cAIc/CLAUDE.md

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Running the App

# Development
./venv/bin/uvicorn app:app --host 0.0.0.0 --port 8080 --reload

# Production (via systemd)
sudo systemctl restart jarvischat

# Direct run
./venv/bin/python app.py

Dependencies

./venv/bin/pip install -r requirements.txt
# Also requires: psutil jinja2 python-multipart pypdf (not in requirements.txt)

Architecture

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.

Request Flow: /api/chat

1. User message saved to DB → conversation created if new
2. process_remember_command() intercepts "remember that..." / "forget about..." first
3. Optional upload_context_id → fetches document text from upload_context table, injects [ATTACHED DOCUMENT] into system prompt
4. build_system_prompt() assembles: profile + FTS5 memory search + Qdrant RAG + preset + skills + uploaded doc
5. Streamed to llama-server (/v1/chat/completions, stream: true, logprobs: true) via SSE
6. Auto web search trigger: if perplexity > 15.0 OR response matches REFUSAL_PATTERNS, re-queries with SearXNG results
7. Final response saved to DB; SSE done event sent with perplexity + tokens/sec

Request Flow: /api/search (explicit search)

Bypasses perplexity/refusal — queries SearXNG directly then asks llama-server to summarize results.

Request Flow: /api/upload

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.

Request Flow: /api/ingest

Bearer-token-authenticated terminal RAG hook. Accepts raw text, chunks via chunk_text(), embeds via Ollama /api/embeddings, upserts to Qdrant.

Memory System

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.

Key Constants (config.py)

• LLAMA_SERVER_BASE — http://192.168.50.108:8081 (ultron llama-server, RPC offloads to jarvis GPU)
• SEARXNG_BASE — http://localhost:8888
• PERPLEXITY_THRESHOLD — 15.0
• EMBED_URL — http://192.168.50.210:11434/api/embeddings (Ollama on jarvis)
• VERSION — current version string

External Services

Service	Required	Port
llama-server (ultron)	Yes	8081 + RPC :50052 (jarvis GPU)
SearXNG	No	8888
wttr.in	No	weather shortcut
rocm-smi	No	AMD GPU stats
Qdrant (ultron)	No	6333 — RAG vector search
Ollama (jarvis)	No	11434 — embeddings only

Database

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.

SSE Protocol

All streaming endpoints yield data: {json}\n\n. Key event shapes:

• {token, conversation_id} — streaming token
• {searching: true} — web search triggered
• {search_results: N} — N results retrieved
• {done: true, perplexity, tokens_per_sec, searched?} — terminal event
• {error: "...", error_key: "..."} — error with incident key

Deployment

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.