Scoring with SDK

import { LaminarClient, Laminar, observe } from "@lmnr-ai/lmnr";

const laminarClient = new LaminarClient({
  projectApiKey: "your-project-api-key",
});

let traceId: string | null = null;
let spanId: string | null = null;

// First, capture your LLM calls
await observe(
  {
    name: "chat_completion",
    input: { messages: [...] },
  },
  async () => {
    // Your LLM call here
    const output = { content: "AI response" };

    // Capture IDs while we're inside span context
    traceId = Laminar.getTraceId();
    spanId = Laminar.getLaminarSpanContext()?.spanId ?? null;

    return output;
  }
);

// IMPORTANT: Flush data to ensure it reaches the backend
await Laminar.flush();

if (!traceId || !spanId) {
  throw new Error("No recorded trace/span found");
}

// Score by trace ID (attaches to root span)
await laminarClient.evaluators.score({
  name: "quality",
  traceId,
  score: 0.95,
  metadata: { model: "gpt-4" }
});

// Score by span ID (attaches to that span)
await laminarClient.evaluators.score({
  name: "relevance", 
  spanId,
  score: 0.87
});

from lmnr import LaminarClient, Laminar
import time

laminar_client = LaminarClient(project_api_key="your-project-api-key")

trace_id = None
span_id = None

# First, capture your LLM calls
with Laminar.start_as_current_span(
    name = "chat_completion",
    input = {"messages": [
      # ...
    ]},
) as span:

    # Your LLM call here
    Laminar.set_span_output({"content": "AI response"})

    # Capture IDs while we're inside span context
    trace_id = Laminar.get_trace_id()
    span_context = Laminar.get_laminar_span_context()
    span_id = span_context.span_id if span_context else None

# IMPORTANT: Flush data to ensure it reaches the backend
Laminar.flush()

if not trace_id or not span_id:
    raise ValueError("No recorded trace/span found")

# Score by trace ID (attaches to root span)
laminar_client.evaluators.score(
    name="quality",
    trace_id=trace_id, 
    score=0.95,
    metadata={"model": "gpt-4"}
)

# Score by span ID
# In production, ensure this runs after the span is recorded/ingested.
time.sleep(1)

laminar_client.evaluators.score(
    name="relevance",
    span_id=span_id,
    score=0.87
)