Add research assistant example using Chainlit
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unclecode
281
docs/examples/chainlit_review.py
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281
docs/examples/chainlit_review.py
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from openai import AsyncOpenAI
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from chainlit.types import ThreadDict
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import chainlit as cl
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from chainlit.input_widget import Select, Switch, Slider
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client = AsyncOpenAI()
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# Instrument the OpenAI client
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cl.instrument_openai()
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settings = {
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"model": "gpt-3.5-turbo",
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"temperature": 0.5,
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"max_tokens": 500,
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"top_p": 1,
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"frequency_penalty": 0,
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"presence_penalty": 0,
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}
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@cl.action_callback("action_button")
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async def on_action(action: cl.Action):
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print("The user clicked on the action button!")
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return "Thank you for clicking on the action button!"
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@cl.set_chat_profiles
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async def chat_profile():
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return [
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cl.ChatProfile(
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name="GPT-3.5",
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markdown_description="The underlying LLM model is **GPT-3.5**.",
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icon="https://picsum.photos/200",
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),
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cl.ChatProfile(
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name="GPT-4",
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markdown_description="The underlying LLM model is **GPT-4**.",
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icon="https://picsum.photos/250",
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),
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]
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@cl.on_chat_start
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async def on_chat_start():
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settings = await cl.ChatSettings(
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[
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Select(
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id="Model",
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label="OpenAI - Model",
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values=["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-4", "gpt-4-32k"],
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initial_index=0,
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),
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Switch(id="Streaming", label="OpenAI - Stream Tokens", initial=True),
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Slider(
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id="Temperature",
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label="OpenAI - Temperature",
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initial=1,
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min=0,
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max=2,
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step=0.1,
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),
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Slider(
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id="SAI_Steps",
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label="Stability AI - Steps",
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initial=30,
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min=10,
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max=150,
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step=1,
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description="Amount of inference steps performed on image generation.",
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),
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Slider(
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id="SAI_Cfg_Scale",
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label="Stability AI - Cfg_Scale",
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initial=7,
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min=1,
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max=35,
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step=0.1,
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description="Influences how strongly your generation is guided to match your prompt.",
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),
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Slider(
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id="SAI_Width",
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label="Stability AI - Image Width",
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initial=512,
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min=256,
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max=2048,
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step=64,
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tooltip="Measured in pixels",
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),
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Slider(
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id="SAI_Height",
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label="Stability AI - Image Height",
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initial=512,
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min=256,
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max=2048,
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step=64,
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tooltip="Measured in pixels",
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),
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]
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).send()
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chat_profile = cl.user_session.get("chat_profile")
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await cl.Message(
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content=f"starting chat using the {chat_profile} chat profile"
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).send()
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print("A new chat session has started!")
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cl.user_session.set("session", {
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"history": [],
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"context": []
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})
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image = cl.Image(url="https://c.tenor.com/uzWDSSLMCmkAAAAd/tenor.gif", name="cat image", display="inline")
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# Attach the image to the message
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await cl.Message(
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content="You are such a good girl, aren't you?!",
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elements=[image],
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).send()
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text_content = "Hello, this is a text element."
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elements = [
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cl.Text(name="simple_text", content=text_content, display="inline")
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]
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await cl.Message(
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content="Check out this text element!",
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elements=elements,
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).send()
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elements = [
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cl.Audio(path="./assets/audio.mp3", display="inline"),
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]
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await cl.Message(
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content="Here is an audio file",
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elements=elements,
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).send()
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await cl.Avatar(
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name="Tool 1",
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url="https://avatars.githubusercontent.com/u/128686189?s=400&u=a1d1553023f8ea0921fba0debbe92a8c5f840dd9&v=4",
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).send()
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await cl.Message(
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content="This message should not have an avatar!", author="Tool 0"
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).send()
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await cl.Message(
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content="This message should have an avatar!", author="Tool 1"
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).send()
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elements = [
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cl.File(
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name="quickstart.py",
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path="./quickstart.py",
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display="inline",
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),
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]
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await cl.Message(
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content="This message has a file element", elements=elements
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).send()
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# Sending an action button within a chatbot message
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actions = [
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cl.Action(name="action_button", value="example_value", description="Click me!")
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]
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await cl.Message(content="Interact with this action button:", actions=actions).send()
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# res = await cl.AskActionMessage(
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# content="Pick an action!",
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# actions=[
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# cl.Action(name="continue", value="continue", label="✅ Continue"),
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# cl.Action(name="cancel", value="cancel", label="❌ Cancel"),
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# ],
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# ).send()
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# if res and res.get("value") == "continue":
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# await cl.Message(
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# content="Continue!",
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# ).send()
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# import plotly.graph_objects as go
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# fig = go.Figure(
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# data=[go.Bar(y=[2, 1, 3])],
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# layout_title_text="An example figure",
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# )
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# elements = [cl.Plotly(name="chart", figure=fig, display="inline")]
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# await cl.Message(content="This message has a chart", elements=elements).send()
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# Sending a pdf with the local file path
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# elements = [
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# cl.Pdf(name="pdf1", display="inline", path="./pdf1.pdf")
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# ]
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# cl.Message(content="Look at this local pdf!", elements=elements).send()
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@cl.on_settings_update
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async def setup_agent(settings):
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print("on_settings_update", settings)
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@cl.on_stop
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def on_stop():
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print("The user wants to stop the task!")
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@cl.on_chat_end
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def on_chat_end():
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print("The user disconnected!")
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@cl.on_chat_resume
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async def on_chat_resume(thread: ThreadDict):
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print("The user resumed a previous chat session!")
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# @cl.on_message
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async def on_message(message: cl.Message):
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cl.user_session.get("session")["history"].append({
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"role": "user",
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"content": message.content
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})
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response = await client.chat.completions.create(
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messages=[
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{
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"content": "You are a helpful bot",
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"role": "system"
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},
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*cl.user_session.get("session")["history"]
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],
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**settings
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)
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# Add assitanr message to the history
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cl.user_session.get("session")["history"].append({
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"role": "assistant",
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"content": response.choices[0].message.content
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})
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# msg.content = response.choices[0].message.content
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# await msg.update()
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# await cl.Message(content=response.choices[0].message.content).send()
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@cl.on_message
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async def on_message(message: cl.Message):
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cl.user_session.get("session")["history"].append({
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"role": "user",
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"content": message.content
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})
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msg = cl.Message(content="")
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await msg.send()
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stream = await client.chat.completions.create(
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messages=[
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{
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"content": "You are a helpful bot",
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"role": "system"
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},
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*cl.user_session.get("session")["history"]
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],
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stream = True,
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**settings
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)
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async for part in stream:
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if token := part.choices[0].delta.content or "":
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await msg.stream_token(token)
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# Add assitanr message to the history
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cl.user_session.get("session")["history"].append({
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"role": "assistant",
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"content": msg.content
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})
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await msg.update()
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if __name__ == "__main__":
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from chainlit.cli import run_chainlit
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run_chainlit(__file__)
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