📚 Building a RAG Model

🚀 Overview

This notebook demonstrates the implementation of a Retrieval-Augmented Generation (RAG) model to improve the accuracy of generated answers by incorporating external knowledge. The workflow involves:

✅ Dependencies: Installation of required packages such as langchain and langgraph.
✅ Data Preprocessing: Text is segmented and prepared using langchain-text-splitters for better retrieval.
✅ Model Implementation: A RAG pipeline is developed to retrieve relevant documents and generate improved responses.
✅ Evaluation: BLEU scores are calculated to assess the model’s performance before and after applying RAG.

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⚡️ Installation

To install the required packages, run the following command:

pip install --quiet --upgrade langchain-text-splitters langchain-community langgraph

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📖 Usage

1. Open the Notebook: Launch the rag_model.ipynb in Jupyter Notebook or Google Colab.
2. Run All Cells: Execute the cells sequentially to preprocess the data, implement the model, and evaluate results.
3. Evaluate Results: Compare BLEU scores to measure the improvement achieved by the RAG model.

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📝 Notes

• The initial markdown section is titled "Dependencies."
• The first code cell installs necessary packages:

%pip install --quiet --upgrade langchain-text-splitters langchain-community langgraph

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📊 Results

The performance improvement is evaluated using BLEU scores:

• Before RAG: Baseline results without document retrieval.
• After RAG: Enhanced results by incorporating relevant information into the responses.

Happy Coding! 🎉