RAG Agent with LangChain, Qdrant, and Ollama

This repository contains a minimal example of a Retrieval-Augmented Generation (RAG) agent built with LangChain, Qdrant, and Ollama. The agent retrieves relevant documents from a local Qdrant vector store and generates answers using an Ollama language model.

Prerequisites

• Python 3.10+
• Qdrant server running locally (default port 6333).
  • Create a collection named rag_collection and populate it with embeddings.
• Ollama server running locally (default port 11434).
  • Ensure the model llama3.1 (or any other supported model) is available.

Installation

# Clone the repository
git clone https://git.brojs.ru/kuzakhmetovartur/agent-s-rag-pamyatyu.git
cd agent-s-rag-pamyatyu

# 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

Usage

python -m src.main

You will be prompted to enter a question. The agent will retrieve relevant documents from Qdrant and generate an answer using Ollama. Type exit or quit to terminate the program.

Project Structure

agent-s-rag-pamyatyu/
├── requirements.txt
├── src/
│   └── main.py
└── README.md

• requirements.txt – lists all Python dependencies, including langchain-qdrant and langchain-ollama.
• src/main.py – contains the RAG agent implementation.
• README.md – this documentation file.

Troubleshooting

• Missing dependencies: Ensure you ran pip install -r requirements.txt.
• Qdrant connection errors: Verify Qdrant is running and the collection name matches rag_collection.
• Ollama connection errors: Verify Ollama is running and the model name is correct.

License

This project is provided as-is for educational purposes. Feel free to modify and extend it.

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