{
  "id": 6825241,
  "title": "# I Built a RAG Document Assistant with FastAPI, React, FAISS and Ollama",
  "url": "https://urgent.news/2026/09/11/i-built-a-rag-document-assistant-with-fastapi-react-faiss-and-ollama",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-09-11T23:29:08.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/abderaouf-b/-i-built-a-rag-document-assistant-with-fastapi-react-faiss-and-ollama-1bgj"
  },
  "original_language": "en",
  "account": "I created a Retrieval-Augmented Generation (RAG) document assistant using FastAPI, React, FAISS, and Ollama. During my learning journey, I understood that RAG is more complex than simply sending documents to an LLM. Several factors, such as document splitting, embedding generation, relevant chunk retrieval, and evaluation of retrieval effectiveness, impact the final outcome. The application, CloudRAG, allows users to upload documents and ask questions about their content. Instead of directly sending questions to the LLM, the assistant first searches the uploaded documents for relevant information, which it then provides to the LLM as context. The simplified workflow consists of document extraction, chunking, embeddings generation, FAISS for vector search, relevant chunks, and finally, the LLM's response along with sources. The backend was built using Python FastAPI, SQLite, and SQLAlchemy, while the RAG pipeline utilized SentenceTransformers, FAISS, and Ollama. React and Vite front-end were employed for the user interface. Docker and Pytest facilitated development. FAISS was chosen for local embedding storage and search, making it an apt choice for this version of the project. Throughout the project, I emphasized the importance of document processing, chunking, embeddings, retrieval, and LLM deployment. I also explored Qdrant Cloud integration but ultimately settled on the local FAISS setup for deployment. The project aimed to be an educational endeavor, providing insights into the intricacies of building a RAG application. The source code is available on GitHub (https://github.com/abderaoufsec/CloudRAG), and I welcome any feedback, particularly from those who have improved RAG retrieval quality.",
  "summary": "I Built a RAG Document Assistant with FastAPI, React, FAISS and Ollama I've been learning more about Retrieval-Augmented Generation (RAG), and I wanted to understand what actually happens inside a RAG application. So instead of building another simple chatbot, I decided to build a complete application around documents. The result is CloudRAG : https://github.com/abderaoufsec/CloudRAG What does it…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}