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AuditGlue/System alternative/iso27DYI architecture with LLM.md

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+---
+tags:
+  - iso27DIY
+  - architecture
+  - LLMgenerated
+---
+# iso27DYI architecture with LLM
+
+For connecting WeWeb (frontend) and Supabase (backend) with LLM functionality, you have several technology options:
+- **OpenAI API** or **Anthropic API** directly from your middleware
+- **Supabase JavaScript client** for database operations
+- **WeWeb API calls** to your middleware endpoints
+
+Since WeWeb works well with REST APIs and Supabase has excellent API support, you can build a middleware layer:
+
+1. **Node.js/Express API** - Create API endpoints that:
+    - Receive requests from WeWeb
+    - Query Supabase for data
+    - Call LLM APIs (OpenAI, Anthropic, etc.)
+    - Return processed responses to WeWeb
+2. **Supabase Edge Functions** - Use Supabase's serverless functions to:
+    - Handle LLM processing directly within your Supabase environment
+    - Access your database without additional API calls
+    - Deploy TypeScript/JavaScript functions that WeWeb can call
+3. **Serverless Functions** (Vercel, Netlify, or AWS Lambda):
+    - Deploy functions that orchestrate between WeWeb, Supabase, and LLMs
+    - Good for scaling and keeping costs predictable
+
+
+The **Supabase Edge Functions** approach is often the most elegant since it keeps everything in your existing Supabase ecosystem and supports TypeScript natively.
+
+``` typescript
+// Edge function that:
+// 1. Receives user input from WeWeb
+// 2. Queries Supabase database for context
+// 3. Calls OpenAI/Anthropic API directly
+// 4. Returns response to WeWeb
+
+```
+
+**For your slot-filling + content generation use case:**
+
+- Direct API calls to LLMs work perfectly
+- Supabase handles your data needs
+- WeWeb handles the UI interactions
+
+## Self-hosting the LLM
+For self-hosting an LLM with an easy API, here are the simplest options:
+
+**Ollama (Easiest)**
+
+- Install Ollama on your server
+- Pull models like `llama3`, `mistral`, `codellama`
+- Built-in REST API at `http://localhost:11434`
+- Simple JSON requests, OpenAI-compatible endpoints
+- Works great for development and moderate production use
+
+**vLLM (Best for Production)**
+
+- High-performance serving with OpenAI-compatible API
+- Excellent throughput and batching
+- Simple command: `vllm serve model-name --api-key your-key`
+- API available at `http://localhost:8000/v1/`
+
+**Text Generation Inference (TGI)**
+
+- Hugging Face's production-ready solution
+- Docker container deployment
+- OpenAI-compatible API
+- Good performance optimization
+
+**For your Supabase setup:**
+
+1. Deploy Ollama/vLLM on a server (or even locally for development)
+2. From your Supabase Edge Functions, make HTTP requests to your LLM API
+3. No need to change your WeWeb → Supabase → LLM flow
+
+**Quick start with Ollama:**
+
+```bash
+# Install and run
+curl -fsSL https://ollama.ai/install.sh | sh
+ollama serve
+ollama pull llama3
+```
+
+Then your Edge Functions can call `POST http://your-server:11434/api/generate`
+