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ChatOllama

ChatOllama is an open source chatbot platform built with Nuxt 3, supporting a wide range of language models and advanced features including knowledge bases, realtime voice chat, and Model Context Protocol (MCP) integration.

Supported Language Models

• OpenAI / Azure OpenAI
• Anthropic
• Google Gemini
• Groq
• Moonshot
• Ollama
• OpenAI API compatible service providers

Key Features

• Multi-modal Chat - Text and image input support
• Knowledge Bases - RAG (Retrieval Augmented Generation) with document upload
• Realtime Voice Chat - Voice conversations with Gemini 2.0 Flash
• Model Context Protocol (MCP) - Extensible tool integration
• Vector Databases - Chroma and Milvus support
• Docker Support - Easy deployment with Docker Compose
• Internationalization - Multi-language support

Quick Start

Choose your preferred deployment method:

Option 1: Docker (Recommended)

The easiest way to get started. Download docker-compose.yaml and run:

docker compose up

Initialize the database on first run:

docker compose exec chatollama npx prisma migrate dev

Access ChatOllama at http://localhost:3000

Option 2: Development Setup

For development or customization:

1. Prerequisites

   • Node.js 18+ and pnpm
   • Ollama server running on http://localhost:11434
   • ChromaDB or Milvus vector database

2. Installation

   git clone git@github.com:sugarforever/chat-ollama.git
   cd chat-ollama
   cp .env.example .env
   pnpm install
   pnpm prisma-migrate
   pnpm dev

Vector Database Configuration

ChatOllama supports two vector databases. Configure in your .env file:

# Choose: chroma or milvus
VECTOR_STORE=chroma
CHROMADB_URL=http://localhost:8000
MILVUS_URL=http://localhost:19530

ChromaDB Setup (Default)

docker run -d -p 8000:8000 chromadb/chroma

Configuration

Environment Variables

Key configuration options in .env:

# Database
DATABASE_URL=file:../../chatollama.sqlite

# Server
PORT=3000
HOST=

# Vector Database
VECTOR_STORE=chroma
CHROMADB_URL=http://localhost:8000

# Optional: API Keys for commercial models
OPENAI_API_KEY=your_openai_key
ANTHROPIC_API_KEY=your_anthropic_key
GOOGLE_API_KEY=your_gemini_key
GROQ_API_KEY=your_groq_key
MOONSHOT_API_KEY=your_moonshot_key

# Optional: Proxy settings
NUXT_PUBLIC_MODEL_PROXY_ENABLED=false
NUXT_MODEL_PROXY_URL=http://127.0.0.1:1080

# Optional: Cohere for reranking
COHERE_API_KEY=your_cohere_key

Advanced Features

Model Context Protocol (MCP)

ChatOllama integrates with MCP to extend AI capabilities through external tools and data sources. MCP servers are managed through a user-friendly interface in Settings.

Supported Transport Types:

• STDIO - Command-line tools (most common)
• Server-Sent Events (SSE) - HTTP-based streaming
• Streamable HTTP - HTTP-based communication

Configuration via Settings UI:

1. Navigate to Settings → MCP
2. Click "Add Server" to create a new MCP server
3. Configure server details:
   • Name: Descriptive server name
   • Transport: Choose STDIO, SSE, or Streamable HTTP
   • Command/Args (STDIO): Executable path and arguments
   • URL (SSE/HTTP): Server endpoint URL
   • Environment Variables: API keys and configuration
   • Enable/Disable: Toggle server status

STDIO Server Example:

Name: Filesystem Tools
Transport: stdio
Command: uvx
Args: mcp-server-filesystem
Environment Variables:
  PATH: ${PATH}

Migration from Legacy Config: If you have an existing .mcp-servers.json file:

pnpm exec ts-node scripts/migrate-mcp-servers.ts

Popular MCP Servers:

• mcp-server-filesystem - File system operations
• mcp-server-git - Git repository management
• mcp-server-sqlite - SQLite database queries
• mcp-server-brave-search - Web search capabilities

How MCP Works in Chat: When MCP servers are enabled, their tools become available to AI models during conversations. The AI can automatically call these tools to:

• Read/write files when discussing code
• Search the web for current information
• Query databases for specific data
• Perform system operations as needed

Tools are loaded dynamically and integrated seamlessly into the chat experience.

Realtime Voice Chat

Enable voice conversations with Gemini 2.0 Flash:

1. Set your Google API key in Settings
2. Enable "Realtime Chat" in Settings
3. Click the microphone icon to start voice conversations
4. Access via /realtime page

Knowledge Bases

Create knowledge bases for RAG conversations:

1. Create Knowledge Base - Name and configure chunking parameters
2. Upload Documents - PDF, DOCX, TXT files supported
3. Chat with Knowledge - Reference your documents in conversations

Supported Vector Databases:

• ChromaDB (default) - Lightweight, easy setup
• Milvus - Production-scale vector database

Data Storage

Docker Deployment:

• Vector Data - Stored in Docker volumes (chromadb_volume)
• Relational Data - SQLite database at ~/.chatollama/chatollama.sqlite
• Redis - Session and caching data

Development:

• Database - Local SQLite file
• Vector Store - External ChromaDB/Milvus instance

Development

Project Structure

chatollama/
├── components/          # Vue components
├── pages/              # Nuxt pages (routing)
├── server/             # API routes and server logic
├── prisma/             # Database schema and migrations
├── locales/            # Internationalization files
├── config/             # Configuration files
└── docker-compose.yaml # Docker deployment

Available Scripts

# Development
pnpm dev                # Start development server
pnpm build             # Build for production
pnpm preview           # Preview production build

# Database
pnpm prisma-migrate    # Run database migrations
pnpm prisma-generate   # Generate Prisma client
pnpm prisma-push       # Push schema changes

Contributing

1. Keep dependencies updated: pnpm install after each git pull
2. Run migrations: pnpm prisma-migrate when schema changes
3. Follow conventions: Use TypeScript, Vue 3 Composition API, and Tailwind CSS
4. Test thoroughly: Verify both Docker and development setups

Tech Stack

• Frontend: Nuxt 3, Vue 3, Nuxt UI, Tailwind CSS
• Backend: Nitro (Nuxt server), Prisma ORM
• Database: SQLite (development), PostgreSQL (production ready)
• Vector DB: ChromaDB, Milvus
• AI/ML: LangChain, Ollama, OpenAI, Anthropic, Google AI
• Deployment: Docker, Docker Compose

Join Our Community

Join our Discord community for support, discussions, and updates:

Discord Invite Link

• #technical-discussion - For contributors and technical discussions
• #customer-support - Get help with usage issues and troubleshooting
• #general - Community chat and announcements

License

MIT License