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traceAI-qdrant

OpenTelemetry instrumentation for Qdrant vector database.

Installation

pip install traceAI-qdrant

Quick Start

from fi_instrumentation import register
from traceai_qdrant import QdrantInstrumentor
from qdrant_client import QdrantClient
from qdrant_client.models import VectorParams, Distance, PointStruct

trace_provider = register(project_name="my-rag-app")
QdrantInstrumentor().instrument(tracer_provider=trace_provider)

client = QdrantClient(":memory:")
client.create_collection("documents", vectors_config=VectorParams(size=384, distance=Distance.COSINE))

client.upsert("documents", points=[
    PointStruct(id=1, vector=[0.1]*384, payload={"text": "Hello"}),
    PointStruct(id=2, vector=[0.2]*384, payload={"text": "World"}),
])

results = client.search("documents", query_vector=[0.15]*384, limit=5)

Instrumented Operations

Operation Span Name Description
search qdrant search Vector similarity search
upsert qdrant upsert Insert or update points
delete qdrant delete Delete points
retrieve qdrant retrieve Get points by ID
scroll qdrant scroll Iterate through points
recommend qdrant recommend Recommendation search
count qdrant count Count points

License

Apache License 2.0

Metadata

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