Vector Database

Chroma (ChromaDB) Monitoring & Query Tracing

Auto-instrument every ChromaDB collection operation, query, add, update, get, with zero code changes. Chroma spans integrate as child spans inside LangChain traces for complete RAG observability.

Free tier · No credit card · 2-minute setup

What you get with Fluiq for Chroma

Collection query tracing

Every collection.query() call is traced with the query text, n_results, where filters, and end-to-end latency including embedding generation time.

RAG pipeline integration

Chroma spans automatically appear as children inside LangChain traces, full end-to-end RAG pipeline visibility without extra code.

Add & update tracing

Document addition, update, and deletion operations are traced with record counts and timing, monitor ingestion performance alongside query performance.

Setup

Add Fluiq to your Chroma app in 2 lines

import fluiq
fluiq.instrument(api_key="fl_...")  # patches ChromaDB automatically

import chromadb

client = chromadb.PersistentClient(path="./chroma_db")
collection = client.get_or_create_collection("my-docs")

# Add is traced with record count:
collection.add(
    documents=["LLM observability is critical for production AI"],
    ids=["doc1"],
    metadatas=[{"source": "guide"}],
)

# Query is traced with latency and result count:
results = collection.query(
    query_texts=["What is LLM monitoring?"],
    n_results=5,
)

What Fluiq instruments in Chroma

Every call to these methods is automatically traced, no decorators, no wrappers, no manual spans.

Collection.query()
Collection.add()
Collection.update()
Collection.upsert()
Collection.get()
Collection.delete()
Collection.count()
Client.create_collection()

Start tracing Chroma in 2 minutes

Free tier. No credit card. Full traces, security scanning, and evals on your first Chroma call.

50,000 free traces / month · 1,000 evals / month · 14-day retention