菜单
Chroma — AI-native embedding database. In-process, lightweight vector store with automatic embedding, metadata filtering, and full-text search. Simplest path from prototype to production RAG.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/mkurman/zorai --skill chromadb命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| name | chromadb |
| description | Chroma — AI-native embedding database. In-process, lightweight vector store with automatic embedding, metadata filtering, and full-text search. Simplest path from prototype to production RAG. |
| tags | ["chromadb","vector-database","embeddings","rag","semantic-search","python","zorai"] |
Chroma is an AI-native embedding database optimized for RAG workflows. Lightweight, in-process, with automatic embedding via sentence-transformers, metadata filtering, and semantic search — no separate server required. Fastest path from prototype to production.
uv pip install chromadb
import chromadb
client = chromadb.PersistentClient(path="./chroma_data")
collection = client.create_collection(name="documents")
# Add documents with metadata
collection.add(
documents=["Paris is the capital of France.", "Berlin is the capital of Germany."],
metadatas=[{"country": "France"}, {"country": "Germany"}],
ids=["doc1", "doc2"],
)
# Query with filter
results = collection.query(
query_texts=["What is the capital of France?"],
n_results=3,
where={"country": "France"},
)
print(results["documents"][0])