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Chroma

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

技能元数据


来源	可选 — 通过 `hermes skills install official/mlops/chroma`
路径	`optional-skills/mlops/chroma`
版本	`1.0.0`
作者	Orchestra Research
许可证	MIT
依赖项	`chromadb`, `sentence-transformers`
平台	linux, macos, windows
标签	`RAG`, `Chroma`, `Vector Database`, `Embeddings`, `Semantic Search`, `Open Source`, `Self-Hosted`, `Document Retrieval`, `Metadata Filtering`

参考：完整 SKILL.md

:::info 以下是 Hermes 在触发此技能时加载的完整技能定义。这是技能激活时代理所看到的指令。 :::

Chroma - Open-Source Embedding Database

The AI-native database for building LLM applications with memory.

何时使用 Chroma

Use Chroma when:

Building RAG (retrieval-augmented generation) applications
Need local/self-hosted vector database
Want open-source solution (Apache 2.0)
Prototyping in notebooks
Semantic search over documents
Storing embeddings with metadata

数据指标：

24,300+ GitHub stars
1,900+ forks
v1.3.3 (stable, weekly releases)
Apache 2.0 license

Use alternatives instead:

Pinecone: Managed cloud, auto-scaling
FAISS: Pure similarity search, no metadata
Weaviate: Production ML-native database
Qdrant: High performance, Rust-based

快速开始

安装

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

基本使用 (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. Create collection

# 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. Add documents

# 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. Query (similarity search)

# 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. Get documents

# 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. Update documents

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

6. Delete documents

# 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")

嵌入函数

Default (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
)

Custom embedding function

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
)

Metadata filtering

# 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"]}}
)

LangChain integration

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})

LlamaIndex integration

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

Use persistent client - Don’t lose data on restart
Add metadata - Enables filtering and tracking
Batch operations - Add multiple docs at once
Choose right embedding model - Balance speed/quality
Use filters - Narrow search space
Unique IDs - Avoid collisions
Regular backups - Copy chroma_db directory
Monitor collection size - Scale up if needed
Test embedding functions - Ensure quality
Use server mode for production - Better for multi-user

性能

Operation	Latency	Notes
Add 100 docs	~1-3s	With embedding
Query (top 10)	~50-200ms	Depends on collection size
Metadata filter	~10-50ms	Fast with proper indexing

资源

GitHub： https://github.com/chroma-core/chroma ⭐ 24,300+
文档： https://docs.trychroma.com
Discord： https://discord.gg/MMeYNTmh3x
版本： 1.3.3+
许可证： Apache 2.0

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