LangChain Parent-Child Retriever for better RAG
In this video, we'll learn about the advanced capabilities of Langchain's Parent-Child Retriever in the context of retrieval-augmented generation (RAG) applications. The tutorial showcases how embedding chunks of text with links to larger 'parent' chunks can enhance the context provided to language models. It demonstrates practical steps in a Jupyter notebook, including loading a text document, converting it to a Langchain document, and storing it in Chroma DB. The video further explains the creation of parent and child splitters, embedding chunks using the Fast Embed library, and utilizing an in-memory store for parent documents. We'll see a comparison between querying parent and child retrievers and observe the impact on the quality of responses from a language model. We'll then use the Zephyr LLM with Ollama to compare answers to questions with the two retrievers.
#Langchain #RAG #retrievalaugmenetedgeneration #RAGapps #LanguageModeling #AIDevelopment #AIApplications
Notebook: https://github.com/mneedham/LearnData...