Learning Timeline
Key Insights
Data Exploration vs. Strict Schemas
If you're unsure of the document structure, you can use an empty dictionary object to let the LLM autonomously determine the important key-value pairs.
The Benefits of Dot Notation
Using Pydantic allows you to access AI outputs like standard code objects (dot notation), which is much cleaner and less error-prone than manual JSON parsing.
Debugging with Inspect History
Always use 'inspect_history' to see how prompts are automatically constructed. This helps you understand how input and output fields are mapped before reaching the LLM.
Step by Step
Defining Pydantic Schemas for Document Data Extraction
- Define Pydantic classes to specify the data structure you want to extract (e.g., filing date, form type, transactions).
- Build a document analyzer 'Signature' by setting the 'Input Field' that will receive the document (text and images via attachments).
- Pass the 'document_schema' parameter into the Signature using the created Pydantic class to enforce a specific output format.
- Use 'Chain of Thought' (CoT) alongside the Signature to process documents in-depth.
- Access the extracted data using 'dot notation' (e.g., response.document_schema.filing_date) for direct integration into your code.
- Use the 'inspect_history' function to view the 'raw dump' of system messages and see how the AI processes inputs behind the scenes.