Summary:

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

• Introduction of how to split the document e.g., CharacterSplitter, Sentence Transformer, NLTK ,etc. - Document of text splitters - Github

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.

• Finally, creating a Chat need Memory component, so the course demonstrate ConversationalRetrievalChain with ConversationBufferMemory and finally show how to create the UI wrapping the QA chatbot

#llm #chatgpt #langchain #deeplearning-ai