chatboti 0.2.0

Raglet - RAG starter kit with FastAPI, MCP, and multiple LLM providers

Chatboti

A simple chatbot example demonstrating how to build an AI agent that uses MCP (Model Context Protocol) to query a RAG database. Includes CLI, web UI, and Docker deployment options.

What It Does

An LLM-powered agent answers questions by searching a simple speaker database using semantic search. The agent uses MCP tools to find the best matching speaker for any topic.

Quick Start

Prerequisites

• Python 3.13+
• uv package manager

Installation

uv sync

Configuration

Create a .env file:

CHAT_SERVICE=openai
OPENAI_API_KEY=your-api-key-here

Supported services: openai, bedrock

Three Ways to Run

1. Web UI

uv run chatboti ui-chat

Opens a browser with an interactive chat interface.

2. CLI Chat

uv run chatboti cli-chat

Interactive terminal chat with the agent.

3. Docker

uv run chatboti docker

Builds and runs a Docker container. The Dockerfile is configured for CI/ECS deployment with:

• Port 80
• Health endpoint at /health

How It Works

User Query → Agent (LLM) → MCP Tools → RAG Database → Response

1. Agent - An LLM that decides when to use tools
2. MCP Server - Provides tools via Model Context Protocol
3. RAG Service - Semantic search using embeddings on speaker data

Project Structure

chatboti/
├── cli.py           # CLI commands
├── agent.py         # LLM agent with MCP client
├── mcp_server.py    # MCP server with RAG tools
├── rag.py           # Embeddings and semantic search
├── server.py        # FastAPI web server
├── index.html       # Web UI
└── data/            # Speaker database (CSV + embeddings)

API Endpoints

• / - Web chat interface
• /chat - Chat API endpoint
• /health - Health check
• /info - Service configuration

Environment Variables

Variable	Description
CHAT_SERVICE	LLM provider: openai or bedrock (required)
EMBED_SERVICE	Embedding provider (defaults to CHAT_SERVICE)
OPENAI_API_KEY	OpenAI API key
AWS_PROFILE	AWS profile for Bedrock