RAG Agent with ChromaDB and Web Search

This project implements a simple Retrieval-Augmented Generation (RAG) agent that uses ChromaDB for vector storage and OpenAI embeddings for text representation. The agent exposes two HTTP endpoints:

POST /ingest – ingest documents into the vector store.
POST /query – retrieve the most similar documents for a given query.

Features

Vector Store: ChromaDB collection named rag_collection.
Embeddings: OpenAI text-embedding-ada-002 (configurable).
API: FastAPI based, can be run locally or in Docker.
No Qdrant: The implementation uses only ChromaDB as required.

Prerequisites

Python 3.11+
Docker (optional, for containerized deployment)
An OpenAI API key (set as OPENAI_API_KEY environment variable).

Setup

Local

# Clone the repository
git clone https://git.brojs.ru/kuzakhmetovartur/ekzamen-rag-agent-s-chromadb-i-veb-poisk.git
cd ekzamen-rag-agent-s-chromadb-i-veb-poisk

# Create virtual environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Set OpenAI API key
export OPENAI_API_KEY="sk-..."

# Run the server
uvicorn src.main:app --reload

The API will be available at http://127.0.0.1:8000.

Docker

# Build the image
docker build -t rag-agent .

# Run the container
docker run -d -p 8000:8000 --env OPENAI_API_KEY="sk-..." rag-agent

API Usage

Ingest Documents

curl -X POST http://localhost:8000/ingest \
     -H "Content-Type: application/json" \
     -d '{
           "documents": [
             {"content": "The quick brown fox jumps over the lazy dog."},
             {"content": "Python is a versatile programming language."}
           ]
         }'

Query

curl -X POST http://localhost:8000/query \
     -H "Content-Type: application/json" \
     -d '{
           "query": "What is Python?",
           "k": 3
         }'

Notes

The vector store is persisted in memory by default. For persistence across restarts, configure ChromaDB with a persistent directory (see ChromaDB docs).
The agent currently only returns the raw similarity search results. Integration with a language model for generation can be added later.
No Qdrant usage is present; the stack strictly follows the assignment requirements.

License

MIT License

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