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RAG Pipeline for conversational feedback

rag-pipeline

This project showcases the creation of a conversational Retrieval-Augmented Generation (RAG) pipeline system using LangChain, designed for engaging and informative discussions about video game releases. The advanced notebook provided guides users in building a RAG that leverages a quantized Mistral-7B for efficient yet powerful information retrieval and generation. I focused on December 2023 video game releases, using data from Gamespot's biggest game releases of December 2023 as a knowledge base. This project highlights key concepts in Conversational AI, offering an in-depth look at RAG and its application in creating advanced, context-aware conversational agents with LangChain.

🚀 About Me

Passionate self-taught artificial intelligence developer (AI) and machine learning engineer (ML) with a formal background in technical support, network security, system administration.

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Twitter / X - https://twitter.com/TMobley96

My YouTube Channel - https://www.youtube.com/@PapaAI334

Running LLMs on Your Local Machine - https://tjmobley.hashnode.dev/taking-control-of-ai-running-llms-on-your-local-machine

Linkedin - https://www.linkedin.com/in/1tmobley/

Hugging Face - https://huggingface.co/tmobley96

Acknowledgements

Madhav Thaker's tutorial on building a RAG pipeline with langchain

LangChain Documentation

Gamespot's biggest game releases of December 2023

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