Pydantic: How to use LLM to convert unstructured data to structured data?

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AI Summary

- **Summary: Converting Unstructured Data to Structured Data with LLM**  
  - Introduction to using LLM and Pydantic for data structuring.  
  - Follow-up to a previous video on Pydantic (link in description).  
  - Example: Structuring Cricket World Cup finals data.  
  - Steps:  
    - Import [[[[OpenAI]]]] library.  
    - Import `Instructor` from Pydantic for [[[[OpenAI]]]] completions.  
    - Use `typing` for generic type variables.  
    - Define a data type for key-value pair extraction.  
    - Apply `Instructor.patch` to enhance [[[[OpenAI]]]] completions.  
    - Create a `GenericDetail` class with `BaseModel` and generic data type.  
    - Read `cricket.txt` file for input data.  
    - Define [[[[OpenAI]]]] chat completion with `GenericDetail` data type.  
    - Use GPT-3.5 Turbo model for processing.  
    - Instruct LLM to extract winners from the data.  
    - Run code in terminal:  
      - Activate virtual environment from previous video.  
      - Install Pydantic, [[[[OpenAI]]]], and Instructor.  
      - Export [[[[OpenAI]]]] API key.  
      - Execute `Python app.py`.  
  - Result: Unstructured data converted to structured format.  
  - Promises more Pydantic-related videos.  
  - Encourages viewers to like, share, subscribe, and watch AI content on the channel.