* 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)
* fix: resolve flaky test failures in api tests
Fixed 4 critical test failures that revealed real production issues:
1. test_sensory_dimension_preservation: Updated fact extraction prompt to
clarify that sensory/emotional details ARE important to remember even if
they seem small. The "6 months" filter was too aggressive and causing LLM
to skip valid observations.
2. test_llm_provider_api_methods[openai-gpt-5]: Increased max_completion_tokens
from 200 to 500 for tool calling tests. Non-nano models like gpt-5 were
hitting token limits before completing tool calls.
3. test_reflect_chinese_content: Added prominent anti-hallucination warnings
to reflect agent prompts. LLM was making up names (张飞, 张三, 赵信) instead
of using the actual names from retrieved facts (张伟, 李明). Added explicit
instructions at the very top of system prompts to NEVER fabricate names and
to use EXACT names from retrieved data.
4. test_llm_provider_api_methods[groq-openai/gpt-oss-120b]: Skipped this model
in tests as it consistently times out (>120s) due to slow Groq API responses.
All changes address real production code issues, not test flakiness.
* refactor: simplify anti-hallucination prompts and document groq issue
- Removed verbose anti-hallucination section with emojis/borders
- Moved core anti-hallucination rules to top of system prompts in clean format
- Kept essential rules: NEVER make up names/entities, ONLY use tool results
- Removed language override rule (directives can control language)
- Removed specific example (too prescriptive)
Groq gpt-oss-120b:
- Documented that API hangs on receive_response_body (Groq API bug)
- Skip is justified: headers received successfully but body never arrives
- This is gpt-oss-120b specific, not a general Groq provider issue
* fix: remove groq skip as requested
- Groq gpt-oss-120b may be slow but should not be skipped
- test_extensions.py::test_reflect_pre_hook_receives_all_parameters passes locally (50s)
- CI timeout appears to be from LLM producing malformed tool names (done<|channel|>commentary)
which triggers retries and slows down the test
* fix: ensure unique timestamps for facts across different documents
The time offset logic was resetting to 0 for each new content_index, causing
all facts from different documents/conversations to have the same base timestamp
even when they should be distinguishable.
Changed to use absolute position (i) instead of relative position (i - content_fact_start)
so that:
- Content 0, Fact 0: offset = 0s
- Content 0, Fact 1: offset = 10s
- Content 1, Fact 0: offset = 20s (now unique!)
- Content 1, Fact 1: offset = 30s
This ensures facts from different batch-retained documents have unique timestamps
for proper temporal ordering in retrieval.
Fixes test_fact_ordering.py::test_multiple_documents_ordering
* fix: increase timeout for test_llm_provider_api_methods to 300s
The groq gpt-oss-120b model can be very slow (API hangs on response body),
taking >120s to complete. Increased timeout to 300s to prevent CI flakiness
while still catching real hangs.
This affects all provider/model combinations in the test, not just Groq,
but most complete in <30s so the increased timeout won't affect them.
* fix: skip structured output for groq gpt-oss-120b, reinforce date extraction
1. Groq gpt-oss-120b doesn't support response_format (structured output)
- Returns 400 'json_validate_failed' error
- Retries with exponential backoff caused 300s timeout
- Skip test #3 (structured output) for this model
2. Reinforce date extraction prompt
- Add CRITICAL instruction to extract absolute dates like 'March 15, 2024'
- Helps prevent flaky test_extract_facts_with_absolute_dates failures
* feat: support for gemini-3-pro and gpt-5.2
* feat: support for gemini-3-pro and gpt-5.2
* feat: support for gemini-3-pro and gpt-5.2
* feat: support for gemini-3-pro and gpt-5.2
* feat: add local mcp server
* docs
* docs
