How to extract table data from PDFs in Databricks

TL;DR

AI extract pulls specific values from a PDF table into separate columns and rows in Databricks. Start by ensuring the table HTML is loaded, then specify target fields like CPT code and ICD description. Use a tiny regex to split rows and explode data, ultimately producing clean, numeric columns after type conversion.

Transcript

What's going on everybody? Welcome back to another video. Today we're going to be extracting data from our documents in data bricks. [music] Now, in our last lesson, we used AI parse document to specify what things we wanted to actually pull out of our PDFs. Now, we want to use AI extract to pull the specific values that we want and put it into col... Read More

Key Insights

• AI extract pulls specific values from a PDF table into columns and rows.
• The code converts non array inputs to arrays to satisfy parameter types.
• Multiple CPT codes require an explode step to capture all rows.
• Regular expressions isolate each table row from HTML between TR tags.
• Headers are filtered out to avoid non data rows in the result.
• A CTE is used to separate data rows from raw HTML data.
• Data rows are validated by ensuring only TD elements are kept.
• Numeric fields can be converted from strings after extraction.

Questions & Answers

Q: A real search query question about the video (e.g. 'How to...', 'What is...', 'Why does...', 'When should...'). Question 1

How to extract table data from a PDF in Databricks using AI extract. The method starts by preparing the HTML table, then specifying the target fields such as CPT code and ICD description. It handles errors by converting inputs to an array, expands all rows with a regular expression, and filters out header rows. Finally, a data rows CTE is used to structure the data into clean columns for analysis.

Q: Question 2

What is the role of AI extract in this workflow. AI extract is used to pull specific values from the HTML table embedded in the PDF. By targeting fields like CPT code or description, it returns structured data rather than a raw HTML snippet. This makes later steps, such as converting strings to numbers, straightforward.

Q: Question 3

How are multiple rows per PDF handled in this approach. The code uses a small regular expression to take each TR row from the table HTML and treat it as a separate data row. An explode-like process converts all matching rows into individual records, enabling per row extraction and downstream column creation.

Q: Question 4

Why is there a need to filter out headers in the extracted data. The first rows of an HTML table often contain column headers, which are not actual data. Filtering with a condition that only keeps rows containing TD elements ensures only meaningful data rows are processed.

Q: Question 5

How do you ensure numeric fields become numbers. After extraction, the fields such as build amount and paid amount may be strings. The workflow notes that you can convert these values to numeric types to enable arithmetic operations and proper data analysis.

Q: Question 6

What is the purpose of the data rows CTE in this tutorial. The data rows CTE isolates the actual data rows from raw HTML, making subsequent joins and selections simpler. It provides a stable base for applying the AI extract logic to each row.

Q: Question 7

What is the overall takeaway from this video. The key idea is that AI extract can transform unstructured PDF data into a structured table by extracting specific fields, handling multiple rows, and cleaning the data into usable columns. The approach is practical for Databricks workflows and sets up future steps like AI classify.

Q: Question 8

How does the workflow handle errors and type issues. The video demonstrates encountering a data type mismatch and resolving it by converting the parameter to an array type. This highlights the importance of matching expected types when passing data into AI based functions.

Summary & Key Takeaways

• This video demonstrates using AI extract to pull data from a PDF table into a structured format in Databricks. It covers preparing the HTML table, selecting fields, handling multiple rows, and cleaning data for numeric conversion. The approach relies on a combination of AI extract, explode, and regex logic to achieve row-wise extraction.

• The method starts with extracting values for CPT codes, ICD descriptions, build amount, and paid amount, then expands to all rows using a regex rule. It shows how to filter out header rows and apply a data rows CTE to keep the data clean and usable.

• The tutorial emphasizes practical steps over theory, highlighting how simple commands can transform unstructured PDF data into structured columns and rows in Databricks notebooks. It ends with a preview of applying AI classify in the next lesson.