Update app.py

app.py

@@ -17,7 +17,7 @@ load_index_from_storage,
     set_global_service_context,
 )
 #from langchain.embeddings import HuggingFaceEmbeddings, HuggingFaceInstructEmbeddings 
-#from langchain_community.embeddings import HuggingFaceInstructEmbeddings 
+from langchain_community.embeddings import HuggingFaceInstructEmbeddings 
 #from llama_index.embeddings.huggingface import  HuggingFaceInstructEmbeddings
 from g4f import Provider, models
 
@@ -35,7 +35,7 @@ g4f.debug.logging = True  # Enable logging
 #print(g4f.version)  # Check version
 #print(g4f.Provider.Ails.params)  
 
-"""
+
 #documents = SimpleDirectoryReader('data').load_data()
 model_kwargs = {'device': 'cpu'}
 encode_kwargs = {'normalize_embeddings': True}
@@ -43,7 +43,7 @@ embed_model = HuggingFaceInstructEmbeddings(
     model_name="hkunlp/instructor-xl", model_kwargs=model_kwargs,
     encode_kwargs=encode_kwargs 
 ) 
-"""
+
 #from langchain_community.embeddings import HuggingFaceInstructEmbeddings
 
 model_name = "hkunlp/instructor-xl"
@@ -52,10 +52,10 @@ encode_kwargs = {'normalize_embeddings': True}
 
 from llama_index.core import Settings
 
-embed_model = InstructorEmbedding()
+#embed_model = InstructorEmbedding(model_name)
 
 Settings.embed_model = embed_model
-Settings.chunk_size = 512
+#Settings.chunk_size = 512
 llm= LLM = G4FLLM(
         model=models.gpt_35_turbo_16k,
     )
@@ -63,7 +63,7 @@ llm= LLM = G4FLLM(
 Settings.llm = LangChainLLM(llm=llm)
 #embed_model=embed_model)
 
-#Settings.service_context = ServiceContext.from_defaults(chunk_size=5512, llm=llm, embed_model=embed_model )
+Settings.service_context = ServiceContext.from_defaults(chunk_size=5512, llm=llm, embed_model=embed_model )
 
 
 # rebuild storage context