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Pinecone is a vector database with broad functionality.

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Installation and Setup

Install the Python SDK: 
pip install langchain-pinecone

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Vector store

There exists a wrapper around Pinecone indexes, allowing you to use it as a vectorstore, whether for semantic search or example selection. 
from langchain_pinecone import PineconeVectorStore
For a more detailed walkthrough of the Pinecone vectorstore, see this notebook

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Sparse Vector store

LangChain’s PineconeSparseVectorStore enables sparse retrieval using Pinecone’s sparse English model. It maps text to sparse vectors and supports adding documents and similarity search. 
from langchain_pinecone import PineconeSparseVectorStore

# Initialize sparse vector store
vector_store = PineconeSparseVectorStore(
    index=my_index,
    embedding_model="pinecone-sparse-english-v0"
)
# Add documents
vector_store.add_documents(documents)
# Query
results = vector_store.similarity_search("your query", k=3)
For a more detailed walkthrough, see the Pinecone Sparse Vector Store notebook.

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Sparse Embedding

LangChain’s PineconeSparseEmbeddings provides sparse embedding generation using Pinecone’s pinecone-sparse-english-v0 model. 
from langchain_pinecone.embeddings import PineconeSparseEmbeddings

# Initialize sparse embeddings
sparse_embeddings = PineconeSparseEmbeddings(
    model="pinecone-sparse-english-v0"
)
# Embed a single query (returns SparseValues)
query_embedding = sparse_embeddings.embed_query("sample text")

# Embed multiple documents (returns list of SparseValues)
docs = ["Document 1 content", "Document 2 content"]
doc_embeddings = sparse_embeddings.embed_documents(docs)
For more detailed usage, see the Pinecone Sparse Embeddings notebook.

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Retrievers

pip install pinecone pinecone-text

from langchain_community.retrievers import (
    PineconeHybridSearchRetriever,
)
For more detailed information, see this notebook.

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Self Query retriever

Pinecone vector store can be used as a retriever for self-querying.
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