mcp-local-analyst

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mcp-local-analyst

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Talk to your data locally 💬📊. A private AI Data Analyst built with the Model Context Protocol (MCP), Ollama, and SQLite. Turn natural language into SQL queries without data leaving your machine. Includes a Dockerized Streamlit UI

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# MCP Local Analyst
![Demo](https://raw.githubusercontent.com/fabiothiroki/mcp-local-analyst/main/docs/demo.gif)

Talk to your data locally 💬📊. A private AI Data Analyst built with the Model Context Protocol (MCP), Ollama, and SQLite. Turn natural language into SQL queries without data leaving your machine. Includes a Dockerized Streamlit UI

📝 **[Read the full article on Medium](https://medium.com/gitconnected/chat-with-your-database-for-free-build-a-private-ai-agent-in-python-87bc0245ca8f)**

## Getting Started

### Prerequisites

Before running the application, make sure you have the following installed:

1. **Docker & Docker Compose** - Required for running the application in containers
   - Install Docker Desktop from [docker.com](https://www.docker.com/products/docker-desktop)
   - Includes Docker Compose by default

2. **Ollama** - For running local LLM models
   - Download from [ollama.ai](https://ollama.ai)
   - After installation, pull a model: `ollama pull mistral` (or your preferred model)
   - Ollama will run as a service on `http://localhost:11434`

### Installation & Running Locally

1. Clone the repository

2. Ensure Ollama is running:
   ```bash
   ollama serve
   ```
   (Keep this running in a separate terminal)

3. Start the application with Docker Compose:
   ```bash
   docker-compose up --build
   ```

4. Open your browser and navigate to:
   ```
   http://localhost:8501
   ```

### Configuration

- Modify the database by editing `src/seed_data.py` if needed
- Configure model selection and parameters in the application UI
- Data is stored in the `data/` directory

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