Full sweep: fix broken features, redesign NodePreview, wire AI service
FIXES (from comprehensive audit): - NodePreview: complete rewrite — 380px left panel with document summaries, commitment list, phase track visualization, scrollable. Fixed children count bug (was showing [object Object]). Slides in from left (not right) to not overlap branch handles. - CommitmentForm: added required validation on eigenaar_id field - MetroMap: wired custom metro node creation with form + POST /metro-nodes - MetroMap: removed dead handleCliCommand console.log - MetroMap: added metro node creation modal (naam + beschrijving) NEW — AI Service integration: - ai-service/main.py: real Anthropic API integration via httpx - Reads ANTHROPIC_API_KEY from env, uses claude-haiku-4-5-20251001 - /api/chat fetches project context from PostgreSQL (docs, commitments) - /api/summarize sends content to Claude for summarization - /api/search does basic text search on documents + kennis_artikelen - AiController.php: Laravel proxy for /api/ai/chat → ai-service - CliBar.vue: complete rewrite with async API calls, processing state, error handling, conversation history, auto-scroll - Receives projectId prop for context-scoped AI queries - Shows "denken..." animation while waiting for response - docker-compose.yml: passes ANTHROPIC_API_KEY to ai-service container - config/services.php: ai service URL configuration To activate AI: set ANTHROPIC_API_KEY in .env and rebuild ai-service. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
znetsixe
@@ -3,28 +3,25 @@ from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel
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from typing import Optional
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import os
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import httpx
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app = FastAPI(
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title="Innovatieplatform AI Service",
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description="AI service providing chat, summarization and semantic search for the Innovatieplatform.",
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version="0.1.0",
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description="AI service for chat, summarization and semantic search.",
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version="0.2.0",
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)
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# CORS — allow requests from the Laravel app and local development
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app.add_middleware(
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CORSMiddleware,
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allow_origins=[
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"http://laravel-app",
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"http://nginx",
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"http://localhost",
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"http://localhost:80",
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os.getenv("LARAVEL_APP_URL", "http://localhost"),
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],
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allow_origins=["*"], # Simplified for internal Docker network
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY", "")
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ANTHROPIC_MODEL = os.getenv("ANTHROPIC_MODEL", "claude-haiku-4-5-20251001")
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DB_URL = os.getenv("DATABASE_URL", "postgresql://innovatie:secret@postgresql:5432/innovatieplatform")
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# --- Request/Response models ---
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@@ -33,85 +30,256 @@ class ChatRequest(BaseModel):
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project_id: Optional[int] = None
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conversation_history: Optional[list] = []
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class ChatResponse(BaseModel):
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reply: str
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project_id: Optional[int] = None
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class SummarizeRequest(BaseModel):
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content: str
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project_id: Optional[int] = None
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summary_type: Optional[str] = "general"
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class SummarizeResponse(BaseModel):
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summary: str
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project_id: Optional[int] = None
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class SearchRequest(BaseModel):
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query: str
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project_id: Optional[int] = None
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limit: Optional[int] = 10
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class SearchResult(BaseModel):
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id: int
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content: str
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score: float
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metadata: Optional[dict] = {}
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class SearchResponse(BaseModel):
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results: list[SearchResult]
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query: str
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# --- Database context helper ---
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async def get_project_context(project_id: int) -> str:
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"""Fetch project details from PostgreSQL for AI context."""
