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6d: Add semantic search API endpoint (GET /notes/semantic-search) #137

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opened 2026-03-09 13:56:38 +00:00 by forgejo_admin · 2 comments

forgejo_admin commented

2026-03-09 13:56:38 +00:00

Owner

Lineage

plan-2026-02-26-tf-modularize-postgres → Phase 6 (Vector Search) → Phase 6d (Semantic Search)

Repo

forgejo_admin/pal-e-docs

User Story

As an AI agent
I want to search platform knowledge by semantic meaning
So that I can find relevant notes and blocks even when the exact keywords don't match

Context

Phase 6a deployed Ollama (GPU, qwen3-embedding:4b). Phase 6b added pgvector to the blocks table. Phase 6c built the async embedding worker and backfilled 5K+ blocks. The embeddings exist — this issue adds the query endpoint.

The endpoint mirrors the existing GET /notes/search (tsvector full-text search at routes/notes.py:169-243) but operates at block granularity using pgvector cosine similarity.

Query flow: Receive query text → embed via Ollama (POST /api/embed) → cosine similarity search on blocks.embedding (HNSW index) → return ranked blocks with note context.

Key implementation details from the embedding worker:

Ollama URL: PALDOCS_OLLAMA_URL env var (default http://ollama.ollama.svc.cluster.local:11434)
Model: PALDOCS_EMBEDDING_MODEL env var (default qwen3-embedding:4b)
Instruction prefix for documents: "Represent this platform knowledge base section for retrieval: " — the query may need a different prefix (e.g. "Represent this query for retrieving relevant documents: "). Check qwen3-embedding docs and decide.
Vector dimension: 768
Cosine distance operator: <=> (0 = identical, 2 = opposite; similarity = 1 - distance)
Only blocks with embedding_status = 'completed' and non-null embedding should be searched

File Targets

Files to modify:

src/pal_e_docs/routes/notes.py — add GET /notes/semantic-search route (follow search_notes pattern at line 169)
src/pal_e_docs/schemas.py — add SemanticSearchResult response model

Files to create:

tests/test_semantic_search.py — endpoint tests (or add to existing test file if appropriate)

Files to reference (read, don't modify):

src/pal_e_docs/embedding_worker.py — Ollama client pattern (get_embedding at line 177), instruction prefix, block text extraction
src/pal_e_docs/models.py — Block model with embedding (Vector(768)) and embedding_status columns (line 200)
alembic/versions/l2g3h4i5j6k7_add_vector_embeddings.py — schema reference

Files NOT to touch:

src/pal_e_docs/embedding_worker.py — owned by 6c
alembic/versions/ — no schema changes needed

Acceptance Criteria

GET /notes/semantic-search?q=deployment+strategy&limit=5 returns up to 5 blocks ranked by cosine similarity
Each result includes: slug, title, note_type, status, project, anchor_id, block_type, content_snippet, similarity
content_snippet is extracted plain text from the block's JSON content (reuse or adapt the extraction logic from embedding_worker.py:79-169)
Filters work: note_type, project, status, tags (same filter set as search_notes)
Returns 501 if database is not PostgreSQL (match search_notes pattern)
Returns 503 or graceful error if Ollama is unavailable
Empty query returns 422 validation error
Query with no matching results returns empty list []

Test Expectations

Unit test: mock Ollama response, verify SQL query uses cosine distance operator
Unit test: verify filters (note_type, project, tags) are applied correctly
Unit test: verify 501 on non-PostgreSQL
Unit test: verify error handling when Ollama is down
Run command: pytest tests/ -k test_semantic

