5 commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Nicolò Boschi
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914ba7962c
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perf: 3-phase retain pipeline — fix deadlocks, cap temporal links, query-time entity expansion (#722)
* perf: 3-phase retain pipeline — fix deadlocks, cap temporal links, query-time entity expansion
Major retain pipeline overhaul addressing deadlocks, write amplification,
and TimeoutErrors. Restructures retain into three phases:
Phase 1: Entity resolution on separate connection (read-heavy)
Phase 2: Core write transaction (atomic) — facts, unit_entities, links
Phase 3: Best-effort display data (error-isolated) — entity viz links, stats
Key changes:
- Sorted bulk INSERT FROM unnest() prevents deadlocks
- Temporal links capped to top-20 per unit (95% reduction)
- Batched semantic ANN via temp table + LATERAL
- Query-time entity expansion via unit_entities self-join
- Entity viz links moved to Phase 3 (post-transaction)
- HINDSIGHT_API_RETAIN_MAX_CONCURRENT config (default: 32)
* fix: increase semantic link top_k from 5 to 20
The hardcoded top_k=5 was artificially limiting semantic link creation.
Link expansion retrieval can consume up to budget (50-200) semantic
neighbors per seed set, but each fact only had 5 outgoing edges — making
the bidirectional graph very sparse.
Increasing to 20 gives retrieval 4x more edges to work with. The ANN
probe cost is unchanged (same HNSW traversal per fact, just returning
more rows). INSERT cost is negligible (~14k rows via bulk INSERT).
Also: all 18 TimeoutErrors in the latest benchmark (beam-1m-u20) were
from Gemini LLM calls, zero from the database — confirming the entity
resolution split eliminated DB timeouts entirely.
* perf: move semantic ANN search to Phase 1 to avoid transaction timeouts
The batched LATERAL ANN query (700 HNSW probes) was the last remaining
source of DB TimeoutErrors — all 29 in the latest benchmark were from
create_semantic_links_batch inside the Phase 2 write transaction.
Split semantic link creation into three phases:
- Phase 1 (separate conn, autocommit): ANN search via temp table + LATERAL.
No transaction locks, no contention with concurrent writers.
- Phase 2 (write transaction): within-batch numpy similarities (instant) +
INSERT of both within-batch and Phase 1 ANN results. No DB reads.
- Phase 3 (flush_pending_stats): future hook point for re-checking ANN
results after commit to catch links missed by concurrent batches.
Also adds 7 unit tests for compute_semantic_links_within_batch covering
empty input, identical/orthogonal embeddings, threshold filtering, top_k
cap, and tuple structure validation.
* fix: handle placeholder unit_ids in Phase 1 ANN search (not valid UUIDs)
* test: add Phase 1 ANN cross-batch test + configurable test PG port
- New test_semantic_links_phase1_ann_cross_batch verifies that the Phase 1
ANN search with placeholder unit IDs correctly creates cross-batch
semantic links after remapping to real IDs.
- Test PG port now configurable via HINDSIGHT_TEST_PG_PORT env var
(default: 5556) to avoid conflicts with running benchmark daemons.
* perf: remove retry_with_backoff from retain, set semaphore default to 4
Remove retry_with_backoff from _run_db_work and _run_delta_db_work:
- Deadlocks are prevented by sorted bulk INSERT (no need for retry)
- Transient timeouts are handled by the worker poller's task-level retry
(3 attempts, 60s spacing) which is better than rapid internal retries
that amplify I/O pressure during contention storms
Set HINDSIGHT_API_RETAIN_MAX_CONCURRENT default from 32 to 4:
- The semaphore gates Phase 1 (ANN + entity resolution) + Phase 2 (writes)
- At 4 concurrent, HNSW index I/O is manageable; at 10+ concurrent the
probes saturate disk and cause cascading timeouts
- LLM extraction still runs at full parallelism (semaphore acquired after)
* fix: add fact_type filter to Phase 1 ANN query to use per-bank HNSW indexes
The LATERAL ANN query was falling back to sequential scan + sort (90ms/probe)
because the per-bank HNSW indexes are partial indexes filtered on fact_type.
Without fact_type in the WHERE clause, PostgreSQL couldn't use them.
Fix: iterate over ('world', 'experience') and run one HNSW-indexed ANN per
type. EXPLAIN shows 8ms/probe (was 90ms) — 11x faster.
