Koan AI Integration

Koan AI Integration

Core Principle

AI capabilities integrate seamlessly with entity patterns. Store embeddings on entities, use vector repositories for search, and leverage standard Entity patterns for AI-enriched data.

Quick Reference

Chat Endpoints

public class ChatController : ControllerBase
{
    private readonly IAi _ai;

    [HttpPost]
    public async Task<IActionResult> Chat(
        [FromBody] ChatRequest request,
        CancellationToken ct)
    {
        var response = await _ai.ChatAsync(new AiChatRequest
        {
            Model = "gpt-4",
            Messages = request.Messages,
            SystemPrompt = "You are a helpful assistant.",
            Temperature = 0.7
        }, ct);

        return Ok(new { message = response.Content, usage = response.Usage });
    }
}

Entity with Embeddings

[DataAdapter("weaviate")] // Force vector database
public class ProductSearch : Entity<ProductSearch>
{
    public string ProductId { get; set; } = "";
    public string Description { get; set; } = "";

    [VectorField]
    public float[] DescriptionEmbedding { get; set; } = Array.Empty<float>();

    // Semantic search
    public static async Task<List<ProductSearch>> SimilarTo(
        string query,
        CancellationToken ct = default)
    {
        return await Vector<ProductSearch>.SearchAsync(query, limit: 10, ct);
    }
}

RAG Workflow

public class KnowledgeBaseService
{
    private readonly IAi _ai;

    public async Task<string> AnswerQuestion(string question, CancellationToken ct)
    {
        // 1. Find relevant documents via vector search
        var relevantDocs = await KnowledgeDocument.SimilarTo(question, ct);

        // 2. Build context from documents
        var context = string.Join("\n\n", relevantDocs.Select(d => d.Content));

        // 3. Query AI with context
        var response = await _ai.ChatAsync(new AiChatRequest
        {
            Model = "gpt-4",
            SystemPrompt = $"Answer based on this context:\n\n{context}",
            Messages = new[] { new AiMessage { Role = "user", Content = question } }
        }, ct);

        return response.Content;
    }
}

Configuration

{
  "Koan": {
    "AI": {
      "Providers": {
        "Primary": {
          "Type": "OpenAI",
          "ApiKey": "{OPENAI_API_KEY}",
          "Model": "gpt-4"
        },
        "Fallback": {
          "Type": "Ollama",
          "BaseUrl": "http://localhost:11434",
          "Model": "llama2"
        }
      }
    },
    "Data": {
      "Sources": {
        "Vectors": {
          "Adapter": "weaviate",
          "ConnectionString": "http://localhost:8080"
        }
      }
    }
  }
}

When This Skill Applies

• ✅ Integrating AI features
• ✅ Semantic search
• ✅ Chat interfaces
• ✅ Embeddings generation
• ✅ RAG workflows
• ✅ AI-enriched entities

Reference Documentation

• Full Guide: docs/guides/ai-integration.md
• Vector How-To: docs/guides/ai-vector-howto.md
• Sample: samples/S5.Recs/ (AI recommendation engine)
• Sample: samples/S16.PantryPal/ (Vision AI integration)