gramps
90 lines
3.8 KiB
Markdown
90 lines
3.8 KiB
Markdown
# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Running the App
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```bash
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# Development
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./venv/bin/uvicorn app:app --host 0.0.0.0 --port 8080 --reload
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# Production (via systemd)
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sudo systemctl restart jarvischat
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# Direct run
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./venv/bin/python app.py
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```
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## Dependencies
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```bash
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./venv/bin/pip install -r requirements.txt
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# Also requires: psutil jinja2 python-multipart pypdf (not in requirements.txt)
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```
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## Architecture
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Modular FastAPI app — `app.py` wires routers, middleware, and lifespan. SQLite database auto-created at `jarvischat.db` on first run. No build step, single `templates/index.html`.
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### Request Flow: `/api/chat`
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1. User message saved to DB → conversation created if new
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2. `process_remember_command()` intercepts "remember that..." / "forget about..." first
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3. Optional `upload_context_id` → fetches document text from `upload_context` table, injects `[ATTACHED DOCUMENT]` into system prompt
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4. `build_system_prompt()` assembles: profile + FTS5 memory search + Qdrant RAG + preset + skills + uploaded doc
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5. Streamed to llama-server (`/v1/chat/completions`, `stream: true`, `logprobs: true`) via SSE
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6. **Auto web search trigger**: if perplexity > 15.0 OR response matches `REFUSAL_PATTERNS`, re-queries with SearXNG results
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7. Final response saved to DB; SSE `done` event sent with perplexity + tokens/sec
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### Request Flow: `/api/search` (explicit search)
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Bypasses perplexity/refusal — queries SearXNG directly then asks llama-server to summarize results.
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### Request Flow: `/api/upload`
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Multipart file upload → PDF/text extraction + chunking → optional Qdrant upsert + SQLite context storage (1hr expiry). Supports `mode=(context|ingest|both)`. Images upload as storage only — model cannot process image content.
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### Request Flow: `/api/ingest`
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Bearer-token-authenticated terminal RAG hook. Accepts raw text, chunks via `chunk_text()`, embeds via Ollama `/api/embeddings`, upserts to Qdrant.
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### Memory System
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FTS5 virtual table (`memories`) in SQLite. `search_memories()` uses BM25 ranking. `process_remember_command()` intercepts "remember that..." / "forget about..." before the message reaches the model and returns a confirmation string.
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### Key Constants (`config.py`)
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- `LLAMA_SERVER_BASE` — `http://192.168.50.108:8081` (ultron llama-server, RPC offloads to jarvis GPU)
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- `SEARXNG_BASE` — `http://localhost:8888`
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- `PERPLEXITY_THRESHOLD` — `15.0`
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- `EMBED_URL` — `http://192.168.50.210:11434/api/embeddings` (Ollama on jarvis)
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- `VERSION` — current version string
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### External Services
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| Service | Required | Port |
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|---------|----------|------|
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| **llama-server** (ultron) | Yes | 8081 + RPC :50052 (jarvis GPU) |
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| **SearXNG** | No | 8888 |
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| **wttr.in** | No | weather shortcut |
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| **rocm-smi** | No | AMD GPU stats |
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| **Qdrant** (ultron) | No | 6333 — RAG vector search |
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| **Ollama** (jarvis) | No | 11434 — embeddings only |
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### Database
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`get_db()` opens a new connection per request (no pool). `init_db()` runs at startup via FastAPI `lifespan`. Tables: `conversations`, `messages`, `settings`, `profile` (singleton id=1), `memories` (FTS5), `upload_context`. Default settings seeded but never overwritten.
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### SSE Protocol
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All streaming endpoints yield `data: {json}\n\n`. Key event shapes:
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- `{token, conversation_id}` — streaming token
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- `{searching: true}` — web search triggered
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- `{search_results: N}` — N results retrieved
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- `{done: true, perplexity, tokens_per_sec, searched?}` — terminal event
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- `{error: "...", error_key: "..."}` — error with incident key
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### Deployment
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Runs as systemd service under user `jarvischat`, working directory `/opt/jarvischat`. Logs via syslog (`journalctl -u jarvischat`). Version bumps via git tag + commit, deployed via `git pull && systemctl restart jarvischat`.
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