Sampling

from langfuse import Langfuse, get_client
import os
 
# Method 1: Set environment variable
os.environ["LANGFUSE_SAMPLE_RATE"] = "0.5"  # As string in env var
langfuse = get_client()
 
# Method 2: Initialize with constructor parameter then get client
Langfuse(sample_rate=0.5)  # 50% of traces will be sampled
langfuse = get_client()

from langfuse import observe, Langfuse, get_client
 
# Initialize the client with sampling
Langfuse(sample_rate=0.3)  # 30% of traces will be sampled
 
@observe()
def process_data():
    # Only ~30% of calls to this function will generate traces
    # The decision is made at the trace level (first span)
    pass

from langfuse.decorators import langfuse_context, observe
 
os.environ["LANGFUSE_SAMPLE_RATE"] = '0.5'
 
@observe()
def fn():
    pass
 
fn()

from langfuse import Langfuse
 
# Either set the environment variable or the constructor parameter. The latter takes precedence.
os.environ["LANGFUSE_SAMPLE_RATE"] = '0.5'
langfuse = Langfuse(sample_rate=0.5)
 
trace = langfuse.trace(
  name="Rap Battle",
)

import { Langfuse } from "langfuse";
 
const langfuse = new Langfuse({
  sampleRate: 0.5,
});

# Either set the environment variable or configure the openai import. The latter takes precedence.
os.environ["LANGFUSE_SAMPLE_RATE"] = '0.5'
 
from langfuse.openai import openai
openai.langfuse_sample_rate = 0.5
 
completion = openai.chat.completions.create(
  name="test-chat",
  model="gpt-3.5-turbo",
  messages=[
    {"role": "system", "content": "You are a calculator."},
    {"role": "user", "content": "1 + 1 = "}],
)

import OpenAI from "openai";
import { observeOpenAI } from "langfuse";
 
const openai = observeOpenAI(new OpenAI(), {
  clientInitParams: {
    sampleRate: 0.5,
  },
});

from langfuse.callback import CallbackHandler
 
# Either set the environment variable or the constructor parameter. The latter takes precedence.
os.environ["LANGFUSE_SAMPLE_RATE"] = '0.5'
handler = CallbackHandler(
  sample_rate=0.5
)

import { CallbackHandler } from "langfuse-langchain";
 
const handler = new CallbackHandler({
  sampleRate: 0.5,
});

import { registerOTel } from "@vercel/otel";
import { LangfuseExporter } from "langfuse-vercel";
 
export function register() {
  registerOTel({
    serviceName: "langfuse-vercel-ai-nextjs-example",
    traceExporter: new LangfuseExporter({ sampleRate: 0.5 }),
  });
}

import os
from langfuse.llama_index import LlamaIndexInstrumentor
 
# Either set the environment variable or the constructor parameter. The latter takes precedence.
os.environ["LANGFUSE_SAMPLE_RATE"] = '0.5'
instrumentor = LlamaIndexInstrumentor(sample_rate=0.5)

from llama_index.core import Settings
from llama_index.core.callbacks import CallbackManager
from langfuse import langfuse
 
# Either set the environment variable or the constructor parameter. The latter takes precedence.
os.environ["LANGFUSE_SAMPLE_RATE"] = '0.5'
langfuse_callback_handler = LlamaIndexCallbackHandler(sample_rate=0.5)
 
Settings.callback_manager = CallbackManager([langfuse_callback_handler])