Introduction to Structured Outputs
Ollama's latest announcement highlights a significant advancement in AI technology—support for structured outputs. This feature allows users to constrain model outputs to a specific format defined by a JSON schema, providing precision and customization previously unavailable in large language model responses. Ollama’s upgrades to both their Python package and JavaScript library ensure that developers can easily incorporate structured outputs into their workflows.
Structured outputs have a myriad of applications, including:
- Parsing documents
- Extracting data from images
- Structuring large language model responses
By imposing a structure on the model's output, developers can refine responses into a desired format rather than receiving unstructured text.
Getting Started with Ollama's Updates
To utilize this feature, Ollama users must upgrade their Python package or JavaScript library. Even if you already have Ollama installed, an upgrade is necessary to access the new capabilities. The process is straightforward:
- For Python users:
pip install Ullama - For JavaScript users:
npm install ollama
Although updates have been applied to both the Python package and the JavaScript library, this article will focus on Python implementations.
Implementation with Python
Activation and Installation
To begin experimenting with structured outputs, it's recommended to work in a virtual environment. Create a directory, such as Ollama_Demo, for your experiments and activate a virtual environment like CONDA. Install the upgraded Ollama library into this environment using:
pip install Ullama
This step downloads and installs the latest package, ensuring compatibility with structured outputs.
Overcoming JSON Schema Errors
During installation, you might encounter errors related to JSON schema. Should this occur, visit the Ollama website to download the latest package for your operating system, further ensuring compatibility.
Use Cases of Structured Outputs
Example 1: Data Extraction
Structured outputs excel in data extraction scenarios where text models can parse and return structured data. Consider a case involving pet description extraction:
# Example schema for extracting pet details
details_schema = {
'name': str,
'animal': str,
'age': int,
'color': str,
'favorite_toy': str
}
By defining such a schema, you can parse text like:
I have two pets: a cat named Luna, who is five years old and loves playing with yarn. She has gray fur. I also have a 2-year-old black cat named Loki who loves tennis balls.
The model responds with structured data conforming to the schema.
Example 2: Image Description
Structured outputs can also handle image data, yielding a breakdown according to a predefined schema. Here's a scenario describing an image:
Input Image: A tropical beach scene with a palm tree and ocean.
Output Schema:
image_description = {
'summary': str,
'objects': List[str],
'scene': str,
'time_of_day': str,
'settings': str,
'text': str
}
Based on this input, the model provides:
- Summary: Depiction of a tropical beach setting.
- Objects: Palm tree, ocean, sand, etc.
- Time of Day: Afternoon
- Setting: Outdoor
Best Practices for Structured Outputs
When working with structured outputs in Ollama, consider the following tips for optimal results:
- Utilize Pydantic (Python) or Zod (JavaScript): These libraries aid in schema definition, improving the structuring process.
- Include "Return as JSON": In your prompt, use this phrase to instruct the model to output in JSON format.
- Adjust Temperature for Determinism: Set the temperature to zero for deterministic responses, suitable for queries with fixed answers, like capital cities or actors in films.
Conclusion
Structured outputs in Ollama open new horizons in AI application, allowing developers to create highly customized and precise responses from AI models. Whether dealing with text or image data, structured outputs foster clarity and order, enhancing both development and user experience.
As this feature is explored further, it promises to redefine how we interact with language models by bridging the gap between raw data and actionable information.
Stay tuned for further updates and innovations in structured outputs from Ollama, paving the way for future AI advancements.
Midjourney prompt for the cover image: Illustration of a futuristic digital interface showing structured data output with a person interacting with visualized JSON schemas, set in a high-tech environment, side angle, vibrant colors, Sketch Cartoon Style.
DATA EXTRACTION, IMAGE DESCRIPTION, AI, PYTHON, JSON SCHEMA, OLLAMA, YOUTUBE, STRUCTURED OUTPUTS