Video version at https://youtu.be/REjWp_2Dt30 Hypertext Transfer Protocol (HTTP) provides us with a set of basic methods to describe what our request is doing. Generally, we’re working with GET or POST...

When building agents using Semantic Kernel that integrate with SLMs (Small Language Models) /LLMs (Large Language Models), you typically send and receive multiple responses. A common pattern is to add all user and agent message to the ChatHistory object. In the past, I have done this by implementing a service class to handle these in .NET web applications. You can see an example of a controller which creates the chat history object for unique sessions for a webchat experience here: [HttpPost]   public async Task Post([FromBody] ChatRequest chatRequest)   {       // Use a fixed session ID for simplicity, or generate a unique one per user/session       var sessionId = default-session;       var history = _chatHistoryService.GetOrCreateHistory(sessionId);       // Add user input       history.AddUserMessage(chatRequest.Message);       var openAIProm

When building agents using Semantic Kernel that integrate with SLMs (Small Language Models) /LLMs (Large Language Models), you typically send and receive multiple responses. A common pattern is to add all user and agent message to the ChatHistory object. In the past, I have done this by implementing a service class to handle these in .NET web applications. You can see an example of a controller which creates the chat history object for unique sessions for a webchat experience here: [HttpPost]   public async Task Post([FromBody] ChatRequest chatRequest)   {       // Use a fixed session ID for simplicity, or generate a unique one per user/session       var sessionId = default-session;       var history = _chatHistoryService.GetOrCreateHistory(sessionId);       // Add user input       history.AddUserMessage(chatRequest.Message);       var openAIProm

When building agents using Semantic Kernel that integrate with SLMs (Small Language Models) /LLMs (Large Language Models), you typically send and receive multiple responses. A common pattern is to add all user and agent message to the ChatHistory object. In the past, I have done this by implementing a service class to handle these in .NET web applications. You can see an example of a controller which creates the chat history object for unique sessions for a webchat experience here: [HttpPost]   public async Task Post([FromBody] ChatRequest chatRequest)   {       // Use a fixed session ID for simplicity, or generate a unique one per user/session       var sessionId = default-session;       var history = _chatHistoryService.GetOrCreateHistory(sessionId);       // Add user input       history.AddUserMessage(chatRequest.Message);       var openAIProm

When building agents using Semantic Kernel that integrate with SLMs (Small Language Models) /LLMs (Large Language Models), you typically send and receive multiple responses. A common pattern is to add all user and agent message to the ChatHistory object. In the past, I have done this by implementing a service class to handle these in .NET web applications. You can see an example of a controller which creates the chat history object for unique sessions for a webchat experience here: [HttpPost]   public async Task Post([FromBody] ChatRequest chatRequest)   {       // Use a fixed session ID for simplicity, or generate a unique one per user/session       var sessionId = default-session;       var history = _chatHistoryService.GetOrCreateHistory(sessionId);       // Add user input       history.AddUserMessage(chatRequest.Message);       var openAIProm

When building agents using Semantic Kernel that integrate with SLMs (Small Language Models) /LLMs (Large Language Models), you typically send and receive multiple responses. A common pattern is to add all user and agent message to the ChatHistory object. In the past, I have done this by implementing a service class to handle these in .NET web applications. You can see an example of a controller which creates the chat history object for unique sessions for a webchat experience here: [HttpPost]   public async Task Post([FromBody] ChatRequest chatRequest)   {       // Use a fixed session ID for simplicity, or generate a unique one per user/session       var sessionId = default-session;       var history = _chatHistoryService.GetOrCreateHistory(sessionId);       // Add user input       history.AddUserMessage(chatRequest.Message);       var openAIProm

When building agents using Semantic Kernel that integrate with SLMs (Small Language Models) /LLMs (Large Language Models), you typically send and receive multiple responses. A common pattern is to add all user and agent message to the ChatHistory object. In the past, I have done this by implementing a service class to handle these in .NET web applications. You can see an example of a controller which creates the chat history object for unique sessions for a webchat experience here: [HttpPost]   public async Task Post([FromBody] ChatRequest chatRequest)   {       // Use a fixed session ID for simplicity, or generate a unique one per user/session       var sessionId = default-session;       var history = _chatHistoryService.GetOrCreateHistory(sessionId);       // Add user input       history.AddUserMessage(chatRequest.Message);       var openAIProm

When building agents using Semantic Kernel that integrate with SLMs (Small Language Models) /LLMs (Large Language Models), you typically send and receive multiple responses. A common pattern is to add all user and agent message to the ChatHistory object. In the past, I have done this by implementing a service class to handle these in .NET web applications. You can see an example of a controller which creates the chat history object for unique sessions for a webchat experience here: [HttpPost]   public async Task Post([FromBody] ChatRequest chatRequest)   {       // Use a fixed session ID for simplicity, or generate a unique one per user/session       var sessionId = default-session;       var history = _chatHistoryService.GetOrCreateHistory(sessionId);       // Add user input       history.AddUserMessage(chatRequest.Message);       var openAIProm

