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""" | ||
Demonstrates how to use the `ChatInterface` to create a chatbot using | ||
[LangChain Expression Language](https://python.langchain.com/docs/expression_language/) (LCEL). | ||
""" | ||
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from operator import itemgetter | ||
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import panel as pn | ||
from langchain.memory import ConversationSummaryBufferMemory | ||
from langchain_core.output_parsers import StrOutputParser | ||
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder | ||
from langchain_core.runnables import RunnableLambda, RunnablePassthrough | ||
from langchain_openai import ChatOpenAI | ||
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pn.extension() | ||
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SYSTEM_PROMPT = "Try to be a silly comedian." | ||
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async def callback(contents, user, instance): | ||
message = "" | ||
inputs = {"input": contents} | ||
async for token in chain.astream(inputs): | ||
message += token | ||
yield message | ||
memory.save_context(inputs, {"output": message}) | ||
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model = ChatOpenAI(model="gpt-3.5-turbo") | ||
memory = ConversationSummaryBufferMemory(return_messages=True, llm=model) | ||
prompt = ChatPromptTemplate.from_messages( | ||
[ | ||
("system", SYSTEM_PROMPT), | ||
MessagesPlaceholder(variable_name="history"), | ||
("human", "{input}"), | ||
] | ||
) | ||
output_parser = StrOutputParser() | ||
chain = ( | ||
RunnablePassthrough.assign( | ||
history=RunnableLambda(memory.load_memory_variables) | itemgetter("history") | ||
) | ||
| prompt | ||
| model | ||
| output_parser | ||
) | ||
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chat_interface = pn.chat.ChatInterface( | ||
pn.chat.ChatMessage( | ||
"Offer a topic and ChatGPT will try to be funny!", user="System" | ||
), | ||
callback=callback, | ||
callback_user="ChatGPT", | ||
) | ||
chat_interface.servable() |