From a58856fa00b55a4f75269fc1e827cab713ca49fd Mon Sep 17 00:00:00 2001
From: Lars <Lars@stommer.de>
Date: Tue, 2 Sep 2025 10:19:42 +0200
Subject: [PATCH] Dateien nach "app" hochladen

---
 app/__init__.py   | Bin 0 -> 1024 bytes
 app/config.py     |  20 ++++++++++++++++++++
 app/embeddings.py |  22 ++++++++++++++++++++++
 app/main.py       |  18 ++++++++++++++++++
 4 files changed, 60 insertions(+)
 create mode 100644 app/__init__.py
 create mode 100644 app/config.py
 create mode 100644 app/embeddings.py
 create mode 100644 app/main.py

diff --git a/app/__init__.py b/app/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..06d7405020018ddf3cacee90fd4af10487da3d20
GIT binary patch
literal 1024
ScmZQz7zLvtFd70QH3R?z00031

literal 0
HcmV?d00001

diff --git a/app/config.py b/app/config.py
new file mode 100644
index 0000000..5452a90
--- /dev/null
+++ b/app/config.py
@@ -0,0 +1,20 @@
+
+from __future__ import annotations
+import os
+from functools import lru_cache
+
+class Settings:
+    # Qdrant
+    QDRANT_URL: str = os.getenv("QDRANT_URL", "http://127.0.0.1:6333")
+    QDRANT_API_KEY: str | None = os.getenv("QDRANT_API_KEY")
+    COLLECTION_PREFIX: str = os.getenv("MINDNET_PREFIX", "mindnet")
+    VECTOR_SIZE: int = int(os.getenv("MINDNET_VECTOR_SIZE", "384"))
+    DISTANCE: str = os.getenv("MINDNET_DISTANCE", "Cosine")
+    # Embeddings
+    MODEL_NAME: str = os.getenv("MINDNET_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
+    # API
+    DEBUG: bool = os.getenv("DEBUG", "false").lower() == "true"
+
+@lru_cache
+def get_settings() -> Settings:
+    return Settings()
diff --git a/app/embeddings.py b/app/embeddings.py
new file mode 100644
index 0000000..2f23562
--- /dev/null
+++ b/app/embeddings.py
@@ -0,0 +1,22 @@
+
+from __future__ import annotations
+from typing import List
+from functools import lru_cache
+
+from .config import get_settings
+
+# Lazy import so startup stays fast
+@lru_cache
+def _load_model():
+    from sentence_transformers import SentenceTransformer
+    settings = get_settings()
+    model = SentenceTransformer(settings.MODEL_NAME, device="cpu")
+    return model
+
+def embed_texts(texts: List[str]) -> list[list[float]]:
+    model = _load_model()
+    # Normalize to list of str
+    texts = [t if isinstance(t, str) else str(t) for t in texts]
+    vecs = model.encode(texts, normalize_embeddings=True, convert_to_numpy=False)
+    # Ensure pure-python list of floats
+    return [list(map(float, v)) for v in vecs]
diff --git a/app/main.py b/app/main.py
new file mode 100644
index 0000000..0098913
--- /dev/null
+++ b/app/main.py
@@ -0,0 +1,18 @@
+
+from __future__ import annotations
+from fastapi import FastAPI
+from .config import get_settings
+from .routers.embed_router import router as embed_router
+from .routers.qdrant_router import router as qdrant_router
+
+def create_app() -> FastAPI:
+    app = FastAPI(title="mindnet API", version="0.1.0")
+    s = get_settings()
+    @app.get("/healthz")
+    def healthz():
+        return {"status": "ok", "qdrant": s.QDRANT_URL, "prefix": s.COLLECTION_PREFIX}
+    app.include_router(embed_router)
+    app.include_router(qdrant_router)
+    return app
+
+app = create_app()