SDK instead.
>
> Do not deploy this configuration to shared environments or production. For
> that, use the `anthropic` provider with an API key from the
> [Anthropic Console](https://console.anthropic.com/). Usage counts against
> your Claude Pro/Max subscription limits.
## Prerequisites
- Host has `claude` CLI installed (e.g., `npm install -g @anthropics/claude-code`)
and `claude auth login` has been run successfully.
- `~/.claude.json` and `~/.claude/.credentials.json` exist on the host.
- Host `claude` CLI version is **2.1.128 or newer** — the version bundled with
`claude-agent-sdk` 0.5.x has a protocol incompatibility in containers, so
the recipe overrides it with the host binary.
## Quick start
```bash
# Set your host UID/GID (defaults to 1000:1000 if unset)
export HOST_UID=$(id -u)
export HOST_GID=$(id -g)
docker compose -f docker/docker-compose/claude-code/docker-compose.yaml up -d
```
- API: http://localhost:8888
- Control Plane: http://localhost:9999
## Post-setup (one-time)
After the container starts for the first time, run these commands to fix
permissions and symlink the host `claude` binary into `$PATH`:
```bash
# Make ~/.claude writable by your UID (the CLI writes session/project state)
docker exec --user 0:0 hindsight-claude-code chown $(id -u):$(id -g) /home/hindsight/.claude
docker exec --user 0:0 hindsight-claude-code chmod 755 /home/hindsight/.claude
# Symlink the host claude binary into PATH
docker exec --user 0:0 hindsight-claude-code \
ln -sf /home/hindsight/.local/share/claude/versions/2.1.128 /usr/local/bin/claude
```
If you set `CLAUDE_CLI_VERSION` to a version other than `2.1.128`, update the
symlink path accordingly.
## Notes on the bind-mount surface (every flag is load-bearing)
- **Host `claude` binary required** — the image ships only `claude-agent-sdk`,
not the CLI itself.
- **SDK bundled-binary override** — the override of
`claude_agent_sdk/_bundled/claude` works around a protocol issue in the
bundled v2.1.121 binary inside containers. Once `claude-agent-sdk` ships
with v2.1.128+ this override can be dropped. Set `CLAUDE_CLI_VERSION` to
match your installed version.
- **Single-file credential mounts** — credentials are mounted as individual
`:ro` files rather than a whole-directory `:ro` mount of `~/.claude`,
because the CLI writes session/project state at runtime and a read-only
directory mount silently breaks it.
- **`--user` / `user:`** — the `user: ${HOST_UID}:${HOST_GID}` pattern
requires `chmod 755 /home/hindsight`, which is built into the image since
v0.6.0 (see [#1481](https://github.com/vectorize-io/hindsight/issues/1481)).
- **`~/.hindsight-docker` data directory** — the pg0 data bind mount must be
writable by your host UID (see
[#1483](https://github.com/vectorize-io/hindsight/issues/1483)).
- **Verified** on `linux/amd64` against `ghcr.io/vectorize-io/hindsight:latest`
v0.5.6+.
## Using a different Claude CLI version
If your host has a `claude` version other than 2.1.128, set
`CLAUDE_CLI_VERSION` before starting:
```bash
export CLAUDE_CLI_VERSION=2.2.0
docker compose -f docker/docker-compose/claude-code/docker-compose.yaml up -d
```
Then update the post-setup symlink to match:
```bash
docker exec --user 0:0 hindsight-claude-code \
ln -sf /home/hindsight/.local/share/claude/versions/2.2.0 /usr/local/bin/claude
```
@@ -1,44 +0,0 @@
name: hindsight-claude-code
# Run Hindsight with the claude-code LLM provider, using your host machine's
# Claude Pro/Max subscription credentials. Linux/amd64 only for now.
#
# Quick start:
# docker compose -f docker/docker-compose/claude-code/docker-compose.yaml up -d
#
# See README.md for prerequisites, post-setup steps, and important caveats.
services:
hindsight:
image: ghcr.io/vectorize-io/hindsight:latest
container_name: hindsight-claude-code
user: "${HOST_UID:-1000}:${HOST_GID:-1000}"
ports:
- "127.0.0.1:8888:8888"
- "127.0.0.1:9999:9999"
environment:
HOME: /home/hindsight
USER: hindsight
LOGNAME: hindsight
PATH: /usr/local/bin:/usr/bin:/bin:/app/api/.venv/bin
HINDSIGHT_API_LLM_PROVIDER: claude-code
volumes:
# ── Persistent data ────────────────────────────────────────────
# Writable pg0 data directory. Must be writable by HOST_UID.
- ${HOME:-.}/.hindsight-docker:/home/hindsight/.pg0
# ── Claude credentials (read-only, single-file mounts) ────────
# A whole-directory :ro mount of ~/.claude silently breaks the
# CLI, which writes session/project state at runtime — so we
# mount only the two credential files.
- ${HOME}/.claude/.credentials.json:/home/hindsight/.claude/.credentials.json:ro
- ${HOME}/.claude.json:/home/hindsight/.claude.json:ro
# ── Claude CLI install (read-only) ─────────────────────────────
- ${HOME}/.local/share/claude:/home/hindsight/.local/share/claude:ro
# ── SDK bundled-binary override ────────────────────────────────
# The claude-agent-sdk 0.5.x image bundles v2.1.121 which has a
# protocol incompatibility in containers. Override it with the
# host's v2.1.128+ binary. Drop this mount once claude-agent-sdk
# ships with v2.1.128+.
- ${HOME}/.local/share/claude/versions/${CLAUDE_CLI_VERSION:-2.1.128}:/app/api/.venv/lib/python3.11/site-packages/claude_agent_sdk/_bundled/claude:ro
@@ -1,34 +0,0 @@
# Example: custom Hindsight image with non-default local models baked in.
#
# Use this pattern in production when you run a non-default embedder or
# reranker. Baking models into the image removes the runtime dependency on
# HuggingFace and lets the container registry handle caching per node, so
# you don't need a model-cache PVC.
#
# Built on top of the slim image so only the deps and models you actually
# use end up in the final image.
FROM ghcr.io/vectorize-io/hindsight:latest-slim
# Install the local-ml deps required to load sentence-transformers /
# cross-encoder models at runtime. Pinned ranges mirror hindsight-api-slim's
# `local-ml` extra in hindsight-api-slim/pyproject.toml. Use `uv pip
# install` against the image's venv explicitly: the slim image's venv was
# created by `uv sync` and does not ship its own `pip`, so a bare
# `pip install` would fall back to user site-packages and not be visible
# to the runtime python.
RUN uv pip install --python /app/api/.venv/bin/python --no-cache \
'sentence-transformers>=3.3.0' \
'transformers>=4.53.0' \
'torch>=2.6.0'
# Pre-download the models you want to use. Replace these with your own.
# The defaults bundled in the full image are BAAI/bge-small-en-v1.5 and
# cross-encoder/ms-marco-MiniLM-L-6-v2; here we pick multilingual variants
# as a concrete non-default example.
ARG EMBEDDER=sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
ARG RERANKER=cross-encoder/mmarco-mMiniLMv2-L12-H384-v1
ENV HF_HUB_DOWNLOAD_TIMEOUT=600
RUN python -c "\
from sentence_transformers import SentenceTransformer, CrossEncoder; \
SentenceTransformer('${EMBEDDER}'); \
CrossEncoder('${RERANKER}')"
@@ -1,81 +0,0 @@
# Hindsight with Custom Local Models
Example Docker Compose setup that builds a Hindsight image with **non-default
local embedder and reranker models baked in at build time**.
This is the recommended pattern for production when you use a non-default
local model: the container registry caches model layers per node, pod
startup is deterministic, and you don't need a model-cache PVC (or any
runtime dependency on HuggingFace).
## When to use this
- You override `HINDSIGHT_API_EMBEDDINGS_LOCAL_MODEL` or
`HINDSIGHT_API_RERANKER_LOCAL_MODEL` to a non-default model.
- You want pod startup to be deterministic and offline-capable.
- You'd otherwise reach for a Helm `modelCache` PVC just to avoid
re-downloading models.
If you're using the **default** local models, the published full image
(`ghcr.io/vectorize-io/hindsight:latest`) already bakes them in — you don't
need this example.
If you're using **external** providers (TEI, OpenAI, Cohere, ...) for
embeddings and reranking, use the slim image directly — no models are
needed in the image.
## Quick start
```bash
export OPENAI_API_KEY=sk-xxx
docker compose -f docker/docker-compose/custom-models/docker-compose.yaml up --build
```
- API: http://localhost:8888
- Control Plane: http://localhost:9999
## Using your own models
Override the build args to bake different models:
```bash
docker compose -f docker/docker-compose/custom-models/docker-compose.yaml build \
--build-arg EMBEDDER=your-org/your-embedder \
--build-arg RERANKER=your-org/your-reranker
```
Then update the matching `HINDSIGHT_API_EMBEDDINGS_LOCAL_MODEL` and
`HINDSIGHT_API_RERANKER_LOCAL_MODEL` values in `docker-compose.yaml` so the
runtime points at the same model IDs.
## Verifying the models are baked in
`docker-compose.yaml` sets `HF_HUB_OFFLINE=1` and `TRANSFORMERS_OFFLINE=1`
so that any attempt to download a model at runtime fails loudly instead of
silently re-downloading. If the container starts and serves recall queries
with these set, the models are correctly baked in.
You can also inspect the image directly:
```bash
docker run --rm --entrypoint sh hindsight-custom-models-hindsight \
-c 'ls ~/.cache/huggingface/hub/'
```
## Why not a model-cache PVC?
The Helm chart exposes an optional `api.persistence.modelCache` PVC for
caching downloaded models across pod restarts. Compared to baking models
into the image:
- A PVC adds storage cost — one PVC per worker replica with
`volumeClaimTemplates`.
- `ReadWriteOnce` (the default) pins pods to a node.
- The PVC needs lifecycle management on `helm uninstall` / `helm upgrade`
— without `helm.sh/resource-policy: keep` it is deleted on uninstall;
with it, storage keeps billing forever until manually cleaned up.
- Pod startup still depends on HuggingFace being reachable on first run.
Image layers, by contrast, are pulled once per node and cached for free by
the container runtime, with no orphaned-storage cleanup story.
@@ -1,44 +0,0 @@
name: hindsight-custom-models
# Example: run a custom Hindsight image with non-default local models baked
# in at build time, so pod startup does not depend on HuggingFace at runtime.
#
# Quick start:
# export OPENAI_API_KEY=sk-xxx
# docker compose -f docker/docker-compose/custom-models/docker-compose.yaml up --build
#
# Required environment variables:
# - OPENAI_API_KEY (or configure another LLM provider via HINDSIGHT_API_LLM_*)
services:
hindsight:
build:
context: .
dockerfile: Dockerfile
# Override at build time to bake different models:
# docker compose build --build-arg EMBEDDER=your-org/your-embedder
args:
EMBEDDER: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
RERANKER: cross-encoder/mmarco-mMiniLMv2-L12-H384-v1
container_name: hindsight-custom-models
ports:
- "8888:8888"
- "9999:9999"
environment:
HINDSIGHT_API_LLM_PROVIDER: ${HINDSIGHT_API_LLM_PROVIDER:-openai}
HINDSIGHT_API_LLM_API_KEY: ${OPENAI_API_KEY:-your-api-key}
# Point Hindsight at the models baked into the image above.
HINDSIGHT_API_EMBEDDINGS_PROVIDER: local
HINDSIGHT_API_EMBEDDINGS_LOCAL_MODEL: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
HINDSIGHT_API_RERANKER_PROVIDER: local
HINDSIGHT_API_RERANKER_LOCAL_MODEL: cross-encoder/mmarco-mMiniLMv2-L12-H384-v1
# Fail fast if a model is missing from the image instead of silently
# falling back to a HuggingFace download at runtime.
HF_HUB_OFFLINE: "1"
TRANSFORMERS_OFFLINE: "1"
volumes:
- pg_data:/home/hindsight/.pg0
volumes:
pg_data:
-103
View File
@@ -1,103 +0,0 @@
# Hindsight with a local llama.cpp server sidecar
Example Docker Compose setup that runs Hindsight against a **local
llama.cpp server**, fully offline, with no external API key required.
## Architecture
```
┌────────────┐ HTTP /v1/chat/completions ┌──────────────────────────────┐
│ hindsight │ ──────────────────────────▶ │ llama.cpp server (sidecar) │
│ (API + CP) │ │ ghcr.io/ggml-org/llama.cpp │
└────────────┘ └──────────────────────────────┘
```
`llama.cpp` runs as its own container and exposes an OpenAI-compatible
HTTP API. Hindsight talks to it via the standard `openai` LLM provider
with `HINDSIGHT_API_LLM_BASE_URL` pointed at the sidecar.
This pattern follows
[*Hosting llama-server with Docker* (ServiceStack)](https://servicestack.net/posts/hosting-llama-server).
### Why a sidecar and not the in-process `llamacpp` provider?
Hindsight does ship an in-process `llamacpp` provider that spawns
`llama-cpp-python`, but the **published `ghcr.io/vectorize-io/hindsight`
image deliberately omits `llama-cpp-python`** to keep the image small and
avoid bundling native inference libraries that most users don't need.
Trying to set `HINDSIGHT_API_LLM_PROVIDER=llamacpp` against the published
image fails with `ModuleNotFoundError: No module named 'llama_cpp'`.
The sidecar approach side-steps that entirely: the official llama.cpp
image is used as-is for inference, Hindsight is used as-is for memory.
Clean separation, no derived images.
## Quick start
```bash
docker compose -f docker/docker-compose/local-llm/docker-compose.yaml up
```
- API: http://localhost:8888
- Control Plane: http://localhost:9999
**First boot downloads ~3.5 GB** (Gemma 4 E2B Q4_K_M GGUF) into the
`llama_models` named volume. Subsequent boots reuse it.
Hindsight only starts after llama.cpp's `/health` endpoint reports
healthy, so the API will appear "stuck" for a few minutes on the first
run while the model downloads.
## Using a different model
Override the HuggingFace repo / file in `docker-compose.yaml`:
```yaml
environment:
LLAMA_ARG_HF_REPO: bartowski/Qwen2.5-7B-Instruct-GGUF
LLAMA_ARG_HF_FILE: Qwen2.5-7B-Instruct-Q4_K_M.gguf
```
Also update `HINDSIGHT_API_LLM_MODEL` on the `hindsight` service to a
matching alias (the value is sent to llama-server as the OpenAI `model`
field — llama-server is lenient about this but it shows up in logs).
## GPU acceleration
The default compose file targets CPU because not everyone has a GPU. On
CPU, Gemma 4 E2B runs at ~2-3 tokens/sec — fine for a smoke test, but the
retain pipeline (which makes several multi-hundred-token LLM calls per
memory) will time out against Hindsight's default LLM timeout. **For any
real use, run on a GPU.**
### NVIDIA
1. Switch the `llama` service image from `:server` to `:server-cuda`.
2. Uncomment the `LLAMA_ARG_N_GPU_LAYERS: "999"` env var (offload all
layers to GPU).
3. Uncomment the `deploy.resources.reservations.devices` block.
4. Install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
on the host.
The compose file has all four spots marked with inline comments.
### Apple Silicon / ROCm / Vulkan
The official `ghcr.io/ggml-org/llama.cpp` image only ships CPU and CUDA
variants. For Metal (Apple Silicon), ROCm (AMD), or Vulkan backends,
build llama.cpp yourself with the appropriate flags and reference the
image you build instead. Docker Desktop on macOS cannot pass through the
host GPU to a Linux container in any case — for Apple Silicon, run
llama-server directly on the host and only put Hindsight in Docker.
## Caveats
- llama.cpp's HTTP API is OpenAI-compatible but not 100% feature-parity.
Function/tool calling support depends on the chat template baked into
the GGUF; some retain/reflect flows may behave differently than against
a hosted OpenAI model.
- Small GGUFs (~3 B params) are useful for smoke testing but will
underperform a hosted frontier model on retain quality. Use a larger
GGUF (7-13 B params) for production-quality memory.
- The `llama_models` named volume persists the GGUF across `docker
compose down`/`up` so the model is downloaded once, not every restart.
@@ -1,74 +0,0 @@
name: hindsight-local-llm
# Example: run Hindsight against a local llama.cpp server sidecar — fully
# offline, no external API key needed.
#
# Pattern follows https://servicestack.net/posts/hosting-llama-server :
# llama.cpp runs as its own container exposing an OpenAI-compatible HTTP
# API, and Hindsight talks to it via the `openai` LLM provider with a
# custom `base_url`. This means we can use the published Hindsight image
# unchanged — no derived Dockerfile, no `llama-cpp-python` install on top.
#
# Quick start:
# docker compose -f docker/docker-compose/local-llm/docker-compose.yaml up
#
# First boot downloads the default Gemma 4 E2B GGUF (~3.5 GB) into the
# `llama_models` volume; subsequent boots reuse it.
services:
llama:
image: ghcr.io/ggml-org/llama.cpp:server
container_name: hindsight-local-llm-llama
environment:
LLAMA_ARG_HOST: 0.0.0.0
LLAMA_ARG_PORT: "8080"
# Auto-download a small GGUF from HuggingFace on first start.
# Override these to use a different model.
LLAMA_ARG_HF_REPO: bartowski/google_gemma-4-E2B-it-GGUF
LLAMA_ARG_HF_FILE: google_gemma-4-E2B-it-Q4_K_M.gguf
LLAMA_ARG_CTX_SIZE: "8192"
# Uncomment for NVIDIA GPU (and switch image to :server-cuda):
# LLAMA_ARG_N_GPU_LAYERS: "999"
volumes:
# llama-server stores HuggingFace downloads under ~/.cache/huggingface
# (not ~/.cache/llama.cpp), so mount the named volume there to avoid
# re-downloading the GGUF on every recreate.
- llama_models:/root/.cache/huggingface
healthcheck:
test: ["CMD-SHELL", "curl -fsS http://localhost:8080/health || exit 1"]
interval: 10s
timeout: 5s
retries: 60
start_period: 30s
# For NVIDIA GPU acceleration, swap the image above to
# `ghcr.io/ggml-org/llama.cpp:server-cuda` and uncomment:
# deploy:
# resources:
# reservations:
# devices:
# - driver: nvidia
# count: all
# capabilities: [gpu]
hindsight:
image: ghcr.io/vectorize-io/hindsight:latest
container_name: hindsight-local-llm
depends_on:
llama:
condition: service_healthy
ports:
- "8888:8888"
- "9999:9999"
environment:
# llama-server is OpenAI-compatible, so use the `openai` provider and
# point base_url at the sidecar. The API key is unused by llama-server
# but Hindsight requires the env var to be set.
HINDSIGHT_API_LLM_PROVIDER: openai
HINDSIGHT_API_LLM_BASE_URL: http://llama:8080/v1
HINDSIGHT_API_LLM_API_KEY: not-needed
HINDSIGHT_API_LLM_MODEL: gemma-4-e2b-it
volumes:
- pg_data:/home/hindsight/.pg0
volumes:
pg_data:
llama_models:
@@ -51,8 +51,6 @@ services:
# Control Plane config
HINDSIGHT_CP_DATAPLANE_API_URL: http://localhost:8888
# Optional: Require a shared access key for Control Plane UI access
# HINDSIGHT_CP_ACCESS_KEY: your-secret-key
volumes:
# Persist embedded pg0 database
- hindsight_data:/app/data
@@ -1,7 +0,0 @@
# PostgreSQL with pgvector and ParadeDB pg_search extensions.
#
# The official ParadeDB image ships PostgreSQL with pg_search and pgvector
# already installed, so no build steps are required. We pin to the PG17
# variant for parity with the other Hindsight docker-compose examples
# (vchord, pg_textsearch).
FROM paradedb/paradedb:latest-pg17
@@ -1,96 +0,0 @@
name: hindsight
# Docker Compose file for Hindsight with PostgreSQL and ParadeDB pg_search.
#
# pg_search is the only BM25 backend supported by Hindsight that works with
# Citus, so this is the recommended setup for horizontally scaled deployments.
#
# Usage:
# docker compose -f docker/docker-compose/pg_search/docker-compose.yaml up -d
#
# Required environment variables:
# - HINDSIGHT_DB_PASSWORD: Password for the PostgreSQL user
# - Configure LLM provider variables as needed (see the hindsight service)
#
# 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_API_TEXT_SEARCH_EXTENSION_PG_SEARCH_TOKENIZER: ParadeDB pg_search
# tokenizer for new BM25 indexes (default: empty, uses ParadeDB default)
services:
db:
# Use ParadeDB image which bundles pgvector + pg_search
build:
context: .
dockerfile: Dockerfile
container_name: hindsight-db
restart: always
ports:
- "5437:5432"
environment:
POSTGRES_USER: ${HINDSIGHT_DB_USER:-hindsight_user}
POSTGRES_PASSWORD: ${HINDSIGHT_DB_PASSWORD:-hindsight_password}
POSTGRES_DB: ${HINDSIGHT_DB_NAME:-hindsight_db}
volumes:
- pg_data:/var/lib/postgresql/data
networks:
- hindsight-net
pg-search-init:
build:
context: .
dockerfile: Dockerfile
depends_on:
- db
environment:
- PGPASSWORD=${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_search 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_search
HINDSIGHT_API_TEXT_SEARCH_EXTENSION_PG_SEARCH_TOKENIZER: ${HINDSIGHT_API_TEXT_SEARCH_EXTENSION_PG_SEARCH_TOKENIZER:-}
depends_on:
- db
networks:
- hindsight-net
networks:
hindsight-net:
driver: bridge
volumes:
pg_data:
-23
View File
@@ -1,23 +0,0 @@
# PostgreSQL with pgvector and pgroonga extensions.
#
# pgroonga is a multilingual full-text search extension built on Groonga.
# It works out of the box for CJK (Chinese, Japanese, Korean) and other
# non-whitespace-segmented languages via the TokenBigram tokenizer.
FROM groonga/pgroonga:latest-debian-pg17
# Install pgvector on top of the pgroonga base image (which already provides
# pgroonga and the Groonga library).
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential \
git \
postgresql-server-dev-17 \
&& rm -rf /var/lib/apt/lists/*
RUN cd /tmp && \
git clone --branch v0.8.0 https://github.com/pgvector/pgvector.git && \
cd pgvector && \
make && \
make install
RUN rm -rf /tmp/pgvector && \
apt-get purge -y --auto-remove build-essential git postgresql-server-dev-17
@@ -1,91 +0,0 @@
name: hindsight
# Docker Compose file for Hindsight with PostgreSQL and pgroonga
#
# pgroonga provides multilingual BM25 indexing that works out of the box for
# CJK (Chinese, Japanese, Korean) and other non-whitespace-segmented languages.
# Use this recipe if your bank content is not English/European.
#
# docker compose -f docker/docker-compose/pgroonga/docker-compose.yaml down && \
# sleep 2 && \
# docker compose -f docker/docker-compose/pgroonga/docker-compose.yaml 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_PASSWORD: PostgreSQL password (default: hindsight_password)
services:
db:
build:
context: .
dockerfile: Dockerfile
container_name: hindsight-db
restart: always
ports:
- "5439:5432"
environment:
POSTGRES_USER: ${HINDSIGHT_DB_USER:-hindsight_user}
POSTGRES_PASSWORD: ${HINDSIGHT_DB_PASSWORD:-hindsight_password}
POSTGRES_DB: ${HINDSIGHT_DB_NAME:-hindsight_db}
volumes:
- pg_data:/var/lib/postgresql/data
networks:
- hindsight-net
pgroonga-init:
build:
context: .
dockerfile: Dockerfile
depends_on:
- db
environment:
- PGPASSWORD=${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 pgroonga 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: pgroonga
depends_on:
- db
networks:
- hindsight-net
networks:
hindsight-net:
driver: bridge
volumes:
pg_data:
@@ -1,6 +1,6 @@
name: hindsight
# Docker Compose file for Hindsight with PostgreSQL and vectorchord
# docker compose -f docker/docker-compose/vchord/docker-compose.yaml down && sleep 2 && docker compose -f docker/docker-compose/vchord/docker-compose.yaml up -d
# 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)
-10
View File
@@ -50,8 +50,6 @@ WORKDIR /app/api
# Sync dependencies using appropriate extras based on INCLUDE_LOCAL_MODELS
# local-ml: torch, sentence-transformers, transformers, einops, flashrank, mlx (optional)
# embedded-db: pg0-embedded (always included for embedded PostgreSQL support)
# ONNX Runtime embeddings are intentionally not bundled into the official
# standalone image; install the local-onnx extra in custom images when needed.
RUN if [ "$INCLUDE_LOCAL_MODELS" = "true" ]; then \
uv sync --extra local-ml --extra embedded-db; \
else \
@@ -174,10 +172,6 @@ USER hindsight
# "Permission denied" error when mounting a fresh root-owned volume.
RUN mkdir -p /home/hindsight/.pg0
# Make /home/hindsight traversable when running with --user UID:GID overrides
# (default 0700 blocks traversal by non-owner UIDs needed for bind-mount ownership matching)
RUN chmod 755 /home/hindsight
ENV PATH="/app/api/.venv/bin:${PATH}"
# Pre-download tiktoken encoding (ALWAYS - required for token counting even in air-gapped envs)
@@ -334,10 +328,6 @@ USER hindsight
# "Permission denied" error when mounting a fresh root-owned volume.
RUN mkdir -p /home/hindsight/.pg0
# Make /home/hindsight traversable when running with --user UID:GID overrides
# (default 0700 blocks traversal by non-owner UIDs needed for bind-mount ownership matching)
RUN chmod 755 /home/hindsight
ENV PATH="/app/api/.venv/bin:${PATH}"
# Pre-download tiktoken encoding (ALWAYS - required for token counting even in air-gapped envs)
+7 -78
View File
@@ -10,90 +10,19 @@ set -e
# loss scenarios where a container restart caused the data directory to be
# wiped despite a volume mount being present.
# =============================================================================
pg0_has_pg_version() {
local pg0_data_dir="$1"
# pg0 has used more than one on-disk layout. Newer standalone images keep
# PostgreSQL data under instances/<name>/data, while older volumes may have
# placed PG_VERSION at or one level below the mount.
[ -f "$pg0_data_dir/PG_VERSION" ] && return 0
compgen -G "$pg0_data_dir"/*/PG_VERSION > /dev/null 2>&1 && return 0
compgen -G "$pg0_data_dir"/instances/*/data/PG_VERSION > /dev/null 2>&1 && return 0
return 1
}
check_pg0_data_integrity() {
local pg0_data_dir="$1"
if [ ! -d "$pg0_data_dir" ]; then
return 0
fi
PG0_DATA_DIR="${HOME}/.pg0"
if [ -d "$PG0_DATA_DIR" ]; then
# Look for actual PostgreSQL data directories (pg0 creates subdirs per instance)
if pg0_has_pg_version "$pg0_data_dir"; then
echo "✅ Existing pg0 data directory detected at $pg0_data_dir"
elif [ "$(ls -A "$pg0_data_dir" 2>/dev/null)" ]; then
echo "⚠️ WARNING: pg0 data directory exists at $pg0_data_dir but no PG_VERSION found."
if compgen -G "$PG0_DATA_DIR"/*/PG_VERSION > /dev/null 2>&1; then
echo "✅ Existing pg0 data directory detected at $PG0_DATA_DIR"
elif [ "$(ls -A "$PG0_DATA_DIR" 2>/dev/null)" ]; then
echo "⚠️ WARNING: pg0 data directory exists at $PG0_DATA_DIR but no PG_VERSION found."
echo " This may indicate data corruption or an incomplete previous shutdown."
echo " If you see all migrations running from scratch after this, your data may have been lost."
echo " See: https://github.com/vectorize-io/hindsight/issues/675"
fi
return 0
}
# =============================================================================
# Embedded pg0 writability pre-check (#1483)
#
# The container runs as the unprivileged `hindsight` user (UID 1000). When the
# pg0 data directory is a host bind mount (e.g. `-v $HOME/dir:/home/hindsight/.pg0`)
# that is not owned by UID 1000 — the default on macOS Docker Desktop and most
# non-1000 Linux hosts — pg0 fails with the opaque "Permission denied (os error
# 13)". We cannot chown it ourselves without root (and the image is deliberately
# rootless), so we surface an actionable message up front instead.
#
# Docker *named* volumes are seeded with the image directory's ownership (UID
# 1000) on first use, so they avoid this entirely — hence the named-volume
# recommendation below and in the README.
# =============================================================================
check_pg0_writable() {
local pg0_data_dir="$1"
# Only relevant for embedded pg0; an external database doesn't use this dir.
if [ -n "${HINDSIGHT_API_DATABASE_URL:-}" ]; then
return 0
fi
mkdir -p "$pg0_data_dir" 2>/dev/null || true
if touch "$pg0_data_dir/.hindsight-write-test" 2>/dev/null; then
rm -f "$pg0_data_dir/.hindsight-write-test" 2>/dev/null || true
return 0
fi
echo "❌ The embedded database directory $pg0_data_dir is not writable by this container (UID $(id -u))."
echo ""
echo " A host directory was bind-mounted but is not owned by the container user (UID 1000)."
echo " Hindsight runs rootless and cannot fix this for you. Choose one:"
echo ""
echo " • Recommended — use a Docker named volume (auto-owned by the container):"
echo " -v hindsight-data:/home/hindsight/.pg0"
echo ""
echo " • Or keep the host path and run as your host user, chowning it to match:"
echo " sudo chown -R \$(id -u):\$(id -g) <host-directory>"
echo " docker run --user \$(id -u):\$(id -g) -e HOME=/home/hindsight ..."
echo ""
echo " See https://github.com/vectorize-io/hindsight/issues/1483"
return 1
}
if [ "${HINDSIGHT_START_ALL_SOURCE_ONLY:-false}" = "true" ]; then
return 0 2>/dev/null || exit 0
fi
check_pg0_data_integrity "${HOME}/.pg0"
check_pg0_writable "${HOME}/.pg0" || exit 1
# Service flags (default to true if not set)
ENABLE_API="${HINDSIGHT_ENABLE_API:-true}"
ENABLE_CP="${HINDSIGHT_ENABLE_CP:-true}"
@@ -227,7 +156,7 @@ PIDS=()
# Start API if enabled
if [ "$ENABLE_API" = "true" ]; then
cd /app/api
API_HEALTH_URL="${HINDSIGHT_API_HEALTH_URL:-http://localhost:${HINDSIGHT_API_PORT:-8888}/health}"
API_HEALTH_URL="${HINDSIGHT_API_HEALTH_URL:-http://localhost:8888/health}"
API_STARTUP_WAIT_SECONDS="${HINDSIGHT_API_STARTUP_WAIT_SECONDS:-300}"
# Run API directly - Python's PYTHONUNBUFFERED=1 handles output buffering
-121
View File
@@ -1,121 +0,0 @@
#!/bin/bash
set -euo pipefail
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
HINDSIGHT_START_ALL_SOURCE_ONLY=true
source "$SCRIPT_DIR/start-all.sh"
unset HINDSIGHT_START_ALL_SOURCE_ONLY
TMP_DIR="$(mktemp -d)"
trap 'chmod -R u+rwx "$TMP_DIR" 2>/dev/null || true; rm -rf "$TMP_DIR"' EXIT
assert_contains() {
local output="$1"
local expected="$2"
if [[ "$output" != *"$expected"* ]]; then
echo "Expected output to contain: $expected"
echo "Actual output:"
echo "$output"
exit 1
fi
}
assert_not_contains() {
local output="$1"
local unexpected="$2"
if [[ "$output" == *"$unexpected"* ]]; then
echo "Expected output not to contain: $unexpected"
echo "Actual output:"
echo "$output"
exit 1
fi
}
assert_empty() {
local output="$1"
if [ -n "$output" ]; then
echo "Expected no output, got:"
echo "$output"
exit 1
fi
}
mkdir -p "$TMP_DIR/empty"
assert_empty "$(check_pg0_data_integrity "$TMP_DIR/empty")"
mkdir -p "$TMP_DIR/direct"
touch "$TMP_DIR/direct/PG_VERSION"
direct_output="$(check_pg0_data_integrity "$TMP_DIR/direct")"
assert_contains "$direct_output" "Existing pg0 data directory detected"
assert_not_contains "$direct_output" "WARNING"
mkdir -p "$TMP_DIR/legacy/instance"
touch "$TMP_DIR/legacy/instance/PG_VERSION"
legacy_output="$(check_pg0_data_integrity "$TMP_DIR/legacy")"
assert_contains "$legacy_output" "Existing pg0 data directory detected"
assert_not_contains "$legacy_output" "WARNING"
mkdir -p "$TMP_DIR/nested/instances/hindsight/data"
touch "$TMP_DIR/nested/instances/hindsight/data/PG_VERSION"
nested_output="$(check_pg0_data_integrity "$TMP_DIR/nested")"
assert_contains "$nested_output" "Existing pg0 data directory detected"
assert_not_contains "$nested_output" "WARNING"
mkdir -p "$TMP_DIR/nonempty/instances/hindsight"
touch "$TMP_DIR/nonempty/instances/hindsight/instance.json"
nonempty_output="$(check_pg0_data_integrity "$TMP_DIR/nonempty")"
assert_contains "$nonempty_output" "WARNING: pg0 data directory exists"
echo "start-all pg0 integrity checks passed"
# =============================================================================
# check_pg0_writable (#1483)
# These rely on filesystem permissions, which root bypasses; skip under root.
# =============================================================================
if [ "$(id -u)" != "0" ]; then
# Writable directory: returns 0, prints nothing, leaves no artifact behind.
mkdir -p "$TMP_DIR/writable"
writable_output="$(check_pg0_writable "$TMP_DIR/writable")"
assert_empty "$writable_output"
if [ -e "$TMP_DIR/writable/.hindsight-write-test" ]; then
echo "check_pg0_writable left its write-test file behind"
exit 1
fi
# Non-writable directory: returns 1 with actionable guidance.
mkdir -p "$TMP_DIR/readonly"
chmod 000 "$TMP_DIR/readonly"
set +e
readonly_output="$(check_pg0_writable "$TMP_DIR/readonly" 2>&1)"
readonly_rc=$?
