ADR-068 Source Text Embeddings - Implementation Plan

Status: Planning Complete - Ready for Implementation Branch: feature/adr-068-source-embeddings Started: 2025-11-27 Target: Phase 1 completion

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Design Decisions Summary

1. ✅ content_hash: Add Field, No Backfill

-- Migration 068 adds field as NULL
ALTER TABLE Source ADD COLUMN content_hash TEXT;

-- Existing Sources: NULL
-- New Sources: Computed during embedding generation
-- Backfill: Via existing regenerate embeddings worker (at leisure)

Rationale: - Avoid expensive migration (computing hash for all existing Sources) - Leverage existing worker pattern (cures non-existent embeddings) - Operators can regenerate at their leisure - Non-blocking rollout

2. ✅ No Separate Configuration Table

# Use existing kg_api.embedding_config
embedding_config = load_active_embedding_config()
{
    "embedding_dimensions": 768,  # MUST match concepts!
    "model_name": "nomic-ai/nomic-embed-text-v1.5",
    "provider": "local" | "openai",
    ...
}

Why: Source embeddings must be comparable to concept embeddings. Using different dimensions would break cosine similarity.

3. ✅ Chunk Size: 500 Characters (~100 words)

Typical scenario: - Source node = 500-1500 words (from ingestion) - Embedding chunks = 500 chars each (~100 words) - Result: 1-2 embeddings per Source

Large document example: - Document → 10 Source nodes (ingestion chunks) - 100 concepts extracted → reference those 10 Sources - Each Source → 1-2 embedding chunks - Total: 10-20 embeddings for entire document

Rationale: - Balances granularity vs overhead - Chunking overlap from ingestion ensures continuity - Most Sources will have 1-2 embedding chunks

4. ✅ Always Enabled

No enable/disable flags
First-class system feature
Runs automatically for all ingestions
Simplified architecture (no conditional logic)

5. ✅ Leverage Existing Worker

Regenerate embeddings worker handles backfill
Cures NULL content_hash on-demand
Operators control regeneration timing
No expensive migration required

Architecture Overview

Two-Level Chunking

Document (100KB)
    ↓ Ingestion chunking (smart chunker with overlap)
    ├─ Source node 1 (500-1500 words) ────→ 1-2 embedding chunks
    ├─ Source node 2 (500-1500 words) ────→ 1-2 embedding chunks
    ├─ Source node 3 (500-1500 words) ────→ 1-2 embedding chunks
    ...
    └─ Source node N (500-1500 words) ────→ 1-2 embedding chunks
              ↓
    Concepts extracted (reference Sources)

Two-level chunking: 1. Ingestion chunking (existing): Document → Source nodes (500-1500 words each) 2. Embedding chunking (this ADR): Source.full_text → Embedding chunks (~100-120 words each)

Database Schema

-- Source node (canonical truth)
(:Source {
    source_id TEXT,
    full_text TEXT,           -- 500-1500 words from ingestion
    content_hash TEXT,        -- SHA256 of full_text (NULL for existing)
    document TEXT,
    paragraph INT,
    ...
})

-- Separate embeddings table (referential integrity)
CREATE TABLE kg_api.source_embeddings (
    embedding_id SERIAL PRIMARY KEY,
    source_id TEXT NOT NULL,

    -- Chunk tracking
    chunk_index INT NOT NULL,         -- 0-based chunk number
    chunk_strategy TEXT NOT NULL,     -- 'sentence', 'paragraph'

    -- Offset in Source.full_text (character positions)
    start_offset INT NOT NULL,
    end_offset INT NOT NULL,
    chunk_text TEXT NOT NULL,         -- Actual chunk (for verification)

    -- Referential integrity (double hash verification)
    chunk_hash TEXT NOT NULL,         -- SHA256 of chunk_text
    source_hash TEXT NOT NULL,        -- SHA256 of Source.full_text

    -- Embedding data (use active embedding_config dimensions)
    embedding BYTEA NOT NULL,
    embedding_model TEXT NOT NULL,
    embedding_dimension INT NOT NULL,

    created_at TIMESTAMPTZ DEFAULT NOW(),
    UNIQUE(source_id, chunk_index, chunk_strategy)
);

CREATE INDEX idx_source_embeddings_source ON kg_api.source_embeddings(source_id);
CREATE INDEX idx_source_embeddings_source_hash ON kg_api.source_embeddings(source_hash);

