feat(chat): Add follow up questions functionality (#2241)

This pull request adds the ability to ask follow up questions in the
chat. It also includes various code improvements and bug fixes.

backend/modules/brain/knowledge_brain_qa.py

@@ -181,6 +181,8 @@ class KnowledgeBrainQA(BaseModel, QAInterface):
             brain_id=brain_id,
             chat_id=chat_id,
             streaming=streaming,
+            max_input=self.max_input,
+            max_tokens=self.max_tokens,
             **kwargs,
         )
 

backend/modules/brain/rags/quivr_rag.py

@@ -5,14 +5,17 @@ from uuid import UUID
 from langchain.chains import ConversationalRetrievalChain
 from langchain.embeddings.ollama import OllamaEmbeddings
 from langchain.llms.base import BaseLLM
-from langchain.memory import ConversationBufferMemory
 from langchain.prompts import HumanMessagePromptTemplate
 from langchain.schema import format_document
 from langchain_community.chat_models import ChatLiteLLM
-from langchain_core.messages import SystemMessage, get_buffer_string
+from langchain_core.messages import SystemMessage
 from langchain_core.output_parsers import StrOutputParser
-from langchain_core.prompts import ChatPromptTemplate, PromptTemplate
-from langchain_core.runnables import RunnableLambda, RunnablePassthrough
+from langchain_core.prompts import (
+    ChatPromptTemplate,
+    MessagesPlaceholder,
+    PromptTemplate,
+)
+from langchain_core.runnables import RunnablePassthrough
 from langchain_openai import OpenAIEmbeddings
 from llm.utils.get_prompt_to_use import get_prompt_to_use
 from logger import get_logger
@@ -53,6 +56,7 @@ ANSWER_PROMPT = ChatPromptTemplate.from_messages(
                 "When answering use markdown or any other techniques to display the content in a nice and aerated way.  Use the following pieces of context from files provided by the user to answer the users question in the same language as the user question. Your name is Quivr. You're a helpful assistant.  If you don't know the answer with the context provided from the files, just say that you don't know, don't try to make up an answer."
             )
         ),
+        MessagesPlaceholder(variable_name="chat_history", optional=False),
         HumanMessagePromptTemplate.from_template(template_answer),
     ]
 )
@@ -201,23 +205,17 @@ class QuivrRAG(BaseModel):
 
     def get_chain(self):
         retriever_doc = self.get_retriever()
-        memory = ConversationBufferMemory(
-            return_messages=True, output_key="answer", input_key="question"
-        )
-
-        loaded_memory = RunnablePassthrough.assign(
-            chat_history=RunnableLambda(memory.load_memory_variables)
-            | itemgetter("history"),
-        )
 
+        _inputs = RunnablePassthrough()
         standalone_question = {
             "standalone_question": {
-                "question": lambda x: x["question"],
-                "chat_history": lambda x: get_buffer_string(x["chat_history"]),
+                "question": itemgetter("question"),
+                "chat_history": itemgetter("chat_history"),
             }
             | CONDENSE_QUESTION_PROMPT
             | ChatLiteLLM(temperature=0, model=self.model)
             | StrOutputParser(),
+            "chat_history": itemgetter("chat_history"),
         }
 
         prompt_custom_user = self.prompt_to_use()
@@ -230,12 +228,14 @@ class QuivrRAG(BaseModel):
             "docs": itemgetter("standalone_question") | retriever_doc,
             "question": lambda x: x["standalone_question"],
             "custom_instructions": lambda x: prompt_to_use,
+            "chat_history": itemgetter("chat_history"),
         }
 
         final_inputs = {
             "context": lambda x: self._combine_documents(x["docs"]),
             "question": itemgetter("question"),
             "custom_instructions": itemgetter("custom_instructions"),
+            "chat_history": itemgetter("chat_history"),
         }
 
         # And finally, we do the part that returns the answers
@@ -246,4 +246,4 @@ class QuivrRAG(BaseModel):
             "docs": itemgetter("docs"),
         }
 
-        return loaded_memory | standalone_question | retrieved_documents | answer
+        return _inputs | standalone_question | retrieved_documents | answer

backend/modules/chat/dto/chats.py

@@ -4,7 +4,7 @@ from uuid import UUID
 
 from modules.chat.dto.outputs import GetChatHistoryOutput
 from modules.notification.entity.notification import Notification
-from pydantic import BaseModel, ConfigDict
+from pydantic import BaseModel
 
 
 class ChatMessage(BaseModel):