set -e
chmod 755 "$TMP_DIR/readonly"
if [ "$readonly_rc" -eq 0 ]; then
echo "check_pg0_writable should fail on a non-writable directory"
exit 1
fi
assert_contains "$readonly_output" "not writable"
assert_contains "$readonly_output" "hindsight-data:/home/hindsight/.pg0"
assert_contains "$readonly_output" "--user"
# External database configured: skip the check regardless of dir perms.
mkdir -p "$TMP_DIR/extdb"
chmod 000 "$TMP_DIR/extdb"
set +e
HINDSIGHT_API_DATABASE_URL="postgres://x" check_pg0_writable "$TMP_DIR/extdb" >/dev/null 2>&1
extdb_rc=$?
set -e
chmod 755 "$TMP_DIR/extdb"
if [ "$extdb_rc" -ne 0 ]; then
echo "check_pg0_writable should skip when an external database is configured"
exit 1
fi
echo "start-all pg0 writability checks passed"
else
echo "⚠️ Running as root; skipping pg0 writability checks (permissions are bypassed)."
fi
+6
View File
@@ -0,0 +1,6 @@
dependencies:
- name: postgresql
repository: https://charts.bitnami.com/bitnami
version: 15.5.38
digest: sha256:f67c7612736803ece8a669f8ca6b0555f3b78557bc0ecb732aa2e43f0df7750d
generated: "2025-12-10T17:20:57.058794+01:00"
+2 -2
View File
@@ -2,8 +2,8 @@ apiVersion: v2
name: hindsight
description: Hindsight helm chart
type: application
version: 0.8.4
appVersion: "0.8.4"
version: 0.5.6
appVersion: "0.5.6"
keywords:
- ai
- memory
+3 -3
View File
@@ -66,13 +66,13 @@ helm install hindsight ./helm/hindsight -n hindsight --create-namespace -f value
| Parameter | Description | Default |
|-----------|-------------|---------|
| `version` | Default image tag for all components | Chart `appVersion` |
| `version` | Default image tag for all components | `0.1.0` |
| `api.enabled` | Enable the API component | `true` |
| `api.image.repository` | API image repository | `ghcr.io/vectorize-io/hindsight-api` |
| `api.image.repository` | API image repository | `hindsight/api` |
| `api.image.tag` | API image tag (defaults to `version`) | - |
| `api.service.port` | API service port | `8888` |
| `controlPlane.enabled` | Enable the control plane | `true` |
| `controlPlane.image.repository` | Control plane image repository | `ghcr.io/vectorize-io/hindsight-control-plane` |
| `controlPlane.image.repository` | Control plane image repository | `hindsight/control-plane` |
| `controlPlane.image.tag` | Control plane image tag (defaults to `version`) | - |
| `controlPlane.service.port` | Control plane service port | `3000` |
| `postgresql.enabled` | Deploy PostgreSQL as subchart | `true` |
+4 -10
View File
@@ -13,6 +13,9 @@
# - Any other env vars you want to inject
# existingSecret: "my-hindsight-secret"
# Global settings
replicaCount: 1
# Image settings for api
api:
enabled: true
@@ -67,12 +70,6 @@ api:
# Persistent volume for local model cache (reranker, embeddings)
# Models are downloaded to /home/hindsight/.cache on first use.
# Without persistence, models are re-downloaded on every pod restart.
#
# For production, prefer baking models into a custom image instead of
# enabling this PVC: image layers are pulled once per node and cached
# for free, while a PVC adds storage cost, pins pods to a node
# (ReadWriteOnce), and needs lifecycle management on uninstall/upgrade.
# See docs: developer/installation#bundling-custom-models-in-a-custom-image
persistence:
modelCache:
enabled: false
@@ -171,10 +168,7 @@ worker:
# affinity: {}
# Persistent volume for local model cache (reranker, embeddings)
# Uses volumeClaimTemplates since worker is a StatefulSet — one PVC per
# replica. For production, prefer baking models into a custom image; see
# api.persistence.modelCache above and docs:
# developer/installation#bundling-custom-models-in-a-custom-image
# Uses volumeClaimTemplates since worker is a StatefulSet.
persistence:
modelCache:
enabled: false
+1 -1
View File
@@ -1,6 +1,6 @@
{
"name": "@vectorize-io/hindsight-all",
"version": "0.8.4",
"version": "0.5.6",
"description": "Node.js programmatic lifecycle manager for Hindsight — embeds a local hindsight daemon in a Node application. Pair with @vectorize-io/hindsight-client for memory operations.",
"main": "dist/index.js",
"types": "dist/index.d.ts",
+2 -2
View File
@@ -4,12 +4,12 @@ build-backend = "setuptools.build_meta"
[project]
name = "hindsight-all-slim"
version = "0.8.4"
version = "0.5.6"
description = "Hindsight: Agent Memory That Works Like Human Memory - Slim All-in-One Bundle"
readme = "README.md"
requires-python = ">=3.11"
dependencies = [
"hindsight-api-slim==0.8.4",
"hindsight-api-slim>=0.4.17",
"hindsight-client>=0.0.7",
"hindsight-embed>=0.1.0",
]
+3 -3
View File
@@ -4,12 +4,12 @@ build-backend = "hatchling.build"
[project]
name = "hindsight-all"
version = "0.8.4"
version = "0.5.6"
description = "Hindsight: Agent Memory That Works Like Human Memory - All-in-One Bundle"
readme = "README.md"
requires-python = ">=3.11"
dependencies = [
"hindsight-api-slim[all]==0.8.4",
"hindsight-api-slim[all]>=0.4.17",
"hindsight-client>=0.0.7",
"hindsight-embed>=0.1.0",
]
@@ -21,7 +21,7 @@ hindsight-embed = { workspace = true }
[project.optional-dependencies]
local-llm = [
"hindsight-api-slim[local-llm]==0.8.4",
"hindsight-api-slim[local-llm]>=0.4.17",
]
test = [
"pytest>=7.0.0",
+1 -1
View File
@@ -386,7 +386,7 @@ def test_embedded_ui_flag(llm_config):
# Verify UI is reachable and reports connected dataplane
ui_url = client.ui_url
assert isinstance(ui_url, str) and ui_url, "ui_url should be a non-empty string"
assert ui_url, "ui_url should be set"
health_url = f"{ui_url}/api/health"
with urllib.request.urlopen(health_url, timeout=10) as resp:
+2 -2
View File
@@ -99,7 +99,7 @@ hindsight-api
## Docker
```bash
docker run -it --name hindsight --restart unless-stopped -p 8888:8888 \
docker run --rm -it -p 8888:8888 \
-e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
ghcr.io/vectorize-io/hindsight:latest
@@ -121,7 +121,7 @@ This runs a stdio-based MCP server that can be used directly with MCP-compatible
- **Entity Graph** — Automatic entity extraction and relationship tracking
- **Temporal Reasoning** — Native support for time-based queries
- **Disposition Traits** — Configurable skepticism, literalism, and empathy influence opinion formation
- **Three Memory Types** — World facts, experience facts (the bank's own actions), and observations
- **Three Memory Types** — World facts, bank actions, and formed opinions with confidence scores
## Documentation
+1 -8
View File
@@ -4,13 +4,6 @@ Memory System for AI Agents.
Temporal + Semantic Memory Architecture using PostgreSQL with pgvector.
"""
# Cap native ML thread pools (OpenBLAS/OpenMP/MKL) before any import pulls in
# numpy/torch/onnxruntime — they read these env vars only at load time. See
# hindsight_api/_thread_limits.py for the rationale.
from ._thread_limits import apply_default_thread_limits
apply_default_thread_limits()
from .config import HindsightConfig, get_config
from .engine.cross_encoder import CrossEncoderModel, LocalSTCrossEncoder, RemoteTEICrossEncoder
from .engine.embeddings import Embeddings, LocalSTEmbeddings, RemoteTEIEmbeddings
@@ -53,4 +46,4 @@ __all__ = [
"RemoteTEICrossEncoder",
"LLMConfig",
]
__version__ = "0.8.4"
__version__ = "0.5.6"
@@ -1,85 +0,0 @@
"""Helpers for ParadeDB pg_search index configuration."""
from __future__ import annotations
import re
from collections.abc import Sequence
PG_SEARCH_TOKENIZER_ENV = "HINDSIGHT_API_TEXT_SEARCH_EXTENSION_PG_SEARCH_TOKENIZER"
_SIMPLE_TOKENIZERS = {
"unicode_words",
"simple",
"whitespace",
"literal",
"literal_normalized",
"chinese_compatible",
"icu",
"jieba",
"source_code",
}
_TOKENIZER_ALIASES = {
"chinese_lindera": "lindera(chinese)",
"japanese_lindera": "lindera(japanese)",
"korean_lindera": "lindera(korean)",
"lindera_chinese": "lindera(chinese)",
"lindera_japanese": "lindera(japanese)",
"lindera_korean": "lindera(korean)",
}
def normalize_pg_search_tokenizer(value: str | None) -> str:
"""Validate and normalize a ParadeDB pg_search tokenizer setting.
Returns an empty string when unset. The returned value is safe to embed after
``pdb.`` in a CREATE INDEX expression.
"""
tokenizer = (value or "").strip().lower()
if not tokenizer:
return ""
if tokenizer in _TOKENIZER_ALIASES:
return _TOKENIZER_ALIASES[tokenizer]
if tokenizer in _SIMPLE_TOKENIZERS:
return tokenizer
lindera_match = re.fullmatch(r"lindera\((chinese|japanese|korean)\)", tokenizer)
if lindera_match:
return tokenizer
ngram_match = re.fullmatch(r"(ngram|edge_ngram)\((\d{1,3}),\s*(\d{1,3})\)", tokenizer)
if ngram_match:
kind, min_gram, max_gram = ngram_match.groups()
min_value = int(min_gram)
max_value = int(max_gram)
if min_value <= 0 or min_value > max_value:
raise ValueError(
f"Invalid {PG_SEARCH_TOKENIZER_ENV}: {value!r}. "
"ngram and edge_ngram require positive min/max gram sizes with min <= max."
)
return f"{kind}({min_value},{max_value})"
raise ValueError(
f"Invalid {PG_SEARCH_TOKENIZER_ENV}: {value!r}. "
"Supported values are: unicode_words, simple, whitespace, literal, "
"literal_normalized, chinese_compatible, icu, jieba, source_code, "
"chinese_lindera, japanese_lindera, korean_lindera, or "
"lindera(chinese|japanese|korean), ngram(min,max), or edge_ngram(min,max)."
)
def pg_search_bm25_columns(
key_field: str,
text_fields: Sequence[str],
tokenizer: str | None,
) -> str:
"""Build a ParadeDB BM25 column list for CREATE INDEX."""
normalized = normalize_pg_search_tokenizer(tokenizer)
if not normalized:
return ", ".join([key_field, *text_fields])
return ", ".join([key_field, *(f"({field}::pdb.{normalized})" for field in text_fields)])
@@ -1,107 +0,0 @@
"""Process-level caps for native ML thread pools.
OpenBLAS, OpenMP, and MKL each spawn a worker pool sized to the host CPU count
the first time they are loaded (numpy pulls in OpenBLAS eagerly; torch and
onnxruntime load their pools lazily on first inference). Hindsight already
parallelizes at the request level via thread-pool executors (embeddings on the
default executor, the reranker on its own pool), so these native intra-op pools
oversubscribe the CPU: on a many-core host the process accumulates 100+ native
threads, which inflates memory and, under contention, can degrade throughput.
We bound each pool to ``_MAX_NATIVE_THREADS`` (or the available CPU count, if
smaller). "Available" is the CPU budget actually granted to the process, not
``os.cpu_count()``: in a CPU-limited container ``os.cpu_count()`` still reports
the host's cores, so sizing pools by it oversubscribes the container's real
quota — the exact failure mode this guards against. We therefore take the
smallest of the CPU-affinity set, the cgroup CPU quota, and ``os.cpu_count()``.
Every cap is applied with ``setdefault`` so an operator who has deliberately
tuned one of these variables keeps their value. This must run *before* numpy,
torch, or onnxruntime are imported — those libraries read the variables only at
load time — which is why it is invoked at the very top of
``hindsight_api/__init__.py``, ahead of the package's other imports.
"""
from __future__ import annotations
import os
# Native threading env vars, each read by the respective library at load time.
_NATIVE_THREAD_VARS = (
"OMP_NUM_THREADS", # OpenMP — torch, onnxruntime, some BLAS builds
"OPENBLAS_NUM_THREADS", # OpenBLAS — numpy's default BLAS
"MKL_NUM_THREADS", # Intel MKL — numpy/torch when MKL-backed
"NUMEXPR_NUM_THREADS", # numexpr expression engine
)
# Upper bound on intra-op threads per native pool. Bounds runaway growth on
# many-core hosts without serialising single-request inference.
_MAX_NATIVE_THREADS = 16
def _quota_to_cpus(quota: int, period: int) -> int | None:
"""Whole CPUs from a CFS quota/period pair, or None if unlimited."""
if quota > 0 and period > 0:
# Floor (never round up) so we never exceed the granted budget.
return max(1, quota // period)
return None
def _parse_cgroup_v2_cpu_max(text: str) -> int | None:
"""Parse cgroup v2 ``cpu.max`` ("<quota> <period>", or "max <period>")."""
parts = text.split()
if len(parts) >= 2 and parts[0] != "max":
try:
return _quota_to_cpus(int(parts[0]), int(parts[1]))
except ValueError:
return None
return None
def _cgroup_cpu_quota() -> int | None:
"""Effective CPUs from the cgroup CPU quota, or None if unlimited/unknown."""
try: # cgroup v2
with open("/sys/fs/cgroup/cpu.max") as fh:
return _parse_cgroup_v2_cpu_max(fh.read())
except OSError:
pass
try: # cgroup v1
with open("/sys/fs/cgroup/cpu/cpu.cfs_quota_us") as fh:
quota = int(fh.read())
with open("/sys/fs/cgroup/cpu/cpu.cfs_period_us") as fh:
period = int(fh.read())
return _quota_to_cpus(quota, period)
except (OSError, ValueError):
return None
def _available_cpu_count() -> int:
"""CPUs actually available to this process.
The smallest of the CPU-affinity set (cpuset / ``--cpuset-cpus``), the
cgroup CPU quota (``--cpus``), and ``os.cpu_count()`` — each captures a
different way the budget can be constrained, and the last alone overcounts
inside a limited container.
"""
candidates = [os.cpu_count() or 1]
if hasattr(os, "sched_getaffinity"):
try:
candidates.append(len(os.sched_getaffinity(0)))
except OSError:
pass
quota = _cgroup_cpu_quota()
if quota is not None:
candidates.append(quota)
return max(1, min(candidates))
def default_native_thread_count() -> int:
"""Per-pool cap: ``_MAX_NATIVE_THREADS``, or available CPUs if fewer."""
return min(_MAX_NATIVE_THREADS, _available_cpu_count())
def apply_default_thread_limits() -> None:
"""Cap native ML thread pools unless the operator has set the var already."""
value = str(default_native_thread_count())
for var in _NATIVE_THREAD_VARS:
os.environ.setdefault(var, value)
@@ -1,225 +0,0 @@
"""Shared PostgreSQL vector-extension dispatch helpers."""
from __future__ import annotations
import logging
import os
from sqlalchemy import text
from sqlalchemy.engine import Connection
logger = logging.getLogger(__name__)
# Extensions a user can set via HINDSIGHT_API_VECTOR_EXTENSION.
CONFIGURABLE_EXTENSIONS = ("pgvector", "pgvectorscale", "vchord", "scann")
# Extensions detect_vector_extension() can return. pg_diskann is a runtime-only
# resolution from a configured "pgvectorscale" backend on Azure (uses a different
# WITH clause), never a value the user sets directly.
RESOLVED_EXTENSIONS = (*CONFIGURABLE_EXTENSIONS, "pg_diskann")
# Backwards-compatible alias for older imports.
VALID_EXTENSIONS = CONFIGURABLE_EXTENSIONS
SCANN_MIN_ROWS_FOR_AUTO_INDEX = 10_000
_EXTENSION_NAMES = {
"pgvector": "vector",
"pgvectorscale": "vectorscale",
"vchord": "vchord",
"scann": "alloydb_scann",
}
_INDEX_USING_CLAUSES = {
"pgvector": "USING hnsw (embedding vector_cosine_ops)",
"pgvectorscale": "USING diskann (embedding vector_cosine_ops) WITH (num_neighbors = 50)",
"pg_diskann": "USING diskann (embedding vector_cosine_ops) WITH (max_neighbors = 50)",
"vchord": "USING vchordrq (embedding vector_cosine_ops)",
"scann": "USING scann (embedding cosine) WITH (mode = 'AUTO')",
}
_INDEX_TYPE_KEYWORDS = {
"pgvector": "hnsw",
"pgvectorscale": "diskann",
"pg_diskann": "diskann",
"vchord": "vchordrq",
"scann": "scann",
}
# Per-backend ANN search-time tuning GUCs. Each entry is a tuple of
# (guc_name, value) pairs the caller can apply with SET or SET LOCAL.
#
# - pgvector exposes hnsw.ef_search. The 60 / 200 pair is unchanged from the
# pre-dispatcher code (internal benchmarks tuned around our embedding count
# and recall floor; see the link_utils / pool init call sites for the
# latency-vs-recall framing).
# - vchord exposes vchordrq.probes, but its shape must match the index's
# build.internal.lists hierarchy. VectorChord 1.1 added per-index fallback
# parameters for this reason: a session GUC overrides every vchordrq index,
# and a single value can be invalid for listless or mixed-layout indexes.
# Hindsight's built-in vchord clause does not set lists, so the safe default
# is no session-level probe override; deployments that partition vchordrq
# indexes should attach probes to the index storage parameters instead.
# - pgvectorscale / pg_diskann / scann do not expose an equivalent per-statement
# knob in the engine today, so the dispatcher returns no statements for them.
_ANN_TUNING_LOW_LATENCY: dict[str, tuple[tuple[str, str], ...]] = {
"pgvector": (("hnsw.ef_search", "60"),),
}
_ANN_TUNING_HIGH_RECALL: dict[str, tuple[tuple[str, str], ...]] = {
"pgvector": (("hnsw.ef_search", "200"),),
}
_EXTENSION_INSTALL_SQL = {
"pgvector": ("CREATE EXTENSION IF NOT EXISTS vector",),
"pgvectorscale": (
"CREATE EXTENSION IF NOT EXISTS vector",
"CREATE EXTENSION IF NOT EXISTS vectorscale CASCADE",
),
"vchord": ("CREATE EXTENSION IF NOT EXISTS vchord CASCADE",),
"scann": (
"CREATE EXTENSION IF NOT EXISTS vector",
"CREATE EXTENSION IF NOT EXISTS alloydb_scann CASCADE",
),
}
_INSTALL_HINTS = {
"pgvector": "CREATE EXTENSION vector;",
"pgvectorscale": "CREATE EXTENSION vector; then CREATE EXTENSION vectorscale CASCADE; (or pg_diskann on Azure)",
"vchord": "CREATE EXTENSION vchord CASCADE;",
"scann": "CREATE EXTENSION vector; then CREATE EXTENSION alloydb_scann CASCADE;",
}
def configured_vector_extension() -> str:
"""Return the user-configured vector backend extension.
Reads ``HINDSIGHT_API_VECTOR_EXTENSION`` (default ``"pgvector"``) and
validates it via :func:`validate_extension`. This is the single source of
truth for runtime code that needs to dispatch behaviour by vector backend;
callers should prefer this over reading the env var directly, so the
default value and the lookup mechanism live in one place.
"""
return validate_extension(os.getenv("HINDSIGHT_API_VECTOR_EXTENSION", "pgvector"))
def validate_extension(name: str) -> str:
"""Return a normalized configurable vector extension name or raise.
Used at the user-facing config boundary; pg_diskann is rejected here because
it is a detection-time alias, never a value the user sets directly.
"""
ext = name.lower()
if ext not in CONFIGURABLE_EXTENSIONS:
valid = ", ".join(CONFIGURABLE_EXTENSIONS)
raise ValueError(f"Invalid vector_extension: {name}. Must be one of: {valid}")
return ext
def _normalize_resolved(name: str) -> str:
"""Normalize either a user-configurable or detect-time extension name."""
ext = name.lower()
if ext not in RESOLVED_EXTENSIONS:
valid = ", ".join(RESOLVED_EXTENSIONS)
raise ValueError(f"Unknown vector extension: {name}. Must be one of: {valid}")
return ext
def pg_extension_name(ext: str) -> str:
"""Return the PostgreSQL extension name for a configured vector backend."""
return _EXTENSION_NAMES[validate_extension(ext)]
def index_using_clause(ext: str) -> str:
"""Return the CREATE INDEX USING clause for the vector backend."""
return _INDEX_USING_CLAUSES[_normalize_resolved(ext)]
def index_type_keyword(ext: str) -> str:
"""Return the keyword that identifies this index type in pg_indexes.indexdef."""
return _INDEX_TYPE_KEYWORDS[_normalize_resolved(ext)]
def minimum_rows_for_index(ext: str) -> int:
"""Return the minimum populated embedding rows before creating this index type."""
return SCANN_MIN_ROWS_FOR_AUTO_INDEX if _normalize_resolved(ext) == "scann" else 0
def should_defer_index_creation(ext: str, row_count: int) -> bool:
"""Return True when index creation should wait for more embeddings."""
minimum_rows = minimum_rows_for_index(ext)
return minimum_rows > 0 and row_count < minimum_rows
def ann_search_tuning_settings(ext: str, *, kind: str) -> tuple[tuple[str, str], ...]:
"""Return per-backend (guc_name, value) pairs for ANN search-time tuning.
``kind`` is ``"low_latency"`` for retain-side link probing (smaller probe
count, lower recall, lower latency) and ``"high_recall"`` for connection
init in the pool (larger probe count, higher recall). Callers wrap each
pair with ``SET LOCAL`` or ``SET`` themselves so the same dispatcher works
for both transaction-scoped and session-scoped use. Returns an empty tuple
for backends without an equivalent knob.
"""
if kind == "low_latency":
table = _ANN_TUNING_LOW_LATENCY
elif kind == "high_recall":
table = _ANN_TUNING_HIGH_RECALL
else:
raise ValueError(f"Unknown ANN tuning kind: {kind!r}")
return table.get(_normalize_resolved(ext), ())
def uses_per_bank_vector_indexes(ext: str) -> bool:
"""Return whether the backend should create per-bank partial vector indexes."""
return _normalize_resolved(ext) != "scann"
def bootstrap_extension(conn: Connection, ext: str) -> None:
"""Install the configured vector extension and any prerequisites if possible."""
normalized = validate_extension(ext)
for statement in _EXTENSION_INSTALL_SQL[normalized]:
conn.execute(text(statement))
def detect_vector_extension(conn: Connection, vector_extension: str = "pgvector") -> str:
"""Validate the configured vector extension exists and return the index backend."""
configured_ext = validate_extension(vector_extension)
if configured_ext == "pgvectorscale":
pgvector_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vector'")).scalar()
if not pgvector_check:
raise RuntimeError(
"DiskANN (pgvectorscale/pg_diskann) requires pgvector to be installed. "
f"Install it with: {_INSTALL_HINTS['pgvectorscale']}"
)
vectorscale_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vectorscale'")).scalar()
pg_diskann_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'pg_diskann'")).scalar()
if vectorscale_check:
logger.debug("Using vector extension: pgvectorscale (DiskANN)")
return "pgvectorscale"
if pg_diskann_check:
logger.debug("Using vector extension: pg_diskann (Azure DiskANN)")
return "pg_diskann"
raise RuntimeError(
"Configured vector extension 'pgvectorscale' not found. Install either:\n"
" - pgvectorscale (open source): CREATE EXTENSION vectorscale CASCADE;\n"
" - pg_diskann (Azure): CREATE EXTENSION pg_diskann CASCADE;"
)
extension_name = pg_extension_name(configured_ext)
extension_check = conn.execute(
text("SELECT 1 FROM pg_extension WHERE extname = :extension_name"),
{"extension_name": extension_name},
).scalar()
if not extension_check:
raise RuntimeError(
f"Configured vector extension '{configured_ext}' not found. "
f"Install it with: {_INSTALL_HINTS[configured_ext]}"
)
logger.debug("Using configured vector extension: %s", configured_ext)
return configured_ext
+33 -191
View File
@@ -17,9 +17,7 @@ import asyncpg
import typer
from ..config import DEFAULT_DATABASE_SCHEMA, HindsightConfig
from ..engine.memory_engine import _current_schema
from ..engine.schema import fq_table_explicit as _fq_table
from ..engine.transfer import export_bank
from ..extensions import TenantExtension, load_extension
from ..pg0 import parse_pg0_url, resolve_database_url
@@ -32,60 +30,22 @@ logger = logging.getLogger(__name__)
app = typer.Typer(name="hindsight-admin", help="Hindsight administrative commands")
# Tables to backup/restore in foreign-key dependency order (parents first).
# Restore COPYs in this order and TRUNCATEs in reverse, so every child must
# appear after the tables it references.
#
# This must cover EVERY persistent PostgreSQL table in the schema — a missing
# entry silently drops that table's data on restore (and, worse, restore's
# `TRUNCATE banks CASCADE` wipes any FK-to-banks child like mental_models even
# when it was never backed up). test_admin_backup_restore.py asserts this list
# equals the live schema's tables, so adding a migration that creates a table
# without adding it here fails CI. Oracle-only tables (e.g. observation_sources)
# are intentionally absent — admin backup/restore is PostgreSQL-only.
# Tables to backup/restore in dependency order
# Import must happen in this order due to foreign key constraints
BACKUP_TABLES = [
"banks",
"documents",
"entities",
"chunks",
"memory_units",
"invalidated_memory_units",
"unit_entities",
"entity_cooccurrences",
"memory_links",
"observation_history",
"mental_models",
"mental_model_history",
"knowledge_pages",
"directives",
"async_operations",
"webhooks",
"file_storage",
"audit_log",
"llm_requests",
"graph_maintenance_queue",
]
MANIFEST_VERSION = "1"
async def _admin_connect(db_url: str) -> asyncpg.Connection:
"""Open a raw asyncpg connection to an admin DB URL.
``resolve_database_url`` handles both plain ``postgres://`` (passthrough) and
``pg0://`` (boots the embedded server and returns its real libpq URL), so this
is the only step needed to connect. JSON codecs are registered so ``jsonb``
columns decode to Python objects (used by the export row dumps).
"""
is_pg0, instance_name, _ = parse_pg0_url(db_url)
if is_pg0:
typer.echo(f"Starting embedded PostgreSQL (instance: {instance_name})...")
conn = await asyncpg.connect(await resolve_database_url(db_url))
for type_name in ("json", "jsonb"):
await conn.set_type_codec(type_name, encoder=json.dumps, decoder=json.loads, schema="pg_catalog")
return conn
async def _backup(database_url: str, output_path: Path, schema: str = "public") -> dict[str, Any]:
"""Backup all tables to a zip file using binary COPY protocol."""
conn = await asyncpg.connect(database_url)
@@ -257,10 +217,14 @@ async def _run_migration(
schema: str | None = None,
base_schema: str = DEFAULT_DATABASE_SCHEMA,
embedding_dimension: int | None = None,
ensure_extensions: bool = True,
) -> list[str]:
"""Resolve database URL and run migrations for one schema or all discovered schemas."""
from ..migrations import run_migrations_for_schemas
from ..migrations import (
ensure_embedding_dimension,
ensure_text_search_extension,
ensure_vector_extension,
run_migrations,
)
is_pg0, instance_name, _ = parse_pg0_url(db_url)
if is_pg0:
@@ -281,21 +245,31 @@ async def _run_migration(
# Preserve order while removing duplicates.
schemas = list(dict.fromkeys(schemas))
# Migrate up to `migration_concurrency` schemas at once (each in its own
# process); within a schema the work stays sequential. Run off the event
# loop so the process pool's blocking joins don't stall it.
await asyncio.to_thread(
run_migrations_for_schemas,
resolved_url,
schemas,
concurrency=config.migration_concurrency,
migration_database_url=config.migration_database_url,
embedding_dimension=embedding_dimension,
vector_extension=config.vector_extension,
text_search_extension=config.text_search_extension,
pg_search_tokenizer=config.text_search_extension_pg_search_tokenizer,
ensure_extensions=ensure_extensions,
)
for schema in schemas:
run_migrations(resolved_url, schema=schema, migration_database_url=config.migration_database_url)
if embedding_dimension is not None:
for schema in schemas:
ensure_embedding_dimension(
resolved_url,
embedding_dimension,
schema=schema,
vector_extension=config.vector_extension,
)
for schema in schemas:
ensure_vector_extension(
resolved_url,
vector_extension=config.vector_extension,
schema=schema,
)
for schema in schemas:
ensure_text_search_extension(
resolved_url,
text_search_extension=config.text_search_extension,
schema=schema,
)
return schemas
@@ -313,18 +287,6 @@ def run_db_migration(
"--embedding-dimension",
help="Expected embedding dimension to enforce after migrations. Omit to skip dimension sync.",
),
skip_extension_reconcile: bool = typer.Option(
False,
"--skip-extension-reconcile",
help=(
"Skip the post-migration vector / text-search index reconcile. This step only does "
"work when the configured backend (HINDSIGHT_API_VECTOR_EXTENSION / "
"HINDSIGHT_API_TEXT_SEARCH_EXTENSION) differs from a schema's existing indexes — a "
"rare, operator-driven change. Skipping it makes a no-change re-migration over many "
"tenant schemas much faster. Only use when you have NOT changed the backend; a "
"backend change still needs a normal run to reshape the indexes."
),
),
):
"""Run database migrations to the latest version."""
config = HindsightConfig.from_env()
@@ -338,8 +300,6 @@ def run_db_migration(
typer.echo(f"Running database migrations for schema: {schema}...")
else:
typer.echo("Running database migrations for base schema and all discovered tenant schemas...")
if skip_extension_reconcile:
typer.echo("Skipping post-migration extension reconcile (--skip-extension-reconcile).")
schemas = asyncio.run(
_run_migration(
@@ -347,130 +307,12 @@ def run_db_migration(
schema=schema,
base_schema=config.database_schema,
embedding_dimension=embedding_dimension,
ensure_extensions=not skip_extension_reconcile,
)
)
typer.echo(f"Database migrations completed successfully for {len(schemas)} schema(s)")
async def _run_export_bank(db_url: str, bank_id: str, output: Path, schema: str, include_history: bool) -> int:
"""Export a whole bank to a ZIP archive."""
conn = await _admin_connect(db_url)
try:
# export_bank resolves table names via fq_table (the _current_schema
# contextvar); set it so the raw connection targets the right schema.
_current_schema.set(schema)
data = await export_bank(conn, bank_id, include_history=include_history)
finally:
await conn.close()
output.write_bytes(data)
return len(data)
@app.command(name="export-bank")
def export_bank_command(
bank_id: str = typer.Option(..., "--bank", "-b", help="Bank id to export."),
output: Path = typer.Option(..., "--output", "-o", help="Path to write the .zip archive."),
schema: str | None = typer.Option(
None,
"--schema",
"-s",
help="Database schema the bank lives in. Defaults to the configured base schema.",
),
include_history: bool = typer.Option(
False,
"--include-history",
help="Also export operational history (audit_log, llm_requests). Off by default.",
),
):
"""Export an entire bank to a portable ZIP (no embeddings — regenerated on import).
