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Based on the provided extracts, there are several tutorials and examples that guide you through the process of building a vector index, retrieving embeddings, and storing them in Pinecone using LlamaIndex. However, explicit information on parallelizing these processes is not provided in the extracts. Here's a general guide on how to proceed:
- Initialize Pinecone and create an index:
You need to initialize Pinecone with your API key and environment, then create an index with the desired parameters. Note that you should not save your API keys in the code or add pinecone_env to your repo for security reasons. Here's an example of how to do this:
import pinecone
import os
api_key = os.environ["PINECONE_API_KEY"]
environment = os.environ["PINECONE_ENVIRONMENT"]
pinecone.init(api_key=api_key, environment=environment)
index_name = "llamaindex-rag-fs"
pinecone.create_index(index_name, dimension=1536, metric="euclidean", pod_type="p1")
pinecone_index = pinecone.Index(index_name)
Source:
LlamaIndex > En > Latest > Examples > Low_level > OpenAI > Setup ()
- Create a PineconeVectorStore:
This is a simple wrapper abstraction to use in LlamaIndex. You can create it as follows:
from llama_index.vector_stores import PineconeVectorStore
vector_store = PineconeVectorStore(pinecone_index=pinecone_index)
Source:
LlamaIndex > En > Latest > Examples > Low_level > OpenAI > Setup ()
- Load documents into the PineconeVectorStore:
You can load documents into the PineconeVectorStore using high-level ingestion abstractions with VectorStoreIndex.from_documents. Here's an example:
