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Documentation Index

Fetch the complete documentation index at: https://docs.tensorlake.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Progress API

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Getting the Request Context

First, get access to the request context in your function: 
from tensorlake.applications import RequestContext, function

@function()
def my_function(data: str) -> str:
    # Get the current request context
    ctx = RequestContext.get()

    # Now you can use ctx.progress.update()
    ctx.progress.update(1, 10, "Starting processing...")

    return "done"

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Method: progress.update()

Stream progress updates to monitoring systems and frontends. 
ctx.progress.update(
    current: int | float,
    total: int | float,
    message: str | None = None,
    attributes: dict[str, str] | None = None
)
Parameters:
ParameterTypeRequiredDescription
currentint | float✅ YesCurrent step or percentage complete
totalint | float✅ YesTotal steps or 100 for percentage
messagestr | None❌ NoHuman-readable progress message
attributesdict[str, str] | None❌ NoAdditional metadata (key-value pairs)

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Basic Usage

Simple progress tracking: 
@function()
def process_items(items: list) -> dict:
    ctx = RequestContext.get()

    for i, item in enumerate(items):
        # Update progress: current step, total steps
        ctx.progress.update(i + 1, len(items))
        process(item)

    return {"processed": len(items)}
With a message: 
@function()
def multi_step_workflow() -> str:
    ctx = RequestContext.get()

    ctx.progress.update(1, 3, "Fetching data from API...")
    data = fetch_data()

    ctx.progress.update(2, 3, "Processing data...")
    processed = process_data(data)

    ctx.progress.update(3, 3, "Storing results...")
    store_results(processed)

    return "complete"
With additional metadata: 
@function()
def batch_processor(items: list) -> dict:
    ctx = RequestContext.get()
    errors = 0

    for i, item in enumerate(items):
        try:
            process(item)
        except Exception:
            errors += 1

        # Include metadata about the processing
        ctx.progress.update(
            current=i + 1,
            total=len(items),
            message=f"Processing item {i + 1}",
            attributes={
                "error_count": str(errors),
                "success_rate": f"{((i + 1 - errors) / (i + 1) * 100):.1f}%"
            }
        )

    return {"total": len(items), "errors": errors}

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Using Percentages

You can use percentages instead of step counts: 
@function()
def long_operation() -> str:
    ctx = RequestContext.get()

    # 0-100 scale
    ctx.progress.update(0, 100, "Starting...")

    # 25% complete
    ctx.progress.update(25, 100, "Quarter way through...")

    # 50% complete
    ctx.progress.update(50, 100, "Halfway done...")

    # 100% complete
    ctx.progress.update(100, 100, "Finished!")

    return "done"

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Consuming Progress Streams

Progress updates are available through the Tensorlake API in real-time.

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Polling for Progress Updates

# Get progress updates for a specific request
curl -X GET \
  "https://api.tensorlake.ai/applications/{application}/requests/{request_id}/progress" \
  -H "Authorization: Bearer $TENSORLAKE_API_KEY"
Response: 
{
  "current": 45,
  "total": 100,
  "message": "Processing batch 3 of 10",
  "attributes": {
    "batch_id": "batch_003",
    "records_processed": "4500"
  },
  "timestamp": 1704067200000
}

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Learn More

Request Context

Full context API reference.