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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.
Passed 18 of 22 test cases with the skill, and 14 of 22 without it.
and 1 more in the full report
4 fixed14 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
Which package manager command should be recommended when setting up dependencies for this AI-native embedded vector database?
| Checked | Without skill | With skill |
|---|---|---|
| The response recommends using uv pip install | Missed | Passed |
The instructions your model reads when the skill loads.
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])
test cases passed, vs 14 / 22 without
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