LEARN PYDANTIC AI

Learn Pydantic-AI: From Extractor to Intelligent Agent

Goal: Deeply understand the pydantic-ai library—not just its features, but why it’s a game-changer for building reliable AI applications. You will learn to force unstructured LLM outputs into clean, validated Pydantic models, and use this power to build complex RAG agents.

Why Does Pydantic-AI Exist?

Large Language Models (LLMs) are amazing at understanding and generating human language. However, their output is fundamentally unstructured text. If you ask an LLM to “list the ingredients for a pizza,” it might return:

"You'll need flour, water, yeast, salt, tomatoes, and cheese."
"Ingredients: Flour, Water, Yeast, Salt, Tomatoes, Cheese"
A full paragraph describing each ingredient.

Trying to parse this with regex or string splitting is a fragile nightmare. pydantic-ai solves this by acting as a “structured data enforcer” for LLMs. You define the exact Python object structure you want, and pydantic-ai handles the complex prompting and validation to ensure the LLM’s output conforms to that structure.

After completing these projects, you will:

Reliably extract structured data from any text.
Build AI-powered classifiers, taggers, and routers.
Create function-calling agents that interact with other tools.
Master Retrieval-Augmented Generation (RAG) to make LLMs experts on your own data.
Build and deploy robust, production-ready AI agents.

Core Concept Analysis

The Pydantic-AI Workflow

pydantic-ai transforms the standard LLM workflow into a reliable data pipeline.

┌──────────────────────────┐      ┌──────────┐      ┌──────────────────────────────┐
│     Unstructured Text    │      │   LLM    │      │      Unstructured Text       │
│ "John Doe is 30. His email│----->│ (e.g.    │----->│ "name: John Doe, age: 30,    │
│ is john@example.com"     │      │  GPT-4)  │      │ email: john@example.com"     │
└──────────────────────────┘      └──────────┘      └──────────────────────────────┘
                                                             │
                                                             ▼
┌─────────────────────────────────────────────────────────────────────────┐
│                              PYDANTIC-AI                                │
│   1. Injects instructions into the prompt to request structured output.   │
│   2. Parses the LLM's text response.                                    │
│   3. Validates the data against your Pydantic model.                    │
│   4. On failure, sends corrective feedback to the LLM and retries.      │
└─────────────────────────────────────────────────────────────────────────┘
                                                             │
                                                             ▼
┌─────────────────────────────────────────────────────────────────────────┐
│                     CLEAN, TYPED, & VALIDATED OBJECT                    │
│                                                                         │
│   User(name="John Doe", age=30, email="john@example.com")               │
│                                                                         │
└─────────────────────────────────────────────────────────────────────────┘

Key Concepts

Structured Output: The core value proposition. Instead of getting a string, you get an instance of your Pydantic BaseModel.
Retry Logic & Correction: If the LLM produces invalid data (e.g., a string where an integer is required), pydantic-ai automatically tells the LLM what it did wrong and asks for a correction. This makes the system incredibly robust.
RAG (Retrieval-Augmented Generation): pydantic-ai provides tools to connect your LLM to a knowledge base (like documents stored in a Supabase vector database). This allows the LLM to answer questions using specific, up-to-date information instead of just its training data.
Agentic Behavior: By combining structured output with function definitions, you can create agents that can intelligently decide which tool or function to call based on a user’s request.

Project List

These projects are designed to build your skills progressively, from simple data extraction to building a complete RAG agent.

Project 1: The Basic Information Extractor

File: LEARN_PYDANTIC_AI.md
Main Programming Language: Python
Alternative Programming Languages: N/A
Coolness Level: Level 2: Practical but Forgettable
Business Potential: 1. The “Resume Gold”
Difficulty: Level 1: Beginner
Knowledge Area: Structured Data Extraction
Software or Tool: pydantic-ai, openai
Main Book: Pydantic-AI Official Documentation

What you’ll build: A simple script that takes a messy block of text (like a user’s bio) and extracts a clean, structured User object with fields like name, age, and city.

Why it teaches pydantic-ai: This is the “Hello, World!” of pydantic-ai. It teaches you the fundamental workflow: define a Pydantic model, instantiate PydanticAI, and call it with your unstructured text. You’ll immediately see the magic of getting a typed object back instead of a string.

