> ## Documentation Index
> Fetch the complete documentation index at: https://docs.hipocap.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Scoring with SDK
## Create Evaluator Score
Create a score for a span using either a trace ID or span ID. When using a trace ID, the score will be attached to the root span of that trace.
### Code Examples
```javascript TypeScript theme={null}
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
});
```
```python Python theme={null}
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
)
```
## Viewing Scores in UI
When you create evaluator scores, they will appear in your Laminar dashboard attached to the corresponding spans:
