from brokle import Brokle

client = Brokle(api_key="bk_...")

# Query spans that may need human review
spans = list(client.query.query_iter(
    filter="service.name=chatbot AND status=error",
))

# Add them to an annotation queue
span_ids = [s.span_id for s in spans]
result = client.annotations.add_spans(
    "queue_id",
    span_ids,
    priority=5,
)
print(f"Added {result['created']} items to queue")

# List pending items
items = client.annotations.list_items("queue_id", status="pending")
for item in items["items"]:
    print(f"{item['object_id']}: {item['status']}")

import { Brokle } from 'brokle';

const client = new Brokle({ apiKey: 'bk_...' });

// Query spans that may need human review
const spans = [];
for await (const span of client.query.queryIter({
  filter: 'service.name=chatbot AND status=error',
})) {
  spans.push(span);
}

// Add them to an annotation queue
const spanIds = spans.map((s) => s.spanId);
const result = await client.annotations.addSpans(
  "queue_id",
  spanIds,
  { priority: 5 },
);
console.log(`Added ${result.created} items to queue`);

// List pending items
const items = await client.annotations.listItems("queue_id", {
  status: "pending",
});
for (const item of items.items) {
  console.log(`${item.objectId}: ${item.status}`);
}

result = client.annotations.add_traces(
    "queue_id",
    ["trace_id_1", "trace_id_2", "trace_id_3"],
    priority=5,
    metadata={"reason": "low_confidence"},
)
print(f"Added {result['created']} traces")

const result = await client.annotations.addTraces(
  "queue_id",
  ["trace_id_1", "trace_id_2", "trace_id_3"],
  { priority: 5, metadata: { reason: "low_confidence" } },
);
console.log(`Added ${result.created} traces`);

result = client.annotations.add_spans(
    "queue_id",
    ["span_id_1", "span_id_2"],
    priority=10,
    metadata={"category": "error_responses"},
)

const result = await client.annotations.addSpans(
  "queue_id",
  ["span_id_1", "span_id_2"],
  { priority: 10, metadata: { category: "error_responses" } },
);

result = client.annotations.add_items("queue_id", [
    {"object_id": "trace_1", "object_type": "trace", "priority": 5},
    {"object_id": "span_1", "object_type": "span", "priority": 10, "metadata": {"urgent": True}},
])

const result = await client.annotations.addItems("queue_id", [
  { objectId: "trace_1", objectType: "trace", priority: 5 },
  { objectId: "span_1", objectType: "span", priority: 10, metadata: { urgent: true } },
]);

# List all pending items
result = client.annotations.list_items("queue_id", status="pending")
print(f"Total: {result['total']}")
for item in result["items"]:
    print(f"{item['object_id']}: priority={item['priority']}")

# Paginate through items
result = client.annotations.list_items("queue_id", limit=20, offset=0)

// List all pending items
const result = await client.annotations.listItems("queue_id", {
  status: "pending",
});
console.log(`Total: ${result.total}`);
for (const item of result.items) {
  console.log(`${item.objectId}: priority=${item.priority}`);
}

// Paginate through items
const page = await client.annotations.listItems("queue_id", {
  limit: 20,
  offset: 0,
});

from brokle import Brokle
from brokle.scorers import LengthCheck, Contains

client = Brokle(api_key="bk_...")

# 1. Query production spans
spans = list(client.query.query_iter(
    filter="gen_ai.provider.name=openai AND service.name=chatbot",
))

# 2. Run automated scorers
results = client.experiments.run(
    name="automated-triage",
    spans=spans,
    extract_input=lambda s: {"prompt": s.input},
    extract_output=lambda s: s.output,
    scorers=[LengthCheck(min_length=20), Contains(substring="sorry")],
)

# 3. Find low-quality items to route to humans
low_quality_span_ids = []
for item in results.items:
    avg_score = sum(s.value for s in item.scores) / len(item.scores) if item.scores else 0
    if avg_score < 0.5 and item.span_id:
        low_quality_span_ids.append(item.span_id)

# 4. Route to annotation queue for human review
if low_quality_span_ids:
    result = client.annotations.add_spans(
        "review_queue_id",
        low_quality_span_ids,
        priority=5,
        metadata={"source": "automated_triage"},
    )
    print(f"Routed {result['created']} spans for human review")

import { Brokle } from 'brokle';
import { LengthCheck, Contains } from 'brokle/scorers';

const client = new Brokle({ apiKey: 'bk_...' });

// 1. Query production spans
const spans = [];
for await (const span of client.query.queryIter({
  filter: 'gen_ai.provider.name=openai AND service.name=chatbot',
})) {
  spans.push(span);
}

// 2. Run automated scorers
const results = await client.experiments.run({
  name: "automated-triage",
  spans,
  extractInput: (s) => ({ prompt: s.input }),
  extractOutput: (s) => s.output,
  scorers: [LengthCheck({ minLength: 20 }), Contains({ substring: "sorry" })],
});

// 3. Find low-quality items to route to humans
const lowQualitySpanIds = results.items
  .filter((item) => {
    const avg = item.scores.length
      ? item.scores.reduce((sum, s) => sum + s.value, 0) / item.scores.length
      : 0;
    return avg < 0.5 && item.spanId;
  })
  .map((item) => item.spanId!);

// 4. Route to annotation queue for human review
if (lowQualitySpanIds.length > 0) {
  const result = await client.annotations.addSpans(
    "review_queue_id",
    lowQualitySpanIds,
    { priority: 5, metadata: { source: "automated_triage" } },
  );
  console.log(`Routed ${result.created} spans for human review`);
}