vectorstore

app.py

@@ -34,13 +34,7 @@ login(token=HF_KEY)
 
 class BSIChatbot:
     def __init__(self, model_paths: Dict[str, str], docs_path: str):
-        #self.embedding_model = None
-        self.embedding_model = HuggingFaceEmbeddings(
-            model_name=self.word_and_embed_model_path,
-            multi_process=True,
-            model_kwargs={"device": "cuda"},
-            encode_kwargs={"normalize_embeddings": True},
-        )
+        self.embedding_model = None
         self.llmpipeline = None
         self.llmtokenizer = None
         self.vectorstore = None
@@ -58,7 +52,12 @@ class BSIChatbot:
         raw_knowledge_base = []
 
         # Initialize embedding model
-
+        self.embedding_model = HuggingFaceEmbeddings(
+            model_name=self.word_and_embed_model_path,
+            multi_process=True,
+            model_kwargs={"device": "cuda"},
+            encode_kwargs={"normalize_embeddings": True},
+        )
 
         if rebuild_embeddings:
             # Load documents
@@ -98,9 +97,9 @@ class BSIChatbot:
     @spaces.GPU
     def retrieve_similar_embedding(self, query: str):
         #lazy load
-        if (self.vectorstore == None):
-            self.vectorstore = FAISS.load_local(os.path.join(self.docs, "_embeddings"), self.embedding_model,
-                                                allow_dangerous_deserialization=True)
+        #if (self.vectorstore == None):
+        #   self.vectorstore = FAISS.load_local(os.path.join(self.docs, "_embeddings"), self.embedding_model,
+        #                                        allow_dangerous_deserialization=True)
         print("DBG: Vectorstore Status retriever:", self.vectorstore)
         query = f"Instruct: Given a search query, retrieve the relevant passages that answer the query\nQuery:{query}"
         return self.vectorstore.similarity_search(query=query, k=20)