700 probes × 8ms × 2 types = ~11s total (was ~63s via seq scan).
* fix: scope temporal links by fact_type + add integration tests
Temporal links now filter by fact_type in the LATERAL query — world facts
only link to world facts, experience to experience. This matches how
retrieval filters results and avoids wasted cross-type link rows.
New integration tests:
- test_semantic_ann_uses_hnsw_index: verifies Phase 1 ANN creates
cross-batch semantic links (tests fact_type filter + placeholder remap)
- test_temporal_links_scoped_by_fact_type: verifies world facts get
temporal links to other world facts but NOT to experience facts
* fix: tolerate individual chunk LLM failures instead of failing entire batch
Changed asyncio.gather(*tasks) to asyncio.gather(*tasks, return_exceptions=True)
in both chunk-level and content-level fact extraction. A single chunk timeout
(e.g., Gemini >90s) no longer discards all other successfully extracted facts.
For a 50MB document with 17k chunks, even a 2% chunk failure rate previously
caused 0 completions (entire batch discarded). Now 16,700 facts are extracted
and only the 300 failed chunks are skipped with a warning log.
* fix: batch temporal LATERAL query for large documents (16k+ chunks)
The LATERAL query for temporal links passed all unit_ids at once into
unnest(), causing PostgreSQL timeouts on documents with 16k+ chunks.
Split into batches of 500 units per query to keep each under the
command_timeout.
Also identified: HNSW index creation on shared pg0 instances with
50k+ existing units exceeds the 60s command_timeout. This is a
test infrastructure issue (shared pg0 accumulates data) but also
affects production when creating new banks on large instances.
* feat: streaming chunk batching for large documents (RETAIN_CHUNK_BATCH_SIZE)
Process chunks in mini-batches of N (default 500), committing each batch
to the DB before starting the next. This prevents OOM kills on large
documents (50MB / 17k+ chunks) by keeping only ~500 facts + embeddings
in memory at a time instead of 50k+.
Each mini-batch goes through the full Phase 1 → 2 → 3 pipeline
independently, sharing the same document_id. On recovery (process dies
mid-way), delta retain detects already-committed chunks via content_hash
and skips them — only remaining chunks get re-extracted.
Config: HINDSIGHT_API_RETAIN_CHUNK_BATCH_SIZE (default: 500, 0 to disable)
Per-bank configurable via the hierarchical config system.
Tests:
- test_streaming_chunk_batching_produces_same_facts
- test_streaming_chunk_batching_recovery (delta retain skips committed chunks)
- test_streaming_disabled_for_small_docs
* perf(retain): producer-consumer pipeline + deferred semantic ANN
Replace the sequential streaming loop with a producer-consumer pipeline:
- LLM producer fires concurrent chunk extractions (semaphore-bounded)
- DB consumer drains queue in batches, runs Phase 1+2+3 per batch
- LLM and DB work overlap instead of running sequentially
Defer semantic links to a single final ANN pass after all batches commit:
- Remove within-batch semantic links from Phase 2 (was 2.6s/batch)
- Run parallel ANN (4 connections) after all facts committed
- top_k reduced from 50 to 20 (recall uses at most 20 neighbors)
- Recovery via operation result_metadata checkpoint
Additional optimizations:
- skip_exists_check on temporal/causal link INSERT (saves ~0.5s/batch)
- WHERE EXISTS guard on semantic link INSERT (handles document upsert)
- timeout=300s on ANN queries and bulk INSERT for large banks
- Demote [ANN] debug logs to logger.debug()
- Fix docstring typos (agent_id → bank_id)
- Fix content_index remapping in producer-consumer batches
- Fix delta retain passing contents vs delta_contents
50MB benchmark (mock LLM): 9.2 min (was 23 min) — 2.5x faster.
BEAM 10m benchmark: zero deadlocks, zero DB errors.
