Semantic Kernel 入门系列：🥑Memory内存

了解的运作原理之后，就可以开始使用Semantic Kernel来制作应用了。

Semantic Kernel将embedding的功能封装到了Memory中，用来存储上下文信息，就好像电脑的内存一样，而LLM就像是CPU一样，我们所需要做的就是从内存中取出相关的信息交给CPU处理就好了。

内存配置

使用Memory需要注册 embedding模型，目前使用的就是 text-embedding-ada-002。同时需要为Kernel添加MemoryStore，用于存储更多的信息，这里Semantic Kernel提供了一个 VolatileMemoryStore，就是一个普通的内存存储的MemoryStore。

var kernel = Kernel.Builder.Configure(c =>

{

	c.AddOpenAITextCompletionService("openai", "text-davinci-003", Environment.GetEnvironmentVariable("MY_OPEN_AI_API_KEY"));

	c.AddOpenAIEmbeddingGenerationService("openai", "text-embedding-ada-002", Environment.GetEnvironmentVariable("MY_OPEN_AI_API_KEY"));

})

.WithMemoryStorage(new VolatileMemoryStore())

.Build();

信息存储

完成了基础信息的注册后，就可以往Memroy中存储信息了。

const string MemoryCollectionName = "aboutMe";

await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info1", text: "My name is Andrea");

await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info2", text: "I currently work as a tourist operator");

await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info3", text: "I currently live in Seattle and have been living there since 2005");

await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info4", text: "I visited France and Italy five times since 2015");

await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info5", text: "My family is from New York");

SaveInformationAsync 会将text的内容通过 embedding 模型转化为对应的文本向量，存放在的MemoryStore中。其中CollectionName如同数据库的表名，Id就是Id。

语义搜索

完成信息的存储之后，就可以用来语义搜索了。

直接使用Memory.SearchAsync方法，指定对应的Collection，同时提供相应的查询问题，查询问题也会被转化为embedding，再在MemoryStore中计算查找最相似的信息。

var questions = new[]

{

	"what is my name?",

	"where do I live?",

	"where is my family from?",

	"where have I travelled?",

	"what do I do for work?",

};

foreach (var q in questions)

{

	var response = await kernel.Memory.SearchAsync(MemoryCollectionName, q).FirstOrDefaultAsync();

	Console.WriteLine(q + " " + response?.Metadata.Text);

}

// output

/*

what is my name? My name is Andrea

where do I live? I currently live in Seattle and have been living there since 2005

where is my family from? My family is from New York

where have I travelled? I visited France and Italy five times since 2015

what do I do for work? I currently work as a tourist operator

*/

到这个时候，即便不需要进行总结归纳，光是这样的语义查找，都会很有价值。

引用存储

除了添加信息以外，还可以添加引用，像是非常有用的参考链接之类的。

const string memoryCollectionName = "SKGitHub";

var githubFiles = new Dictionary<string, string>()

{

	["https://github.com/microsoft/semantic-kernel/blob/main/README.md"]

		= "README: Installation, getting started, and how to contribute",

	["https://github.com/microsoft/semantic-kernel/blob/main/samples/notebooks/dotnet/2-running-prompts-from-file.ipynb"]

		= "Jupyter notebook describing how to pass prompts from a file to a semantic skill or function",

	["https://github.com/microsoft/semantic-kernel/blob/main/samples/notebooks/dotnet/Getting-Started-Notebook.ipynb"]

		= "Jupyter notebook describing how to get started with the Semantic Kernel",

	["https://github.com/microsoft/semantic-kernel/tree/main/samples/skills/ChatSkill/ChatGPT"]

		= "Sample demonstrating how to create a chat skill interfacing with ChatGPT",

	["https://github.com/microsoft/semantic-kernel/blob/main/dotnet/src/SemanticKernel/Memory/Volatile/VolatileMemoryStore.cs"]

		= "C# class that defines a volatile embedding store",

	["https://github.com/microsoft/semantic-kernel/tree/main/samples/dotnet/KernelHttpServer/README.md"]

		= "README: How to set up a Semantic Kernel Service API using Azure Function Runtime v4",

	["https://github.com/microsoft/semantic-kernel/tree/main/samples/apps/chat-summary-webapp-react/README.md"]

		= "README: README associated with a sample starter react-based chat summary webapp",

};

foreach (var entry in githubFiles)

{

	await kernel.Memory.SaveReferenceAsync(

		collection: memoryCollectionName,

		description: entry.Value,

		text: entry.Value,

		externalId: entry.Key,

		externalSourceName: "GitHub"

	);

}

同样的，使用SearchAsync搜索就行。

string ask = "I love Jupyter notebooks, how should I get started?";

Console.WriteLine("===========================\n" +

					"Query: " + ask + "\n");

var memories = kernel.Memory.SearchAsync(memoryCollectionName, ask, limit: 5, minRelevanceScore: 0.77);

var i = 0;

await foreach (MemoryQueryResult memory in memories)

{

	Console.WriteLine($"Result {++i}:");

	Console.WriteLine("  URL:     : " + memory.Metadata.Id);

	Console.WriteLine("  Title    : " + memory.Metadata.Description);

	Console.WriteLine("  ExternalSource: " + memory.Metadata.ExternalSourceName);

	Console.WriteLine("  Relevance: " + memory.Relevance);

	Console.WriteLine();

}

//output

/*

===========================

Query: I love Jupyter notebooks, how should I get started?

