mirror of
https://github.com/langchain-ai/langchain-teacher.git
synced 2026-07-01 15:19:06 -04:00
Harrison Chase
88 lines
3.6 KiB
Python
88 lines
3.6 KiB
Python
from langchain.callbacks.base import BaseCallbackHandler
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from langchain.chat_models import ChatOpenAI
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from langchain.schema import HumanMessage, AIMessage
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import streamlit as st
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from langsmith import Client
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client = Client()
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st.set_page_config(page_title="LangChain: Getting Started Class", page_icon="🦜")
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st.title("🦜 LangChain: Getting Started Class")
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button_css =""".stButton>button {
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color: #4F8BF9;
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border-radius: 50%;
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height: 2em;
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width: 2em;
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font-size: 4px;
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}"""
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st.markdown(f'<style>{button_css}</style>', unsafe_allow_html=True)
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class StreamHandler(BaseCallbackHandler):
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def __init__(self, container, initial_text=""):
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self.container = container
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self.text = initial_text
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def on_llm_new_token(self, token: str, **kwargs) -> None:
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self.text += token
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self.container.markdown(self.text)
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with open("guide.txt", "r") as f:
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guide = f.read()
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from langchain.chat_models import ChatOpenAI
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from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder, HumanMessagePromptTemplate
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from langchain.schema import SystemMessage
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from langchain.memory import ConversationBufferMemory
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template = """The below is a "Getting Started" guide for LangChain. You are an expert educator, and are responsible for walking the user through this getting started guide. You should make sure to guide them along, encouraging them to progress when appropriate. If they ask questions not related to this getting started guide, you should politely decline to answer and resume trying to teach them about LangChain!
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Please limit any responses to only one concept or step at a time. Make sure they fully understand that before moving on to the next. This is an interactive lesson - do not lecture them, but rather engage and guide them along!
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When they have finished the guide, congragulate them and tell them to move onto the next section.
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-----------------
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{content}""".format(content=guide)
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prompt_template = ChatPromptTemplate(messages = [SystemMessage(content=template), MessagesPlaceholder(variable_name="chat_history"), HumanMessagePromptTemplate.from_template("{input}")])
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from langchain.chains import LLMChain
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def send_feedback(run_id, score):
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client.create_feedback(run_id, "user_score", score=score)
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if "messages" not in st.session_state:
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st.session_state["messages"] = [AIMessage(content="Welcome! This short course with help you started with LangChain, and will cover LLMs, prompts, output parsers, and LLMChains.Before doing this, you should have a Python environment set up. Do you have that done?")]
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for msg in st.session_state["messages"]:
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if isinstance(msg, HumanMessage):
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st.chat_message("user").write(msg.content)
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else:
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st.chat_message("assistant").write(msg.content)
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if prompt := st.chat_input():
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st.chat_message("user").write(prompt)
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with st.chat_message("assistant"):
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stream_handler = StreamHandler(st.empty())
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model = ChatOpenAI(streaming=True, callbacks=[stream_handler], model="gpt-4")
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chain = LLMChain(prompt=prompt_template, llm=model)
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response = chain({"input":prompt, "chat_history":st.session_state.messages[-20:]}, include_run_info=True)
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st.session_state.messages.append(HumanMessage(content=prompt))
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st.session_state.messages.append(AIMessage(content=response[chain.output_key]))
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run_id = response["__run"].run_id
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col_blank, col_text, col1, col2 = st.columns([10, 2,1,1])
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with col_text:
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st.text("Feedback:")
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with col1:
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st.button("👍", on_click=send_feedback, args=(run_id, 1))
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with col2:
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st.button("👎", on_click=send_feedback, args=(run_id, 0))
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