Carries documents, facts, observations, bank config, mental models, directives
and webhooks so the bank can be imported into a new instance configured with a
different embedding model / vector / text-search backend.
"""
config = HindsightConfig.from_env()
if not config.database_url:
typer.echo("Error: Database URL not configured.", err=True)
typer.echo("Set HINDSIGHT_API_DATABASE_URL environment variable.", err=True)
raise typer.Exit(1)
target_schema = schema or config.database_schema or DEFAULT_DATABASE_SCHEMA
typer.echo(f"Exporting bank '{bank_id}' from schema '{target_schema}'...")
size = asyncio.run(_run_export_bank(config.database_url, bank_id, output, target_schema, include_history))
typer.echo(f"Exported bank '{bank_id}' to {output} ({size} bytes)")
async def _run_import_bank(archive_path: Path, schema: str, target_bank_id: str | None, include_history: bool):
"""Boot a MemoryEngine (for the target's embedding model) and restore a bank archive."""
# MemoryEngine is heavy (loads embeddings); import it lazily so other admin
# commands don't pay for it. _current_schema is imported at module top.
from ..engine.memory_engine import MemoryEngine
from ..models import RequestContext
archive_bytes = archive_path.read_bytes()
# run_migrations=True so a fresh target instance is provisioned at this
# instance's embedding dimension / vector / text-search backend before restore.
engine = MemoryEngine(run_migrations=True)
await engine.initialize()
try:
_current_schema.set(schema)
context = RequestContext(internal=True, user_initiated=True)
return await engine.import_bank_async(
archive_bytes,
context,
target_bank_id=target_bank_id,
include_history=include_history,
)
finally:
await engine.close()
@app.command(name="import-bank")
def import_bank_command(
archive: Path = typer.Option(..., "--archive", "-a", help="Path to the .zip produced by export-bank."),
schema: str | None = typer.Option(
None, "--schema", "-s", help="Target schema. Defaults to the configured base schema."
),
target_bank: str | None = typer.Option(
None, "--target-bank", help="Override the bank id (defaults to the archive's source bank)."
),
include_history: bool = typer.Option(
False, "--include-history", help="Also restore operational history if present in the archive."
),
):
"""Restore a whole bank from an export-bank archive into THIS instance.
Re-embeds facts with this instance's configured embedding model and rebuilds
links and indexes — the import half of a cross-instance migration. Run against
an instance configured with the desired embedding / vector / text-search backend.
The target bank must not already exist (import restores a whole bank, not a merge).
"""
config = HindsightConfig.from_env()
if not config.database_url:
typer.echo("Error: Database URL not configured.", err=True)
typer.echo("Set HINDSIGHT_API_DATABASE_URL environment variable.", err=True)
raise typer.Exit(1)
target_schema = schema or config.database_schema or DEFAULT_DATABASE_SCHEMA
typer.echo(f"Importing bank archive '{archive}' into schema '{target_schema}'...")
result = asyncio.run(_run_import_bank(archive, target_schema, target_bank, include_history))
typer.echo(
f"Imported bank '{result.bank_id}': {result.documents_imported} doc(s), "
f"{result.facts_imported} fact(s), {result.observations_imported} observation(s), "
f"{result.mental_models_imported} mental model(s), "
f"{result.mental_model_history_imported} mm-history row(s), {result.directives_imported} directive(s), "
f"{result.webhooks_imported} webhook(s), {result.history_rows_imported} history row(s)"
)
async def _decommission_worker(db_url: str, worker_id: str, schema: str = "public") -> int:
"""Release all tasks owned by a worker, setting them back to pending status."""
is_pg0, instance_name, _ = parse_pg0_url(db_url)
@@ -1,105 +0,0 @@
"""Add a composite index on memory_links(bank_id, link_type) (PostgreSQL).
``bank_id`` was added to ``memory_links`` in ``c5d6e7f8a9b0`` precisely so that
bank-scoped reads (e.g. the stats endpoint) could filter on the link table
directly instead of joining ``memory_units`` — that JOIN took 18+ seconds on
banks with millions of links. The column landed without an index, so every
``bank_id = $1`` predicate still falls back to a sequential scan over the whole
table.
This adds the missing btree. It is composite on ``(bank_id, link_type)`` rather
than ``bank_id`` alone because the hot query is the stats endpoint's
``SELECT link_type, COUNT(*) ... WHERE bank_id = $1 GROUP BY link_type``: a
``(bank_id, link_type)`` index serves that filter, grouping and count as an
index-only scan, never touching the heap, whereas a ``bank_id``-only index would
still have to read every matching row to recover ``link_type``. ``link_type`` is
low-cardinality (only ``temporal``/``semantic``/``caused_by`` are written —
entity edges were dropped in ``e9b2c7d1f3a4``), so the trailing column adds
little to the index size while removing the heap fetch.
The Oracle baseline (``o1a2b3c4d5e6``) already creates ``idx_ml_bank_id`` on
``memory_links(bank_id)``; that single-column index already covers Oracle's
bank-scoped filter, so the Oracle slot here is intentionally absent and only the
PostgreSQL dialect gets the composite index.
``memory_links`` can hold tens of millions of rows, so the index is built
CONCURRENTLY to avoid taking a write lock on the table. CONCURRENTLY cannot run
inside a transaction block, so the statement runs in an ``autocommit_block()``;
``IF NOT EXISTS`` keeps it idempotent across retries and re-migrated tenant
schemas. A CONCURRENTLY build interrupted partway (lock conflict, disk
pressure, signal) leaves the index behind as *invalid*; ``IF NOT EXISTS`` would
then skip over it forever, so the upgrade first drops any invalid leftover of
this name before (re)creating it.
Revision ID: 2071c7518f88
Revises: a1d3f5b7c9e2
Create Date: 2026-06-16
"""
from collections.abc import Sequence
from alembic import context, op
from sqlalchemy import text
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "2071c7518f88"
down_revision: str | Sequence[str] | None = "a1d3f5b7c9e2"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
_INDEX_NAME = "idx_memory_links_bank_id_link_type"
def _get_schema_prefix() -> str:
"""Schema-qualifier for raw SQL on PG (multi-tenant search_path)."""
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
def _pg_upgrade() -> None:
bind = op.get_bind()
# `or None` collapses an unset option and an explicit empty string into NULL
# so the COALESCE below falls back to current_schema() in both cases.
target_schema = context.config.get_main_option("target_schema") or None
schema = _get_schema_prefix()
# CREATE INDEX CONCURRENTLY cannot run inside a transaction block; the
# autocommit_block runs each statement outside Alembic's migration
# transaction.
with op.get_context().autocommit_block():
# A CONCURRENTLY build that errored on a previous run leaves an INVALID
# index of this name behind. `CREATE INDEX ... IF NOT EXISTS` would see
# that relation and skip, so bank_id queries would keep seq-scanning.
# Drop only the invalid leftover — never a healthy index — so the retry
# actually rebuilds a usable one.
leftover_invalid = bind.execute(
text(
"SELECT NOT i.indisvalid "
"FROM pg_class c "
"JOIN pg_index i ON c.oid = i.indexrelid "
"JOIN pg_namespace n ON c.relnamespace = n.oid "
"WHERE c.relname = :index_name "
" AND n.nspname = COALESCE(:target_schema, current_schema())"
),
{"index_name": _INDEX_NAME, "target_schema": target_schema},
).scalar()
if leftover_invalid:
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}{_INDEX_NAME}")
# IF NOT EXISTS keeps the create idempotent across retries and schemas.
op.execute(f"CREATE INDEX CONCURRENTLY IF NOT EXISTS {_INDEX_NAME} ON {schema}memory_links(bank_id, link_type)")
def _pg_downgrade() -> None:
schema = _get_schema_prefix()
with op.get_context().autocommit_block():
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}{_INDEX_NAME}")
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade)
@@ -15,11 +15,6 @@ from pgvector.sqlalchemy import Vector
from sqlalchemy import text
from sqlalchemy.dialects import postgresql
from hindsight_api._pg_search import (
PG_SEARCH_TOKENIZER_ENV,
normalize_pg_search_tokenizer,
pg_search_bm25_columns,
)
from hindsight_api.alembic._dialect import run_for_dialect
# revision identifiers, used by Alembic.
@@ -31,79 +26,59 @@ depends_on: str | Sequence[str] | None = None
def _detect_vector_extension() -> str:
"""
Detect or validate vector extension for this immutable migration revision.
Detect or validate vector extension: 'pgvector', 'vchord', or 'pgvectorscale'.
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 == "pgvectorscale":
# pgvectorscale/DiskANN requires pgvector
pgvector_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vector'")).scalar()
if not pgvector_check:
raise RuntimeError(
"DiskANN requires pgvector. Install with: CREATE EXTENSION vector; then vectorscale or pg_diskann CASCADE;"
)
# Check for either vectorscale (open source) or pg_diskann (Azure)
vectorscale_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vectorscale'")).scalar()
pg_diskann_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'pg_diskann'")).scalar()
if vectorscale_check:
return "pgvectorscale"
if pg_diskann_check:
elif pg_diskann_check:
return "pg_diskann"
raise RuntimeError(
"Configured vector extension 'pgvectorscale' not found. Install either:\n"
" - pgvectorscale: CREATE EXTENSION vectorscale CASCADE;\n"
" - pg_diskann (Azure): CREATE EXTENSION pg_diskann CASCADE;"
)
if vector_extension == "vchord":
else:
raise RuntimeError(
"Configured vector extension 'pgvectorscale' not found. Install either:\n"
" - pgvectorscale: CREATE EXTENSION vectorscale CASCADE;\n"
" - pg_diskann (Azure): CREATE EXTENSION pg_diskann CASCADE;"
)
elif 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"
if vector_extension == "scann":
scann_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'alloydb_scann'")).scalar()
if not scann_check:
raise RuntimeError(
"Configured vector extension 'scann' not found. Install it with: CREATE EXTENSION alloydb_scann CASCADE;"
)
return "scann"
if vector_extension == "pgvector":
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"
raise ValueError(
"Invalid HINDSIGHT_API_VECTOR_EXTENSION: "
f"{vector_extension}. Must be 'pgvector', 'vchord', 'pgvectorscale', or 'scann'"
)
def _vector_index_using_clause(ext: str) -> str:
if ext == "pgvectorscale":
return "USING diskann (embedding vector_cosine_ops) WITH (num_neighbors = 50)"
if ext == "pg_diskann":
return "USING diskann (embedding vector_cosine_ops) WITH (max_neighbors = 50)"
if ext == "vchord":
return "USING vchordrq (embedding vector_cosine_ops)"
if ext == "scann":
return "USING scann (embedding cosine) WITH (mode = 'AUTO')"
return "USING hnsw (embedding vector_cosine_ops)"
else:
raise ValueError(
f"Invalid HINDSIGHT_API_VECTOR_EXTENSION: {vector_extension}. Must be 'pgvector', 'vchord', or 'pgvectorscale'"
)
def _detect_text_search_extension() -> str:
"""
Detect or validate text search extension: 'native', 'vchord', 'pg_textsearch',
'pgroonga', or 'pg_search'. Respects HINDSIGHT_API_TEXT_SEARCH_EXTENSION env var.
Detect or validate text search extension: 'native', 'vchord', or 'pg_textsearch'.
Respects HINDSIGHT_API_TEXT_SEARCH_EXTENSION env var.
Creates the extension if needed.
pgroonga is treated as native here so the initial schema still creates valid
tsvector columns. ensure_text_search_extension() at startup converts the
schema to pgroonga structures (drops the tsvector column, builds a pgroonga
index on the base text column).
"""
text_search_extension = os.getenv("HINDSIGHT_API_TEXT_SEARCH_EXTENSION", "native").lower()
@@ -131,35 +106,14 @@ def _detect_text_search_extension() -> str:
# Extension truly doesn't exist - re-raise the error
raise
return "pg_textsearch"
elif text_search_extension == "pg_search":
# ParadeDB pg_search — true BM25 over base columns, Citus-compatible.
try:
op.execute("CREATE EXTENSION IF NOT EXISTS pg_search 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_search'")).fetchone()
if not result:
# Extension truly doesn't exist - re-raise the error
raise
return "pg_search"
elif text_search_extension == "native":
return "native"
elif text_search_extension == "pgroonga":
# ensure_text_search_extension() at runtime converts to pgroonga.
# Treat as native here so the initial schema still creates valid columns.
return "native"
else:
raise ValueError(
f"Invalid HINDSIGHT_API_TEXT_SEARCH_EXTENSION: {text_search_extension}. "
"Must be 'native', 'vchord', 'pg_textsearch', 'pgroonga', or 'pg_search'"
f"Invalid HINDSIGHT_API_TEXT_SEARCH_EXTENSION: {text_search_extension}. Must be 'native', 'vchord', or 'pg_textsearch'"
)
def _pg_search_tokenizer() -> str:
return normalize_pg_search_tokenizer(os.getenv(PG_SEARCH_TOKENIZER_ENV))
def _pg_upgrade() -> None:
"""Upgrade schema - create all tables from scratch."""
@@ -315,9 +269,8 @@ def _pg_upgrade() -> None:
ALTER TABLE memory_units
ADD COLUMN search_vector bm25_catalog.bm25vector
""")
elif text_search_ext in ("pg_textsearch", "pg_search"):
# Timescale pg_textsearch / ParadeDB pg_search: dummy TEXT column for
# consistency (indexes operate on base columns directly).
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
@@ -360,11 +313,36 @@ def _pg_upgrade() -> None:
)
# Create vector index - conditional based on available extension
vector_ext = _detect_vector_extension()
if vector_ext != "scann":
op.execute(f"""
if vector_ext == "pgvectorscale":
# Use DiskANN index for pgvectorscale (disk-based, scalable)
op.execute("""
CREATE INDEX idx_memory_units_embedding ON memory_units
{_vector_index_using_clause(vector_ext)}
USING diskann (embedding vector_cosine_ops)
WITH (num_neighbors = 50)
""")
elif vector_ext == "pg_diskann":
# Use DiskANN index for pg_diskann (Azure)
op.execute("""
CREATE INDEX idx_memory_units_embedding ON memory_units
USING diskann (embedding vector_cosine_ops)
WITH (max_neighbors = 50)
""")
elif 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
@@ -382,17 +360,6 @@ def _pg_upgrade() -> None:
USING bm25(text)
WITH (text_config='english')
""")
elif text_search_ext == "pg_search":
# ParadeDB pg_search BM25 index on (id, text, context). The key_field
# reloption is required and must match the table's primary key column.
bm25_cols = pg_search_bm25_columns("id", ("text", "context"), _pg_search_tokenizer())
op.execute(
"""
CREATE INDEX idx_memory_units_text_search ON memory_units
USING bm25 ({bm25_cols})
WITH (key_field='id')
""".format(bm25_cols=bm25_cols)
)
else: # native
# Native PostgreSQL GIN index
op.execute("""
@@ -1,117 +0,0 @@
"""Repair mental_models.subtype on databases stuck at m3rg3h3ad5f6
Three production deployments reported `column "subtype" of relation
"mental_models" does not exist` on `create_mental_model` even after their
container reported `Database migrations completed successfully` and
`alembic_version` advanced to `m3rg3h3ad5f6` (see issue #1553, #1553#1
confirmations from @4Lienau and @khanhduyvt0101).
Both `h3c4d5e6f7g8_mental_models_v4` and `d5y6z7a8b9c0_backfill_mental_models_subtype`
were meant to ensure `subtype` exists, but on databases that came through the
`reflections -> mental_models` rename chain *and* whose alembic_version
advanced past `d5y6z7a8b9c0` along an alternate path during the divergent-heads
reorganization, neither column-add actually fired. The result is a head-tagged
database with a v3-shaped `mental_models` table missing six columns:
``subtype``, ``description``, ``entity_id``, ``observations``, ``links``,
``last_updated``.
This migration sits at the current head (`m3rg3h3ad5f6`) so every affected
deployment will pick it up on next container start. It mirrors the column-add
block from `d5y6z7a8b9c0_backfill_mental_models_subtype` using
``ADD COLUMN IF NOT EXISTS`` so it is a no-op on databases where the columns
are already present.
Revision ID: 86f7a033d372
Revises: m3rg3h3ad5f6
Create Date: 2026-05-14
"""
from collections.abc import Sequence
from alembic import context, op
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "86f7a033d372"
down_revision: str | Sequence[str] | None = "m3rg3h3ad5f6"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _pg_schema_prefix() -> str:
"""Schema-qualifier for raw SQL on PG (multi-tenant search_path)."""
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
def _pg_upgrade() -> None:
"""Idempotently ensure mental_models has the v4 column set.
Safe to re-apply on databases that already received the columns via
`h3c4d5e6f7g8_mental_models_v4` or `d5y6z7a8b9c0_backfill_mental_models_subtype` —
every column-add uses ``IF NOT EXISTS`` and the constraint is recreated
from scratch with the canonical v4 allowlist.
"""
schema = _pg_schema_prefix()
bare_schema = schema.strip(".").strip('"') if schema else ""
schema_clause = f"AND table_schema = '{bare_schema}'" if bare_schema else ""
# Wrapped in a DO block so the existence check skips databases that
# predate the reflections -> mental_models rename chain (no table to
# repair). On those, every ALTER below would error.
op.execute(
f"""
DO $$
BEGIN
IF EXISTS (
SELECT 1 FROM information_schema.tables
WHERE table_name = 'mental_models'
{schema_clause}
) THEN
-- Add the six v4 columns idempotently.
ALTER TABLE {schema}mental_models
ADD COLUMN IF NOT EXISTS subtype VARCHAR(32) NOT NULL DEFAULT 'structural';
ALTER TABLE {schema}mental_models
ADD COLUMN IF NOT EXISTS description TEXT NOT NULL DEFAULT '';
ALTER TABLE {schema}mental_models
ADD COLUMN IF NOT EXISTS entity_id UUID;
ALTER TABLE {schema}mental_models
ADD COLUMN IF NOT EXISTS observations JSONB DEFAULT '{{"observations": []}}'::jsonb;
ALTER TABLE {schema}mental_models
ADD COLUMN IF NOT EXISTS links VARCHAR[];
ALTER TABLE {schema}mental_models
ADD COLUMN IF NOT EXISTS last_updated TIMESTAMP WITH TIME ZONE;
-- Recreate the CHECK constraint with the canonical v4 allowlist.
-- Existing rows with subtype = 'directive' (possible on databases
-- that ran the o0j1k2l3m4n5 directive-only path) are rewritten to
-- 'structural' first so the constraint add succeeds.
UPDATE {schema}mental_models SET subtype = 'structural' WHERE subtype = 'directive';
ALTER TABLE {schema}mental_models DROP CONSTRAINT IF EXISTS ck_mental_models_subtype;
ALTER TABLE {schema}mental_models
ADD CONSTRAINT ck_mental_models_subtype
CHECK (subtype IN ('structural', 'emergent', 'pinned', 'learned'));
CREATE INDEX IF NOT EXISTS idx_mental_models_subtype
ON {schema}mental_models(bank_id, subtype);
END IF;
END$$;
"""
)
def _pg_downgrade() -> None:
"""No-op: dropping these columns would corrupt v4 application code."""
pass
def upgrade() -> None:
# PG-only: Oracle's baseline (o1a2b3c4d5e6) creates mental_models with its
# own subtype shape (chk_mm_subtype IN ('directive', 'pinned')) and a
# different table topology, so this PG-shaped repair does not apply.
run_for_dialect(pg=_pg_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade)
@@ -1,128 +0,0 @@
"""Make memory_links.from_unit_id and memory_links.to_unit_id FKs deferrable.
Revision ID: 9f8e7d6c5b4a
Revises: o1a2b3c4d5e6
Create Date: 2026-05-03
Background
----------
Concurrent retain (which INSERTs into ``memory_links``) and any code path
that DELETEs a row whose deletion cascades into ``memory_links`` (e.g.
delta-retain superseding chunks, which CASCADEs chunks → memory_units →
memory_links) can deadlock under sustained single-tenant write load.
The deadlock cycle:
* Tx A: ``DELETE FROM chunks WHERE chunk_id = ANY(...)``
→ CASCADE acquires row locks on memory_units, then on memory_links rows
where ``to_unit_id`` matches the deleted units.
* Tx B: ``INSERT INTO memory_links (...)`` referencing one of the same
memory_units rows.
→ The immediate FK check takes ``FOR KEY SHARE`` on those memory_units
rows.
The two transactions take row locks on the same memory_units rows in
opposite orders depending on which side started first. PostgreSQL detects
the cycle and aborts one transaction; the loser is killed mid-batch, the
winner continues. Workers then retry, but under sustained write load the
pattern repeats.
Fix
---
Make both ``memory_links → memory_units`` FKs (``from_unit_id`` and
``to_unit_id``) ``DEFERRABLE INITIALLY DEFERRED``. This pushes the FK
check from INSERT time to COMMIT time:
* INSERT no longer takes ``FOR KEY SHARE`` on the memory_units row → no
contention with the cascading DELETE's row lock.
* At COMMIT the engine validates referential integrity in one shot. If a
cascade-DELETE has since removed the referenced unit, the INSERT
transaction commits OR fails with a clean FK violation (sqlstate
23503) instead of a deadlock (sqlstate 40P01).
The ``WHERE EXISTS`` filter already in ``_bulk_insert_links`` continues to
filter out the typical "stale unit_id" case at INSERT time; the deferred
FK is only the backstop for the narrow race window between the EXISTS
probe and COMMIT. ``ON DELETE CASCADE`` semantics are unchanged — only
the *timing* of the constraint check moves.
The ``entity_id`` FK on ``memory_links`` is not changed; entities are not
involved in the observed deadlock cycle and leaving the constraint
immediate keeps the error message specific when an entity row is missing.
"""
from collections.abc import Sequence
from alembic import context, op
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "9f8e7d6c5b4a"
down_revision: str | Sequence[str] | None = "o1a2b3c4d5e6"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _get_schema_prefix() -> str:
"""Schema-qualifier for raw SQL on PG (multi-tenant search_path)."""
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
# The two FK constraints installed by the initial schema migration
# (5a366d414dce_initial_schema), mapped to the column they constrain.
# They reference memory_units(id) with ON DELETE CASCADE — that
# semantics is preserved; only the deferral attribute changes.
_FK_COLUMNS: dict[str, str] = {
"fk_memory_links_from_unit_id_memory_units": "from_unit_id",
"fk_memory_links_to_unit_id_memory_units": "to_unit_id",
}
def _pg_upgrade() -> None:
schema = _get_schema_prefix()
# PostgreSQL doesn't allow altering the deferrability of an existing
# constraint with ALTER CONSTRAINT — the constraint must be dropped
# and recreated. DROP IF EXISTS makes the migration safe to re-run
# on schemas where the constraint was already recreated.
for fk_name, column in _FK_COLUMNS.items():
op.execute(f"ALTER TABLE {schema}memory_links DROP CONSTRAINT IF EXISTS {fk_name}")
op.execute(
f"""
ALTER TABLE {schema}memory_links
ADD CONSTRAINT {fk_name}
FOREIGN KEY ({column})
REFERENCES {schema}memory_units (id)
ON DELETE CASCADE
DEFERRABLE INITIALLY DEFERRED
"""
)
def _pg_downgrade() -> None:
schema = _get_schema_prefix()
# Revert to the default (NOT DEFERRABLE) form so a downgrade actually
# restores the prior schema state, even though that re-introduces the
# deadlock window.
for fk_name, column in _FK_COLUMNS.items():
op.execute(f"ALTER TABLE {schema}memory_links DROP CONSTRAINT IF EXISTS {fk_name}")
op.execute(
f"""
ALTER TABLE {schema}memory_links
ADD CONSTRAINT {fk_name}
FOREIGN KEY ({column})
REFERENCES {schema}memory_units (id)
ON DELETE CASCADE
"""
)
def upgrade() -> None:
# PG-only: Oracle's deferrable-FK semantics differ and the deadlock
# cycle was only observed on PostgreSQL. Oracle slot intentionally
# absent → no-op there.
run_for_dialect(pg=_pg_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade)
@@ -1,85 +0,0 @@
"""Repair: widen the remaining live ``bank_id`` columns from VARCHAR(64) to TEXT on PostgreSQL.
Follow-up to ``c3e5a7b9d1f4`` (issue #2106), which widened the two *history*
tables (``observation_history``, ``mental_model_history``) to ``TEXT`` after the
narrow ``VARCHAR(64)`` declaration bricked startup. The same VARCHAR(64) / TEXT
inconsistency still affects the live tables that store a user-supplied
``bank_id``:
* ``directives`` -- created VARCHAR(64) in ``p1k2l3m4n5o6``
* ``mental_models`` -- VARCHAR(64) (origin ``pinned_reflections`` in
``n9i0j1k2l3m4``; recreated in ``h3c4d5e6f7g8``)
``mental_model_versions`` is intentionally *not* widened here: it is created in
``j5e6f7g8h9i0`` but dropped (``DROP TABLE ... CASCADE``) in ``o0j1k2l3m4n5`` and
never recreated on the upgrade path, so it does not exist at head. Issuing
``ALTER TABLE mental_model_versions ...`` would raise ``UndefinedTable`` and --
because migrations run inside the lifespan-startup transaction -- roll the whole
migration back, bricking the API. (It is unrelated to the live
``mental_model_history`` table widened by ``c3e5a7b9d1f4``.)
``banks.bank_id`` is ``TEXT`` (unbounded), so a deployment can create a bank
whose id exceeds 64 chars -- the 78-char hierarchical org-unit shape reported in
issue #2106 -- and the bank insert succeeds. The next write that propagates that
id (``create_directive``, ``create_mental_model`` / consolidation, or
mental-model versioning) then aborts with::
psycopg2.errors.StringDataRightTruncation: value too long for type
character varying(64)
i.e. a 500 on core write endpoints, instead of the startup brick that
``c3e5a7b9d1f4`` already repaired.
``ALTER COLUMN ... TYPE TEXT`` is a no-op on a column that is already ``TEXT``,
so every upgrade path converges on ``TEXT``. These tables are per-tenant (they
live in each tenant schema, not ``public``), so this runs for every migrated
schema via the search-path-aware prefix -- the same mechanism as
``c3e5a7b9d1f4``.
PostgreSQL only: these tables are created by PostgreSQL-only migrations
(``run_for_dialect(pg=...)``); on Oracle they are absent or already
``VARCHAR2(256)`` (consistent, never truncates), so the Oracle slot is
intentionally absent -- mirroring ``c3e5a7b9d1f4``.
Revision ID: a1d3f5b7c9e2
Revises: c3e5a7b9d1f4
Create Date: 2026-06-13
"""
from collections.abc import Sequence
from alembic import context, op
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "a1d3f5b7c9e2"
down_revision: str | Sequence[str] | None = "c3e5a7b9d1f4"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _get_schema_prefix() -> str:
"""Schema-qualifier for raw SQL on PG (multi-tenant search_path)."""
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
def _pg_upgrade() -> None:
schema = _get_schema_prefix()
op.execute(f"ALTER TABLE {schema}directives ALTER COLUMN bank_id TYPE TEXT")
op.execute(f"ALTER TABLE {schema}mental_models ALTER COLUMN bank_id TYPE TEXT")
def _pg_downgrade() -> None:
# No-op: narrowing back to VARCHAR(64) could truncate real data and would
# re-introduce the bug this migration repairs. The column types are owned by
# the migrations that created the tables.
pass
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade)
@@ -7,7 +7,6 @@ the stored fact text.
- vchord: text_signals included in tokenize() at insert time
- native: search_vector GENERATED column regenerated to include text_signals
- pg_textsearch: no change (index only supports a single base column)
- pg_search: BM25 index dropped and recreated to include text_signals
Revision ID: a2b3c4d5e6f7
Revises: z1u2v3w4x5y6
@@ -19,11 +18,6 @@ from collections.abc import Sequence
from alembic import context, op
from hindsight_api._pg_search import (
PG_SEARCH_TOKENIZER_ENV,
normalize_pg_search_tokenizer,
pg_search_bm25_columns,
)
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "a2b3c4d5e6f7"
@@ -41,10 +35,6 @@ def _detect_text_search_extension() -> str:
return os.getenv("HINDSIGHT_API_TEXT_SEARCH_EXTENSION", "native").lower()
def _pg_search_tokenizer() -> str:
return normalize_pg_search_tokenizer(os.getenv(PG_SEARCH_TOKENIZER_ENV))
def _pg_upgrade() -> None:
schema = _get_schema_prefix()
table = f"{schema}memory_units"
@@ -72,16 +62,6 @@ def _pg_upgrade() -> None:
CREATE INDEX IF NOT EXISTS idx_memory_units_text_search
ON {table} USING gin(search_vector)
""")
elif text_search_ext == "pg_search":
# ParadeDB pg_search: drop the existing BM25 index and recreate it
# to include text_signals alongside text and context.
bm25_cols = pg_search_bm25_columns("id", ("text", "context", "text_signals"), _pg_search_tokenizer())
op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_text_search")
op.execute(f"""
CREATE INDEX idx_memory_units_text_search ON {table}
USING bm25 ({bm25_cols})
WITH (key_field='id')
""")
# vchord: tokenize() call in fact_storage.py is updated to include text_signals at insert time
# pg_textsearch: no change — index operates on the base `text` column only
@@ -106,15 +86,6 @@ def _pg_downgrade() -> None:
CREATE INDEX idx_memory_units_text_search
ON {table} USING gin(search_vector)
""")
elif text_search_ext == "pg_search":
# Restore the original (id, text, context) BM25 index without text_signals.
bm25_cols = pg_search_bm25_columns("id", ("text", "context"), _pg_search_tokenizer())
op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_text_search")
op.execute(f"""
CREATE INDEX idx_memory_units_text_search ON {table}
USING bm25 ({bm25_cols})
WITH (key_field='id')
""")
op.execute(f"ALTER TABLE {table} DROP COLUMN IF EXISTS text_signals")
@@ -40,20 +40,20 @@ def _get_schema_prefix() -> str:
def _pg_upgrade() -> None:
schema = _get_schema_prefix()
# CREATE INDEX CONCURRENTLY cannot run inside a transaction block; an
# autocommit_block runs it outside Alembic's migration transaction.
with op.get_context().autocommit_block():
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_source_memory_ids "
f"ON {schema}memory_units USING GIN (source_memory_ids) "
f"WHERE source_memory_ids IS NOT NULL"
)
# CREATE INDEX CONCURRENTLY cannot run inside a transaction block.
# Commit the current Alembic transaction first.
op.execute("COMMIT")
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_source_memory_ids "
f"ON {schema}memory_units USING GIN (source_memory_ids) "
f"WHERE source_memory_ids IS NOT NULL"
)
def _pg_downgrade() -> None:
schema = _get_schema_prefix()
with op.get_context().autocommit_block():
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_source_memory_ids")
op.execute("COMMIT")
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_source_memory_ids")
def upgrade() -> None:
@@ -11,8 +11,7 @@ was configured.
This migration detects the mismatch and recreates the affected indexes with
the correct type. Skipped entirely when the configured extension is pgvector
(the default) or scann. ScaNN uses global vector indexes because empty or tiny
per-bank indexes cannot be built safely on AlloyDB.
(the default), since those indexes are already correct.
