Spaces:
Sleeping
Sleeping
| from openai import AsyncOpenAI # importing openai for API usage | |
| import chainlit as cl # importing chainlit for our app | |
| from chainlit.prompt import Prompt, PromptMessage # importing prompt tools | |
| from chainlit.playground.providers import ChatOpenAI # importing ChatOpenAI tools | |
| from dotenv import load_dotenv | |
| import asyncio | |
| import datetime | |
| from aimakerspace.text_utils import TextFileLoader, CharacterTextSplitter | |
| from aimakerspace.vectordatabase import VectorDatabase | |
| from aimakerspace.openai_utils.prompts import ( | |
| UserRolePrompt, | |
| SystemRolePrompt, | |
| ) | |
| load_dotenv() | |
| RAQA_PROMPT_TEMPLATE = """ | |
| Use the provided context to answer the user's query. | |
| You may not answer the user's query unless there is specific context in the following text. | |
| If you do not know the answer, or cannot answer, please respond with "I don't know". | |
| Context: | |
| {context} | |
| """ | |
| raqa_prompt = SystemRolePrompt(RAQA_PROMPT_TEMPLATE) | |
| USER_PROMPT_TEMPLATE = """ | |
| User Query: | |
| {user_query} | |
| """ | |
| user_prompt = UserRolePrompt(USER_PROMPT_TEMPLATE) | |
| vector_db = None | |
| async def start_chat(): | |
| #Create the Vector Database for King Lear | |
| global vector_db | |
| text_loader = TextFileLoader("data/KingLear.txt") | |
| documents = text_loader.load_documents() | |
| text_splitter = CharacterTextSplitter() | |
| split_documents = text_splitter.split_texts(documents) | |
| vector_db = VectorDatabase() | |
| vector_db = asyncio.run(vector_db.abuild_from_list(split_documents)) | |
| settings = { | |
| "model": "gpt-3.5-turbo", | |
| "temperature": 0, | |
| "max_tokens": 500, | |
| "top_p": 1, | |
| "frequency_penalty": 0, | |
| "presence_penalty": 0, | |
| } | |
| cl.user_session.set("settings", settings) | |
| async def main(message: cl.Message): | |
| settings = cl.user_session.get("settings") | |
| client = AsyncOpenAI() | |
| context_list = vector_db.search_by_text(message.content, k=4) | |
| context_prompt = "" | |
| for context in context_list: | |
| context_prompt += context[0] + "\n" | |
| formatted_system_prompt = raqa_prompt.create_message(context=context_prompt) | |
| formatted_user_prompt = user_prompt.create_message(user_query=message.content) | |
| print(formatted_system_prompt) | |
| print(formatted_user_prompt) | |
| prompt = Prompt( | |
| provider=ChatOpenAI.id, | |
| messages=[ | |
| PromptMessage( | |
| role="system", | |
| template=RAQA_PROMPT_TEMPLATE, | |
| formatted=formatted_system_prompt['content'], | |
| ), | |
| PromptMessage( | |
| role="user", | |
| template=USER_PROMPT_TEMPLATE, | |
| formatted=formatted_user_prompt['content'], | |
| ), | |
| ], | |
| inputs={"context": context_prompt, | |
| "user_query": message.content}, | |
| settings=settings, | |
| ) | |
| msg = cl.Message(content="") | |
| async for stream_resp in await client.chat.completions.create( | |
| messages=[m.to_openai() for m in prompt.messages], stream=True, **settings | |
| ): | |
| token = stream_resp.choices[0].delta.content | |
| if not token: | |
| token = "" | |
| await msg.stream_token(token) | |
| # Update the prompt object with the completion | |
| prompt.completion = msg.content | |
| msg.prompt = prompt | |
| # Send and close the message stream | |
| await msg.send() | |