Building a Vision App with Ollama Structured Outputs

Summary

In this video, the speaker explores the powerful new feature of structured outputs in Ollama, showcasing its ability to improve data extraction from text and images. By leveraging Python (Pydantic) or JavaScript (Zod), users can easily structure and validate outputs, enhancing tasks like Named Entity Recognition (NER) and image-to-text conversion. The video demonstrates practical applications such as extracting song titles from album covers and identifying book details. It highlights the flexibility of running models locally or in the cloud and encourages fine-tuning for improved results in various use cases.

Takeaways

• 😀 Ollama now supports fully structured outputs, enhancing the ability to parse and extract data from text and images locally without the need for external APIs.
• 😀 The introduction of structured outputs allows users to use Python with Pydantic or JavaScript with Zod to structure the data and validate it more easily.
• 😀 This feature enables accurate Named Entity Recognition (NER) tasks, allowing extraction of organizations, products, people, and locations from text.
• 😀 Ollama's structured outputs help streamline data extraction for simple tasks without overengineering, making it suitable for creating lightweight, task-specific apps.
• 😀 Users can leverage Ollama's local models (e.g., Llama 3.1 or 3.2) or cloud-based options, such as Google Cloud’s serverless instances, for scalable model deployment.
• 😀 By using system prompts and fine-tuning models, users can improve accuracy in extracting specific types of data, like product names or person identification.
• 😀 Ollama allows easy swapping between its SDK and OpenAI endpoints, offering flexibility for local and cloud-based applications.
• 😀 The vision model in Ollama is capable of extracting text and identifying objects in images, like books and album covers, even with challenging inputs.
• 😀 Using structured outputs in Ollama, users can build applications like an album cover track list extractor, making the task automated and repeatable.
• 😀 Structured outputs provide a private, efficient solution for extracting data locally, reducing dependency on external APIs and improving data privacy.
• 😀 Users can fine-tune models like Llama for more specific use cases, improving accuracy over time, especially when working with specialized tasks like OCR or entity extraction.

Q & A

What is the main feature introduced in Ollama that is discussed in the video?

The main feature introduced is structured outputs, which allows users to structure and parse data from both text and images in a more organized way, especially using Python classes with Pydantic for structuring outputs.

How does Ollama's structured output system differ from its previous adjacent mode?

Ollama's previous adjacent mode worked to a degree but didn't always provide exactly what users wanted. The new structured output system offers full support for structured parsing, making it more reliable and flexible for extracting data from various sources.

What is the role of Pydantic in the structured output feature for Python users?

Pydantic is used to define classes that structure the outputs. Users can easily set up their own classes to ensure the output is formatted according to their needs, providing better validation and organization of the extracted data.

Can you use Ollama's structured outputs with models other than those provided by Ollama? If so, how?

Yes, you can use Ollama's structured output feature with models from other providers, like OpenAI. You can swap out the Ollama SDK for an OpenAI endpoint and still run the feature locally, leveraging the same principles for structuring outputs.

What is the significance of running Ollama models locally or in serverless environments?

Running Ollama models locally ensures privacy and reduces the need to rely on external APIs, offering a more cost-effective and secure solution. Serverless environments, like those on Google Cloud, allow for on-demand model usage, where you're billed only for the time the model is running.

What are some potential applications of structured outputs, as demonstrated in the video?

Structured outputs can be applied to a variety of tasks, such as entity extraction from text (NER), analyzing images for object detection, or extracting data from images like album covers or book spines to create structured metadata for use in apps or databases.

How does the system handle different models for structured outputs, and how can users experiment with them?

Users can experiment with different models by adjusting system prompts and selecting models like Llama 3.1 or 3.2. The video highlights how the use of different models can lead to varied results, and users can fine-tune models to improve accuracy for specific tasks.

What is the advantage of using a system prompt when working with structured outputs?

A system prompt helps guide the model's behavior, making it more effective in extracting relevant data. The video shows how adding a system prompt led to better results in entity extraction and image description tasks, improving accuracy and consistency.

Can structured outputs help with tasks beyond entity extraction, like image analysis or app development?

Yes, structured outputs are versatile. For example, they can be used for extracting text and metadata from images (e.g., album covers or book spines) and creating simple applications that automate such tasks. This avoids the need for complex agent frameworks.

How does the video demonstrate using structured outputs in a real-world application?

The video demonstrates a practical application by extracting track listings and album details from images of album covers. The structured output process involves passing images through a vision model, then structuring the extracted information into usable formats like markdown files for storage or further processing.