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Extract validated fields from unstructured documents
Convert documents into fixed-field JSON records while keeping validation in the application.
Goal
Extract document metadata in batches for later retrieval or review.
Steps
- Define Pydantic/JSON Schema fields and enums, then prepare synthetic documents and edge cases. [extract]
- Follow the notebook’s tool-use extraction pattern with the input text and field definitions; test summary, entity, or sentiment outputs separately. [extract]
- Validate required fields, types, enums, and source evidence; mark unverifiable values missing instead of guessing. [extract]
How to verify
Valid examples match the schema; typos, missing fields, and out-of-range cases are rejected or flagged by the application.
Troubleshooting
If the tool call lacks a complete object, inspect required schema fields and input boundaries; do not treat prose as field data.
Permissions
Prerequisites
- Python 3.11+, anthropic SDK, an API key, and redacted synthetic or authorized documents.
Permissions
- Do not send IDs, secrets, or out-of-scope data; keep extracted output in controlled storage.
Limitations
Structured results are extraction candidates; business truth and document provenance need independent checks. Do not automate eligibility/payment decisions.
Pinned upstream source
- Repository
- anthropics/anthropic-cookbook
- File path
- tool_use/extracting_structured_json.ipynb
- Pinned commit
9c1e493ddd2b82df399d2a61b9c806c539e18be6- Source checked
- 2026-10-14 · Reviewed against the pinned upstream source
Repository metrics
These values describe the upstream repository, not popularity or likes for an individual skill or playbook.
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License: MIT
Main topic
Turn ecommerce campaign materials into a reviewable weekly brief