Observability for OpenAI with Honeycomb

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

Route the traces to Honeycomb’s endpoint and set the API key

TRACELOOP_BASE_URL=https://api.honeycomb.io
TRACELOOP_HEADERS="x-honeycomb-team=<YOUR_API_KEY>"

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 Honeycomb

Step 1

Step 2

Discover use cases

Trace prompts and completions

Trace your RAG retrieval pipeline

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