"""
import os
@@ -40,47 +39,39 @@ def _get_schema_prefix() -> str:
return f'"{schema}".' if schema else ""
def _validate_extension(name: str) -> str:
ext = name.lower()
if ext not in {"pgvector", "pgvectorscale", "vchord", "scann"}:
raise ValueError(
f"Invalid HINDSIGHT_API_VECTOR_EXTENSION: {ext}. Must be 'pgvector', 'vchord', 'pgvectorscale', or 'scann'"
)
return ext
def _index_type_keyword(ext: str) -> str:
def _target_index_type() -> str | None:
"""Return the target index type, or None if pgvector (no fix needed)."""
ext = os.getenv("HINDSIGHT_API_VECTOR_EXTENSION", "pgvector").lower()
if ext == "pgvectorscale":
return "diskann"
if ext == "vchord":
elif ext == "vchord":
return "vchordrq"
if ext == "scann":
return "scann"
return "hnsw"
return None
def _vector_index_using_clause(ext: str) -> str:
def _vector_index_using_clause() -> str:
"""Return the USING clause based on the configured vector extension."""
ext = os.getenv("HINDSIGHT_API_VECTOR_EXTENSION", "pgvector").lower()
if ext == "pgvectorscale":
return "USING diskann (embedding vector_cosine_ops) WITH (num_neighbors = 50)"
if ext == "vchord":
return "USING vchordrq (embedding vector_cosine_ops)"
if ext == "scann":
return "USING scann (embedding cosine) WITH (mode = 'AUTO')"
return "USING hnsw (embedding vector_cosine_ops)"
elif ext == "vchord":
return "USING vchordrq (embedding vector_l2_ops)"
else:
return "USING hnsw (embedding vector_cosine_ops)"
def _pg_upgrade() -> None:
ext = _validate_extension(os.getenv("HINDSIGHT_API_VECTOR_EXTENSION", "pgvector"))
if ext in {"pgvector", "scann"}:
target = _target_index_type()
if target is None:
# pgvector — indexes are already HNSW, nothing to fix
return
target = _index_type_keyword(ext)
bind = op.get_bind()
schema_name = context.config.get_main_option("target_schema")
schema = _get_schema_prefix()
table_ref = f'"{schema_name}".memory_units' if schema_name else "memory_units"
banks_ref = f'"{schema_name}".banks' if schema_name else "banks"
using_clause = _vector_index_using_clause(ext)
using_clause = _vector_index_using_clause()
pg_schema = schema_name or "public"
rows = bind.execute(text(f"SELECT bank_id, internal_id FROM {banks_ref}")).fetchall() # noqa: S608
@@ -126,8 +117,8 @@ def _pg_upgrade() -> None:
def _pg_downgrade() -> None:
# Downgrade recreates indexes as HNSW (the original hardcoded behavior)
ext = _validate_extension(os.getenv("HINDSIGHT_API_VECTOR_EXTENSION", "pgvector"))
if ext in {"pgvector", "scann"}:
target = _target_index_type()
if target is None:
return
bind = op.get_bind()
@@ -1,52 +0,0 @@
"""Add managed flag to knowledge_pages.
The knowledge base is managed by clients (CRUD over folders/pages). ``managed``
lets a client tag a node as system-owned vs. hand-authored; it carries no
server-side behaviour.
Revision ID: a5b6c7d8e9f0
Revises: a9b8c7d6e5f4
Create Date: 2026-06-26
"""
from collections.abc import Sequence
from alembic import context, op
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "a5b6c7d8e9f0"
down_revision: str | Sequence[str] | None = "a9b8c7d6e5f4"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _pg_schema_prefix() -> str:
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
def _pg_upgrade() -> None:
schema = _pg_schema_prefix()
op.execute(f"ALTER TABLE {schema}knowledge_pages ADD COLUMN IF NOT EXISTS managed BOOLEAN NOT NULL DEFAULT false")
def _pg_downgrade() -> None:
schema = _pg_schema_prefix()
op.execute(f"ALTER TABLE {schema}knowledge_pages DROP COLUMN IF EXISTS managed")
def _oracle_upgrade() -> None:
op.execute("ALTER TABLE knowledge_pages ADD (managed NUMBER(1) DEFAULT 0 NOT NULL)")
def _oracle_downgrade() -> None:
op.execute("ALTER TABLE knowledge_pages DROP COLUMN managed")
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade, oracle=_oracle_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade, oracle=_oracle_downgrade)
@@ -1,253 +0,0 @@
"""Move mental-model and observation history into dedicated tables.
Both histories were accumulated in a single JSONB/CLOB ``history`` column
(``mental_models.history`` and ``memory_units.history``), appended to on every
update. That design has two problems:
1. **Unbounded growth on observations.** The observation write path appended a
snapshot on every update with no cap at all, so a frequently-reinforced
observation grew its ``history`` array until it crossed Postgres's hard 256MB
jsonb limit (SQLSTATE 54000), after which every further UPDATE failed and the
row was stuck.
2. **Wrong-axis cap on mental models.** The mental-model cap bounded the *number*
of entries (50), not their *size* — a single large reflect snapshot could
still blow the budget — and rewrote the whole array (plus TOAST) on every
refresh, defeating HOT updates.
This migration creates one row per history entry in two dedicated tables, with
an index that makes "most recent N for this item" cheap, then drops the old
columns. The cap is now enforced at write time as a bounded DELETE of the
oldest over-cap rows (see config ``*_HISTORY_MAX_ENTRIES``).
Revision ID: a7b8c9d0e1f2
Revises: d3e4f5a6b7c8
Create Date: 2026-06-05
"""
from collections.abc import Sequence
from alembic import context, op
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "a7b8c9d0e1f2"
down_revision: str | Sequence[str] | None = "d3e4f5a6b7c8"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _get_schema_prefix() -> str:
"""Schema-qualifier for raw SQL on PG (multi-tenant search_path)."""
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
# ---------------------------------------------------------------------------
# PostgreSQL
# ---------------------------------------------------------------------------
def _pg_upgrade() -> None:
schema = _get_schema_prefix()
# Both tables share the same shape: surrogate id, FK to the parent, bank_id,
# the snapshot payload as a single JSONB ``content`` blob, and changed_at.
# The payload is per-row (one change per row) so it stays small — this is NOT
# the old single-column-grows-forever design; growth is bounded by row count
# plus the write-time cap. Folding the previous_* fields into one JSONB keeps
# the schema dialect-simple (no array columns) and flexible.
# --- mental_model_history -------------------------------------------------
# content: {"previous_content": ..., "previous_reflect_response": {...}}
op.execute(
f"""
CREATE TABLE IF NOT EXISTS {schema}mental_model_history (
id BIGINT GENERATED BY DEFAULT AS IDENTITY PRIMARY KEY,
mental_model_id VARCHAR(64) NOT NULL,
bank_id TEXT NOT NULL,
content JSONB NOT NULL,
changed_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now(),
FOREIGN KEY (mental_model_id, bank_id)
REFERENCES {schema}mental_models(id, bank_id) ON DELETE CASCADE
)
"""
)
op.execute(
"CREATE INDEX IF NOT EXISTS idx_mm_history_model "
f"ON {schema}mental_model_history (bank_id, mental_model_id, changed_at DESC, id DESC)"
)
# --- observation_history --------------------------------------------------
# content: {"previous_text", "previous_tags", "previous_occurred_start",
# "previous_occurred_end", "previous_mentioned_at", "new_source_memory_ids"}
op.execute(
f"""
CREATE TABLE IF NOT EXISTS {schema}observation_history (
id BIGINT GENERATED BY DEFAULT AS IDENTITY PRIMARY KEY,
observation_id UUID NOT NULL,
bank_id TEXT NOT NULL,
content JSONB NOT NULL,
changed_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now(),
FOREIGN KEY (observation_id)
REFERENCES {schema}memory_units(id) ON DELETE CASCADE
)
"""
)
op.execute(
"CREATE INDEX IF NOT EXISTS idx_observation_history_obs "
f"ON {schema}observation_history (observation_id, changed_at DESC, id DESC)"
)
# --- backfill mental models ----------------------------------------------
# Explode each row's history array into rows, preserving chronological order
# via WITH ORDINALITY so the IDENTITY id tie-breaks oldest->newest correctly.
# changed_at is promoted to its own column; the rest of the element becomes
# ``content`` (the ``- 'changed_at'`` strips the now-redundant key).
op.execute(
f"""
INSERT INTO {schema}mental_model_history (mental_model_id, bank_id, content, changed_at)
SELECT mm.id, mm.bank_id,
e - 'changed_at',
COALESCE(NULLIF(e->>'changed_at', '')::timestamptz, now())
FROM {schema}mental_models mm
CROSS JOIN LATERAL jsonb_array_elements(mm.history) WITH ORDINALITY a(e, ord)
WHERE mm.history IS NOT NULL
AND jsonb_typeof(mm.history) = 'array'
AND jsonb_array_length(mm.history) > 0
ORDER BY mm.id, mm.bank_id, ord
"""
)
# --- backfill observations -----------------------------------------------
op.execute(
f"""
INSERT INTO {schema}observation_history (observation_id, bank_id, content, changed_at)
SELECT mu.id, mu.bank_id,
e - 'changed_at',
COALESCE(NULLIF(e->>'changed_at', '')::timestamptz, now())
FROM {schema}memory_units mu
CROSS JOIN LATERAL jsonb_array_elements(mu.history) WITH ORDINALITY a(e, ord)
WHERE mu.fact_type = 'observation'
AND mu.history IS NOT NULL
AND jsonb_typeof(mu.history) = 'array'
AND jsonb_array_length(mu.history) > 0
ORDER BY mu.id, ord
"""
)
# --- drop the legacy columns ---------------------------------------------
op.execute(f"ALTER TABLE {schema}mental_models DROP COLUMN IF EXISTS history")
op.execute(f"ALTER TABLE {schema}memory_units DROP COLUMN IF EXISTS history")
def _pg_downgrade() -> None:
schema = _get_schema_prefix()
# Re-add the columns (empty — historical content is not reconstructed back
# into the array form; the dedicated tables are dropped below).
op.execute(f"ALTER TABLE {schema}mental_models ADD COLUMN IF NOT EXISTS history JSONB DEFAULT '[]'::jsonb")
op.execute(f"ALTER TABLE {schema}memory_units ADD COLUMN IF NOT EXISTS history JSONB DEFAULT '[]'::jsonb")
op.execute(f"DROP INDEX IF EXISTS {schema}idx_observation_history_obs")
op.execute(f"DROP TABLE IF EXISTS {schema}observation_history")
op.execute(f"DROP INDEX IF EXISTS {schema}idx_mm_history_model")
op.execute(f"DROP TABLE IF EXISTS {schema}mental_model_history")
# ---------------------------------------------------------------------------
# Oracle 23ai
# ---------------------------------------------------------------------------
def _oracle_upgrade() -> None:
# Same single-JSONB shape as PG: ``content`` holds the snapshot payload as a
# CLOB IS JSON. The legacy per-element JSON object (minus changed_at, promoted
# to its own column) is carried through verbatim on backfill — the array
# columns the previous design needed are gone.
op.execute(
"""
CREATE TABLE IF NOT EXISTS mental_model_history (
id NUMBER GENERATED BY DEFAULT AS IDENTITY,
mental_model_id VARCHAR2(256) NOT NULL,
bank_id VARCHAR2(256) NOT NULL,
content CLOB NOT NULL
CONSTRAINT mmh_content_json CHECK (content IS JSON),
changed_at TIMESTAMP WITH TIME ZONE DEFAULT SYSTIMESTAMP NOT NULL,
CONSTRAINT pk_mental_model_history PRIMARY KEY (id),
CONSTRAINT fk_mmh_model FOREIGN KEY (mental_model_id, bank_id)
REFERENCES mental_models(id, bank_id) ON DELETE CASCADE
)
"""
)
op.execute(
"CREATE INDEX idx_mm_history_model ON mental_model_history (bank_id, mental_model_id, changed_at DESC, id DESC)"
)
op.execute(
"""
CREATE TABLE IF NOT EXISTS observation_history (
id NUMBER GENERATED BY DEFAULT AS IDENTITY,
observation_id RAW(16) NOT NULL,
bank_id VARCHAR2(256) NOT NULL,
content CLOB NOT NULL
CONSTRAINT oh_content_json CHECK (content IS JSON),
changed_at TIMESTAMP WITH TIME ZONE DEFAULT SYSTIMESTAMP NOT NULL,
CONSTRAINT pk_observation_history PRIMARY KEY (id),
CONSTRAINT fk_oh_obs FOREIGN KEY (observation_id)
REFERENCES memory_units(id) ON DELETE CASCADE
)
"""
)
op.execute(
"CREATE INDEX idx_observation_history_obs ON observation_history (observation_id, changed_at DESC, id DESC)"
)
bind = op.get_bind()
# Backfill via JSON_TABLE. ``content`` is the whole element (FORMAT JSON PATH
# '$'); changed_at is also promoted to its own column. Backfilled content may
# therefore still carry a redundant changed_at key, which the read path
# ignores in favour of the column — harmless, and avoids JSON surgery here.
bind.exec_driver_sql(
"""
INSERT INTO mental_model_history (mental_model_id, bank_id, content, changed_at)
SELECT mm.id, mm.bank_id, jt.content, NVL(jt.changed_at, SYSTIMESTAMP)
FROM mental_models mm,
JSON_TABLE(mm.history, '$[*]' COLUMNS (
seq FOR ORDINALITY,
content CLOB FORMAT JSON PATH '$',
changed_at TIMESTAMP WITH TIME ZONE PATH '$.changed_at'
)) jt
WHERE mm.history IS NOT NULL
ORDER BY mm.id, mm.bank_id, jt.seq
"""
)
bind.exec_driver_sql(
"""
INSERT INTO observation_history (observation_id, bank_id, content, changed_at)
SELECT mu.id, mu.bank_id, jt.content, NVL(jt.changed_at, SYSTIMESTAMP)
FROM memory_units mu,
JSON_TABLE(mu.history, '$[*]' COLUMNS (
seq FOR ORDINALITY,
content CLOB FORMAT JSON PATH '$',
changed_at TIMESTAMP WITH TIME ZONE PATH '$.changed_at'
)) jt
WHERE mu.fact_type = 'observation' AND mu.history IS NOT NULL
ORDER BY mu.id, jt.seq
"""
)
op.execute("ALTER TABLE mental_models DROP COLUMN history")
op.execute("ALTER TABLE memory_units DROP COLUMN history")
def _oracle_downgrade() -> None:
op.execute("ALTER TABLE mental_models ADD history CLOB DEFAULT '[]' NOT NULL")
op.execute("ALTER TABLE memory_units ADD history CLOB DEFAULT '[]'")
op.execute("DROP TABLE observation_history CASCADE CONSTRAINTS")
op.execute("DROP TABLE mental_model_history CASCADE CONSTRAINTS")
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade, oracle=_oracle_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade, oracle=_oracle_downgrade)
@@ -1,110 +0,0 @@
"""Add knowledge_pages table (knowledge-base hierarchy).
The knowledge base organizes synthesized mental models into a navigable tree of
**folders** and **pages**. A page references the mental model that holds its
content (``mental_model_id``); a folder is a pure container (``mental_model_id``
NULL). Hierarchy is a single self-referential ``parent_id`` so folders can nest
arbitrarily. Content stays in ``mental_models`` — this table is metadata + tree
structure only.
Revision ID: a9b8c7d6e5f4
Revises: b57a7c9e0d13
Create Date: 2026-06-25
"""
from collections.abc import Sequence
from alembic import context, op
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "a9b8c7d6e5f4"
down_revision: str | Sequence[str] | None = "b57a7c9e0d13"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _pg_schema_prefix() -> str:
"""Schema-qualifier for raw SQL on PG (multi-tenant search_path)."""
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
def _pg_upgrade() -> None:
schema = _pg_schema_prefix()
# parent_id self-FK cascades so deleting a folder row removes its whole
# subtree of rows in one shot. The mental_model FK is composite (matches the
# mental_models (id, bank_id) PK) and cascades too, so deleting a page's
# mental model removes the page row — folders skip the FK because a NULL
# column in a composite FK is not enforced (MATCH SIMPLE).
op.execute(
f"""
CREATE TABLE IF NOT EXISTS {schema}knowledge_pages (
id VARCHAR(64) NOT NULL,
bank_id TEXT NOT NULL,
parent_id VARCHAR(64),
kind VARCHAR(16) NOT NULL,
name TEXT NOT NULL,
mental_model_id VARCHAR(64),
sort_order INTEGER NOT NULL DEFAULT 0,
created_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now(),
updated_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now(),
CONSTRAINT pk_knowledge_pages PRIMARY KEY (id),
CONSTRAINT ck_knowledge_pages_kind CHECK (kind IN ('folder', 'page')),
CONSTRAINT fk_kp_bank FOREIGN KEY (bank_id)
REFERENCES {schema}banks(bank_id) ON DELETE CASCADE,
CONSTRAINT fk_kp_parent FOREIGN KEY (parent_id)
REFERENCES {schema}knowledge_pages(id) ON DELETE CASCADE,
CONSTRAINT fk_kp_mm FOREIGN KEY (mental_model_id, bank_id)
REFERENCES {schema}mental_models(id, bank_id) ON DELETE CASCADE
)
"""
)
op.execute(
f"CREATE INDEX IF NOT EXISTS idx_kp_bank_parent ON {schema}knowledge_pages (bank_id, parent_id, sort_order)"
)
def _pg_downgrade() -> None:
schema = _pg_schema_prefix()
op.execute(f"DROP INDEX IF EXISTS {schema}idx_kp_bank_parent")
op.execute(f"DROP TABLE IF EXISTS {schema}knowledge_pages")
def _oracle_upgrade() -> None:
op.execute(
"""
CREATE TABLE IF NOT EXISTS knowledge_pages (
id VARCHAR2(64) NOT NULL,
bank_id VARCHAR2(256) NOT NULL,
parent_id VARCHAR2(64),
kind VARCHAR2(16) NOT NULL,
name CLOB NOT NULL,
mental_model_id VARCHAR2(64),
sort_order NUMBER DEFAULT 0 NOT NULL,
created_at TIMESTAMP WITH TIME ZONE DEFAULT SYSTIMESTAMP NOT NULL,
updated_at TIMESTAMP WITH TIME ZONE DEFAULT SYSTIMESTAMP NOT NULL,
CONSTRAINT pk_knowledge_pages PRIMARY KEY (id),
CONSTRAINT ck_knowledge_pages_kind CHECK (kind IN ('folder', 'page')),
CONSTRAINT fk_kp_bank FOREIGN KEY (bank_id)
REFERENCES banks(bank_id) ON DELETE CASCADE,
CONSTRAINT fk_kp_parent FOREIGN KEY (parent_id)
REFERENCES knowledge_pages(id) ON DELETE CASCADE,
CONSTRAINT fk_kp_mm FOREIGN KEY (mental_model_id, bank_id)
REFERENCES mental_models(id, bank_id) ON DELETE CASCADE
)
"""
)
op.execute("CREATE INDEX idx_kp_bank_parent ON knowledge_pages (bank_id, parent_id, sort_order)")
def _oracle_downgrade() -> None:
op.execute("DROP TABLE knowledge_pages CASCADE CONSTRAINTS")
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade, oracle=_oracle_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade, oracle=_oracle_downgrade)
@@ -1,156 +0,0 @@
"""Repair: install maintenance routines on the ``public`` / base-schema run.
The original maintenance-routines migration (``e5f6a7b8c9d0``) only created the
shared ``public.banks_needing_consolidation()`` and
``public.schemas_with_expired_rows(...)`` routines when the run had *no*
``target_schema`` at all. But the single-tenant runtime always migrates an
explicit schema — which defaults to ``public`` — so on every default
PostgreSQL deployment the migration was stamped as applied while the functions
were never created. Background maintenance then logs::
Retention sweep failed for llm_requests: function public.schemas_with_expired_rows(...) does not exist
Consolidation reconcile discovery failed: function public.banks_needing_consolidation() does not exist
See https://github.com/vectorize-io/hindsight/issues/2056.
Because ``e5f6a7b8c9d0`` is already stamped on affected ``0.8.0`` databases,
editing it would not re-run it there. This forward migration re-installs the
functions idempotently (``CREATE OR REPLACE``) on the run that targets the
shared ``public`` schema (base run with no ``target_schema``, or an explicit
``target_schema=public``), self-healing already-upgraded deployments and
covering fresh upgrades from earlier versions.
Per-tenant runs against a non-``public`` schema still skip it: re-issuing
``CREATE OR REPLACE FUNCTION public....`` from each concurrent tenant migration
aborts with ``tuple concurrently updated`` on the ``pg_proc`` catalog row, and
the base/public run has already created the functions for every tenant to use.
Runs that target ``public`` are serialized by the per-schema migration advisory
lock, so only one wins the create.
PostgreSQL only — the worker poller and these tables are not wired for Oracle,
so the Oracle slot is intentionally absent (mirrors ``e5f6a7b8c9d0``).
Revision ID: b2d4f6a8c1e3
Revises: e5f6a7b8c9d0
Create Date: 2026-06-08
"""
from collections.abc import Sequence
from alembic import context, op
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "b2d4f6a8c1e3"
down_revision: str | Sequence[str] | None = "e5f6a7b8c9d0"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _should_install_public_routines(target_schema: str | None) -> bool:
"""True for the run that must (re)create the shared ``public.*`` routines.
The routines physically live in ``public`` (hard-coded ``public.`` qualifier
in the SQL below), so they must be installed exactly once — on the base run
(no ``target_schema``) or on the run that explicitly targets ``public``. A
run against any other tenant schema skips it to avoid concurrent
``CREATE OR REPLACE`` on the same ``pg_proc`` row.
"""
return not target_schema or target_schema == "public"
def _pg_upgrade() -> None:
if not _should_install_public_routines(context.config.get_main_option("target_schema")):
return
# Banks with eligible-but-unscheduled facts and no in-flight consolidation.
# Auto-consolidation is filtered here only at the bank level (cheap prune);
# the full hierarchical resolution (global -> tenant -> bank, plus
# enable_observations) is done by the caller for the small returned set.
op.execute(
"""
CREATE OR REPLACE FUNCTION public.banks_needing_consolidation()
RETURNS TABLE(schema_name text, bank_id text)
LANGUAGE plpgsql STABLE
AS $fn$
DECLARE
sch text;
BEGIN
FOR sch IN
SELECT n.nspname
FROM pg_class c
JOIN pg_namespace n ON n.oid = c.relnamespace
WHERE c.relname = 'memory_units' AND c.relkind = 'r'
LOOP
RETURN QUERY EXECUTE format($q$
SELECT %1$L::text, m.bank_id
FROM %1$I.memory_units m
JOIN %1$I.banks b ON b.bank_id = m.bank_id
WHERE m.consolidated_at IS NULL
AND m.consolidation_failed_at IS NULL
AND m.fact_type IN ('experience', 'world')
AND COALESCE(b.config -> 'enable_auto_consolidation', 'true'::jsonb) <> 'false'::jsonb
AND NOT EXISTS (
SELECT 1 FROM %1$I.async_operations o
WHERE o.bank_id = m.bank_id
AND o.operation_type = 'consolidation'
AND o.status IN ('pending', 'processing')
)
GROUP BY m.bank_id
$q$, sch);
END LOOP;
END;
$fn$;
"""
)
# Schemas holding at least one row of p_table older than p_days. p_ts_col is
# the timestamp column to compare. Returns nothing when p_days <= 0
# (retention disabled).
op.execute(
"""
CREATE OR REPLACE FUNCTION public.schemas_with_expired_rows(
p_table text, p_ts_col text, p_days int
)
RETURNS SETOF text
LANGUAGE plpgsql STABLE
AS $fn$
DECLARE
sch text;
has_expired boolean;
BEGIN
IF p_days IS NULL OR p_days <= 0 THEN
RETURN;
END IF;
FOR sch IN
SELECT n.nspname
FROM pg_class c
JOIN pg_namespace n ON n.oid = c.relnamespace
WHERE c.relname = p_table AND c.relkind = 'r'
LOOP
EXECUTE format(
'SELECT EXISTS (SELECT 1 FROM %I.%I WHERE %I < NOW() - make_interval(days => $1))',
sch, p_table, p_ts_col
) INTO has_expired USING p_days;
IF has_expired THEN
RETURN NEXT sch;
END IF;
END LOOP;
END;
$fn$;
"""
)
def _pg_downgrade() -> None:
# No-op: ``e5f6a7b8c9d0`` owns the lifecycle of these functions and drops
# them on its own downgrade. This migration only ever (re)creates them, so
# there is nothing to undo without racing that migration's DROP.
pass
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade)
@@ -37,35 +37,37 @@ def _get_schema_prefix() -> str:
def _pg_upgrade() -> None:
schema = _get_schema_prefix()
# CREATE INDEX CONCURRENTLY cannot run inside a transaction block; an
# autocommit_block runs each statement outside Alembic's migration transaction.
with op.get_context().autocommit_block():
# Partial index on occurred_start (covers "occurred_start BETWEEN $4 AND $5")
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_bank_occurred_start "
f"ON {schema}memory_units(bank_id, fact_type, occurred_start) "
f"WHERE occurred_start IS NOT NULL"
)
# Partial index on occurred_end (covers "occurred_end BETWEEN $4 AND $5")
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_bank_occurred_end "
f"ON {schema}memory_units(bank_id, fact_type, occurred_end) "
f"WHERE occurred_end IS NOT NULL"
)
# Partial index on mentioned_at (covers "mentioned_at BETWEEN $4 AND $5")
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_bank_mentioned_at "
f"ON {schema}memory_units(bank_id, fact_type, mentioned_at) "
f"WHERE mentioned_at IS NOT NULL"
)
# Partial index on occurred_start (covers "occurred_start BETWEEN $4 AND $5")
op.execute("COMMIT")
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_bank_occurred_start "
f"ON {schema}memory_units(bank_id, fact_type, occurred_start) "
f"WHERE occurred_start IS NOT NULL"
)
# Partial index on occurred_end (covers "occurred_end BETWEEN $4 AND $5")
op.execute("COMMIT")
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_bank_occurred_end "
f"ON {schema}memory_units(bank_id, fact_type, occurred_end) "
f"WHERE occurred_end IS NOT NULL"
)
# Partial index on mentioned_at (covers "mentioned_at BETWEEN $4 AND $5")
op.execute("COMMIT")
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_bank_mentioned_at "
f"ON {schema}memory_units(bank_id, fact_type, mentioned_at) "
f"WHERE mentioned_at IS NOT NULL"
)
def _pg_downgrade() -> None:
schema = _get_schema_prefix()
with op.get_context().autocommit_block():
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_bank_mentioned_at")
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_bank_occurred_end")
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_bank_occurred_start")
op.execute("COMMIT")
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_bank_mentioned_at")
op.execute("COMMIT")
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_bank_occurred_end")
op.execute("COMMIT")
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_bank_occurred_start")
def upgrade() -> None:
@@ -1,61 +0,0 @@
"""Add bank_stats_cache table for distributed get_bank_stats caching
Revision ID: b57a7c9e0d13
Revises: c3f7a1b9d2e4
Create Date: 2026-07-01
get_bank_stats aggregates over memory_links / unit_entities — a multi-second scan
on banks with millions of rows. The result was cached per-process (in-memory), so
every API worker recomputed it once per TTL and the first caller after expiry
stalled. This table backs a shared, cross-process TTL cache: one worker's compute
is written here and served to all the others.
PostgreSQL only. Oracle keeps the in-process cache (the runtime picks the backing
store by dialect), so the Oracle upgrade slot is intentionally absent.
"""
from collections.abc import Sequence
from alembic import context, op
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "b57a7c9e0d13"
down_revision: str | Sequence[str] | None = "c3f7a1b9d2e4"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _get_schema_prefix() -> str:
"""Schema-qualifier for raw SQL on PG (multi-tenant search_path)."""
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
def _pg_upgrade() -> None:
schema = _get_schema_prefix()
# One row per bank: payload is the full get_bank_stats result, computed_at
# drives logical TTL expiry. Rows are overwritten in place (ON CONFLICT), so
# the table never grows beyond the number of banks and needs no purge job.
op.execute(
f"""
CREATE TABLE IF NOT EXISTS {schema}bank_stats_cache (
bank_id TEXT PRIMARY KEY,
payload JSONB NOT NULL,
computed_at TIMESTAMPTZ NOT NULL DEFAULT now()
)
"""
)
def _pg_downgrade() -> None:
schema = _get_schema_prefix()
op.execute(f"DROP TABLE IF EXISTS {schema}bank_stats_cache")
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade) # oracle slot intentionally absent → no-op
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade)
@@ -1,106 +0,0 @@
"""Add graph_maintenance_queue table
Queue of memory_units whose outgoing temporal/semantic links lost a
neighbour to a delete. Drained by the async graph_maintenance worker,
which tops the unit's links back up using the same probes retain runs.
The queue only targets the link-recompute pass. The worker also runs
bank-wide sweeps (orphan-entity prune, stale-cooccurrence prune) on each
invocation; those don't need per-target queueing.
Revision ID: b5a4c3e2f1d8
Revises: e9b2c7d1f3a4
Create Date: 2026-05-27
"""
from collections.abc import Sequence
from alembic import context, op
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "b5a4c3e2f1d8"
down_revision: str | Sequence[str] | None = "e9b2c7d1f3a4"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _pg_schema_prefix() -> str:
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
def _pg_upgrade() -> None:
schema = _pg_schema_prefix()
# Composite PK gives us natural ON CONFLICT DO NOTHING dedup when the same
# unit is enqueued from overlapping deletes. No FK to memory_units: if the
# unit is deleted between enqueue and drain, the worker observes it's gone
# and skips — a cascade would erase the work order, but that work has
# already been satisfied (no surviving row to maintain).
op.execute(
f"""
CREATE TABLE IF NOT EXISTS {schema}graph_maintenance_queue (
bank_id TEXT NOT NULL,
unit_id UUID NOT NULL,
enqueued_at TIMESTAMPTZ NOT NULL DEFAULT now(),
PRIMARY KEY (bank_id, unit_id)
)
"""
)
op.execute(
f"""
CREATE INDEX IF NOT EXISTS idx_graph_maintenance_queue_bank_enqueued
ON {schema}graph_maintenance_queue (bank_id, enqueued_at)
"""
)
def _pg_downgrade() -> None:
schema = _pg_schema_prefix()
op.execute(f"DROP INDEX IF EXISTS {schema}idx_graph_maintenance_queue_bank_enqueued")
op.execute(f"DROP TABLE IF EXISTS {schema}graph_maintenance_queue")
def _oracle_execute_ignoring_955(sql: str) -> None:
"""Run a CREATE statement and swallow ORA-00955 (object already exists).
Mirrors the helper in the Oracle baseline migration so reruns stay safe
on a database where the table was created by an earlier partial run.
"""
block = (
"BEGIN "
"EXECUTE IMMEDIATE :stmt; "
"EXCEPTION WHEN OTHERS THEN "
"IF SQLCODE = -955 THEN NULL; ELSE RAISE; END IF; "
"END;"
)
op.get_bind().exec_driver_sql(block, {"stmt": sql.strip()})
def _oracle_upgrade() -> None:
_oracle_execute_ignoring_955(
"""
CREATE TABLE graph_maintenance_queue (
bank_id VARCHAR2(256) NOT NULL,
unit_id RAW(16) NOT NULL,
enqueued_at TIMESTAMP WITH TIME ZONE DEFAULT SYSTIMESTAMP NOT NULL,
CONSTRAINT pk_graph_maintenance_queue PRIMARY KEY (bank_id, unit_id)
)
"""
)
_oracle_execute_ignoring_955(
"CREATE INDEX idx_graph_maintenance_queue_bank_enqueued ON graph_maintenance_queue (bank_id, enqueued_at)"
)
def _oracle_downgrade() -> None:
op.execute("DROP INDEX idx_graph_maintenance_queue_bank_enqueued")
op.execute("DROP TABLE graph_maintenance_queue")
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade, oracle=_oracle_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade, oracle=_oracle_downgrade)
@@ -1,91 +0,0 @@
"""Backfill entity_cooccurrences.last_cooccurred from memory_units event time
Revision ID: b5d4e3f2a1c9
Revises: o1a2b3c4d5e6
Create Date: 2026-04-24
The writer path in `entity_resolver.link_units_to_entities_batch` historically
stamped `entity_cooccurrences.last_cooccurred` with `datetime.now(UTC)` at
flush time, ignoring the source memory unit's event date. For normal online
retains that's fine (now ≈ event time), but for any corpus that was
backfilled in a single session — migrating from another memory system, for
example — every co-occurrence collapsed to the import moment, which hid the
underlying knowledge timeline from the dashboard's entity graph recency heat
and from any downstream consumer of the column.
The writer is fixed in the same change set to propagate the unit's event_date;
this migration repairs historical rows by reading the true event time off
`unit_entities × memory_units` (falling back to `created_at` when
`mentioned_at` / `occurred_start` are NULL, so rows never regress).
Oracle slot is intentionally absent: the Oracle baseline (`o1a2b3c4d5e6`)
landed days before this fix, so any Oracle deployment runs the corrected
writer against an effectively empty `entity_cooccurrences` — there is no
historical residue on Oracle to repair. PG-only matches the asymmetry of
the data, not negligence.
