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

# ClickHouse 集成

> 使用 LangChain Python 与 ClickHouse 向量存储集成。

> [ClickHouse](https://clickhouse.com/) 是一个用于实时应用和分析的开源数据库，具有完整的 SQL 支持。ClickHouse 支持精确向量搜索（例如，使用 `L2Distance` 等距离函数）和使用向量相似性索引的近似向量搜索（在 ClickHouse 25.8+ 中可用）。详情请参阅[精确和近似向量搜索](https://clickhouse.com/docs/engines/table-engines/mergetree-family/annindexes)。

本页展示如何使用与 `ClickHouse` 向量存储相关的功能。

## 设置

首先使用 docker 设置一个本地 clickhouse 服务器：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
! docker run -d -p 8123:8123 -p 9000:9000 --name langchain-clickhouse-server --ulimit nofile=262144:262144 -e CLICKHOUSE_SKIP_USER_SETUP=1 clickhouse/clickhouse-server:26.2
```

你需要安装 `langchain-community` 和 `clickhouse-connect` 才能使用此集成。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pip install -qU langchain-community clickhouse-connect
```

### 凭证

此笔记本无需凭证，只需确保你已按上述方式安装了相关包。

如果你想获得一流的模型调用自动追踪，也可以通过取消注释以下内容来设置你的 [LangSmith](/langsmith/home) API 密钥：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"
```

## 实例化

<EmbeddingTabs />

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# | output: false
# | echo: false
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_community.vectorstores import Clickhouse, ClickhouseSettings

settings = ClickhouseSettings(table="clickhouse_example")
vector_store = Clickhouse(embeddings, config=settings)
```

## 管理向量存储

创建向量存储后，我们可以通过添加和删除不同项目与之交互。

### 向向量存储添加项目

我们可以使用 `add_documents` 函数向向量存储添加项目。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from uuid import uuid4

from langchain_core.documents import Document

document_1 = Document(
    page_content="I had chocolate chip pancakes and scrambled eggs for breakfast this morning.",
    metadata={"source": "tweet"},
)

document_2 = Document(
    page_content="The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.",
    metadata={"source": "news"},
)

document_3 = Document(
    page_content="Building an exciting new project with LangChain - come check it out!",
    metadata={"source": "tweet"},
)

document_4 = Document(
    page_content="Robbers broke into the city bank and stole $1 million in cash.",
    metadata={"source": "news"},
)

document_5 = Document(
    page_content="Wow! That was an amazing movie. I can't wait to see it again.",
    metadata={"source": "tweet"},
)

document_6 = Document(
    page_content="Is the new iPhone worth the price? Read this review to find out.",
    metadata={"source": "website"},
)

document_7 = Document(
    page_content="The top 10 soccer players in the world right now.",
    metadata={"source": "website"},
)

document_8 = Document(
    page_content="LangGraph is the best framework for building stateful, agentic applications!",
    metadata={"source": "tweet"},
)

document_9 = Document(
    page_content="The stock market is down 500 points today due to fears of a recession.",
    metadata={"source": "news"},
)

document_10 = Document(
    page_content="I have a bad feeling I am going to get deleted :(",
    metadata={"source": "tweet"},
)

documents = [
    document_1,
    document_2,
    document_3,
    document_4,
    document_5,
    document_6,
    document_7,
    document_8,
    document_9,
    document_10,
]
uuids = [str(uuid4()) for _ in range(len(documents))]

vector_store.add_documents(documents=documents, ids=uuids)
```

### 从向量存储删除项目

我们可以使用 `delete` 函数按 ID 从向量存储中删除项目。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
vector_store.delete(ids=uuids[-1])
```

## 查询向量存储

创建向量存储并添加相关文档后，你很可能希望在链或代理运行期间对其进行查询。

### 直接查询

#### 相似性搜索

执行简单的相似性搜索可以按如下方式进行：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
results = vector_store.similarity_search(
    "LangChain provides abstractions to make working with LLMs easy", k=2
)
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")
```

#### 带分数的相似性搜索

你也可以带分数进行搜索：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
results = vector_store.similarity_search_with_score("Will it be hot tomorrow?", k=1)
for res, score in results:
    print(f"* [SIM={score:3f}] {res.page_content} [{res.metadata}]")
```

## 过滤

你可以直接访问 ClickHouse SQL where 语句。你可以按照标准 SQL 编写 `WHERE` 子句。

**注意**：请注意 SQL 注入，此接口不得由最终用户直接调用。

如果你在设置中自定义了 `column_map`，你可以使用如下过滤器进行搜索：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
meta = vector_store.metadata_column
results = vector_store.similarity_search_with_relevance_scores(
    "What did I eat for breakfast?",
    k=4,
    where_str=f"{meta}.source = 'tweet'",
)
for res in results:
    print(f"* {res.page_content} [{res.metadata}]")
```

#### 其他搜索方法

本笔记本未涵盖多种其他搜索方法，例如 MMR 搜索或按向量搜索。

### 通过转换为检索器进行查询

你也可以将向量存储转换为检索器，以便在你的链中更轻松地使用。

以下是将向量存储转换为检索器，然后使用简单查询和过滤器调用检索器的方法。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
retriever = vector_store.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={"k": 1, "score_threshold": 0.5, "where_str": "metadata.source = 'news'"},
)
retriever.invoke("Stealing from the bank is a crime")
```

## 用于检索增强生成

有关如何将此向量存储用于检索增强生成 (RAG) 的指南，请参阅以下部分：

* [教程](/oss/python/langchain/rag)
* [操作指南：使用 RAG 进行问答](https://python.langchain.com/docs/how_to/#qa-with-rag)
* [检索概念文档](https://python.langchain.com/docs/concepts/retrieval)

更多内容，请查看[使用 Astra DB 的完整 RAG 模板](https://github.com/langchain-ai/langchain/tree/master/templates/rag-astradb)。

***

***

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