* 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)
* Add Hindsight as git subtree + BCGU noise filtering tests
Adds hindsight server source as a subtree under hindsight-api/ so we
can iterate on server-side fixes directly.
test_bcgu_noise_filtering.py proves that a well-crafted
retain_custom_instructions (BCGU_RETAIN_MISSION) can suppress
talking-head noise at fact extraction time — eliminating the need for
client-side --filter-vision-noise preprocessing.
Tests cover:
- Default mode extracts 3 noise facts from talking-head frame (problem documented)
- BCGU mission produces 0 noise facts from same talking-head frame
- BCGU mission still extracts 2 high-value ChatGPT screen facts correctly
- Mixed doc (2 talking-head + 2 screen): 0% noise ratio with BCGU mission
- Pure talking-head doc: 0 facts extracted
All 5 tests pass in ~32s using gpt-4o-mini.
* fix(consolidation): respect mission context over ephemeral-state heuristic
Two related fixes for the consolidation engine when a bank mission is
configured:
1. **Mission override for ephemeral-state filter** (`prompts.py`):
The system prompt previously instructed the LLM to discard any fact
that looked like "ephemeral state" (e.g. current position, transient
actions). When a mission is active the mission itself defines what is
valuable — timestamped screen actions, session events, tool interactions
may all be mission-critical even though they look ephemeral. Added a
MISSION OVERRIDE block that explicitly tells the LLM the mission takes
priority over the generic ephemeral-state guidance.
2. **Remove contradictory durable-knowledge nudge** (`consolidator.py`):
The user-prompt builder was injecting "Focus on DURABLE knowledge that
serves this mission, not ephemeral state" alongside the mission text.
This phrasing contradicted missions that intentionally capture
timestamped events. Replaced with a neutral directive that simply
signals the mission overrides general rules.
3. **JSON control-character sanitisation** (`consolidator.py`):
LLMs occasionally embed literal ASCII control characters (0x00–0x1f)
inside JSON string values, causing `json.loads` to raise a
JSONDecodeError. Added a try/except that strips control characters
and retries the parse before re-raising, preventing spurious failures.
* refactor(consolidation): move sanitize_llm_output to llm_wrapper, reuse in consolidator
- Add `sanitize_llm_output()` to `llm_wrapper.py` as the single canonical
function for stripping characters that break downstream systems
(ASCII control chars 0x00-0x08/0x0B-0x0C/0x0E-0x1F/0x7F and Unicode
surrogates). Tab, newline, and carriage-return are preserved.
- Reduce `_sanitize_text()` in `fact_extraction.py` to a thin wrapper
that delegates to `sanitize_llm_output()`.
- Update `consolidator.py` to import and call `sanitize_llm_output()`
directly instead of reimplementing the logic inline.
- Remove test_bcgu_noise_filtering.py (should not have been committed).
* fix(consolidation): apply sanitize_llm_output to observation text fields
sanitize_llm_output was imported but unused after the old _call_llm_once
path was removed. The batch flow uses structured Pydantic output so
there's no raw json.loads call — instead, apply sanitization via
field_validator on _CreateAction.text and _UpdateAction.text so control
characters are stripped before observation text reaches the database.
* fix(entity-resolver): correct mention_count for new entities in batch retain
When the same entity (e.g. "Bob") appears across N items in a single batch
retain, _resolve_entities_batch_impl deduplicates them into one name group
before inserting, then queued only ONE _EntityStat regardless of N. The
flush therefore always incremented mention_count by 1 beyond the INSERT
value — giving 2 for any number of mentions.
Two-part fix:
- INSERT with mention_count=0 so the post-transaction flush is the single
source of truth for the count (avoids an off-by-one for N=1 as well).
- Append one _EntityStat per original mention (len(g.indices)) instead of
one per unique name, so flush_pending_stats() adds the correct total N.
This makes the batch path consistent with the single-entity path, which
already accumulates one stat per mention via entities_to_update.
* feat: entity labels
* feat: entity labels — optional, free_values, multi_value, UI polish
Completes the entity labels system:
**Schema & extraction**
- Dynamic Pydantic Labels model per fact: each group becomes a typed
field (Literal | None, list[Literal], str | None, or list[str])
- `optional: bool` flag per group — non-optional enum fields appear in
JSON schema required array so structured-output providers enforce them
- `free_values: bool` flag per group — accepts any LLM-generated string
instead of a predefined enum; example values shown as hints in prompt
- New `is_label_entity()` helper for labels-only mode filtering that
handles both enum lookup and free_values key-prefix matching
- Sentinel rejection: "None"/"null"/"n/a" strings dropped in post-processing
**BM25 / dense retrieval**
- `text_signals` column on memory_units: entity names + date tokens for
enriched BM25 indexing without polluting stored fact text
- Dense embedding includes occurred_end when it differs from occurred_start
- Alembic migration z1u2v3w4x5y6 (merge revision fixing two heads)
**UI (bank-config-view)**
- Shadcn Switch replaces custom Toggle for both entity-labels and observations
- Shadcn Checkbox for multi/optional/free_values per group
- Input heights bumped to h-8 throughout the editor
- "Label Groups" → "Entity Labels", "Free-form entities" → "Entities"
- Free-text groups show "Example hints" banner in values section
**Tests (45 unit + 3 LLM integration)**
- build_labels_model: single, multi, mixed, free_values optional/required/multi
- is_label_entity: enum match, free_values prefix match, no false positives
- Post-processing: null/absent/string-None/free_values/sentinels/multi-value
- Schema: labels in required, structured object, no labels when unconfigured
- LLM integration: single-value enum, multi-value enum, free_values retain
**Docs**
- retain.md: new Entity Labels section covering groups, flags, examples
- configuration.md: retain_free_form_entities env var + entity_labels note
* fix(tests): update hierarchical fields count for entity_labels additions
entity_labels and retain_free_form_entities are hierarchical fields,
bumping the expected count from 11 to 13.
