Semantic KernelSamplesVector Database Aug 16, 2023 Post comments count0 Post likes count1 GUEST POST: Semantic Kernel and Weaviate: Orchestrating interactions around LLMs with long-term memory Zain Hasan The Emerging LLM Stack In a recent interview, the co-founder of Cohere stated - “For...
Combining a vector database, the cloud, and a framework like Semantic Kernel becomes a powerful combination in building Generative AI applications. So, let’s take a journey on how to use the semantic search and vector database,Qdrant, running on Azure cloud and integrated ...
使用VectorStoreRecordKeyAttribute指示 key 字段,使用VectorStoreRecordDataAttribute指示数据字段,VectorStoreRecordVector指示向量字段。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 publicclassUserModel{[VectorStoreRecordKey]publicstring UserId{get;set;}[VectorStoreRecordData]publicstring UserName{get;set;}[...
使用VectorStoreRecordKeyAttribute指示 key 字段,使用VectorStoreRecordDataAttribute指示数据字段,VectorStoreRecordVector指示向量字段。 public class UserModel { [VectorStoreRecordKey] public string UserId { get;set; } [VectorStoreRecordData] public string UserName { get;set; } [VectorStoreRecordData] public...
//OpenAI API Key//builder.WithOpenAITextEmbeddingGenerationService("text-embedding-ada-002",//"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");//内存配置//也可以使用 Vector Database 向量数据库//docker pull qdrant/qdrant//docker run -p 6333:6333 qdrant/qdrant//使用Memory需要注册 ...
Semantic Kernel:用Embedding做客服(RAG) RAG(Retrieval-Augmented Generation)是一种结合信息检索和生成模型的自然语言处理方法。它通过检索相关文档片段作为生成模型的上下文,提高生成文本的准确性和相关性。RAG广泛应用于问答系统、对话系统和文本摘要等领域,兼具高效性和灵活性。公司的客户机器人都特别适合。
将Weaviate Vector Store 连接器 NuGet 包添加到项目。 .NET CLI dotnetaddpackage Microsoft.SemanticKernel.Connectors.Weaviate--prerelease 可以使用语义内核提供的扩展方法将向量存储添加到可用的KernelBuilder依赖项注入容器或IServiceCollection依赖项注入容器。 Weaviate 向量存...
Semantic Kernel's Memory provides abstractions over embedding models, vector databases, and other data to simplify context management for AI applications. Memory is agnostic to the underlying LLM or Vector DB, offering a uniform developer experience. You can configure memory features to store data in...
An example that shows how to use Semantic Kernel and Kernel Memory to work with embeddings in a .NET application using SQL Server as Vector Database. - marcominerva/OpenAIEmbeddingSample
{ "DBConnection": { "DbType": "Sqlite", "ConnectionStrings": "Data Source=AntSK.db;" }, "KernelMemory": { "VectorDb": "Disk", "ConnectionString": "Host=;Port=;Database=antsk;Username=;Password=", "TableNamePrefix": "km-" }, "FileDir": { "DirectoryPath": "D:\\git\\AntBla...