Documentation Index
Fetch the complete documentation index at: https://docs.getnetra.ai/llms.txt
Use this file to discover all available pages before exploring further.
Installation
Install both the Netra SDK and Pinecone:
pip install netra-sdk pinecone-client
Usage
Initialize the Netra SDK to automatically trace all Pinecone operations:
from netra import Netra
from pinecone import Pinecone
import os
# Initialize Netra
Netra.init(
headers=f"x-api-key={os.environ.get('NETRA_API_KEY')}",
trace_content=True
)
# Create Pinecone client - automatically traced
pc = Pinecone(api_key=os.environ.get('PINECONE_API_KEY'))
index = pc.Index("my-index")
# Upsert vectors
index.upsert(vectors=[{
"id": "vec1",
"values": [0.1, 0.2, 0.3],
"metadata": {"text": "Sample document"}
}])
Index Operations
Trace index creation and management:
from netra.decorators import task
from netra import SpanWrapper
@task()
def create_index(pc: Pinecone, name: str, dimension: int):
span = SpanWrapper("pinecone-create-index", {
"index.name": name,
"index.dimension": dimension
}).start()
pc.create_index(
name=name,
dimension=dimension,
metric="cosine",
spec={"serverless": {"cloud": "aws", "region": "us-west-2"}}
)
span.end()
Vector Upsert
Trace vector insertions:
from netra.decorators import task
from netra import SpanWrapper, ActionModel
@task()
def upsert_vectors(index, vectors: list):
span = SpanWrapper("pinecone-upsert", {
"vectors.count": len(vectors)
}).start()
result = index.upsert(vectors=vectors)
span.set_action([ActionModel(
action="upsert",
action_type="database.upsert",
success=True,
affected_records=[{"id": v["id"]} for v in vectors],
metadata={"index": index._index_name, "upserted": result["upserted_count"]}
)])
span.set_attribute("upserted.count", result["upserted_count"])
span.end()
return result
Vector Search
Trace similarity searches:
from netra.decorators import workflow
from netra import SpanWrapper
@workflow()
def search_vectors(index, query: list[float], top_k: int = 5):
span = SpanWrapper("pinecone-search", {
"query.dimension": len(query),
"top_k": top_k
}).start()
results = index.query(
vector=query,
top_k=top_k,
include_metadata=True
)
span.set_attribute("results.count", len(results["matches"]))
span.end()
return results
Namespace Operations
Trace namespace-specific operations:
from netra.decorators import task
from netra import SpanWrapper
@task()
def query_namespace(index, namespace: str, query: list[float]):
span = SpanWrapper("pinecone-namespace-query", {
"namespace": namespace
}).start()
results = index.query(
vector=query,
top_k=10,
namespace=namespace
)
span.set_attribute("results.count", len(results["matches"]))
span.end()
return results
Next Steps
