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-# task-6a186500-ekzamen-strukturirovannyy
+# Structured Output – Pydantic
 
-Решения домашних заданий
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+> 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”.
+
+---
+
+## 📦 Installation
+
+```bash
+# 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.
+
+---
+
+## 📁 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.
+
+---
+
+## 🚀 Running the script
+
+```bash
+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
+
+```bash
+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
+
+```bash
+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.
+```
+
+---
+
+## 📄 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.
+
+---
+
+## 🛠️ 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.
+
+---
+
+## 📄 License
+
+MIT © 2026 – see [LICENSE](LICENSE) (if present).
+
+---
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