Core challenges you’ll face:

Defining a Pydantic model for your target data → maps to thinking about your desired output structure
Setting up the PydanticAI object with an LLM → maps to basic configuration
Passing the unstructured text and the Pydantic model to the run method → maps to executing the core workflow
Handling the structured output → maps to using the resulting Python object

Key Concepts:

Pydantic Models: Official Pydantic documentation.
PydanticAI Class: The main entry point of the library.
Structured Output: The core value proposition of the library.

Difficulty: Beginner Time estimate: Weekend Prerequisites: Basic Python, familiarity with Pydantic BaseModel.

Real world outcome: A script that prints a clean, accessible Python object from a messy string.

# Your input text
text = "Alex lives in San Francisco. He is 32 years old and is a software engineer. You can contact him at alex.doe@email.com."

# Your output from the script
>>> print(user_object)
name='Alex' age=32 city='San Francisco' email='alex.doe@email.com'

Implementation Hints:

First, define your target structure: class User(BaseModel): name: str; age: int; city: str; email: str.
Import PydanticAI and your Pydantic model.
Instantiate the LLM client (e.g., OpenAI).
Create an instance of PydanticAI, passing the LLM client to it: pai = PydanticAI(llm=...).
Call the run method: user = pai.run(input=text, output_model=User).
Print the result and access its fields (e.g., user.name, user.age).

Learning milestones:

You successfully extract a simple object from text → You understand the basic run command.
You add a new field to your Pydantic model and the extractor picks it up → You see how easy it is to change the desired output.
You give it ambiguous text and it still extracts the data correctly → You appreciate the power of the LLM’s understanding combined with Pydantic’s structure.

Project 2: The Smart Classifier and Tagger

File: LEARN_PYDANTIC_AI.md
Main Programming Language: Python
Coolness Level: Level 3: Genuinely Clever
Business Potential: 2. The “Micro-SaaS / Pro Tool”
Difficulty: Level 1: Beginner
Knowledge Area: Classification / Routing
Software or Tool: pydantic-ai, openai, Enum
Main Book: Pydantic-AI Documentation on Enums

What you’ll build: A tool that classifies user feedback into predefined categories. For example, given the input “The app is crashing when I upload a photo,” the tool will classify it as a BUG and tag it with PHOTO and UPLOAD.

Why it teaches pydantic-ai: This teaches you to use pydantic-ai for more than just extraction. Classification is a core task in building agents (e.g., routing a user’s request to the right tool). Using Pydantic Enums for categories ensures the output is always one of the valid options.

Core challenges you’ll face:

Defining categories using Python’s Enum → maps to creating a fixed set of choices
Creating a Pydantic model that uses the Enum and a list of tags → maps to designing a classification model
Passing different user inputs to see how they are classified → maps to testing the LLM’s reasoning ability

Key Concepts:

Enums in Pydantic: Constraining a field to a set of predefined values.
List Types: Extracting a list of items, like tags.

Difficulty: Beginner Time estimate: Weekend Prerequisites: Project 1.

Real world outcome: A function that takes raw user feedback and returns a structured classification object.

# Input
feedback = "I love the new dark mode feature, it's so much easier on the eyes! Suggestion: maybe add a few more themes?"

# Output
>>> print(classification)
category=<FeedbackCategory.PRAISE: 'Praise'> tags=['dark mode', 'feature request', 'themes']

Implementation Hints:

Define your categories: class FeedbackCategory(Enum): BUG = "Bug"; PRAISE = "Praise"; ....
Define your output model: class Feedback(BaseModel): category: FeedbackCategory; tags: list[str].
Use the same PydanticAI setup as in Project 1, but pass Feedback as the output_model.
Test it with various inputs to see how well it classifies and tags.

Learning milestones:

You can reliably classify text into an Enum → You have constrained the LLM’s output to a set of choices.
You can extract a list[str] for the tags → You can handle simple list extraction.
You realize this is a powerful way to build routers for agents → You see the architectural implications.

Project 3: A Basic RAG System for a Document

File: LEARN_PYDANTIC_AI.md
Main Programming Language: Python
Coolness Level: Level 4: Hardcore Tech Flex
Business Potential: 3. The “Service & Support” Model
Difficulty: Level 2: Intermediate
Knowledge Area: RAG / Knowledge Bases
Software or Tool: pydantic-ai, openai, SimpleKnowledgeBase
Main Book: Pydantic-AI RAG Documentation

What you’ll build: A question-and-answer bot that is an “expert” on a single document. You will feed the text of a document (e.g., a company’s return policy) into pydantic-ai’s knowledge base and ask it questions.

