rag-with-qdrant

A simple Proof of Concept (PoC) for a Retrieval-Augmented Generation (RAG) service using:

• FastAPI → backend API
• Qdrant → vector database

This service allows:

• Uploading documents (PDF, DOCX, TXT)
• Extracting and splitting text into chunks
• Generating embeddings for each chunk
• Storing embeddings in Qdrant
• Answering questions by searching relevant chunks via vector similarity

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🚀 Running the Project

docker-compose up

Qdrant

• REST API → http://localhost:6333
• Web UI → http://localhost:6333/dashboard

FastAPI

• API → http://localhost:8000

Run locally in development mode:

uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

or via Docker-compose:

docker-compose up --build

🗂️ API Endpoints (Planned)

File Upload

• POST /upload-file
  • Receive uploaded files (PDF, DOCX, TXT)
  • Extract and split text into chunks
  • Generate embeddings
  • Store vectors in Qdrant

Ask a Question

• POST /ask
  • Receive a question
  • Generate question embedding
  • Search Qdrant for similar vectors
  • Return relevant context to answer the question

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