Weaviate Monitoring & Vector Search Tracing
Auto-instrument every Weaviate query and insert, near-text, near-vector, batch operations, with zero code changes. Weaviate spans integrate as child spans inside LangChain traces for full RAG pipeline visibility.
Free tier · No credit card · 2-minute setup
What you get with Fluiq for Weaviate
Vector search tracing
Weaviate query.get() and near-text/near-vector searches are traced with the class, properties, filters, and end-to-end latency including Weaviate server time.
Batch insert tracing
Bulk inserts via client.batch are traced with item counts, batch size, and total latency, monitor data pipeline throughput alongside query performance.
RAG child spans
Weaviate spans automatically appear as children of LangChain traces, see retrieval latency, result counts, and cost attribution rolled up into the parent RAG trace.
Add Fluiq to your Weaviate app in 2 lines
import fluiq
fluiq.instrument(api_key="fl_...") # patches Weaviate client automatically
import weaviate
client = weaviate.connect_to_local()
# Near-text search is traced with latency and result counts:
result = (
client.query
.get("Article", ["title", "content", "source"])
.with_near_text({"concepts": ["AI observability", "LLM monitoring"]})
.with_limit(5)
.with_additional(["distance"])
.do()
)
# Batch insert is traced with item count:
with client.batch.dynamic() as batch:
batch.add_object({"title": "Fluiq Guide", "content": "..."})What Fluiq instruments in Weaviate
Every call to these methods is automatically traced, no decorators, no wrappers, no manual spans.
client.query.get().do()client.query.get().with_near_text().do()client.query.get().with_near_vector().do()client.data_object.create()client.batch.add_data_object()client.schema.create_class()client.data_object.get_by_id()Start tracing Weaviate in 2 minutes
Free tier. No credit card. Full traces, security scanning, and evals on your first Weaviate call.
50,000 free traces / month · 1,000 evals / month · 14-day retention