Templates — API Reference

Package: dodil.k3.tables.v1 · Service: TableService

ListTemplates here returns the Scriptum template catalog filtered to warehouse-compatible templates — templates tagged warehouse_compatible: true whose output schema can back a Delta table. Use the IDs returned here when calling CreateTablePipeline.

Same catalog, different filter: this is the same underlying Scriptum catalog as Pipelines → Templates → ListTemplates, but pre-filtered. The Pipelines endpoint returns all templates; this endpoint returns only those that emit structured rows.

RPC	HTTP
`ListTemplates`	`GET /:bucket/tables/_templates`

gRPC setup — grpcurl, endpoints, reflection, and field-name casing — is covered once in Conventions → Using gRPC.

What’s in the warehouse-compatible catalog

Every template here ships with K3 and is production-grade. Pick by use case + modality:

Template	What it does	Modalities
`classification`	Document type / topic / language / sensitivity classification	text, pdf
`document_triage`	Rapid intake triage — classify + extract entities	text, pdf, docx, html, email
`entity_pii_extraction`	Structured entities + PII detection	text, pdf
`image_understanding`	Image analysis (ML object detection + OCR + LLM vision reasoning)	image
`ocr_extraction`	Text extraction from scanned / image-based documents	pdf, image
`sentiment_intent_analysis`	Sentiment, intent, topics, urgency, toxicity	text, pdf
`summarization`	Multi-level document summarization + keyword extraction	text, pdf, docx, html
`translation`	Translate text to target language	text
`audio_transcription`	Whisper transcripts (+ optional diarization)	audio, video
`code_intelligence`	Source-code symbols / dependencies / architecture	text
`object_detection`	YOLOv8m object detection in images / video keyframes	image, video
`video_surveillance`	Object tracking + activity classification	video
`product_catalog_enrichment`	Product attribute enrichment from images + descriptions	image, text
`review_analysis`	Customer review sentiment + toxicity + keyword extraction	text, pdf

Full descriptions, labels, and the typed ScriptContract for each template are at Pipelines → Templates → The catalog.

`ListTemplates`

Request

HTTP

All warehouse-compatible templates:


curl -sS "https://k3.dev.dodil.io/kb-prod/tables/_templates" \
  -H "Authorization: Bearer $DODIL_TOKEN"

Filter by category:


curl -sS "https://k3.dev.dodil.io/kb-prod/tables/_templates?category=analysis" \
  -H "Authorization: Bearer $DODIL_TOKEN"

Free-text search:


curl -sS "https://k3.dev.dodil.io/kb-prod/tables/_templates?search=ocr" \
  -H "Authorization: Bearer $DODIL_TOKEN"

Response

HTTP


{
  "templates": [
    {
      "id": "entity_pii_extraction",
      "name": "Entity & PII Extraction",
      "description": "Extract structured entities and detect PII from any document.",
      "tags": ["analysis", "pii"],
      "labels": {
        "category": "analysis",
        "modality": "text, pdf",
        "warehouse_compatible": "true",
        "status": "ready"
      },
      "category": "analysis",
      "modalities": ["text", "pdf"],
      "acceptedExtensions": ["pdf", "txt", "docx"],
      "acceptedContentTypes": ["application/pdf", "text/plain"]
    }
  ]
}

Using a template — the canonical flow


# 1. Browse — pick a template
curl -sS "https://k3.dev.dodil.io/kb-prod/tables/_templates" \
  -H "Authorization: Bearer $DODIL_TOKEN" | jq '.templates[] | {id, name, modalities, acceptedExtensions}'
 
# 2. Create a pipeline-bound table from it
curl -sS -X POST "https://k3.dev.dodil.io/kb-prod/tables/pipelines" \
  -H "Authorization: Bearer $DODIL_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "bucket": "kb-prod",
    "name": "entities",
    "templateId": "entity_pii_extraction",
    "folderPrefix": "intake"
  }'
 
# 3. Upload a document under intake/ — K3 auto-extracts rows into the table
dodil k3 object create ./contract.pdf -b kb-prod -k intake/contracts/acme-2026.pdf
 
# 4. Query the auto-extracted rows
dodil k3 table query --bucket kb-prod --sql "
  SELECT entity_type, entity_value FROM entities
  WHERE source_key = 'intake/contracts/acme-2026.pdf' AND is_pii = true
"

For the full recipe (including how to inspect the auto-generated ingest rule + per-object jobs), see Recipes → Pipeline-bound table.

output_columns is always empty here — warehouse templates use lazy schema materialization. K3 doesn’t know the column layout until the first ingest job runs. After first ingest, DescribeTable returns the real schema.

Templates — API Reference

What’s in the warehouse-compatible catalog

ListTemplates

Request

HTTP

gRPC

Response

HTTP

gRPC

Using a template — the canonical flow

See also

`ListTemplates`