Structured Output – Pydantic

Extract a validated Pydantic object from raw text without manual parsing.

This repository contains the solution for the exam task “Structured output (Pydantic)” from the LangChain course “Generating Structured Outputs”.

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📦 Installation

# Create and activate a virtual environment (optional but recommended)
python -m venv .venv
source .venv/bin/activate   # Windows: .\.venv\Scripts\activate

# Install dependencies
pip install langchain-core langchain-openai pydantic python-dotenv

Requirements

• Python 3.10+
• LangChain ≥ 1.0.0

The project uses the OpenAI LLM via langchain_openai. If you prefer another provider (e.g., Ollama) replace the import and configuration accordingly.

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📁 Project layout

├── solution.py          # Main script – contains models, chain, CLI
└── .env                 # Optional: store your OPENAI_API_KEY here

.env
Add a line OPENAI_API_KEY=sk-… to use the OpenAI API without passing the key on the command line.

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🚀 Running the script

python solution.py [--text TEXT] [--model MODEL]

• --text TEXT – raw text to parse.
  If omitted, the script will prompt you for input or use one of the built‑in examples.
• --model MODEL – optional LLM model name (default: gpt-4o-mini).

Example 1 – Person description

python solution.py --text "John Doe is a senior software engineer at Acme Corp. He loves Python, Docker and Kubernetes."

Output

Model: PersonInfo
{
  "name": "John Doe",
  "age": null,
  "profession": "senior software engineer at Acme Corp.",
  "skills": ["Python", "Docker", "Kubernetes"]
}
Summary: Parsed as a person profile.

Example 2 – Meeting notes

python solution.py --text "Meeting on 2024-05-28 with Alice, Bob and Carol. Topics: project roadmap, budget allocation. Decisions: approve Q3 budget, assign tasks to team."

Output

Model: MeetingNotes
{
  "date": "2024-05-28",
  "participants": ["Alice", "Bob", "Carol"],
  "topics": ["project roadmap", "budget allocation"],
  "decisions": ["approve Q3 budget", "assign tasks to team"]
}
Summary: Parsed as meeting notes.

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📄 How it works

1. Pydantic models – PersonInfo and MeetingNotes, each field annotated with a description for the LLM.
2. Prompt template – instructs the model to output JSON that matches one of the schemas.
3. Output parser – PydanticOutputParser validates the returned JSON against the chosen schema.
4. Schema selection – a simple heuristic (keyword search) decides whether the input describes a person or a meeting, then runs the appropriate chain.

The script prints both the full parsed object (model_dump()) and a short human‑readable summary.

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🛠️ Customisation

• Change the LLM model by editing DEFAULT_MODEL in solution.py.
• Add more heuristics for schema selection.
• Extend the models with additional fields or validation rules.

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📄 License

MIT © 2026 – see LICENSE (if present).

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