feat: solution for 'Экзамен: Структурированный вывод (Pydantic)'

README.md

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+# Structured Output Extraction with LangChain and Pydantic
+
+This project demonstrates how to extract structured data from raw text using LangChain and Pydantic.  
+It supports two schemas:
+
+- **PersonInfo** – name, age, profession, skills
+- **MeetingNotes** – date, participants, topics, decisions, next steps
+
+The CLI automatically selects the appropriate schema based on the input text and prints the parsed object and a short summary.
+
+## Prerequisites
+
+- Python 3.10+
+- An OpenAI API key (or compatible LLM provider)
+
+## Installation
+
+```bash
+# Create a virtual environment (optional but recommended)
+python -m venv .venv
+source .venv/bin/activate   # On Windows use `.venv\Scripts\activate`
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+## Configuration
+
+Create a `.env` file in the project root with your OpenAI key:
+
+```
+OPENAI_API_KEY=sk-...
+```
+
+## Usage
+
+Run the CLI:
+
+```bash
+python src/cli.py run
+```
+
+You will be prompted to provide text or a file path.  
+If no input is given, example texts for both schemas are displayed.
+
+### Example
+
+```bash
+python src/cli.py run --file example.txt
+```
+
+The output will look like:
+
+```
+Detected schema: person
+
+Parsed object:
+{
+  "name": "Анна",
+  "age": 28,
+  "profession": "Python-разработчик",
+  "skills": [
+    "FastAPI",
+    "Docker"
+  ]
+}
+
+Summary:
+Person: Анна, age=28, profession=Python-разработчик, skills=FastAPI, Docker
+```
+
+## Project Structure
+
+```
+src/
+├── __init__.py
+├── cli.py
+├── main.py
+└── models.py
+requirements.txt
+README.md
+```
+
+## License
+
+MIT License