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This repository houses a collection of projects built using LangChain, focusing on Retrieval Augmented Generation (RAG) systems and AI chatbots. Explore various implementations, from basic RAG models to more sophisticated chatbot interactions.

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Kunal70616c/Simple-RAG-Langchain

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Simple-RAG-Langchain

This repository explores the use of LangChain for building Retrieval Augmented Generation (RAG) systems and AI chatbots.

Key Features:

  • RAG Implementations:
    • Various RAG architectures are explored, including basic RAG, RAG with metadata, and different retrieval strategies.
    • Examples of RAG in action for answering questions and engaging in conversations.
  • Chatbot Development:
    • Chatbot implementations with varying levels of sophistication.
    • Integration with external services like Firebase for conversation storage.
  • Text Splitting:
    • Techniques for effectively splitting text for RAG systems.

Repository Structure:

  • data: Contains data used for the projects, such as documents for RAG systems.
  • db: Stores any database files used by the projects.
  • scripts: Contains scripts for data processing, model training, or other tasks.
  • notebooks: Jupyter notebooks for exploratory analysis and experiments.
  • models: Stores trained models or checkpoints.

Getting Started:

  1. Clone the repository:
    git clone https://github.com/Kunal70616c/Simple-RAG-Langchain.git

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This repository houses a collection of projects built using LangChain, focusing on Retrieval Augmented Generation (RAG) systems and AI chatbots. Explore various implementations, from basic RAG models to more sophisticated chatbot interactions.

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