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Updated 3 months ago
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322 response self pinecone index query
322 response self pinecone index query
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Obelix
2 years ago
Β·
-> 322 response = self._pinecone_index.query(
323 vector=query_embedding,
324 sparse_vector=sparse_vector,
AttributeError: 'str' object has no attribute 'query'
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Logan M
2 years ago
Did you create the pinecone_index just like the notebook?
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Obelix
2 years ago
Yep. I am actually doing it again right now to see where I dropped the ball.
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Obelix
2 years ago
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Logan M
2 years ago
Ah, I think the documents have to be originally inserted by llama index too
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Obelix
2 years ago
π¦
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Logan M
2 years ago
You probably want to load the data using a loader instead?
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Logan M
2 years ago
https://llamahub.ai/l/pinecone
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Obelix
2 years ago
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.
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Obelix
2 years ago
Its like trying to fit a square in a circle
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Logan M
2 years ago
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
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Obelix
2 years ago
Understood. Thank you
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