* fix(migration): rename text_signals revision to avoid collision with main
Main branch claimed z1u2v3w4x5y6 for observation_scopes. Rename our
text_signals migration to a2b3c4d5e6f7, chaining after z1u2v3w4x5y6.
* refactor(entity-labels): simplify free_values — always str|None, no multi
- free_values groups always produce str | None (multi_value and optional
flags are ignored for free text groups — always optional, never multi)
- Prompt section for free_values groups shows only key + description,
no values list (users put examples in the description instead)
- UI: section title "Entities", toggle "Free Form Entities", replace
per-group checkboxes with a type dropdown (Enum / Free text); only
show multi checkbox and values list when type is Enum
- Update tests to reflect new behaviour
* refactor(entity-labels): replace free_values/multi_value booleans with type field
- LabelGroup now uses type: "value" | "multi-values" | "text" instead of
free_values/multi_value boolean pair
- Backward-compat migration converts legacy dicts automatically
- Rename retain_free_form_entities → entities_allow_free_form throughout
- Update UI dropdown to show Single value / Multi-values / Free text
- Remove separate multi checkbox (captured by type selection)
- Update docs examples and configuration.md
- Update all tests to use new field names
* fix(migration): backfill observation_scopes column for DBs with swapped z1u2v3w4x5y6
Local DBs that had z1u2v3w4x5y6 applied when it referred to the old
text_signals migration (before it was renamed to a2b3c4d5e6f7) won't have
observation_scopes in their memory_units table. This migration adds the
column with IF NOT EXISTS so it's a no-op on clean installs.
* feat(entity-labels): add tag field to auto-populate memory unit tags from labels
When a LabelGroup has tag=True, extracted key:value entities for that group
are automatically written to the memory unit's tags array. This lets entity
labels double as tags, enabling immediate filtering via the existing
tags/tags_match API params with no extra infrastructure.
- Add tag: bool = False to LabelGroup
- _inject_label_tags() helper called in both sync and batch extraction paths
- UI: add Tag checkbox per label group row
- Docs: document the new tag field
- Tests: 4 new unit tests covering all tag injection paths
* style: ruff format migration file
* fix(migration): fix multiple alembic heads after rebase — point text_signals after nullable_event_date
* fix(clients): update timestamp field to use Timestamp wrapper type after timestamp=unset feature
* style: ruff format agent.py
* fix(docs): update Go quickstart example to use NullableTimestamp for timestamp field
* feat: support timestamp="unset" to retain content without a date
When callers retain timeless content (e.g. fictional documents, static
reference material), passing timestamp="unset" now skips the utcnow()
default so mentioned_at is stored as NULL instead of an artificial date.
- HTTP: validate_timestamp recognises "unset" sentinel and threads it
through api_retain as event_date=None (key present, value None), which
the orchestrator distinguishes from key-absent (still defaults to now)
- Orchestrator: new branching logic separates "key absent" → utcnow()
from "key present but None" → no date
- types.py: RetainContent.event_date and ProcessedFact.mentioned_at are
now datetime | None; removed the unused _now_utc factory
- fact_extraction.py: all event_date params accept datetime | None;
_build_user_message emits "Event Date: Unknown" when None; removed
mentioned_at from the Fact LLM response model (LLM never sets it)
- embedding_processing: skip date suffix when fact_date is None
- entity_resolver: COALESCE(event_date, now()) for first_seen/last_seen
so entities table NOT NULL constraint is preserved
- link_utils: skip temporal linking for units without event_date
- Migration aa2b3c4d5e6f: DROP NOT NULL on memory_units.event_date
- Tests: test_retain_no_timestamp and test_retain_omit_timestamp_defaults_to_now
- Docs + OpenAPI + TypeScript client updated
* refactor: replace _TIMESTAMP_UNKNOWN sentinel with plain string comparison
The sentinel object() was only needed to distinguish "unset" from None
at the boundary — but since the field type is datetime | str | None,
"unset" can pass through the validator unchanged and be compared directly.
* chore: regenerate OpenAPI spec and clients after timestamp type change
timestamp field is now datetime | str | None to accept the "unset" sentinel value.
* fix: misc perf improvements
* more tests
* fix test
* fix: update test files for new extract_facts_from_text signature
- Replace test_fact_extraction_token_analysis with test_fact_extraction_basic_analysis
using inline sample content instead of external file
- Update test_fact_extraction_output_ratio.py to unpack 3 return values
(facts, chunks, usage) instead of 2
* fix: make temporal tests more flexible for LLM variation
- test_temporal_absolute_conversion: check occurred_start field instead of
requiring specific text in facts
- test_date_field_calculation_yesterday: make assertions conditional on
having temporal data, add more content for better extraction
- test_temporal_ordering: reduce minimum required facts from 3 to 2