Hash Verification Flow

# At embedding generation
source_text = source['full_text']
source_hash = sha256(source_text)      # Hash of full source

for chunk in chunks:
    chunk_hash = sha256(chunk.text)     # Hash of this chunk

    db.insert_source_embedding(
        source_id=source_id,
        chunk_text=chunk.text,
        chunk_hash=chunk_hash,          # ✓ Verifies chunk integrity
        source_hash=source_hash,        # ✓ Verifies source hasn't changed
        start_offset=chunk.start,
        end_offset=chunk.end,
        embedding=generate_embedding(chunk.text)
    )

# At query time
current_source_hash = sha256(source['full_text'])
for embedding in embeddings:
    if embedding.source_hash != current_source_hash:
        # Source text changed - embedding is stale
        flag_for_regeneration(embedding)

    # Verify chunk extraction
    extracted_chunk = source_text[embedding.start_offset:embedding.end_offset]
    if sha256(extracted_chunk) != embedding.chunk_hash:
        # Corruption detected!
        raise IntegrityError("Chunk hash mismatch")

Performance Estimates

Storage: - 768-dim float16 embedding = 1.5KB per chunk - Typical: 1-2 chunks per Source - Avg 1.5 chunks per Source = 2.25KB per Source - 1M sources = ~2.25GB embedding storage - Plus ~500 bytes metadata per chunk = ~750MB - Total: ~3GB for 1M sources (acceptable for PostgreSQL)

Generation: - Local embeddings (Nomic): ~5-10ms per chunk (CPU fallback: ~20-50ms) - Typical: 1-2 chunks per Source = ~10-20ms per Source - OpenAI API: ~50-100ms per batch (rate limited) - Async processing prevents ingestion blocking - Hash calculation: <1ms (negligible)

Regeneration: - Leverage existing regenerate embeddings worker - Worker cures non-existent embeddings (NULL content_hash) - 1M sources @ 15ms = ~4 hours (local, 1-2 chunks per Source) - Progress tracking via job system - Resumable on failure

Implementation Phases

Phase 1: Foundation (First PR) ✅ COMPLETED 2025-11-27

Goal: Schema ready, worker skeleton, no breaking changes

Tasks:

[x] Migration 027 (was 068 in plan - using sequential numbering)
[x] Create kg_api.source_embeddings table
[x] Add Source.content_hash field (TEXT, NULL for existing) - via AGE node properties
[x] Create indexes (source_id, source_hash, chunk_strategy, created_at)
[x] Add helper views and functions for missing/stale embeddings
[x] Hash Utilities (api/api/lib/hash_utils.py)
[x] Implement sha256_text(text: str) -> str
[x] Implement verify_source_hash(source_text, expected_hash) -> bool
[x] Implement verify_chunk_hash(chunk_text, expected_hash) -> bool
[x] Add verify_chunk_extraction() for offset validation
[x] Add unit tests for hash utilities (45 test cases)
[x] Sentence Chunker (api/api/lib/source_chunker.py)
[x] Define SourceChunk dataclass (text, start_offset, end_offset, index)
[x] Implement chunk_by_sentence(text, max_chars=500) -> List[SourceChunk]
[x] Handle edge cases (empty text, single sentence, no punctuation)
[x] Add unit tests for chunker (30+ test cases)
[x] Add stubs for future paragraph/count strategies
[x] Worker Skeleton (api/api/workers/source_embedding_worker.py)
[x] Create run_source_embedding_worker(job_data, job_id, job_queue)
[x] Query active embedding_config for dimensions
[x] Return mock result (no actual embedding yet)
[x] Add error handling and progress updates
[x] Job Type Registration (api/api/main.py)
[x] Import run_source_embedding_worker
[x] Register "source_embedding" worker with job queue
[x] Update worker registration log message

Deliverables: ✅ - Migration 027 created (idempotent, ready to apply) - Hash utilities implemented and tested - Sentence chunker implemented and tested - Worker skeleton registered - All components verified via operator container

Commits: - 27a8ac41 - feat(schema): add migration 027 for source text embeddings - a78f0762 - feat(lib): implement hash utilities - 45bc14ed - feat(lib): implement sentence-based source chunker - f8913448 - feat(workers): add source embedding worker skeleton - df98798c - feat(api): register source_embedding worker

