* fix: resolve remaining Dependabot security alerts
- Regenerate package-lock.json so npm overrides take effect
(serialize-javascript, handlebars, path-to-regexp, brace-expansion)
- Upgrade Pygments 2.19.2 -> 2.20.0 in crewai and integration-tests
lockfiles (fixes ReDoS via GUID matching)
* fix: resolve duplicate alembic revision ID d6e7f8a9b0c1
Two migrations shared the same revision ID: the merge migration
(drop_documents_metadata_column) and the trigram index migration
(case_insensitive_entities_trgm_index). Assign a new unique ID
to the trigram migration and update the downstream dependency.
* chore: fix lint formatting for generated and existing files
* 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
Remove the BFS spreading activation and MPFP (Multi-Path Fact Propagation)
graph retrieval strategies, leaving link_expansion as the sole graph
retrieval algorithm. Rename MPFPTimings to GraphRetrievalTimings and
mpfp_timings field to graph_timings since the timing struct is used by
LinkExpansionRetriever.
Deleted:
- hindsight-api-slim/hindsight_api/engine/search/mpfp_retrieval.py
- hindsight-api-slim/tests/test_mpfp_retrieval.py
Removed config: HINDSIGHT_API_MPFP_TOP_K_NEIGHBORS
* fix(db): respect vector extension config in per-bank index migration
Migration d5e6f7a8b9c0 hardcoded HNSW when creating per-bank partial
vector indexes, ignoring HINDSIGHT_API_VECTOR_EXTENSION. This caused
banks migrated from pre-v0.4.18 to get HNSW indexes even when
pgvectorscale (DiskANN) or vchord was configured.
- Fix the original migration to read the vector extension config
- Add migration a4b5c6d7e8f9 to detect and recreate mismatched indexes
(skipped entirely when extension is pgvector, since those are correct)
* chore: regenerate openapi.json for v0.4.22 version bump
* feat: expose document_metadata in API and control plane
Add document_metadata (sourced from retain_params.metadata) to both
list and get document endpoints. Display it in the control plane
documents table and detail panel. Drop the unused metadata column
from the documents table (was always stored as empty {}).
* fix: code review fixes for document_metadata feature
- Remove unnecessary `import json as _json` (json already imported at module level)
- Simplify redundant truthiness checks in retain_params parsing
- Regenerate OpenAPI spec and client SDKs (Python, TypeScript, Go)
- Add tests for document_metadata in get_document and list_documents
* feat(ui): improve documents table and detail panel
- Relative timestamps with full date on hover
- Remove context column from table
- Metadata shown as k=v badges (blue, like tags)
- Size in bytes instead of chars
- Document IDs wrap instead of truncating
- Detail panel wider (560px)
- Retain params: context, event_date, metadata badges
* fix(claude-code): implement tool_choice support for forced tool calls
The call_with_tools() method now properly handles the tool_choice parameter
to force specific tool calls. Previously, the parameter was accepted but ignored,
causing the reflect agent to fail when trying to force specific tools on each
iteration.
Fixes#732
Changes:
- When tool_choice forces a specific function: filter allowed_tools to only
that tool (with mcp prefix) and add a strong system prompt instruction
- When tool_choice is 'required': add instruction that model must call at
least one tool
- When tool_choice is 'none': clear allowed_tools and mcp_servers to disable
all tools
- When tool_choice is 'auto' (default): no change (existing behavior)
This matches the approach used in the OpenAI provider while adapting to the
Claude Agent SDK's lack of native tool_choice parameter by using allowed_tools
filtering and system prompt instructions.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* style: fix ruff formatting in alembic migration
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
The stats endpoint JOINs memory_links to memory_units just to filter
by bank_id. With 8.2M+ links per bank this takes 18+ seconds, and
the control plane polls every 10s — perpetually blocking the server.
Add bank_id column directly to memory_links so the query can filter
on ml.bank_id instead of mu.bank_id, letting Postgres push the filter
down before the JOIN.
- Migration: add bank_id TEXT NOT NULL, backfill from memory_units
- All 4 INSERT paths (temporal, semantic, entity, causal) now write bank_id
- Stats query filters on ml.bank_id instead of mu.bank_id
* fix(migration): use IF EXISTS when dropping chunk FK constraint
The migration unconditionally dropped memory_units_chunk_fkey, but
depending on the order in which migrations were applied the constraint
may not exist. Use raw SQL with IF EXISTS so the drop is safe regardless.
* fix(migration): make chunk FK add idempotent with DO block
The previous fix only handled the DROP side with IF EXISTS. The ADD side
could still fail with DuplicateObject when the FK already existed on a
schema that was provisioned after the base migration ran.
Wrap the ADD CONSTRAINT in a DO block to catch duplicate_object and
continue, making the migration fully idempotent in both directions.
