Using JSON Schema and a framework like Semantic Kernel allows you to control the format of AI-generated responses, ensuring that the output is structured, predictable, and easy to use. Whether you use a Pydantic
Agent with Plugins - PythonEnhance your agent with custom tools (plugins) and structured output:import asyncio from typing import Annotated from pydantic import BaseModel from semantic_kernel.agents import ChatCompletionAgent from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion, OpenAI...
Using o3-mini in Python with Semantic Kernel is just as straightforward. We can utilize the SK OpenAI connector classes to call the model. Below is an example how to use Semantic Kernel targeting o3-mini and enabling high reasoning effort: Copy importasynciofromsemantic_kernel.connectors.ai....
Java: Add Support for OpenAI new Structured Outputs. microsoft/semantic-kernel-java#168 Open papa-pep commented Aug 27, 2024 It looks like OpenAI SDK work has been completed! Looking forward to having this in SK soon! 👍 3 👀 1 RogerBarreto changed the title Python: .Net: OpenAI...
The kernel parameter metadata. Create a new model by parsing and validating input data from keyword arguments. Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model. self is explicitly positio
近年来,基于卷积神经网络的方法在语义分割上取得了显著的进步,应用到自动驾驶和图像编辑上面。基于CNN方法的难点是需要标注大量的数据集来包含可能的场景。但是训练的模型对未知的场景,泛化能力一般,特别是训练集和测试集之间有差别时。例如,目标外观的分布和场景的分布在不同城市间是不同的,甚至在同一城市中光照和...
Semantic Kernel follows the OpenTelemetry Semantic Convention for Observability. This means that the logs, metrics, and traces emitted by Semantic Kernel are structured and follow a common schema. This ensures that you can more effectively analyze the telemetry data emitted by Semantic Kernel....
structured-output learningweakly-supervised learningloss functionsWe address the task of annotating images with semantic tuples. Solving this problem requires an algorithm which is able to deal with hundreds of classes for each argument of the tuple. In such contexts, data sparsity becomes a key ...
Bumps the dotnet group in /docs/ai/how-to/snippets/semantic-kernel with 1 update: Microsoft.SemanticKernel. Updates Microsoft.SemanticKernel from 1.31.0 to 1.32.0 Release notes Sourced from Micros...
摘要:有监督的基于卷积神经网络语义分割方法需要依赖像素级的ground truth,对于未见过的图像泛化能力差(这体现在源域和目标域之间图像差异较大),此外,图像标注过程也是繁琐费力的事情,因此,需要找到域自适应的方法,将源域的label自适应到目标域中。本文基于语义分割,提出对抗学习的域自适应方法。也就是说,源域和目标...