title: “Chroma — Open-source embedding database for AI applications” sidebar_label: “Chroma” description: “Open-source embedding database for AI applications”

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Chroma

专为人工智能应用设计的开源嵌入数据库。可用于存储嵌入向量及元数据，支持向量和全文搜索，并能根据元数据进行过滤。该数据库提供简洁的 4 种功能 API，可从笔记本环境扩展至生产级集群。适用于语义搜索、RAG 应用以及文档检索场景，尤其适合本地开发及开源项目使用。

技能元数据

参考：完整的 SKILL.md 文件

:::info 以下是当触发该技能时 Hermes 会加载的完整技能定义。技能处于激活状态时，智能体将依据此内容执行操作。

::

Chroma —— 开源嵌入数据库

专为构建具备记忆功能的 LLM 应用而设计的原生人工智能数据库。

何时使用 Chroma

以下情况适合使用 Chroma：

• 开发 RAG（检索增强生成）应用
• 需要本地/自托管的向量数据库
• 寻求开源解决方案（遵循 Apache 2.0 许可协议）
• 在笔记本环境中进行原型设计 | 对文档进行语义搜索 | | 同时存储嵌入向量及元数据 |

相关数据统计：

• GitHub 星标数超过 24,300 个
• 代码复用次数超过 1,900 次
• 当前版本为 v1.3.3，稳定发布且每周都有新版本
• 采用 Apache 2.0 许可协议

如需替代方案，可选择：

• Pinecone：托管型云服务，具备自动扩展功能
• FAISS：纯相似度搜索工具，不支持元数据处理
• Weaviate：专为生产环境设计的原生机器学习数据库
• Qdrant：高性能数据库，基于 Rust 语言开发

快速入门

安装

# Python
pip install chromadb

# JavaScript/TypeScript
npm install chromadb @chroma-core/default-embed

基本用法（Python）

import chromadb

# Create client
client = chromadb.Client()

# Create collection
collection = client.create_collection(name="my_collection")

# Add documents
collection.add(
    documents=["This is document 1", "This is document 2"],
    metadatas=[{"source": "doc1"}, {"source": "doc2"}],
    ids=["id1", "id2"]
)

# Query
results = collection.query(
    query_texts=["document about topic"],
    n_results=2
)

print(results)

核心操作

1. 创建集合

# Simple collection
collection = client.create_collection("my_docs")

# With custom embedding function
from chromadb.utils import embedding_functions

openai_ef = embedding_functions.OpenAIEmbeddingFunction(
    api_key="your-key",
    model_name="text-embedding-3-small"
)

collection = client.create_collection(
    name="my_docs",
    embedding_function=openai_ef
)

# Get existing collection
collection = client.get_collection("my_docs")

# Delete collection
client.delete_collection("my_docs")

2. 添加文档

# Add with auto-generated IDs
collection.add(
    documents=["Doc 1", "Doc 2", "Doc 3"],
    metadatas=[
        {"source": "web", "category": "tutorial"},
        {"source": "pdf", "page": 5},
        {"source": "api", "timestamp": "2025-01-01"}
    ],
    ids=["id1", "id2", "id3"]
)

# Add with custom embeddings
collection.add(
    embeddings=[[0.1, 0.2, ...], [0.3, 0.4, ...]],
    documents=["Doc 1", "Doc 2"],
    ids=["id1", "id2"]
)

3. 查询（相似度搜索）

# Basic query
results = collection.query(
    query_texts=["machine learning tutorial"],
    n_results=5
)

# Query with filters
results = collection.query(
    query_texts=["Python programming"],
    n_results=3,
    where={"source": "web"}
)

# Query with metadata filters
results = collection.query(
    query_texts=["advanced topics"],
    where={
        "$and": [
            {"category": "tutorial"},
            {"difficulty": {"$gte": 3}}
        ]
    }
)

# Access results
print(results["documents"])      # List of matching documents
print(results["metadatas"])      # Metadata for each doc
print(results["distances"])      # Similarity scores
print(results["ids"])            # Document IDs

4. 获取文档

# Get by IDs
docs = collection.get(
    ids=["id1", "id2"]
)

# Get with filters
docs = collection.get(
    where={"category": "tutorial"},
    limit=10
)

# Get all documents
docs = collection.get()

5. 更新文档

# Update document content
collection.update(
    ids=["id1"],
    documents=["Updated content"],
    metadatas=[{"source": "updated"}]
)

