ekzamen-rag-agent-s-chromadb-i-veb-poisk/README.md

RAG Agent with ChromaDB & Tavily

This project implements a Retrieval-Augmented Generation (RAG) agent that uses ChromaDB for vector storage and Tavily for web search.
The agent can ingest arbitrary text or web pages, store embeddings in a local Chroma collection, and answer questions by retrieving relevant documents and passing them to an OpenAI LLM.

Important

The original repository used Qdrant. All references to Qdrant have been removed.
Only ChromaDB and Tavily are used.

Prerequisites

Component	Version	Notes
Python	3.9+	Tested on 3.10
OpenAI API	Any key	Required for embeddings and LLM
Tavily API	Any key	Required for web search

Set the following environment variables before running:

export OPENAI_API_KEY="your-openai-key"
export TAVILY_API_KEY="your-tavily-key"

Installation

# 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: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

requirements.txt contains:

chromadb>=0.4
tavily>=0.1
langchain>=0.0.350
openai>=1.0

Note

: The exact versions may vary; the above are the minimal compatible versions.

Usage

The agent is a single script src/index.py. It supports two commands:

1. Ingest

python src/index.py ingest <url_or_text>

• If <url_or_text> starts with http:// or https://, the script treats it as a URL, fetches the content via Tavily, and stores it.
• Otherwise, it treats the argument as raw text and stores it directly.

Example:

python src/index.py ingest https://en.wikipedia.org/wiki/OpenAI

2. Query

python src/index.py query "<your question>"

The script retrieves relevant documents from the Chroma collection and asks OpenAI to generate an answer.

Example:

python src/index.py query "What is OpenAI?"

Project Structure

.
├── src
│   └── index.py          # Main script
├── README.md
└── requirements.txt

How It Works

1. Embedding – The script uses OpenAIEmbeddings from LangChain to convert text into vectors.
2. Vector Store – Chroma stores these vectors locally in ~/.rag_agent/chromadb.
3. Retrieval – When a query is made, the nearest vectors are fetched.
4. Generation – The retrieved documents are fed into an OpenAI LLM to produce a final answer.

Troubleshooting

• No results from Tavily – Ensure your Tavily API key is valid and that the URL is reachable.
• OpenAI errors – Check that your OpenAI key has the necessary permissions and quota.
• Chroma storage issues – The data directory is ~/.rag_agent/chromadb. Delete it to reset the collection.

License

This project is released under the MIT License.