How to Turn PDFs Into Databricks Tables with IDP

TL;DR

Use AI Parse Document to convert uploaded PDFs into parsed content, then classify invoices, purchase orders, and receipts before extracting selected fields into separate Databricks tables. Storing intermediate results in replaceable tables and converting document elements into readable text makes the workflow easier to inspect, reuse, and rerun when additional files are uploaded.

Transcript

What's going on everybody? Welcome back to another video. Today we're building our full project using IDP and data bricks. [music] Now, I'm super excited for this project because this is a really great use case for using IDP. We're going to be working with financial data and we're going to have invoices, purchase orders, and receipts and we're goin... Read More

Key Insights

• Intelligent document processing is used to transform unstructured financial documents into structured, usable data inside Databricks, with invoices, purchase orders, and receipts serving as the project inputs.
• A Databricks volume is the initial storage location for the PDF files, allowing the SQL notebook to access invoices, purchase orders, and receipts from one catalog path.
• The read_files function loads file paths, modification times, lengths, and content, but the raw content is not yet a useful document representation until it is processed with AI Parse Document.
• AI Parse Document produces parsed content containing document elements, text, tables, totals, and other information that can later be selected according to the requirements of each document category.
• A replaceable intermediate table preserves file paths and parsed results for reuse in later queries, while avoiding errors when the same table-creation statement is run again.
• Readable document text is created by transforming parsed document elements, attempting to cast each element's content to a string, and joining the results with newline separators through concat_ws.
• AI classification separates invoices, purchase orders, and receipts so that each category can be associated with its own desired fields and destination table instead of combining every file type in one table.
• The completed workflow is designed for repeated use: upload additional files, run the established code, and populate the relevant tables with newly extracted data from those documents.

Questions & Answers

Q: How do you turn PDFs into Databricks tables with IDP?

Upload the PDF files to a Databricks catalog volume, read them in a SQL notebook with read_files, and pass their content to AI Parse Document. Store the resulting file paths and parsed content in an intermediate table. Next, convert the document elements into readable text, classify each document type, extract the required fields, and route those fields into the appropriate tables.

Q: Why should invoices, purchase orders, and receipts be classified?

Invoices, purchase orders, and receipts contain different information, so combining every extracted value in one table would not fit the project's intended structure. AI classification groups and categorizes the documents before extraction. The classification result can then determine which information should be selected from each file and which destination table should receive the structured data.

Q: What does AI Parse Document do in the Databricks workflow?

AI Parse Document processes the file content returned by read_files and creates a document-aware parsed result. That result can include document elements, readable text, tables, invoice information, website information, totals, and other available content. The parsed representation provides the source from which the workflow can select as much or as little information as the project requires.

Q: How are PDF files loaded from a Databricks volume?

The files are first uploaded into a newly created catalog volume that contains the invoices, purchase orders, and receipts. A SQL notebook then uses a select statement with read_files and the volume path. The query returns the available files together with information such as their paths, modification times, lengths, and content, preparing the content for document parsing.

Q: Why create a replaceable parsed-data table?

A replaceable table stores each file path and its parsed document content so later queries can reuse the results. Using create or replace also supports rerunning the statement when the table already exists, instead of receiving an error that it has already been created. A create statement may report that no rows were returned even though the table was created successfully.

Q: How is parsed document content made easier to read?

The parsed document elements are transformed into strings and joined into one text value. The SQL uses concat_ws with a newline separator, transforms the elements found under the parsed content's document structure, and applies try cast to each element's content. The resulting doc text representation is more human-readable and can also make the following classification step easier to inspect.

Q: What information can be extracted from an invoice PDF?

The workflow can extract as much or as little information as needed from an invoice. The tutorial specifically considers basic values such as the invoice ID and total amount, while noting that addresses and other information can also be selected. Parsed results may also contain item tables, website information, total information, and other text available in the source document.

Q: How can the IDP pipeline process newly uploaded files?

The project is designed so additional documents can be placed into the volume after the parsing, classification, extraction, and table-loading logic has been established. The code can then be run through the same process again. This allows newly uploaded files to be parsed, categorized, and converted into new structured data within the appropriate tables without rebuilding the workflow.

Summary & Key Takeaways

• The project begins by uploading invoices, purchase orders, and receipts into a new Databricks catalog volume. A SQL notebook reads the files with read_files, returning metadata, paths, and binary content. Because the initial content is not directly usable, the workflow passes it to AI Parse Document for document-aware processing.

• Parsed document results contain document elements, tables, invoice details, website information, totals, and other available text. The tutorial stores each file path and its parsed content in a replaceable table named parsed data. This intermediate table allows later SQL queries to reuse the processed documents without repeating every preceding expression.

• The parsed elements are transformed and combined with concat_ws into a newline-separated doc text field that is easier for people to inspect. This readable representation can then support document classification, field extraction, and routing into separate tables. Once established, the workflow can be rerun after more files are uploaded.