Semantic Kernel:图识文-编程知识

Semantic Kernel:图识文

news/2025/3/4 8:36:30/文章来源:https://www.cnblogs.com/axzxs2001/p/18746011

　　多模态是每个LLM具有的能力，图片又是最常见的信息载体，GPT对图片的识别也很早就有了，随着GPT版本的迭代，效果越来越好。SK也是在很多就适配了图识文，只不过最近版本才支持本地图片的上传。（有点晚）

图片场景识别：

using Microsoft.SemanticKernel.ChatCompletion;
using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Connectors.OpenAI;var chatModelId = "gpt-4o";
var key = File.ReadAllText(@"C:\GPT\key.txt");
#pragma warning disable SKEXP0070
#pragma warning disable SKEXP0010
#pragma warning disable SKEXP0001
#pragma warning disable SKEXP0110
var kernel = Kernel.CreateBuilder().AddOpenAIChatCompletion(chatModelId, key).Build();var chat = kernel.GetRequiredService<IChatCompletionService>();
var chatHistory = new ChatHistory();
chatHistory.AddUserMessage(new ChatMessageContentItemCollection
{new TextContent("请说明这是那里，什么样的天气，大家在干什么?一共有多少人"),new ImageContent(File.ReadAllBytes("tam.jpg"),"image/jpeg")
});
var settings = new Dictionary<string, object>
{["max_tokens"] = 1000,["temperature"] = 0.2,["top_p"] = 0.8,["presence_penalty"] = 0.0,["frequency_penalty"] = 0.0
};var content = chat.GetStreamingChatMessageContentsAsync(chatHistory, new PromptExecutionSettings
{ExtensionData = settings
});
await foreach (var item in content)
{Console.Write(item.Content);
}
Console.ReadLine();

图片：

结果：

文字识别：

using Microsoft.SemanticKernel.ChatCompletion;
using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Connectors.OpenAI;var chatModelId = "gpt-4o";
var key = File.ReadAllText(@"C:\GPT\key.txt");
#pragma warning disable SKEXP0070
#pragma warning disable SKEXP0010
#pragma warning disable SKEXP0001
#pragma warning disable SKEXP0110
var kernel = Kernel.CreateBuilder().AddOpenAIChatCompletion(chatModelId, key).Build();var chat = kernel.GetRequiredService<IChatCompletionService>();
var chatHistory = new ChatHistory();
chatHistory.AddUserMessage(new ChatMessageContentItemCollection
{new TextContent("请识别图片上的文字，并输出"),new ImageContent(File.ReadAllBytes("japancard.png"),"image/jpeg")
});
var settings = new Dictionary<string, object>
{["max_tokens"] = 1000,["temperature"] = 0.2,["top_p"] = 0.8,["presence_penalty"] = 0.0,["frequency_penalty"] = 0.0
};var content = chat.GetStreamingChatMessageContentsAsync(chatHistory, new PromptExecutionSettings
{ExtensionData = settings
});
await foreach (var item in content)
{Console.Write(item.Content);
}
Console.ReadLine();