Initial upload

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