Pinecone 集成

from langchain_pinecone import PineconeVectorStore

from langchain_pinecone import PineconeSparseVectorStore

# 初始化稀疏向量存储
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)

from langchain_pinecone.embeddings import PineconeSparseEmbeddings

# 初始化稀疏嵌入
sparse_embeddings = PineconeSparseEmbeddings(
    model="pinecone-sparse-english-v0"
)
# 嵌入单个查询（返回 SparseValues）
query_embedding = sparse_embeddings.embed_query("sample text")

# 嵌入多个文档（返回 SparseValues 列表）
docs = ["Document 1 content", "Document 2 content"]
doc_embeddings = sparse_embeddings.embed_documents(docs)

from langchain_community.retrievers import (
    PineconeHybridSearchRetriever,
)