"""
from collections.abc import Sequence
from alembic import context, op
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "b5d4e3f2a1c9"
down_revision: str | Sequence[str] | None = "o1a2b3c4d5e6"
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 _pg_upgrade() -> None:
schema = _get_schema_prefix()
# Recompute last_cooccurred from the true event time per entity pair.
# COALESCE picks the first non-null of mentioned_at / occurred_start /
# created_at so banks without event-time metadata still see a sane value
# (equivalent to the pre-fix behaviour) instead of NULL.
#
# The self-join on `unit_entities` is O(k²) per memory_unit in the number
# of distinct entities mentioned (k). For typical units k is small (single
# digits), but a bank with units containing hundreds of entities and tens
# of millions of co-occurrence rows may want to run this off-hours — the
# whole UPDATE is one statement, so it locks every targeted ec row for
# the duration. The migration is one-time; subsequent online writes
# already carry event time via the writer fix.
op.execute(
f"""
UPDATE {schema}entity_cooccurrences ec
SET last_cooccurred = sub.event_time
FROM (
SELECT
LEAST(ue1.entity_id, ue2.entity_id) AS e1,
GREATEST(ue1.entity_id, ue2.entity_id) AS e2,
MAX(COALESCE(mu.mentioned_at, mu.occurred_start, mu.created_at)) AS event_time
FROM {schema}memory_units mu
JOIN {schema}unit_entities ue1 ON ue1.unit_id = mu.id
JOIN {schema}unit_entities ue2 ON ue2.unit_id = mu.id AND ue1.entity_id <> ue2.entity_id
GROUP BY 1, 2
) sub
WHERE ec.entity_id_1 = sub.e1 AND ec.entity_id_2 = sub.e2
"""
)
def _pg_downgrade() -> None:
# No-op: the previous column value was `now()` at the time of write and
# isn't recoverable. Rolling back the code is sufficient — new writes will
# revert to the old behaviour for subsequent retains.
pass
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade) # oracle slot intentionally absent — see header
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade)
@@ -1,152 +0,0 @@
"""Re-create vchord vector indexes with vector_cosine_ops
Revision ID: b8c9d0e1f2a3
Revises: 86f7a033d372
Create Date: 2026-05-20
vchordrq operator classes are bound 1:1 to operators in PostgreSQL:
vector_l2_ops only matches ``<->``, while every Hindsight ANN query uses
``<=>`` (cosine distance). The previous vchord mapping used vector_l2_ops,
so vchord deployments could never use the index — every ANN query fell
back to a sequential scan with per-row cosine computation.
This migration finds any vchordrq index built with vector_l2_ops in the
target schema and re-creates it with vector_cosine_ops, using
``CREATE INDEX CONCURRENTLY`` so it can run online. It is a no-op when:
* the configured vector extension is not vchord, or
* no matching indexes exist (already on cosine ops).
Only PostgreSQL is affected; the Oracle 23ai dialect uses its own native
vector index and does not depend on this mapping.
"""
from __future__ import annotations
import re
from collections.abc import Sequence
from alembic import context, op
from sqlalchemy import text
from hindsight_api._vector_index import configured_vector_extension
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "b8c9d0e1f2a3"
down_revision: str | Sequence[str] | None = "86f7a033d372"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _pg_schema_prefix() -> str:
"""Schema-qualifier for raw SQL on PG (multi-tenant search_path)."""
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
def _rebuild_vchordrq_indexes(old_ops: str, new_ops: str) -> None:
"""Rebuild vchordrq indexes using ``old_ops`` so they use ``new_ops``.
Each index is rebuilt with CREATE INDEX CONCURRENTLY under a fresh name,
then the old index is dropped and the new one renamed to take its place.
Must be called inside an ``autocommit_block()`` because CONCURRENTLY
cannot run inside a transaction.
"""
bind = op.get_bind()
# `or None` collapses both unset and explicit empty-string Alembic options
# into NULL so the COALESCE below falls back to current_schema() in either
# case. Without it, an empty-string option would filter on `schemaname = ''`
# and skip every real schema.
target_schema = context.config.get_main_option("target_schema") or None
prefix = _pg_schema_prefix()
rows = bind.execute(
text(
"SELECT indexname, indexdef FROM pg_indexes "
"WHERE schemaname = COALESCE(:target_schema, current_schema()) "
"AND indexdef ILIKE '%vchordrq%' "
"AND indexdef ILIKE :ops_like"
),
{"target_schema": target_schema, "ops_like": f"%{old_ops}%"},
).fetchall()
for idx_name, indexdef in rows:
# pg_get_indexdef() emits the canonical form `CREATE INDEX <name> ON …`,
# so <name> is the first textual occurrence — both substitutions below
# rely on that.
new_def = indexdef.replace(old_ops, new_ops, 1)
temp_name = f"{idx_name}__opclass_swap"
new_def = new_def.replace(idx_name, temp_name, 1)
new_def = re.sub(
r"^CREATE\s+INDEX\b",
"CREATE INDEX CONCURRENTLY IF NOT EXISTS",
new_def,
count=1,
)
# CREATE INDEX CONCURRENTLY can leave the partial index as INVALID if a
# previous run errored (disk pressure, lock conflict, signal). Without
# this drop the CONCURRENTLY IF NOT EXISTS below would skip creation,
# then we'd drop the original and rename the broken index into its
# place — silently restoring the seq-scan bug this migration fixes.
op.execute(f'DROP INDEX IF EXISTS {prefix}"{temp_name}"')
op.execute(new_def)
# Even on a clean run CONCURRENTLY can finish with indisvalid = false
# (e.g. constraint violation during the second build scan). Refuse to
# promote in that case so we never alias an INVALID index over a working
# one.
is_valid = bind.execute(
text(
"SELECT i.indisvalid "
"FROM pg_class c "
"JOIN pg_index i ON c.oid = i.indexrelid "
"JOIN pg_namespace n ON c.relnamespace = n.oid "
"WHERE c.relname = :name "
" AND n.nspname = COALESCE(:target_schema, current_schema())"
),
{"name": temp_name, "target_schema": target_schema},
).scalar()
if not is_valid:
raise RuntimeError(
f"vchordrq index rebuild produced an INVALID index ({temp_name}); "
"drop it manually and re-run the migration."
)
# DROP + RENAME atomically. A crash between the two would leave
# `temp_name` as a valid orphan and the canonical name missing —
# next run's `pg_indexes` filter (looking for vector_l2_ops) wouldn't
# find anything to recover from, so the index would stay gone. PG
# runs the DO block in its own server-side transaction, so either
# both succeed or both roll back.
op.execute(
f"""
DO $$
BEGIN
DROP INDEX IF EXISTS {prefix}"{idx_name}";
ALTER INDEX {prefix}"{temp_name}" RENAME TO "{idx_name}";
END $$;
"""
)
def _pg_upgrade() -> None:
if configured_vector_extension() != "vchord":
return
with op.get_context().autocommit_block():
_rebuild_vchordrq_indexes("vector_l2_ops", "vector_cosine_ops")
def _pg_downgrade() -> None:
if configured_vector_extension() != "vchord":
return
with op.get_context().autocommit_block():
_rebuild_vchordrq_indexes("vector_cosine_ops", "vector_l2_ops")
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade)
@@ -47,18 +47,17 @@ def _pg_upgrade() -> None:
schema = _get_schema_prefix()
# GIN index on canonical_name enables sub-millisecond trigram similarity queries
# (% operator, similarity()) instead of full-table scans across all bank entities.
# CREATE INDEX CONCURRENTLY cannot run inside a transaction block.
with op.get_context().autocommit_block():
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS entities_canonical_name_trgm_idx "
f"ON {schema}entities USING GIN (canonical_name gin_trgm_ops)"
)
op.execute("COMMIT")
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS entities_canonical_name_trgm_idx "
f"ON {schema}entities USING GIN (canonical_name gin_trgm_ops)"
)
def _pg_downgrade() -> None:
schema = _get_schema_prefix()
with op.get_context().autocommit_block():
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}entities_canonical_name_trgm_idx")
op.execute("COMMIT")
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}entities_canonical_name_trgm_idx")
# Note: not dropping pg_trgm extension as other indexes may depend on it
@@ -1,45 +0,0 @@
"""Merge graph_maintenance_queue and vchord_cosine_opclass heads.
Revision ID: c1d2e3f4a5b6
Revises: b5a4c3e2f1d8, b8c9d0e1f2a3
Create Date: 2026-05-29
PRs #1668 (vchord cosine opclass) and #1772 (async link recompute) both
branched off the same parent and were merged onto main without rebasing,
leaving two parallel Alembic heads. This is a structural merge revision
with no schema changes — its only job is to unify the DAG so
``alembic upgrade head`` is unambiguous again.
"""
from collections.abc import Sequence
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "c1d2e3f4a5b6"
down_revision: str | Sequence[str] | None = ("b5a4c3e2f1d8", "b8c9d0e1f2a3")
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _pg_upgrade() -> None:
pass
def _pg_downgrade() -> None:
pass
def _oracle_upgrade() -> None:
pass
def _oracle_downgrade() -> None:
pass
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade, oracle=_oracle_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade, oracle=_oracle_downgrade)
@@ -1,71 +0,0 @@
"""Unique page name per folder in knowledge_pages.
The folder curator can fire concurrently (folder-create trigger + the
post-consolidation sweep), and an in-process lock can't serialize runs that
execute in different threads/loops. A partial unique index on
(bank_id, parent, lower(name)) for pages makes duplicate-named pages in the same
folder impossible at the DB level — the second concurrent insert fails and the
curator treats it as "already exists".
PostgreSQL only: the Oracle ``name`` column is a CLOB and cannot back a
functional unique index; Oracle relies on the in-process serialization instead.
Revision ID: c3d4e5f6a7b8
Revises: a5b6c7d8e9f0
Create Date: 2026-06-26
"""
from collections.abc import Sequence
from alembic import context, op
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "c3d4e5f6a7b8"
down_revision: str | Sequence[str] | None = "a5b6c7d8e9f0"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _pg_schema_prefix() -> str:
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
def _pg_upgrade() -> None:
schema = _pg_schema_prefix()
# First drop any pre-existing duplicate pages (created by the racy curator
# before this guard existed), keeping the earliest row of each duplicate set,
# so the unique index can be built. Their backing mental models are left in
# place (harmless orphans).
op.execute(
f"""
DELETE FROM {schema}knowledge_pages a
USING {schema}knowledge_pages b
WHERE a.kind = 'page' AND b.kind = 'page'
AND a.bank_id = b.bank_id
AND COALESCE(a.parent_id, '') = COALESCE(b.parent_id, '')
AND lower(a.name) = lower(b.name)
AND a.ctid > b.ctid
"""
)
# COALESCE(parent_id, '') so root-level pages (NULL parent) are also unique by
# name — NULLs would otherwise compare distinct and allow duplicates.
op.execute(
"CREATE UNIQUE INDEX IF NOT EXISTS uq_kp_folder_pagename "
f"ON {schema}knowledge_pages (bank_id, COALESCE(parent_id, ''), lower(name)) "
"WHERE kind = 'page'"
)
def _pg_downgrade() -> None:
schema = _pg_schema_prefix()
op.execute(f"DROP INDEX IF EXISTS {schema}uq_kp_folder_pagename")
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade) # oracle slot intentionally absent (CLOB name)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade)
@@ -1,75 +0,0 @@
"""Repair: widen ``*_history.bank_id`` from VARCHAR(64) to TEXT on PostgreSQL.
The original split-history migration (``a7b8c9d0e1f2``) declared
``observation_history.bank_id`` and ``mental_model_history.bank_id`` as
``VARCHAR(64)`` on PostgreSQL. But ``memory_units.bank_id`` — the backfill
source for observations — is ``TEXT`` (unbounded), as are ``banks``,
``documents`` and ``entities``. Any deployment whose ``bank_id`` exceeds 64
characters aborts the backfill ``INSERT`` with::
psycopg2.errors.StringDataRightTruncation: value too long for type
character varying(64)
Because the migration runs in ``lifespan`` startup inside a transaction, the
whole migration rolls back and the API never comes up — unrecoverable from the
running container. See https://github.com/vectorize-io/hindsight/issues/2106.
``a7b8c9d0e1f2`` itself has been corrected to create the column as ``TEXT``,
which unblocks deployments that *failed* (the migration rolled back, so it
re-runs the fixed DDL). This forward migration covers deployments that already
*succeeded* with the narrow ``VARCHAR(64)`` column — where editing
``a7b8c9d0e1f2`` has no effect because it will not re-run — by widening the
column in place. ``ALTER COLUMN ... TYPE TEXT`` is a no-op on a column that is
already ``TEXT`` (fresh installs and re-run failures), so every upgrade path
converges on ``TEXT``.
The history tables are per-tenant (they live in each tenant schema, not
``public``), so this runs for every migrated schema via the search-path-aware
prefix — unlike the shared-``public`` routines repaired in ``b2d4f6a8c1e3``.
PostgreSQL only. On Oracle both ``memory_units.bank_id`` and the history
``bank_id`` columns are already ``VARCHAR2(256)`` (consistent, never
truncates), so the Oracle slot is intentionally absent.
Revision ID: c3e5a7b9d1f4
Revises: c9a1b2d3e4f5
Create Date: 2026-06-10
"""
from collections.abc import Sequence
from alembic import context, op
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "c3e5a7b9d1f4"
down_revision: str | Sequence[str] | None = "c9a1b2d3e4f5"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _get_schema_prefix() -> str:
"""Schema-qualifier for raw SQL on PG (multi-tenant search_path)."""
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
def _pg_upgrade() -> None:
schema = _get_schema_prefix()
op.execute(f"ALTER TABLE {schema}observation_history ALTER COLUMN bank_id TYPE TEXT")
op.execute(f"ALTER TABLE {schema}mental_model_history ALTER COLUMN bank_id TYPE TEXT")
def _pg_downgrade() -> None:
# No-op: narrowing back to VARCHAR(64) could truncate real data and would
# re-introduce the bug this migration repairs. The column type is owned by
# ``a7b8c9d0e1f2``'s lifecycle.
pass
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade)
@@ -1,144 +0,0 @@
"""Backfill search_vector for native-backend observations.
Observations created or updated by the consolidator landed with a NULL
``search_vector`` under the ``native`` text-search backend: the
single-row INSERT/UPDATE paths in ``consolidator.py`` never populated the
tsvector (only the batch raw-fact path in ``ops_postgresql.insert_facts_batch``
did). Those observations were therefore invisible to the BM25 retrieval arm
until they were re-written by a later consolidation pass. The writer is fixed
in the same change set (all four consolidator sites now call
``to_tsvector($lang, COALESCE(text, ''))``); this migration repairs the
historical residue so existing observations become BM25-searchable without a
re-ingest.
Scope mirrors the writer fix exactly:
* Only the ``native`` backend is touched. The gate is the column *type*:
under ``native`` ``search_vector`` is a regular (non-generated) tsvector
column; under ``vchord`` it is a ``bm25vector`` and under
``pg_textsearch`` / ``pgroonga`` / ``pg_search`` it is a dummy ``text``
column. ``_is_regular_tsvector`` is true only for ``native``, so every
other backend is a no-op.
* The tsvector is built from the observation's own ``text`` only — matching
the consolidator INSERT/UPDATE paths (entity / source / temporal signals
are intentionally excluded; the other retrieval arms cover those).
* Only ``fact_type = 'observation'`` rows with a NULL ``search_vector`` are
rewritten. Raw facts already carry a populated tsvector, and the
``IS NULL`` predicate makes the migration idempotent and re-runnable.
The configured ``HINDSIGHT_API_TEXT_SEARCH_EXTENSION_NATIVE_LANGUAGE`` is used
so backfilled rows are lexically identical to newly-created observations. The
value is validated as a PG identifier (mirroring
``HindsightConfig.validate``) before being embedded as a SQL literal.
This is a single UPDATE per schema: it locks the targeted observation rows for
its duration. It is one-time and only touches unpopulated rows, so subsequent
online writes (which now carry the tsvector via the writer fix) are unaffected.
Oracle slot is intentionally absent: the consolidator INSERT/UPDATE paths that
this repairs are PostgreSQL-specific (``ops_postgresql``), and the native
tsvector ``search_vector`` column only exists on PostgreSQL. There is no Oracle
residue to repair.
Revision ID: c3f7a1b9d2e4
Revises: f4d1c2b3a5e6
Create Date: 2026-06-29
"""
import os
import re
from collections.abc import Sequence
from alembic import context, op
from sqlalchemy import Connection, text
from hindsight_api.alembic._dialect import run_for_dialect
from hindsight_api.config import (
DEFAULT_TEXT_SEARCH_EXTENSION_NATIVE_LANGUAGE,
ENV_TEXT_SEARCH_EXTENSION_NATIVE_LANGUAGE,
)
revision: str = "c3f7a1b9d2e4"
down_revision: str | Sequence[str] | None = "f4d1c2b3a5e6"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
# Matches HindsightConfig.validate(): a tsvector regconfig name embedded as a
# SQL literal must be a bare PG identifier.
_PG_IDENTIFIER = re.compile(r"[a-zA-Z_][a-zA-Z0-9_]*")
def _schema_prefix() -> str:
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
def _schema_name() -> str:
return (context.config.get_main_option("target_schema") or "public").strip('"')
def _native_language() -> str:
"""Configured native tsvector language, validated as a PG identifier."""
lang = os.getenv(
ENV_TEXT_SEARCH_EXTENSION_NATIVE_LANGUAGE,
DEFAULT_TEXT_SEARCH_EXTENSION_NATIVE_LANGUAGE,
)
if not _PG_IDENTIFIER.fullmatch(lang):
return DEFAULT_TEXT_SEARCH_EXTENSION_NATIVE_LANGUAGE
return lang
def _is_regular_tsvector(conn: Connection, schema: str, table: str) -> bool:
"""True iff ``schema.table.search_vector`` is a non-generated tsvector column.
This is the ``native`` backend signature. ``vchord`` (bm25vector) and
``pg_textsearch`` / ``pgroonga`` / ``pg_search`` (dummy text column) all
fail this check, so the backfill is a no-op for them.
"""
row = conn.execute(
text(
"""
SELECT is_generated, udt_name
FROM information_schema.columns
WHERE table_schema = :schema
AND table_name = :table
AND column_name = 'search_vector'
"""
),
{"schema": schema, "table": table},
).fetchone()
if not row:
return False
is_generated, udt_name = row[0], row[1]
return udt_name == "tsvector" and is_generated != "ALWAYS"
def _pg_upgrade() -> None:
conn = op.get_bind()
schema_name = _schema_name()
if not _is_regular_tsvector(conn, schema_name, "memory_units"):
# Non-native backend (or column absent) — nothing to backfill.
return
schema_prefix = _schema_prefix()
lang = _native_language()
op.execute(
f"""
UPDATE {schema_prefix}memory_units
SET search_vector = to_tsvector('{lang}'::regconfig, COALESCE(text, ''))
WHERE fact_type = 'observation' AND search_vector IS NULL
"""
)
def _pg_downgrade() -> None:
# No-op: backfilled rows are indistinguishable from observations that were
# populated by the post-fix writer, and reverting either to NULL would
# re-break BM25 retrieval. The column simply stays populated.
pass
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade)
@@ -1,158 +0,0 @@
"""Make maintenance routines resilient to schemas that vanish mid-scan.
``public.banks_needing_consolidation()`` and
``public.schemas_with_expired_rows(...)`` snapshot the set of schemas owning a
target table from ``pg_class`` and then run a dynamic query against each schema
in turn. That is a time-of-check/time-of-use race: a schema (or its tables) can
be dropped — a tenant being deleted, or a tenant migration that recreates
tables — between the snapshot and the per-schema query, which then aborts the
whole routine with::
relation "<schema>.memory_units" does not exist
relation "<schema>.audit_log" does not exist
In the test suite this surfaces as cross-worker contamination: the multi-tenant
maintenance test creates and drops ~100 ``mt<hash>_NNN`` schemas while
``test_maintenance_routines`` (on another xdist worker, same DB) calls the
routines. In production the background maintenance loop hits the same race when
a tenant is removed or mid-migration.
Wrap each per-schema query in its own ``BEGIN ... EXCEPTION`` block so a schema
that disappears (``undefined_table`` / ``invalid_schema_name`` /
``undefined_column``) is skipped instead of aborting the scan. The routines stay
``CREATE OR REPLACE`` and PostgreSQL-only, and are (re)installed only on the run
that targets the shared ``public`` schema — same gating as the original
install (``e5f6a7b8c9d0``) and its repair (``b2d4f6a8c1e3``).
Revision ID: c7e9f1a3b5d2
Revises: e1f2a3b4c5d6
Create Date: 2026-06-19
"""
from collections.abc import Sequence
from alembic import context, op
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "c7e9f1a3b5d2"
down_revision: str | Sequence[str] | None = "e1f2a3b4c5d6"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _should_install_public_routines(target_schema: str | None) -> bool:
"""True for the run that must (re)create the shared ``public.*`` routines.
The routines physically live in ``public``, so they are installed exactly
once — on the base run (no ``target_schema``) or the run that explicitly
targets ``public``. Mirrors ``b2d4f6a8c1e3``.
"""
return not target_schema or target_schema == "public"
def _pg_upgrade() -> None:
if not _should_install_public_routines(context.config.get_main_option("target_schema")):
return
# Same body as b2d4f6a8c1e3, but each per-schema query runs in its own
# subtransaction so a schema dropped mid-scan is skipped, not fatal.
op.execute(
"""
CREATE OR REPLACE FUNCTION public.banks_needing_consolidation()
RETURNS TABLE(schema_name text, bank_id text)
LANGUAGE plpgsql STABLE
AS $fn$
DECLARE
sch text;
BEGIN
FOR sch IN
SELECT n.nspname
FROM pg_class c
JOIN pg_namespace n ON n.oid = c.relnamespace
WHERE c.relname = 'memory_units' AND c.relkind = 'r'
LOOP
BEGIN
RETURN QUERY EXECUTE format($q$
SELECT %1$L::text, m.bank_id
FROM %1$I.memory_units m
JOIN %1$I.banks b ON b.bank_id = m.bank_id
WHERE m.consolidated_at IS NULL
AND m.consolidation_failed_at IS NULL
AND m.fact_type IN ('experience', 'world')
AND COALESCE(b.config -> 'enable_auto_consolidation', 'true'::jsonb) <> 'false'::jsonb
AND NOT EXISTS (
SELECT 1 FROM %1$I.async_operations o
WHERE o.bank_id = m.bank_id
AND o.operation_type = 'consolidation'
AND o.status IN ('pending', 'processing')
)
GROUP BY m.bank_id
$q$, sch);
EXCEPTION
-- Schema or its tables vanished between the pg_class
-- snapshot and this query (tenant dropped or migrating).
WHEN undefined_table OR invalid_schema_name OR undefined_column THEN
CONTINUE;
END;
END LOOP;
END;
$fn$;
"""
)
op.execute(
"""
CREATE OR REPLACE FUNCTION public.schemas_with_expired_rows(
p_table text, p_ts_col text, p_days int
)
RETURNS SETOF text
LANGUAGE plpgsql STABLE
AS $fn$
DECLARE
sch text;
has_expired boolean;
BEGIN
IF p_days IS NULL OR p_days <= 0 THEN
RETURN;
END IF;
FOR sch IN
SELECT n.nspname
FROM pg_class c
JOIN pg_namespace n ON n.oid = c.relnamespace
WHERE c.relname = p_table AND c.relkind = 'r'
LOOP
BEGIN
EXECUTE format(
'SELECT EXISTS (SELECT 1 FROM %I.%I WHERE %I < NOW() - make_interval(days => $1))',
sch, p_table, p_ts_col
) INTO has_expired USING p_days;
EXCEPTION
-- Schema or its table vanished mid-scan; skip it.
WHEN undefined_table OR invalid_schema_name OR undefined_column THEN
CONTINUE;
END;
IF has_expired THEN
RETURN NEXT sch;
END IF;
END LOOP;
END;
$fn$;
"""
)
def _pg_downgrade() -> None:
# No-op: e5f6a7b8c9d0 owns these functions' lifecycle and drops them on its
# own downgrade. This migration only re-installs them (the resilient body is
# a strict superset of the previous behaviour), so there is nothing to undo
# without racing that migration's DROP.
pass
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade)
@@ -1,108 +0,0 @@
"""Add invalidated_memory_units table for curation (edit/invalidate).
Curation keeps the recall hot-path (``memory_units``) clean by *moving*
invalidated facts into a sibling archive table rather than flagging them in
place. If a row is in ``memory_units`` it is live; if it is in
``invalidated_memory_units`` it has been retired. Recall/consolidation/graph
queries never need a state predicate — the rows simply aren't there.
The archive mirrors ``memory_units`` column-for-column — except ``embedding``,
which it never keeps: the archive is cold storage, never a recall surface, and
revert recomputes the embedding from the unit's text/dates/entities. Keeping no
archive vector also means a later embedding-model switch (which re-dimensions
``memory_units``) can't trip a dimension mismatch on the move (#2209). Plus:
- ``invalidation_reason`` optional free text recorded on invalidate
- ``invalidated_at`` when it was retired
- ``entity_ids`` snapshot of the unit's entity associations, so revert
can restore them (``unit_entities`` is cascade-deleted
when the live row is removed)
This migration also adds ``edited_at`` to ``memory_units``: set whenever a user
edits a memory's fields (text, context, dates, fact_type, entities) via curation.
NULL means never manually modified; a non-NULL value answers "has the user ever
changed this?" with the time of the last edit (distinct from ``updated_at``,
which background operations also bump). It is added to ``memory_units`` *before*
the archive is cloned below, so the archive inherits the column and the marker
travels with a fact when it is invalidated.
Revision ID: c9a1b2d3e4f5
Revises: b2d4f6a8c1e3
Create Date: 2026-06-03
"""
from collections.abc import Sequence
from alembic import context, op
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "c9a1b2d3e4f5"
down_revision: str | Sequence[str] | None = "b2d4f6a8c1e3"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _pg_schema_prefix() -> str:
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
def _pg_upgrade() -> None:
schema = _pg_schema_prefix()
# Add edited_at to the live table FIRST so the archive's LIKE clone below
# inherits it (keeps the two tables column-for-column identical for round-trip).
op.execute(f"ALTER TABLE {schema}memory_units ADD COLUMN IF NOT EXISTS edited_at TIMESTAMPTZ")
# LIKE ... INCLUDING DEFAULTS clones every memory_units column (incl.
# edited_at) so an invalidated row can move back verbatim. We deliberately
# omit indexes/constraints — the archive is cold storage, not a recall
# surface; only the lookups below need indexing.
op.execute(
f"CREATE TABLE IF NOT EXISTS {schema}invalidated_memory_units (LIKE {schema}memory_units INCLUDING DEFAULTS)"
)
# ...then drop the inherited embedding: the archive never stores one (revert
# recomputes it), so it isn't created here only to be dropped again later by
# d4f6a8c2e1b3. That migration still runs as a no-op (DROP ... IF EXISTS) on
# fresh DBs and does the real drop on DBs created before this column was removed.
op.execute(f"ALTER TABLE {schema}invalidated_memory_units DROP COLUMN IF EXISTS embedding")
op.execute(
f"ALTER TABLE {schema}invalidated_memory_units "
f"ADD COLUMN IF NOT EXISTS invalidation_reason TEXT, "
f"ADD COLUMN IF NOT EXISTS invalidated_at TIMESTAMPTZ DEFAULT now(), "
f"ADD COLUMN IF NOT EXISTS entity_ids UUID[]"
)
op.execute(f"CREATE UNIQUE INDEX IF NOT EXISTS idx_invalidated_mu_id ON {schema}invalidated_memory_units (id)")
op.execute(
f"CREATE INDEX IF NOT EXISTS idx_invalidated_mu_bank "
f"ON {schema}invalidated_memory_units (bank_id, invalidated_at)"
)
# Deleting a document (or bank) should clear its archived facts too, mirroring
# the memory_units → documents cascade.
op.execute(
f"""
DO $$ BEGIN
IF NOT EXISTS (SELECT 1 FROM pg_constraint WHERE conname = 'invalidated_mu_document_fkey') THEN
ALTER TABLE {schema}invalidated_memory_units
ADD CONSTRAINT invalidated_mu_document_fkey
FOREIGN KEY (document_id, bank_id)
REFERENCES {schema}documents(id, bank_id) ON DELETE CASCADE;
END IF; END $$;
"""
)
def _pg_downgrade() -> None:
schema = _pg_schema_prefix()
# Drops the archive (and its inherited edited_at) wholesale, then removes
# edited_at from the live table.
op.execute(f"DROP TABLE IF EXISTS {schema}invalidated_memory_units")
op.execute(f"ALTER TABLE {schema}memory_units DROP COLUMN IF EXISTS edited_at")
def upgrade() -> None:
# PG-only: Oracle gets the table from the baseline snapshot, matching the
# convention used by sibling column/index migrations in this tree.
run_for_dialect(pg=_pg_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade)
@@ -50,35 +50,39 @@ def _get_schema_prefix() -> str:
def _pg_upgrade() -> None:
schema = _get_schema_prefix()
# CREATE INDEX CONCURRENTLY cannot run inside a transaction block; an
# autocommit_block runs each statement outside Alembic's migration
# transaction. IF NOT EXISTS makes each statement idempotent on retry.
with op.get_context().autocommit_block():
# Index for the semantic *incoming* direction in link_expansion_retrieval.py.
# Replaces the BitmapAnd of idx_memory_links_to_unit ∩ idx_memory_links_link_type
# with a single composite index scan.
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_links_to_type_weight "
f"ON {schema}memory_links(to_unit_id, link_type, weight DESC)"
)
# CREATE INDEX CONCURRENTLY cannot run inside a transaction block.
# Commit the current Alembic transaction, then issue each CONCURRENTLY
# statement in its own implicit autocommit transaction.
# IF NOT EXISTS makes each statement idempotent if the migration is retried.
# Covering index for entity co-occurrence expansion.
# Enables an index-only scan: entity_id and to_unit_id are read from the
# index leaf pages instead of the heap, eliminating ~2 500 random heap-page
# reads per expansion query.
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_links_entity_covering "
f"ON {schema}memory_links(from_unit_id) "
f"INCLUDE (to_unit_id, entity_id) "
f"WHERE link_type = 'entity'"
)
# Index for the semantic *incoming* direction in link_expansion_retrieval.py.
# Replaces the BitmapAnd of idx_memory_links_to_unit ∩ idx_memory_links_link_type
# with a single composite index scan.
op.execute("COMMIT")
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_links_to_type_weight "
f"ON {schema}memory_links(to_unit_id, link_type, weight DESC)"
)
# Covering index for entity co-occurrence expansion.
# Enables an index-only scan: entity_id and to_unit_id are read from the
# index leaf pages instead of the heap, eliminating ~2 500 random heap-page
# reads per expansion query.
op.execute("COMMIT")
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_links_entity_covering "
f"ON {schema}memory_links(from_unit_id) "
f"INCLUDE (to_unit_id, entity_id) "
f"WHERE link_type = 'entity'"
)
def _pg_downgrade() -> None:
schema = _get_schema_prefix()
with op.get_context().autocommit_block():
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_links_entity_covering")
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_links_to_type_weight")
op.execute("COMMIT")
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_links_entity_covering")
op.execute("COMMIT")
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_links_to_type_weight")
def upgrade() -> None:
@@ -1,96 +0,0 @@
"""Add llm_requests table for per-bank LLM request tracing.
Stores one row per logical LLM call Hindsight makes (success and failure),
capturing the input messages, model output, token usage (input/output/cached/
total), finish reason, and caller metadata. Disabled by default at the
application layer (HINDSIGHT_API_LLM_TRACE_ENABLED); this migration only
creates the table.
PostgreSQL only — the tracing subsystem is not wired for Oracle, so the Oracle
slot is intentionally absent (mirrors the audit_log table).
Revision ID: d3e4f5a6b7c8
Revises: c1d2e3f4a5b6
Create Date: 2026-06-01
"""
from collections.abc import Sequence
from alembic import context, op
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "d3e4f5a6b7c8"
down_revision: str | Sequence[str] | None = "c1d2e3f4a5b6"
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 _pg_upgrade() -> None:
schema = _get_schema_prefix()
op.execute(
f"""
CREATE TABLE IF NOT EXISTS {schema}llm_requests (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
bank_id TEXT,
operation TEXT,
scope TEXT,
-- OTel-style grouping: trace_id is shared by every LLM call of one
-- operation invocation (e.g. all calls of a single reflect run);
-- parent_span_id is that operation span; span_id is this call.
trace_id TEXT,
span_id TEXT,
parent_span_id TEXT,
provider TEXT,
model TEXT,
status TEXT NOT NULL,
started_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
ended_at TIMESTAMPTZ,
duration_ms INTEGER,
input_tokens INTEGER,
output_tokens INTEGER,
cached_tokens INTEGER,
total_tokens INTEGER,
input JSONB,
output JSONB,
error TEXT,
llm_info JSONB DEFAULT '{{}}'::jsonb,
metadata JSONB DEFAULT '{{}}'::jsonb
)
"""
)
op.execute(
f"CREATE INDEX IF NOT EXISTS idx_llm_requests_bank_started ON {schema}llm_requests (bank_id, started_at DESC)"
)
op.execute(
f"CREATE INDEX IF NOT EXISTS idx_llm_requests_status_started ON {schema}llm_requests (status, started_at DESC)"
)
op.execute(f"CREATE INDEX IF NOT EXISTS idx_llm_requests_started ON {schema}llm_requests (started_at DESC)")
op.execute(
f"CREATE INDEX IF NOT EXISTS idx_llm_requests_trace ON {schema}llm_requests (bank_id, trace_id, started_at)"
)
def _pg_downgrade() -> None:
schema = _get_schema_prefix()
op.execute(f"DROP INDEX IF EXISTS {schema}idx_llm_requests_started")
op.execute(f"DROP INDEX IF EXISTS {schema}idx_llm_requests_status_started")
op.execute(f"DROP INDEX IF EXISTS {schema}idx_llm_requests_bank_started")
op.execute(f"DROP TABLE IF EXISTS {schema}llm_requests")
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade)
@@ -33,27 +33,26 @@ def _get_schema_prefix() -> str:
def _pg_upgrade() -> None:
schema = _get_schema_prefix()
# DROP + CREATE CONCURRENTLY must run outside a transaction block; an
# autocommit_block runs them outside Alembic's migration transaction.
with op.get_context().autocommit_block():
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_source_memory_ids")
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_source_memory_ids "
f"ON {schema}memory_units USING GIN (source_memory_ids) "
f"WITH (fastupdate=off) "
f"WHERE source_memory_ids IS NOT NULL"
)
# DROP + CREATE CONCURRENTLY must run outside a transaction block.
op.execute("COMMIT")
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_source_memory_ids")
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_source_memory_ids "
f"ON {schema}memory_units USING GIN (source_memory_ids) "
f"WITH (fastupdate=off) "
f"WHERE source_memory_ids IS NOT NULL"
)
def _pg_downgrade() -> None:
schema = _get_schema_prefix()
with op.get_context().autocommit_block():
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_source_memory_ids")
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_source_memory_ids "
f"ON {schema}memory_units USING GIN (source_memory_ids) "
f"WHERE source_memory_ids IS NOT NULL"
)
op.execute("COMMIT")
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_source_memory_ids")
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_source_memory_ids "
f"ON {schema}memory_units USING GIN (source_memory_ids) "
f"WHERE source_memory_ids IS NOT NULL"
)
def upgrade() -> None:
@@ -1,93 +0,0 @@
"""Drop the embedding column from the curation archive (invalidated_memory_units).
The archive is cold storage, never a recall surface, so it has no business
keeping an embedding. Earlier curation code copied the live row's embedding into
``invalidated_memory_units`` on invalidate; the engine now leaves it out on
invalidate and recomputes it on revert, so the column is dead weight.
Dropping it makes "the archive holds no embedding" a schema-enforced invariant
rather than a convention the move queries have to honour, and removes a latent
failure mode (#2209): after an embedding-model switch the live tables are
re-dimensioned but the archive was not, so a stale old-dimension embedding in
the archive tripped a vector-dimension mismatch on the INSERT … SELECT
round-trip. With no column at all, there is nothing to mismatch.
The creation sites no longer add the column (the PG ``LIKE`` clone in
c9a1b2d3e4f5 drops it; the Oracle baseline omits it), so on a fresh database
this migration is a no-op (DROP ... IF EXISTS / Oracle ORA-00904 swallow). It
does the real work on databases created before the column was removed there.
DROP COLUMN is a metadata-only operation on both PostgreSQL and Oracle 23ai (no
table rewrite), so it is cheap even across many tenant schemas. The downgrade
re-adds an unconstrained vector column (any dimension) — empty, since the
embeddings are intentionally discarded.
Revision ID: d4f6a8c2e1b3
Revises: a1d3f5b7c9e2
Create Date: 2026-06-15
"""
from collections.abc import Sequence
from alembic import context, op
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "d4f6a8c2e1b3"
down_revision: str | Sequence[str] | None = "a1d3f5b7c9e2"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _pg_schema_prefix() -> str:
"""Schema-qualifier for raw SQL on PG (multi-tenant search_path)."""
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
def _pg_upgrade() -> None:
schema = _pg_schema_prefix()
op.execute(f"ALTER TABLE {schema}invalidated_memory_units DROP COLUMN IF EXISTS embedding")
def _pg_downgrade() -> None:
schema = _pg_schema_prefix()
# Unconstrained `vector` (no dimension) so the re-added column accepts any
# model's embeddings; it comes back empty regardless.
op.execute(f"ALTER TABLE {schema}invalidated_memory_units ADD COLUMN IF NOT EXISTS embedding vector")
def _oracle_upgrade() -> None:
# Oracle has no `DROP COLUMN IF EXISTS`; swallow ORA-00904 (column does not
# exist) so the migration is idempotent and safe on a fresh schema whose
# baseline already omits the column.
op.execute(
"""
BEGIN
EXECUTE IMMEDIATE 'ALTER TABLE invalidated_memory_units DROP COLUMN embedding';
EXCEPTION WHEN OTHERS THEN
IF SQLCODE != -904 THEN RAISE; END IF;
END;
"""
)
def _oracle_downgrade() -> None:
# Swallow ORA-01430 (column already exists) for idempotency.
op.execute(
"""
BEGIN
EXECUTE IMMEDIATE 'ALTER TABLE invalidated_memory_units ADD (embedding VECTOR)';
EXCEPTION WHEN OTHERS THEN
IF SQLCODE != -1430 THEN RAISE; END IF;
END;
"""
)
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade, oracle=_oracle_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade, oracle=_oracle_downgrade)
@@ -6,11 +6,10 @@ Create Date: 2026-03-11
This migration:
1. Adds internal_id UUID column to banks (stable identifier for index naming)
2. For non-ScaNN backends, drops the global vector index (competes with
per-bank partial indexes)
3. For non-ScaNN backends, creates per-(bank_id, fact_type) partial vector
indexes for all existing banks using the configured vector extension
(HNSW for pgvector, DiskANN for pgvectorscale, vchordrq for vchord).
2. Drops the global vector index (competes with per-bank partial indexes)
3. Creates per-(bank_id, fact_type) partial vector indexes for all existing banks
using the configured vector extension (HNSW for pgvector, DiskANN for
pgvectorscale, vchordrq for vchord).
(new banks get indexes created at bank-creation time via bank_utils.create_bank_vector_indexes)
Why per-(bank, fact_type) indexes:
@@ -18,8 +17,6 @@ Why per-(bank, fact_type) indexes:
clause, because the idx_memory_units_bank_id B-tree index always wins at planning time.
- Per-(bank, fact_type) partial indexes have both predicates matching → planner selects them.
- The global vector index competes for larger partitions (world, observation) and must be dropped.
- AlloyDB ScaNN uses global vector indexes with filtered vector search instead
because empty or tiny per-bank indexes cannot be built safely.
"""
import os
@@ -42,30 +39,22 @@ _FACT_TYPES: dict[str, str] = {
}
def _configured_vector_extension() -> str:
ext = os.getenv("HINDSIGHT_API_VECTOR_EXTENSION", "pgvector").lower()
if ext not in {"pgvector", "pgvectorscale", "vchord", "scann"}:
raise ValueError(
f"Invalid HINDSIGHT_API_VECTOR_EXTENSION: {ext}. Must be 'pgvector', 'vchord', 'pgvectorscale', or 'scann'"
)
return ext
def _vector_index_using_clause(ext: str) -> str:
if ext == "pgvectorscale":
return "USING diskann (embedding vector_cosine_ops) WITH (num_neighbors = 50)"
if ext == "vchord":
return "USING vchordrq (embedding vector_cosine_ops)"
if ext == "scann":
return "USING scann (embedding cosine) WITH (mode = 'AUTO')"
return "USING hnsw (embedding vector_cosine_ops)"
def _get_schema_prefix() -> str:
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
def _vector_index_using_clause() -> str:
"""Return the USING clause based on the configured vector extension."""
ext = os.getenv("HINDSIGHT_API_VECTOR_EXTENSION", "pgvector").lower()
if ext == "pgvectorscale":
return "USING diskann (embedding vector_cosine_ops) WITH (num_neighbors = 50)"
elif ext == "vchord":
return "USING vchordrq (embedding vector_l2_ops)"
else:
return "USING hnsw (embedding vector_cosine_ops)"
def _pg_upgrade() -> None:
schema = _get_schema_prefix()
@@ -75,14 +64,6 @@ def _pg_upgrade() -> None:
)
op.execute(f"ALTER TABLE {schema}banks ADD CONSTRAINT banks_internal_id_unique UNIQUE (internal_id)")
ext = _configured_vector_extension()
if ext == "scann":
# ScaNN should keep/use a global vector index. Per-bank partial indexes
# are created while banks are empty and can fail AlloyDB's ScaNN build
# requirements, so this migration leaves vector index reconciliation to
# runtime ensure_vector_extension once enough rows exist.
return
# 2. Drop any fact_type-only partial indexes that may exist from prior migrations
# (bank_id B-tree always wins over them when bank_id is in the WHERE clause)
op.execute(f"DROP INDEX IF EXISTS {schema}idx_mu_emb_world")
@@ -98,7 +79,7 @@ def _pg_upgrade() -> None:
schema_name = context.config.get_main_option("target_schema")
table_ref = f'"{schema_name}".memory_units' if schema_name else "memory_units"
banks_ref = f'"{schema_name}".banks' if schema_name else "banks"
using_clause = _vector_index_using_clause(ext)
using_clause = _vector_index_using_clause()
rows = bind.execute(text(f"SELECT bank_id, internal_id FROM {banks_ref}")).fetchall() # noqa: S608
for row in rows:
@@ -128,7 +109,7 @@ def _pg_downgrade() -> None:
rows = bind.execute(text(f"SELECT internal_id FROM {banks_ref}")).fetchall() # noqa: S608
for row in rows:
internal_id = str(row[0]).replace("-", "")[:16]
for ft_short in _FACT_TYPES.values():
for ft_short in _HNSW_FACT_TYPES.values():
idx_name = f"idx_mu_emb_{ft_short}_{internal_id}"
bind.execute(text(f"DROP INDEX IF EXISTS {schema}{idx_name}"))
@@ -1,133 +0,0 @@
"""Drop indexes that are unused or redundant with composite indexes.
Code audit identified the following indexes as either dead (no code path
exercises them) or fully covered by composite indexes the planner already
prefers:
memory_links:
1. idx_memory_links_entity_covering — entity co-occurrence expansion was
rewritten to traverse unit_entities instead of memory_links, so no code
path filters memory_links on (link_type = 'entity').
2. idx_memory_links_from_unit — redundant. idx_memory_links_from_type_weight
(from_unit_id, link_type, weight DESC) leads with the same column and
answers every from_unit_id = X query.
3. idx_memory_links_to_unit — redundant. idx_memory_links_to_type_weight
(to_unit_id, link_type, weight DESC) leads with the same column.
4. idx_memory_links_link_type — no application query filters on link_type
alone; the composite indexes above serve every (from/to + link_type)
predicate.
entities:
5. idx_entities_canonical_name — superseded by
entities_canonical_name_lower_trgm_idx (case-insensitive lookups).
6. entities_canonical_name_trgm_idx — superseded by the lowercase variant
in migration 2eee35aa3cfc, but the original was never dropped on schemas
that ran the prior migration.
documents:
7. idx_documents_retain_params — GIN index on retain_params JSONB; no query
uses jsonb containment on this column.
8. idx_documents_content_hash — content-hash lookups happen on the chunks
table (chunks.content_hash, indexed separately).
unit_entities:
9. idx_unit_entities_entity — defensive drop. Migration h3i4j5k6l7m8 already
issues DROP INDEX IF EXISTS for this; this re-runs the drop idempotently
to cover any schema that missed the previous migration.
All drops use CONCURRENTLY + IF EXISTS so they neither block writers nor
fail on schemas where the index is already gone.
Revision ID: e1b2c3d4f5a6
Revises: p4q5r6s7t8u9
Create Date: 2026-05-26
"""
from collections.abc import Sequence
from alembic import context, op
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "e1b2c3d4f5a6"
down_revision: str | Sequence[str] | None = "p4q5r6s7t8u9"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
_PG_INDEXES_TO_DROP: tuple[str, ...] = (
"idx_memory_links_entity_covering",
"idx_memory_links_from_unit",
"idx_memory_links_to_unit",
"idx_memory_links_link_type",
"idx_entities_canonical_name",
"entities_canonical_name_trgm_idx",
"idx_documents_retain_params",
"idx_documents_content_hash",
"idx_unit_entities_entity",
)
def _schema_prefix() -> str:
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
def _pg_upgrade() -> None:
schema = _schema_prefix()
# DROP INDEX CONCURRENTLY cannot run inside a transaction block; an
# autocommit_block drops out of Alembic's migration transaction so each
# statement runs in its own autocommit. IF EXISTS makes each statement
# idempotent across schemas that already dropped (or never had) the index.
with op.get_context().autocommit_block():
for index_name in _PG_INDEXES_TO_DROP:
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}{index_name}")
def _pg_downgrade() -> None:
schema = _schema_prefix()
# Recreate the dropped indexes in the same shape the prior migrations used,
# so a downgrade leaves the schema in the state the previous head expected.
# CREATE INDEX CONCURRENTLY cannot run inside a transaction block.
with op.get_context().autocommit_block():
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_links_entity_covering "
f"ON {schema}memory_links(from_unit_id) "
f"INCLUDE (to_unit_id, entity_id) "
f"WHERE link_type = 'entity'"
)
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_links_from_unit ON {schema}memory_links(from_unit_id)"
)
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_links_to_unit ON {schema}memory_links(to_unit_id)"
)
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_links_link_type ON {schema}memory_links(link_type)"
)
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_entities_canonical_name ON {schema}entities(canonical_name)"
)
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS entities_canonical_name_trgm_idx "
f"ON {schema}entities USING GIN (canonical_name gin_trgm_ops)"
)
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_documents_retain_params "
f"ON {schema}documents USING GIN (retain_params)"
)
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_documents_content_hash ON {schema}documents(content_hash)"
)
op.execute(
f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_unit_entities_entity ON {schema}unit_entities(entity_id)"
)
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade)
@@ -1,39 +0,0 @@
"""Merge two divergent migration heads.
``d4f6a8c2e1b3`` (drop the curation-archive embedding column) and
``2071c7518f88`` (add the memory_links(bank_id, link_type) index) were authored
in parallel off the same parent (``a1d3f5b7c9e2``) and merged independently,
leaving the DAG with two heads. This is a no-op merge that re-unifies them so
``alembic upgrade head`` is unambiguous again (enforced by
``tests/test_alembic_dag.py::test_single_head``).
Revision ID: e1f2a3b4c5d6
Revises: d4f6a8c2e1b3, 2071c7518f88
Create Date: 2026-06-16
"""
from collections.abc import Sequence
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "e1f2a3b4c5d6"
down_revision: str | Sequence[str] | None = ("d4f6a8c2e1b3", "2071c7518f88")
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _pg_upgrade() -> None:
# Pure DAG merge — both parents already applied their schema changes.
pass
def _pg_downgrade() -> None:
pass
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade)
@@ -1,153 +0,0 @@
"""Add server-side routines for background maintenance sweeps.
Installs two PL/pgSQL discovery routines in the ``public`` schema. Both loop
over every schema that actually holds the relevant table (via ``pg_class``), so
a single function call covers all tenants in one round-trip instead of the
per-tenant query storm that a client-side loop would create at thousands of
tenants.
- ``public.banks_needing_consolidation()`` -> (schema_name, bank_id) for banks
that have eligible-but-unscheduled facts (``consolidated_at IS NULL AND
consolidation_failed_at IS NULL`` for consolidatable fact types), have
auto-consolidation not explicitly disabled at the bank level, and have no
consolidation operation already pending/processing. Drives the periodic
reconcile that re-schedules consolidation after a terminal failure left facts
stranded (see HINDSIGHT_API_CONSOLIDATION_RECONCILE_INTERVAL_SECONDS).
- ``public.schemas_with_expired_rows(p_table, p_ts_col, p_days)`` -> schema
names that hold at least one ``p_table`` row older than ``p_days``. Drives the
cross-tenant retention sweeps for ``audit_log`` and ``llm_requests``; the loop
then issues a DELETE only against the returned schemas.
These are read-only (STABLE) discovery routines — the caller performs the
enqueue/DELETE — so installing them never mutates data.
PostgreSQL only — the worker poller and these tables are not wired for Oracle,
so the Oracle slot is intentionally absent (mirrors the audit_log / llm_requests
table migrations). The routines live in ``public`` and are CREATE OR REPLACE, so
running this migration once per tenant schema is idempotent.
Revision ID: e5f6a7b8c9d0
Revises: a7b8c9d0e1f2
Create Date: 2026-06-05
"""
from collections.abc import Sequence
from alembic import context, op
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "e5f6a7b8c9d0"
down_revision: str | Sequence[str] | None = "a7b8c9d0e1f2"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _is_base_schema_run() -> bool:
"""True only for the base-schema migration (no per-tenant target_schema).
These routines live in the shared ``public`` schema, so they must be created
exactly once. Running ``CREATE OR REPLACE FUNCTION public....`` again from each
concurrent per-tenant migration aborts with ``tuple concurrently updated`` on
the ``pg_proc`` catalog row, so tenant runs skip it (the base run already
created the function for every tenant to use).
"""
return not context.config.get_main_option("target_schema")
def _pg_upgrade() -> None:
if not _is_base_schema_run():
return
# Banks with eligible-but-unscheduled facts and no in-flight consolidation.
# Auto-consolidation is filtered here only at the bank level (cheap prune);
# the full hierarchical resolution (global -> tenant -> bank, plus
# enable_observations) is done by the caller for the small returned set.
op.execute(
"""
CREATE OR REPLACE FUNCTION public.banks_needing_consolidation()
RETURNS TABLE(schema_name text, bank_id text)
LANGUAGE plpgsql STABLE
AS $fn$
DECLARE
sch text;
BEGIN
FOR sch IN
SELECT n.nspname
FROM pg_class c
JOIN pg_namespace n ON n.oid = c.relnamespace
WHERE c.relname = 'memory_units' AND c.relkind = 'r'
LOOP
RETURN QUERY EXECUTE format($q$
SELECT %1$L::text, m.bank_id
FROM %1$I.memory_units m
JOIN %1$I.banks b ON b.bank_id = m.bank_id
WHERE m.consolidated_at IS NULL
AND m.consolidation_failed_at IS NULL
AND m.fact_type IN ('experience', 'world')
AND COALESCE(b.config -> 'enable_auto_consolidation', 'true'::jsonb) <> 'false'::jsonb
AND NOT EXISTS (
SELECT 1 FROM %1$I.async_operations o
WHERE o.bank_id = m.bank_id
AND o.operation_type = 'consolidation'
AND o.status IN ('pending', 'processing')
)
GROUP BY m.bank_id
$q$, sch);
END LOOP;
END;
$fn$;
"""
)
# Schemas holding at least one row of p_table older than p_days. p_ts_col is
# the timestamp column to compare. Returns nothing when p_days <= 0
# (retention disabled).
op.execute(
"""
CREATE OR REPLACE FUNCTION public.schemas_with_expired_rows(
p_table text, p_ts_col text, p_days int
)
RETURNS SETOF text
LANGUAGE plpgsql STABLE
AS $fn$
DECLARE
sch text;
has_expired boolean;
BEGIN
IF p_days IS NULL OR p_days <= 0 THEN
RETURN;
END IF;
FOR sch IN
SELECT n.nspname
FROM pg_class c
JOIN pg_namespace n ON n.oid = c.relnamespace
WHERE c.relname = p_table AND c.relkind = 'r'
LOOP
EXECUTE format(
'SELECT EXISTS (SELECT 1 FROM %I.%I WHERE %I < NOW() - make_interval(days => $1))',
sch, p_table, p_ts_col
) INTO has_expired USING p_days;
IF has_expired THEN
RETURN NEXT sch;
END IF;
END LOOP;
END;
$fn$;
"""
)
def _pg_downgrade() -> None:
if not _is_base_schema_run():
return
op.execute("DROP FUNCTION IF EXISTS public.banks_needing_consolidation()")
op.execute("DROP FUNCTION IF EXISTS public.schemas_with_expired_rows(text, text, int)")
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade)
@@ -1,91 +0,0 @@
"""Drop materialized entity rows from memory_links.
Entity edges are no longer stored in ``memory_links``. The /graph endpoint
derives them on demand from ``unit_entities``, and recall already used the
``unit_entities`` self-join. Storing entity rows duplicated state we never
read from the link table — on a 10k-unit benchmark bank, entity rows were
53% of all link rows (~190 MB after indexes) and recall never touched them.
This migration deletes ``memory_links`` rows with ``link_type = 'entity'``.
``idx_memory_links_entity_covering`` was already dropped by migration
``e1b2c3d4f5a6``; we still issue ``DROP INDEX IF EXISTS`` defensively in case
this migration runs against an older snapshot that predates that one.
Revision ID: e9b2c7d1f3a4
Revises: e1b2c3d4f5a6
Create Date: 2026-05-26
"""
from collections.abc import Sequence
from alembic import context, op
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "e9b2c7d1f3a4"
down_revision: str | Sequence[str] | None = "e1b2c3d4f5a6"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _pg_schema_prefix() -> str:
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
def _pg_upgrade() -> None:
schema = _pg_schema_prefix()
# Drop the partial covering index first so the bulk DELETE doesn't churn it.
# DROP INDEX CONCURRENTLY, and the DO block's per-batch COMMIT, both require
# running outside Alembic's migration transaction — an autocommit_block
# commits it and switches the connection to autocommit for the duration.
with op.get_context().autocommit_block():
op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_links_entity_covering")
# Delete entity rows. Chunked to keep individual transactions small on
# large banks (the perf-medium bench had ~345k entity rows; production
# banks can be much larger).
op.execute(
f"""
DO $$
DECLARE
deleted INTEGER;
BEGIN
LOOP
DELETE FROM {schema}memory_links
WHERE ctid IN (
SELECT ctid FROM {schema}memory_links
WHERE link_type = 'entity'
LIMIT 50000
);
GET DIAGNOSTICS deleted = ROW_COUNT;
EXIT WHEN deleted = 0;
COMMIT;
END LOOP;
END$$;
"""
)
def _pg_downgrade() -> None:
# Cannot reconstruct deleted entity links — the writer was path-dependent
# on retain order. New retains will not produce entity rows either, so the
# partial index would stay empty. Leave both no-op.
pass
def _oracle_upgrade() -> None:
op.execute("DELETE FROM memory_links WHERE link_type = 'entity'")
def _oracle_downgrade() -> None:
pass
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade, oracle=_oracle_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade, oracle=_oracle_downgrade)
@@ -1,110 +0,0 @@
"""Add server-side routine for cron-scheduled mental model refresh.
Installs ``public.mental_models_with_cron()`` — a discovery routine that returns
every mental model carrying a non-empty ``trigger->>'refresh_cron'`` across all
tenant schemas in one round-trip (the same per-schema scan as the other
maintenance routines from ``e5f6a7b8c9d0``). The maintenance loop evaluates each
candidate's cron expression in Python (``croniter``) against ``last_refreshed_at``
to decide whether a scheduled refresh is due — cron arithmetic isn't expressible
in plain SQL — and only the cron *candidate set* is discovered here.
Models that already have a ``refresh_mental_model`` operation pending/processing
are excluded so a slow refresh isn't double-queued (mirrors the in-flight guard
in ``banks_needing_consolidation``). Each per-schema query runs in its own
``BEGIN ... EXCEPTION`` subtransaction so a schema dropped mid-scan (tenant
deletion / migration) is skipped, not fatal — same resilience as
``c7e9f1a3b5d2``.
Read-only (STABLE) discovery routine — the caller performs the refresh enqueue —
so installing it never mutates data. PostgreSQL only: the worker poller and the
maintenance loop are PG-only (Oracle slot intentionally absent, mirroring
``e5f6a7b8c9d0``). The routine lives in ``public`` and is CREATE OR REPLACE, so
it is installed exactly once (base / ``public`` run) to avoid the
``tuple concurrently updated`` race on concurrent per-tenant runs.
Revision ID: f4d1c2b3a5e6
Revises: c7e9f1a3b5d2
Create Date: 2026-06-23
"""
from collections.abc import Sequence
from alembic import context, op
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "f4d1c2b3a5e6"
down_revision: str | Sequence[str] | None = "c7e9f1a3b5d2"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _should_install_public_routines(target_schema: str | None) -> bool:
"""True for the run that must (re)create the shared ``public.*`` routine.
The routine physically lives in ``public``, so it is installed exactly once —
on the base run (no ``target_schema``) or the run that explicitly targets
``public``. Mirrors ``c7e9f1a3b5d2``.
"""
return not target_schema or target_schema == "public"
def _pg_upgrade() -> None:
if not _should_install_public_routines(context.config.get_main_option("target_schema")):
return
op.execute(
"""
CREATE OR REPLACE FUNCTION public.mental_models_with_cron()
RETURNS TABLE(schema_name text, bank_id text, mental_model_id text,
refresh_cron text, last_refreshed_at timestamptz)
LANGUAGE plpgsql STABLE
AS $fn$
DECLARE
sch text;
BEGIN
FOR sch IN
SELECT n.nspname
FROM pg_class c
JOIN pg_namespace n ON n.oid = c.relnamespace
WHERE c.relname = 'mental_models' AND c.relkind = 'r'
LOOP
BEGIN
RETURN QUERY EXECUTE format($q$
SELECT %1$L::text, mm.bank_id::text, mm.id::text,
mm.trigger->>'refresh_cron', mm.last_refreshed_at
FROM %1$I.mental_models mm
WHERE COALESCE(mm.trigger->>'refresh_cron', '') <> ''
AND NOT EXISTS (
SELECT 1 FROM %1$I.async_operations o
WHERE o.bank_id = mm.bank_id
AND o.operation_type = 'refresh_mental_model'
AND o.status IN ('pending', 'processing')
AND o.task_payload->>'mental_model_id' = mm.id::text
)
$q$, sch);
EXCEPTION
-- Schema or its tables vanished between the pg_class
-- snapshot and this query (tenant dropped or migrating).
WHEN undefined_table OR invalid_schema_name OR undefined_column THEN
CONTINUE;
END;
END LOOP;
END;
$fn$;
"""
)
def _pg_downgrade() -> None:
if not _should_install_public_routines(context.config.get_main_option("target_schema")):
return
op.execute("DROP FUNCTION IF EXISTS public.mental_models_with_cron()")
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade)
@@ -1,31 +0,0 @@
"""Merge divergent heads from deferrable FK and cooccurrence backfill
Revision ID: m3rg3h3ad5f6
Revises: 9f8e7d6c5b4a, b5d4e3f2a1c9
Create Date: 2026-05-04
"""
from collections.abc import Sequence
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "m3rg3h3ad5f6"
down_revision: tuple[str, ...] = ("9f8e7d6c5b4a", "b5d4e3f2a1c9")
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def _pg_upgrade() -> None:
pass
def _pg_downgrade() -> None:
pass
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade)
@@ -16,11 +16,6 @@ from collections.abc import Sequence
from alembic import context, op
from sqlalchemy import text
from hindsight_api._pg_search import (
PG_SEARCH_TOKENIZER_ENV,
normalize_pg_search_tokenizer,
pg_search_bm25_columns,
)
from hindsight_api.alembic._dialect import run_for_dialect
# revision identifiers, used by Alembic.
@@ -37,78 +32,60 @@ def _get_schema_prefix() -> str:
def _detect_vector_extension() -> str:
"""Detect or validate vector extension for this immutable migration revision."""
"""
Detect or validate vector extension: 'pgvector', 'vchord', or 'pgvectorscale'.
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 == "pgvectorscale":
# pgvectorscale/DiskANN requires pgvector
pgvector_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vector'")).scalar()
if not pgvector_check:
raise RuntimeError(
"DiskANN requires pgvector. Install with: CREATE EXTENSION vector; then vectorscale or pg_diskann CASCADE;"
)
# Check for either vectorscale (open source) or pg_diskann (Azure)
vectorscale_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vectorscale'")).scalar()
pg_diskann_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'pg_diskann'")).scalar()
if vectorscale_check:
return "pgvectorscale"
if pg_diskann_check:
elif pg_diskann_check:
return "pg_diskann"
raise RuntimeError(
"Configured vector extension 'pgvectorscale' not found. Install either:\n"
" - pgvectorscale: CREATE EXTENSION vectorscale CASCADE;\n"
" - pg_diskann (Azure): CREATE EXTENSION pg_diskann CASCADE;"
)
if vector_extension == "vchord":
else:
raise RuntimeError(
"Configured vector extension 'pgvectorscale' not found. Install either:\n"
" - pgvectorscale: CREATE EXTENSION vectorscale CASCADE;\n"
" - pg_diskann (Azure): CREATE EXTENSION pg_diskann CASCADE;"
)
elif 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"
if vector_extension == "scann":
scann_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'alloydb_scann'")).scalar()
if not scann_check:
raise RuntimeError(
"Configured vector extension 'scann' not found. Install it with: CREATE EXTENSION alloydb_scann CASCADE;"
)
return "scann"
if vector_extension == "pgvector":
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"
raise ValueError(
"Invalid HINDSIGHT_API_VECTOR_EXTENSION: "
f"{vector_extension}. Must be 'pgvector', 'vchord', 'pgvectorscale', or 'scann'"
)
def _vector_index_using_clause(ext: str) -> str:
if ext == "pgvectorscale":
return "USING diskann (embedding vector_cosine_ops) WITH (num_neighbors = 50)"
if ext == "pg_diskann":
return "USING diskann (embedding vector_cosine_ops) WITH (max_neighbors = 50)"
if ext == "vchord":
return "USING vchordrq (embedding vector_cosine_ops)"
if ext == "scann":
return "USING scann (embedding cosine) WITH (mode = 'AUTO')"
return "USING hnsw (embedding vector_cosine_ops)"
else:
raise ValueError(
f"Invalid HINDSIGHT_API_VECTOR_EXTENSION: {vector_extension}. Must be 'pgvector', 'vchord', or 'pgvectorscale'"
)
def _detect_text_search_extension() -> str:
"""
Detect or validate text search extension: 'native', 'vchord', 'pg_textsearch',
'pgroonga', or 'pg_search'. Respects HINDSIGHT_API_TEXT_SEARCH_EXTENSION env var.
Detect or validate text search extension: 'native', 'vchord', or 'pg_textsearch'.
Respects HINDSIGHT_API_TEXT_SEARCH_EXTENSION env var.
Creates the extension if needed.
pgroonga is treated as native here so this migration still creates valid
tsvector columns; ensure_text_search_extension() at startup converts the
reflections table (renamed from pinned_reflections in p1k2l3m4n5o6) to
pgroonga structures. The learnings table is dropped in p1k2l3m4n5o6 so its
transient native-style column never reaches steady state.
