Observability for OpenAI with Splunk

Step 1

Install the Traceloop SDK and initialize it. It will automatically log all calls to OpenAI, with prompts and completions as separate spans.

import openai
from traceloop.sdk import Traceloop
from traceloop.sdk.decorators import workflow

Traceloop.init(app_name="joke_generation_service")

@workflow(name="joke_creation")
def create_joke():
    completion = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": "Tell me a joke about opentelemetry"}],
    )

    return completion.choices[0].message.content

import * as traceloop from "@traceloop/node-server-sdk";
import OpenAI from "openai";

Traceloop.init({ app_name="joke_generation_service" })
const openai = new OpenAI();

class MyLLM {
  @traceloop.workflow("joke_creation")
  async create_joke():
      completion = await openai.chat.completions.create({
          model: "gpt-3.5-turbo",
          messages: [{"role": "user", "content": "Tell me a joke about opentelemetry"}],
      })

      return completion.choices[0].message.content
}

Step 2

Configure your collector to collect traces and send them to Splunk Observability Cloud; point OpenLLMetry to send traces to your Splunk collector

# In your Splunk collector configuration
receivers:
  otlp:
    protocols:
      grpc:
        endpoint: "0.0.0.0:4317"
      http:
        endpoint: "0.0.0.0:4318"
exporters:
  # Traces
  sapm:
    access_token: "${SPLUNK_ACCESS_TOKEN}"
    endpoint: "https://ingest.${SPLUNK_REALM}.signalfx.com/v2/trace"
    sending_queue:
      num_consumers: 32
 pipelines:
  traces:
    receivers: [jaeger, otlp, sapm, zipkin]
    processors:
    - memory_limiter
    - batch
    #- resource/add_environment
    exporters: [sapm]
        
# In the environment of your application
TRACELOOP_BASE_URL=http://splunk-otel-collector:4318

Docs

Observability for OpenAI with Splunk

Step 1

Step 2

Discover use cases

Trace prompts and completions

Trace your RAG retrieval pipeline

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