环境

from langfuse import get_client, observe
import os
 
# Set the environment variable
# Alternatively, set via .env file and load via dotenv
os.environ["LANGFUSE_TRACING_ENVIRONMENT"] = "production"
 
# Get the client (will use environment variable)
langfuse = get_client()
 
# All operations will now be associated with the "production" environment
with langfuse.start_as_current_observation(as_type="span", name="my-operation") as span:
    # Your code here
    pass
 
@observe
def main():
    return "Hello"
 
main()

export LANGFUSE_TRACING_ENVIRONMENT=production

from opentelemetry import trace
from opentelemetry.trace import Status, StatusCode
 
tracer = trace.get_tracer(__name__)
 
with tracer.start_as_current_observation("my-operation") as span:
    # Set environment using Langfuse-specific attribute
    span.set_attribute("langfuse.environment", "staging")
 
    # Or using OpenTelemetry convention
    span.set_attribute("deployment.environment.name", "staging")

from langfuse import Langfuse
from langfuse.openai import openai
 
# Either set the environment variable or configure the Langfuse client
os.environ["LANGFUSE_TRACING_ENVIRONMENT"] = "production"
 
langfuse = Langfuse(environment="production")
 
# the integration will use the instantiated client under the hood
completion = openai.chat.completions.create(
  model="gpt-3.5-turbo",
  messages=[
    {"role": "system", "content": "You are a calculator."},
    {"role": "user", "content": "1 + 1 = "}],
)

LANGFUSE_TRACING_ENVIRONMENT=production

import OpenAI from "openai";
import { observeOpenAI } from "@langfuse/openai";
 
const openai = observeOpenAI(new OpenAI());

from langfuse.callback import CallbackHandler
 
# Either set the environment variable or the constructor parameter. The latter takes precedence.
os.environ["LANGFUSE_TRACING_ENVIRONMENT"] = "production"
handler = CallbackHandler()

import { CallbackHandler } from "langfuse-langchain";
 
const handler = new CallbackHandler({
  environment: "production",
});

import { registerOTel } from "@vercel/otel";
import { LangfuseExporter } from "langfuse-vercel";
 
export function register() {
  registerOTel({
    serviceName: "langfuse-vercel-ai-nextjs-example",
    traceExporter: new LangfuseExporter({ environment: "production" }),
  });
}