jarvisChat v0.11.0

You have a garage full of retired office PCs, a GPU that was mid-range when Obama was president, and a burning desire to chat with a language model without renting some billionaire's server farm. Congratulations — you've found your people.

jarvisChat is a chat UI that grew limbs. It started as a single-file Python script because OpenWebUI wouldn't install on Debian 13, and somewhere along the way it learned to file paperwork (file attachments), write things down (RAG ingest), boss around other computers (AMQP clustering), and check its own pulse (hardware self-assessment). It now does all the things you didn't ask for, plus a few you might actually use.

Under the hood: FastAPI + SQLite + Jinja2 on Python 3.13. No Docker. It stitches together mismatched hardware via llama.cpp RPC — your gaming PC's dusty RX 580, the NUC in the closet, that old workstation from 2017 — and spreads inference across them like peanut butter on stale bread. It shouldn't work, but somehow it does.

Developer wiki: docs/wiki/Home.md

What's New in v0.11.0

File & Document Attachments (v1.9.0–v1.10.0)

• POST /api/upload — multipart file upload with PDF/text extraction; modes: context (chat injection), ingest (RAG corpus), both
• DELETE /api/upload/{id} — removes upload from SQLite + Qdrant
• PATCH /api/upload/{id}/link — associates upload with a conversation
• GET /api/upload/by-conversation/{id} — list attachments per conversation
• Paperclip UI — file picker, preview pill, image thumbnails, gallery overlay
• Attachment indicators — 📎 badge on conversations with attachments
• Chat context injection — upload_context_id prepends document text to system prompt

Terminal RAG Hook — POST /api/ingest (v0.11.0)

• Bearer token auth (same key as /v1/chat/completions)
• Chunking via shared chunk_text() helper, embed via Ollama, upsert to Qdrant
• jc-ingest.sh — PROMPT_COMMAND shell script for autonomous terminal history ingestion

v1.8.0 Foundation (refactor & fixes)

• Modular refactor — single-file app.py split into config.py, db.py, auth.py, security.py, memory.py, search.py, rag.py, gpu.py, and routers/ package
• Perplexity auto-search fixed — logprobs: true now properly extracted from stream chunks
• All /api/models endpoints target LLAMA_SERVER_BASE (llama-server) not Ollama
• RAG embedding via Ollama at http://192.168.50.210:11434
• Origin check applies to all API methods, rejects absent Origin/Referer

Features

• Persistent Memory — SQLite FTS5 full-text search for fast, relevant memory retrieval
• Web Search — SearXNG integration for automatic web lookups when the model is uncertain
• Explicit Search — Search button to force web search without waiting for model uncertainty
• Profile Injection — Custom system prompt injected into every conversation
• System Presets — Save and switch between different system prompts
• Real-time Stats — CPU, RAM, GPU, VRAM monitoring in sidebar
• Token Thermometer — Visual context window usage indicator
• Streaming Responses — Server-sent events for real-time token display
• Conversation History — SQLite-backed chat persistence with mass-delete option
• Model Switching — Change inference models on the fly
• Skills Framework — Built-in skill registry with per-skill enable/disable controls

File Structure

/opt/jarvischat/
├── app.py              # FastAPI app entry point
├── config.py           # Constants, env vars, limits, skill registry
├── db.py               # SQLite schema, connection factory
├── auth.py             # PIN-based guest/admin sessions, auth routes
├── security.py         # Rate limiting, origin checks, IP allowlist, audit
├── memory.py           # FTS5 memory CRUD, remember/forget commands
├── search.py           # SearXNG integration, perplexity, refusal detection
├── rag.py              # Qdrant vector search + system prompt assembly
├── gpu.py              # AMD GPU stats via rocm-smi
├── routers/
│   ├── chat.py         # /api/chat streaming endpoint
│   ├── search_route.py # /api/search explicit search endpoint
│   ├── completions.py  # /v1/chat/completions OpenAI-compat endpoint
│   ├── conversations.py# Conversation CRUD
│   ├── memories.py     # Memory CRUD API
│   ├── models.py       # Model listing, system stats
│   ├── presets.py      # System prompt presets
│   ├── profile.py      # User profile
│   ├── settings.py     # Runtime settings
│   ├── skills.py       # Skills management
│   ├── upload.py       # File attachment endpoints
│   └── ingest.py       # Terminal RAG ingest
├── static/
│   └── logo.png        # Logo image (optional)
├── templates/
│   └── index.html      # Frontend
└── tests/              # 110 pytest tests

Requirements

• Python 3.11+ (tested on 3.13)
• llama-server running locally or on network (OpenAI-compatible API on port 8081)
• SearXNG (optional, for web search)

Installation

Fresh Install

# Create directory and venv
sudo mkdir -p /opt/jarvischat
sudo chown $USER:$USER /opt/jarvischat
cd /opt/jarvischat
python3 -m venv venv

# Install dependencies
./venv/bin/pip install fastapi uvicorn httpx psutil jinja2 python-multipart pypdf

# Set admin PIN before first startup (4 digits)
export JARVISCHAT_ADMIN_PIN=4827

# Create subdirectories
mkdir -p templates static

# Copy files
# (copy all .py files to /opt/jarvischat/)
# (copy routers/ directory to /opt/jarvischat/)
# (copy templates/index.html to /opt/jarvischat/templates/)

WARNING: Do not use 1234 as your admin PIN unless you accept weak local security.

