feat: Introduce weighted and rrf reranker implementations

Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>

llama_stack/providers/utils/memory/vector_store.py

@@ -32,6 +32,10 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 
 log = logging.getLogger(__name__)
 
+# Constants for reranker types
+RERANKER_TYPE_RRF = "rrf"
+RERANKER_TYPE_WEIGHTED = "weighted"
+
 
 def parse_pdf(data: bytes) -> str:
     # For PDF and DOC/DOCX files, we can't reliably convert to string
@@ -204,7 +208,13 @@ class EmbeddingIndex(ABC):
 
     @abstractmethod
     async def query_hybrid(
-        self, embedding: NDArray, query_string: str, k: int, score_threshold: float
+        self,
+        embedding: NDArray,
+        query_string: str,
+        k: int,
+        score_threshold: float,
+        reranker_type: str,
+        reranker_params: dict[str, Any] | None = None,
     ) -> QueryChunksResponse:
         raise NotImplementedError()
 
@@ -251,15 +261,29 @@ class VectorDBWithIndex:
         k = params.get("max_chunks", 3)
         mode = params.get("mode")
         score_threshold = params.get("score_threshold", 0.0)
+
+        # Get ranker configuration
+        ranker = params.get("ranker")
+        if ranker is None:
+            # Default to RRF with impact_factor=60.0
+            reranker_type = RERANKER_TYPE_RRF
+            reranker_params = {"impact_factor": 60.0}
+        else:
+            reranker_type = ranker.type
+            reranker_params = (
+                {"impact_factor": ranker.impact_factor} if ranker.type == RERANKER_TYPE_RRF else {"alpha": ranker.alpha}
+            )
+
         query_string = interleaved_content_as_str(query)
+        if mode == "keyword":
+            return await self.index.query_keyword(query_string, k, score_threshold)
 
         # Calculate embeddings for both vector and hybrid modes
         embeddings_response = await self.inference_api.embeddings(self.vector_db.embedding_model, [query_string])
         query_vector = np.array(embeddings_response.embeddings[0], dtype=np.float32)
-
-        if mode == "keyword":
-            return await self.index.query_keyword(query_string, k, score_threshold)
-        elif mode == "hybrid":
-            return await self.index.query_hybrid(query_vector, query_string, k, score_threshold)
+        if mode == "hybrid":
+            return await self.index.query_hybrid(
+                query_vector, query_string, k, score_threshold, reranker_type, reranker_params
+            )
         else:
             return await self.index.query_vector(query_vector, k, score_threshold)