Observability for OpenAI with Dynatrace

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

Go to your Dynatrace environment and create a new access token under Manage Access Tokens. The access token needs the following permission scopes that allow the ingest of OpenTelemetry spans, metrics and logs (openTelemetryTrace.ingest, metrics.ingest, logs.ingest). Set the following environment variables.

TRACELOOP_BASE_URL=https://<YOUR_ENV>.live.dynatrace.com\api\v2\otlp
TRACELOOP_HEADERS=Authorization=Api-Token%20<YOUR_ACCESS_TOKEN>

Docs

Discover use cases

Trace prompts and completions

Call OpenAI and see prompts, completions, and token usage for your call.

See Code Example

Trace your RAG retrieval pipeline

Build a RAG pipeline with Chroma and OpenAI. See vectors returned from Chroma, full prompt in OpenAI and responses

See Code Example

Observability for OpenAI with Dynatrace

Step 1

Step 2

Discover use cases

Trace prompts and completions

Trace your RAG retrieval pipeline

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