When building agents using Semantic Kernel that integrate with SLMs (Small Language Models) /LLMs (Large Language Models), you typically send and receive multiple responses. A common pattern is to add all user and agent message to the ChatHistory object. In the past, I have done this by implementing a service class to handle these in .NET web applications. You can see an example of a controller which creates the chat history object for unique sessions for a webchat experience here: [HttpPost]   public async Task Post([FromBody] ChatRequest chatRequest)   {       // Use a fixed session ID for simplicity, or generate a unique one per user/session       var sessionId = default-session;       var history = _chatHistoryService.GetOrCreateHistory(sessionId);       // Add user input       history.AddUserMessage(chatRequest.Message);       var openAIProm

There are various reasons cause the swagger to fail to load the API definition and you will get the error shared in the below screenshot. Below are the possible reasons, Swagger misconfiguration Controller methods without HTTP verbs (HttpGet, HttpPost etc.) Same class name etc…   Fig 1. Failed to load definition error in Swagger Solution: The Swagger can be launched by adding the ‘/swagger’ after your API URL which doesn’t show the error details causing the Swagger to fail to load.

When building agents using Semantic Kernel that integrate with SLMs (Small Language Models) /LLMs (Large Language Models), you typically send and receive multiple responses. A common pattern is to add all user and agent message to the ChatHistory object. In the past, I have done this by implementing a service class to handle these in .NET web applications. You can see an example of a controller which creates the chat history object for unique sessions for a webchat experience here: [HttpPost]   public async Task Post([FromBody] ChatRequest chatRequest)   {       // Use a fixed session ID for simplicity, or generate a unique one per user/session       var sessionId = default-session;       var history = _chatHistoryService.GetOrCreateHistory(sessionId);       // Add user input       history.AddUserMessage(chatRequest.Message);       var openAIProm

When building agents using Semantic Kernel that integrate with SLMs (Small Language Models) /LLMs (Large Language Models), you typically send and receive multiple responses. A common pattern is to add all user and agent message to the ChatHistory object. In the past, I have done this by implementing a service class to handle these in .NET web applications. You can see an example of a controller which creates the chat history object for unique sessions for a webchat experience here: [HttpPost]   public async Task Post([FromBody] ChatRequest chatRequest)   {       // Use a fixed session ID for simplicity, or generate a unique one per user/session       var sessionId = default-session;       var history = _chatHistoryService.GetOrCreateHistory(sessionId);       // Add user input       history.AddUserMessage(chatRequest.Message);       var openAIProm

When building agents using Semantic Kernel that integrate with SLMs (Small Language Models) /LLMs (Large Language Models), you typically send and receive multiple responses. A common pattern is to add all user and agent message to the ChatHistory object. In the past, I have done this by implementing a service class to handle these in .NET web applications. You can see an example of a controller which creates the chat history object for unique sessions for a webchat experience here: [HttpPost]   public async Task Post([FromBody] ChatRequest chatRequest)   {       // Use a fixed session ID for simplicity, or generate a unique one per user/session       var sessionId = default-session;       var history = _chatHistoryService.GetOrCreateHistory(sessionId);       // Add user input       history.AddUserMessage(chatRequest.Message);       var openAIProm

When building agents using Semantic Kernel that integrate with SLMs (Small Language Models) /LLMs (Large Language Models), you typically send and receive multiple responses. A common pattern is to add all user and agent message to the ChatHistory object. In the past, I have done this by implementing a service class to handle these in .NET web applications. You can see an example of a controller which creates the chat history object for unique sessions for a webchat experience here: [HttpPost]   public async Task Post([FromBody] ChatRequest chatRequest)   {       // Use a fixed session ID for simplicity, or generate a unique one per user/session       var sessionId = default-session;       var history = _chatHistoryService.GetOrCreateHistory(sessionId);       // Add user input       history.AddUserMessage(chatRequest.Message);       var openAIProm

When building agents using Semantic Kernel that integrate with SLMs (Small Language Models) /LLMs (Large Language Models), you typically send and receive multiple responses. A common pattern is to add all user and agent message to the ChatHistory object. In the past, I have done this by implementing a service class to handle these in .NET web applications. You can see an example of a controller which creates the chat history object for unique sessions for a webchat experience here: [HttpPost]   public async Task Post([FromBody] ChatRequest chatRequest)   {       // Use a fixed session ID for simplicity, or generate a unique one per user/session       var sessionId = default-session;       var history = _chatHistoryService.GetOrCreateHistory(sessionId);       // Add user input       history.AddUserMessage(chatRequest.Message);       var openAIProm

