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Updated 3 months ago

322 response self pinecone index query

-> 322 response = self._pinecone_index.query(
323 vector=query_embedding,
324 sparse_vector=sparse_vector,

AttributeError: 'str' object has no attribute 'query'
L
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11 comments
Did you create the pinecone_index just like the notebook?
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Yep. I am actually doing it again right now to see where I dropped the ball.
Ah, I think the documents have to be originally inserted by llama index too
You probably want to load the data using a loader instead?
I am trying to reproduce my custom method with what you just shared but I am trying to wrap my mind around how to go about it.
Its like trying to fit a square in a circle
Yea it's like, llama index can insert into and read from its own data it inserted to answer queries.

If you have existing data, then you can only really load it and possibly use it in llama index later
Understood. Thank you
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