NOTE: First boot requires JARVISCHAT_ADMIN_PIN unless you explicitly opt into insecure fallback with JARVISCHAT_ALLOW_DEFAULT_PIN=true.

Systemd Service

Create /etc/systemd/system/jarvischat.service:

[Unit]
Description=jarvisChat - Local Inference Web Interface
After=network.target

[Service]
Type=simple
User=jarvischat
Group=jarvischat
WorkingDirectory=/opt/jarvischat
ExecStart=/opt/jarvischat/venv/bin/uvicorn app:app --host 0.0.0.0 --port 8080
Restart=always
RestartSec=5

[Install]
WantedBy=multi-user.target

sudo systemctl daemon-reload
sudo systemctl enable jarvischat
sudo systemctl start jarvischat

Memory Commands

In chat, natural language triggers memory operations:

You say	What happens
"remember that I prefer Rust over Go"	Stores as preference
"remember that JarvisChat runs on port 8080"	Stores as infrastructure
"note that the deadline is Friday"	Stores as general
"forget about the deadline"	Removes matching memories

Memories are automatically searched based on your message content and injected into the system prompt when relevant.

Memory Topics

Memories are auto-categorized:

• preference — likes, dislikes, choices
• project — active work, repos, tasks
• infrastructure — servers, services, configs
• personal — name, location, background
• general — everything else

API Endpoints

Completions (OpenAI-compatible)

Method	Endpoint	Description
POST	/v1/chat/completions	OpenAI-compatible chat (requires Bearer API key)

Chat & Search

Method	Endpoint	Description
POST	/api/chat	Send message (streaming SSE)
POST	/api/search	Explicit web search (streaming SSE)

File Upload & Ingest

Method	Endpoint	Description
POST	/api/upload	Upload file (multipart, admin)
DELETE	/api/upload/{id}	Delete upload (admin)
PATCH	/api/upload/{id}/link	Link upload to conversation (admin)
GET	/api/upload/by-conversation/{id}	List uploads for conversation
POST	/api/ingest	Ingest text into RAG (Bearer token auth)

Memory

Method	Endpoint	Description
GET	/api/memories	List all memories
POST	/api/memories	Add memory
PUT	/api/memories/{rowid}	Update memory
DELETE	/api/memories/{rowid}	Delete memory
GET	/api/memories/search?q=term	Search memories
GET	/api/memories/stats	Get counts by topic

Models & System

Method	Endpoint	Description
GET	/api/models	List available models
GET	/api/ps	List loaded models
POST	/api/show	Get model info
GET	/api/stats	CPU, RAM, GPU, VRAM stats
GET	/api/search/status	SearXNG availability

Settings & Profile

Method	Endpoint	Description
GET	/api/profile	Get profile content
PUT	/api/profile	Update profile (admin)
GET	/api/profile/default	Get default profile
GET	/api/settings	Get settings
PUT	/api/settings	Update settings (admin)

Conversations

Method	Endpoint	Description
GET	/api/conversations	List conversations
POST	/api/conversations	Create conversation
GET	/api/conversations/{id}	Get conversation with messages
PUT	/api/conversations/{id}	Update conversation title/model
DELETE	/api/conversations/{id}	Delete conversation
DELETE	/api/conversations	Delete ALL conversations

Presets

Method	Endpoint	Description
GET	/api/presets	List presets
POST	/api/presets	Create preset
PUT	/api/presets/{id}	Update preset
DELETE	/api/presets/{id}	Delete preset

Skills

Method	Endpoint	Description
GET	/api/skills	List all skills with state
GET	/api/skills/active	List active skills
PUT	/api/skills/{key}	Toggle skill enabled (admin)

Auth

Method	Endpoint	Description
POST	/api/auth/guest	Create guest session
POST	/api/auth/login	Admin PIN login
POST	/api/auth/logout	Revoke session
GET	/api/auth/session	Check session validity
POST	/api/auth/heartbeat	Extend session TTL

Configuration

Settings are stored in the settings table and include:

• profile_enabled — Inject profile into chats (true/false)
• search_enabled — Auto web search (true/false)
• memory_enabled — Memory injection (true/false)
• skills_enabled — Skills framework (true/false)
• default_model — Default inference model

Testing

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

All 110 tests use tmp_path fixtures + monkeypatched httpx.AsyncClient. No external services needed.

License

MIT

Repository

Gitea: ssh://gitea@llgit.llamachile.tube:1319/gramps/jarvisChat.git