This project demonstrates how to leverage Gemini-pro with Langchain for text generation, multimodal applications, and creating a Retrieval Augmented Generation (RAG) system to extract answers from PDFs.
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Text Generation: Generate high-quality responses based on your queries using Gemini-pro.
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Multimodal Tasks: Handle text-based multimodal tasks using Langchain.
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RAG-based PDF Q&A: Extract answers from PDFs using a combination of Gemini and Chroma embeddings.
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PDF Processing: Load, split, and process PDFs to answer specific questions.
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Document Interaction: Query your documents using the Langchain retrieval-based approach.
- Clone the repository:
git clone https://github.com/whoatharva/Gemini-Project
cd Gemini-Project
- Create a virtual environment and activate it:
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`- Install the required packages:
pip install -r requirements.txt4.Set up environment variables:
- Create a .env file in the root of your project and add your Google API key:
GOOGLE_API_KEY=your-google-api-key- Python: Programming language used.
- Google Generative AI: For text generation and embeddings.
- Langchain: For constructing chains and document processing.
- ChromaDB: For building vector indexes and retrieving document embeddings.
- pypdf: For loading and processing PDFs.
To run this project, you will need to add the following environment variables to your .env file
GOOGLE_API_KEY="YOUR_API_KEY_HERE"
Contributions are welcome! Feel free to submit pull requests or open issues for suggestions or improvements.