Documentation Index
Fetch the complete documentation index at: https://docs.trelent.com/llms.txt
Use this file to discover all available pages before exploring further.
Use one connector (source) and one output (delivery mechanism) to submit a job. You’ll receive a job_id that you can poll until results are ready.
{
"job_id": "550e8400-e29b-41d4-a716-446655440000"
}
import { DataIngestionClient, DataIngestionConfig } from "@trelent/data-ingestion";
import type { JobInput } from "@trelent/data-ingestion";
const client = new DataIngestionClient();
const job: JobInput = {
connector: {
type: "url",
urls: [
"https://example.com/file.pdf",
],
},
output: {
type: "s3-signed-url",
},
};
const { job_id } = await client.submitJob(job);
console.log("Submitted job:", job_id);
You can also configure the client via environment variables: TRELENT_DATA_INGESTION_API_URL and TRELENT_DATA_INGESTION_API_TOKEN.
from trelent_data_ingestion_sdk.client import DataIngestionClient
from trelent_data_ingestion_sdk.config import SDKConfig
from trelent_data_ingestion_sdk.models import JobInput, UrlConnector, S3SignedUrlOutput
client = DataIngestionClient()
job = JobInput(
connector=UrlConnector(type="url", urls=[
"https://example.com/file.pdf",
]),
output=S3SignedUrlOutput(type="s3-signed-url"),
)
resp = client.submit_job(job)
print("Submitted job:", resp.job_id)
You can configure via environment variables: DATA_INGESTION_API_URL and DATA_INGESTION_API_TOKEN.
S3 connector example (optional)
If your files are in S3, use the S3 connector. Each object key becomes the stable identifier echoed in responses.
import type { JobInput } from "@trelent/data-ingestion";
const job: JobInput = {
connector: {
type: "s3",
bucket_name: "my-docs-bucket",
prefixes: [
{ prefix: "docs/", recursive: true },
"videos/demo.mp4",
],
},
output: { type: "bucket", bucket_name: "example-output-bucket", prefix: "processed/" },
};
from trelent_data_ingestion_sdk.models import JobInput, S3Connector, BucketOutput, S3Prefix
job = JobInput(
connector=S3Connector(
type="s3",
bucket_name="my-docs-bucket",
prefixes=[S3Prefix(prefix="docs/", recursive=True), "videos/demo.mp4"],
),
output=BucketOutput(type="bucket", bucket_name="example-output-bucket", prefix="processed/")
)