End-to-End GenAI RAG App

This project implements an end-to-end Retrieval-Augmented Generation (RAG) application using Langchain and Llama2. The app retrieves relevant information from a document store and generates intelligent responses by augmenting the retrieved data with Llama2’s generative AI capabilities.

Key Features:

• Document Loading: Easily load and preprocess various document types (e.g., PDFs).
• Text Chunking: Automatically split large text into manageable chunks for efficient retrieval.
• Embeddings Generation: Use Hugging Face models to generate vector embeddings for documents.
• Pinecone Vector Storage: Store and manage document embeddings in Pinecone’s vector database.
• RAG Workflow: Combine retrieved documents with generative AI to produce informative responses.

Tech Stack:

• Langchain: For chaining together retrieval and generation tasks.
• Llama2: To handle generative responses.
• Pinecone: As the vector store for fast retrieval of relevant documents.
• Hugging Face: For generating embeddings from text.

Installation

1. Clone the repository:

   git clone https://github.com/your-username/your-repo-name.git
   cd your-repo-name

2. Install the required dependencies:

   pip install -r requirements.txt

   pip install -r requirements.txt -i https://pypi.org/simple

3. Set up your environment variables (e.g., PINECONE_API_KEY, PINECONE_ENVIRONMENT):

   cp .env.example .env

4. Run the application:

   python main.py

Usage

1. Load documents into the app.
2. Generate embeddings using Hugging Face models.
3. Use Pinecone for efficient vector retrieval.
4. Generate informative, context-rich responses using Llama2.

License

• This project is licensed under the MIT License. See the LICENSE file for more details.