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Updated 2 months ago

llama_index/docs/examples/metadata_extra...

I'm reading this doc on MetadataExtraction.
and with using the QuestionsAnsweredExtractor(questions=3, llm=llm), I see that it generates
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 'questions_this_excerpt_can_answer': '1. How many countries does Uber operate in?\n2. What is the total gross bookings of Uber in 2019?\n3. How many trips did Uber facilitate in 2019?'}


My understanding is when doing a Vector Search a Document/Node's Text is searched. So how does it know how to search questions_this_excerpt_can_answer without specifying it as a metafilter using qdrant for example.

https://github.com/run-llama/llama_index/blob/main/docs/examples/metadata_extraction/MetadataExtractionSEC.ipynb
L
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5 comments
When nodes are embedded by llama-index, the metadata is included in the embedding

It's something like embed_model.get_text_embedding(node.get_content(metadata_mode=MetadataMode.EMBED))
You can actually configure which metadata keys to include/exclude too
You can try see an example now actually
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from llama_index.schema import MetadataMode

print(node.get_content(metadata_mode=MetadataMode.EMBED))
the node/document object is HIGHLY customizable in this aspect
ahh I see. I thought metadata were not part of the embedded search. good to know thanks.
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