Hi What causes this error when using pinecone as a vec...

Hi

What causes this error when using pinecone as a vectorstore?

HTTP response body: The index exceeds the project quota of 1 pods by 1 pods. Upgrade your account or change the project settings to increase the quota.

11 comments

kkapa.ai

Please stand by @Matt Thompson as I search the knowledge base. We are experiencing especially high traffic right now and might take longer than usual. :wave:

kkapa.ai

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Hmm, I don't know enough to give you a confident answer yet. However, you can refer to the GPT index documentation (https://gpt-index.readthedocs.io/en/latest) and example notebooks (https://github.com/jerryjliu/gpt_index/tree/main/examples/data_connectors) for more information on using Pinecone as a vector store with GPT index.
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MMatt Thompson

@kapa.ai can you tell me about using a vector store like pinecone?

kkapa.ai

Let me check the knowledge base for you again. One moment! :books:

kkapa.ai

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Sure! To use Pinecone as a vector store with GPT index, you need to create a Pinecone index and then build a GPTPineconeIndex from your documents. Here's an example of how to do this:

First, create a Pinecone index:

Plain Text

import pinecone

api_key = "api_key"
pinecone.init(api_key=api_key, environment="us-west1-gcp")
pinecone.create_index(
    "quickstart", 
    dimension=1536, 
    metric="euclidean", 
    pod_type="p1"
)
index = pinecone.Index("quickstart")

Load your documents and build the GPTPineconeIndex:

Plain Text

from gpt_index import GPTPineconeIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()
metadata_filters = {"title": "paul_graham_essay"}

index = GPTPineconeIndex.from_documents(
    documents, pinecone_index=index, metadata_filters=metadata_filters
)

Query the index:

Plain Text

response = index.query("What did the author do growing up?")

For more information and examples, you can refer to the GPT index documentation on vector stores (https://gpt-index.readthedocs.io/en/latest/how_to/integrations/vector_stores.html) and the example notebooks (https://github.com/jerryjliu/gpt_index/tree/main/examples/data_connectors).
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

MMatt Thompson

@kapa.ai can you tell me about the pinecone index variables?

kkapa.ai

Let me check the knowledge base for you again. One moment! :books:

kkapa.ai

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Certainly! The GPTPineconeIndex is a data structure where nodes are keyed by embeddings, and those embeddings are stored within a Pinecone index. During index construction, the document texts are chunked up, converted to nodes with text, and then encoded in document embeddings stored within Pinecone.

Here are the parameters for the GPTPineconeIndex:

documents: The documents you want to index.
pinecone_index: The Pinecone index instance.
metadata_filters: Optional dictionary for filtering metadata.
pinecone_kwargs: Optional dictionary for Pinecone index kwargs (deprecated).
insert_kwargs: Optional dictionary for insert kwargs during upsert call.
query_kwargs: Optional dictionary for query kwargs during query call.
delete_kwargs: Optional dictionary for delete kwargs during delete call.

To create a GPTPineconeIndex, you can use the following code snippet:

Plain Text

from gpt_index import GPTPineconeIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()
index = GPTPineconeIndex(documents, pinecone_index=index)

For more information, you can refer to the GPT index documentation on vector store index (https://gpt-index.readthedocs.io/en/latest/reference/indices/vector_store.html) and the example notebooks (https://github.com/jerryjliu/gpt_index/tree/main/examples/vector_indices/PineconeIndexDemo.ipynb).
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

MMatt Thompson

@kapa.ai any documents on the pinecone dimensions, pod type and and metrics?

kkapa.ai

Let me check the knowledge base for you again. One moment! :books:

kkapa.ai

@Matt Thompson That's a great question, but unfortunately I am only capable of responding to a maximum of three questions per thread. Don't hesitate to create a new thread for any further questions you have though. I'm here to help!

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