"""
text_search_extension = os.getenv("HINDSIGHT_API_TEXT_SEARCH_EXTENSION", "native").lower()
@@ -136,33 +113,14 @@ def _detect_text_search_extension() -> str:
# Extension truly doesn't exist - re-raise the error
raise
return "pg_textsearch"
elif text_search_extension == "pg_search":
# ParadeDB pg_search — true BM25 over base columns, Citus-compatible.
try:
op.execute("CREATE EXTENSION IF NOT EXISTS pg_search CASCADE")
except Exception:
conn = op.get_bind()
result = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'pg_search'")).fetchone()
if not result:
raise
return "pg_search"
elif text_search_extension == "native":
return "native"
elif text_search_extension == "pgroonga":
# Treat as native here; ensure_text_search_extension() converts the
# reflections table to pgroonga structures at runtime.
return "native"
else:
raise ValueError(
f"Invalid HINDSIGHT_API_TEXT_SEARCH_EXTENSION: {text_search_extension}. "
"Must be 'native', 'vchord', 'pg_textsearch', 'pgroonga', or 'pg_search'"
f"Invalid HINDSIGHT_API_TEXT_SEARCH_EXTENSION: {text_search_extension}. Must be 'native', 'vchord', or 'pg_textsearch'"
)
def _pg_search_tokenizer() -> str:
return normalize_pg_search_tokenizer(os.getenv(PG_SEARCH_TOKENIZER_ENV))
def _pg_upgrade() -> None:
"""Create learnings and pinned_reflections tables."""
schema = _get_schema_prefix()
@@ -200,12 +158,28 @@ def _pg_upgrade() -> None:
# Indexes for learnings
op.execute(f"CREATE INDEX idx_learnings_bank_id ON {schema}learnings(bank_id)")
# Create vector index based on detected extension. ScaNN is deferred because
# this table is empty during migration and AlloyDB rejects empty ScaNN builds.
if vector_ext != "scann":
# Create vector index based on detected extension
if vector_ext == "pgvectorscale":
op.execute(f"""
CREATE INDEX idx_learnings_embedding ON {schema}learnings
{_vector_index_using_clause(vector_ext)}
USING diskann (embedding vector_cosine_ops)
WITH (num_neighbors = 50)
""")
elif vector_ext == "pg_diskann":
op.execute(f"""
CREATE INDEX idx_learnings_embedding ON {schema}learnings
USING diskann (embedding vector_cosine_ops)
WITH (max_neighbors = 50)
""")
elif 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)")
@@ -230,18 +204,6 @@ def _pg_upgrade() -> None:
CREATE INDEX idx_learnings_text_search ON {schema}learnings
USING bm25(text) WITH (text_config='english')
""")
elif text_search_ext == "pg_search":
# ParadeDB pg_search: dummy TEXT column; BM25 index is built directly over (id, text)
# with key_field='id' (matches the table's primary key).
bm25_cols = pg_search_bm25_columns("id", ("text",), _pg_search_tokenizer())
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 ({bm25_cols})
WITH (key_field='id')
""")
else: # native
# Native PostgreSQL: tsvector with automatic generation
op.execute(f"""
@@ -275,12 +237,28 @@ def _pg_upgrade() -> None:
# Indexes for pinned_reflections
op.execute(f"CREATE INDEX idx_pinned_reflections_bank_id ON {schema}pinned_reflections(bank_id)")
# Create vector index based on detected extension. ScaNN is deferred because
# this table is empty during migration and AlloyDB rejects empty ScaNN builds.
if vector_ext != "scann":
# Create vector index based on detected extension
if vector_ext == "pgvectorscale":
op.execute(f"""
CREATE INDEX idx_pinned_reflections_embedding ON {schema}pinned_reflections
{_vector_index_using_clause(vector_ext)}
USING diskann (embedding vector_cosine_ops)
WITH (num_neighbors = 50)
""")
elif vector_ext == "pg_diskann":
op.execute(f"""
CREATE INDEX idx_pinned_reflections_embedding ON {schema}pinned_reflections
USING diskann (embedding vector_cosine_ops)
WITH (max_neighbors = 50)
""")
elif 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)")
@@ -306,18 +284,6 @@ def _pg_upgrade() -> None:
USING bm25(content)
WITH (text_config='english')
""")
elif text_search_ext == "pg_search":
# ParadeDB pg_search: dummy TEXT column; BM25 index over (id, name, content)
# with key_field='id'.
bm25_cols = pg_search_bm25_columns("id", ("name", "content"), _pg_search_tokenizer())
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 ({bm25_cols})
WITH (key_field='id')
""")
else: # native
# Native PostgreSQL: tsvector with automatic generation
op.execute(f"""
@@ -122,7 +122,6 @@ _TABLES: tuple[str, ...] = (
text_signals CLOB,
consolidation_failed_at TIMESTAMP WITH TIME ZONE,
search_vector CLOB,
edited_at TIMESTAMP WITH TIME ZONE,
created_at TIMESTAMP WITH TIME ZONE DEFAULT SYSTIMESTAMP NOT NULL,
updated_at TIMESTAMP WITH TIME ZONE DEFAULT SYSTIMESTAMP NOT NULL,
CONSTRAINT pk_memory_units PRIMARY KEY (id),
@@ -139,50 +138,6 @@ _TABLES: tuple[str, ...] = (
PARTITION BY LIST (bank_id) AUTOMATIC
(PARTITION p_default VALUES ('__default__'))
""",
# Cold archive for curation: invalidated facts are MOVED here out of
# memory_units so the recall hot-path never sees them. Mirrors memory_units
# plus invalidation bookkeeping and an entity-id snapshot for lossless revert.
# No `embedding` column: the archive is cold storage and revert recomputes the
# embedding, so there is no archive vector to fall out of sync with the live
# model's dimension on a model switch (#2209).
"""
CREATE TABLE IF NOT EXISTS invalidated_memory_units (
id RAW(16) NOT NULL,
bank_id VARCHAR2(256) NOT NULL,
document_id VARCHAR2(512),
chunk_id VARCHAR2(512),
text CLOB NOT NULL,
context CLOB,
event_date TIMESTAMP WITH TIME ZONE NOT NULL,
occurred_start TIMESTAMP WITH TIME ZONE,
occurred_end TIMESTAMP WITH TIME ZONE,
mentioned_at TIMESTAMP WITH TIME ZONE,
fact_type VARCHAR2(64) DEFAULT 'world' NOT NULL,
confidence_score BINARY_DOUBLE,
access_count NUMBER(10) DEFAULT 0 NOT NULL,
consolidated_at TIMESTAMP WITH TIME ZONE,
observation_scopes CLOB CONSTRAINT imu_obs_scopes_json CHECK (observation_scopes IS JSON OR observation_scopes IS NULL),
tags CLOB DEFAULT '[]' NOT NULL,
metadata CLOB DEFAULT '{}' NOT NULL
CONSTRAINT imu_metadata_json CHECK (metadata IS JSON),
proof_count NUMBER(10) DEFAULT 1,
source_memory_ids CLOB,
history CLOB DEFAULT '[]'
CONSTRAINT imu_history_json CHECK (history IS JSON OR history IS NULL),
text_signals CLOB,
consolidation_failed_at TIMESTAMP WITH TIME ZONE,
search_vector CLOB,
edited_at TIMESTAMP WITH TIME ZONE,
created_at TIMESTAMP WITH TIME ZONE DEFAULT SYSTIMESTAMP NOT NULL,
updated_at TIMESTAMP WITH TIME ZONE DEFAULT SYSTIMESTAMP NOT NULL,
invalidation_reason CLOB,
invalidated_at TIMESTAMP WITH TIME ZONE DEFAULT SYSTIMESTAMP,
entity_ids CLOB CONSTRAINT imu_entity_ids_json CHECK (entity_ids IS JSON OR entity_ids IS NULL),
CONSTRAINT pk_invalidated_memory_units PRIMARY KEY (id),
CONSTRAINT fk_imu_document FOREIGN KEY (document_id, bank_id)
REFERENCES documents(id, bank_id) ON DELETE CASCADE
)
""",
"""
CREATE TABLE IF NOT EXISTS entities (
id RAW(16) DEFAULT SYS_GUID() NOT NULL,
@@ -264,7 +219,7 @@ _TABLES: tuple[str, ...] = (
CONSTRAINT pk_mental_models PRIMARY KEY (id, bank_id),
CONSTRAINT fk_mm_bank FOREIGN KEY (bank_id) REFERENCES banks(bank_id) ON DELETE CASCADE,
CONSTRAINT fk_mm_entity FOREIGN KEY (entity_id) REFERENCES entities(id) ON DELETE SET NULL,
CONSTRAINT chk_mm_subtype CHECK (subtype IN ('directive', 'pinned'))
CONSTRAINT chk_mm_subtype CHECK (subtype IN ('structural', 'emergent', 'pinned', 'learned'))
)
""",
"""
@@ -301,7 +256,7 @@ _TABLES: tuple[str, ...] = (
completed_at TIMESTAMP WITH TIME ZONE,
CONSTRAINT pk_async_operations PRIMARY KEY (operation_id),
CONSTRAINT fk_ao_bank FOREIGN KEY (bank_id) REFERENCES banks(bank_id) ON DELETE CASCADE,
CONSTRAINT chk_ao_status CHECK (status IN ('pending', 'processing', 'completed', 'failed', 'cancelled'))
CONSTRAINT chk_ao_status CHECK (status IN ('pending', 'processing', 'completed', 'failed'))
)
""",
"""
@@ -1,170 +0,0 @@
"""Drop GENERATED expression on tsvector search_vector columns.
The search_vector tsvector column was originally GENERATED ALWAYS with a
hardcoded ``to_tsvector('english', ...)`` expression. To support configurable
``HINDSIGHT_API_TEXT_SEARCH_EXTENSION_NATIVE_LANGUAGE``, we convert it to a
regular tsvector column that the application populates at INSERT time via
``to_tsvector($lang, ...)``.
Existing rows retain their English-derived lexemes — switching the configured
language only affects newly-written rows. Users who need to backfill existing
rows in a different language can run an admin UPDATE after this migration.
Only the ``native`` text-search backend is affected. ``vchord``, ``pg_textsearch``,
and ``pgroonga`` use other column types or no column at all.
Revision ID: p4q5r6s7t8u9
Revises: 86f7a033d372
Create Date: 2026-05-08
"""
from collections.abc import Sequence
from dataclasses import dataclass
from alembic import context, op
from sqlalchemy import Connection, text
from hindsight_api.alembic._dialect import run_for_dialect
revision: str = "p4q5r6s7t8u9"
down_revision: str | Sequence[str] | None = "86f7a033d372"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
@dataclass(frozen=True)
class _TsvectorTableSpec:
"""Native-backend tsvector table targeted by this migration.
``upgrade`` is a one-way DROP EXPRESSION; ``downgrade`` re-attaches the
original GENERATED expression so the schema returns to the state created
by the initial migration (and a2b3c4d5e6f7_add_text_signals_column for
memory_units).
"""
table: str
generated_expression: str
# Tables that may have a GENERATED tsvector ``search_vector`` column under the
# native backend. Note: the ``learnings`` table was dropped in
# p1k2l3m4n5o6_new_knowledge_architecture and ``pinned_reflections`` was renamed
# to ``reflections`` in the same migration.
_NATIVE_TSVECTOR_TABLES: tuple[_TsvectorTableSpec, ...] = (
_TsvectorTableSpec(
table="memory_units",
generated_expression=(
"to_tsvector('english', COALESCE(text, '') || ' ' || "
"COALESCE(context, '') || ' ' || COALESCE(text_signals, ''))"
),
),
_TsvectorTableSpec(
table="reflections",
generated_expression="to_tsvector('english', COALESCE(name, '') || ' ' || content)",
),
)
def _schema_prefix() -> str:
schema = context.config.get_main_option("target_schema")
return f'"{schema}".' if schema else ""
def _is_generated_tsvector(conn: Connection, schema: str, table: str) -> bool:
"""Return True iff ``schema.table.search_vector`` is a GENERATED tsvector column."""
row = conn.execute(
text(
"""
SELECT is_generated, udt_name
FROM information_schema.columns
WHERE table_schema = :schema
AND table_name = :table
AND column_name = 'search_vector'
"""
),
{"schema": schema, "table": table},
).fetchone()
if not row:
return False
is_generated, udt_name = row[0], row[1]
return is_generated == "ALWAYS" and udt_name == "tsvector"
def _is_regular_tsvector(conn: Connection, schema: str, table: str) -> bool:
"""Return True iff ``schema.table.search_vector`` is a non-generated tsvector column."""
row = conn.execute(
text(
"""
SELECT is_generated, udt_name
FROM information_schema.columns
WHERE table_schema = :schema
AND table_name = :table
AND column_name = 'search_vector'
"""
),
{"schema": schema, "table": table},
).fetchone()
if not row:
return False
is_generated, udt_name = row[0], row[1]
return udt_name == "tsvector" and is_generated != "ALWAYS"
def _table_exists(conn: Connection, schema: str, table: str) -> bool:
return bool(
conn.execute(
text(
"""
SELECT 1 FROM information_schema.tables
WHERE table_schema = :schema AND table_name = :table
"""
),
{"schema": schema, "table": table},
).fetchone()
)
def _pg_upgrade() -> None:
schema_prefix = _schema_prefix()
schema_name = (context.config.get_main_option("target_schema") or "public").strip('"')
conn = op.get_bind()
for spec in _NATIVE_TSVECTOR_TABLES:
if not _table_exists(conn, schema_name, spec.table):
continue
if not _is_generated_tsvector(conn, schema_name, spec.table):
# Either the column doesn't exist (non-native backend) or it's
# already a regular tsvector — nothing to do.
continue
op.execute(f"ALTER TABLE {schema_prefix}{spec.table} ALTER COLUMN search_vector DROP EXPRESSION")
def _pg_downgrade() -> None:
schema_prefix = _schema_prefix()
schema_name = (context.config.get_main_option("target_schema") or "public").strip('"')
conn = op.get_bind()
for spec in _NATIVE_TSVECTOR_TABLES:
if not _table_exists(conn, schema_name, spec.table):
continue
# Only restore the GENERATED expression if a non-generated tsvector
# column exists — otherwise the table is on a different backend.
if not _is_regular_tsvector(conn, schema_name, spec.table):
continue
# Drop and recreate to re-attach the GENERATED expression. Index will be
# recreated by re-running ensure_text_search_extension on next startup.
op.execute(f"DROP INDEX IF EXISTS {schema_prefix}idx_{spec.table}_text_search")
op.execute(f"ALTER TABLE {schema_prefix}{spec.table} DROP COLUMN search_vector")
op.execute(
f"ALTER TABLE {schema_prefix}{spec.table} "
f"ADD COLUMN search_vector tsvector GENERATED ALWAYS AS ({spec.generated_expression}) STORED"
)
op.execute(f"CREATE INDEX idx_{spec.table}_text_search ON {schema_prefix}{spec.table} USING gin(search_vector)")
def upgrade() -> None:
run_for_dialect(pg=_pg_upgrade)
def downgrade() -> None:
run_for_dialect(pg=_pg_downgrade)
@@ -16,7 +16,9 @@ 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
from hindsight_api.alembic._dialect import run_for_dialect
@@ -1,97 +0,0 @@
"""Client-disconnect detection that works behind ``BaseHTTPMiddleware``.
``Request.is_disconnected()`` is the obvious way to notice an abandoned HTTP
request, but it is silently broken once any ``@app.middleware("http")``
(Starlette ``BaseHTTPMiddleware``) is installed: that middleware runs the route
in a child task behind anyio memory streams, so the ``http.disconnect`` ASGI
event never reaches the route's ``Request``. This app has such middlewares, so
the recall/reflect cancellation in #2122/#2127 never actually fired in
production — the disconnect was never observed.
This pure-ASGI middleware sits *outside* the ``BaseHTTPMiddleware`` layer, where
it still owns the real ``receive`` channel. For the recall and reflect routes it
drains ``receive`` in a background task and trips a :class:`CancellationToken`
the moment ``http.disconnect`` arrives, stashing the token on the ASGI ``scope``.
The route copies that token onto its ``RequestContext`` and the engine checks it
at stage boundaries — so abandoned work stops instead of running to completion.
It only wraps recall/reflect (small JSON bodies); every other request — uploads,
MCP streams, etc. — passes straight through untouched, so there is no buffering
or latency cost elsewhere.
"""
from __future__ import annotations
import asyncio
import contextlib
from collections.abc import Awaitable, Callable, MutableMapping
from typing import Any
from ..cancellation import CancellationToken
# Key under which the per-request CancellationToken is stored on the ASGI scope.
# A dedicated top-level scope key (not scope["state"]) avoids any interaction
# with Starlette's per-request state copying.
SCOPE_CANCELLATION_TOKEN = "hindsight.cancellation_token"
_CLIENT_DISCONNECTED_REASON = "client disconnected"
Scope = MutableMapping[str, Any]
Receive = Callable[[], Awaitable[MutableMapping[str, Any]]]
Send = Callable[[MutableMapping[str, Any]], Awaitable[None]]
def _should_monitor(path: str) -> bool:
"""Only the two long-running, abandon-prone read endpoints need monitoring."""
return path.endswith("/memories/recall") or path.endswith("/reflect")
class ClientDisconnectCancellationMiddleware:
"""Trip a scope-level CancellationToken when the client disconnects.
Must be installed *outside* any ``BaseHTTPMiddleware`` so it owns the real
ASGI ``receive`` channel.
"""
def __init__(self, app: Callable) -> None:
self.app = app
async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None:
if scope["type"] != "http" or not _should_monitor(scope.get("path", "")):
await self.app(scope, receive, send)
return
token = CancellationToken()
scope[SCOPE_CANCELLATION_TOKEN] = token
# The downstream app still needs to read the request body, so we cannot
# simply consume `receive` ourselves. Instead a single pump task drains
# the real channel, forwards every message to a queue the app reads from,
# and trips the token the instant `http.disconnect` shows up — which the
# app would otherwise never pull once it has finished reading the body.
queue: asyncio.Queue = asyncio.Queue()
async def pump() -> None:
while True:
message = await receive()
if message["type"] == "http.disconnect":
token.cancel(_CLIENT_DISCONNECTED_REASON)
await queue.put(message)
return
await queue.put(message)
async def proxied_receive() -> MutableMapping[str, Any]:
return await queue.get()
pump_task = asyncio.create_task(pump())
try:
await self.app(scope, proxied_receive, send)
finally:
pump_task.cancel()
with contextlib.suppress(asyncio.CancelledError):
await pump_task
def get_scope_cancellation_token(scope: Scope) -> CancellationToken | None:
"""Return the CancellationToken the middleware attached, if any."""
return scope.get(SCOPE_CANCELLATION_TOKEN)
File diff suppressed because it is too large Load Diff
+1 -23
View File
@@ -9,7 +9,7 @@ from fastmcp import FastMCP
from hindsight_api import MemoryEngine
from hindsight_api import __version__ as HINDSIGHT_VERSION
from hindsight_api.config import DEFAULT_MCP_RECALL_DESCRIPTION, DEFAULT_MCP_RETAIN_DESCRIPTION, _get_raw_config
from hindsight_api.config import _get_raw_config
from hindsight_api.engine.memory_engine import _current_schema
from hindsight_api.extensions import MCPExtension, load_extension
from hindsight_api.extensions.tenant import AuthenticationError
@@ -78,19 +78,6 @@ def get_current_mcp_authenticated() -> bool:
return _current_mcp_authenticated.get()
def _build_mcp_tool_descriptions(extra_instructions: str | None) -> tuple[str | None, str | None]:
"""Return custom retain/recall descriptions when server-level MCP instructions are set."""
if not isinstance(extra_instructions, str):
return None, None
extra_instructions = extra_instructions.strip()
if not extra_instructions:
return None, None
suffix = f"\n\nAdditional instructions: {extra_instructions}"
return DEFAULT_MCP_RETAIN_DESCRIPTION + suffix, DEFAULT_MCP_RECALL_DESCRIPTION + suffix
def create_mcp_server(memory: MemoryEngine, multi_bank: bool = True) -> FastMCP:
"""
Create and configure the Hindsight MCP server.
@@ -120,14 +107,11 @@ def create_mcp_server(memory: MemoryEngine, multi_bank: bool = True) -> FastMCP:
"update_mental_model",
"delete_mental_model",
"refresh_mental_model",
"clear_mental_model",
"list_directives",
"create_directive",
"delete_directive",
"list_memories",
"get_memory",
"update_memory",
"invalidate_memory",
"list_documents",
"get_document",
"delete_document",
@@ -148,10 +132,6 @@ def create_mcp_server(memory: MemoryEngine, multi_bank: bool = True) -> FastMCP:
allowed = frozenset(global_config.mcp_enabled_tools)
base_tools = (base_tools if base_tools is not None else _ALL_TOOLS) & allowed
retain_description, recall_description = _build_mcp_tool_descriptions(
getattr(global_config, "mcp_instructions", None)
)
# Configure and register tools using shared module
config = MCPToolsConfig(
bank_id_resolver=get_current_bank_id,
@@ -161,8 +141,6 @@ def create_mcp_server(memory: MemoryEngine, multi_bank: bool = True) -> FastMCP:
mcp_authenticated_resolver=get_current_mcp_authenticated, # Propagate MCP pre-auth flag
include_bank_id_param=multi_bank,
tools=base_tools,
retain_description=retain_description,
recall_description=recall_description,
)
register_mcp_tools(mcp, memory, config)
-263
View File
@@ -1,263 +0,0 @@
"""Open Knowledge Format (OKF) projection for knowledge pages.
Knowledge pages are a *read-only* OKF view over the existing mental models: each
mental model is projected into an OKF document — a markdown body with YAML
frontmatter (``type`` required; ``title``/``description``/``tags``/``timestamp``
optional) — and pages are linked into a constellation graph via shared tags.
See the Open Knowledge Format spec:
https://github.com/GoogleCloudPlatform/knowledge-catalog/tree/main/okf
This module is intentionally pure: every function transforms the mental-model
dicts returned by ``MemoryEngine.list_mental_models`` / ``get_mental_model`` and
never touches the database. That keeps the OKF contract unit-testable without a
DB or LLM and lets the HTTP layer stay a thin wrapper.
"""
from __future__ import annotations
from collections.abc import Callable
from dataclasses import dataclass, field
from typing import Any
# OKF requires exactly one frontmatter field — ``type``. We default to this when
# a page does not declare one via a ``type:<x>`` tag.
DEFAULT_PAGE_TYPE = "knowledge-page"
# A page declares its OKF ``type`` through a tag of the form ``type:runbook``.
# This keeps the projection schema-free (no new mental_models column): the type
# is lifted from the existing tags array.
TYPE_TAG_PREFIX = "type:"
INDEX_FILENAME = "index.md"
# Deterministic, colour-blind-friendly palette. Type → colour is stable across
# requests so the constellation keeps the same colours between reloads.
_PALETTE = (
"#0074d9", # blue
"#2ecc40", # green
"#b10dc9", # purple
"#ff851b", # orange
"#39cccc", # teal
"#f012be", # magenta
"#3d9970", # olive
"#ff4136", # red
)
_EDGE_COLOR = "#9aa5b1"
@dataclass(frozen=True)
class PageType:
"""A page's OKF ``type`` and the tags that remain after the type tag is split off."""
type: str
display_tags: list[str]
@dataclass(frozen=True)
class KnowledgeGraph:
"""Cytoscape-style node/edge graph of knowledge pages linked by shared tags."""
nodes: list[dict[str, Any]] = field(default_factory=list)
edges: list[dict[str, Any]] = field(default_factory=list)
def _color_for(key: str) -> str:
"""Stable colour for a string key (FNV-ish hash into the fixed palette)."""
h = 0
for ch in key:
h = (h * 31 + ord(ch)) & 0xFFFFFFFF
return _PALETTE[h % len(_PALETTE)]
def _scalar(value: Any) -> str:
"""Emit a YAML-safe double-quoted scalar.
We always double-quote so arbitrary page names / source queries can't be
misread as YAML special forms (``true``, ``2026-01-01``, ``- x``, etc.).
"""
text = str(value)
escaped = text.replace("\\", "\\\\").replace('"', '\\"').replace("\n", "\\n").replace("\r", "")
return f'"{escaped}"'
def page_type(tags: list[str] | None) -> PageType:
"""Split an OKF ``type`` out of the tag list.
The first ``type:<x>`` tag wins; all ``type:`` tags are removed from the
returned ``display_tags`` so they don't pollute the constellation's
shared-tag edges. Falls back to :data:`DEFAULT_PAGE_TYPE`.
"""
resolved = DEFAULT_PAGE_TYPE
display: list[str] = []
for tag in tags or []:
if tag.startswith(TYPE_TAG_PREFIX):
suffix = tag[len(TYPE_TAG_PREFIX) :].strip()
if suffix and resolved == DEFAULT_PAGE_TYPE:
resolved = suffix
continue
display.append(tag)
return PageType(type=resolved, display_tags=display)
def _timestamp(mm: dict[str, Any]) -> str | None:
return mm.get("last_refreshed_at") or mm.get("created_at")
def frontmatter(mm: dict[str, Any]) -> dict[str, Any]:
"""Build the ordered OKF frontmatter mapping for a mental model.
``None``/empty values are dropped by :func:`render_frontmatter`.
"""
pt = page_type(mm.get("tags"))
return {
"id": mm.get("id"),
"type": pt.type,
"title": mm.get("name"),
"description": mm.get("source_query"),
"tags": pt.display_tags,
"timestamp": _timestamp(mm),
}
def render_frontmatter(fm: dict[str, Any]) -> str:
"""Render a frontmatter mapping into a ``---`` fenced YAML block."""
lines = ["---"]
for key, value in fm.items():
if value is None:
continue
if isinstance(value, list):
if not value:
continue
lines.append(f"{key}:")
lines.extend(f" - {_scalar(item)}" for item in value)
else:
lines.append(f"{key}: {_scalar(value)}")
lines.append("---")
return "\n".join(lines)
def render_document(mm: dict[str, Any]) -> str:
"""Render a full OKF document: frontmatter block + markdown body."""
body = (mm.get("content") or "").strip()
return f"{render_frontmatter(frontmatter(mm))}\n\n{body}\n" if body else f"{render_frontmatter(frontmatter(mm))}\n"
def page_filename(page_id: str) -> str:
"""OKF bundle filename for a page id."""
return f"{page_id}.md"
def log_filename(page_id: str) -> str:
"""OKF reserved per-page history filename."""
return f"{page_id}.log.md"
def render_index(nodes: list[dict[str, Any]]) -> str:
"""Render the reserved ``index.md`` — nested OKF navigation over the tree.
``nodes`` is the flat folder/page list (each with ``id``, ``kind``, ``name``,
``parent_id``); folders nest their children, pages link to their ``.md``.
"""
fm = render_frontmatter({"type": "index", "title": "Knowledge base"})
lines = [fm, "", "# Knowledge base", ""]
children: dict[Any, list[dict[str, Any]]] = {}
for node in nodes:
children.setdefault(node.get("parent_id"), []).append(node)
def walk(parent: Any, depth: int) -> None:
ordered = sorted(children.get(parent, []), key=lambda n: (n.get("sort_order", 0), n.get("name") or ""))
for node in ordered:
indent = " " * depth
if node.get("kind") == "folder":
lines.append(f"{indent}- **{node['name']}/**")
walk(node["id"], depth + 1)
else:
description = node.get("source_query") or node.get("description")
link = f"{indent}- [{node['name']}](./{page_filename(node['id'])})"
lines.append(f"{link}{description}" if description else link)
walk(None, 0)
if len(lines) == 4:
lines.append("_No knowledge pages yet._")
return "\n".join(lines) + "\n"
def render_log(mm: dict[str, Any], history: list[dict[str, Any]]) -> str:
"""Render the reserved per-page ``log.md`` from refresh history.
Each history entry is ``{previous_content, previous_reflect_response,
changed_at}`` (newest first), capturing the content *before* a refresh.
"""
name = mm.get("name") or mm.get("id")
fm = render_frontmatter({"type": "log", "title": f"{name} — history"})
lines = [fm, "", f"# {name} — history", ""]
if not history:
lines.append("_No refresh history._")
return "\n".join(lines) + "\n"
for entry in history:
changed_at = entry.get("changed_at") or "unknown"
previous = (entry.get("previous_content") or "").strip()
lines.append(f"## {changed_at}")
lines.append("")
lines.append(previous if previous else "_(empty)_")
lines.append("")
return "\n".join(lines).rstrip() + "\n"
def knowledge_graph(
pages: list[dict[str, Any]],
cluster_for: "Callable[[dict[str, Any]], str] | None" = None,
) -> KnowledgeGraph:
"""Derive the constellation graph: pages as nodes, shared tags as edges.
Two pages are linked when they share at least one (non-``type:``) tag; the
edge weight is the number of shared tags. Each node's cluster (``type`` field
+ colour) comes from ``cluster_for(page)`` — the knowledge base groups by
parent folder; the default groups by OKF ``type``.
"""
nodes: list[dict[str, Any]] = []
tag_sets: list[tuple[str, frozenset[str]]] = []
for mm in pages:
page_id = mm["id"]
pt = page_type(mm.get("tags"))
cluster = cluster_for(mm) if cluster_for else pt.type
tag_sets.append((page_id, frozenset(pt.display_tags)))
nodes.append(
{
"data": {
"id": page_id,
"label": mm.get("name") or page_id,
"type": cluster,
"tagCount": len(pt.display_tags),
"color": _color_for(cluster),
}
}
)
edges: list[dict[str, Any]] = []
for i in range(len(tag_sets)):
source_id, source_tags = tag_sets[i]
if not source_tags:
continue
for j in range(i + 1, len(tag_sets)):
target_id, target_tags = tag_sets[j]
shared = source_tags & target_tags
if not shared:
continue
edges.append(
{
"data": {
"id": f"{source_id}--{target_id}",
"source": source_id,
"target": target_id,
"sharedTags": sorted(shared),
"weight": len(shared),
"color": _EDGE_COLOR,
}
}
)
return KnowledgeGraph(nodes=nodes, edges=edges)
@@ -1,85 +0,0 @@
"""Cooperative cancellation for long-running engine operations.
Recall runs as a staged pipeline whose heavy stages — graph expansion and
cross-encoder reranking — execute in worker threads (``run_in_executor``) that
asyncio task cancellation cannot interrupt once they have started. Cancelling
the awaiting task only unblocks the ``await``; the thread keeps burning CPU to
completion. So rather than rely on task cancellation, callers thread a
``CancellationToken`` through ``RequestContext`` and the engine checks it at
stage boundaries (``raise_if_cancelled``), bailing out *before* dispatching the
next expensive stage.
This is cooperative by design: it cannot stop a computation already inside a
worker thread, but it does stop an abandoned recall from progressing into — or
past — that work, which is what starves the instance in issue #2122. The token
lives on ``RequestContext``, so any operation that receives one (recall today;
reflect/consolidation/MCP later) can adopt the same checkpoints, and any driver
(client disconnect today; a deadline tomorrow) can fire it.
"""
from __future__ import annotations
import asyncio
class OperationCancelledError(Exception):
"""Raised at a checkpoint when the operation has been cancelled.
Carries the ``reason`` set by whoever cancelled (e.g. "client disconnected")
so the HTTP layer can translate it into the appropriate status code instead
of a generic 500.
NOTE: this is a plain ``Exception`` on purpose, NOT ``BaseException``. The
recall/reflect pipelines have broad ``except Exception`` handlers that would
otherwise swallow it — those handlers re-raise ``OperationCancelledError``
explicitly (see ``_search_with_retries``) so cancellation propagates to the
HTTP layer. A ``BaseException`` would dodge those handlers but also slip past
legitimate ``isinstance(result, Exception)`` checks (e.g. the reflect agent's
``asyncio.gather(..., return_exceptions=True)`` tool-result handling), which
expect every non-tuple result to be an ``Exception``.
"""
def __init__(self, reason: str = "operation cancelled") -> None:
super().__init__(reason)
self.reason = reason
class CancellationToken:
"""A one-shot, cooperative cancellation signal.
Cheap to poll (``raise_if_cancelled``) at stage boundaries and awaitable
(``wait``) so a driver task can block until cancellation. Safe to share
across an engine call tree; polling is a no-op until something cancels, and
cancellation is idempotent (the first reason wins).
"""
__slots__ = ("_event", "_reason")
def __init__(self) -> None:
self._event = asyncio.Event()
self._reason = "operation cancelled"
def cancel(self, reason: str = "operation cancelled") -> None:
"""Signal cancellation. Idempotent; the first reason recorded wins."""
if not self._event.is_set():
self._reason = reason
self._event.set()
@property
def cancelled(self) -> bool:
"""Whether cancellation has been signalled."""
return self._event.is_set()
@property
def reason(self) -> str:
"""The reason recorded by the first ``cancel`` call."""
return self._reason
def raise_if_cancelled(self) -> None:
"""Raise ``OperationCancelledError`` if cancellation has been signalled."""
if self._event.is_set():
raise OperationCancelledError(self._reason)
async def wait(self) -> None:
"""Block until cancellation is signalled."""
await self._event.wait()
File diff suppressed because it is too large Load Diff
@@ -8,7 +8,6 @@ Config values are resolved on every request to ensure consistency across
multiple API servers.