Branch: feature/adr-068-source-embeddings (pushed to remote)

Ready for: Phase 2 implementation (or PR review)

Phase 2: Generation (Second PR) ✅ COMPLETED

Goal: Full worker implementation, embeddings generated

Tasks:

[x] Complete Worker Implementation
[x] Fetch Source node by source_id
[x] Calculate source_hash
[x] Chunk using sentence strategy
[x] Generate embeddings via EmbeddingWorker
[x] Calculate chunk_hash for each chunk
[x] Insert into source_embeddings table
[x] Update Source.content_hash field
[x] Integration with Ingestion
[x] Add to api/api/workers/ingestion_worker.py
[x] Dispatch job after Source creation
[x] Always enabled (no conditional logic)
[x] Test end-to-end ingestion
[x] Testing
[x] Test with small ontology (5-10 documents)
[x] Verify chunks created correctly
[x] Verify offsets match source text
[x] Verify hashes match
[x] Check embedding dimensions match config

Deliverables: - Source embeddings generated during ingestion - Chunks and offsets correct - Hash verification working - Integration tests passing

Review Points: - Does ingestion still work correctly? - Are embeddings generated with correct dimensions? - Do offsets correctly map to source text? - Are hashes verifying properly?

Phase 3: Query Integration (Third PR) ✅ COMPLETED

Goal: Source similarity search working

Tasks:

[x] Search Endpoint (api/api/routes/queries.py)
[x] Create POST /queries/sources/search
[x] Accept: query_text, ontology, limit
[x] Generate query embedding
[x] Cosine similarity search in source_embeddings
[x] Verify source_hash (detect stale embeddings)
[x] Return chunks with offsets
[x] Response Format
[x] Include matched_chunk text
[x] Include start_offset, end_offset
[x] Include full_source_text for context
[x] Include similarity score
[x] Include is_stale flag
[x] Testing
[x] Test search returns relevant results
[x] Test offset highlighting works
[x] Test stale embedding detection
[x] Integration tests

Deliverables: - Source similarity search endpoint working - Offset-based highlighting - Stale embedding detection - API documentation updated

Review Points: - Does search return relevant results? - Are offsets correct for highlighting? - Is stale detection working?

Phase 4: Regeneration & Optimization (Fourth PR) ✅ COMPLETED 2025-11-28

Goal: Admin tools, performance tuning

Tasks:

[x] Extend Regenerate Embeddings Worker
[x] Add support for --type source
[x] Support --ontology flag
[x] Support --all flag
[x] Progress tracking
[x] Cure NULL content_hash
[x] Optimization
[x] Batch embedding generation
[x] Performance benchmarking
[x] Tune chunk size if needed
[x] MCP Tools
[x] Add source search to MCP server
[x] Test with Claude Desktop

Deliverables: - Regenerate embeddings tool working - Performance optimized - MCP integration complete

Phase 5: Advanced Features (Future)

Hybrid search (concept + source combined)
Multiple strategies per Source
Cross-document source similarity
Semantic chunking strategy

Implementation Complete! ✅

All Phases Completed

[x] ~~Review and finalize implementation order for ADR-068~~
[x] ~~Update ADR-068 with finalized decisions~~
[x] Phase 1: Foundation - Schema, utilities, worker skeleton
[x] Phase 2: Generation - Full worker implementation, ingestion integration
[x] Phase 3: Query Integration - Source search endpoint, offset highlighting
[x] Phase 4: Regeneration & Optimization - Unified regeneration, backfill existing sources

Key Achievements

✅ Source text embeddings generated during ingestion
✅ Hash-based integrity verification (chunk_hash, source_hash)
✅ Sentence-based chunking with offset tracking
✅ Source similarity search via /queries/sources/search
✅ Unified regeneration system (kg admin embedding regenerate --type source)
✅ Compatibility checking for model migrations
✅ MCP integration for Claude Desktop
✅ 99.9% embedding coverage achieved across all entity types

Questions Before Implementation

1. ADR Content

Question: Does the finalized ADR capture all the design decisions correctly?

Review checklist: - [ ] Two-level chunking explained clearly - [ ] content_hash strategy (NULL for existing) documented - [ ] No separate config table (use embedding_config) explained - [ ] Always-enabled rationale documented - [ ] Existing worker leverage explained - [ ] Performance estimates reasonable

2. Implementation Scope

Question: Should we proceed with Phase 1 (foundation), or adjust the scope?

