feat: initial RAG stack with README and env example

.env.example

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+# OpenAI API Key (https://platform.openai.com/api-keys)
+OPENAI_API_KEY=sk-proj-...
+
+# PostgreSQL Passwort
+POSTGRES_PASSWORD=dein-sicheres-passwort

.gitignore

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+```
+```
+postgres/
+ollama/
+open-webui/
+*.sql
+.env

README.md

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+# apply4jobs RAG Stack
+
+KI-basierter Chat-Assistent für apply4jobs.de – beantwortet Recruiter-Fragen zu Sebastian Fröhlich auf Basis seiner Bewerbungsunterlagen.
+
+## Architektur
+
+\`\`\`
+apply4jobs.de (Next.js)
+    └── ChatWidget → https://ai.apply4jobs.de/api/v1/chat/completions
+            └── FastAPI RAG-Bridge (Port 8000)
+                    ├── multilingual-e5-small (Embeddings)
+                    ├── pgvector / PostgreSQL (Vektordatenbank)
+                    └── OpenAI GPT-4o-mini (Generierung)
+\`\`\`
+
+## Stack
+
+| Service | Image | Port |
+|---------|-------|------|
+| PostgreSQL + pgvector | pgvector/pgvector:pg16 | 5432 |
+| Ollama | ollama/ollama:latest | 11434 |
+| Open WebUI | ghcr.io/open-webui/open-webui:main | 3000 |
+| RAG API | python:3.11-slim (custom) | 8000 |
+
+## Voraussetzungen
+
+- Docker + Docker Compose v2
+- Ubuntu 22.04 LTS
+- Domain mit SSL (Let's Encrypt)
+- OpenAI API Key (GPT-4o-mini)
+
+## Setup
+
+### 1. Repository klonen
+
+\`\`\`bash
+git clone <repo-url> /opt/apply4jobs
+cd /opt/apply4jobs
+\`\`\`
+
+### 2. Umgebungsvariablen konfigurieren
+
+\`\`\`bash
+cp .env.example .env
+nano .env
+\`\`\`
+
+\`\`\`env
+OPENAI_API_KEY=sk-proj-...
+POSTGRES_PASSWORD=dein-sicheres-passwort
+\`\`\`
+
+### 3. Stack starten
+
+\`\`\`bash
+docker compose up -d
+\`\`\`
+
+### 4. Ollama-Modelle pullen
+
+\`\`\`bash
+docker exec ollama ollama pull llama3.2:3b
+docker exec ollama ollama pull nomic-embed-text
+\`\`\`
+
+### 5. Datenbank importieren
+
+\`\`\`bash
+docker exec -i postgres psql -U anythingllm -d anythingllm < backup.sql
+\`\`\`
+
+## RAG API Endpunkte
+
+| Methode | Endpoint | Beschreibung |
+|---------|----------|-------------|
+| GET | /health | Health Check |
+| GET | /v1/models | Verfügbare Modelle |
+| POST | /retrieve | Kontext aus pgvector abrufen |
+| POST | /v1/chat/completions | OpenAI-kompatibler Chat-Endpunkt |
+
+## Kosten
+
+| Posten | Kosten/Monat |
+|--------|-------------|
+| Hetzner CPX42 | ~22 € |
+| OpenAI GPT-4o-mini | ~1-3 € |
+| **Gesamt** | **~23-25 €** |
+
+## Wartung
+
+\`\`\`bash
+# Logs anzeigen
+docker compose logs -f rag-api
+
+# Container neu starten
+docker compose restart rag-api
+
+# Nach Code-Änderungen neu bauen
+docker compose up -d --build rag-api
+
+# SSL-Zertifikat testen
+certbot renew --dry-run
+\`\`\`
+
+## Sicherheitshinweise
+
+- .env niemals ins Git-Repository committen
+- DSGVO: Datenschutzhinweis auf apply4jobs.de erforderlich
+- Chat-Logs werden nicht persistiert
+- Hetzner Rechenzentrum Deutschland = DSGVO-konform
+

docker-compose.yaml

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+services:
+  postgres:
+    image: pgvector/pgvector:pg16
+    container_name: postgres
+    restart: unless-stopped
+    environment:
+      POSTGRES_USER: anythingllm
+      POSTGRES_PASSWORD: any0203thing78llm
+      POSTGRES_DB: anythingllm
+    volumes:
+      - ./postgres:/var/lib/postgresql/data
+    ports:
+      - "127.0.0.1:5432:5432"
+
+  ollama:
+    image: ollama/ollama:latest
+    container_name: ollama
+    restart: unless-stopped
+    volumes:
+      - ./ollama:/root/.ollama
+    ports:
+      - "127.0.0.1:11434:11434"
+
+  open-webui:
+    image: ghcr.io/open-webui/open-webui:main
+    container_name: open-webui
+    restart: unless-stopped
+    depends_on:
+      - ollama
+    environment:
+      - OLLAMA_BASE_URL=http://ollama:11434
+    volumes:
+      - ./open-webui:/app/backend/data
+    ports:
+      - "127.0.0.1:3000:8080"
+
+  rag-api:
+    build: ./rag-api
+    container_name: rag-api
+    restart: unless-stopped
+    depends_on:
+      - postgres
+    env_file:
+      - .env
+    ports:
+      - "127.0.0.1:8000:8000"
+
+networks:
+  default:
+    name: apply4jobs-network