* refactor(retain): remove legacy fallback code paths
- Remove process_entities_batch (legacy single-connection entity processing)
- Remove extract_entities_batch_optimized (only caller was the above)
- Remove fallback entity processing inside Phase 2 transaction
- Remove legacy ANN inline fallback in create_semantic_links_batch
- Remove fallback entity_links direct-insert path in Phase 3
- Make resolved_entity_ids/entity_to_unit/unit_to_entity_ids required params
* refactor(retain): replace tuple returns with dataclasses, remove dead code
- Add EntityResolutionResult and Phase1Result dataclasses in types.py
- Replace 4-tuple return from _pre_resolve_phase1 with Phase1Result
- Remove dead `entity_links = []` variables in retain_batch and _try_delta_retain
- Remove unused `confidence_score` parameter from orchestrator.retain_batch
and _retain_batch_async_internal (was accepted but never used)
* fix(entity-resolver): remove LIKE full-scan fallbacks, use index-only trigram matching
The entity resolution query had LIKE '%...' substring conditions that bypassed
the GIN trigram index, causing full sequential scans of the entities table.
On banks with 10k+ entities, this caused TimeoutErrors (observed in BEAM 10m).
Changes:
- Remove LIKE fallbacks, use trigram % operator only (GIN index-based)
- Lower similarity threshold from 0.3 to 0.15 to catch substring relationships
- Use LOWER() on both sides for case-insensitive matching
- Migration: recreate GIN trigram index on LOWER(canonical_name)
* fix: remove schema prefix from index names in trigram migration
* fix(delta-retain): use same chunk_size as streaming path (3000 vs 120000)
_chunk_contents_for_delta defaulted to chunk_size=120000 while the streaming
path used 3000. On retry, delta re-chunked the document with different
boundaries, found 0 matching chunks, and fell through to full re-extraction.
This wasted all LLM calls on already-committed chunks.
Fix: use the same default (3000) so chunk hashes match on recovery.
* fix(retain): persist generated document_id in operation metadata for retry recovery
When no document_id is provided, retain generates a UUID. On retry, a new UUID
was generated, making delta retain and streaming chunk-hash recovery unable to
find previously committed chunks. All LLM extraction was wasted on retry.
Fix: resolve document_id early in retain_batch (before delta), persist it to
operation result_metadata, and recover it on retry. Both delta and streaming
paths now see the same document_id across attempts.
* refactor(retain): unify into single streaming pipeline, remove non-streaming path
All retains now go through the producer-consumer streaming pipeline,
regardless of document size. Small documents are processed as a single batch.
This eliminates the maintenance burden of two separate code paths.
Also fix document upsert: compare content hash to distinguish recovery
(same content, partially committed) from update (different content, needs
cascade-delete). Previously, existing chunks always triggered recovery mode.
* refactor(retain): remove dead code, replace raw dicts with Phase3Context dataclass
- Remove dead _handle_zero_facts_documents (no callers after path unification)
- Remove unused imports: defaultdict, EntityLink
- Replace raw dict phase3_context with typed Phase3Context dataclass
- Update _build_and_insert_entity_links_phase3 to use typed parameter
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Nicolò Boschi
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baf5447de2
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refactor: replace LLMProvider classmethods with from_env() and document missing config fields (#816)
CI failures are unrelated to this PR: - test_mental_models_dimension_change_empty_table: database OID error (infrastructure flake) - test_reflect_searches_mental_models_when_available: LLM-dependent assertion (flaky) |
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Nicolò Boschi
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865fb91298
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fix(tests): use random port for pg0 in tests to avoid port conflicts (#801)
pg0 supports auto-assigning a free port when port=None. This avoids test failures when port 5556 is already in use by another process. |
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Nicolò Boschi
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ea662d062e
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feat: fact_types and mental model exclusion filters for reflect (#615)
