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LLMInsights.py

@@ -127,7 +127,8 @@ class HallucinatonEvaluater:
 
 
 
-@st.cache_resource
+#@st.cache_resource
+@st.cache_data
 def initialize_vectorstore():
 
     webpage_loader = WebBaseLoader("https://www.tredence.com/case-studies/forecasting-app-installs-for-a-large-retailer-in-the-us").load()    
@@ -144,6 +145,7 @@ def initialize_vectorstore():
     retriever = vectorstore.as_retriever()
     st.session_state['vectorstore'] = vectorstore
     st.session_state['docadd'] = 0
+    print("st.session_state['docadd'] ", st.session_state['docadd'])
 
     return retriever
 
@@ -218,7 +220,7 @@ def scoring_eval(question: str, answer: str, context: str):
         }        
     )
     print("got score")
-    st.markdown('<h1 style="color:#100170;font-size:24px;">Score</h1>', unsafe_allow_html=True)
+    st.markdown('<h1 style="color:#100170;font-size:24px;">Completion Score</h1>', unsafe_allow_html=True)
     st.text_area(label=" ", value=score, height=30)
     #return {"hallucination_score": hallucination_score}    
     #time.sleep(10)
@@ -247,18 +249,18 @@ And you'll need to submit your grading for the correctness, comprehensiveness an
 - Correctness: If the answer correctly answer the question, below are the details for different scores:
   - Score 0: the answer is completely incorrect, doesn’t mention anything about the question or is completely contrary to the correct answer.
       - For example, when asked “How to terminate a databricks cluster”, the answer is empty string, or content that’s completely irrelevant, or sorry I don’t know the answer.
-  - Score 4: the answer provides some relevance to the question and answer one aspect of the question correctly.
+  - Score 50: the answer provides some relevance to the question and answer one aspect of the question correctly.
       - Example:
           - Question: How to terminate a databricks cluster
           - Answer: Databricks cluster is a cloud-based computing environment that allows users to process big data and run distributed data processing tasks efficiently.
           - Or answer:  In the Databricks workspace, navigate to the "Clusters" tab. And then this is a hard question that I need to think more about it
-  - Score 7: the answer mostly answer the question but is missing or hallucinating on one critical aspect.
+  - Score 75: the answer mostly answer the question but is missing or hallucinating on one critical aspect.
       - Example:
           - Question: How to terminate a databricks cluster”
           - Answer: “In the Databricks workspace, navigate to the "Clusters" tab.
           Find the cluster you want to terminate from the list of active clusters.
           And then you’ll find a button to terminate all clusters at once”
-  - Score 10: the answer correctly answer the question and not missing any major aspect
+  - Score 100: the answer correctly answer the question and not missing any major aspect
       - Example:
           - Question: How to terminate a databricks cluster
           - Answer: In the Databricks workspace, navigate to the "Clusters" tab.
@@ -311,11 +313,16 @@ with tab1:
   with st.form(" RAG with evaluation - scoring & hallucination "):
     #tab1.subheader(''' # RAG App''')
     initialize_vectorstore()
-    if st.session_state['docadd'] == 1:
-        retriever = st.session_state['retriever']
-    else:
+    time.sleep(2)
+    try:
+        if st.session_state['docadd'] == 1:
+            retriever = st.session_state['retriever']
+        else:
+            retriever = initialize_vectorstore()
+    except:
+        st.session_state['docadd'] = 0
         retriever = initialize_vectorstore()
-    
+
     #print("lenght in tab1,  ", len(vectorstore.serialize_to_bytes()))
     options = ["true", "false"]