Update RFC-0003-reranker-api.md

rfcs/RFC-0003-reranker-api.md

@@ -286,57 +286,68 @@ Reranks query–document pairs based on a specified strategy. It requires a `mod
 
 ```python
 class LocalReranker(RerankerProvider):
-
     def __init__(
         self,
         model_id: Optional[str] = None,
-        rerank_strategy: RerankingStrategy = RerankingStrategy.DEFAULT,
+        rerank_strategy: RerankingStrategy = RerankingStrategy.DEFAULT
     ) -> None:
-
         self.model_id = model_id
         self.rerank_strategy = rerank_strategy
-        self.model = asyncio.run(self._initialize_model())
+        self.model = None
+    async def initialize(self) -> None:
+        """Asynchronously initializes the model."""
+        self.model = await self._initialize_model()
 
     async def _initialize_model(self) -> Any:
+        """Loads or registers the model asynchronously."""
         if not self.model_id:
             raise ValueError(
                 "No model_id provided, but a model is required for this reranking strategy."
             )
 
-        if self.rerank_strategy == RerankingStrategy.LLM_RERANK:
-            # Placeholder for an LLM-based model loader
-            return self.model_id
+        # Ensure models_registry is accessible
+        if not hasattr(self, "models_registry"):
+            raise AttributeError("models_registry is not defined.")
 
-        # Default: use model_id for cross-encoder/embedding-based model loading
-        return self.model_id
+        # Attempt to get a pre-registered model
+        model = await self.models_registry.get_model(self.model_id)
+        if model:
+            return model
+
+        # Otherwise, register a new one
+        return await self.models_registry.register_model(
+            model_id=self.model_id,
+            provider_model_id=self.model_id,
+            metadata={"ranking": "512"}
+        )
 
     async def compute_scores(self, query: str, chunks: List[Any]) -> np.ndarray:
+        """Computes reranking scores for the given query and chunks."""
         if self.rerank_strategy == RerankingStrategy.NONE:
             return np.zeros(len(chunks))
 
+        # Ensure the model is initialized before use
+        if not self.model:
+            raise RuntimeError("Model is not initialized. Call `await initialize()` first.")
+
         # Create (query, content) pairs from chunks
         pairs = [(query, chunk.content) for chunk in chunks]
 
-        # Compute scores in a non-blocking way
+        # Compute scores asynchronously
         scores = await asyncio.to_thread(self.model.predict, pairs)
 
+        # Apply reranking strategy
         if self.rerank_strategy == RerankingStrategy.DEFAULT:
             return scores
-
         elif self.rerank_strategy == RerankingStrategy.BOOST:
-            # Placeholder: apply a boost factor to scores
-            return scores
-
+            return scores  # Placeholder for boost logic
         elif self.rerank_strategy == RerankingStrategy.HYBRID:
-            # Placeholder: combine cross-encoder scores with embedding-based scores
-            return scores
-
+            return scores  # Placeholder for hybrid logic
         elif self.rerank_strategy == RerankingStrategy.LLM_RERANK:
-            # Placeholder: use an LLM to compute scores
-            return scores
-
+            return scores  # Placeholder for LLM rerank logic
         else:
             raise ValueError(f"Unknown reranking strategy: {self.rerank_strategy}")
+
 ```
 
 #### ExternalReranker