Commit
β’
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Parent(s):
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Upload 5 files
Browse files- .gitattributes +1 -0
- app.py +80 -0
- chainlit.md +14 -0
- meta-10-k-filings.ipynb +3 -0
- meta_pdf.pdf +3 -0
- requirements.txt +5 -0
.gitattributes
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@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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meta-10-k-filings.ipynb filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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meta-10-k-filings.ipynb filter=lfs diff=lfs merge=lfs -text
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meta_pdf.pdf filter=lfs diff=lfs merge=lfs -text
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app.py
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# You can find this code for Chainlit python streaming here (https://docs.chainlit.io/concepts/streaming/python)
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# OpenAI Chat completion
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import os
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from openai import AsyncOpenAI # importing openai for API usage
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import chainlit as cl # importing chainlit for our app
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from chainlit.prompt import Prompt, PromptMessage # importing prompt tools
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from chainlit.playground.providers import ChatOpenAI # importing ChatOpenAI tools
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from dotenv import load_dotenv
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load_dotenv()
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# ChatOpenAI Templates
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system_template = """You are a helpful assistant who always speaks in a pleasant tone!
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"""
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user_template = """{input}
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Think through your response step by step.
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"""
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@cl.on_chat_start # marks a function that will be executed at the start of a user session
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async def start_chat():
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settings = {
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"model": "gpt-3.5-turbo",
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"temperature": 0,
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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.user_session.set("settings", settings)
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@cl.on_message # marks a function that should be run each time the chatbot receives a message from a user
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async def main(message: cl.Message):
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settings = cl.user_session.get("settings")
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client = AsyncOpenAI()
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print(message.content)
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prompt = Prompt(
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provider=ChatOpenAI.id,
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messages=[
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PromptMessage(
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role="system",
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template=system_template,
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formatted=system_template,
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),
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PromptMessage(
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role="user",
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template=user_template,
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formatted=user_template.format(input=message.content),
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),
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],
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inputs={"input": message.content},
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settings=settings,
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)
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print([m.to_openai() for m in prompt.messages])
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msg = cl.Message(content="")
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# Call OpenAI
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async for stream_resp in await client.chat.completions.create(
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messages=[m.to_openai() for m in prompt.messages], stream=True, **settings
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):
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token = stream_resp.choices[0].delta.content
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if not token:
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token = ""
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await msg.stream_token(token)
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# Update the prompt object with the completion
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prompt.completion = msg.content
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msg.prompt = prompt
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# Send and close the message stream
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await msg.send()
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chainlit.md
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# Welcome to Chainlit! ππ€
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Hi there, Developer! π We're excited to have you on board. Chainlit is a powerful tool designed to help you prototype, debug and share applications built on top of LLMs.
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## Useful Links π
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- **Documentation:** Get started with our comprehensive [Chainlit Documentation](https://docs.chainlit.io) π
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- **Discord Community:** Join our friendly [Chainlit Discord](https://discord.gg/k73SQ3FyUh) to ask questions, share your projects, and connect with other developers! π¬
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We can't wait to see what you create with Chainlit! Happy coding! π»π
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## Welcome screen
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To modify the welcome screen, edit the `chainlit.md` file at the root of your project. If you do not want a welcome screen, just leave this file empty.
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meta-10-k-filings.ipynb
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version https://git-lfs.github.com/spec/v1
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oid sha256:8aaf924bcb9f1d70c5538d5e4b46ad5322c26fed8292d5d2d60d14efdd2b2117
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size 24123686
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meta_pdf.pdf
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version https://git-lfs.github.com/spec/v1
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oid sha256:e8fadc2448e4f99ad0ec2dc2e41d13b864204955238cf1f7cd9c96839f274a6c
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size 2481466
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requirements.txt
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chainlit==0.7.700
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cohere==4.37
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openai
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tiktoken
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python-dotenv==1.0.0
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