* feat: add fact_types and mental model exclusion filters to reflect and mental models Adds three new filtering options to both the reflect endpoint and mental model creation/refresh: - `fact_types`: restrict which fact types (world, experience, observation) are retrieved. Disables irrelevant agent tools entirely (no wasted tokens). - `exclude_mental_models`: skip the search_mental_models tool altogether. - `exclude_mental_model_ids`: exclude specific mental models by ID (merged with the existing self-exclusion logic during mental model refresh). For mental models, options are persisted in the existing `trigger` JSONB column so they are automatically applied on every refresh. The `UpdateMentalModelRequest` already proxies `trigger`, so no extra endpoint changes are needed. Also fixes the test fixture (`pg0_db_url` in conftest.py) to correctly resolve pg0:// URLs and run migrations before tests, which was causing all DB-dependent tests to fail with "relation public.banks does not exist" when HINDSIGHT_API_DATABASE_URL=pg0://uuuu. * fix: guard against disabled-tool hallucination and regenerate clients - Add enabled_tools guard in reflect agent: if an LLM calls a tool that was excluded (e.g. recall when fact_types=["observation"]), return an error result instead of executing it - Regenerate OpenAPI spec and all SDK clients (Go, Python, TypeScript) to include new fact_types / exclude_mental_models fields * fix: add missing ReflectRequest fields in Rust CLI struct initializers * fix: filter hallucinated tool calls before trace to prevent disabled tools appearing in results * chore: merge main, fix lint formatting and update skills openapi.json * feat: expose fact_types, exclude_mental_models, exclude_mental_model_ids in control plane UI * fix: add missing trigger fields to MentalModel type in control plane api.ts * fix: add missing trigger fields to local MentalModel interface in mental-models-view * feat: tabbed mental model dialogs (Basic / Options tabs) * refactor: shared FactTypeFilter component, tabbed mental model dialogs use General tab, clean up labels * feat: pill-style toggle buttons for fact type filter (blue/emerald/amber per type) * fix: add spacing between Fact Types label and pills, rename to Exclude all mental models |
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Nicolò Boschi
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15ea23d5d6
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feat: introduce hindsight-api-slim and hindsight-all-slim packages (#560)
* feat: introduce hindsight-api-slim and hindsight-all-slim packages Closes #552 - Move all source code from hindsight-api/ to new hindsight-api-slim/ - hindsight-api-slim has heavy ML deps (torch, sentence-transformers, transformers, einops, flashrank, mlx, mlx-lm, safetensors) and pg0-embedded as optional extras: [local-ml], [embedded-db], [all] - hindsight-api becomes a zero-code meta-package depending on hindsight-api-slim[all] for full backward compatibility - Add hindsight-all-slim meta-package: hindsight-api-slim + client + embed - hindsight-all updated to depend on hindsight-api-slim[all] - pg0.py: lazy-import pg0 with clear ImportError pointing to [embedded-db] - Dockerfile: replace sed hack with proper uv sync --extra flags - Update release.yml, test.yml, lint.sh, release.sh, CLAUDE.md and all path references throughout the repo * refactor: rename hindsight/ directory to hindsight-all/ * docs: document hindsight-api-slim and hindsight-all-slim package variants Add package variants table and extras explanation to installation.md * docs: remove emojis from installation.md, use professional tone * docs: link Docker slim variant to pip package variants section * docs: consolidate Docker image variants into single table * ci: fix working-directory paths after package restructure - Replace all hindsight-api → hindsight-api-slim in test.yml - Replace hindsight → hindsight-all in test.yml - Add --extra embedded-db to test-embed API install step * ci: add local-ml and embedded-db extras to API sync steps These extras were previously implicit in the old hindsight-api package (which bundled everything). Now that hindsight-api-slim uses optional extras, we must explicitly request local-ml and embedded-db in CI. * ci: add API install step with embedded-db to test-embed smoke test The smoke test starts hindsight-api as a daemon, which requires pg0-embedded. Add a dedicated install step for hindsight-api-slim with embedded-db extra so the daemon can start successfully. * ci: remove --no-install-project when using optional extras When --no-install-project is combined with --extra, the optional deps are not installed because extras require the project to be active. Remove --no-install-project from steps that need local-ml or embedded-db. * ci: fix ordering of uv sync steps to preserve optional extras When uv sync runs for a different workspace member, it removes optional extras installed for other members. Fix by always running extra-requiring API sync last, after other workspace member syncs. Also remove --no-install-project from embedded-db sync in test-embed, as --no-install-project prevents optional extras from being active. * ci: add local-ml extra to test-embed API install for smoke test The smoke test starts the full API server which needs sentence-transformers for local embeddings (default provider). Add local-ml extra to the install. * ci: simplify extras with --all-extras and add slim pip smoke test - Replace explicit --extra local-ml --extra embedded-db with --all-extras for cleaner, more maintainable sync steps - Add test-pip-slim job: tests hindsight-api-slim[embedded-db] without local ML models, using Cohere for embeddings/reranking (mirrors Docker slim smoke test approach) * ci: simplify slim smoke test to health check only (mirrors Docker test) |
Renamed from hindsight-api/tests/conftest.py (Browse further)