Update app1.py

app1.py

@@ -240,5 +240,35 @@ if query:
             """
         )
 
+        context_relevancy_evaluation_chain = LLMChain(llm=llm_judge, prompt=context_relevancy_checker_prompt, output_key="relevancy_response")
+
+        response_crisis = context_relevancy_evaluation_chain.invoke({"context":context,"retriever_query":query})
+
+        pick_relevant_context_chain = LLMChain(llm=llm_judge, prompt=relevant_prompt, output_key="context_number")
+
+        relevant_response = pick_relevant_context_chain.invoke({"relevancy_response":response_crisis['relevancy_response']})
+
+        relevant_contexts_chain = LLMChain(llm=llm_judge, prompt=context_prompt, output_key="relevant_contexts")
+
+        contexts = relevant_contexts_chain.invoke({"context_number":relevant_response['context_number'],"context":context})
+
+        response_chain = LLMChain(llm=rag_llm,prompt=final_prompt,output_key="final_response")
+
+        response = chain.invoke({"query":query,"context":contexts['relevant_contexts']})
+
+        # Orchestrate using SequentialChain
+        context_management_chain = SequentialChain(
+            chains=[context_relevancy_evaluation_chain ,pick_relevant_context_chain, relevant_contexts_chain,response_chain],
+            input_variables=["context","retriever_query","query"],
+            output_variables=["relevancy_response", "context_number","relevant_contexts","final_response"]
+        )
+
+        final_output = context_management_chain({"context":context,"retriever_query":query,"query":query})
+
+        
+
+
+        
+