Retrieval Augmented Generation on audio data with LangChain and Chroma

In this tutorial, we learned how to build a retrieval augmented generation (RAG) model using LangChain with audio data. We combined several tools such as AssemblyAI for transcribing the audio files, HuggingFace's tokenizers and transformers libraries for embedding the transcriptions, Chroma for creating a vector database, and OpenAI's GPT-3.5 for generating responses based on the retrieved information. To implement this model, we followed these steps: 1. Load audio files with AssemblyAI loader and transcribe them into text format. 2. Use HuggingFace's transformers library to embed the transcriptions into vectors. 3. Store the vector representations of the audio transcriptions in a Chroma vector database. 4. Perform queries with GPT-3.5 using the stored audio content as context for generating responses. We also demonstrated how to run the application and provided an example response along with the source information. Finally, we mentioned additional learning resources such as our blog tutorials section and YouTube channel.