日志级别

from langfuse import observe, get_client
 
@observe()
def my_function():
    langfuse = get_client()
 
    # ... processing logic ...
    # Update the current span with a warning level
    langfuse.update_current_span(
        level="WARNING",
        status_message="This is a warning"
    )

from langfuse import get_client
 
langfuse = get_client()
 
# Using context managers (recommended)
with langfuse.start_as_current_observation(as_type="span", name="my-operation") as span:
    # Set level and status message on creation
    with span.start_as_current_observation(
        name="potentially-risky-operation",
        level="WARNING",
        status_message="Operation may fail"
    ) as risky_span:
        # ... do work ...
 
        # Or update level and status message later
        risky_span.update(
            level="ERROR",
            status_message="Operation failed with unexpected input"
        )
 
# You can also update the currently active span without a direct reference
with langfuse.start_as_current_observation(as_type="span", name="another-operation"):
    # ... some processing ...
    langfuse.update_current_span(
        level="DEBUG",
        status_message="Processing intermediate results"
    )

langfuse = get_client()
 
with langfuse.start_as_current_observation(
    as_type="generation",
    name="llm-call",
    model="gpt-4o",
    level="DEFAULT"  # Default level
) as generation:
    # ... make LLM call ...
 
    if error_detected:
        generation.update(
            level="ERROR",
            status_message="Model returned malformed output"
        )

import { startActiveObservation, startObservation } from "@langfuse/tracing";
 
await startActiveObservation("context-manager", async (span) => {
  span.update({
    input: { query: "What is the capital of France?" },
  });
 
  updateActiveObservation({
    level: "WARNING",
    statusMessage: "This is a warning",
  });
});

import { observe, updateActiveObservation } from "@langfuse/tracing";
 
// An existing function
async function fetchData(source: string) {
  updateActiveObservation({
    level: "WARNING",
    statusMessage: "This is a warning",
  });
 
  // ... logic to fetch data
  return { data: `some data from ${source}` };
}
 
// Wrap the function to trace it
const tracedFetchData = observe(fetchData, {
  name: "observe-wrapper",
});
 
const result = await tracedFetchData("API");

import { startObservation } from "@langfuse/tracing";
 
const span = startObservation("manual-observation", {
  input: { query: "What is the capital of France?" },
});
 
span.update({
  level: "WARNING",
  statusMessage: "This is a warning",
});
 
span.update({ output: "Paris" }).end();