RAG with Gemma 3

Models
1

RAG with Gemma 3: Intelligent Document Analysis

This project combines the power of Google’s Gemma 3 model with the RAG (Retrieval-Augmented Generation) architecture to create an intelligent document analysis solution. The tool allows users to upload, process, and query PDF documents, generating precise and contextually relevant answers based on the extracted content.

Features

  • PDF Loading: Extracts text from PDF documents and prepares it for analysis.
  • Semantic Retrieval: Uses embeddings and FAISS to find relevant sections of the document.
  • Answer Generation: Utilizes the Gemma 3 model to generate contextually accurate responses.
  • Intelligent Querying: Enables users to ask complex questions and receive clear answers.

Technologies Used

  • Gemma 3: Google’s language model for generating responses.
  • LangChain: Framework for integrating document processing pipelines.
  • FAISS: Vector database for efficient semantic searches.
  • PyPDF: Library for loading and processing PDFs.
  • Hugging Face Transformers: For embeddings and natural language processing.

This project is a powerful example of how RAG and Gemma 3 can be combined to create a robust document analysis tool. Feel free to share your thoughts, suggestions, or questions below!

link
[#ia #gemma3 #rag #inovação #nlp #datascience #tech #linkedintech | Bruno Henrique](https://Link download)