> ## Documentation Index
> Fetch the complete documentation index at: https://nvd-54.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Pinecone 集成

> 使用 LangChain Python 与 Pinecone 集成。

> [Pinecone](https://docs.pinecone.io/docs/overview) 是一个向量数据库 with broad functionality.

## 安装和设置

安装 Python SDK：

<CodeGroup>
  ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  pip install langchain-pinecone
  ```

  ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  uv add langchain-pinecone
  ```
</CodeGroup>

## 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。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_pinecone import PineconeVectorStore
```

有关 Pinecone vectorstore, see [this notebook](/oss/python/integrations/vectorstores/pinecone)

### 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.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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](/oss/python/integrations/vectorstores/pinecone_sparse).

### Sparse embedding

LangChain's `PineconeSparseEmbeddings` provides sparse embedding generation using Pinecone's `pinecone-sparse-english-v0` model.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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](/oss/python/integrations/vectorstores/pinecone_sparse).

## 检索器

### Pinecone hybrid search

<CodeGroup>
  ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  pip install pinecone pinecone-text
  ```

  ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  uv add pinecone pinecone-text
  ```
</CodeGroup>

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_community.retrievers import (
    PineconeHybridSearchRetriever,
)
```

有关更多详细信息，请参阅 [this notebook](/oss/python/integrations/retrievers/pinecone_hybrid_search).

### Self query retriever

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

***

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  <Callout icon="terminal-2">
    [将这些文档连接](/use-these-docs) 到 Claude、VSCode 等工具，通过 MCP 获取实时答案。
  </Callout>

  <Callout icon="edit">
    [在 GitHub 上编辑此页面](https://github.com/langchain-ai/docs/edit/main/src/oss/python/integrations/providers/pinecone.mdx)或[提交 issue](https://github.com/langchain-ai/docs/issues/new/choose).
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