* feat: add audit log for feature usage tracking
Add full auditability for all mutating and core API operations across
HTTP, MCP, and system (worker) transports. Audit entries record raw
request/response as JSONB, timing (started_at/ended_at), action, and
transport type.
Backend:
- New audit_log table with JSONB columns for expandability without
future migrations (merge migration of 3 existing heads)
- AuditLogger with fire-and-forget writes via asyncio.create_task
- @audited decorator on 28 HTTP route handlers
- MCP tool audit wrapping for 16 auditable tools
- Worker task execution wrapped with audit_context
- List endpoint with action, transport, date range filters + pagination
- Stats endpoint with per-day counts for charting
- Configurable retention sweep (concurrent-safe DELETE)
Config (env-only, static):
- HINDSIGHT_API_AUDIT_LOG_ENABLED (default: false)
- HINDSIGHT_API_AUDIT_LOG_ACTIONS (comma-separated allowlist, empty=all)
- HINDSIGHT_API_AUDIT_LOG_RETENTION_DAYS (default: -1, keep forever)
Control Plane:
- New "Audit Logs" tab on bank configuration page
- Line chart showing request volume (today/7d/30d) with action filter
- Filterable table with action, transport, date range filters
- Paginated list with detail dialog showing raw request/response JSON
Tests:
- 13 tests covering list, filters, pagination, stats, disabled mode,
action allowlist, and ordering
* fix: split 3-way merge migration into two 2-way merges
Alembic doesn't support 3-parent merge migrations. Split into a no-op
merge of 2 heads (b1c2d3e4f5g6) followed by the audit_log table
migration merging the third head.
* fix: correct merge migration to merge actual 2 heads
The original analysis incorrectly identified 3 heads. There were only 2
(a3b4c5d6e7f8 and c8e5f2a3b4d1). Remove the unnecessary intermediate
merge migration and fix the audit_log migration to merge these 2 heads.
* fix: use 'heads' instead of 'head' in migration runner
Alembic's upgrade('head') fails when multiple heads exist (e.g. from
namespace package overlaps between hindsight-api and hindsight-api-slim).
Using 'heads' (plural) handles this gracefully by upgrading all branches.
* chore: regenerate OpenAPI spec with audit log endpoints
* chore: regenerate TypeScript client and docs skill OpenAPI spec
Python and Go clients still need regeneration (requires Docker).
* chore: regenerate all client SDKs (Python, Go, TypeScript)
Adds generated audit log API clients for Python (audit_api.py),
Go (api_audit.go), and TypeScript client type updates.
* feat(retain): delta retain — skip LLM re-extraction for unchanged chunks on upsert
When upserting a document (same document_id), instead of deleting all
facts and re-extracting from scratch, compare chunk content hashes
and only process changed/new chunks. Unchanged chunks keep their
existing facts, entities, and links.
- Add content_hash column to chunks table (migration b3c4d5e6f7a8)
- Add chunk delta comparison functions in chunk_storage.py
- Add delta_mode to fact_storage.handle_document_tracking (skip full delete)
- Add update_memory_units_tags for propagating tag changes to existing facts
- Refactor orchestrator into _try_delta_retain and _full_retain paths
- Automatic fallback to full retain for pre-migration data or all-changed scenarios
- Fix ty type error in metrics.py (resource module import on Windows)
- 16 new tests covering entities, links, tags, metadata, edge cases
* refactor(retain): deduplicate delta and full retain paths
Extract shared _insert_facts_and_links() and _extract_and_embed()
functions used by both the full retain and delta retain paths.
Remove delta_mode flag from handle_document_tracking — delta path
uses dedicated upsert_document_metadata() instead.
* chore: regenerate clients, openapi spec, and lockfile
* chore: regenerate docs skill
On managed PostgreSQL services (e.g. Azure Flexible Server), the pg_trgm
extension may not be available, causing two failures:
1. Migration c1a2b3d4e5f6 crashes on CREATE EXTENSION
2. Even if migration is bypassed, the default 'trigram' entity lookup
strategy uses the % operator which requires pg_trgm, causing retain
background tasks to fail silently
Changes:
- Migration now gracefully skips pg_trgm and index creation if the
extension cannot be loaded
- EntityResolver auto-detects pg_trgm availability on first use and
falls back to 'full' lookup strategy with a warning log
Co-authored-by: coder999999999 <coder999999999@users.noreply.github.com>
* fix: prevent silent memory loss on consolidation LLM failure
When all LLM retries are exhausted during consolidation, memories were
being marked consolidated_at unconditionally, permanently excluding them
from future consolidation runs without producing any observations.