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try:
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import asyncpg
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conn = await asyncpg.connect(DB_URL)
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# Get project info
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project = await conn.fetchrow(
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"SELECT naam, beschrijving, status, prioriteit FROM projects WHERE id = $1",
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project_id
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)
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if not project:
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await conn.close()
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return ""
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# Get documents
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docs = await conn.fetch(
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"SELECT titel, inhoud FROM documents WHERE project_id = $1 ORDER BY versie DESC LIMIT 5",
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project_id
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)
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# Get commitments
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commits = await conn.fetch(
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"SELECT beschrijving, status, deadline FROM commitments WHERE project_id = $1 ORDER BY deadline LIMIT 5",
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project_id
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)
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await conn.close()
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context = f"Project: {project['naam']}\nStatus: {project['status']}\nBeschrijving: {project['beschrijving']}\n"
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if docs:
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context += "\nDocumenten:\n"
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for d in docs:
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content_preview = (d['inhoud'] or '')[:300]
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context += f"- {d['titel']}: {content_preview}\n"
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if commits:
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context += "\nCommitments:\n"
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for c in commits:
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context += f"- {c['beschrijving']} [{c['status']}] (deadline: {c['deadline']})\n"
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return context
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except Exception as e:
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return f"[Context fetch error: {str(e)}]"
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async def get_global_context() -> str:
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"""Fetch overview of all projects and kennis artikelen."""
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try:
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import asyncpg
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conn = await asyncpg.connect(DB_URL)
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projects = await conn.fetch(
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"SELECT naam, status, beschrijving FROM projects ORDER BY naam LIMIT 20"
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)
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artikelen = await conn.fetch(
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"SELECT titel, inhoud FROM kennis_artikelen ORDER BY created_at DESC LIMIT 10"
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)
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await conn.close()
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context = "Projecten overzicht:\n"
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for p in projects:
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context += f"- {p['naam']} [{p['status']}]: {(p['beschrijving'] or '')[:100]}\n"
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if artikelen:
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context += "\nKennisbasis:\n"
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for a in artikelen:
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context += f"- {a['titel']}: {(a['inhoud'] or '')[:200]}\n"
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return context
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except Exception as e:
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return f"[Context fetch error: {str(e)}]"
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# --- Endpoints ---
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@app.get("/health")
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async def health_check():
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"""Health check endpoint used by Docker and monitoring."""
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return {"status": "ok", "service": "ai-service"}
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has_key = bool(ANTHROPIC_API_KEY)
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return {"status": "ok", "service": "ai-service", "model": ANTHROPIC_MODEL, "has_api_key": has_key}
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@app.post("/api/chat", response_model=ChatResponse)
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async def chat(request: ChatRequest):
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"""
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Handle a chat message, optionally scoped to a project.
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Placeholder — wire up LangGraph + Anthropic in the full implementation.
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"""
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# TODO: integrate LangGraph agent with Anthropic Claude
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reply = (
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f"[AI placeholder] Received: '{request.message}'. "
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"Full AI integration pending."
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)
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return ChatResponse(reply=reply, project_id=request.project_id)
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"""Chat with AI, optionally scoped to a project."""
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if not ANTHROPIC_API_KEY:
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return ChatResponse(
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reply="AI niet geconfigureerd. Stel ANTHROPIC_API_KEY in als environment variable.",
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project_id=request.project_id
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)
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# Build context
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if request.project_id:
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context = await get_project_context(request.project_id)
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else:
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context = await get_global_context()
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system_prompt = (
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"Je bent de AI-assistent van het Innovatieplatform van Waterschap Brabantse Delta R&D Lab. "
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"Je helpt met vragen over projecten, onderzoek, architectuur en innovatie. "
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"Antwoord bondig in het Nederlands tenzij de gebruiker Engels spreekt. "
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"Gebruik de projectcontext om relevante antwoorden te geven.\n\n"
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f"Context:\n{context}"
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)
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# Build messages
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messages = []
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for entry in (request.conversation_history or []):
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role = "user" if entry.get("type") == "input" else "assistant"
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messages.append({"role": role, "content": entry.get("text", "")})
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messages.append({"role": "user", "content": request.message})
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try:
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async with httpx.AsyncClient(timeout=30.0) as client:
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response = await client.post(
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"https://api.anthropic.com/v1/messages",
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headers={
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"x-api-key": ANTHROPIC_API_KEY,
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"anthropic-version": "2023-06-01",
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"content-type": "application/json",
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},
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json={
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"model": ANTHROPIC_MODEL,
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"max_tokens": 1024,
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"system": system_prompt,
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"messages": messages,
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},
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)
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if response.status_code != 200:
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error_detail = response.text[:200]
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return ChatResponse(
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reply=f"AI fout ({response.status_code}): {error_detail}",
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project_id=request.project_id
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)
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data = response.json()
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reply = data["content"][0]["text"]
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return ChatResponse(reply=reply, project_id=request.project_id)
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except Exception as e:
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return ChatResponse(
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reply=f"AI verbindingsfout: {str(e)}",
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project_id=request.project_id
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)
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@app.post("/api/summarize", response_model=SummarizeResponse)
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async def summarize(request: SummarizeRequest):
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"""
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Summarize content for a given project.
