Project 6: The Pydantic-AI Documentation Agent

Build a capstone agent that answers questions about PydanticAI docs with strict schema validation and citations.

Quick Reference

Attribute Value
Difficulty Level 4: Expert
Time Estimate 16-24 hours
Language Python
Prerequisites Projects 1-5, RAG basics
Key Topics doc ingestion, retrieval, citations, validation

1. Learning Objectives

By completing this project, you will:

  1. Ingest and index documentation.
  2. Retrieve relevant sections with metadata filters.
  3. Produce schema-validated answers with citations.
  4. Enforce refusal when evidence is missing.
  5. Evaluate accuracy and citation coverage.

2. Theoretical Foundation

2.1 Documentation Agents

Docs agents must be precise: every claim should be traceable to a documented source.

3. Project Specification

3.1 What You Will Build

A doc QA agent that answers PydanticAI questions with citations and validated outputs.

3.2 Functional Requirements

  1. Ingestor for docs (markdown, HTML).
  2. Vector index with metadata.
  3. Answer schema with citations and limitations.
  4. Refusal logic for missing evidence.
  5. Evaluation on doc question set.

3.3 Non-Functional Requirements

  • Deterministic evaluation with fixed test set.
  • Clear source links in output.
  • Robust error handling for index issues.

4. Solution Architecture

4.1 Components

Component Responsibility
Ingestor Load and chunk docs
Index Store embeddings + metadata
Retriever Fetch relevant chunks
Answerer Produce validated output
Evaluator Score accuracy

5. Implementation Guide

5.1 Project Structure

LEARN_PYDANTIC_AI/P06-doc-agent/
├── src/
│   ├── ingest.py
│   ├── index.py
│   ├── retrieve.py
│   ├── answer.py
│   └── eval.py

5.2 Implementation Phases

Phase 1: Ingest + index (5-8h)

  • Ingest docs and build index.
  • Checkpoint: doc sections retrievable by query.

Phase 2: Answering + validation (5-8h)

  • Generate answers with schema validation.
  • Checkpoint: outputs include citations.

Phase 3: Evaluation (4-8h)

  • Evaluate on a doc QA set.
  • Checkpoint: citation coverage reported.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit schema citations required
Integration retrieval relevant chunks
Regression eval accuracy stable

6.2 Critical Test Cases

  1. Output without citations is rejected.
  2. Missing evidence triggers refusal.
  3. Answers reference correct doc sections.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Hallucinated claims no evidence enforce refusal logic
Wrong citations links mismatch attach chunk IDs
Slow retrieval high latency tune index parameters

8. Extensions & Challenges

Beginner

  • Add a simple web UI.
  • Add doc section previews.

Intermediate

  • Add reranking for better retrieval.
  • Add topic filters.

Advanced

  • Add continual indexing for doc updates.
  • Add evaluation dashboard.

9. Real-World Connections

  • Developer assistants need reliable doc answers.
  • Enterprise knowledge bases require citations.

10. Resources

  • PydanticAI documentation
  • RAG system best practices

11. Self-Assessment Checklist

  • I can build a doc QA agent with citations.
  • I can enforce schema validation.
  • I can evaluate accuracy reliably.

12. Submission / Completion Criteria

Minimum Completion:

  • Doc ingestion + retrieval
  • Schema-validated answers

Full Completion:

  • Refusal logic
  • Evaluation report

Excellence:

  • Reranking and dashboards
  • Continuous indexing

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