Building an Extraction Pipeline

Extraction

Part of: Structured Outputs

Now assemble the whole module into one real thing: a pipeline that takes a messy paragraph of customer email and emits a clean, validated record ready for a database. This is extraction , pulling structured fields out of unstructured text, and it is the single most common production use of structured outputs. What it is An extraction pipeline is an end-to-end flow: unstructured input goes in, a validated structured record comes out. The model does the understanding; the surrounding code enforces the contract. You've built every piece in this module, extraction is where they click together into a system you can trust. The shape of the data you want is the target schema . The pipeline's whole job is to fill that schema from free text and refuse to emit anything that doesn't satisfy it. How it works The pipeline chains the lessons in order: 1. Define the target schema. Decide the exact fields: name (string), email (string), order total (number). 2. Prompt with structure enforced. Use JSON mode or function calling so the model returns JSON shaped to that schema, not prose (lessons 2 and 3). 3. Parse. json.loads the response inside a try/except (lesson 2). 4. Validate the values. Apply

Challenge: Full extraction with verdict