Set Trace Environments
Set your environments during tracing for better debugging
Included on the Enterprise plan. Book a demo, opens in a new tab. Included on the Team plan. Included on the Starter plan. Not included on the Free plan.
Overview
The environment feature allows you to specify which environment your traces are coming from. This is useful for separating traces from different environments in "development", "staging", "production", or "testing".
Configure Environment
You can configure the environment with the CONFIDENT_TRACE_ENVIRONMENT environment variable.
export CONFIDENT_TRACE_ENVIRONMENT="staging"Alternatively, you can set the environment directly in code:
from openai import OpenAI
from deepeval.tracing import observe, trace_manager
trace_manager.configure(environment="production")
client = OpenAI()
@observe()
def llm_app(query: str):
return client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": query}]
).choices[0].message.content
llm_app("Write me a poem.")import OpenAI from 'openai';
import { observe, traceManager } from 'deepeval/tracing';
traceManager.configure({ environment: "production" });
const openai = new OpenAI();
const llmApp = async (query: string) => {
const result = await openai.chat.completions.create({
model: "gpt-4o",
messages: [{ role: "user", content: query }]
})
return result.choices[0].message.content;
};
const observedLlmApp = observe({ type: "llm", model: "gpt-4o", fn: llmApp });
observedLlmApp("Write me a poem.");The environment can be either "production", "staging", or "development", and helps you identify where your traces are coming from.
Last updated on