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Hmm, I don't know enough to give you a confident answer yet about creating a Pinecone Index from a set of PDFs using GPTPineconeIndex. The provided extracts do not cover the process of extracting text from PDFs. However, once you have extracted the text from the PDFs, you can follow the steps in the extracts to create a Pinecone Index using GPTPineconeIndex.
Here's an example of how to create a Pinecone Index using GPTPineconeIndex with extracted text from documents:
- Import necessary libraries:
from gpt_index import GPTPineconeIndex, SimpleDirectoryReader
import pinecone
- Initialize Pinecone with your API key and environment:
api_key = "api_key"
pinecone.init(api_key=api_key, environment="us-east1-gcp")
- Create a Pinecone Index:
pinecone.create_index("quickstart", dimension=1536, metric="euclidean", pod_type="p1")
index = pinecone.Index("quickstart")
- Load the extracted text from your PDFs into a suitable data structure (e.g., a list of dictionaries with keys like 'title' and 'content').
- Create a GPTPineconeIndex using the extracted text and the Pinecone Index:
gpt_index = GPTPineconeIndex(documents, pinecone_index=index)
For more information on GPTPineconeIndex, you can refer to the
documentation (
https://gpt-index.readthedocs.io/en/latest/reference/indices/vector_store.html).
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