LangChain Chat with Your Data



The LangChain Chat with Your Data course provides a step-by-step exploration of RAG EssentialsRAG Essentials

RAG Essentials :

How to split documents for embedding
How to Create high-quality embeddings.
How to choose vector store
How to search in order to retrieve most useful and relevance docum...
, guiding users through fundamental processes. The course covers loading various document types using methods like website loaders, directory loaders, and YouTube audio loaders,

1. Document Loaders

  • Introduction of how to loading different type of document, through website loader, directory loader, pdf loader, Youtube Audio loader - Document Loaders - Github

2. Text Splitters

3. Vector Stores and Embeddings

  • Vector stores and Embedding - They shows that we first embed the document into embedding using OpenAI embedding, however there are various embedding in the LangChain (Embedding), also the course introduce how to create a persist vector store using ChromaDB, again several vector store supported by the LangChain (Vector Stores)

4. Retrieval Strategies

  • Retrieve the relevant document with different type of retrievals such as similarity search, maximal marginal relevance, using self-query to specified the metadata filtering, compression the response from LLM (through ContextualCompressionRetriever) - See more (Retrievers). Finally, they also demonstrate the traditional retriever such as SVMRetriever and TFIDFRetriever

5. Creating Q and A

  • Demonstrate how to create Question Answering by using RetrievalQA chain (RetrievalQA)

6. Enhancing them with Memory to create a Q/A Chat Bots.

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