RAG Agent with Ollama
This project implements a simple Retrieval-Augmented Generation (RAG) agent that uses Ollama for both embeddings and LLM inference.
The agent stores documents in memory, retrieves the most relevant ones for a query, and generates an answer using the retrieved context.
Features
- Embeddings – Uses Ollama’s embedding endpoint (
ollama.embeddings) with caching. - LLM – Uses Ollama’s chat endpoint (
ollama.chat) for generation. - RAG – Cosine similarity based retrieval of top‑k documents.
- FastAPI – Exposes a REST API for adding documents and asking questions.
- Docker – Containerized with Ollama and FastAPI.
Setup
# Clone the repo
git clone https://git.brojs.ru/kuzakhmetovartur/agent-s-rag-pamyatyu.git
cd agent-s-rag-pamyatyu
# Build Docker image
docker build -t rag-agent .
# Run container
docker run -p 8000:8000 rag-agent
The API will be available at http://localhost:8000.
API Endpoints
| Method | Path | Description |
|---|---|---|
| POST | /documents | Add a document to the agent. |
| POST | /ask | Ask a question; returns answer. |
Example
# Add a document
curl -X POST http://localhost:8000/documents \
-H "Content-Type: application/json" \
-d '{"text":"Python is a programming language."}'
# Ask a question
curl -X POST http://localhost:8000/ask \
-H "Content-Type: application/json" \
-d '{"query":"What is Python?"}'
Dependencies
ollama– Ollama client for embeddings and chat.fastapi– Web framework.uvicorn– ASGI server.numpy– Numerical operations.pydantic– Data validation.
All dependencies are listed in requirements.txt.
License
MIT License
This implementation follows the assignment constraints: only Ollama is used for embeddings and LLM, no OpenAI services are involved.