Add get_category_allocations() to YNABService that fetches all categories
grouped by category group (using the categories API for group structure),
showing assigned and available amounts for every non-hidden category.
Add the corresponding ynab_category_allocations agent tool so the LLM
can answer questions about per-category budgets even when a category has
no spending activity this month.
Also remove the [:10] cap in ynab_category_spending so all spending
categories are returned — previously categories ranked below 10th by
activity (e.g. Coffee slush fund) were silently dropped.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replaces the gws CLI subprocess approach with google-api-python-client.
Adds get_calendar_events, create_calendar_event, gmail_search_emails, and
gmail_get_email as LangChain tools; all admin-gated and auto-enabled when
token.json is present. Includes uv-runnable scripts/google_auth.py for
one-time OAuth2 setup.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Native reply threading: inbound replies continue their conversation via
SendBlue message_handle lookup; standalone messages create a new conversation
- Our responses are sent with reply_to so they thread visually in iMessage
- Typing indicator fires before agent invocation and stops in a finally block
- Add extract_agent_text() to handle list-type AIMessage content and strip
<think> tokens from Qwen3 — applied to iMessage, WhatsApp, and web chat
- ConversationMessage gains a nullable message_handle field (migration needed)
- /threads command lists recent iMessage conversations
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Image messages previously ran two sequential LLM generations: a full
non-streamed vision call to describe the image, then a second agent call
with the description pasted in as text. The agent's llama-server model is
already vision-capable, so attach the image to the user message as an
OpenAI-style image_url content part and let the single agent call see it.
Roughly halves image-turn latency and streams from the start.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
analyze_user_image now runs input bytes through Pillow before base64-
encoding, converting HEIC/HEIF and any other non-JPEG format to JPEG.
pillow_heif is already registered at module load so HEIC opens cleanly.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Upgrades pypetkitapi to 1.28.0, which adds the Eversweet Ultra (W7H
fountain with AI camera). The poller now extracts per-pet drinking
events (drink_over/pet_detect records) into a new drinking_events
table, plus Ultra-specific alerts for waste tank full and filter
expiry. Uncertain state fields (cwt/wt/pump) are logged for threshold
tuning against real data.
Exposes the data via a new /api/pets/drinking-events endpoint, a
Fountain Activity dashboard section, and a simbakit_drinking_activity
LangChain tool so the agent can answer hydration questions.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Wires up the existing MealieService to the conversational agent with tools
for querying today's meals, weekly meal plans, full recipes, shopping lists,
and creating new meal plan entries. Adds env vars to docker-compose and
updates the system prompt so the agent knows when to use each tool.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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>
Strip markdown formatting (bold, italic, headers, code, links, lists) from
LLM responses before sending via iMessage. Add scheduled messages feature
with CRUD API, background scheduler loop, and admin frontend panel.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Two fixes:
- Convert wikilinks to display text instead of stripping them entirely.
[[Noah]] becomes "Noah", [[target|display]] becomes "display". This
was causing names and references in wikilinks to be invisible to search.
- Switch _get_obsidian_indexed_files to async engine to avoid stale reads
from the separate sync engine, which caused files to be re-indexed
every cycle.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Update from journal/YYYY/YYYY-MM-DD.md to
50 - Journal/YYYY/MM/YYYY-MM-DD.md to match the actual Obsidian vault
folder layout.
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>
Browser <img> tags can't attach JWT headers, causing 401s. The image
endpoint now returns a time-limited presigned S3 URL via authenticated
API call, which the frontend fetches and uses directly.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Use the same llama-server (OpenAI-compatible API) for vision analysis
that the main agent uses, with OpenAI fallback. Sends images as base64
in the standard OpenAI vision message format.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Users can now attach images in the web chat for Simba to analyze using
Ollama's gemma3 vision model. Images are stored in Garage (S3-compatible)
and displayed in chat history.
Also fixes aerich migration config by extracting TORTOISE_CONFIG into a
standalone config/db.py module, removing the stale aerich_config.py, and
adding missing MODELS_STATE to migration 3.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The get_transactions() method was truncating results to 50 by default,
causing incomplete transaction data. The YNAB API returns all matching
transactions in a single response, so this limit was unnecessary and
caused count/total inconsistencies.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Convert date strings to datetime.date objects before passing to API (strict Pydantic validation rejects strings)
- Use txn.var_date instead of txn.date (renamed in SDK v2 to avoid Python builtin conflict)
- Migrate BudgetsApi → PlansApi and update method names accordingly
Co-Authored-By: Claude Sonnet 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