IntegrationsVector Databases

Pinecone

Trace Pinecone vector database operations with Respan.
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Add the Docs MCP to your AI coding tool to get help building with Respan. No API key needed.

1{
2  "mcpServers": {
3    "respan-docs": {
4      "url": "https://mcp.respan.ai/mcp/docs"
5    }
6  }
7}

What is Pinecone?

Pinecone is a managed vector database designed for machine learning applications. It provides low-latency vector search at scale with features like namespaces, metadata filtering, and serverless deployment.

Setup

1

Install packages

$pip install respan-ai opentelemetry-instrumentation-pinecone pinecone
2

Set environment variables

$export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"
$export PINECONE_API_KEY="YOUR_PINECONE_API_KEY"
$export OTEL_EXPORTER_OTLP_ENDPOINT="https://api.respan.ai/api"
$export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer $RESPAN_API_KEY"
3

Run a traced example

1from respan import Respan
2
3# Auto-discover and activate all installed instrumentors
4respan = Respan(is_auto_instrument=True)
5
6from pinecone import Pinecone, ServerlessSpec
7
8# Initialize Pinecone
9pc = Pinecone()
10
11# Create an index
12pc.create_index(
13    name="documents",
14    dimension=4,
15    metric="cosine",
16    spec=ServerlessSpec(cloud="aws", region="us-east-1"),
17)
18index = pc.Index("documents")
19
20# Upsert vectors with metadata
21index.upsert(
22    vectors=[
23        {"id": "doc1", "values": [0.1, 0.2, 0.3, 0.4], "metadata": {"topic": "observability"}},
24        {"id": "doc2", "values": [0.5, 0.6, 0.7, 0.8], "metadata": {"topic": "search"}},
25        {"id": "doc3", "values": [0.2, 0.3, 0.4, 0.5], "metadata": {"topic": "embeddings"}},
26    ],
27    namespace="articles",
28)
29
30# Query for similar vectors
31results = index.query(
32    vector=[0.1, 0.2, 0.3, 0.4],
33    top_k=2,
34    namespace="articles",
35    include_metadata=True,
36)
37for match in results["matches"]:
38    print(f"{match['id']} (score: {match['score']:.4f}) — {match['metadata']['topic']}")
39
40respan.flush()
4

View your trace

Open the Traces page to see your Pinecone operation spans.

Always call respan.flush() before your process exits. Without it, pending spans may be lost.

What gets traced

  • Index creation and management
  • Vector upsert and delete operations
  • Similarity search queries and scores
  • Namespace operations
  • Metadata filtering
  • Operation latency

Learn more