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Signed-off-by: ChengZi <chen.zhang@zilliz.com> Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Dan O'Donovan <dan.odonovan@gmail.com> Co-authored-by: Tom Daniel Grande <tomdgrande@gmail.com> Co-authored-by: Grande <Tom.Daniel.Grande@statsbygg.no> Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: ccurme <chester.curme@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> Co-authored-by: Tomaz Bratanic <bratanic.tomaz@gmail.com> Co-authored-by: ZhangShenao <15201440436@163.com> Co-authored-by: Friso H. Kingma <fhkingma@gmail.com> Co-authored-by: ChengZi <chen.zhang@zilliz.com> Co-authored-by: Nuno Campos <nuno@langchain.dev> Co-authored-by: Morgante Pell <morgantep@google.com>
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)