similarity score in response fix

code/modules/vector_db.py

@@ -5,6 +5,7 @@ from langchain.vectorstores import FAISS, Chroma
 from langchain.schema.vectorstore import VectorStoreRetriever
 from langchain.callbacks.manager import CallbackManagerForRetrieverRun
 from langchain.schema.document import Document
+from langchain_core.callbacks import AsyncCallbackManagerForRetrieverRun
 
 try:
     from modules.embedding_model_loader import EmbeddingModelLoader
@@ -17,12 +18,15 @@ except:
     from constants import *
     from helpers import *
 
+from typing import List
+
 
 class VectorDBScore(VectorStoreRetriever):
+
     # See https://github.com/langchain-ai/langchain/blob/61dd92f8215daef3d9cf1734b0d1f8c70c1571c3/libs/langchain/langchain/vectorstores/base.py#L500
     def _get_relevant_documents(
-        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-    ):
+            self, query: str, *, run_manager: CallbackManagerForRetrieverRun
+    ) -> List[Document]:
         docs_and_similarities = (
             self.vectorstore.similarity_search_with_relevance_scores(
                 query, **self.search_kwargs
@@ -35,6 +39,22 @@ class VectorDBScore(VectorStoreRetriever):
         docs = [doc for doc, _ in docs_and_similarities]
         return docs
 
+    async def _aget_relevant_documents(
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
+    ) -> List[Document]:
+        docs_and_similarities = (
+            self.vectorstore.similarity_search_with_relevance_scores(
+                query, **self.search_kwargs
+            )
+        )
+        # Make the score part of the document metadata
+        for doc, similarity in docs_and_similarities:
+            doc.metadata["score"] = similarity
+
+        docs = [doc for doc, _ in docs_and_similarities]
+        return docs
+
+
 
 class VectorDB:
     def __init__(self, config, logger=None):
@@ -160,7 +180,7 @@ class VectorDB:
                     + self.config["embedding_options"]["model"],
                 ),
                 self.embedding_model,
-                allow_dangerous_deserialization=True,
+                # allow_dangerous_deserialization=True, <- unexpected keyword argument to load_local
             )
         elif self.db_option == "Chroma":
             self.vector_db = Chroma(