Document Indexing

Overview

Extract structured metadata from fetched documents using LLM:

• Content type: blog, tutorial, guide, reference, etc.
• Topics & Tools: Main subjects and technologies
• Structure: Code examples, procedures, narrative

Creates DocumentMetadata records for search and clustering.

Quick Start

# Index single document
kurt index 5494cc13

# Batch index (async, 5-10x faster)
kurt index --url-prefix https://example.com/

# Re-index with custom concurrency
kurt index --url-prefix https://example.com/ --force --max-concurrent 10

Prerequisites: Documents must be FETCHED (kurct content fetch)

Commands

# Single
kurt index <doc-id>
kurt index <doc-id> --force

# Batch (async parallel)
kurt index --url-prefix <url>
kurt index --url-contains <string>
kurt index --max-concurrent 10     # Default: 5

# Filters
kurt index --status FETCHED --url-prefix <url>

Content Types

BLOG | TUTORIAL | GUIDE | REFERENCE | WHITEPAPER | CASE_STUDY | FAQ | CHANGELOG | MARKETING | OTHER

Extracted Metadata

{
  "content_type": "TUTORIAL",
  "extracted_title": "Machine Learning Guide",
  "primary_topics": ["Machine Learning", "Python"],
  "tools_technologies": ["TensorFlow", "Pandas"],
  "has_code_examples": true,
  "has_step_by_step_procedures": true,
  "has_narrative_structure": false
}

Performance

• Sequential: ~3-5s per document
• Parallel (5 concurrent): ~1s per document avg
• Example: 92 docs in 30s (parallel) vs 5 mins (sequential)

Python API

from kurt.indexing import extract_document_metadata, batch_extract_document_metadata
import asyncio

# Single
result = extract_document_metadata("abc-123")

# Batch
results = asyncio.run(batch_extract_document_metadata(
    ["abc-123", "def-456"],
    max_concurrent=5
))

Troubleshooting

Next Steps

• ingest-content-skill - Fetch documents first
• document-management-skill - Query and manage documents