download HF models during image build

download_models.py

@@ -0,0 +1,54 @@
+"""
+Pre-download Hugging Face models during Docker image build.
+
+This script loads the models to trigger download and caching.
+"""
+import os
+import sys
+
+print("🔽 Downloading Hugging Face models during build...")
+
+# Model configurations from settings.yaml
+EMBEDDING_MODEL = "BAAI/bge-m3"
+RERANKER_MODEL = "BAAI/bge-reranker-v2-m3"
+
+try:
+    print(f"📦 Downloading embedding model: {EMBEDDING_MODEL}")
+    from langchain_community.embeddings import HuggingFaceEmbeddings
+    
+    # Load embedding model (will download if not cached)
+    embeddings = HuggingFaceEmbeddings(
+        model_name=EMBEDDING_MODEL,
+        model_kwargs={"device": "cpu"},  # Use CPU during build
+        encode_kwargs={"normalize_embeddings": True},
+        show_progress=True,
+    )
+    
+    # Trigger actual download by encoding a small text
+    test_text = "test"
+    _ = embeddings.embed_query(test_text)
+    print(f"✅ Embedding model downloaded: {EMBEDDING_MODEL}")
+
+except Exception as e:
+    print(f"⚠️ Warning: Could not download embedding model: {e}")
+    # Don't exit on error - allow build to continue (model will download at runtime)
+    pass
+
+try:
+    print(f"📦 Downloading reranker model: {RERANKER_MODEL}")
+    from sentence_transformers import CrossEncoder
+    
+    # Load reranker model (will download if not cached)
+    reranker = CrossEncoder(RERANKER_MODEL)
+    
+    # Trigger actual download by running inference
+    test_pairs = [("test query", "test document")]
+    _ = reranker.predict(test_pairs)
+    print(f"✅ Reranker model downloaded: {RERANKER_MODEL}")
+
+except Exception as e:
+    print(f"⚠️ Warning: Could not download reranker model: {e}")
+    # Don't exit on error - allow build to continue (model will download at runtime)
+    pass
+
+print("✅ All models downloaded and cached successfully!")