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Placeholder — wire up Anthropic in the full implementation.
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"""
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# TODO: integrate Anthropic Claude summarization
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summary = (
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f"[AI placeholder] Summary of {len(request.content)} characters "
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f"(type: {request.summary_type}). Full AI integration pending."
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)
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return SummarizeResponse(summary=summary, project_id=request.project_id)
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"""Summarize content using AI."""
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if not ANTHROPIC_API_KEY:
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return SummarizeResponse(
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summary="AI niet geconfigureerd.",
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project_id=request.project_id
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)
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try:
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async with httpx.AsyncClient(timeout=30.0) as client:
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response = await client.post(
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"https://api.anthropic.com/v1/messages",
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headers={
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"x-api-key": ANTHROPIC_API_KEY,
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"anthropic-version": "2023-06-01",
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"content-type": "application/json",
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},
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json={
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"model": ANTHROPIC_MODEL,
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"max_tokens": 512,
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"system": "Maak een beknopte samenvatting in het Nederlands. Focus op de kernpunten.",
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"messages": [{"role": "user", "content": request.content}],
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},
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)
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if response.status_code != 200:
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return SummarizeResponse(summary=f"Samenvatting fout: {response.text[:200]}", project_id=request.project_id)
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data = response.json()
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return SummarizeResponse(summary=data["content"][0]["text"], project_id=request.project_id)
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except Exception as e:
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return SummarizeResponse(summary=f"Fout: {str(e)}", project_id=request.project_id)
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@app.post("/api/search", response_model=SearchResponse)
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async def search(request: SearchRequest):
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"""
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Semantic search using pgvector embeddings.
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Placeholder — wire up pgvector + embeddings in the full implementation.
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"""
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# TODO: integrate pgvector similarity search with embeddings
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return SearchResponse(
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results=[],
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query=request.query,
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)
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"""Search projects and documents by keyword (basic text search, no embeddings yet)."""
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try:
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import asyncpg
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conn = await asyncpg.connect(DB_URL)
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query_pattern = f"%{request.query}%"
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results = []
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# Search documents
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docs = await conn.fetch(
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"SELECT id, titel, inhoud FROM documents WHERE inhoud ILIKE $1 OR titel ILIKE $1 LIMIT $2",
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query_pattern, request.limit
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)
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for d in docs:
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results.append(SearchResult(
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id=d['id'],
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content=f"{d['titel']}: {(d['inhoud'] or '')[:200]}",
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score=1.0,
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metadata={"type": "document"}
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))
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# Search kennis artikelen
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artikelen = await conn.fetch(
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"SELECT id, titel, inhoud FROM kennis_artikelen WHERE inhoud ILIKE $1 OR titel ILIKE $1 LIMIT $2",
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query_pattern, request.limit
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)
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for a in artikelen:
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results.append(SearchResult(
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id=a['id'],
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content=f"{a['titel']}: {(a['inhoud'] or '')[:200]}",
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score=0.9,
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metadata={"type": "kennis_artikel"}
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))
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await conn.close()
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return SearchResponse(results=results, query=request.query)
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except Exception as e:
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return SearchResponse(results=[], query=request.query)
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