rerank model

RAG/rag_DocumentSearcher.py

@@ -12,7 +12,7 @@ headers = {"Content-Type": "application/json"}
 host = "https://search-opensearchservi-shjckef2t7wo-iyv6rajdgxg6jas25aupuxev6i.us-west-2.es.amazonaws.com/"
 
 parent_dirname = "/".join((os.path.dirname(__file__)).split("/")[0:-1])
-@st.cache_resource
+
 def query_(awsauth,inputs, session_id,search_types):
 
     print("using index: "+st.session_state.input_index)

utilities/invoke_models.py

@@ -11,7 +11,7 @@ import streamlit as st
 #import torch
 
 region = 'us-east-1'
-@st.cache_resource
+
 def get_bedrock_client():
     return boto3.client(
     'bedrock-runtime',
@@ -34,7 +34,7 @@ bedrock_runtime_client = get_bedrock_client()
 #     max_length = 16
 #     num_beams = 4
 #     gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
-@st.cache_resource
+
 def invoke_model(input):
     response = bedrock_runtime_client.invoke_model(
         body=json.dumps({
@@ -47,7 +47,7 @@ def invoke_model(input):
     
     response_body = json.loads(response.get("body").read())
     return response_body.get("embedding")
-@st.cache_resource
+
 def invoke_model_mm(text,img):
     body_ = {
             "inputText": text,
@@ -68,7 +68,7 @@ def invoke_model_mm(text,img):
     response_body = json.loads(response.get("body").read())
     #print(response_body)
     return response_body.get("embedding")
-@st.cache_resource
+
 def invoke_llm_model(input,is_stream):
     if(is_stream == False):
         response = bedrock_runtime_client.invoke_model( 
@@ -149,7 +149,7 @@ def invoke_llm_model(input,is_stream):
     #     stream = response.get('body')
         
     #     return stream
-@st.cache_resource        
+        
 def read_from_table(file,question):
     print("started table analysis:")
     print("-----------------------")
@@ -185,7 +185,7 @@ def read_from_table(file,question):
              )
     agent_res = agent.invoke(question)['output']
     return agent_res
-@st.cache_resource    
+    
 def generate_image_captions_llm(base64_string,question):
     
     # ant_client = Anthropic()