Ollama Structured Output - REVOLUTIONISING AI API Backends!

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AI Summary

Video Summary

Structured Outputs with Olor and Llama 3.2

• Introduction to Structured Outputs:
  • Olor transforms responses from large language models into structured JSON format.
  • Benefits include no manual data parsing, automated image analysis, and reliable data structuring.

Building a Pet Management System

• System Overview:
  • The system allows adding pet information through natural language input.
  • It uses a Flask API backend to parse and structure data using Olor.

Development Pipeline

1. Extract JSON from Unstructured Input:
   • Install necessary packages: ol pantic floss floss course and optionally open AI.
   • Define a schema for the data (e.g., pet name, age, color, favorite toy).
   • Use the Llama 3.2 model to convert unstructured data into structured JSON.
2. Extract JSON from an Image:
   • Use the Llama 3.2 Vision model to analyze images and identify pets.
   • The model returns structured data about the pets found in the image.
3. Create an API:
   • Use Flask to create API endpoints.
   • The API takes unstructured data and returns structured data after saving it to a database.
4. Create a User Interface:
   • Use a UI generated by clo AI to display structured data.
   • The UI allows adding pets through a single input field, which is then parsed and displayed.

Running the Code

• Execution Steps:
  • Run the code in the terminal using python app.py or python api.py.
  • Use curl command to interact with the API and post unstructured data.

Conclusion

• The video demonstrates how to build an application that uses AI to structure data from both text and images.
• The complete code is provided in the video description for viewers to use.

Note

• No URLs or detailed CLI commands were provided in the text for extraction.