draft

fastembed/config/dense_models.json

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+{
+  "models": [
+    {
+      "model": "BAAI/bge-base-en",
+      "dim": 768,
+      "description": "Text embeddings, Unimodal (text), English...",
+      "license": "mit",
+      "size_in_GB": 0.42,
+      "sources": {
+        "hf": "Qdrant/fast-bge-base-en",
+        "url": "https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en.tar.gz"
+      },
+      "model_file": "model_optimized.onnx"
+    }
+  ]
+}

fastembed/model_loader.py

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+from pathlib import Path
+import json
+from typing import Dict, List
+
+
+class ModelLoader:
+    def __init__(self):
+        self.config_dir = Path(__file__).parent / "configs"
+        self._models: Dict[str, List[Dict]] = {}
+
+    def load_models(self, model_type: str) -> List[Dict]:
+        if model_type not in self._models:
+            config_path = self.config_dir / f"{model_type}_models.json"
+            with open(config_path) as f:
+                self._models[model_type] = json.load(f)["models"]
+        return self._models[model_type]