Pinecone 集成

使用 LangChain Python 与 Pinecone 集成。

Pinecone 是一个向量数据库 with broad functionality.

安装和设置

安装 Python SDK：

pip

pip install langchain-pinecone

向量存储

存在一个围绕 Pinecone indexes, 的封装器，允许您将其用作向量存储， 无论是用于语义搜索还是示例选择。

from langchain_pinecone import PineconeVectorStore

有关 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"
)
# 添加文档
vector_store.add_documents(documents)
# 查询
results = vector_store.similarity_search("your query", k=3)

有关更详细的演练，请参阅 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)

有关更详细的用法，请参阅 Pinecone Sparse Embeddings notebook.

检索器

Pinecone hybrid search

pip

pip install pinecone pinecone-text

from langchain_community.retrievers import (
    PineconeHybridSearchRetriever,
)

有关更多详细信息，请参阅 this notebook.

Self query retriever

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

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