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Developer builds open-source RAG document assistant with local LLM

By PulseAugur Editorial · [1 sources] ·

A developer has created CloudRAG, an open-source Retrieval-Augmented Generation (RAG) document assistant. The application allows users to upload documents and ask questions, with the system first searching for relevant information within the documents before sending it to a local LLM for an answer. The project utilizes Python, FastAPI, React, FAISS for local embedding storage and search, and Ollama for the LLM, with plans to integrate Qdrant in the future. AI

IMPACT Provides a practical example of building a local RAG system, highlighting key components and challenges for developers.

RANK_REASON The item describes the creation of a specific software tool and its technical implementation.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Developer builds open-source RAG document assistant with local LLM

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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Abdraouf Benabdsselem ·

    # I Built a RAG Document Assistant with FastAPI, React, FAISS and Ollama

    <h1> I Built a RAG Document Assistant with FastAPI, React, FAISS and Ollama </h1> <p>I've been learning more about Retrieval-Augmented Generation (RAG), and I wanted to understand what actually happens inside a RAG application.</p> <p>So instead of building another simple chatbot…

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