* perf: replace window-function retrieval with UNION ALL + per-bank HNSW indexes
The previous retrieve_semantic_bm25_combined() used ROW_NUMBER() OVER (PARTITION
BY fact_type ...) which forced a full sequential scan — pgvector cannot use HNSW
indexes when a window function partitions on the same column as the ORDER BY.
Changes:
- retrieval.py: rewrite to UNION ALL of per-fact_type subqueries; each arm has
its own ORDER BY embedding <=> $1 LIMIT n, enabling partial HNSW index scans.
Semantic arms over-fetch 5x (min 100) for HNSW approximation; trimmed in Python.
- memory_engine.py: set hnsw.ef_search=200 at pool init (persistent per-connection,
no per-query SET/RESET overhead).
- bank_utils.py: add create_bank_hnsw_indexes / drop_bank_hnsw_indexes for
per-(bank_id, fact_type) partial HNSW index lifecycle management.
- fact_storage.py / bank_utils.py: create per-bank indexes on fresh bank insert.
- memory_engine.py delete_bank: drop per-bank indexes via DELETE...RETURNING to
avoid a separate round-trip.
- Migration a3b4c5d6e7f8: add interim fact_type-only partial indexes.
- Migration d5e6f7a8b9c0: add internal_id UUID UNIQUE to banks, replace
fact_type-only indexes with per-(bank, fact_type) partial HNSW indexes, drop
the global idx_memory_units_embedding that competed with them.
Why per-(bank, fact_type) not just per-fact_type:
The idx_memory_units_bank_id B-tree index always wins over fact_type-only partial
indexes when bank_id appears in the WHERE clause. Including bank_id in the partial
index predicate removes the B-tree from consideration and lets the planner choose
HNSW. The global HNSW index must also be dropped to avoid competing for the larger
fact_type partitions (world, observation).
* refactor: collapse two HNSW migrations into one
* refactor: generate bank internal_id in Python before insert
Instead of relying on DEFAULT gen_random_uuid() and RETURNING internal_id,
generate the UUID in application code before the INSERT. This means we
always know the value upfront and can call create_bank_hnsw_indexes
immediately without needing a DB round-trip to retrieve the assigned ID.
Also adds tests for HNSW index lifecycle and retrieve_semantic_bm25_combined.
* fix: correct migration and prevent global HNSW index recreation
Migration fixes:
- Add text() wrappers for raw SQL in d5e6f7a8b9c0 (SQLAlchemy 2.0 compat)
- Drop stale fact_type-only partial indexes (idx_mu_emb_world/observation/experience)
that may exist from prior migrations on the same DB
migrations.py fix:
- Skip global HNSW index creation when per-bank partial HNSW indexes already
exist on memory_units (idx_mu_emb_* pattern). Without this, the post-migration
vector index check detects no %embedding% named index and recreates the global
idx_memory_units_embedding, which defeats the per-bank index strategy.
Verified with EXPLAIN ANALYZE on 66K-row bank: all three fact_type arms use
their per-bank HNSW index scan (idx_mu_emb_worl/expr/obsv_<uid16>).
* fix: use correct embeddings.encode() in test
* feat: support timescale pg_textsearch as text search extension
* refactor: deduplicate text search query in retrieve_semantic_bm25_combined
Instead of maintaining 3 complete query copies (native, vchord, pg_textsearch),
now we:
- Build backend-specific parts (score_expr, order_by, where_filter)
- Use a single query template with injected backend-specific parts
This makes maintenance easier - changes to the semantic CTE or overall structure
only need to be made once.
* feat: support for other text and vector search pg extensions
* test: increase timeout for test_batch_chunking_behavior to account for VectorChord BM25 tokenization overhead
* feat: support for other text and vector search pg extensions
* Improve LongMemEval benchmark with structured prompts and better options
- Add --context-format option with 'json' (original) and 'structured' modes
- Structured format groups facts with source chunks for better LLM comprehension
- Add detailed instructions for date calculations, relative time handling, and abstention
- Add --source-results flag to read failed questions from a different file
- Allow --category to be combined with --max-instances for sampling
- Fix Gemini structured output by passing response_schema parameter
- Add retry logic for empty Gemini responses with block reason logging
- Add judge prompt comparison documentation
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* fix recall in benchmarks
* Improve LongMemEval prompt and Gemini error handling
- Add JSONDecodeError retry for Gemini truncated responses
- Increase max_tokens to 32768 for thinking models
- Add counting/disambiguation guidance to structured prompt
- Add "when in doubt, undercount" and overlap detection rules
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* Add connection error retry and preference question guidance
- Add APIConnectionError retry for OpenAI client (server disconnects)
- Add recommendation/preference question guidance to structured prompt
- Instruct model to build on user's existing tools/experiences
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* Make reasoning optional
* Seed for LLM through Groq
* fix entity and observations
* Increase graph retrieval neighbor limit for expanded entities
Doubled the neighbor limit multiplier from 10 to 20 in graph retrieval.
With expanded entity extraction (now including objects and concepts like
"kitchen"), facts share more common entities, causing the previous limit
to arbitrarily exclude relevant results. This fix ensures better recall
for questions about related items (e.g., kitchen items).
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* Expand entity extraction to include objects and concepts
Updated entity extraction prompt to include:
- Specific objects (coffee maker, toaster, car, laptop, kitchen)
- Abstract concepts/themes (friendship, career growth, loss, celebration)
- Places and organizations (IKEA, Goodwill, New York)
This enables better fact linking through shared entities. For example,
kitchen appliances now share a "kitchen" entity, allowing graph traversal
to find related facts like "replaced coffee maker" when querying about
"kitchen items".
Works in conjunction with the increased neighbor limit to improve recall.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
---------
Co-authored-by: Chris Bartholomew <chris.bartholomew@vectorize.io>
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: andrew <andrew.neeser@me.com>
