---
title: "向量检索Chroma使用和服务端docker部署"
description: "介绍向量检索数据库Chroma的服务端Docker部署，以及客户端基本的使用"
image: "https://wileyzhang.com/posts/cover/34871ffa-62bc-4995-af00-6faaca757ac8_0811b826df695886a5343f75e23b570b.png"
url: "https://wileyzhang.com/posts/chroma-docker"
date: "2024-08-23"
updated: "2026-08-19"
type: "blog-post"
tags: ["RAG", "VectorStore"]
reading_time_minutes: 1
estimated_tokens: 655
---

# 部署

1. clone项目，自行打包docker（docker hub 镜像更新落后）

```python
git clone https://github.com/chroma-core/chroma.git
```

2. 生成随机Tokens令牌

```python
import secrets

# 生成一个随机的Token令牌
token = secrets.token_urlsafe(32)  # 生成一个32字节的URL安全令牌
print(token)
```

3. 创建环境变量`.chroma_env`文件，写入下面的内容

```python
CHROMA_SERVER_AUTHN_CREDENTIALS="your-token"
CHROMA_SERVER_AUTHN_PROVIDER="chromadb.auth.token_authn.TokenAuthenticationServerProvider"
```

4. 启动

```python
docker compose up -d
```

# 使用

1. 连接

```python
import chromadb

client = chromadb.HttpClient(host="127.0.0.1",
port=8000,
settings=chromadb.Settings(
chroma_client_auth_provider="chromadb.auth.token_authn.TokenAuthClientProvider",
chroma_client_auth_credentials="your_token"))
```

2. 创建colletion，并使用兼容langchain的embedding

embedding 部署见[LLM部署\(docker+vllm+embedding+rerank\) 支持工具调用](https://wileyzhang.com/posts/llm-docker-vllm-embedding-rerank)

```python

from chromadb.utils import embedding_functions
from langchain_openai import OpenAIEmbeddings
embedding = embedding_functions.create_langchain_embedding(OpenAIEmbeddings())
collection = client.get_or_create_collection(name="test", embedding_function=embedding)
```

3. 添加数据

```python
collection.add(ids=["1"],
documents="test",
metadatas={"author":"admin"})
```

4. 查询collection数据量

```python
collection.count()
```

5. 简单查询collection

```python
collection = client.get_collection("test", embedding_function=embedding)
r = collection.query(query_texts="test", n_results=1)
```

---

> 本文由 WileyZhang 原创，首发于 [Wiley Blog](https://wileyzhang.com/posts/chroma-docker)。

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