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0deb98ac0c
**Description:** Currently, the `langchain_pinecone` library forces the `async_req` (asynchronous required) argument to Pinecone to `True`. This design choice causes problems when deploying to environments that do not support multiprocessing, such as AWS Lambda. In such environments, this restriction can prevent users from successfully using `langchain_pinecone`. This PR introduces a change that allows users to specify whether they want to use asynchronous requests by passing the `async_req` parameter through `**kwargs`. By doing so, users can set `async_req=False` to utilize synchronous processing, making the library compatible with AWS Lambda and other environments that do not support multithreading. **Issue:** This PR does not address a specific issue number but aims to resolve compatibility issues with AWS Lambda by allowing synchronous processing. **Dependencies:** None, that I'm aware of. --------- Co-authored-by: Erick Friis <erick@langchain.dev> |
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| poetry.lock | ||
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| README.md | ||
langchain-pinecone
This package contains the LangChain integration with Pinecone.
Installation
pip install -U langchain-pinecone
And you should configure credentials by setting the following environment variables:
PINECONE_API_KEYPINECONE_INDEX_NAME
Usage
The PineconeVectorStore class exposes the connection to the Pinecone vector store.
from langchain_pinecone import PineconeVectorStore
embeddings = ... # use a LangChain Embeddings class
vectorstore = PineconeVectorStore(embeddings=embeddings)