Project 1: Structured Data Extractor

Build a LangChain pipeline that extracts structured fields from unstructured text with schema validation and retry logic.

Quick Reference

Attribute Value
Difficulty Level 2: Intermediate
Time Estimate 6-10 hours
Language Python or JavaScript
Prerequisites JSON schema basics, LLM API familiarity
Key Topics output parsing, schema validation, retries

1. Learning Objectives

By completing this project, you will:

  1. Define strict schemas for extraction targets.
  2. Use LangChain output parsers with validation.
  3. Implement retries for malformed outputs.
  4. Measure extraction accuracy on sample data.
  5. Log failures and fixes for debugging.

2. Theoretical Foundation

2.1 Structured Extraction

LLMs are probabilistic. Schema validation converts probabilistic outputs into deterministic contracts.

3. Project Specification

3.1 What You Will Build

A CLI or small API that takes raw text (emails, tickets, invoices) and returns validated JSON fields.

3.2 Functional Requirements

  1. Schema definition with required fields.
  2. Output parser that enforces schema.
  3. Retry strategy for invalid outputs.
  4. Accuracy report on a small dataset.
  5. Error logs with raw model output.

3.3 Non-Functional Requirements

  • Deterministic mode with temperature 0.
  • Traceability for each extraction run.
  • Configurable schemas per input type.

4. Solution Architecture

4.1 Components

Component Responsibility
Prompt Template Describe extraction task
Output Parser Validate JSON fields
Retry Wrapper Re-run on parse failure
Reporter Track accuracy and errors

5. Implementation Guide

5.1 Project Structure

LEARN_LANGCHAIN_PROJECTS/P01-structured-data-extractor/
├── src/
│   ├── schema.py
│   ├── chain.py
│   ├── retry.py
│   ├── eval.py
│   └── cli.py

5.2 Implementation Phases

Phase 1: Schema + parser (2-3h)

  • Define schemas for 1-2 document types.
  • Checkpoint: invalid outputs fail fast.

Phase 2: Chain + retries (2-4h)

  • Build extraction chain with retries.
  • Checkpoint: retries reduce invalid outputs.

Phase 3: Evaluation (2-3h)

  • Score accuracy on sample inputs.
  • Checkpoint: report shows precision/recall.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit schema validation missing fields
Integration chain valid JSON output
Regression retries recover from bad format

6.2 Critical Test Cases

  1. Missing required field triggers retry.
  2. Malformed JSON is caught and fixed.
  3. Output matches schema exactly.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Overly strict schema many failures allow optional fields
Prompt drift inconsistent output lock format in prompt
Silent parsing errors bad data log raw output

8. Extensions & Challenges

Beginner

  • Add a second schema type.
  • Add CSV export for results.

Intermediate

  • Add confidence scores per field.
  • Add human review queue.

Advanced

  • Add active learning for hard cases.
  • Add automatic schema inference.

9. Real-World Connections

  • Customer support uses structured extraction for routing.
  • Finance uses extraction for invoices and receipts.

10. Resources

  • LangChain output parser docs
  • JSON schema references
  • “AI Engineering” (reliability patterns)

11. Self-Assessment Checklist

  • I can design extraction schemas.
  • I can handle invalid outputs with retries.
  • I can measure extraction accuracy.

12. Submission / Completion Criteria

Minimum Completion:

  • Schema-validated extraction
  • Retry on invalid output

Full Completion:

  • Evaluation report
  • Error logging

Excellence:

  • Confidence scoring
  • Human review workflow

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/LEARN_LANGCHAIN_PROJECTS.md.