Constraints

Follow the exact route pattern from search_notes in routes/notes.py — same parameter style, same raw SQL approach (not ORM), same filter logic
Reuse the Ollama embedding call pattern from embedding_worker.py:get_embedding() — extract it into a shared utility if clean, or inline if simpler
The HNSW index (ix_blocks_embedding) handles the cosine search efficiently — do NOT add application-level similarity thresholds (let the caller decide based on returned scores)
Keep the Ollama call async-friendly if the route is async (match existing route patterns)
Block content snippet extraction: prefer reusing extract_block_text() from the embedding worker if it can be imported cleanly, otherwise duplicate the minimal logic needed
limit parameter: default 10, max 100 (match search_notes)

Response Schema

class SemanticSearchResult(BaseModel):
    slug: str              # note slug
    title: str             # note title
    note_type: str | None
    status: str | None
    project: str | None
    anchor_id: str         # block anchor for direct section access
    block_type: str        # heading, paragraph, list, table, code
    content_snippet: str   # plain text extracted from block content
    similarity: float      # cosine similarity (0-1, higher = more similar)

SQL Pattern

SELECT b.id, b.anchor_id, b.block_type, b.content,
       n.slug, n.title, n.note_type, n.status,
       p.slug AS project,
       1 - (b.embedding <=> :query_vec) AS similarity
FROM blocks b
JOIN notes n ON b.note_id = n.id
LEFT JOIN projects p ON n.project_id = p.id
WHERE b.embedding IS NOT NULL
  AND b.embedding_status = 'completed'
  -- plus filters for note_type, status, project, tags
ORDER BY b.embedding <=> :query_vec
LIMIT :limit

Checklist

PR opened
Tests pass
No unrelated changes
PR description includes Closes #N

phase-postgres-6d-semantic-search — phase note
PR #130 — embedding pipeline (6c) that this depends on
decision-phase6-vector-search-architecture — architectural decisions

