Advanced RAG - Self Querying Retrieval

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Semantic search is powerful, but it's not the answer to everything. For example, you don't need a semantic search if you're searching for a year or a specific name. Instead, use direct lookups for those cases. A semantic search should be used to extract meaning from text, like when looking for a movie and specifying the year or searching for music on Spotify by artist name. In this video, we'll see how to use Qdrant filtering and self-querying retrieval with LangChain.

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what if the text you want to query, is so large that you need to chunk it. Will you have then multipe chunks in the database with the same metadata?
for e.g if you ask your chatbot a question it constructs the query which filters only the relevant chunks, and you end up for example with 10 chunks of the same document wich are then used as context?

artmusic

Why is it called self querying, and not LLM-based query generation?

Wigglylove

This will add an additional LLM call into any query. Which is not so good.

eck

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