Initial upload

recipes/ai/memory/README.md

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+
+# Memory Stack (External Ollama)
+
+## Deploy
+```
+bash deploy.sh http://<OLLAMA-IP>:<PORT>
+```
+
+## Test
+```
+curl http://localhost:8085/health
+```

recipes/ai/memory/compose.yaml

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+
+version: "3.8"
+services:
+  qdrant:
+    image: qdrant/qdrant:latest
+    container_name: memory-qdrant
+    volumes:
+      - /srv/docker/services/memory/qdrant:/qdrant/storage
+    ports:
+      - "127.0.0.1:6333:6333"
+    restart: unless-stopped
+
+  memory-api:
+    build:
+      context: ./memory-api
+    container_name: memory-api
+    environment:
+      - QDRANT_URL=http://qdrant:6333
+      - OLLAMA_API={{OLLAMA_API}}
+      - COLLECTION_NAME=chat-memory
+    ports:
+      - "127.0.0.1:8085:8085"
+    depends_on:
+      - qdrant
+    restart: unless-stopped

recipes/ai/memory/deploy.sh

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+
+#!/usr/bin/env bash
+set -euo pipefail
+
+ensure_root
+detect_pkg_manager
+install_docker
+
+if ask_to_install "RAG Memory Stack (Qdrant + Memory API)"; then
+  log "=== RAG Memory Stack Installation ==="
+
+  read -rp "Ollama API URL (z.B. http://127.0.0.1:11434): " OLLAMA_API_URL
+  OLLAMA_API_URL=${OLLAMA_API_URL:-http://127.0.0.1:11434}
+
+  BASE="/srv/docker/services/memory"
+  $SUDO mkdir -p "$BASE/qdrant"
+  $SUDO cp -r "$(dirname "${BASH_SOURCE[0]}")/memory-api" "$BASE/"
+  $SUDO cp "$(dirname "${BASH_SOURCE[0]}")/compose.yaml" "$BASE/docker-compose.yml"
+  cd "$BASE"
+
+  $SUDO sed -i "s|{{OLLAMA_API}}|$OLLAMA_API_URL|g" docker-compose.yml
+
+  log "🚀 Starte RAG Memory Stack..."
+  $SUDO docker compose up -d --build
+
+  log "Attempting to pull embedding model from remote Ollama..."
+  $SUDO curl -s -X POST "$OLLAMA_API_URL/api/pull" -H 'Content-Type: application/json' -d '{"name": "nomic-embed-text"}' || log "Notice: Model pull failed (possibly using a gateway). Continuing."
+
+  log "✅ RAG Memory Stack läuft unter: http://<server-ip>:8085"
+else
+  log "${YELLOW}⏭ RAG Memory Stack übersprungen.${NC}"
+fi

recipes/ai/memory/memory-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 app.py .
+EXPOSE 8085
+CMD ["python", "app.py"]

recipes/ai/memory/memory-api/app.py

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+
+from fastapi import FastAPI
+import requests, os
+from qdrant_client import QdrantClient
+from qdrant_client.models import PointStruct
+import hashlib
+import json
+
+app = FastAPI()
+
+QDRANT_URL = os.getenv("QDRANT_URL")
+OLLAMA_API = os.getenv("OLLAMA_API")
+COLLECTION_NAME = os.getenv("COLLECTION_NAME", "chat-memory")
+
+client = QdrantClient(url=QDRANT_URL)
+
+@app.get("/health")
+def health():
+    return {"status": "ok", "qdrant": QDRANT_URL, "ollama": OLLAMA_API}
+
+def embed(text):
+    r = requests.post(f"{OLLAMA_API}/api/embeddings", json={"model":"nomic-embed-text","prompt":text})
+    return r.json()["embedding"]
+
+@app.post("/store")
+def store(item: dict):
+    text = item["text"]
+    metadata = item.get("metadata", {})
+    vec = embed(text)
+    pid = hashlib.sha256(text.encode()).hexdigest()
+    client.upsert(collection_name=COLLECTION_NAME, points=[PointStruct(id=pid, vector=vec, payload={"text": text, **metadata})])
+    return {"stored": True}
+
+@app.post("/search")
+def search(query: dict):
+    q = query["text"]
+    top_k = query.get("top_k", 5)
+    vec = embed(q)
+    result = client.search(collection_name=COLLECTION_NAME, query_vector=vec, limit=top_k)
+    return [{"score": r.score, "text": r.payload["text"]} for r in result]

recipes/ai/memory/memory-api/requirements.txt

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+fastapi
+uvicorn
+requests
+qdrant-client