LLMKit 3.0.0

LLMKit

LLMKit is a thread-safe .NET library that provides a unified interface for interacting with various Large Language Models (LLMs) including OpenAI, Gemini, and DeepSeek.

Features

• Unified interface for multiple LLM providers
• Thread-safe implementation
• Built-in conversation management (stores up to 15 messages by default)
• ✨ NEW! Multimodal support (text + images)
• Fluent API for message building
• Configurable parameters (tokens, temperature, etc.)
• Comprehensive error handling with automatic retries
• Dependency injection support
• Cancellation token support
• Custom endpoint support for all providers

Installation

Package Manager

Install-Package LLMKit

.NET CLI

dotnet add package LLMKit

Clone Repository

git clone https://github.com/MohammedJayyab/LLMKit.git

Requirements

• .NET 8.0 or later
• Valid API keys for the LLM providers

Quick Start

using LLMKit;
using LLMKit.Providers;

// Using statement ensures proper disposal
using var client = new LLMClient(
    new OpenAIProvider(apiKey: "your-api-key", model: "gpt-3.5-turbo")
);

// Conversation history is automatically managed
string response = await client.GenerateTextAsync("What is the capital of France?");
Console.WriteLine(response);

Usage Examples

Conversation Management

using var client = new LLMClient(new OpenAIProvider("your-api-key", "gpt-3.5-turbo"));

// First message
string response1 = await client.GenerateTextAsync("Hello, how are you?");
Console.WriteLine(response1);

// Follow-up questions maintain conversation context automatically
string response2 = await client.GenerateTextAsync("What's the weather like?");
Console.WriteLine(response2);

// Get the formatted conversation history
string history = client.GetFormattedConversation();
Console.WriteLine(history);

// Clear the conversation if needed
client.ClearConversation();

✨ Multimodal Support (Image + Text)

// Generate a response to a message with an image
string response = await client.GenerateTextWithImageAsync(
    "What can you see in this image?", 
    "path/to/your/image.jpg"
);

Custom Parameters

// Create client with custom parameters
var client = new LLMClient(
    provider: new OpenAIProvider("your-api-key", "gpt-3.5-turbo"),
    maxTokens: 1000,
    temperature: 0.7,
    maxMessages: 20  // Store up to 20 messages in conversation history
);

✨ Setting a System Message

// Set or update the system message
client.SetSystemMessage("You are a helpful assistant specialized in biology.");

Dependency Injection

services.AddSingleton<ILLMProvider>(sp => 
    new OpenAIProvider(
        apiKey: Configuration["OpenAI:ApiKey"],
        model: Configuration["OpenAI:Model"]
    )
);
services.AddSingleton<LLMClient>();

Error Handling

try
{
    string response = await client.GenerateTextAsync("What is the capital of France?");
    Console.WriteLine(response);
}
catch (LLMException ex)
{
    Console.WriteLine($"Error: {ex.Message}");
}

Cancellation

using var cts = new CancellationTokenSource(TimeSpan.FromSeconds(30));
string response = await client.GenerateTextAsync(
    "What is the capital of France?",
    cts.Token
);

Supported Providers

OpenAI

var provider = new OpenAIProvider(
    apiKey: "your-api-key",
    model: "gpt-3.5-turbo"
);

Gemini

var provider = new GeminiProvider(
    apiKey: "your-api-key",
    model: "gemini-2.0-flash"
);

DeepSeek

var provider = new DeepSeekProvider(
    apiKey: "your-api-key",
    model: "deepseek-chat"
);

Custom Endpoints

Each provider supports custom endpoints. If not provided, the library will use the default endpoint for that provider.

// OpenAI with custom endpoint
var client = new LLMClient(new OpenAIProvider(
    apiKey: "your-api-key",
    model: "gpt-3.5-turbo",
    endpoint: new Uri("https://api.openai.com/v1/chat/completions")
));

// Gemini with custom endpoint
var client = new LLMClient(new GeminiProvider(
    apiKey: "your-api-key",
    model: "gemini-2.0-flash",
    endpoint: new Uri("https://generativelanguage.googleapis.com/v1beta/models")
));

// DeepSeek with custom endpoint
var client = new LLMClient(new DeepSeekProvider(
    apiKey: "your-api-key",
    model: "deepseek-chat",
    endpoint: new Uri("https://api.deepseek.com/v1/chat/completions")
));

Documentation 📚

For more detailed information, please visit our Wiki:

• Getting Started Guide
• Architecture Overview
• API Reference
• Examples
• Contributing Guidelines
• Roadmap

License

MIT License. See LICENSE for details.

Support

For issues or questions, please open an issue in the GitHub repository.

☕ Donate

If you find this project helpful, consider buying me a coffee to support its development: