Agent with RAG Memory
This project implements a retrieval‑augmented generation (RAG) agent that uses Qdrant as the vector store and Ollama for local LLM inference.
The agent follows the latest LangChain API and is fully configurable via environment variables.
Features
- Qdrant vector store (via
langchain-qdrant) - Ollama local LLM integration (via
langchain-ollama) - Retrieval‑augmented generation with conversation memory
- Simple CLI interface for quick testing
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
Configuration
Create a .env file in the project root (or set environment variables directly):
# Qdrant
QDRANT_HOST=localhost
QDRANT_PORT=6333
QDRANT_API_KEY= # leave empty if no key
# Ollama
OLLAMA_HOST=localhost
OLLAMA_PORT=11434
OLLAMA_MODEL=llama3
# Optional: collection name
QDRANT_COLLECTION=documents
Note
: The Qdrant instance must be running and accessible at the specified host/port.
The Ollama server must be running locally and expose the chosen model.
Usage
Adding Documents
from src.agent import Agent
from langchain_core.documents import Document
agent = Agent()
docs = [
Document(page_content="Python is a programming language.", metadata={"source": "python.txt"}),
Document(page_content="LangChain is a framework for LLM applications.", metadata={"source": "langchain.txt"}),
]
agent.add_documents(docs)
Querying the Agent
python -m src.agent "What is LangChain?"
or from Python:
response = agent.run("What is LangChain?")
print(response["answer"])
The response will include the answer and the source documents used.
Project Structure
agent-s-rag-pamyatyu/
├── src/
│ ├── agent.py
│ ├── config.py
│ └── vector_store.py
├── requirements.txt
├── pyproject.toml
└── README.md
License
MIT License