Chat Completion¶

The Xians platform provides a simple chat completion capability through the SemanticRouterHub.ChatCompletionAsync static method. This functionality allows you to send a direct prompt to an LLM and receive a response without the complexity of system prompts or chat history.

Overview¶

The ChatCompletionAsync method leverages Microsoft's SemanticKernel framework to provide straightforward LLM interactions. Unlike the full routing capabilities that include system prompts, chat history, and function calling, this method focuses on simple prompt-to-response interactions.

Usage¶

var response = await SemanticRouterHub.ChatCompletionAsync(prompt, options);

Parameters¶

prompt (string): The direct prompt to send to the LLM
options (RouterOptions, optional): Configuration options for the completion

Configuration Options¶

The RouterOptions class allows you to configure the LLM behavior:

var options = new RouterOptions
{
    ProviderName = "openai",           // or "azureopenai"
    ModelName = "gpt-4",
    ApiKey = "your-api-key",
    Temperature = 0.3,                 // Controls randomness (default: 0.3)
    MaxTokens = 1000                   // Maximum response length (default: 10000)
};

Available Options¶

Property	Description	Default
`ProviderName`	LLM provider ("openai", "azureopenai")	From environment/settings
`ModelName`	Model to use (e.g., "gpt-4", "gpt-3.5-turbo")	From environment/settings
`DeploymentName`	Azure OpenAI deployment name	From environment/settings
`Endpoint`	Custom endpoint URL	From environment/settings
`ApiKey`	API key for the provider	From environment/settings
`Temperature`	Randomness control (0.0-1.0)	0.3
`MaxTokens`	Maximum tokens in response	10000

Example Usage¶

Here's a practical example from a chat interceptor that analyzes assistant messages:

string prompt = @"Analyze the following assistant message and determine if it contains 
    a direct request for contract information: " + userMessage;

var analysis = await SemanticRouterHub.ChatCompletionAsync(prompt);

Key Characteristics¶

No System Prompt: The method sends only your provided prompt
No Chat History: Each call is independent with no conversation context
No Function Calling: Functions/tools are disabled for simple text responses
SemanticKernel Based: Uses Microsoft SemanticKernel framework internally
Provider Agnostic: Supports OpenAI and Azure OpenAI providers

When to Use¶

Use ChatCompletionAsync when you need:

Simple prompt-to-response interactions
Text analysis or generation tasks
Independent LLM calls without conversation context
Quick LLM evaluations or classifications

For more complex scenarios involving system prompts, chat history, or function calling, use the full routing capabilities instead.