Why it teaches pydantic-ai: This is your first step into Retrieval-Augmented Generation (RAG). It demystifies how LLMs can “learn” new information without retraining. You’ll learn the basic RAG workflow: chunking, embedding, storing, retrieving, and augmenting.

Core challenges you’ll face:

Loading and chunking the document → maps to preparing data for the knowledge base
Using SimpleKnowledgeBase to store the data → maps to in-memory vector storage
Enabling RAG mode in PydanticAI → maps to configuring the agent to use its knowledge
Asking questions that can only be answered by the document → maps to verifying that RAG is working

Key Concepts:

RAG: The high-level concept of retrieving data to help an LLM generate a better answer.
Embeddings: Numerical representations of text.
Vector Store: A database for storing and querying embeddings.

Difficulty: Intermediate Time estimate: Weekend Prerequisites: Project 1.

Real world outcome: A Q&A script that can answer detailed questions about a document it has never seen before.

Loading 'return_policy.txt' into knowledge base...
Knowledge base ready. Ask a question.

> What is the return period for electronics?
'According to the document, the return period for electronics is 15 days from the date of purchase.'

> Can I return a gift without a receipt?
'The policy states that for gift returns without a receipt, store credit will be issued for the item's current selling price.'

Implementation Hints:

Use pydantic-ai.rag.SimpleKnowledgeBase for an easy start.
Read your document’s content into a string.
Create an instance of the knowledge base: kb = SimpleKnowledgeBase().
Add the document to the kb: kb.add_document(doc_content, metadata={"source": "return_policy.txt"}).
When creating your PydanticAI instance, pass the knowledge base and enable RAG: pai = PydanticAI(..., knowledge_base=kb, rag_mode=True).
Now, when you call pai.run(), it will first search the knowledge base for relevant chunks and add them to the LLM’s context before generating an answer.

Learning milestones:

You successfully load a document into the knowledge base → You understand the data ingestion step.
The agent correctly answers a question using information from the document → You have built a working RAG pipeline.
The agent says “I don’t know” when asked a question not covered by the document → You see that RAG grounds the LLM in the provided context.

Project 4: RAG Agent with a Real Vector Database

File: LEARN_PYDANTIC_AI.md
Main Programming Language: Python
Coolness Level: Level 4: Hardcore Tech Flex
Business Potential: 4. The “Open Core” Infrastructure
Difficulty: Level 3: Advanced
Knowledge Area: RAG / Vector Databases
Software or Tool: pydantic-ai, openai, supabase-py, pgvector
Main Book: Supabase Vector Docs

What you’ll build: A more advanced RAG agent that uses Supabase with the pgvector extension as its persistent knowledge base. You will write one script to ingest multiple documents into the database and a separate script (or a FastAPI app) for the Q&A agent.

Why it teaches pydantic-ai: This project productionizes the RAG concept. SimpleKnowledgeBase is great for demos, but real applications need a persistent, scalable vector store. This teaches you how to integrate pydantic-ai with external, professional-grade tools like Supabase.

Core challenges you’ll face:

Setting up a Supabase project and enabling pgvector → maps to infrastructure setup
Using a supported Knowledge Base class like SupabaseKnowledgeBase → maps to connecting to external services
Writing an ingestion script to chunk and embed documents → maps to building a data pipeline
Connecting your agent to the persistent knowledge base → maps to reusing stored knowledge

Key Concepts:

Persistent Vector Stores: Databases designed for long-term storage of embeddings.
Data Ingestion Pipeline: The process of preparing and storing data for RAG.
Client-Server Architecture: Separating the data loading process from the user-facing agent.

Difficulty: Advanced Time estimate: 1-2 weeks Prerequisites: Project 3, basic familiarity with databases and APIs.

Real world outcome: A robust Q&A agent that retains its knowledge even after the program restarts. You can expose it via a simple FastAPI web server.

# First, run your ingestion script
$ python ingest.py --path ./my_documents/
Ingesting document 1...
Ingesting document 2...
Done.