### Lineage `plan-2026-02-26-tf-modularize-postgres` → Phase 6 (Vector Search) → Phase 6d (Semantic Search) ### Repo `forgejo_admin/pal-e-docs` ### User Story As an AI agent I want to search platform knowledge by semantic meaning So that I can find relevant notes and blocks even when the exact keywords don't match ### Context Phase 6a deployed Ollama (GPU, qwen3-embedding:4b). Phase 6b added pgvector to the blocks table. Phase 6c built the async embedding worker and backfilled 5K+ blocks. The embeddings exist — this issue adds the query endpoint. The endpoint mirrors the existing `GET /notes/search` (tsvector full-text search at `routes/notes.py:169-243`) but operates at block granularity using pgvector cosine similarity. **Query flow:** Receive query text → embed via Ollama (`POST /api/embed`) → cosine similarity search on `blocks.embedding` (HNSW index) → return ranked blocks with note context. **Key implementation details from the embedding worker:** - Ollama URL: `PALDOCS_OLLAMA_URL` env var (default `http://ollama.ollama.svc.cluster.local:11434`) - Model: `PALDOCS_EMBEDDING_MODEL` env var (default `qwen3-embedding:4b`) - Instruction prefix for documents: `"Represent this platform knowledge base section for retrieval: "` — the query may need a different prefix (e.g. `"Represent this query for retrieving relevant documents: "`). Check qwen3-embedding docs and decide. - Vector dimension: 768 - Cosine distance operator: `<=>` (0 = identical, 2 = opposite; similarity = 1 - distance) - Only blocks with `embedding_status = 'completed'` and non-null `embedding` should be searched ### File Targets Files to modify: - `src/pal_e_docs/routes/notes.py` — add `GET /notes/semantic-search` route (follow `search_notes` pattern at line 169) - `src/pal_e_docs/schemas.py` — add `SemanticSearchResult` response model Files to create: - `tests/test_semantic_search.py` — endpoint tests (or add to existing test file if appropriate) Files to reference (read, don't modify): - `src/pal_e_docs/embedding_worker.py` — Ollama client pattern (`get_embedding` at line 177), instruction prefix, block text extraction - `src/pal_e_docs/models.py` — Block model with `embedding` (Vector(768)) and `embedding_status` columns (line 200) - `alembic/versions/l2g3h4i5j6k7_add_vector_embeddings.py` — schema reference Files NOT to touch: - `src/pal_e_docs/embedding_worker.py` — owned by 6c - `alembic/versions/` — no schema changes needed ### Acceptance Criteria - [ ] `GET /notes/semantic-search?q=deployment+strategy&limit=5` returns up to 5 blocks ranked by cosine similarity - [ ] Each result includes: `slug`, `title`, `note_type`, `status`, `project`, `anchor_id`, `block_type`, `content_snippet`, `similarity` - [ ] `content_snippet` is extracted plain text from the block's JSON content (reuse or adapt the extraction logic from `embedding_worker.py:79-169`) - [ ] Filters work: `note_type`, `project`, `status`, `tags` (same filter set as `search_notes`) - [ ] Returns 501 if database is not PostgreSQL (match `search_notes` pattern) - [ ] Returns 503 or graceful error if Ollama is unavailable - [ ] Empty query returns 422 validation error - [ ] Query with no matching results returns empty list `[]` ### Test Expectations - [ ] Unit test: mock Ollama response, verify SQL query uses cosine distance operator - [ ] Unit test: verify filters (note_type, project, tags) are applied correctly - [ ] Unit test: verify 501 on non-PostgreSQL - [ ] Unit test: verify error handling when Ollama is down - [ ] Run command: `pytest tests/ -k test_semantic` ### Constraints - Follow the exact route pattern from `search_notes` in `routes/notes.py` — same parameter style, same raw SQL approach (not ORM), same filter logic - Reuse the Ollama embedding call pattern from `embedding_worker.py:get_embedding()` — extract it into a shared utility if clean, or inline if simpler - The HNSW index (`ix_blocks_embedding`) handles the cosine search efficiently — do NOT add application-level similarity thresholds (let the caller decide based on returned scores) - Keep the Ollama call async-friendly if the route is async (match existing route patterns) - Block content snippet extraction: prefer reusing `extract_block_text()` from the embedding worker if it can be imported cleanly, otherwise duplicate the minimal logic needed - `limit` parameter: default 10, max 100 (match `search_notes`) ### Response Schema ```python class SemanticSearchResult(BaseModel): slug: str # note slug title: str # note title note_type: str | None status: str | None project: str | None anchor_id: str # block anchor for direct section access block_type: str # heading, paragraph, list, table, code content_snippet: str # plain text extracted from block content similarity: float # cosine similarity (0-1, higher = more similar) ``` ### SQL Pattern ```sql SELECT b.id, b.anchor_id, b.block_type, b.content, n.slug, n.title, n.note_type, n.status, p.slug AS project, 1 - (b.embedding <=> :query_vec) AS similarity FROM blocks b JOIN notes n ON b.note_id = n.id LEFT JOIN projects p ON n.project_id = p.id WHERE b.embedding IS NOT NULL AND b.embedding_status = 'completed' -- plus filters for note_type, status, project, tags ORDER BY b.embedding <=> :query_vec LIMIT :limit ``` ### Checklist - [ ] PR opened - [ ] Tests pass - [ ] No unrelated changes - [ ] PR description includes `Closes #N` ### Related - `phase-postgres-6d-semantic-search` — phase note - PR #130 — embedding pipeline (6c) that this depends on - `decision-phase6-vector-search-architecture` — architectural decisions

forgejo_admin referenced this issue from a commit

2026-03-09 14:01:33 +00:00

Add GET /notes/semantic-search endpoint for vector similarity search

forgejo_admin referenced this issue from a pull request that will close it,

2026-03-09 14:02:23 +00:00

Add semantic search API endpoint (GET /notes/semantic-search) #138

forgejo_admin commented

2026-03-09 14:04:37 +00:00

Author

Owner

PR #138 Review

BLOCKERS

None. The implementation is solid and ready to merge.