"""
import asyncio
import json
import logging
from dataclasses import asdict, replace
@@ -19,8 +18,6 @@ from hindsight_api.config import (
HindsightConfig,
_get_raw_config,
normalize_config_dict,
validate_retain_chunking_config,
validate_retain_completion_token_budget,
)
from hindsight_api.engine.memory_engine import fq_table
from hindsight_api.extensions.tenant import TenantExtension
@@ -32,35 +29,6 @@ if TYPE_CHECKING:
logger = logging.getLogger(__name__)
def _validate_retain_strategy_chunking(base_config: HindsightConfig, strategies: Any) -> None:
"""Validate retain strategy chunking with the same semantics as apply_strategy()."""
if not isinstance(strategies, dict):
return
configurable = HindsightConfig.get_configurable_fields()
for strategy_name, overrides in strategies.items():
if not isinstance(overrides, dict):
raise ValueError(f"Invalid retain strategy {strategy_name!r}: must be an object")
filtered = {k: v for k, v in overrides.items() if k in configurable}
if not filtered:
continue
try:
resolved = replace(base_config, **filtered)
validate_retain_chunking_config(
resolved.retain_chunk_size,
resolved.retain_structured_chunk_size,
)
validate_retain_completion_token_budget(
llm_provider=resolved.llm_provider,
retain_max_completion_tokens=resolved.retain_max_completion_tokens,
retain_chunk_size=resolved.retain_chunk_size,
retain_llm_model=resolved.retain_llm_model,
llm_model=resolved.llm_model,
retain_llm_provider=resolved.retain_llm_provider,
)
except ValueError as e:
raise ValueError(f"Invalid retain strategy {strategy_name!r}: {e}") from e
class ConfigResolver:
"""Resolves hierarchical configuration with tenant/bank overrides."""
@@ -78,26 +46,6 @@ class ConfigResolver:
self._configurable_fields = HindsightConfig.get_configurable_fields()
self._credential_fields = HindsightConfig.get_credential_fields()
async def _resolve_parent_config_dict(self, bank_id: str, context: RequestContext | None = None) -> dict[str, Any]:
"""Resolve global + tenant config before bank-level overrides."""
config_dict = asdict(self._global_config)
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}")
return config_dict
async def resolve_full_config(self, bank_id: str, context: RequestContext | None = None) -> HindsightConfig:
"""
Resolve full HindsightConfig for a bank with hierarchical overrides applied.
@@ -117,7 +65,23 @@ class ConfigResolver:
Returns:
Complete HindsightConfig with hierarchical overrides applied
"""
config_dict = await self._resolve_parent_config_dict(bank_id, context)
# 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)
@@ -128,25 +92,6 @@ class ConfigResolver:
# 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)
# Multi-LLM chains are static credential fields (never tenant/bank-overridable),
# but asdict() above flattened their member dataclasses into plain dicts. Restore
# the original typed objects from the global config so the resolved object stays
# well-typed for any consumer that reads them.
resolved_config = replace(
resolved_config,
llm_members=self._global_config.llm_members,
llm_strategy=self._global_config.llm_strategy,
retain_llm_members=self._global_config.retain_llm_members,
retain_llm_strategy=self._global_config.retain_llm_strategy,
reflect_llm_members=self._global_config.reflect_llm_members,
reflect_llm_strategy=self._global_config.reflect_llm_strategy,
consolidation_llm_members=self._global_config.consolidation_llm_members,
consolidation_llm_strategy=self._global_config.consolidation_llm_strategy,
)
validate_retain_chunking_config(
resolved_config.retain_chunk_size,
resolved_config.retain_structured_chunk_size,
)
return resolved_config
async def get_bank_config(self, bank_id: str, context: RequestContext | None = None) -> dict[str, Any]:
@@ -177,83 +122,26 @@ class ConfigResolver:
resolved_config = await self.resolve_full_config(bank_id, context)
config_dict = asdict(resolved_config)
# SECURITY: drop static/infrastructure + credential fields, then permission-filter.
filtered = self._strip_static_and_credential_fields(config_dict)
return await self._apply_permission_filter(filtered, bank_id, context)
# 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}
def _strip_static_and_credential_fields(self, config_dict: dict[str, Any]) -> dict[str, Any]:
"""Keep only configurable, non-credential 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}
SECURITY: excludes static/infrastructure fields and ALL credential fields
(API keys, base URLs, etc.) so a resolved config is safe to return over the API.
"""
return {
k: v for k, v in config_dict.items() if k in self._configurable_fields and k not in self._credential_fields
}
async def _apply_permission_filter(
self, filtered: dict[str, Any], bank_id: str, context: RequestContext | None
) -> dict[str, Any]:
"""Further restrict already-stripped config to the tenant/bank permission allow-list.
On extension error, leaves ``filtered`` unchanged (parity with the historical
single-bank path: a permissions lookup failure must not leak or drop fields).
"""
if not (self.tenant_extension and context):
return filtered
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 get_bank_configs(
self, bank_ids: list[str], context: RequestContext | None = None
) -> dict[str, dict[str, Any]]:
"""Batch variant of :meth:`get_bank_config` for many banks.
Equivalent to calling ``get_bank_config`` per bank, but resolves the
global + tenant base once and loads every bank's ``banks.config`` JSONB
in a single query, instead of one config round-trip per bank. Used by
``list_banks`` to overlay disposition + mission without an N+1.
Returns a mapping of bank_id -> filtered configurable-field dict. A bank
with no config row still appears, mapped to the global+tenant base.
"""
if not bank_ids:
return {}
# Global + tenant base, resolved once (tenant override is per-request, not per-bank).
base_dict = asdict(self._global_config)
# PERMISSIONS: Further filter based on tenant/bank permissions
if self.tenant_extension and context:
try:
tenant_overrides = await self.tenant_extension.get_tenant_config(context)
if tenant_overrides:
normalized_tenant = normalize_config_dict(tenant_overrides)
base_dict.update({k: v for k, v in normalized_tenant.items() if k in self._configurable_fields})
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 tenant config for bulk resolve: {e}")
logger.warning(f"Failed to load permissions for bank {bank_id}: {e}")
# All bank overrides in one query, then merge + strip per bank.
bank_overrides = await self._load_bank_configs(bank_ids)
stripped = {
bank_id: self._strip_static_and_credential_fields({**base_dict, **bank_overrides.get(bank_id, {})})
for bank_id in bank_ids
}
# Permission filter is per-bank; resolve concurrently when an extension is present.
if not (self.tenant_extension and context):
return stripped
permission_filtered = await asyncio.gather(
*(self._apply_permission_filter(stripped[bank_id], bank_id, context) for bank_id in bank_ids)
)
return dict(zip(bank_ids, permission_filtered, strict=True))
return filtered
async def _load_bank_config(self, bank_id: str) -> dict[str, Any]:
"""
@@ -284,53 +172,13 @@ class ConfigResolver:
# Normalize keys (handle both env var format and Python field format)
normalized = normalize_config_dict(config_data)
# Only return active overrides for configurable fields. JSON null is a tombstone
# for "Server Default" in the bank-config UI and should not override defaults.
return {k: v for k, v in normalized.items() if k in self._configurable_fields and v is not None}
# 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 _load_bank_configs(self, bank_ids: list[str]) -> dict[str, dict[str, Any]]:
"""Bulk variant of :meth:`_load_bank_config`: load many banks' overrides in one query.
Returns a mapping of bank_id -> normalized active overrides. Banks with no row
(or an empty/all-tombstone config) are simply absent from the mapping.
"""
result: dict[str, dict[str, Any]] = {}
if not bank_ids:
return result
try:
async with self._backend.acquire() as conn:
rows = await conn.fetch(
f"""
SELECT bank_id, config FROM {fq_table("banks")} WHERE bank_id = ANY($1)
""",
bank_ids,
)
for row in rows:
config_data = row["config"]
if not config_data:
continue
# 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 active overrides for configurable fields. JSON null is a tombstone
# for "Server Default" in the bank-config UI and must not override defaults.
overrides = {
k: v for k, v in normalized.items() if k in self._configurable_fields and v is not None
}
if overrides:
result[row["bank_id"]] = overrides
except Exception as e:
logger.error(f"Failed to bulk-load bank configs: {e}")
return result
async def update_bank_config(
self, bank_id: str, updates: dict[str, Any], context: RequestContext | None = None
) -> None:
@@ -417,46 +265,12 @@ class ConfigResolver:
# Validate recall budget fields
_validate_recall_budget_updates(normalized_updates)
# Validate disposition trait fields (1-5 integer scale)
_validate_disposition_updates(normalized_updates)
chunking_fields_updated = (
"retain_chunk_size" in normalized_updates
or "retain_structured_chunk_size" in normalized_updates
or "retain_strategies" in normalized_updates
)
if chunking_fields_updated:
config_dict = await self._resolve_parent_config_dict(bank_id, context)
active_bank_overrides = await self._load_bank_config(bank_id)
for key, value in normalized_updates.items():
if key not in self._configurable_fields:
continue
if value is None:
active_bank_overrides.pop(key, None)
else:
active_bank_overrides[key] = value
config_dict.update(active_bank_overrides)
base_config = HindsightConfig(**config_dict)
validate_retain_chunking_config(
base_config.retain_chunk_size,
base_config.retain_structured_chunk_size,
)
_validate_retain_strategy_chunking(base_config, base_config.retain_strategies)
# Persist the override. Banks are created lazily (on first retain), so a
# PATCH that precedes any ingestion would otherwise UPDATE zero rows and
# silently no-op while returning 200. Ensure the bank row exists first
# (this also creates its per-bank vector indexes), then merge defensively:
# COALESCE guards against a NULL config column (NULL || jsonb is NULL),
# which would drop the override even when a row is updated.
from .engine.retain.fact_storage import ensure_bank_exists
# Merge with existing config (JSONB || operator)
async with self._backend.acquire() as conn:
await ensure_bank_exists(conn, bank_id, ops=self._backend.ops)
await conn.execute(
f"""
UPDATE {fq_table("banks")}
SET config = COALESCE(config, '{{}}'::jsonb) || $1::jsonb,
SET config = config || $1::jsonb,
updated_at = now()
WHERE bank_id = $2
""",
@@ -534,31 +348,6 @@ def _validate_recall_budget_updates(updates: dict[str, Any]) -> None:
)
_DISPOSITION_KEYS = (
"disposition_skepticism",
"disposition_literalism",
"disposition_empathy",
)
def _validate_disposition_updates(updates: dict[str, Any]) -> None:
"""Validate disposition trait config updates. Raises ValueError on invalid input.
Each trait is an integer on a 1-5 scale (or None to clear the per-bank
override). The read overlay injects the stored value verbatim into a strict
``DispositionTraits(int, ge=1, le=5)``; an out-of-contract value (a float, a
0-1 scale, or an int outside 1-5) accepted here would later 500 the whole
bank list when any bank profile is serialized (issue #2348).
"""
for key in _DISPOSITION_KEYS:
if key in updates:
value = updates[key]
if value is None:
continue
if not isinstance(value, int) or isinstance(value, bool) or not (1 <= value <= 5):
raise ValueError(f"{key} must be an integer between 1 and 5, got {value!r}")
def apply_strategy(config: HindsightConfig, strategy_name: str) -> HindsightConfig:
"""
Apply a named retain strategy's overrides on top of a resolved config.
@@ -566,8 +355,7 @@ def apply_strategy(config: HindsightConfig, strategy_name: str) -> HindsightConf
A strategy is a named set of hierarchical field overrides stored in
config.retain_strategies. Any field in _HIERARCHICAL_FIELDS can be
overridden, including retain_extraction_mode, retain_chunk_size,
retain_structured_chunk_size, entity_labels,
entities_allow_free_form, etc.
entity_labels, entities_allow_free_form, etc.
Unknown strategy names log a warning and return config unchanged.
Unknown or non-hierarchical fields in the strategy are silently ignored.
@@ -589,17 +377,4 @@ def apply_strategy(config: HindsightConfig, strategy_name: str) -> HindsightConf
return config
logger.debug(f"Applying retain strategy '{strategy_name}': {list(filtered.keys())}")
resolved = replace(config, **filtered)
validate_retain_chunking_config(
resolved.retain_chunk_size,
resolved.retain_structured_chunk_size,
)
validate_retain_completion_token_budget(
llm_provider=resolved.llm_provider,
retain_max_completion_tokens=resolved.retain_max_completion_tokens,
retain_chunk_size=resolved.retain_chunk_size,
retain_llm_model=resolved.retain_llm_model,
llm_model=resolved.llm_model,
retain_llm_provider=resolved.retain_llm_provider,
)
return resolved
return replace(config, **filtered)
+34 -96
View File
@@ -4,20 +4,12 @@ Daemon mode support for Hindsight API.
Provides idle timeout for running as a background daemon.
"""
from __future__ import annotations
import asyncio
import logging
import os
import platform
import subprocess
import sys
import time
from pathlib import Path
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from typing import IO
logger = logging.getLogger(__name__)
@@ -28,12 +20,6 @@ DEFAULT_IDLE_TIMEOUT = 0 # 0 = no auto-exit (hindsight-embed passes its own tim
# Allow override via environment variable for profile-specific logs
DAEMON_LOG_PATH = Path(os.getenv("HINDSIGHT_API_DAEMON_LOG", str(Path.home() / ".hindsight" / "daemon.log")))
# Internal env var: set by daemonize() in the re-exec'd child so the child
# skips re-exec and just redirects stdio. Also set by hindsight-embed's
# DaemonEmbedManager so the daemon launched via Popen skips re-exec entirely
# (hindsight-embed's Popen already provides a clean, detached process).
ENV_DAEMON_CHILD = "_HINDSIGHT_DAEMON_CHILD"
class IdleTimeoutMiddleware:
"""ASGI middleware that tracks activity and exits after idle timeout."""
@@ -72,103 +58,55 @@ class IdleTimeoutMiddleware:
os.kill(os.getpid(), signal.SIGTERM)
def _detach_popen_kwargs(log_handle: "IO[bytes]") -> dict:
"""Cross-platform kwargs to spawn a subprocess detached from the caller.
On POSIX, ``start_new_session=True`` calls ``setsid(2)`` so the child
survives the parent's terminal. On Windows we use
``DETACHED_PROCESS | CREATE_NEW_PROCESS_GROUP``.
``log_handle`` receives the child's stdout/stderr so output never leaks
into the parent's terminal.
"""
if platform.system() == "Windows":
detached_process = getattr(subprocess, "DETACHED_PROCESS", 0)
create_new_process_group = getattr(subprocess, "CREATE_NEW_PROCESS_GROUP", 0)
return {
"creationflags": detached_process | create_new_process_group,
"stdin": subprocess.DEVNULL,
"stdout": log_handle,
"stderr": subprocess.STDOUT,
"close_fds": True,
}
return {
"start_new_session": True,
"stdin": subprocess.DEVNULL,
"stdout": log_handle,
"stderr": log_handle,
}
def _redirect_stdio_to_log() -> None:
"""Redirect stdin/stdout/stderr to the daemon log file.
Called in the daemon child process after re-exec.
"""
DAEMON_LOG_PATH.parent.mkdir(parents=True, exist_ok=True)
sys.stdout.flush()
sys.stderr.flush()
with open(os.devnull, "r") as devnull:
os.dup2(devnull.fileno(), sys.stdin.fileno())
log_fd = open(DAEMON_LOG_PATH, "a")
os.dup2(log_fd.fileno(), sys.stdout.fileno())
os.dup2(log_fd.fileno(), sys.stderr.fileno())
def daemonize():
"""Detach the current process into a background daemon.
"""
Fork the current process into a background daemon.
Uses ``subprocess.Popen`` (which maps to ``posix_spawn`` on macOS) to
re-exec the current command in a detached session. This replaces the
traditional double-fork pattern because ``os.fork()`` without ``exec()``
corrupts Apple framework state (XPC, Metal/MPS, ObjC runtime) on macOS,
causing SIGBUS crashes when PyTorch uses the MPS backend.
The function has two code paths controlled by the ``_HINDSIGHT_DAEMON_CHILD``
environment variable:
* **Parent** (env var not set): re-exec the same command via Popen with
``start_new_session=True``, stripping ``--daemon`` from argv and setting
``_HINDSIGHT_DAEMON_CHILD=1``. Then ``sys.exit(0)``.
* **Child** (env var set): redirect stdio to the daemon log file and return.
No fork, no re-exec.
Uses double-fork technique to properly detach from terminal.
On Windows there is no fork model: the spawning parent is expected to
detach us via ``CREATE_NEW_PROCESS_GROUP | DETACHED_PROCESS`` and to
redirect stdout/stderr to ``HINDSIGHT_API_DAEMON_LOG`` before exec.
We still ensure the log directory exists.
detach us via `CREATE_NEW_PROCESS_GROUP | DETACHED_PROCESS` and to
redirect stdout/stderr to HINDSIGHT_API_DAEMON_LOG before exec. We
still ensure the log directory exists so that any file handlers set
up by the calling app have a valid target.
"""
if sys.platform == "win32":
DAEMON_LOG_PATH.parent.mkdir(parents=True, exist_ok=True)
return
# If we are already the daemon child (re-exec'd by a previous daemonize()
# call, or launched by hindsight-embed with the env var set), just redirect
# stdio and return — no re-exec needed.
if os.environ.get(ENV_DAEMON_CHILD) == "1":
_redirect_stdio_to_log()
return
# First fork - detach from parent
try:
pid = os.fork()
if pid > 0:
sys.exit(0)
except OSError as e:
sys.stderr.write(f"fork #1 failed: {e}\n")
sys.exit(1)
# --- Parent path: re-exec ourselves as a detached background process ---
# Decouple from parent environment
os.chdir("/")
os.setsid()
os.umask(0)
# Build child command: same Python, same module entry point, all args
# except --daemon (replaced by the env var).
child_args = [a for a in sys.argv[1:] if a != "--daemon"]
cmd = [sys.executable, "-m", "hindsight_api.main"] + child_args
env = os.environ.copy()
env[ENV_DAEMON_CHILD] = "1"
env["HINDSIGHT_API_DAEMON_LOG"] = str(DAEMON_LOG_PATH)
# Second fork - prevent zombie
pid = os.fork()
if pid > 0:
sys.exit(0)
# Redirect standard file descriptors to log file
DAEMON_LOG_PATH.parent.mkdir(parents=True, exist_ok=True)
with open(DAEMON_LOG_PATH, "ab") as log_handle:
subprocess.Popen(cmd, env=env, **_detach_popen_kwargs(log_handle))
sys.stdout.flush()
sys.stderr.flush()
sys.exit(0)
# Redirect stdin to /dev/null
with open("/dev/null", "r") as devnull:
os.dup2(devnull.fileno(), sys.stdin.fileno())
# Redirect stdout/stderr to log file
log_fd = open(DAEMON_LOG_PATH, "a")
os.dup2(log_fd.fileno(), sys.stdout.fileno())
os.dup2(log_fd.fileno(), sys.stderr.fileno())
def check_daemon_running(port: int = DEFAULT_DAEMON_PORT) -> bool:
@@ -16,59 +16,11 @@ from dataclasses import dataclass, field
from datetime import datetime, timezone
from typing import Any
from pydantic import BaseModel, Field
from ..engine.db_utils import acquire_with_retry
logger = logging.getLogger(__name__)
class AuditLogEntry(BaseModel):
"""A single audit log entry."""
id: str
action: str
transport: str
bank_id: str | None
started_at: str | None
ended_at: str | None
duration_ms: int | None = Field(
default=None,
description="Server-computed duration in milliseconds (started_at → ended_at). Null if not yet completed.",
)
request: dict[str, Any] | None
response: dict[str, Any] | None
metadata: dict[str, Any]
class AuditLogListResponse(BaseModel):
"""Response model for list audit logs endpoint."""
bank_id: str
total: int
limit: int
offset: int
items: list[AuditLogEntry]
class AuditLogStatsBucket(BaseModel):
"""A single time bucket in audit log stats."""
time: str
actions: dict[str, int]
total: int
class AuditLogStatsResponse(BaseModel):
"""Response model for audit log stats endpoint."""
bank_id: str
period: str
trunc: str
start: str
buckets: list[AuditLogStatsBucket]
@dataclass
class AuditEntry:
"""A single audit log entry."""
@@ -107,11 +59,11 @@ def _safe_json(data: Any) -> str | None:
return None
class AuditLogger:
"""Fire-and-forget audit log writer.
_SWEEP_INTERVAL_SECONDS = 3600 # Run retention sweep every hour
Retention of old rows is handled by the background :class:`MaintenanceLoop`.
"""
class AuditLogger:
"""Fire-and-forget audit log writer with optional retention sweep."""
def __init__(
self,
@@ -119,11 +71,14 @@ class AuditLogger:
schema_getter: Callable[[], str],
enabled: bool,
allowed_actions: list[str],
retention_days: int = -1,
) -> None:
self._pool_getter = pool_getter
self._schema_getter = schema_getter
self._enabled = enabled
self._allowed_actions: frozenset[str] | None = frozenset(allowed_actions) if allowed_actions else None
self._retention_days = retention_days
self._sweep_task: asyncio.Task | None = None
def is_enabled(self, action: str) -> bool:
"""Check if audit logging is enabled for this action."""
@@ -173,6 +128,48 @@ class AuditLogger:
except Exception as e:
logger.warning(f"Audit log write failed for action={entry.action}: {e}")
def start_retention_sweep(self) -> None:
"""Start the periodic retention sweep if retention is configured."""
if self._retention_days <= 0 or not self._enabled:
return
try:
self._sweep_task = asyncio.create_task(self._sweep_loop())
except RuntimeError:
logger.debug("Cannot start retention sweep: no running event loop")
async def stop_retention_sweep(self) -> None:
"""Stop the periodic retention sweep."""
if self._sweep_task and not self._sweep_task.done():
self._sweep_task.cancel()
try:
await self._sweep_task
except asyncio.CancelledError:
pass
self._sweep_task = None
async def _sweep_loop(self) -> None:
"""Periodically delete audit log entries older than retention_days."""
while True:
await self._run_sweep()
await asyncio.sleep(_SWEEP_INTERVAL_SECONDS)
async def _run_sweep(self) -> None:
"""Delete expired audit log entries. Concurrent-safe via row-level deletes."""
pool = self._pool_getter()
if pool is None:
return
try:
schema = self._schema_getter()
table = f"{schema}.audit_log"
async with acquire_with_retry(pool, max_retries=1) as conn:
result = await conn.execute(
f"DELETE FROM {table} WHERE started_at < NOW() - INTERVAL '{self._retention_days} days'"
)
if result and result != "DELETE 0":
logger.info(f"Audit log retention sweep: {result}")
except Exception as e:
logger.warning(f"Audit log retention sweep failed: {e}")
@asynccontextmanager
async def audit_context(
@@ -1,34 +0,0 @@
"""Per-bank provider cost attribution via the OpenAI ``user`` field.
Shared by the OpenAI-compatible LLM path and the OpenAI embeddings path so both
tag outbound requests identically. Opt-in via ``HINDSIGHT_API_LLM_SEND_BANK_AS_USER``;
downstream cost gateways (OpenRouter usage accounting, LiteLLM, Helicone) key spend
on the OpenAI ``user`` field.
Note: when enabled, the bank id is transmitted to the upstream provider as the
end-user identifier. Banks that are themselves end-user identifiers are therefore
forwarded to the provider — which is exactly what the OpenAI ``user`` field is for,
but operators should opt in with that in mind.
"""
from typing import Any
def apply_bank_attribution(request: dict[str, Any]) -> None:
"""Tag ``request`` with ``user=<bank_id>`` for per-bank cost attribution.
Mutates ``request`` in place. No-op when the flag is off, no bank is in context,
or the caller already set ``user`` — we never override an explicit value.
"""
if "user" in request:
return
# Lazy imports: memory_engine imports the embeddings/provider modules that call
# this, so a top-level import of memory_engine here would be circular.
from ..config import get_config
from .memory_engine import get_current_bank_id
if not get_config().llm_send_bank_as_user:
return
bank_id = get_current_bank_id()
if bank_id:
request["user"] = bank_id
@@ -1,254 +0,0 @@
"""TTL + coalescing cache for `get_bank_stats`.
`get_bank_stats` aggregates over `memory_links` (and joins to `memory_units`),
which can be a multi-second parallel sequential scan on banks with millions of
rows. The result is intentionally approximate (it powers a UI widget and a
freshness hint inside `reflect`), so caching it for a few tens of seconds is
safe and dramatically reduces planner-driven thrash from clients that poll.
The cache also coalesces concurrent misses on the same key onto a single
in-flight task so that N concurrent callers produce one query rather than N.
"""
from __future__ import annotations
import asyncio
import json
import logging
import time
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Awaitable, Callable
from .db_utils import acquire_with_retry
if TYPE_CHECKING:
from .db.base import DatabaseBackend
logger = logging.getLogger(__name__)
class BankStatsCache:
"""Per-process TTL cache keyed on (schema, bank_id).
`ttl_seconds <= 0` disables caching: each call passes straight through to
the loader. `max_entries` bounds memory in environments with many banks.
"""
def __init__(self, *, ttl_seconds: float, max_entries: int) -> None:
self._ttl = float(ttl_seconds)
self._max_entries = int(max_entries) if max_entries and max_entries > 0 else 0
self._entries: OrderedDict[tuple[str, str], tuple[float, dict[str, Any]]] = OrderedDict()
self._in_flight: dict[tuple[str, str], asyncio.Future[dict[str, Any]]] = {}
self._lock = asyncio.Lock()
@property
def enabled(self) -> bool:
return self._ttl > 0
def _now(self) -> float:
return time.monotonic()
def _get_fresh_unlocked(self, key: tuple[str, str]) -> dict[str, Any] | None:
entry = self._entries.get(key)
if entry is None:
return None
expires_at, value = entry
if expires_at <= self._now():
# Expired — drop so the loader runs again.
self._entries.pop(key, None)
return None
# Mark as recently used for LRU eviction.
self._entries.move_to_end(key)
return value
def _store_unlocked(self, key: tuple[str, str], value: dict[str, Any]) -> None:
if not self.enabled:
return
self._entries[key] = (self._now() + self._ttl, value)
self._entries.move_to_end(key)
if self._max_entries:
while len(self._entries) > self._max_entries:
self._entries.popitem(last=False)
async def get_or_load(
self,
schema: str,
bank_id: str,
loader: Callable[[], Awaitable[dict[str, Any]]],
*,
force_refresh: bool = False,
) -> dict[str, Any]:
"""Return cached stats for `(schema, bank_id)` or call `loader()`.
Concurrent misses on the same key are coalesced onto a single
in-flight loader. When ``force_refresh`` is set the cached value is
ignored: the loader runs and its result replaces the cached entry.
"""
if not self.enabled:
return await loader()
key = (schema, bank_id)
if force_refresh:
value = await loader()
async with self._lock:
self._store_unlocked(key, value)
# Supersede any loader that was in flight for this key.
self._in_flight.pop(key, None)
return value
async with self._lock:
cached = self._get_fresh_unlocked(key)
if cached is not None:
return cached
in_flight = self._in_flight.get(key)
if in_flight is None:
in_flight = asyncio.get_running_loop().create_future()
self._in_flight[key] = in_flight
is_owner = True
else:
is_owner = False
if not is_owner:
return await asyncio.shield(in_flight)
try:
value = await loader()
except BaseException as exc:
async with self._lock:
# Invalidation may have detached this loader and allowed a new
# one to claim the key. Never remove that newer loader's slot.
if self._in_flight.get(key) is in_flight:
self._in_flight.pop(key, None)
if not in_flight.done():
in_flight.set_exception(exc)
# Suppress "Future exception was never retrieved" when no other
# caller was waiting on this loader — we re-raise to the owner
# immediately and the future is a no-op in that case.
in_flight.exception()
raise
async with self._lock:
# Only the loader that still owns the key may populate the cache.
# An invalidated loader can finish for its original callers, but its
# pre-invalidation result must not overwrite a newer load.
if self._in_flight.get(key) is in_flight:
self._store_unlocked(key, value)
self._in_flight.pop(key, None)
if not in_flight.done():
in_flight.set_result(value)
return value
async def invalidate(self, schema: str, bank_id: str) -> None:
"""Drop any cached stats for `(schema, bank_id)`."""
async with self._lock:
key = (schema, bank_id)
self._entries.pop(key, None)
# Detach rather than cancel: existing callers may finish with the
# snapshot they requested, while post-invalidation callers reload.
self._in_flight.pop(key, None)
async def clear(self) -> None:
async with self._lock:
self._entries.clear()
self._in_flight.clear()
class DistributedBankStatsCache:
"""Table-backed (cross-process) TTL cache for `get_bank_stats`.
Same ``get_or_load`` / ``invalidate`` / ``clear`` contract as
:class:`BankStatsCache`, but the store is the per-schema ``bank_stats_cache``
table instead of a per-process dict — so one worker's computation is shared
with every other worker, and no caller recomputes while a fresh row exists.
On a hit, a call is a single primary-key ``SELECT`` (sub-millisecond); only a
miss runs the (expensive) ``loader`` and writes the row back. Concurrent
misses are *not* coalesced across processes (that would need a lock): they
each compute and ``UPSERT``, last write wins — all results are correct, at the
cost of a brief redundant compute at expiry.
Every DB touch is best-effort: if the cache table is unreachable or missing
(e.g. a schema mid-migration), the call degrades to computing without caching
rather than failing ``get_bank_stats``. PostgreSQL only — the engine keeps the
in-process :class:`BankStatsCache` for Oracle.
"""
def __init__(self, *, backend: "DatabaseBackend", ttl_seconds: float) -> None:
self._backend = backend
self._ttl = float(ttl_seconds)
@property
def enabled(self) -> bool:
return self._ttl > 0
@staticmethod
def _qualified(schema: str) -> str:
return f'"{schema}".bank_stats_cache' if schema else "bank_stats_cache"
async def get_or_load(
self,
schema: str,
bank_id: str,
loader: Callable[[], Awaitable[dict[str, Any]]],
*,
force_refresh: bool = False,
) -> dict[str, Any]:
if not self.enabled:
return await loader()
table = self._qualified(schema)
# 1. Fresh row? Single PK lookup; ``payload::text`` sidesteps any
# jsonb->object codec so we always decode the same way. Skipped when
# the caller forces a refresh — then we recompute and overwrite below.
if not force_refresh:
try:
async with acquire_with_retry(self._backend) as conn:
row = await conn.fetchrow(
f"SELECT payload::text AS payload FROM {table} "
f"WHERE bank_id = $1 AND computed_at > now() - make_interval(secs => $2::double precision)",
bank_id,
self._ttl,
)
if row is not None:
return json.loads(row["payload"])
except Exception as exc: # noqa: BLE001 — cache read must never break the endpoint
logger.debug("bank_stats_cache read failed for %s.%s (%s); computing uncached", schema, bank_id, exc)
return await loader()
# 2. Miss — compute, then write the row back (best-effort).
value = await loader()
try:
async with acquire_with_retry(self._backend) as conn:
await conn.execute(
f"INSERT INTO {table} (bank_id, payload, computed_at) VALUES ($1, $2::jsonb, now()) "
f"ON CONFLICT (bank_id) DO UPDATE SET payload = EXCLUDED.payload, computed_at = now()",
bank_id,
json.dumps(value),
)
except Exception as exc: # noqa: BLE001 — a failed write just means no caching this round
logger.warning("bank_stats_cache write failed for %s.%s (%s)", schema, bank_id, exc)
return value
async def invalidate(self, schema: str, bank_id: str) -> None:
"""Drop the cached row so the next read recomputes."""
if not self.enabled:
return
try:
async with acquire_with_retry(self._backend) as conn:
await conn.execute(f"DELETE FROM {self._qualified(schema)} WHERE bank_id = $1", bank_id)
except Exception as exc: # noqa: BLE001 — invalidation must never break the write path
logger.debug("bank_stats_cache invalidate failed for %s.%s (%s)", schema, bank_id, exc)
async def clear(self) -> None:
"""Drop all cached rows in the current schema (best-effort)."""
if not self.enabled:
return
from .memory_engine import get_current_schema
try:
async with acquire_with_retry(self._backend) as conn:
await conn.execute(f"DELETE FROM {self._qualified(get_current_schema())}")
except Exception as exc: # noqa: BLE001
logger.debug("bank_stats_cache clear failed (%s)", exc)
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