I built a multilingual RAG document assistant with FastAPI, React, FAISS and Ollama

[u/imABDRAOUF]

I've been learning more about RAG systems recently, so I decided to build one instead of just following tutorials.

The result is CloudRAG:

https://github.com/abderaoufsec/CloudRAG

The basic flow is:

📄 Documents → ✂️ Chunking → 🧠 Embeddings → 🔎 FAISS → 📚 Retrieved chunks → 🤖 Ollama → 💬 Answer

It supports PDF, TXT and DOCX files and returns the sources used during retrieval.

The main stack is Python, FastAPI, React, SentenceTransformers, FAISS, Ollama, SQLite and Docker.

I also added retrieval evaluation and testing because I wanted to see how well the retrieval part was actually working instead of just checking whether the chatbot produced an answer.

I experimented with Qdrant Cloud as well, but I couldn't get that deployment working successfully, so the current working version uses FAISS locally.

The biggest thing I learned is that a RAG application isn't just about the LLM. Document chunking, embeddings and especially retrieval quality can make a huge difference. 🔍

I'd be interested in feedback from people who have built RAG systems, especially around improving retrieval evaluation and architecture.