NITS

extract_block_text called without note_title (route line 368)
The SQL SELECT already fetches n.title, and extract_block_text accepts an optional note_title parameter (used by the embedding worker to provide heading context like "My Note: Section Heading"). The route calls extract_block_text(row["block_type"], content) without passing it. For heading blocks, the content_snippet will just be the raw heading text (e.g., "Scope") instead of the richer "My Note: Scope". This is cosmetic -- the note title is already in the title response field -- but passing it would make snippets more self-contained:
```
snippet = extract_block_text(row["block_type"], content, note_title=row["title"])
```
test_semantic_search_filters_applied_in_sql does not actually verify filter passthrough
This test sends all filter params, then asserts only mock_post.called. Since the Ollama mock raises ConnectError, the function returns 503 before the SQL is ever built or executed. The test verifies that filters don't cause a crash before the Ollama call (which is useful), but its docstring claims it verifies filters are "passed to SQL", which is misleading. Consider renaming to test_semantic_search_filters_dont_break_request or adding a comment clarifying the actual assertion scope.
_EMBEDDING_MODEL duplicates OLLAMA_MODEL from embedding_worker.py
Both read from os.environ.get("PALDOCS_EMBEDDING_MODEL", "qwen3-embedding:4b"). If someone changes the env var only in one place, they'll silently diverge. Consider importing the constant from embedding_worker or extracting it to config.py. Low risk since the env var is the same, but worth noting for future maintainability.
Minor: b.id selected but unused
The SQL SELECT includes b.id but it's never used in the SemanticSearchResult construction. Not harmful (SQLAlchemy mappings just ignore it), but removing it would tighten the query.
Instruction prefix asymmetry is correct but not documented
The embedding worker uses "Represent this platform knowledge base section for retrieval: " for documents, while the route uses "Represent this query for retrieving relevant documents: " for queries. This is the correct asymmetric prefix pattern for qwen3-embedding (document vs. query intent). A brief comment in the route explaining why the prefix differs from the worker would help future readers.

SOP COMPLIANCE

Branch named after issue: 137-semantic-search-endpoint matches issue #137
PR body follows template: Summary, Changes, Test Plan, Related sections all present
Related references plan slug: plan-2026-02-26-tf-modularize-postgres referenced
Closes #137 present in PR body
No secrets committed: no .env files, credentials, or sensitive data
No scope creep: all 3 changed files are directly scoped to the feature
Tests exist: 12 tests covering 501/SQLite, 422 validation, 503 Ollama errors, instruction prefix, model name, cosine operator, filter passthrough, limit bounds

SECURITY NOTES

SQL injection: SAFE. All user-supplied filter values (note_type, status, project, tags) use SQLAlchemy text() bind parameters (:note_type, :status, etc.). The query vector is passed as :query_vec with a ::vector cast. The where_sql f-string interpolation only inserts pre-built clause strings, never raw user input.
Ollama error handling: COMPLETE. ConnectError, TimeoutException, HTTPStatusError, and empty embeddings are all caught and mapped to 503 with descriptive messages. No stack traces leak to the client.

VERDICT: APPROVED

Clean implementation that mirrors the existing search_notes pattern faithfully. SQL injection surface is properly parameterized. Ollama error handling is thorough. Route ordering is correct (before /{slug} wildcard). The nits above are all non-blocking improvements.

