> ## 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.

# Chroma 集成

> 使用 LangChain Python 集成 Chroma 向量存储。

本笔记介绍如何开始使用 `Chroma` 向量存储。

> [Chroma](https://docs.trychroma.com/getting-started) is a AI-native open-source vector database focused on developer productivity and happiness. Chroma is licensed under Apache 2.0. View the full docs of `Chroma` at [this page](https://docs.trychroma.com/reference/py-collection), and find the API reference for the LangChain integration at [this page](https://reference.langchain.com/python/langchain-chroma/vectorstores/Chroma).

<Info>
  **Chroma Cloud**

  Chroma Cloud powers serverless vector and full-text search. It's extremely fast, cost-effective, scalable and painless. Create a DB and try it out in under 30 seconds with \$5 of free credits.

  [Get started with Chroma Cloud](https://trychroma.com/signup)
</Info>

## 设置

要访问 `Chroma` 向量存储，您需要安装 `langchain-chroma` 集成包。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pip install -qU "langchain-chroma>=0.1.2"
```

### 凭证

您无需任何凭证即可使用 `Chroma` 向量存储，只需安装上述包即可！

如果您是 [Chroma Cloud](https://trychroma.com/signup) 用户，请设置 `CHROMA_TENANT`、`CHROMA_DATABASE` 和 `CHROMA_API_KEY` 环境变量。

When you install the `chromadb` package you also get access to the Chroma CLI, which can set these for you. First, [login](https://docs.trychroma.com/docs/cli/login) via the CLI, and then use the [`connect` command](https://docs.trychroma.com/docs/cli/db):

```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
chroma db connect [db_name] --env-file
```

如果您希望获得一流的模型调用自动追踪功能，还可以通过取消注释以下代码来设置 [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"
```

## 初始化

### 基本初始化

以下是基本初始化，包括使用目录在本地保存数据。

<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")
```

#### 本地运行（内存模式）

您可以通过简单地使用集合名称和向量嵌入提供者实例化 `Chroma` 来在内存中运行 Chroma 服务器：

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

vector_store = Chroma(
    collection_name="example_collection",
    embedding_function=embeddings,
)
```

如果您不需要数据持久化，这是使用 LangChain 构建 AI 应用时进行实验的绝佳选择。

#### 本地运行（持久化数据）

您可以提供 `persist_directory` 参数来在程序多次运行之间保存数据：

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

vector_store = Chroma(
    collection_name="example_collection",
    embedding_function=embeddings,
    persist_directory="./chroma_langchain_db",
)
```

#### Connecting to a chroma Server

If you have a Chroma server running locally, or you have [deployed](https://docs.trychroma.com/guides/deploy/client-server-mode) one yourself, you can connect to it by providing the `host` argument.

For example, you can start a Chroma server running locally with `chroma run`, and then connect it with `host='localhost'`:

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

vector_store = Chroma(
    collection_name="example_collection",
    embedding_function=embeddings,
    host="localhost",
)
```

For other deployments you can use the `port`, `ssl`, and `headers` arguments to customize your connection.

#### Chroma cloud

Chroma Cloud users can also build with LangChain. Provide your `Chroma` instance with your Chroma Cloud API key, tenant, and DB name:

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

vector_store = Chroma(
    collection_name="example_collection",
    embedding_function=embeddings,
    chroma_cloud_api_key=os.getenv("CHROMA_API_KEY"),
    tenant=os.getenv("CHROMA_TENANT"),
    database=os.getenv("CHROMA_DATABASE"),
)
```

### Initialization from client

You can also initialize from a `Chroma` client, which is particularly useful if you want easier access to the underlying database.

#### 本地运行（内存模式）

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

client = chromadb.Client()
```

#### 本地运行（持久化数据）

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

client = chromadb.PersistentClient(path="./chroma_langchain_db")
```

#### Connecting to a chroma Server

For example, if you are running a Chroma server locally (using `chroma run`):

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

client = chromadb.HttpClient(host="localhost", port=8000, ssl=False)
```

#### Chroma cloud

After setting your `CHROMA_API_KEY`, `CHROMA_TENANT`, and `CHROMA_DATABASE`, you can simply instantiate:

