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This example demonstrates incremental document processing using: - ai_parse_document for extracting structured data from PDFs/images - ai_query for LLM-based content analysis - Databricks Workflows with Structured Streaming and serverless compute Key features: - Python notebooks with Structured Streaming for incremental processing - Serverless compute for cost efficiency - Parameterized workflow with catalog, schema, and table names - Checkpointed streaming to process only new data - Visual debugging notebook with interactive bounding boxes
- Add job-level parameters block for catalog and schema (shared across all tasks) - Move optional schedule configuration to top of job definition - Replace Python notebook with Jupyter notebook format including visual outputs
The ipynb format was not preserving the visual output properly. Using .py notebook source and .html export to show outputs correctly.
- Move directory from knowledge_base/ to contrib/ - Replace "workflow" terminology with "job" throughout - Add recursiveFileLookup option for subdirectory file discovery - Make all parameters explicit at job level - Update checkpoint paths to use Unity Catalog volumes instead of /tmp - Remove hardcoded personal paths from notebooks - Add 6 visual screenshots showing parsing capabilities - Update README with Example Output section - Remove HTML output file in favor of Python notebook
- Remove CASE statement handling v1.0 vs v2.0 in text extraction - Use only v2.0 format (parsed:document:elements) - Update README to remove mention of v1.0 support - Keep explicit version parameter in ai_parse_document call
…se_document Aligns folder name with job terminology used throughout the project
Replaced by job_with_ai_parse_document with updated terminology
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Folder Rename
Remove v1.0 Parser References