## PR #138 Review ### BLOCKERS None. The implementation is solid and ready to merge. ### NITS 1. **`extract_block_text` called without `note_title` (route line 368)** The SQL SELECT already fetches `n.title`, and `extract_block_text` accepts an optional `note_title` parameter (used by the embedding worker to provide heading context like `"My Note: Section Heading"`). The route calls `extract_block_text(row["block_type"], content)` without passing it. For heading blocks, the `content_snippet` will just be the raw heading text (e.g., `"Scope"`) instead of the richer `"My Note: Scope"`. This is cosmetic -- the note title is already in the `title` response field -- but passing it would make snippets more self-contained: ```python snippet = extract_block_text(row["block_type"], content, note_title=row["title"]) ``` 2. **`test_semantic_search_filters_applied_in_sql` does not actually verify filter passthrough** This test sends all filter params, then asserts only `mock_post.called`. Since the Ollama mock raises `ConnectError`, the function returns 503 before the SQL is ever built or executed. The test verifies that filters don't cause a crash before the Ollama call (which is useful), but its docstring claims it verifies filters are "passed to SQL", which is misleading. Consider renaming to `test_semantic_search_filters_dont_break_request` or adding a comment clarifying the actual assertion scope. 3. **`_EMBEDDING_MODEL` duplicates `OLLAMA_MODEL` from `embedding_worker.py`** Both read from `os.environ.get("PALDOCS_EMBEDDING_MODEL", "qwen3-embedding:4b")`. If someone changes the env var only in one place, they'll silently diverge. Consider importing the constant from `embedding_worker` or extracting it to `config.py`. Low risk since the env var is the same, but worth noting for future maintainability. 4. **Minor: `b.id` selected but unused** The SQL SELECT includes `b.id` but it's never used in the `SemanticSearchResult` construction. Not harmful (SQLAlchemy mappings just ignore it), but removing it would tighten the query. 5. **Instruction prefix asymmetry is correct but not documented** The embedding worker uses `"Represent this platform knowledge base section for retrieval: "` for documents, while the route uses `"Represent this query for retrieving relevant documents: "` for queries. This is the correct asymmetric prefix pattern for qwen3-embedding (document vs. query intent). A brief comment in the route explaining why the prefix differs from the worker would help future readers. ### SOP COMPLIANCE - [x] Branch named after issue: `137-semantic-search-endpoint` matches issue #137 - [x] PR body follows template: Summary, Changes, Test Plan, Related sections all present - [x] Related references plan slug: `plan-2026-02-26-tf-modularize-postgres` referenced - [x] `Closes #137` present in PR body - [x] No secrets committed: no `.env` files, credentials, or sensitive data - [x] No scope creep: all 3 changed files are directly scoped to the feature - [x] Tests exist: 12 tests covering 501/SQLite, 422 validation, 503 Ollama errors, instruction prefix, model name, cosine operator, filter passthrough, limit bounds ### SECURITY NOTES - **SQL injection: SAFE.** All user-supplied filter values (`note_type`, `status`, `project`, `tags`) use SQLAlchemy `text()` bind parameters (`:note_type`, `:status`, etc.). The query vector is passed as `:query_vec` with a `::vector` cast. The `where_sql` f-string interpolation only inserts pre-built clause strings, never raw user input. - **Ollama error handling: COMPLETE.** `ConnectError`, `TimeoutException`, `HTTPStatusError`, and empty embeddings are all caught and mapped to 503 with descriptive messages. No stack traces leak to the client. ### VERDICT: APPROVED Clean implementation that mirrors the existing `search_notes` pattern faithfully. SQL injection surface is properly parameterized. Ollama error handling is thorough. Route ordering is correct (before `/{slug}` wildcard). The nits above are all non-blocking improvements.

forgejo_admin commented

2026-03-09 14:14:19 +00:00

Author

Owner

PR #138 Review (Re-review after nit fixes)

BLOCKERS

None.