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

client = chromadb.CloudClient()
```

#### Access your chroma DB

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
collection = client.get_or_create_collection("collection_name")
collection.add(ids=["1", "2", "3"], documents=["a", "b", "c"])
```

#### Create a chroma vectorstore

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
vector_store_from_client = Chroma(
    client=client,
    collection_name="collection_name",
    embedding_function=embeddings,
)
```

## 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的项目来与其交互。

### 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"},
    id=1,
)

document_2 = Document(
    page_content="The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.",
    metadata={"source": "news"},
    id=2,
)

document_3 = Document(
    page_content="Building an exciting new project with LangChain - come check it out!",
    metadata={"source": "tweet"},
    id=3,
)

document_4 = Document(
    page_content="Robbers broke into the city bank and stole $1 million in cash.",
    metadata={"source": "news"},
    id=4,
)

document_5 = Document(
    page_content="Wow! That was an amazing movie. I can't wait to see it again.",
    metadata={"source": "tweet"},
    id=5,
)

document_6 = Document(
    page_content="Is the new iPhone worth the price? Read this review to find out.",
    metadata={"source": "website"},
    id=6,
)

document_7 = Document(
    page_content="The top 10 soccer players in the world right now.",
    metadata={"source": "website"},
    id=7,
)

document_8 = Document(
    page_content="LangGraph is the best framework for building stateful, agentic applications!",
    metadata={"source": "tweet"},
    id=8,
)

document_9 = Document(
    page_content="The stock market is down 500 points today due to fears of a recession.",
    metadata={"source": "news"},
    id=9,
)

document_10 = Document(
    page_content="I have a bad feeling I am going to get deleted :(",
    metadata={"source": "tweet"},
    id=10,
)

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)
```

### Update items in vector store

Now that we have added documents to our vector store, we can update existing documents by using the `update_documents` function.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
updated_document_1 = Document(
    page_content="I had chocolate chip pancakes and fried eggs for breakfast this morning.",
    metadata={"source": "tweet"},
    id=1,
)

updated_document_2 = Document(
    page_content="The weather forecast for tomorrow is sunny and warm, with a high of 82 degrees.",
    metadata={"source": "news"},
    id=2,
)

vector_store.update_document(document_id=uuids[0], document=updated_document_1)
# You can also update multiple documents at once
vector_store.update_documents(
    ids=uuids[:2], documents=[updated_document_1, updated_document_2]
)
```

### 从向量存储删除项目

We can also delete items from our vector store as follows:

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

## 查询向量存储

一旦创建了向量存储并添加了相关文档，您很可能希望在链或智能体运行期间对其进行查询。

### 直接查询

#### 相似度搜索

可以按以下方式执行简单的相似度搜索：

```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,
    filter={"source": "tweet"},
)
for res in results:
    print(f"* {res.page_content} [{res.metadata}]")
```

#### 带分数的相似度搜索

如果您想执行相似度搜索并获取对应分数，可以运行：

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

#### Search by vector

You can also search by vector:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
results = vector_store.similarity_search_by_vector(
    embedding=embeddings.embed_query("I love green eggs and ham!"), k=1
)
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")
```

#### 其他搜索方法

There are a variety of other search methods that are not covered in this notebook, such as MMR search. For a full list of the search abilities available for `Chroma` check out the [API reference](https://reference.langchain.com/python/langchain-chroma/vectorstores/Chroma).

### 转换为检索器进行查询

您还可以将向量存储转换为检索器，以便在链中更方便地使用。 For more information on the different search types and kwargs you can pass, please visit the [Chroma API reference](https://reference.langchain.com/python/langchain-chroma/vectorstores/Chroma).

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
retriever = vector_store.as_retriever(
    search_type="mmr", search_kwargs={"k": 1, "fetch_k": 5}
)
retriever.invoke("Stealing from the bank is a crime", filter={"source": "news"})
```

## 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：

* [Tutorials](/oss/python/langchain/rag)
* [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)
* [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)

***

## API 参考

For detailed documentation of all `Chroma` vector store features and configurations head to the [API reference](https://reference.langchain.com/python/langchain-chroma/vectorstores/Chroma)

***

<div className="source-links">
  <Callout icon="terminal-2">
    [连接这些文档](/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/vectorstores/chroma.mdx)或[提交 issue](https://github.com/langchain-ai/docs/issues/new/choose)。
  </Callout>
</div>