rag-api/Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY main.py .
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

rag-api/main.py

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+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+from sentence_transformers import SentenceTransformer
+import psycopg2
+import httpx
+import json
+import os
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["https://apply4jobs.de", "https://www.apply4jobs.de", "http://localhost:3000"],
+    allow_methods=["POST", "GET", "OPTIONS"],
+    allow_headers=["*"],
+)
+
+model = SentenceTransformer("intfloat/multilingual-e5-small")
+
+DB_CONFIG = {
+    "host": "postgres",
+    "port": 5432,
+    "dbname": "anythingllm",
+    "user": "anythingllm",
+    "password": "any0203thing78llm"
+}
+
+OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "")
+OPENAI_MODEL = "gpt-4o-mini"
+
+SYSTEM_PROMPT = """Du bist ein professioneller Assistent für Sebastian Fröhlich.
+Beantworte ausschließlich Fragen zu seiner Person, seinen Fähigkeiten,
+Projekten und Berufserfahrung. Nutze nur die bereitgestellten Dokumente.
+Antworte auf Deutsch oder Englisch je nach Sprache des Recruiters.
+Wenn du eine Frage nicht aus den Dokumenten beantworten kannst, sage das ehrlich.
+Antworte immer in vollständigen, professionellen Sätzen."""
+
+def get_context(query: str, top_k: int = 5) -> str:
+    query_text = f"query: {query}"
+    embedding = model.encode(query_text).tolist()
+
+    conn = psycopg2.connect(**DB_CONFIG)
+    cur = conn.cursor()
+    cur.execute("""
+        SELECT metadata->>'text'
+        FROM anythingllm_vectors
+        WHERE namespace = 'mein-workspace'
+        ORDER BY embedding <=> %s::vector
+        LIMIT %s
+    """, (embedding, top_k))
+    rows = cur.fetchall()
+    cur.close()
+    conn.close()
+
+    return "\n\n---\n\n".join([row[0] for row in rows])
+
+
+class Message(BaseModel):
+    role: str
+    content: str
+
+class ChatRequest(BaseModel):
+    model: str = OPENAI_MODEL
+    messages: list[Message]
+    stream: bool = False
+
+class QueryRequest(BaseModel):
+    query: str
+    top_k: int = 5
+
+
+@app.get("/health")
+def health():
+    return {"status": "ok"}
+
+
+@app.get("/v1/models")
+def list_models():
+    return {
+        "object": "list",
+        "data": [{
+            "id": "sebastian-rag",
+            "object": "model",
+            "created": 1700000000,
+            "owned_by": "apply4jobs"
+        }]
+    }
+
+
+@app.post("/retrieve")
+def retrieve(req: QueryRequest):
+    context = get_context(req.query, req.top_k)
+    return {"context": context, "system_prompt": SYSTEM_PROMPT}
+
+
+@app.post("/v1/chat/completions")
+async def chat(req: ChatRequest):
+    user_message = next(
+        (m.content for m in reversed(req.messages) if m.role == "user"), ""
+    )
+
+    context = get_context(user_message)
+
+    enriched_messages = [
+        {
+            "role": "system",
+            "content": f"{SYSTEM_PROMPT}\n\n## Relevante Dokumente:\n{context}"
+        },
+        *[{"role": m.role, "content": m.content} for m in req.messages]
+    ]
+
+    async def stream_response():
+        async with httpx.AsyncClient(timeout=60) as client:
+            async with client.stream(
+                "POST",
+                "https://api.openai.com/v1/chat/completions",
+                headers={
+                    "Authorization": f"Bearer {OPENAI_API_KEY}",
+                    "Content-Type": "application/json"
+                },
+                json={
+                    "model": OPENAI_MODEL,
+                    "messages": enriched_messages,
+                    "stream": True
+                }
+            ) as response:
+                async for chunk in response.aiter_bytes():
+                    yield chunk
+
+    if req.stream:
+        return StreamingResponse(
+            stream_response(),
+            media_type="text/event-stream"
+        )
+    else:
+        async with httpx.AsyncClient(timeout=60) as client:
+            response = await client.post(
+                "https://api.openai.com/v1/chat/completions",
+                headers={
+                    "Authorization": f"Bearer {OPENAI_API_KEY}",
+                    "Content-Type": "application/json"
+                },
+                json={
+                    "model": OPENAI_MODEL,
+                    "messages": enriched_messages,
+                    "stream": False
+                }
+            )
+            return response.json()

rag-api/requirements.txt

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+fastapi
+uvicorn
+psycopg2-binary
+pgvector
+sentence-transformers