Update app.py

app.py

@@ -144,96 +144,23 @@ st.write("📂 **Vector Store Created:**", st.session_state.vector_created)
 
 
 # ----------------- Query Input -----------------
-query = st.text_input("🔍 Ask a question about the document:")
-
 if query:
     with st.spinner("🔄 Retrieving relevant context..."):
         retriever = st.session_state.vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5})
         retrieved_docs = retriever.invoke(query)
-
-        # Debugging: Show retrieved documents
-        st.write("🔹 Retrieved Documents:", retrieved_docs)
-
-        if not retrieved_docs:
-            st.error("❌ No relevant documents retrieved! Try a different query.")
-        else:
-            # Ensure extracted content is formatted correctly
-            context = [d.page_content for d in retrieved_docs]
-
-            if isinstance(context, list):  # Convert list to string for LLMChain
-                context_str = "\n".join(context)
-            else:
-                context_str = str(context)
-
-            st.success("✅ Context retrieved successfully!")
-            st.write("🔹 Extracted Context:", context_str)
+        context = [d.page_content for d in retrieved_docs]
+        st.success("✅ Context retrieved successfully!")
 
     # ----------------- Run Individual Chains Explicitly -----------------
+    context_relevancy_chain = LLMChain(llm=llm_judge, prompt=PromptTemplate(input_variables=["retriever_query", "context"], template=relevancy_prompt), output_key="relevancy_response")
+    relevant_context_chain = LLMChain(llm=llm_judge, prompt=PromptTemplate(input_variables=["relevancy_response"], template=relevant_context_picker_prompt), output_key="context_number")
+    relevant_contexts_chain = LLMChain(llm=llm_judge, prompt=PromptTemplate(input_variables=["context_number", "context"], template=response_synth), output_key="relevant_contexts")
+    response_chain = LLMChain(llm=rag_llm, prompt=PromptTemplate(input_variables=["query", "context"], template=rag_prompt), output_key="final_response")
 
-    # Fix: Ensuring all required variables are passed
-    context_relevancy_chain = LLMChain(
-        llm=llm_judge,
-        prompt=PromptTemplate(
-            input_variables=["retriever_query", "context"], 
-            template=relevancy_prompt
-        ),
-        output_key="relevancy_response"
-    )
-
-    relevant_context_chain = LLMChain(
-        llm=llm_judge,
-        prompt=PromptTemplate(
-            input_variables=["relevancy_response"],
-            template=relevant_context_picker_prompt
-        ),
-        output_key="context_number"
-    )
-
-    relevant_contexts_chain = LLMChain(
-        llm=llm_judge,
-        prompt=PromptTemplate(
-            input_variables=["context_number", "context"],
-            template=response_synth
-        ),
-        output_key="relevant_contexts"
-    )
-
-    response_chain = LLMChain(
-        llm=rag_llm,
-        prompt=PromptTemplate(
-            input_variables=["query", "context"],
-            template=rag_prompt
-        ),
-        output_key="final_response"
-    )
-
-    # ----------------- Fix: Ensuring All Keys Exist -----------------
-
-    response_crisis = context_relevancy_chain.invoke({
-        "context": context_str,
-        "retriever_query": query
-    })
-
-    # Debugging: Show intermediate response
-    st.write("🔍 Context Relevancy Response:", response_crisis["relevancy_response"])
-
-    relevant_response = relevant_context_chain.invoke({
-        "relevancy_response": response_crisis["relevancy_response"]
-    })
-
-    st.write("🔍 Picked Relevant Contexts:", relevant_response["context_number"])
-
-    contexts = relevant_contexts_chain.invoke({
-        "context_number": relevant_response["context_number"],
-        "context": context_str  # Ensure correct format
-    })
-
-    st.write("🔍 Extracted Relevant Contexts:", contexts["relevant_contexts"])
-
-    final_response = response_chain.invoke({
-        "query": query,
-        "context": contexts["relevant_contexts"]
-    })
+    response_crisis = context_relevancy_chain.invoke({"context": context, "retriever_query": query})
+    relevant_response = relevant_context_chain.invoke({"relevancy_response": response_crisis["relevancy_response"]})
+    contexts = relevant_contexts_chain.invoke({"context_number": relevant_response["context_number"], "context": context})
+    final_response = response_chain.invoke({"query": query, "context": contexts["relevant_contexts"]})
 
     # ----------------- Display All Outputs -----------------
     st.markdown("### Context Relevancy Evaluation")
@@ -246,4 +173,13 @@ if query:
     st.json(contexts["relevant_contexts"])
 
     st.subheader("context_relevancy_evaluation_chain Statement")
+    st.json(final_response["relevancy_response"])
+
+    st.subheader("pick_relevant_context_chain Statement")
+    st.json(final_response["context_number"])
+
+    st.subheader("relevant_contexts_chain Statement")
+    st.json(final_response["relevant_contexts"])
+
+    st.subheader("RAG Response Statement")
     st.json(final_response["final_response"])