chatboti 0.2.1

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

AWS Credentials for Bedrock: When using CHAT_SERVICE=bedrock or EMBED_SERVICE=bedrock, CLI automatically:

• Extracts AWS credentials from your AWS_PROFILE (or default profile)
• Injects them into Docker container as environment variables
• Validates credentials before building image

No manual credential configuration needed - just ensure your AWS profile is configured locally.

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

Model Configuration

The project supports multiple LLM and embedding models configured in summary-evals/models.json:

Chat Models:

• OpenAI: GPT-4o, GPT-4o-mini, GPT-4-turbo, GPT-3.5-turbo
• Bedrock: Amazon Nova Pro
• Ollama: Llama 3.2
• Groq: Llama 3.3-70b-versatile

Embedding Models:

• OpenAI: text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002
• Ollama: nomic-embed-text, mxbai-embed-large, all-minilm
• Bedrock: Amazon Titan Embed Text v2 & v1, Cohere Embed v3

Recent Improvements

Test Suite Refactoring

• Reduced Mock Usage: Replaced mock-based tests with integration tests using real services
• Consolidated Test Utilities: Created shared conftest.py with deterministic embed client
• Parameterized Tests: Added unified search tests for both FAISS and HDF5 backends
• Improved Service Creation: Abstracted RAG service creation with unified create_rag_service() function

Storage Backends

The project supports multiple storage backends for flexibility:

• FAISS + JSON: Standard multi-file format (.faiss + .json)
• HDF5: Single-file format (.h5) with compression and partial loading

Both backends are fully tested and supported in production.