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from datetime import datetime
import streamlit as st
from langchain import LLMChain
from langchain.callbacks.base import BaseCallbackHandler
from langchain.callbacks.tracers.langchain import wait_for_all_tracers
from langchain.callbacks.tracers.run_collector import RunCollectorCallbackHandler
from langchain.chat_models import ChatOpenAI
from langchain.memory import StreamlitChatMessageHistory, ConversationBufferMemory
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.schema.runnable import RunnableConfig
from langsmith import Client
from streamlit_feedback import streamlit_feedback
st.set_page_config(
page_title="Chat LangSmith",
page_icon="🦜",
)
def get_llm_chain(system_prompt: str, memory: ConversationBufferMemory) -> LLMChain:
"""Return a basic LLMChain with memory."""
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
system_prompt + "\nIt's currently {time}.",
),
MessagesPlaceholder(variable_name="chat_history"),
("human", "{input}"),
],
).partial(time=lambda: str(datetime.now()))
llm = ChatOpenAI(temperature=0.7, streaming=True)
return LLMChain(prompt=prompt, llm=llm, memory=memory)
client = Client()
# "# Chat🦜🛠️"
# Initialize State
if "trace_link" not in st.session_state:
st.session_state.trace_link = None
if "run_id" not in st.session_state:
st.session_state.run_id = None
st.sidebar.markdown(
"""
# Menu
""",
)
_DEFAULT_SYSTEM_PROMPT = "You are a helpful chatbot."
system_prompt = st.sidebar.text_area(
"Custom Instructions",
_DEFAULT_SYSTEM_PROMPT,
help="Custom instructions to provide the language model to determine style, personality, etc.",
)
system_prompt = system_prompt.strip().replace("{", "{{").replace("}", "}}")
memory = ConversationBufferMemory(
chat_memory=StreamlitChatMessageHistory(key="langchain_messages"),
return_messages=True,
memory_key="chat_history",
)
chain = get_llm_chain(system_prompt, memory)
if st.sidebar.button("Clear message history"):
print("Clearing message history")
memory.clear()
st.session_state.trace_link = None
st.session_state.run_id = None
# Display chat messages from history on app rerun
# NOTE: This won't be necessary for Streamlit 1.26+, you can just pass the type directly
# https://github.com/streamlit/streamlit/pull/7094
def _get_openai_type(msg):
if msg.type == "human":
return "user"
if msg.type == "ai":
return "assistant"
return msg.role if msg.type == "chat" else msg.type
for msg in st.session_state.langchain_messages:
streamlit_type = _get_openai_type(msg)
avatar = "🦜" if streamlit_type == "assistant" else None
with st.chat_message(streamlit_type, avatar=avatar):
st.markdown(msg.content)
if st.session_state.trace_link:
st.sidebar.markdown(
f'<a href="{st.session_state.trace_link}" target="_blank"><button>Latest Trace: 🛠️</button></a>',
unsafe_allow_html=True,
)
class StreamHandler(BaseCallbackHandler):
def __init__(self, container, initial_text=""):
self.container = container
self.text = initial_text
def on_llm_new_token(self, token: str, **kwargs) -> None:
self.text += token
self.container.markdown(self.text)
run_collector = RunCollectorCallbackHandler()
def _reset_feedback():
st.session_state.feedback_update = None
st.session_state.feedback = None
if prompt := st.chat_input(placeholder="Ask me a question!"):
st.chat_message("user").write(prompt)
_reset_feedback()
with st.chat_message("assistant", avatar="🦜"):
message_placeholder = st.empty()
stream_handler = StreamHandler(message_placeholder)
runnable_config = RunnableConfig(
callbacks=[run_collector, stream_handler],
tags=["Streamlit Chat"],
)
full_response = chain.invoke({"input": prompt}, config=runnable_config)["text"]
message_placeholder.markdown(full_response)
run = run_collector.traced_runs[0]
run_collector.traced_runs = []
st.session_state.run_id = run.id
wait_for_all_tracers()
url = client.read_run(run.id).url
st.session_state.trace_link = url
# Simple feedback section
# Optionally add a thumbs up/down button for feedback
if st.session_state.get("run_id"):
# feedback = streamlit_feedback(
# feedback_type="thumbs",
# key=f"feedback_{st.session_state.run_id}",
# )
# scores = {"👍": 1, "👎": 0}
scores = {"😀": 1, "🙂": 0.75, "😐": 0.5, "🙁": 0.25, "😞": 0}
feedback = streamlit_feedback(
feedback_type="faces",
optional_text_label="[Optional] Please provide an explanation",
key=f"feedback_{st.session_state.run_id}",
)
if feedback:
score = scores[feedback["score"]]
feedback = client.create_feedback(
st.session_state.run_id,
feedback["type"],
score=score,
comment=feedback.get("text", None),
)
st.session_state.feedback = {"feedback_id": str(feedback.id), "score": score}
st.toast("Feedback recorded!", icon="📝")
# # Prompt for more information, if feedback was submitted
# if st.session_state.get("feedback"):
# feedback = st.session_state.get("feedback")
# feedback_id = feedback["feedback_id"]
# score = feedback["score"]
# if score == 0:
# if correction := st.text_input(
# label="What would the correct or preferred response have been?",
# key=f"correction_{feedback_id}",
# ):
# st.session_state.feedback_update = {
# "correction": {"desired": correction},
# "feedback_id": feedback_id,
# }
# elif score == 1:
# if comment := st.text_input(
# label="Anything else you'd like to add about this response?",
# key=f"comment_{feedback_id}",
# ):
# st.session_state.feedback_update = {
# "comment": comment,
# "feedback_id": feedback_id,
# }
# # Update the feedback if additional information was provided
# if st.session_state.get("feedback_update"):
# feedback_update = st.session_state.get("feedback_update")
# feedback_id = feedback_update.pop("feedback_id")
# client.update_feedback(feedback_id, **feedback_update)
# # Clear the comment or correction box
# _reset_feedback()
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