Retrieval Augmented Generation (RAG) is a technique to increase the relevance and transparency of LLM responses. RAG works by supplying the LLM with documents that are relevant to a query.
In this tutorial, we’ll learn how to perform RAG on audio data using LangChain and Python.
APIs:
– AssemblyAI API key: https://www.assemblyai.com/dashboard/signup
– OpenAI API key: https://openai.com/blog/openai-api
Resources:
– Code: https://github.com/AssemblyAI-Examples/rag-langchain-audio-data
– Blog: https://www.assemblyai.com/blog/retrieval-augmented-generation-audio-langchain/
– LangChain webinar series: https://www.youtube.com/@LangChain
Tools used:
– AssemblyAI: https://www.assemblyai.com/
– LangChain: https://www.langchain.com/
– Hugging Face: https://huggingface.co/
– Chroma: https://www.trychroma.com/
– OpenAI: https://openai.com/
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#MachineLearning #deeplearning
0:00 Introduction
0:34 Repo and Blog
0:46 What is Retrieval Augmented Generation (RAG)?
0:59 Implementation overview
1:42 Environment setup
2:54 Imports
4:10 Specifying audio files
4:35 Audio document loader
5:12 Text splitting
6:04 Setting metadata
6:29 Text embedding
6:52 Building the Chroma database
7:10 Building the QA LangChain
7:50 Make application loop
8:50 Running the application
9:28 Asking a question and RAG response
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