RAGent

A Retrieval Augmented Generation (RAG) pipeline using Azure OpenAI and LangChain.

Overview

RAGent is a command-line tool that enables:

• Ingesting and processing documents into a vector database
• Interactive chat with a large language model (LLM) using your documents as context

Installation

# Clone the repository
git clone https://github.com/yourusername/ragent.git
cd ragent

# Install the package
pip install -e .

Configuration

Create a .env file in the root directory with your Azure OpenAI credentials and settings. You can use the provided .env.example as a template:

cp .env.example .env

Then edit the .env file with your Azure OpenAI credentials:

# Azure OpenAI Configuration
RAGENT_AOAI_ENDPOINT="https://your-api-endpoint.openai.azure.com/"
RAGENT_EMBEDDING_DEPLOYMENT_NAME=text-embedding-3-large
RAGENT_EMBEDDING_DEPLOYMENT_VERSION=2025-01-01-preview
RAGENT_LLM_DEPLOYMENT_NAME=gpt-4
RAGENT_LLM_DEPLOYMENT_VERSION=2025-01-01-preview

# Document Processing Configuration
RAGENT_CHUNK_SIZE=500
RAGENT_CHUNK_OVERLAP=50
RAGENT_BATCH_SIZE=100

# Vector Store Configuration
RAGENT_PERSIST_DIR=./chroma_db

Usage

RAGent provides two main commands:

1. Ingest Documents

Process and store documents in the vector database:

ragent ingest <directory_path>

2. Chat with Your Documents

Start an interactive chat session with the LLM using your ingested documents as context:

ragent chat

Requirements

• Python 3.9 or higher
• Azure OpenAI API access

Dependencies

• LangChain
• ChromaDB
• Click
• Python-dotenv

License

MIT