Upload backend/ml/rag/embedder.py with huggingface_hub

backend/ml/rag/embedder.py

@@ -0,0 +1,36 @@
+"""
+Embeds text chunks using sentence-transformers (all-MiniLM-L6-v2).
+Runs fully locally, no API key needed.
+"""
+
+from sentence_transformers import SentenceTransformer
+import numpy as np
+
+MODEL_NAME = "all-MiniLM-L6-v2"  # 384-dim, fast, good quality
+_model = None
+
+
+def get_model() -> SentenceTransformer:
+    global _model
+    if _model is None:
+        print(f"Loading embedding model: {MODEL_NAME}")
+        _model = SentenceTransformer(MODEL_NAME)
+    return _model
+
+
+def embed_texts(texts: list[str]) -> np.ndarray:
+    """
+    Embed a list of strings. Returns (N, 384) float32 array.
+    """
+    model = get_model()
+    embeddings = model.encode(texts, batch_size=64, show_progress_bar=True, normalize_embeddings=True)
+    return embeddings.astype(np.float32)
+
+
+def embed_query(query: str) -> np.ndarray:
+    """
+    Embed a single query string. Returns (384,) float32 array.
+    """
+    model = get_model()
+    embedding = model.encode([query], normalize_embeddings=True)
+    return embedding[0].astype(np.float32)