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This repository provides a comprehensive guide on integrating LlamaIndex with Google Gemini to build an effective Retrieval-Augmented Generation (RAG) system for Question and Answer (Q&A) applications.

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NebeyouMusie/LlamaIndex-And-Google-Gemini

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LlamaIndex And Google Gemini

  • This repository provides a comprehensive guide on integrating LlamaIndex with Google Gemini to build an effective Retrieval-Augmented Generation (RAG) system for Question and Answer (Q&A) applications.

Libraries Used

  • llama-index
  • google-generativeai
  • llama-index-llms-gemini
  • pypdf
  • python-dotenv
  • IPython
  • llama-index-embeddings-gemini

Installation

  1. Prerequisites
    • Git
    • Command line familiarity
  2. Clone the Repository: git clone https://github.com/NebeyouMusie/LlamaIndex-And-Google-Gemini.git
  3. Create and Activate Virtual Environment (Recommended)
    • python -m venv venv
    • source venv/bin/activate for Mac and venv/bin/activate for Windows
  4. Navigate to the projects directory cd ./LlamaIndex-And-Google-Gemini using your terminal
  5. Install Libraries: pip install -r requirements.txt
  6. Enter your GOOGLE_API_KEY in the .env.example file then change the file to .env. You can get your GOOGLE_API_KEY from here
  7. run all cells found in the ./notebook/llamaindex_gemini.ipynb notebook

Collaboration

  • Collaborations are welcomed ❤️

Acknowledgments

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This repository provides a comprehensive guide on integrating LlamaIndex with Google Gemini to build an effective Retrieval-Augmented Generation (RAG) system for Question and Answer (Q&A) applications.

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