issue: memory solved

qaPipeline_chain_only.py

@@ -86,6 +86,14 @@ def get_local_LLAMA2():
     print("\n\n> local LLAMA2 loaded")
     return LLAMA2
 
+memory = ConversationBufferWindowMemory(
+            memory_key="chat_history",
+            input_key="question",
+            output_key = "answer",
+            return_messages=True,
+            k=3
+        )
+
 class QAPipeline:
 
     def __init__(self):
@@ -100,7 +108,7 @@ class QAPipeline:
         self.qa_chain = None
     
     def run_agent(self,query, model, dataset, openai_api_key=None):
-    
+        
         try:
             if (self.llm_name != model) or (self.dataset_name != dataset) or (self.qa_chain == None):
                 self.set_model(model, openai_api_key)
@@ -168,30 +176,22 @@ class QAPipeline:
 
 
     def set_qa_chain(self):
+        print(f"\n> creating agent_chain")
         
         try:
-            memory = ConversationBufferWindowMemory(
-            memory_key="chat_history",
-            input_key="question",
-            output_key = "answer",
-            return_messages=True,
-            k=3
-            )
-
+        
             # Define a custom prompt
             B_INST, E_INST = "[INST]", "[/INST]"
             B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
 
             retrieval_qa_template = (
-            """<<SYS>> 
+            """<<SYS>>
             You are the AI assistant of company boardpac which provide services to company board members related to banking and financial sector.
-            You have 2 tasks to do.
 
-            Task 1: combine the given chat history and user question to come up with a follow-up question
+            First combine the given chat history and user question to come up with a follow-up question
             <chat history>: {chat_history}
 
-            Task 2:
-            Identify the type of the follow-up question using following 3 types and answer accordingly.
+            Then dentify the type of the follow-up question using following 3 types and answer accordingly.
             Answer should be short and simple as possible.
             Dont add any extra details that is not mentioned in the context.
 
@@ -209,18 +209,21 @@ class QAPipeline:
 
             <Type 3>
             If the follow-up question is related to Banking and Financial Services Sector like Banking & Financial regulations, legal framework, governance framework, compliance requirements as per Central Bank regulations.
-            please answer the question based on the context information provided in bottom containing few related documents of central bank acts published in various years.
+            please answer the question based only on the information provided in following central bank documents published in various years.
             The published year is mentioned as the  metadata 'year' of each source document.
-            The content of a bank act of a past year can updated by a bank act from a latest year.
+            Please notice that content of a one document of a past year can updated by a new document from a recent year.
             Always try to answer with latest information and mention the year which information extracted.
             If you dont know the answer say you dont know, dont try to makeup answers.
             Start the answer with code word Boardpac AI(QA):
             </Type 3>
-   
-            <Context information>: {context}
             <</SYS>>
+   
+            [INST]
+            <DOCUMENTS>
+            {context}
+            </DOCUMENTS>
 
-            [INST]Question : {question}[/INST]"""
+            Question : {question}[/INST]"""
             )
 
             retrieval_qa_chain_prompt = PromptTemplate(