Pinecone integrations | LangChain 日本語

Pinecone is a vector database with broad functionality.

Installation and setup

Install the Python SDK:

pip

pip install langchain-pinecone

uv add langchain-pinecone

pipuv

pip install langchain-pinecone

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

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.

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.

Retrievers

Self query retriever

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