Ai_extract in Databricks
Summary
This content details the use of Databricks' `AI_extract` function to pull structured data from PDF documents, building on a previous lesson that used `AI_parse_document` to convert PDFs into HTML tables. The process involves using `AI_extract` to identify and retrieve specific values like CPT codes, ICD codes, descriptions, build amounts, and paid amounts from the HTML table. Initially, `AI_extract` returns only the first instance of a requested value, requiring conversion to an array type to resolve data type mismatches. To handle multiple rows of data, the method employs `explode` in conjunction with `regexp_extract_all` to parse each HTML table row (`<tr>...</tr>`) into individual records. A Common Table Expression (CTE) is then used to filter out header rows, ensuring only actual data rows containing table data (`<td>...</td>`) are processed, resulting in a clean, columnar output.
Key takeaway
For Data Engineers or Data Scientists needing to extract structured data from PDF documents within Databricks, integrate `AI_extract` with SQL's `explode` and `regexp_extract_all` functions. This approach allows for precise extraction of multi-row data from HTML tables, ensuring accuracy and efficiency. You should define CTEs to manage intermediate steps like row parsing and header filtering, resulting in clean, usable columnar data for downstream analysis.
Key insights
Databricks' `AI_extract` function, combined with regex and SQL, efficiently extracts structured data from PDF-derived HTML tables.
Principles
- AI_extract identifies specified values within text.
- Regular expressions can segment HTML content.
- CTEs refine and filter intermediate data.
Method
Convert PDF to HTML, then use `AI_extract` with `explode` and `regexp_extract_all` to iterate through HTML table rows (`<tr>`) and extract specific fields (`<td>`) into structured columns, filtering out headers via a CTE.
In practice
- Use `ARRAY()` to resolve `AI_extract` data type errors.
- Employ `regexp_extract_all` for multi-row HTML parsing.
- Filter header rows using `WHERE tr LIKE '%<td%'`.
Topics
- AI Extract
- PDF Data Extraction
- HTML Table Parsing
- SQL Data Transformation
- Regular Expressions
Best for: Machine Learning Engineer, Data Engineer, Data Scientist
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Editorial summary, takeaway, and curation by AIssential. Original article published by Alex The Analyst.