Fix with two complementary mechanisms:
- Adaptive batch splitting: on LLM failure, the batch is halved and
retried recursively down to batch_size=1, recovering most transient
failures (rate limits, Pydantic validation on long prompts) without
operator intervention
- consolidation_failed_at column: only single-memory batches that still
fail after all retries are marked here instead of consolidated_at, so
they remain visible and retryable
- New API endpoint POST /v1/default/banks/{bank_id}/consolidation/retry-failed
resets these memories for the next consolidation run
* chore: regenerate OpenAPI spec
* fix: rename consolidation endpoint from /retry-failed to /recover
* fix: add consolidation_failed_at column, adaptive batch splitting, and recovery API
- Migration a3b4c5d6e7f8: add consolidation_failed_at TIMESTAMPTZ column to
memory_units with an index for efficient failure queries; properly chains off
g7h8i9j0k1l2 (backsweep_orphan_observations)
- Consolidator: filter pending memories with consolidation_failed_at IS NULL
so failed memories are not re-fetched in an infinite loop
- Consolidator: adaptive batch splitting — when a batch exhausts all 3 LLM
retries, halve it and retry sub-batches recursively; only single-memory
batches that also exhaust all retries get consolidation_failed_at set
- New tests (9 total) covering: adaptive splitting recovers all memories,
larger batch splitting, single-memory permanent failure, exclusion from
next run, partial batch failure, recover resets columns, recover returns
0 when none failed, recover-then-consolidate succeeds, HTTP endpoint
* chore: regenerate Go, Python, TypeScript clients with recover consolidation endpoint
* feat: add Recover Consolidation action to bank Actions dropdown
* style: apply ruff formatting to http.py and config.py
* fix: handle consolidation scope in large batch test mock LLM
The mock LLM was returning {"facts": ...} for ALL calls including consolidation.
Consolidation doesn't use skip_validation=True so it expects a _ConsolidationBatchResponse
instance, not a raw dict. Before this PR consolidation silently swallowed the AttributeError
(failed=False was returned); now failed=True triggers adaptive splitting and timeouts.
Fix: return _ConsolidationBatchResponse() when scope=="consolidation".
* fix: restrict claude-agent-sdk to macOS platform only (no Linux wheel available)
Also fix pre-existing type errors: use setattr for XLM-RoBERTa monkey-patch
and add missing reranker_local_fp16/bucket_batching/batch_size fields to main.py config constructor.
* fix: add UV_INDEX_STRATEGY=unsafe-best-match to fix markupsafe cp314 wheel conflict
PyTorch CPU index serves markupsafe==3.0.3 with only cp314 wheels.
uv's default first-index strategy stops at the first index with any version
even if no compatible wheel exists. unsafe-best-match searches all indices
for the best compatible wheel, falling back to PyPI for markupsafe.
* fix: use explicit pytorch index to prevent markupsafe wheel conflict
Configure the pytorch CPU index as explicit=true in pyproject.toml so it is
ONLY used for torch (via [tool.uv.sources]). All other packages (including
markupsafe) are resolved exclusively from PyPI, preventing the pytorch index
from serving incompatible cp314-only wheels for non-pytorch packages.
Remove UV_INDEX and UV_INDEX_STRATEGY from CI workflow (no longer needed
since the index is now configured in pyproject.toml).
* ci: trigger CI run
* ci: retry trigger
* ci: trigger after remote URL fix
* ci: add workflow_dispatch to unblock manual trigger
* fix: remove empty env blocks left after UV_INDEX removal
* fix: add type: ignore for optional claude_agent_sdk imports (macOS-only)
* fix: correct type: ignore rules for claude_agent_sdk and fix utcnow deprecation
* fix(migration): backsweep orphaned observation memory units
Delete observation rows whose every source_memory_id points to a
deleted memory unit, left behind before PR #580 fixed the chunk FK
cascade and before delete_document() called
_delete_stale_observations_for_memories.
Closes#572 (data cleanup for pre-existing installs).
* fix(migration): broaden backsweep to cover all fact types and bank-level orphans
- Pass 1: delete any memory_units row (all fact_types) whose bank_id no
longer exists in banks — catches orphans from bank deletions that
predate a FK cascade between the two tables.
- Pass 2: delete observation rows whose every source_memory_id points to
a deleted memory unit, regardless of document_id/chunk_id anchors.
* test(migration): verify backsweep removes orphans and preserves legit rows
Adds a focused migration test that:
- Starts a fresh pg0 instance at revision f6g7h8i9j0k1
- Seeds orphaned rows for both backsweep passes (ghost-bank + all-dead-sources)
- Seeds legitimate rows that must survive
- Applies the backsweep migration to head
- Asserts the expected rows are deleted/preserved
The foreign key from memory_units.chunk_id to chunks.chunk_id used
ON DELETE SET NULL, which left ghost memory_units rows (chunk_id nulled
out, no parent document) after a document was deleted. Switching to
ON DELETE CASCADE lets the existing document -> chunks -> memory_units
cascade clean up everything in one pass.
Closes#572
Signed-off-by: JiangNan <1394485448@qq.com>
* 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)
Derek Bouius