Result 1:

  URL:     : https://github.com/microsoft/semantic-kernel/blob/main/samples/notebooks/dotnet/Getting-Started-Notebook.ipynb

  Title    : Jupyter notebook describing how to get started with the Semantic Kernel

  ExternalSource: GitHub

  Relevance: 0.8677381632778319

Result 2:

  URL:     : https://github.com/microsoft/semantic-kernel/blob/main/samples/notebooks/dotnet/2-running-prompts-from-file.ipynb

  Title    : Jupyter notebook describing how to pass prompts from a file to a semantic skill or function

  ExternalSource: GitHub

  Relevance: 0.8162989178955157

Result 3:

  URL:     : https://github.com/microsoft/semantic-kernel/blob/main/README.md

  Title    : README: Installation, getting started, and how to contribute

  ExternalSource: GitHub

  Relevance: 0.8083238591883483

*/

这里多使用了两个参数，一个是limit，用于限制返回信息的条数,只返回最相似的前几条数据，另外一个是minRelevanceScore，限制最小的相关度分数，这个取值范围在0.0 ~ 1.0 之间，1.0意味着完全匹配。

语义问答

将Memory的存储、搜索功能和语义技能相结合，就可以快速的打造一个实用的语义问答的应用了。

只需要将搜索到的相关信息内容填充到 prompt中，然后将内容和问题都抛给LLM，就可以等着得到一个满意的答案了。

const string MemoryCollectionName = "aboutMe";

await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info1", text: "My name is Andrea");

await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info2", text: "I currently work as a tourist operator");

await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info3", text: "I currently live in Seattle and have been living there since 2005");

await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info4", text: "I visited France and Italy five times since 2015");

await kernel.Memory.SaveInformationAsync(MemoryCollectionName, id: "info5", text: "My family is from New York");

var prompt =

"""

It can give explicit instructions or say 'I don't know' if it does not have an answer.

Information about me, from previous conversations:

{{ $fact }}

User: {{ $ask }}

ChatBot:

""";

var skill = kernel.CreateSemanticFunction(prompt);

var ask = "Hello, I think we've met before, remember? my name is...";

var fact = await kernel.Memory.SearchAsync(MemoryCollectionName,ask).FirstOrDefaultAsync();

var context = kernel.CreateNewContext();

context["fact"] = fact?.Metadata?.Text;

context["ask"] = ask;

var resultContext =await skill.InvokeAsync(context);

resultContext.Result.Dump();

//output

/*

Hi there! Yes, I remember you. Your name is Andrea, right?

*/

优化搜索过程

由于这种场景太常见了，所以Semantic Kernel中直接提供了一个技能TextMemorySkill，通过Function调用的方式简化了搜索的过程。

// .. SaveInformations 

// TextMemorySkill provides the "recall" function

kernel.ImportSkill(new TextMemorySkill());

var prompt =

"""

It can give explicit instructions or say 'I don't know' if it does not have an answer.

Information about me, from previous conversations:

{{ recall $ask }}

User: {{ $ask }}

ChatBot:

""";

var skill = kernel.CreateSemanticFunction(prompt);

var ask = "Hello, I think we've met before, remember? my name is...";

var context = kernel.CreateNewContext();

context["ask"] = ask;

context[TextMemorySkill.CollectionParam] = MemoryCollectionName;

var resultContext =await skill.InvokeAsync(context);

resultContext.Result.Dump();

// output

/*

Hi there! Yes, I remember you. Your name is Andrea, right?

*/

这里直接使用 recall 方法，将问题传给了 TextMemorySkill，搜索对应得到结果，免去了手动搜索注入得过程。

内存的持久化

VolatileMemoryStore本身也是易丢失的，往往使用到内存的场景，其中的信息都是有可能长期存储的，起码并不会即刻过期。那么将这些信息的 embedding 能够长期存储起来，也是比较划算的事情。毕竟每一次做 embedding的转化也是需要调接口，需要花钱的。

Semantic Kernel库中包含了SQLite、Qdrant和CosmosDB的实现，自行扩展的话，也只需要实现 IMemoryStore 这个接口就可以了。

至于未来，可能就是专用的 Vector Database 了。

参考资料：