mirror of
https://github.com/langchain-ai/langchain-teacher.git
synced 2026-07-01 15:19:06 -04:00
Harrison Chase
110 lines
3.5 KiB
Python
110 lines
3.5 KiB
Python
import langchain
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from langchain.callbacks.base import BaseCallbackHandler
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from langchain.chat_models import ChatOpenAI, ChatAnthropic
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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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from langchain.chat_models import ChatOpenAI
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content = """Follow the below lesson plan, using information from the blog, cookbook, and interface guide.
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<lesson_plan>
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{lesson}
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</lesson_plan>
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<blog>
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{blog}
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</blog>
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<cookbook>
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{cookbook}
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</cookbook>
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<iterface_guide>
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{interface}
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<interface_guide>"""
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with open("lcel/guide.md") as f:
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guide = f.read()
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with open("lcel/interface.md") as f:
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interface = f.read()
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with open("lcel/blog.txt") as f:
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blog = f.read()
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with open("lcel/lesson.txt") as f:
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lesson = f.read()
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from get_prompt import load_prompt
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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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prompt_template = load_prompt(content = content.format(cookbook=guide, interface=interface, blog=blog, lesson=lesson))
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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 Expression Language. In order to get started, you should have basic familiarity with LangChain and you should have Python environment set up with langchain installed. If you don't have that, please set that up. Let me know when you're ready to proceed!")]
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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-3.5-turbo-16k")
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#model = ChatAnthropic(streaming=True, callbacks=[stream_handler], model="claude-2")
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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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