koan-ai-integration
Chat endpoints, embeddings, RAG workflows, vector search
- Category: discovered
- Tags: github, community
- Version: 1.0.0
- Author: walid
- Downloads: 0
- GitHub stars: 2
- Updated: 2025-11-21
Download the latest version (v1.0.0) from this page.
SKILL.md
---
name: koan-ai-integration
description: Chat endpoints, embeddings, RAG workflows, vector search
---
# 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<T> patterns for AI-enriched data.
## Quick Reference
### Chat Endpoints
```csharp
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
```csharp
[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
```csharp
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 = quot;Answer based on this context:\n\n{context}",
Messages = new[] { new AiMessage { Role = "user", Content = question } }
}, ct);
return response.Content;
}
}
```
### Configuration
```json
{
"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)