What's changed: Added per-section figures (cert-figure-retrofit). New AI-901 Chapter 5 (Domain 2: Content Understanding = multimodal information extraction (docs/forms/images/audio/video → structured), schema/field definition, prebuilt vs custom, positioning vs Document Intelligence/Vision/Speech, build flow = analyzer → schema → analyze → results, combining with RAG/agents)
5.2Building an Information-Extraction App
Understand the flow of building a lightweight information-extraction app with Content Understanding (create an analyzer → define the schema → analyze input → receive structured results), how it combines with Foundry and the SDK, and typical use cases.
The basics of building an extraction app: (1) create an analyzer → (2) define the extraction schema (fields) → (3) analyze files → (4) receive structured results. An analyzer bundles "which content type, with which schema, to extract." Pass a file to it and you get back results (e.g., JSON) with each field filled in. A common pattern: design and test the schema in the Foundry portal, then call analysis from your app via the SDK.
5.2.1The build flow
| Step | What you do |
|---|---|
| 1. Create analyzer | Create one for the target content type |
| 2. Define schema | Specify fields to extract (name, type) |
| 3. Analyze | Pass files and run analysis |
| 4. Get results | Receive structured data with fields filled |
5.2.2Combining with Foundry, generative AI, and agents
Information extraction is useful on its own, but shines when combined with other workloads. Feed the extracted structured data as grounding (RAG) to a generative model and it can answer based on a file’s contents. Embed extraction as one of an agent’s tools and you can build an autonomous flow: "read the document → judge the content → execute the next step." See the big picture: the Foundry models, grounding, and agents from Chapters 2–4 connect with this information extraction.
Scenario: automating invoice processing. (1) Analyze received invoice PDFs with Content Understanding (schema = biller/amount/due date) → (2) cross-check results against internal policy (RAG) → (3) if fine, an agent files it in the accounting system (tool) → (4) large amounts go through human-in-the-loop approval. Information extraction becomes the "front door" linking generative AI, grounding, and agents.
Watch out: (1) the analyzer (the whole extraction setup) vs the schema (field definitions)—the schema is part of the analyzer. (2) Result quality depends on schema clarity and input quality (blurry images reduce accuracy). (3) Extraction "pulls out"; creating summaries/answers is generative AI’s role—they combine but the roles differ. (4) Since sensitive data is involved, do not forget privacy & security.
Common: (1) extraction app flow = create analyzer → define schema → analyze → structured results. (2) "answer grounded in extracted results" = combine with grounding/RAG. (3) "automate read → judge → act" = use extraction as an agent tool. (4) Watch questions on the roles of extraction (pull out) vs generation (create).
5.2.3Section summary
- Build flow: create analyzer → define schema → analyze → get structured results
- Typically design the schema in the Foundry portal and call analysis from the app via the SDK
- Information extraction shines when combined with grounding (RAG) and agent tools
- Quality depends on schema clarity and input quality; privacy for sensitive data is essential
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Quick check
(just a quick review)Q1. What is the basic flow for building an information-extraction app with Content Understanding?
Q2. What technique feeds extracted structured data to a generative model so it answers based on the file content?
Q3. To build "read a document → judge → autonomously execute next step," how is information extraction used?
Q4. Which correctly describes the relationship between an analyzer and a schema?
Q5. Which is a correct caution about the quality/operation of an extraction app?
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