When building agents using Semantic Kernel that integrate with SLMs (Small Language Models) /LLMs (Large Language Models), you typically send and receive multiple responses. A common pattern is to add all user and agent message to the ChatHistory object. In the past, I have done this by implementing a service class to handle these in .NET web applications. You can see an example of a controller which creates the chat history object for unique sessions for a webchat experience here: [HttpPost]   public async Task Post([FromBody] ChatRequest chatRequest)   {       // Use a fixed session ID for simplicity, or generate a unique one per user/session       var sessionId = default-session;       var history = _chatHistoryService.GetOrCreateHistory(sessionId);       // Add user input       history.AddUserMessage(chatRequest.Message);       var openAIProm

When building agents using Semantic Kernel that integrate with SLMs (Small Language Models) /LLMs (Large Language Models), you typically send and receive multiple responses. A common pattern is to add all user and agent message to the ChatHistory object. In the past, I have done this by implementing a service class to handle these in .NET web applications. You can see an example of a controller which creates the chat history object for unique sessions for a webchat experience here: [HttpPost]   public async Task Post([FromBody] ChatRequest chatRequest)   {       // Use a fixed session ID for simplicity, or generate a unique one per user/session       var sessionId = default-session;       var history = _chatHistoryService.GetOrCreateHistory(sessionId);       // Add user input       history.AddUserMessage(chatRequest.Message);       var openAIProm

When building agents using Semantic Kernel that integrate with SLMs (Small Language Models) /LLMs (Large Language Models), you typically send and receive multiple responses. A common pattern is to add all user and agent message to the ChatHistory object. In the past, I have done this by implementing a service class to handle these in .NET web applications. You can see an example of a controller which creates the chat history object for unique sessions for a webchat experience here: [HttpPost]   public async Task Post([FromBody] ChatRequest chatRequest)   {       // Use a fixed session ID for simplicity, or generate a unique one per user/session       var sessionId = default-session;       var history = _chatHistoryService.GetOrCreateHistory(sessionId);       // Add user input       history.AddUserMessage(chatRequest.Message);       var openAIProm

When building agents using Semantic Kernel that integrate with SLMs (Small Language Models) /LLMs (Large Language Models), you typically send and receive multiple responses. A common pattern is to add all user and agent message to the ChatHistory object. In the past, I have done this by implementing a service class to handle these in .NET web applications. You can see an example of a controller which creates the chat history object for unique sessions for a webchat experience here: [HttpPost]   public async Task Post([FromBody] ChatRequest chatRequest)   {       // Use a fixed session ID for simplicity, or generate a unique one per user/session       var sessionId = default-session;       var history = _chatHistoryService.GetOrCreateHistory(sessionId);       // Add user input       history.AddUserMessage(chatRequest.Message);       var openAIProm

When building agents using Semantic Kernel that integrate with SLMs (Small Language Models) /LLMs (Large Language Models), you typically send and receive multiple responses. A common pattern is to add all user and agent message to the ChatHistory object. In the past, I have done this by implementing a service class to handle these in .NET web applications. You can see an example of a controller which creates the chat history object for unique sessions for a webchat experience here: [HttpPost]   public async Task Post([FromBody] ChatRequest chatRequest)   {       // Use a fixed session ID for simplicity, or generate a unique one per user/session       var sessionId = default-session;       var history = _chatHistoryService.GetOrCreateHistory(sessionId);       // Add user input       history.AddUserMessage(chatRequest.Message);       var openAIProm

When building agents using Semantic Kernel that integrate with SLMs (Small Language Models) /LLMs (Large Language Models), you typically send and receive multiple responses. A common pattern is to add all user and agent message to the ChatHistory object. In the past, I have done this by implementing a service class to handle these in .NET web applications. You can see an example of a controller which creates the chat history object for unique sessions for a webchat experience here: [HttpPost]   public async Task Post([FromBody] ChatRequest chatRequest)   {       // Use a fixed session ID for simplicity, or generate a unique one per user/session       var sessionId = default-session;       var history = _chatHistoryService.GetOrCreateHistory(sessionId);       // Add user input       history.AddUserMessage(chatRequest.Message);       var openAIProm

When building agents using Semantic Kernel that integrate with SLMs (Small Language Models) /LLMs (Large Language Models), you typically send and receive multiple responses. A common pattern is to add all user and agent message to the ChatHistory object. In the past, I have done this by implementing a service class to handle these in .NET web applications. You can see an example of a controller which creates the chat history object for unique sessions for a webchat experience here: [HttpPost]   public async Task Post([FromBody] ChatRequest chatRequest)   {       // Use a fixed session ID for simplicity, or generate a unique one per user/session       var sessionId = default-session;       var history = _chatHistoryService.GetOrCreateHistory(sessionId);       // Add user input       history.AddUserMessage(chatRequest.Message);       var openAIProm