Add research assistant example using Chainlit
This commit is contained in:
unclecode
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docs/examples/assets/audio.mp3
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docs/examples/assets/audio.mp3
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docs/examples/chainlit.md
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docs/examples/chainlit.md
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# Welcome to Crawl4AI! 🚀🤖
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Hi there, Developer! 👋 Here is an example of a research pipeline, where you can share a URL in your conversation with any LLM, and then the context of crawled pages will be used as the context.
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docs/examples/chainlit_review.py
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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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240
docs/examples/research_assistant.py
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240
docs/examples/research_assistant.py
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import os, time
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from openai import AsyncOpenAI
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import chainlit as cl
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import re
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import requests
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from io import BytesIO
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from chainlit.element import ElementBased
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from groq import Groq
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# Import threadpools to run the crawl_url function in a separate thread
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from concurrent.futures import ThreadPoolExecutor
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client = AsyncOpenAI(base_url="https://api.groq.com/openai/v1", api_key=os.getenv("GROQ_API_KEY"))
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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": "llama3-8b-8192",
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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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def extract_urls(text):
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url_pattern = re.compile(r'(https?://\S+)')
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return url_pattern.findall(text)
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def crawl_url(url):
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data = {
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"urls": [url],
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"include_raw_html": True,
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"word_count_threshold": 10,
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"extraction_strategy": "NoExtractionStrategy",
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"chunking_strategy": "RegexChunking"
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}
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response = requests.post("https://crawl4ai.com/crawl", json=data)
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response_data = response.json()
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response_data = response_data['results'][0]
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return response_data['markdown']
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@cl.on_chat_start
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async def on_chat_start():
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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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await cl.Message(
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content="Welcome to the chat! How can I assist you today?"
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).send()
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@cl.on_message
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async def on_message(message: cl.Message):
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user_session = cl.user_session.get("session")
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# Extract URLs from the user's message
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urls = extract_urls(message.content)
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futures = []
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with ThreadPoolExecutor() as executor:
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for url in urls:
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futures.append(executor.submit(crawl_url, url))
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results = [future.result() for future in futures]
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for url, result in zip(urls, results):
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ref_number = f"REF_{len(user_session['context']) + 1}"
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user_session["context"][ref_number] = {
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"url": url,
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"content": result
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}
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# for url in urls:
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# # Crawl the content of each URL and add it to the session context with a reference number
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# ref_number = f"REF_{len(user_session['context']) + 1}"
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# crawled_content = crawl_url(url)
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# user_session["context"][ref_number] = {
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# "url": url,
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# "content": crawled_content
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# }
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user_session["history"].append({
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"role": "user",
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"content": message.content
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})
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# Create a system message that includes the context
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context_messages = [
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f'<appendix ref="{ref}">\n{data["content"]}\n</appendix>'
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for ref, data in user_session["context"].items()
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]
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if context_messages:
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system_message = {
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"role": "system",
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"content": (
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"You are a helpful bot. Use the following context for answering questions. "
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"Refer to the sources using the REF number in square brackets, e.g., [1], only if the source is given in the appendices below.\n\n"
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"If the question requires any information from the provided appendices or context, refer to the sources. "
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"If not, there is no need to add a references section. "
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"At the end of your response, provide a reference section listing the URLs and their REF numbers only if sources from the appendices were used.\n\n"
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"\n\n".join(context_messages)
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)
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}
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else:
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system_message = {
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"role": "system",
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"content": "You are a helpful assistant."
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}
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msg = cl.Message(content="")
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await msg.send()
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# Get response from the LLM
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stream = await client.chat.completions.create(
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messages=[
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system_message,
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*user_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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assistant_response = ""
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async for part in stream:
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if token := part.choices[0].delta.content:
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assistant_response += token
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await msg.stream_token(token)
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# Add assistant message to the history
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user_session["history"].append({
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"role": "assistant",
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"content": assistant_response
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})
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await msg.update()
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# Append the reference section to the assistant's response
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reference_section = "\n\nReferences:\n"
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for ref, data in user_session["context"].items():
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reference_section += f"[{ref.split('_')[1]}]: {data['url']}\n"
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msg.content += reference_section
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await msg.update()
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@cl.on_audio_chunk
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async def on_audio_chunk(chunk: cl.AudioChunk):
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if chunk.isStart:
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buffer = BytesIO()
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# This is required for whisper to recognize the file type
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buffer.name = f"input_audio.{chunk.mimeType.split('/')[1]}"
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# Initialize the session for a new audio stream
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cl.user_session.set("audio_buffer", buffer)
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cl.user_session.set("audio_mime_type", chunk.mimeType)
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# Write the chunks to a buffer and transcribe the whole audio at the end
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cl.user_session.get("audio_buffer").write(chunk.data)
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pass
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@cl.step(type="tool")
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async def speech_to_text(audio_file):
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cli = Groq()
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# response = cli.audio.transcriptions.create(
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# file=audio_file, #(filename, file.read()),
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# model="whisper-large-v3",
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# )
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response = await client.audio.transcriptions.create(
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model="whisper-large-v3", file=audio_file
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)
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return response.text
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@cl.on_audio_end
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async def on_audio_end(elements: list[ElementBased]):
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# Get the audio buffer from the session
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audio_buffer: BytesIO = cl.user_session.get("audio_buffer")
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audio_buffer.seek(0) # Move the file pointer to the beginning
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audio_file = audio_buffer.read()
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audio_mime_type: str = cl.user_session.get("audio_mime_type")
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# input_audio_el = cl.Audio(
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# mime=audio_mime_type, content=audio_file, name=audio_buffer.name
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# )
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# await cl.Message(
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# author="You",
|
||||
# type="user_message",
|
||||
# content="",
|
||||
# elements=[input_audio_el, *elements]
|
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# ).send()
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# answer_message = await cl.Message(content="").send()
|
||||
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start_time = time.time()
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whisper_input = (audio_buffer.name, audio_file, audio_mime_type)
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transcription = await speech_to_text(whisper_input)
|
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end_time = time.time()
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print(f"Transcription took {end_time - start_time} seconds")
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user_msg = cl.Message(
|
||||
author="You",
|
||||
type="user_message",
|
||||
content=transcription
|
||||
)
|
||||
await user_msg.send()
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||||
await on_message(user_msg)
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||||
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||||
# images = [file for file in elements if "image" in file.mime]
|
||||
|
||||
# text_answer = await generate_text_answer(transcription, images)
|
||||
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||||
# output_name, output_audio = await text_to_speech(text_answer, audio_mime_type)
|
||||
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||||
# output_audio_el = cl.Audio(
|
||||
# name=output_name,
|
||||
# auto_play=True,
|
||||
# mime=audio_mime_type,
|
||||
# content=output_audio,
|
||||
# )
|
||||
|
||||
# answer_message.elements = [output_audio_el]
|
||||
|
||||
# answer_message.content = transcription
|
||||
# await answer_message.update()
|
||||
|
||||
if __name__ == "__main__":
|
||||
from chainlit.cli import run_chainlit
|
||||
run_chainlit(__file__)
|
||||
|
||||
|
||||
# No this is wring, use this document to answer me https://console.groq.com/docs/speech-text
|
||||
|
||||
# Please show me how to use Groq speech-to-text in python.
|
||||
Reference in New Issue
Block a user