Scaffold full project: Vue 3/Inertia frontend, Docker infra, domain model

- Frontend: Vue 3 + Inertia.js + Pinia + Tailwind CSS with layout and dashboard page
- Infrastructure: Docker Compose with nginx, PHP-FPM, PostgreSQL+pgvector, Redis, Python AI service
- Database: 22 migrations covering all domain entities (projects, phases, commitments, decisions, documents, handover, audit)
- Models: 23 Eloquent models with relationships, casts, and 14 string-backed enums
- AI service: FastAPI scaffold with health, chat, summarize, and search endpoints

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

ai-service/Dockerfile

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+FROM python:3.12-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies needed for psycopg2
+RUN apt-get update && apt-get install -y \
+    libpq-dev \
+    gcc \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy and install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY . .
+
+EXPOSE 8000
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000", "--reload"]

ai-service/app/main.py

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+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from typing import Optional
+import os
+
+app = FastAPI(
+    title="Innovatieplatform AI Service",
+    description="AI service providing chat, summarization and semantic search for the Innovatieplatform.",
+    version="0.1.0",
+)
+
+# CORS — allow requests from the Laravel app and local development
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=[
+        "http://laravel-app",
+        "http://nginx",
+        "http://localhost",
+        "http://localhost:80",
+        os.getenv("LARAVEL_APP_URL", "http://localhost"),
+    ],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+# --- Request/Response models ---
+
+class ChatRequest(BaseModel):
+    message: str
+    project_id: Optional[int] = None
+    conversation_history: Optional[list] = []
+
+
+class ChatResponse(BaseModel):
+    reply: str
+    project_id: Optional[int] = None
+
+
+class SummarizeRequest(BaseModel):
+    content: str
+    project_id: Optional[int] = None
+    summary_type: Optional[str] = "general"
+
+
+class SummarizeResponse(BaseModel):
+    summary: str
+    project_id: Optional[int] = None
+
+
+class SearchRequest(BaseModel):
+    query: str
+    project_id: Optional[int] = None
+    limit: Optional[int] = 10
+
+
+class SearchResult(BaseModel):
+    id: int
+    content: str
+    score: float
+    metadata: Optional[dict] = {}
+
+
+class SearchResponse(BaseModel):
+    results: list[SearchResult]
+    query: str
+
+
+# --- Endpoints ---
+
+@app.get("/health")
+async def health_check():
+    """Health check endpoint used by Docker and monitoring."""
+    return {"status": "ok", "service": "ai-service"}
+
+
+@app.post("/api/chat", response_model=ChatResponse)
+async def chat(request: ChatRequest):
+    """
+    Handle a chat message, optionally scoped to a project.
+    Placeholder — wire up LangGraph + Anthropic in the full implementation.
+    """
+    # TODO: integrate LangGraph agent with Anthropic Claude
+    reply = (
+        f"[AI placeholder] Received: '{request.message}'. "
+        "Full AI integration pending."
+    )
+    return ChatResponse(reply=reply, project_id=request.project_id)
+
+
+@app.post("/api/summarize", response_model=SummarizeResponse)
+async def summarize(request: SummarizeRequest):
+    """
+    Summarize content for a given project.
+    Placeholder — wire up Anthropic in the full implementation.
+    """
+    # TODO: integrate Anthropic Claude summarization
+    summary = (
+        f"[AI placeholder] Summary of {len(request.content)} characters "
+        f"(type: {request.summary_type}). Full AI integration pending."
+    )
+    return SummarizeResponse(summary=summary, project_id=request.project_id)
+
+
+@app.post("/api/search", response_model=SearchResponse)
+async def search(request: SearchRequest):
+    """
+    Semantic search using pgvector embeddings.
+    Placeholder — wire up pgvector + embeddings in the full implementation.
+    """
+    # TODO: integrate pgvector similarity search with embeddings
+    return SearchResponse(
+        results=[],
+        query=request.query,
+    )

ai-service/requirements.txt

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+fastapi>=0.110.0
+uvicorn>=0.27.0
+langgraph>=0.0.40
+langchain>=0.1.0
+anthropic>=0.30.0
+pgvector>=0.2.0
+psycopg2-binary>=2.9.0
+numpy>=1.26.0
+pydantic>=2.0.0
+python-dotenv>=1.0.0
+httpx>=0.27.0