6. 删除文档

# Delete by IDs
collection.delete(ids=["id1", "id2"])

# Delete with filter
collection.delete(
    where={"source": "outdated"}
)

持久化存储

# Persist to disk
client = chromadb.PersistentClient(path="./chroma_db")

collection = client.create_collection("my_docs")
collection.add(documents=["Doc 1"], ids=["id1"])

# Data persisted automatically
# Reload later with same path
client = chromadb.PersistentClient(path="./chroma_db")
collection = client.get_collection("my_docs")

嵌入功能

默认选项（Sentence Transformers）

# Uses sentence-transformers by default
collection = client.create_collection("my_docs")
# Default model: all-MiniLM-L6-v2

OpenAI

from chromadb.utils import embedding_functions

openai_ef = embedding_functions.OpenAIEmbeddingFunction(
    api_key="your-key",
    model_name="text-embedding-3-small"
)

collection = client.create_collection(
    name="openai_docs",
    embedding_function=openai_ef
)

HuggingFace

huggingface_ef = embedding_functions.HuggingFaceEmbeddingFunction(
    api_key="your-key",
    model_name="sentence-transformers/all-mpnet-base-v2"
)

collection = client.create_collection(
    name="hf_docs",
    embedding_function=huggingface_ef
)

自定义嵌入函数

from chromadb import Documents, EmbeddingFunction, Embeddings

class MyEmbeddingFunction(EmbeddingFunction):
    def __call__(self, input: Documents) -> Embeddings:
        # Your embedding logic
        return embeddings

my_ef = MyEmbeddingFunction()
collection = client.create_collection(
    name="custom_docs",
    embedding_function=my_ef
)

元数据过滤

# Exact match
results = collection.query(
    query_texts=["query"],
    where={"category": "tutorial"}
)

# Comparison operators
results = collection.query(
    query_texts=["query"],
    where={"page": {"$gt": 10}}  # $gt, $gte, $lt, $lte, $ne
)

# Logical operators
results = collection.query(
    query_texts=["query"],
    where={
        "$and": [
            {"category": "tutorial"},
            {"difficulty": {"$lte": 3}}
        ]
    }  # Also: $or
)

# Contains
results = collection.query(
    query_texts=["query"],
    where={"tags": {"$in": ["python", "ml"]}}
)

与LangChain的集成

from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter

# Split documents
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)
docs = text_splitter.split_documents(documents)

# Create Chroma vector store
vectorstore = Chroma.from_documents(
    documents=docs,
    embedding=OpenAIEmbeddings(),
    persist_directory="./chroma_db"
)

# Query
results = vectorstore.similarity_search("machine learning", k=3)

# As retriever
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})

与 LlamaIndex 的集成

from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import VectorStoreIndex, StorageContext
import chromadb

# Initialize Chroma
db = chromadb.PersistentClient(path="./chroma_db")
collection = db.get_or_create_collection("my_collection")

# Create vector store
vector_store = ChromaVectorStore(chroma_collection=collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)

# Create index
index = VectorStoreIndex.from_documents(
    documents,
    storage_context=storage_context
)

# Query
query_engine = index.as_query_engine()
response = query_engine.query("What is machine learning?")

服务器模式

# Run Chroma server
# Terminal: chroma run --path ./chroma_db --port 8000

# Connect to server
import chromadb
from chromadb.config import Settings

client = chromadb.HttpClient(
    host="localhost",
    port=8000,
    settings=Settings(anonymized_telemetry=False)
)

# Use as normal
collection = client.get_or_create_collection("my_docs")

最佳实践

1. 使用持久化客户端——避免重启后数据丢失
2. 添加元数据——便于筛选与追踪
3. 批量操作——一次性添加多个文档
4. 选择合适的嵌入模型——在速度与质量间取得平衡
5. 运用过滤器——缩小搜索范围
6. 使用唯一标识符——防止数据冲突
7. 定期备份——复制chroma_db目录
8. 监控集合大小——必要时进行扩展
9. 测试嵌入功能——确保输出质量
10. 生产环境采用服务器模式——更适用于多用户场景

性能表现

资源链接

• GitHub仓库：https://github.com/chroma-core/chroma ⭐ 24,300+星标
• 文档中心：https://docs.trychroma.com
• Discord社区：https://discord.gg/MMeYNTmh3x
• 当前版本：1.3.3+
• 许可证：Apache 2.0