langfuse_manual_trace_with_nested_span_and_generation.py

1from langfuse import Langfuse
2import os
3
4# Initialize the Langfuse client
5# Best practice: use environment variables (LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY, LANGFUSE_HOST)
6langfuse = Langfuse(
7    public_key="pk-lf-...",
8    secret_key="sk-lf-...",
9    host="https://cloud.langfuse.com"
10)
11
12# 1. Create a Trace
13trace = langfuse.trace(
14    name="quickstart-trace",
15    user_id="user-123",
16    metadata={"env": "development"}
17)
18
19# 2. Create a Span (represents a unit of work)
20span = trace.span(
21    name="chat-completion",
22    input={"message": "Hello, how are you?"}
23)
24
25# 3. Create a Generation (specialized span for LLM calls)
26generation = span.generation(
27    name="gpt-3.5-turbo-call",
28    model="gpt-3.5-turbo",
29    model_parameters={"temperature": 0.7},
30    input=[{"role": "user", "content": "Hello, how are you?"}]
31)
32
33# 4. Update the generation with the response
34generation.end(
35    output="I am doing well, thank you!",
36    usage={
37        "prompt_tokens": 10,
38        "completion_tokens": 8,
39        "total_tokens": 18
40    }
41)
42
43# 5. End the span
44span.end(output="I am doing well, thank you!")
45
46# Optional: Flush the events to ensure they are sent to the server
47langfuse.flush()