chromadb
$
npx mdskill add mkurman/zorai/chromadbStore and retrieve document embeddings for RAG tasks.
- Enables semantic search and metadata filtering on text data.
- Depends on sentence-transformers for automatic embedding generation.
- Selects results using vector similarity and optional filters.
- Returns ranked document snippets directly to the agent.
SKILL.md
.github/skills/chromadbView on GitHub ↗
---
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]
---
## Overview
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.
## Installation
```bash
uv pip install chromadb
```
## Basic Usage
```python
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])
```
## References
- [Chroma docs](https://docs.trychroma.com/)
- [Chroma GitHub](https://github.com/chroma-core/chroma)