from brokle import Brokle

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

# Create a dataset
dataset = client.datasets.create(
    name="qa-pairs",
    description="Question-answer test cases"
)

# Insert items
dataset.insert([
    {"input": {"question": "What is 2+2?"}, "expected": {"answer": "4"}},
    {"input": {"question": "Capital of France?"}, "expected": {"answer": "Paris"}},
])

# Iterate over items
for item in dataset:
    print(item.input, item.expected)

import { Brokle } from 'brokle';

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

// Create a dataset
const dataset = await client.datasets.create({
  name: "qa-pairs",
  description: "Question-answer test cases",
});

// Insert items
await dataset.insert([
  { input: { question: "What is 2+2?" }, expected: { answer: "4" } },
  { input: { question: "Capital of France?" }, expected: { answer: "Paris" } },
]);

// Iterate over items (auto-paginated)
for await (const item of dataset) {
  console.log(item.input, item.expected);
}

dataset = client.datasets.create(
    name="my-dataset",
    description="Optional description",
    metadata={"team": "ml", "version": "1.0"},
)
print(dataset.id, dataset.name)

const dataset = await client.datasets.create({
  name: "my-dataset",
  description: "Optional description",
  metadata: { team: "ml", version: "1.0" },
});
console.log(dataset.id, dataset.name);

# Get by ID
dataset = client.datasets.get("01HXYZ...")

# List all datasets (paginated)
datasets = client.datasets.list(limit=10, page=1)
for ds in datasets:
    print(ds.name)

// Get by ID
const dataset = await client.datasets.get("01HXYZ...");

// List all datasets (paginated)
const datasets = await client.datasets.list({ limit: 10, page: 1 });
for (const ds of datasets) {
  console.log(ds.name);
}

# Update metadata
updated = client.datasets.update(
    "01HXYZ...",
    name="renamed-dataset",
    description="Updated description",
)

# Delete
client.datasets.delete("01HXYZ...")

// Update metadata
const updated = await client.datasets.update("01HXYZ...", {
  name: "renamed-dataset",
  description: "Updated description",
});

// Delete
await client.datasets.delete("01HXYZ...");

count = dataset.insert([
    {"input": {"prompt": "Summarize this article"}, "expected": {"summary": "..."}},
    {"input": {"prompt": "Translate to French"}, "metadata": {"language": "fr"}},
])
print(f"Inserted {count} items")

const count = await dataset.insert([
  { input: { prompt: "Summarize this article" }, expected: { summary: "..." } },
  { input: { prompt: "Translate to French" }, metadata: { language: "fr" } },
]);
console.log(`Inserted ${count} items`);

# Auto-paginated iteration
for item in dataset:
    print(item.input, item.expected)

# Manual pagination
items = dataset.get_items(limit=10, page=1)

# Get total count
total = dataset.count()

// Auto-paginated async iteration
for await (const item of dataset) {
  console.log(item.input, item.expected);
}

// Manual pagination
const items = await dataset.getItems({ limit: 10, page: 1 });

// Get total count
const total = await dataset.count();

# Import from JSON or JSONL file
result = dataset.insert_from_json("./data.json")
print(f"Created: {result['created']}, Skipped: {result['skipped']}")

# Import from CSV
result = dataset.insert_from_csv(
    "./qa_pairs.csv",
    column_mapping={
        "input_column": "question",
        "expected_column": "answer",
        "metadata_columns": ["category", "difficulty"],
    },
)

// Import from JSON or JSONL file
const result = await dataset.insertFromJson("./data.json");
console.log(`Created: ${result.created}, Skipped: ${result.skipped}`);

// Import from CSV
const csvResult = await dataset.insertFromCsv("./qa_pairs.csv", {
  inputColumn: "question",
  expectedColumn: "answer",
  metadataColumns: ["category", "difficulty"],
});

# Create items from production traces
result = dataset.from_traces(["trace_id_1", "trace_id_2"])
print(f"Created {result['created']} items from traces")

# Create items from spans
result = dataset.from_spans(["span_id_1", "span_id_2"])

// Create items from production traces
const result = await dataset.fromTraces(["trace_id_1", "trace_id_2"]);
console.log(`Created ${result.created} items from traces`);

// Create items from spans
const spanResult = await dataset.fromSpans(["span_id_1", "span_id_2"]);

# Create a version snapshot
version = dataset.create_version(
    description="Pre-training dataset v1",
    metadata={"experiment_id": "exp_123"},
)
print(f"Version {version['version']} with {version['item_count']} items")

# List all versions
versions = dataset.list_versions()

# Get items from a specific version
result = dataset.get_version_items("version_id", limit=50, offset=0)

# Pin dataset to a version (ensures reproducibility)
dataset.pin_version(version_id="version_id")

# Unpin to return to live items
dataset.pin_version(version_id=None)

// Create a version snapshot
const version = await dataset.createVersion({
  description: "Pre-training dataset v1",
  metadata: { experiment_id: "exp_123" },
});
console.log(`Version ${version.version} with ${version.item_count} items`);

// List all versions
const versions = await dataset.listVersions();

// Get items from a specific version
const { items, total } = await dataset.getVersionItems("version_id", {
  limit: 50,
  offset: 0,
});

// Pin dataset to a version (ensures reproducibility)
await dataset.pinVersion({ versionId: "version_id" });

// Unpin to return to live items
await dataset.pinVersion({ versionId: null });