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Add the Docs MCP to your AI coding tool to get help building with Respan. No API key needed.
{
  "mcpServers": {
    "respan-docs": {
      "url": "https://respan.ai/docs/mcp"
    }
  }
}

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What is Qdrant?

Qdrant is an open-source vector similarity search engine with filtering support, designed for production-ready AI applications.

​
Setup

1

Install packages

pip install respan-tracing qdrant-client openai
2

Set environment variables

export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"
export OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
3

Initialize Respan and query Qdrant

Respan auto-instruments Qdrant — search, upsert, and delete operations are captured as spans.
from respan_tracing import RespanTelemetry
from respan_tracing.decorators import workflow, task
from qdrant_client import QdrantClient
from qdrant_client.models import PointStruct, VectorParams, Distance
from openai import OpenAI

# Initialize — auto-instruments Qdrant
telemetry = RespanTelemetry()
client = OpenAI()
qdrant = QdrantClient(":memory:")  # Use url="http://localhost:6333" for production

# Create collection
qdrant.create_collection(
    collection_name="docs",
    vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
)

docs = [
    "Respan provides observability for LLM applications.",
    "Traces capture the full lifecycle of an LLM request.",
]


def get_embeddings(texts: list[str]) -> list[list[float]]:
    response = client.embeddings.create(model="text-embedding-3-small", input=texts)
    return [item.embedding for item in response.data]


# Seed documents
vectors = get_embeddings(docs)
qdrant.upsert(
    collection_name="docs",
    points=[
        PointStruct(id=i, vector=vectors[i], payload={"text": docs[i]})
        for i in range(len(docs))
    ],
)


@task(name="search_docs")
def search_docs(query: str):
    query_vector = get_embeddings([query])[0]
    results = qdrant.search(
        collection_name="docs", query_vector=query_vector, limit=3
    )
    return [hit.payload["text"] for hit in results]


@workflow(name="rag_pipeline")
def rag_pipeline(query: str):
    context = search_docs(query)
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "system", "content": f"Context: {chr(10).join(context)}"},
            {"role": "user", "content": query},
        ],
    )
    return response.choices[0].message.content


result = rag_pipeline("How does tracing work?")
print(result)
4

View your trace

Open the Traces page to see Qdrant operations as spans in your trace tree.

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Configuration

Qdrant is auto-instrumented via Instruments.QDRANT (Python) or qdrant (JavaScript). No additional configuration is needed. See the Python Tracing SDK reference or JavaScript Tracing SDK reference for configuration options.