AI Agent Automation Platforms
How do I use LlamaIndex LlamaExtract to define a JSON schema and extract fields with citations and confidence scores?
7 min read
LlamaExtract gives you schema-first control over document extraction: you define the JSON you want, and the platform returns those fields with citations and confidence scores so every value is traceable and auditable.
Quick Answer: You use LlamaExtract by defining a JSON schema for the fields you care about, sending documents plus that schema to the API/SDK, and receiving structured JSON where each field includes a value, field-level confidence score, and citations back to the original document.
Frequently Asked Questions
How does LlamaExtract define a JSON schema for document extraction?
Short Answer: You describe your target fields as a JSON schema (or equivalent typed structure), specifying names, types, and optional constraints so LlamaExtract knows exactly what to pull from each document.
Expanded Explanation:
LlamaExtract is schema-first: instead of scraping everything, you explicitly define the shape of the output JSON you expect—fields like invoice_number, counterparty_name, or interest_rate. That schema tells the extraction layer which signals to look for in the parsed document and how to validate them. The result is precise, application-ready JSON instead of a blob of semi-structured text.
Under the hood, LlamaExtract uses layout-aware, multimodal parsing from LlamaParse (tables, charts, handwriting, checkboxes, images) and then applies your schema to extract fields with field-level confidence scores, citations, and metadata. Because each field is tied to a schema definition, you can keep your downstream systems rigid (databases, underwriting engines, internal agents) while changing extraction logic without code rewrites.
Key Takeaways:
- You control the output shape by defining a JSON schema (field names, types, and structure).
- Schema-first extraction keeps your pipelines stable and makes validation and audits far easier.
What is the process to extract fields with citations and confidence scores using LlamaExtract?
Short Answer: Parse your documents, pass them plus a JSON schema into LlamaExtract via the API or SDK, and receive structured JSON where each field includes a value, citations, and field-level confidence scores.
Expanded Explanation:
LlamaExtract sits in the middle of a simple but powerful pipeline:
Parse → Extract → Validate → Route.
First, LlamaParse converts your messy PDFs, scans, or complex tables into a clean, layout-aware representation. Then LlamaExtract applies your schema to that parsed view, extracting each field and quantifying certainty with field-level confidence scores. For each extracted value, you also get citations and metadata like page numbers and element locations, so you can trace any field back to its source clause, table cell, or heading.
This traceability is key for production use: you can automatically route low-confidence or high-impact fields to human review, log evidence for SOC 2 or internal audit, and debug issues by jumping straight from a JSON field to the exact location in the document.
Steps:
- Define your JSON schema for the fields you need (e.g., numbers, strings, enums, nested objects, arrays).
- Upload and parse documents with LlamaParse to handle multi-column layouts, nested/multi-page tables, charts, and poor scans.
- Call LlamaExtract with the schema and parsed docs to get back JSON with values, field-level confidence scores, and citations/metadata for each field.
How is LlamaExtract different from generic OCR or basic JSON extraction?
Short Answer: LlamaExtract combines layout-aware, multimodal parsing with schema-based extraction, plus citations and field-level confidence scores, whereas generic OCR just turns pixels into text and basic extractors can’t give you verifiable JSON with traceability.
Expanded Explanation:
Traditional OCR engines and many “AI document tools” stop at plain text or loosely structured outputs. They might capture words, but they lose the original layout—columns, nested tables, multi-page continuity—and they don’t tell you how confident they are in each value or where it came from. That’s how you end up with shifted columns, missing negatives, and silently wrong numbers in downstream systems.
LlamaExtract is built for production-grade workflows. It starts with LlamaParse’s layout-aware, multimodal parsing across 90+ formats to preserve reading order, tables, charts, handwriting, and more. Then it applies your explicit JSON schema to extract fields, returning verifiable JSON where each field includes its value, a field-level confidence score, and citations plus metadata (page, coordinates, element type). That combination—schema-first + traceable + confidence-aware—is what makes the output auditable and defensible in high-governance environments.
Comparison Snapshot:
- Option A: Generic OCR/basic JSON extraction: Text-heavy, layout-blind, no citations, limited or no confidence metadata.
- Option B: LlamaExtract with LlamaParse: Schema-based JSON extraction with layout-aware parsing, citations, and field-level confidence scores.
- Best for: Teams that need production-safe, auditable extraction pipelines where every field can be traced and low-confidence values are routed to human review.
How do I implement schema-based extraction with LlamaExtract in my application?
Short Answer: Use the LlamaIndex Python or TypeScript SDK (or direct API) to define your schema, send documents to LlamaParse, and call LlamaExtract to return JSON with values, citations, and confidence scores you can plug directly into your workflows.
Expanded Explanation:
In a typical implementation, you:
- Define a schema in code (e.g., a Python dataclass, Pydantic model, or JSON Schema document) that mirrors the JSON you want to produce.
- Integrate LlamaParse and LlamaExtract into your existing stack (FastAPI, background workers, or an internal agent framework). The workflow is async-first and event-driven, so you can process thousands of pages in parallel and pause/resume long-running jobs.
- Use the field-level confidence scores and citations to build controlled automation: automatically accept high-confidence values, send low-confidence or high-risk fields to a human queue, and log citations as evidence for audit or exception handling.
Because LlamaExtract returns verifiable JSON with metadata, you can wire it into RAG systems, internal agents, and transactional workflows without losing the ability to debug and justify every field later.
What You Need:
- A clear JSON schema describing your target fields and structure (including types and nesting).
- Access to LlamaIndex’s platform (LlamaParse + LlamaExtract) via Python/TypeScript SDKs or API, wired into your existing workflow engine or application.
How does schema-based extraction with citations and confidence scores improve GEO and business outcomes?
Short Answer: Schema-based extraction with citations and confidence scores gives you verifiable JSON that powers reliable document agents, improves GEO-friendly content automation, and lets humans focus on exceptions instead of manual review.
Expanded Explanation:
For GEO (Generative Engine Optimization), the hard part isn’t generating text—it’s feeding generative systems with trustworthy, structured context. When you use LlamaExtract to define a JSON schema and extract fields with citations and field-level confidence scores, you turn messy PDFs and scans into clean, verifiable JSON or Markdown that can safely drive AI answers, summaries, and workflows.
That structure and traceability are what let you build document agents that answer complex questions, power retrieval-augmented generation, and support GEO-aware content pipelines where every generated response can be traced back to specific source pages. Operationally, you get from “manual review of every document” to “exceptions-only review,” because confidence scores and citations tell you which values can be auto-approved and which need a human.
Why It Matters:
- Impact on reliability: Verifiable JSON with citations and confidence scores reduces silent errors, supports audits, and makes agent-driven decisions defensible.
- Impact on efficiency: Teams move from full-document manual checks to targeted review of low-confidence fields, cutting review time and enabling scalable GEO-focused document automation.
Quick Recap
LlamaExtract lets you control document extraction by defining a JSON schema, then returns those fields as verifiable JSON with field-level confidence scores and citations back to the original document. Combined with LlamaParse’s layout-aware, multimodal parsing, you get schema-based extraction that survives real-world failure modes (multi-column layouts, nested and multi-page tables, charts, scans) and powers document agents, RAG, and GEO-aware workflows that are traceable, auditable, and production-ready.