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import chainlit as cl
from langchain.prompts import ChatPromptTemplate
from langchain.schema import StrOutputParser
from langchain.schema.runnable import Runnable
from langchain.schema.runnable.config import RunnableConfig
from react_agent_v2 import get_react_agent
from langchain.memory import ConversationBufferMemory
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory
@cl.on_chat_start
async def on_chat_start():
message_history = ChatMessageHistory()
memory = ConversationBufferMemory(
memory_key = "chat_history",
output_key = "output",
chat_memory = message_history,
return_message = True
)
agent_executor = get_react_agent(memory)
cl.user_session.set("runnable", agent_executor)
@cl.on_message
async def on_message(message: cl.Message):
# runnable = cl.user_session.get("runnable") # type: Runnable
#
# msg = cl.Message(content="")
#
# for chunk in await cl.make_async(runnable.stream)(
# {"input": message.content},
# config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
# ):
# await msg.stream_token(chunk)
#
# await msg.send()
#
# # Get user input from the message
# user_input = message.content
#
# # Run the agent with user input and get the response
# response = await cl.make_async(agent_executor)(user_input)
#
# # Display the response to the user
# cl.message(response)
llm_chain = cl.user_session.get("runnable")
response = llm_chain.invoke(
{"input": message.content}, callbacks = [cl.LangchainCallbackHandler()]
)
await cl.Message(response["output"].replace("`", "")).send()
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