mvectors

semantic_search/llm_eval.py

@@ -18,7 +18,7 @@ import streamlit as st
 import re
 from sklearn.metrics import ndcg_score,dcg_score
 from sklearn import preprocessing as pre
-import invoke_models#invoke_llm_model
+import invoke_models as llm#invoke_llm_model
 
 # bedrock_ = boto3.client(
 #     'bedrock-runtime',
@@ -70,7 +70,7 @@ def eval(question, answers):
         search_results += f"Index: {index_}, Description: {desc}\n\n"
         index_ = index_+1
     prompt = prompt.format(query, search_results)
-    response = invoke_llm_model.invoke_llm_model(prompt,False)
+    response = llm.invoke_llm_model(prompt,False)
     #response = textgen_llm(prompt)
     print("Response from LLM: ", response)
     inter_trim =response.split("[")[1]