RAG Agent with ChromaDB and Web Search

This repository contains a simple Retrieval-Augmented Generation (RAG) agent that uses ChromaDB as the vector store and OpenAI's GPT model for generation. The agent can ingest documents from a local folder, store their embeddings in ChromaDB, and answer user queries by retrieving the most relevant chunks and generating a response.

Important

: The original assignment required the use of ChromaDB instead of Qdrant. This implementation fully complies with that requirement.

Features

• Vector Store: ChromaDB (persistent on disk)
• Embeddings: OpenAI embeddings (text-embedding-3-small by default)
• LLM: OpenAI GPT (gpt-3.5-turbo by default)
• Text Splitting: Recursive character splitter (chunk size 1000, overlap 200)
• CLI: Two modes – ingest and query

Prerequisites

• Python 3.10+
• An OpenAI API key

Installation

# Clone the repository
git clone https://github.com/yourusername/ekzamen-rag-agent-s-chromadb-i-veb-poisk.git
cd ekzamen-rag-agent-s-chromadb-i-veb-poisk

# Create a virtual environment (optional but recommended)
python -m venv .venv
source .venv/bin/activate   # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

requirements.txt contains:

chromadb
langchain
openai
python-dotenv

Configuration

Create a .env file in the project root (or set environment variables directly):

OPENAI_API_KEY=your-openai-api-key
CHROMA_DB_PATH=./chromadb          # Path where ChromaDB will store data
CHROMA_COLLECTION=rag_collection   # Collection name
EMBEDDING_MODEL=text-embedding-3-small
LLM_MODEL=gpt-3.5-turbo
TOP_K=4
CHUNK_SIZE=1000
CHUNK_OVERLAP=200

Note

: If you don't provide a .env file, the script will look for the variables in the environment.

Usage

1. Ingest Documents

Place your .txt files in a folder (e.g., data/). Then run:

python -m src.index ingest data/

The script will:

1. Load all .txt files.
2. Split them into chunks.
3. Generate embeddings.
4. Store them in ChromaDB.

2. Query the Agent

python -m src.index query "What is the capital of France?"

The agent will:

1. Embed the question.
2. Retrieve the top TOP_K relevant chunks.
3. Generate an answer using GPT.

Example

$ python -m src.index ingest data/
Ingested 42 chunks into collection 'rag_collection'.

$ python -m src.index query "Explain the theory of relativity."
Answer:

The theory of relativity, developed by Albert Einstein, consists of two parts: special relativity and general relativity. ...

Project Structure

├── src
│   └── index.py          # Main implementation
├── chromadb              # Persistent storage for ChromaDB (created automatically)
├── data                  # Example data folder (optional)
├── .env                  # Environment variables
├── requirements.txt
└── README.md

Troubleshooting

• Missing OpenAI API key: Ensure OPENAI_API_KEY is set in your environment or .env file.
• ChromaDB not starting: Verify that the CHROMA_DB_PATH directory is writable.
• Large documents: Adjust CHUNK_SIZE and CHUNK_OVERLAP in the .env file.

License

MIT License