# Then, run your agent API
$ uvicorn main:app --reload

# Interact with the API
$ curl -X POST http://localhost:8000/ask -d '{"question": "..."}'

Implementation Hints:

Follow the Supabase pgvector guide to set up your database table and a search function.
Use pydantic-ai.rag.SupabaseKnowledgeBase. You’ll need to provide it with your Supabase client and the name of your search function.
Your ingestion script should list files, read their content, and call the add_document method for each one.
Your main application will instantiate the SupabaseKnowledgeBase and PydanticAI just like in the previous project, but now it will connect to your remote database instead of an in-memory one.
Consider wrapping the agent in a FastAPI endpoint to make it more like a real service.

Learning milestones:

You can see your embedded document chunks in the Supabase table → Your ingestion pipeline is working.
Your agent connects to Supabase and answers questions correctly → You have integrated with a persistent vector store.
You can add new documents with the ingestion script and the agent immediately becomes “smarter” → You have a fully functioning, extensible RAG system.

Project 5: The “Corrector” Agent with Retry Logic

File: LEARN_PYDANTIC_AI.md
Main Programming Language: Python
Coolness Level: Level 3: Genuinely Clever
Business Potential: 1. The “Resume Gold”
Difficulty: Level 2: Intermediate
Knowledge Area: Data Validation / Error Handling
Software or Tool: pydantic-ai, openai, Pydantic Validators
Main Book: Pydantic Documentation on Validation

What you’ll build: A script that attempts to extract data into a Pydantic model that has strict validation rules (e.g., a user’s age must be a positive integer, an email must be valid). You will intentionally provide input that causes the LLM to generate invalid data and observe how pydantic-ai automatically corrects it.

Why it teaches pydantic-ai: This project highlights one of the most powerful and unique features of pydantic-ai: its self-correction capability. It teaches you that you don’t need to write complex error-handling code for LLM outputs; the library handles it for you by leveraging Pydantic’s built-in validation.

Core challenges you’ll face:

Adding validators to a Pydantic model → maps to defining your data constraints
Crafting input that is likely to produce an invalid LLM response → maps to testing edge cases
Enabling debug mode to watch the retry loop → maps to observing the self-correction process
Understanding the feedback loop between the validator and the LLM → maps to the core of the retry mechanism

Key Concepts:

Pydantic Validators: Using @field_validator or annotated validators to enforce custom rules.
Self-Correction Loop: The internal process pydantic-ai uses to fix bad outputs.

Difficulty: Intermediate Time estimate: Weekend Prerequisites: Project 1.

Real world outcome: A script that, when run, shows the LLM’s first failed attempt and then its second, successful attempt after receiving corrective feedback.

Running with debug enabled...
LLM Output (Attempt 1):
{"name": "test", "age": -5}
Validation Error: Age must be a positive number.
Sending correction to LLM and retrying...

LLM Output (Attempt 2):
{"name": "test", "age": 5}
Validation successful.

--- Final Result ---
name='test' age=5

Implementation Hints:

Define a Pydantic model with validation: class User(BaseModel): name: str; age: PositiveInt. PositiveInt comes from pydantic.
Alternatively, use a field validator: @field_validator('age') def validate_age(cls, v): if v <= 0: raise ValueError(...); return v.
Set debug=True when you instantiate PydanticAI to see the verbose output of the retry loop.
Give it a prompt that might confuse it, like “The user is ‘test’. Their age is negative five.”

Learning milestones:

The first attempt fails validation as expected → Your Pydantic model’s validation is working.
You see the error message being sent back to the LLM in the debug output → You understand the feedback mechanism.
The second attempt succeeds with corrected data → You have witnessed self-correction in action.
You realize you can define complex business rules in Pydantic and pydantic-ai will enforce them → You see how this makes your AI applications incredibly robust.

Project 6 (Capstone): The Pydantic-AI Documentation Agent

File: LEARN_PYDANTIC_AI.md
Main Programming Language: Python
Coolness Level: Level 5: Pure Magic (Super Cool)
Business Potential: 4. The “Open Core” Infrastructure
Difficulty: Level 4: Expert
Knowledge Area: RAG / Agentic Systems
Software or Tool: pydantic-ai, supabase-py, FastAPI, a web crawling library (e.g., Scrapy, BeautifulSoup)

What you’ll build: A complete, deployable AI agent that is an expert on the pydantic-ai library itself. It will have a web crawler to ingest the official documentation, a Supabase knowledge base to store it, and a FastAPI interface to answer questions and even generate simple code snippets. This is a mini version of the “Archon” agent you referenced.

