Update custom_llm.py

custom_llm.py

@@ -61,21 +61,21 @@ def create_vectorstore():
     
     docs = splitter.create_documents([loader]+web_loader)
     print(docs)
-    # emb_model = HuggingFaceEmbeddings(model_name='sentence-transformers/paraphrase-multilingual-mpnet-base-v2', encode_kwargs={'normalize_embeddings': True})
-    # db = FAISS.from_documents(docs, emb_model)
+    emb_model = HuggingFaceEmbeddings(model_name='sentence-transformers/paraphrase-multilingual-mpnet-base-v2', encode_kwargs={'normalize_embeddings': True})
+    db = FAISS.from_documents(docs, emb_model)
 
-    emb_model = HuggingFaceInferenceAPIEmbeddings(
-        api_key=API_TOKEN, model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2", encode_kwargs={'normalize_embeddings': True}
-    )
+    # emb_model = HuggingFaceInferenceAPIEmbeddings(
+    #     api_key=API_TOKEN, model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2", encode_kwargs={'normalize_embeddings': True}
+    # )
 
-    x = open("wi_knowledge.pkl", 'rb')
+    # x = open("wi_knowledge.pkl", 'rb')
 
     # db = FAISS.deserialize_from_bytes(
     #     embeddings=emb_model, serialized=x
     # ) 
 
-    db = pickle.load(x)
-    print(db)
+    # db = pickle.load(x)
+    # print(db)
     # db.add_documents( splitter.transform_documents(docs) )
     
     return db