Adds simbakit/ as a subfolder service that polls PetKit smart devices
(litter box, fountain, feeder) and exposes pet weight, litter activity,
and device health data via three new LangChain tools.
Also fixes stale alerts bug where cleared conditions (e.g. waste bin
full) persisted because the query used max alert time instead of the
device's last_seen_at timestamp.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Markdown-aware chunking (split on headers before size-based splitting)
- Prepend note filename to each chunk for self-contained context
- Source-filtered retrieval (obsidian/paperless queries stay isolated)
- MMR search with k=8, fetch_k=24 for better recall and diversity
- Add source metadata to Paperless docs and folder metadata to Obsidian docs
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Switch from gws calendar +agenda to gws calendar events list with
explicit timeMin/timeMax and singleEvents=true to include all-day events.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds a get_calendar_events agent tool that shells out to `gws calendar +agenda`
for admin users. Controlled by GOOGLE_CALENDAR_ENABLED env var, with OAuth
credentials mounted from credentials.json.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Consolidate onto PostgreSQL by using pgvector instead of a separate
ChromaDB instance. This removes a Docker volume, a large dependency,
and simplifies the stack without meaningful performance impact at
our document scale.
- Swap langchain-chroma for langchain-postgres (PGVector)
- Use pgvector/pgvector:pg16 Docker image with init script
- Lazy-initialize vector store to avoid eager DB connections
- Add SQL helpers for stats/delete/list (replacing _collection access)
- Remove legacy main.py, chunker, petmd scraper, and /api/query endpoint
Re-index required after deploy (POST /api/rag/index + /index-obsidian).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Give the LangChain agent a save_user_memory tool so users can ask it to
remember preferences and personal facts. Memories are stored per-user in
a new user_memories table and injected into the system prompt on each
conversation turn.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Update llm.py to use OpenAI client with custom base_url for llama-server
- Update agents.py to use ChatOpenAI instead of ChatOllama
- Remove unused ollama imports from main.py, chunker.py, query.py
- Add LLAMA_SERVER_URL and LLAMA_MODEL_NAME env vars
- Remove ollama and langchain-ollama dependencies
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Ryan Chen