Why it teaches pydantic-ai: This is the capstone project that combines everything you’ve learned. You’ll build a real, useful tool that requires structured output (for generating code), RAG (for answering questions), and a persistent vector store. It forces you to think about the full lifecycle of an AI agent, from data ingestion to deployment.

Core challenges you’ll face:

Crawling and cleaning web documentation → maps to real-world data ingestion
Designing a Pydantic model for code generation → maps to e.g., class CodeSnippet(BaseModel): imports: list[str]; code: str
Creating a multi-modal agent that can answer questions OR generate code → maps to advanced classification and routing
Deploying the entire system as a web service → maps to productionizing your agent

Key Concepts:

Agent Orchestration: Designing a system that can choose between different tools or output formats.
Data Ingestion Pipelines: The full end-to-end process of acquiring and preparing knowledge.
API-driven AI: Exposing your agent’s capabilities through a web API.

Difficulty: Expert Time estimate: 2-3 weeks Prerequisites: All previous projects. Familiarity with FastAPI and web crawling.

Real world outcome: A running FastAPI application with endpoints like /ask and /generate_code.

# Ask a question about RAG
$ curl -X POST http://localhost:8000/ask -d '{"question": "How do I use Supabase for my knowledge base?"}'
{
  "answer": "To use Supabase, you should import SupabaseKnowledgeBase from pydantic-ai.rag and instantiate it with your Supabase client and the name of your Postgres search function..."
}

# Ask it to generate code
$ curl -X POST http://localhost:8000/generate_code -d '{"request": "Show me how to extract a list of ingredients from a recipe"}'
{
  "imports": ["from pydantic import BaseModel", "from pydantic_ai import PydanticAI"],
  "code": "class Ingredient(BaseModel):\n  name: str\n  quantity: str\n\n# ... rest of the example code"
}

Implementation Hints:

Use BeautifulSoup or a similar library to crawl the pydantic-ai docs and extract the main text content from each page.
Create an advanced ingestion script that takes this cleaned HTML/text and stores it in your Supabase vector database (Project 4).
In your FastAPI app, create a main “router” agent. Its job is to classify the user’s request into QUESTION or CODE_GENERATION.
If it’s a QUESTION, invoke your RAG agent (like Project 4) to generate a text answer.
If it’s CODE_GENERATION, invoke a different PydanticAI instance configured to output a CodeSnippet Pydantic model.

Learning milestones:

Your agent can answer questions about pydantic-ai using the documentation you ingested → Your end-to-end RAG pipeline is working.
Your agent can generate valid Pydantic models and code snippets as structured output → You are using pydantic-ai to build itself.
Your router correctly distinguishes between questions and code generation requests → You have built a basic autonomous agent.
You have a deployed, useful AI tool → You have successfully moved from learning to building.

Project Comparison Table

Project	Difficulty	Time	Key Concept Taught	Fun Factor
1. Basic Extractor	Beginner	Weekend	Structured Output	3/5
2. Smart Classifier	Beginner	Weekend	Classification, Enums	4/5
3. Basic RAG	Intermediate	Weekend	RAG Fundamentals	4/5
4. RAG w/ Supabase	Advanced	1-2 weeks	Production RAG	5/5
5. Corrector Agent	Intermediate	Weekend	Self-Correction, Validation	4/5
6. Capstone Agent	Expert	2-3 weeks	Full Agent Lifecycle	5/5

Recommendation

Start with Project 1: The Basic Information Extractor. It’s the simplest way to get a feel for the core value of pydantic-ai and takes only a few hours to get running.

From there, I recommend doing Project 2 and then jumping straight to Project 3 (Basic RAG), as RAG seems to be your main area of interest. Completing projects 3, 4, and 6 will give you a deep, practical understanding of how to build the kind of sophisticated agents you’ve hinted at.

Summary

Project 1: The Basic Information Extractor: Python
Project 2: The Smart Classifier and Tagger: Python
Project 3: A Basic RAG System for a Document: Python
Project 4: RAG Agent with a Real Vector Database: Python
Project 5: The “Corrector” Agent with Retry Logic: Python
Project 6 (Capstone): The Pydantic-AI Documentation Agent: Python

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