changes

src/main.py

@@ -16,7 +16,7 @@ from langchain.llms import OpenAI
 from langchain.memory import ConversationBufferMemory
 from langchain.vectorstores import Chroma
 from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.prompts import PromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate, AIMessagePromptTemplate, ChatPromptTemplate, MessagesPlaceholder
+from langchain.prompts import PromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate, AIMessagePromptTemplate, ChatPromptTemplate
 import datetime
 import shutil
 
@@ -97,8 +97,7 @@ def retreival(vector_store, k):
     # Create the chat prompt templates
     messages = [
     SystemMessagePromptTemplate.from_template(system_template),
-    MessagesPlaceholder(variable_name="chat_history"),
-    HumanMessagePromptTemplate.from_template(human_template),
+    HumanMessagePromptTemplate.from_template(human_template)
     # AIMessagePromptTemplate.from_template(ai_template)
     ]
 
@@ -121,7 +120,7 @@ def retreival(vector_store, k):
         memory=memory,
         return_source_documents=True, #When used these 2 properties, the output gets 3 properties: answer, source_document, source_document_score and then have to speocify input and output key in memory for it to work
         combine_docs_chain_kwargs=dict({"prompt": PROMPT})
-    )
+        )
 
     return chain
 
@@ -139,5 +138,9 @@ class ConversationalResponse:
         self.chain = retreival(self.vector_store, self.k)
 
     def __call__(self, question):
-        agent = self.chain(question)
+        chat_history = []
+        agent = self.chain({"question": question
+                            , "chat_history": chat_history
+                            })
+        chat_history.append((question, agent['answer']))
         return agent['answer']