Kaggle Notebook

The Retrieval Augmented Engine (RAG) is a powerful tool for document retrieval, summarization, and interactive question-answering. This project utilizes LangChain and weaviate Db to provide a seamless web application for users to perform these tasks. With RAG, you can easily upload Your documents (Like PDF File, Json File, txt File etc.) and easily ask Questions related your Documents.

Before running the project, make sure you have the following prerequisites:

• Python 3.7+
• LangChain
• weaviate
• An OpenAI API key
• WEAVIATE_API_KEY and WEAVIATE_CLUSTER_URL
• TXT File to upload (You can modily code and use other file)

1. Clone the repository to your local machine:

   git clone https://github.com/chiragjoshi12/LangChain.git
   cd RAG

2. Open Implementing a Retrieval-Augmented Generation (RAG) System with OpenAI's API + weaviate_DB.ipynb File for use Weaviate Database

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2. Open Implementing a Retrieval-Augmented Generation (RAG) System with OpenAI's API.ipynb for use FAISS Database

Chirag Joshi

If you have any questions, suggestions, or would like to discuss this project further, feel free to get in touch with me:

• LinkedIn

I'm open to collaboration and would be happy to connect!