RAG From Scratch: Part 3 (Retrieval)
Переглядів 12,266
3 місяці томуThis is the third video in our series on RAG. The aim of this series is to build up an understanding of RAG from scratch, starting with the basics of indexing, retrieval, and generation. This video focuses on retrieval, covering the process of document search using an index.
Code:
github.com/langchain-ai/rag-f...
Slides:
docs.google.com/presentation/...
@mr.daniish Місяць тому
Lance is the man! love these small bites of gold.
@gnkbhuvan 7 днів тому
Really great explanation sir. Great work from langchain team.
@bqmac43 2 місяці тому
Excellent explanations, Lance! I'm looking forward to the remaining videos.
@noordin85 16 днів тому
Awesome explanation to the basics
@Learn-it-all-Do-it-all 2 місяці тому
Many thanks Lance for this, a great service for the community. In the example, does get_relevant_documents embed the query internally before running the search?
@Orcrambo 2 місяці тому
Yep, when you create the vector store you input the embedding strategy
@Learn-it-all-Do-it-all 2 місяці тому
Thanks @Orcrambo.
@jzam5426 Місяць тому
Thank you! With CSVLoaders a document has both "page_content" and "metadata." Do both get embedded, or only page_content? If page_content alone, does the retriever uses natively (without add instructions) metadata as well?
@zishanahmedshaikh 2 місяці тому
First
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