jarvisChat/AGENTS.md

JarvisChat — Agents Guide

Run

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

Tests

./venv/bin/python -m pytest tests/ -v

All tests use tmp_path fixtures + monkeypatched httpx.AsyncClient.stream. No external services needed. Test factories reset SESSIONS, PIN_ATTEMPTS, RATE_EVENTS globals — be careful not to let test state leak. After the modular refactor, tests import directly from the correct modules (db, security, config, search, rag, memory, routers.*) — not from the old monolithic app namespace.

Architecture

Refactored from single-file (app.py) into modules under project root:

File	Role
app.py	FastAPI app, middleware, router registration
config.py	Constants, env vars, rate/payload limits, built-in skills registry
db.py	SQLite schema, connection factory, settings helpers
auth.py	PIN-based guest/admin sessions, auth routes
security.py	Rate limiting, origin checks, IP allowlist, audit/incident logging
memory.py	FTS5 memory CRUD, remember/forget command parsing
search.py	SearXNG integration, perplexity scoring, refusal detection
rag.py	Qdrant vector search + system prompt assembly
gpu.py	AMD GPU stats via rocm-smi
routers/	One module per endpoint group (chat, search, skills, completions, etc.)

Entrypoint / API keys

• app.py line 148: uvicorn.run(app, ...) when called directly
• config.py line 14: LLAMA_SERVER_BASE defaults to http://192.168.50.108:8081 — llama-server, not standard Ollama port, used by all model endpoints
• config.py line 17: COMPLETIONS_API_KEY read from JARVISCHAT_COMPLETIONS_API_KEY env var or auto-generates a random key — no longer a missing import
• config.py line 13: OLLAMA_BASE is legacy/unused — all endpoints now use LLAMA_SERVER_BASE

Key flows

1. /api/chat → process_remember_command() intercepts "remember that..." / "forget about..." first → else build_system_prompt() (profile + FTS5 memory + Qdrant RAG + preset + skills) → stream from llama-server with logprobs: true → if perplexity > 15.0 OR REFUSAL_PATTERNS match, re-query with SearXNG results
2. /api/search → bypasses perplexity/refusal, queries SearXNG directly → summarizes via llama-server (no raw results leaked in SSE)
3. /v1/chat/completions → OpenAI-compatible for Continue.dev/IDE integration; FIM requests proxied without persistence

Perplexity / auto-search

The upstream request includes "logprobs": true. parse_llama_stream_chunk() extracts per-token logprobs from each chunk's choices[0].logprobs.content[].logprob. The all_logprobs list is populated during streaming, so calculate_perplexity() and is_uncertain() work correctly — auto-search on high perplexity is no longer dead code.

Auth / lockdown

• Guest session by default (POST /api/auth/guest), admin unlock via 4-digit PIN (POST /api/auth/login)
• Admin required for PUT/DELETE/PATCH + all POST except allowlist (/api/chat, /api/search, /api/auth/*)
• IP allowlist, rate limiting, origin checking, payload size limits — all enforced in app.py middleware
• JARVISCHAT_ADMIN_PIN env var required on first boot (or JARVISCHAT_ALLOW_DEFAULT_PIN=true)

Database

• SQLite at jarvischat.db, auto-created by init_db() on startup via FastAPI lifespan
• get_db() opens new connection per request (no pool). Close after use.
• FTS5 virtual table memories for full-text search with BM25 ranking. FTS5 operator keywords (AND, OR, NOT, NEAR) are double-quoted to prevent parse errors.

External services

Service	Required	Port
llama-server (OpenAI-compat API)	Yes	8081 (ultron) or env LLAMA_SERVER_BASE
SearXNG	No	8888
wttr.in	No	weather shortcut bypasses SearXNG; curl UA for plain-text output
rocm-smi	No	AMD GPU stats
Qdrant	No	6333 (ultron) — RAG vector search

Config quirks

• Rate limits and payload caps in config.py — tweak for testing by monkeypatching module attributes (note: patch security.RL_* not config.RL_* since security imports bindings separately)
• ALLOWED_SETTINGS_KEYS in config.py controls which keys the UI can write via /api/settings
• Settings table seeded with defaults (profile_enabled, search_enabled, memory_enabled, skills_enabled, default_model) — never overwritten by init_db()
• Profile table uses singleton row id=1
• RAG embedding requests go to LLAMA_SERVER_BASE at /api/embeddings (port 8081, not 11434)

SSE Protocol

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

• {token, conversation_id} — streaming token
• {searching: true} — web search triggered
• {search_results: N} — N results (no raw_results payload)
• {done: true, perplexity, tokens_per_sec, searched?} — terminal
• {error: "...", error_key: "..."} — error with incident key