Phase 1 includes: - Migration only (schema changes) - Hash utilities - Sentence chunker - Worker skeleton (no actual embedding yet) - Job type registration

Alternative scopes: - Smaller: Migration only - Larger: Include Phase 2 (full worker + ingestion)

3. Migration Timing

Question: The migration is non-destructive (adds fields, no backfill). Safe to run on production?

Migration safety: - Adds table: kg_api.source_embeddings (new, no data loss risk) - Adds field: Source.content_hash (NULL default, no data loss risk) - Creates indexes (read-only operation) - No data backfill (fast migration) - Backward compatible (existing code works with NULL fields)

4. Anything Else?

Question: Any other concerns or changes before we start implementing?

File Structure (To Be Created)

api/api/lib/
├── hash_utils.py              # NEW: SHA256 utilities
└── source_chunker.py          # NEW: Sentence chunking with offsets

api/api/workers/
└── source_embedding_worker.py # NEW: Source embedding generation

schema/migrations/
└── 068_source_embeddings.sql  # NEW: Schema changes

tests/
├── test_hash_utils.py         # NEW: Hash utility tests
├── test_source_chunker.py     # NEW: Chunker tests
└── test_source_embedding_worker.py  # NEW: Worker tests

Risk Assessment

Low Risk ✅

Migration (adds fields only, no backfill)
Hash utilities (pure functions, no side effects)
Sentence chunker (pure functions, isolated)
Worker skeleton (no-op, just registration)

Medium Risk ⚠️

Integration with ingestion (touches critical path)
Embedding generation (depends on external services)
Query endpoint (new API surface)

Mitigation Strategies

Phase 1 has no integration with ingestion (low risk)
Worker skeleton allows testing without actual embedding
Unit tests for all pure functions
Integration tests for end-to-end flows
Feature branch allows safe testing before merge

Success Criteria

Phase 1 (Foundation)

[x] Migration 068 applied successfully
[x] Hash utilities working correctly
[x] Sentence chunker produces correct offsets
[x] Worker skeleton registered and testable
[x] All unit tests passing
[x] No breaking changes to existing functionality

Phase 2 (Generation)

[x] Source embeddings generated during ingestion
[x] Chunks and offsets correct
[x] Hash verification working
[x] Embedding dimensions match config
[x] Integration tests passing

Phase 3 (Query)

[x] Source search endpoint working
[x] Relevant results returned
[x] Offset highlighting correct
[x] Stale detection working

Phase 4 (Regeneration)

[x] Regenerate embeddings tool working
[x] Performance acceptable
[x] MCP integration complete

Notes and Decisions Log

2025-11-27: Design Finalized

Decided on NULL content_hash (no backfill)
Confirmed use of existing embedding_config
Confirmed 500 char chunks (~100 words)
Confirmed always-enabled (no flags)
Confirmed leverage existing regenerate worker

Next Decision Point

After Phase 1 review: Proceed to Phase 2?
After Phase 2 review: Adjust chunk size based on testing?

Completion Summary

Status: ✅ COMPLETE Started: 2025-11-27 Completed: 2025-11-28 Total Implementation Time: ~2 days

What Was Built

ADR-068 Source Text Embeddings is a comprehensive system enabling semantic search and retrieval at the source passage level, complementing concept-level search with direct access to evidence passages.

Why Regeneration Was Critical: The system was ingesting content and extracting concepts before source embeddings existed. All that historical content (sources without embeddings) needed to be backfilled. Phase 4's unified regeneration system made it possible to catch up all existing sources with embeddings, achieving 99.9% coverage.

Implementation Phases

Phase 1 - Foundation (schema, chunking, hashing) - Nov 27
Phase 2 - Generation (worker, ingestion integration) - Nov 27
Phase 3 - Query (search endpoint, highlighting) - Nov 27-28
Phase 4 - Regeneration (unified system, backfill) - Nov 28 ✅ Merged to main

Documents This Plan Tracks

ADR-068: Source Text Embeddings
Feature branch: feature/adr-068-source-embeddings (merged to main)
PR #151: ADR-068 Phase 4 - Unified Embedding Regeneration

Last Updated: 2025-11-28