Log Prompt Templates & Variables

By instrumenting the prompt template, users can take full advantage of the Arize prompt playground. You don't need to deploy a new template version in order to see if prompt text or prompt variables changes have the intended effect. Instead, you can experiment with these changes in the playground UI.

Python

We provide a using_prompt_template context manager (example below) to add a prompt template to the current OpenTelemetry Context. OpenInference auto-instrumentors will read this Context and pass the prompt template fields as span attributes, following the OpenInference semantic conventions. The interface expects the following:

Param	Type	Example
template	str	"Please describe the weather forecast for {city} on {date}"
version	str	"v1.0"
variables	Dict[str]	{"city": "Johannesburg", "date":"July 11"}

Refer to the code below for a working example:

Google Colab

Tutorial that adds tracing to prompt template and variables and logs the traces to the Arize platform.

pip install -qq opentelemetry-api opentelemetry-sdk openinference-semantic-conventions openinference-instrumentation-openai opentelemetry-exporter-otlp arize-otel openai

import os
from getpass import getpass
import openai
import opentelemetry
from arize.otel import register
from openai import OpenAI
from openinference.instrumentation import using_prompt_template
from openinference.instrumentation.openai import OpenAIInstrumentor


os.environ["OPENAI_API_KEY"] = getpass("Enter your Open AI API key: ")

my_first_model = "my first model"

# Setup OTel via our convenience function
tracer_provider = register(
    space_id = "your-space-id", # in app space settings page
    api_key = "your-api-key", # in app space settings page
    project_name = "your-project-name", # name this to whatever you would like
)
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)

# Setup OpenAI
client = OpenAI()

prompt_template = "Please describe the weather forecast for {city} on {date}"
prompt_template_variables = {"city": "Johannesburg", "date":"July 11"}
with using_prompt_template(
    template=prompt_template,
    variables=prompt_template_variables,
    version="v1.0",
    ):
    response = client.chat.completions.create(
      model="gpt-4o-mini",
      messages=[
          {
              "role": "user",
              "content": prompt_template.format(**prompt_template_variables)},
        ]
    )

JS

We provide a setPromptTemplate function which allows you to set a template, version, and variables on context. You can use this utility in conjunction with context.with to set the active context. OpenInference auto instrumentations will then pick up these attributes and add them to any spans created within the context.with callback. The components of a prompt template are:

Param	Type	Example
template	string	"Please describe the weather forecast for {city} on {date}"
version	string	"v1.0"
variables	Record<string, unknown>	{"city": "Johannesburg", "date":"July 11"}

npm install --save @arizeai/openinference-core @opentelemetry/api

All of these are optional. Application of variables to a template will typically happen before the call to an llm and may not be picked up by auto instrumentation. So, this can be helpful to add to ensure you can see the templates and variables while troubleshooting.

import { context } from "@opentelemetry/api"
import { setSession } from "@openinference-core"

context.with(
  setPromptTemplate(
    context.active(),
    { 
      template: "hello {{name}}",
      variables: { name: "world" },
      version: "v1.0"
    }
  ),
  () => {
      // Calls within this block will generate spans with the attributes:
      // "llm.prompt_template.template" = "hello {{name}}"
      // "llm.prompt_template.version" = "v1.0"
      // "llm.prompt_template.variables" = '{ "name": "world" }'
  }
)