Chatbots with RAG: LangChain Full Walkthrough

TL;DR

Learn how to build a chatbot using retrieval augmented generation, which combines openai GPT models and the line train library to answer questions.

Transcript

today we're going to take a look at how we can build a chatbot using retrieval augmented generation from start to finish so we're literally going to start with the assumption that you don't really know anything about chat Bots or how to build one but by the end of this video what we're going to have is a chatbot for those of you that are interested... Read More

Key Insights

• ℹ️ Retrieval augmented generation (RAG) combines language models with external knowledge sources to improve chatbot performance.
• 🐦‍⬛ Language models rely on training data and lack access to real-time or specific information, leading to inaccurate responses for certain queries.
• ❔ RAG enables chatbots to retrieve information from external knowledge bases, enhancing their ability to answer a wider range of questions accurately.
• 🔢 The RAG pipeline involves retrieving relevant knowledge, augmenting the language model's input, and generating responses based on the combined knowledge.
• 🆘 RAG helps address the hallucination and misinformation issues that can occur with language models.
• 🇨🇷 RAG presents token usage and cost considerations, as feeding more information into the language model can slow down its performance and increase costs.

Summary & Key Takeaways

• The content teaches how to build a chatbot using retrieval augmented generation (RAG) with openai GPT 3.5 and the line train library.

• RAG allows the chatbot to access external knowledge sources to answer questions that the language model has not been trained on.

• The process involves setting up a knowledge base, embedding the data, and using the RAG pipeline to retrieve relevant information.