RAG Agent with ChromaDB and Web Search

This project implements a Retrieval-Augmented Generation (RAG) agent that uses ChromaDB as the vector database and performs live web searches to provide up‑to‑date information.

Features

Vector store – Documents are ingested, split into chunks, embedded with OpenAI embeddings, and stored in a persistent ChromaDB collection.
Web search – Uses DuckDuckGo scraping to fetch recent web snippets for a query.
RAG pipeline – Combines local document context and web results, then generates an answer with OpenAI GPT‑3.5‑Turbo.
CLI – Simple command line interface for ingestion and querying.

Prerequisites

Python 3.10+
An OpenAI API key with access to text-embedding-ada-002 and gpt-3.5-turbo.

Setup

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

# Install dependencies
pip install -r requirements.txt

Configuration

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

OPENAI_API_KEY=sk-...
CHROMA_DB_PATH=./chromadb
CHROMA_COLLECTION_NAME=rag_collection

Note

: Do not commit your .env file or API key to version control.

Usage

1. Ingest Documents

python src/main.py ingest path/to/doc1.txt path/to/doc2.txt

The script will read each file, split it into chunks, generate embeddings, and store them in ChromaDB.

2. Query the Agent

python src/main.py query "What is the capital of France?"

The agent will:

Retrieve relevant chunks from the local vector store.
Perform a DuckDuckGo web search for the query.
Combine both sources of information.
Generate a response using OpenAI GPT‑3.5‑Turbo.

Project Structure

src/
├── main.py          # CLI entry point
├── vector_store.py  # ChromaDB ingestion & retrieval
├── web_search.py    # DuckDuckGo web search
requirements.txt
README.md

Testing

The project can be tested with pytest (tests are not included in this minimal example).
If you add tests, run:

pytest

License

MIT License

Feel free to extend the agent with additional features such as custom embeddings, different LLMs, or alternative search APIs.

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