new version

Author: Bessouat40

src/raglight/vectorstore/chroma.py

@@ -46,7 +46,9 @@ def __init__(
         search_type: str = "semantic",
         alpha: float = 0.5,
     ) -> None:
-        super().__init__(persist_directory, embeddings_model, custom_processors, search_type, alpha)
+        super().__init__(
+            persist_directory, embeddings_model, custom_processors, search_type, alpha
+        )
 
         self.persist_directory = persist_directory
         self.host = host

src/raglight/vectorstore/qdrant.py

@@ -39,7 +39,9 @@ def __init__(
                 "Install it with: pip install raglight[qdrant]"
             )
 
-        super().__init__(persist_directory, embeddings_model, custom_processors, search_type, alpha)
+        super().__init__(
+            persist_directory, embeddings_model, custom_processors, search_type, alpha
+        )
 
         self.collection_name = collection_name
         self._classes_collection_name = f"{collection_name}_classes"

src/raglight/vectorstore/vector_store.py

@@ -64,7 +64,9 @@ def _bm25_search(self, question: str, k: int) -> List[Document]:
                 docs.append(Document(page_content=self._bm25.corpus[idx]))
         return docs
 
-    def _rrf(self, ranked_lists: List[List[Document]], k_rrf: int = 60) -> List[Document]:
+    def _rrf(
+        self, ranked_lists: List[List[Document]], k_rrf: int = 60
+    ) -> List[Document]:
         scores: Dict[str, float] = {}
         doc_map: Dict[str, Document] = {}
         for ranked in ranked_lists: