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Setup

  1. Install Langtrace’s SDK and initialize the SDK in your code.
Note: You’ll need API keys from Langtrace and Pinecone. Sign up for Langtrace and/or Pinecone if you haven’t done so already.
Python
# Install the SDK
pip install -U langtrace-python-sdk pinecone
Typescript
npm install @pinecone-database/pinecone-client
  1. Setup environment variables:
Shell
export LANGTRACE_API_KEY=YOUR_LANGTRACE_API_KEY
export PINECONE_API_KEY=YOUR_PINECONE_API_KEY

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Usage

Generate a simple output with your deployment’s model: 
import os
   from langtrace_python_sdk import langtrace # Must precede any llm module imports
langtrace.init(api_key = os.environ['LANGTRACE_API_KEY'])
from pinecone import Pinecone, ServerlessSpec
pc = Pinecone(api_key=os.environ['PINECONE_API_KEY'])

# Create an Index and upsert some data in Pinecone:
pc.create_index(
    name="index",
    dimension=8, # Replace with your model dimensions
    metric="euclidean", # Replace with your model metric
    spec=ServerlessSpec(
        cloud="aws",
        region="us-east-1"
    )
)
index = pc.Index("index")
index.upsert(
  vectors=[
    {"id": "A", "values": [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]},
    {"id": "B", "values": [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]},
    {"id": "C", "values": [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3]},
    {"id": "D", "values": [0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4]}
  ]
)


import { init as langtraceInit } from 'langtrase/typescript-sdk';
import { PineconeClient, ServerlessSpec } from '@pinecone-database/pinecone-client';

langtraceInit({
 apiKey: langtraceApiKey,
 });

// Step 3: Initialize Pinecone client
const pinecone = new PineconeClient({
 apiKey: pineconeApiKey,
});

// Step 4: Create an Index and upsert some data in Pinecone
(async () => {
await pinecone.createIndex({
name: "index",
dimension: 8, // Replace with your model dimensions
metric: "euclidean", // Replace with your model metric
spec: new ServerlessSpec({
cloud: "aws",
region: "us-east-1",
}),
});

const index = pinecone.Index("index");

await index.upsert({
vectors: [
{ id: "A", values: [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1] },
{ id: "B", values: [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2] },
{ id: "C", values: [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3] },
{ id: "D", values: [0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4] },
],
});
})();
You can now view your traces on the Langtrace dashboard: traces Want to see more supported methods? Checkout the sample code in the Langtrace Pinecone Python Example repository.