Pinecone | Respan Docs

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. Respan gives you full observability over every index operation, upsert, and query.

Set up Respan

Create an account at platform.respan.ai and grab an API key.

Run npx @respan/cli setup to set up with your coding agent.

Example projects

Python examples

Tracing

Setup

Install packages

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

Set environment variables

$ export PINECONE_API_KEY="YOUR_PINECONE_API_KEY"
$ export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"

PINECONE_API_KEY is used for Pinecone requests. RESPAN_API_KEY is used to export traces to Respan.

Initialize and run

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

View your trace

Open the Traces page to see your Pinecone operation spans.

Configuration

Parameter	Type	Default	Description
`api_key`	`str \| None`	`None`	Falls back to `RESPAN_API_KEY` env var.
`base_url`	`str \| None`	`None`	Falls back to `RESPAN_BASE_URL` env var.
`instrumentations`	`list`	`[]`	Plugin instrumentations to activate (e.g. `PineconeInstrumentor()`).
`customer_identifier`	`str \| None`	`None`	Default customer identifier for all spans.
`metadata`	`dict \| None`	`None`	Default metadata attached to all spans.
`environment`	`str \| None`	`None`	Environment tag (e.g. `"production"`).

Attributes

In Respan()

1 from respan import Respan
2 from opentelemetry.instrumentation.pinecone import PineconeInstrumentor
3 
4 respan = Respan(
5     instrumentations=[PineconeInstrumentor()],
6     customer_identifier="user_123",
7     metadata={"service": "rag-api", "version": "1.0.0"},
8 )

With propagate_attributes

1 from respan import Respan, propagate_attributes
2 from opentelemetry.instrumentation.pinecone import PineconeInstrumentor
3 
4 respan = Respan(instrumentations=[PineconeInstrumentor()])
5 
6 def search(user_id: str, query_vector):
7     with propagate_attributes(
8         customer_identifier=user_id,
9         thread_identifier="conv_abc_123",
10         metadata={"plan": "pro"},
11     ):
12         return index.query(vector=query_vector, top_k=2, namespace="articles")

Attribute	Type	Description
`customer_identifier`	`str`	Identifies the end user in Respan analytics.
`thread_identifier`	`str`	Groups related messages into a conversation.
`metadata`	`dict`	Custom key-value pairs. Merged with default metadata.