NIT FIX VERIFICATION

All 5 nits from the first review are properly resolved in commit 7cbfe7b:

extract_block_text receives note_title -- FIXED. routes/notes.py line 370 passes note_title=row["title"]. The function signature in embedding_worker.py line 79 already had the note_title: str | None = None parameter. Headings now get proper context in search snippets.
Misleading test docstring reworded -- FIXED. Module docstring in test_semantic_search.py now accurately describes "SQLite (in-memory)" and "mocked via httpx mocking."
_EMBEDDING_MODEL moved to settings.embedding_model -- FIXED. Single source of truth in config.py line 13 (embedding_model: str = "qwen3-embedding:4b"). Both routes/notes.py (line 27) and embedding_worker.py (line 46) read from settings.embedding_model. The env var PALDOCS_EMBEDDING_MODEL works correctly due to the PALDOCS_ prefix in pydantic-settings. No regression -- same default value, same env var name as the old os.environ.get() pattern.
Unused b.id removed from SELECT -- FIXED. SQL query at lines 352-353 selects only b.anchor_id, b.block_type, b.content -- no extraneous columns.
Asymmetric prefix comment added -- FIXED. Lines 28-31 of routes/notes.py explain why query and document prefixes differ and where the document prefix lives (embedding_worker.INSTRUCTION_PREFIX).

NITS

None. The formatting-only changes in embedding_worker.py (dict indentation, 2 ** attempt to 2**attempt, string literal consolidation) are consistent with ruff auto-format from touching the file. Acceptable.

SOP COMPLIANCE

Branch named after issue (137-semantic-search-endpoint)
PR body follows template (Summary, Changes, Test Plan, Related)
Related references plan slug (plan-2026-02-26-tf-modularize-postgres)
Closes #137 present in PR body
Tests exist (12 tests in test_semantic_search.py)
No secrets or credentials committed
No scope creep (minor formatting changes are ruff artifacts)

VERDICT: APPROVED

## PR #138 Review (Re-review after nit fixes) ### BLOCKERS None. ### NIT FIX VERIFICATION All 5 nits from the first review are properly resolved in commit 7cbfe7b: 1. **extract_block_text receives note_title** -- FIXED. `routes/notes.py` line 370 passes `note_title=row["title"]`. The function signature in `embedding_worker.py` line 79 already had the `note_title: str | None = None` parameter. Headings now get proper context in search snippets. 2. **Misleading test docstring reworded** -- FIXED. Module docstring in `test_semantic_search.py` now accurately describes "SQLite (in-memory)" and "mocked via httpx mocking." 3. **_EMBEDDING_MODEL moved to settings.embedding_model** -- FIXED. Single source of truth in `config.py` line 13 (`embedding_model: str = "qwen3-embedding:4b"`). Both `routes/notes.py` (line 27) and `embedding_worker.py` (line 46) read from `settings.embedding_model`. The env var `PALDOCS_EMBEDDING_MODEL` works correctly due to the `PALDOCS_` prefix in pydantic-settings. No regression -- same default value, same env var name as the old `os.environ.get()` pattern. 4. **Unused b.id removed from SELECT** -- FIXED. SQL query at lines 352-353 selects only `b.anchor_id, b.block_type, b.content` -- no extraneous columns. 5. **Asymmetric prefix comment added** -- FIXED. Lines 28-31 of `routes/notes.py` explain why query and document prefixes differ and where the document prefix lives (`embedding_worker.INSTRUCTION_PREFIX`). ### NITS None. The formatting-only changes in `embedding_worker.py` (dict indentation, `2 ** attempt` to `2**attempt`, string literal consolidation) are consistent with ruff auto-format from touching the file. Acceptable. ### SOP COMPLIANCE - [x] Branch named after issue (`137-semantic-search-endpoint`) - [x] PR body follows template (Summary, Changes, Test Plan, Related) - [x] Related references plan slug (`plan-2026-02-26-tf-modularize-postgres`) - [x] `Closes #137` present in PR body - [x] Tests exist (12 tests in `test_semantic_search.py`) - [x] No secrets or credentials committed - [x] No scope creep (minor formatting changes are ruff artifacts) ### VERDICT: APPROVED

forgejo_admin referenced this issue from a commit

2026-03-09 14:17:13 +00:00

Add GET /notes/semantic-search endpoint for vector similarity search

forgejo_admin closed this issue

2026-03-09 14:17:19 +00:00