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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。
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();
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。
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。
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。
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?
- */
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。
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:
https://learn.microsoft.com/en-us/semantic-kernel/concepts-sk/memories
https://github.com/microsoft/semantic-kernel/blob/main/samples/notebooks/dotnet/6-memory-and-embeddings.ipynb
https://github.com/johnmaeda/SK-Recipes/blob/main/e4-memories/notebook.ipynb
https://learn.microsoft.com/en-us/semantic-kernel/concepts-ai/vectordb
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