Update app.py

app.py

@@ -1,95 +1,51 @@
-!pip install -q -U numpy matplotlib plotly pandas scipy scikit-learn openai python-dotenv
-
-from aimakerspace.text_utils import TextFileLoader, CharacterTextSplitter
-from aimakerspace.vectordatabase import VectorDatabase
-import asyncio
-
-text_loader = TextFileLoader("data/KingLear.txt")
-documents = text_loader.load_documents()
-len(documents)
-
-text_splitter = CharacterTextSplitter()
-split_documents = text_splitter.split_texts(documents)
+# You can find this code for Chainlit python streaming here (https://docs.chainlit.io/concepts/streaming/python)
 
+# OpenAI Chat completion
 import os
-import openai
-from getpass import getpass
-
-openai.api_key = getpass("OpenAI API Key: ")
-os.environ["OPENAI_API_KEY"] = openai.api_key
-
-vector_db = VectorDatabase()
-vector_db = asyncio.run(vector_db.abuild_from_list(split_documents))
-
 import sys
-sys.path.append('/home/rlpeter70/LLMO-Cohort-3/Week 1/Thursday - Retrieval Augmented Generation QA Application /aimakerspace/openai_utils')
-
-from aimakerspace.openai_utils.prompts import (
-    UserRolePrompt,
-    SystemRolePrompt,
-    AssistantRolePrompt,
-)
-
-from aimakerspace.openai_utils.chatmodel import ChatOpenAI
-
-chat_openai = ChatOpenAI()
-user_prompt_template = "{content}"
-user_role_prompt = UserRolePrompt(user_prompt_template)
-system_prompt_template = (
-    "You are an expert in {expertise}, you always answer in a kind way."
-)
-system_role_prompt = SystemRolePrompt(system_prompt_template)
-
-messages = [
-    user_role_prompt.create_message(
-        content="What is the best way to write a loop?"
-    ),
-    system_role_prompt.create_message(expertise="Python"),
-]
-
-response = chat_openai.run(messages)
-
-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".
+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
+
+load_dotenv()
+sys.path.append(".")
+import raqa
+from raqa import retrieval_augmented_qa_pipeline
+
+# ChatOpenAI Templates
+system_template = """You are a helpful assistant who always speaks in a pleasant tone!
+"""
 
-Context:
-{context}
+user_template = """{input}
+Think through your response step by step.
 """
 
-raqa_prompt = SystemRolePrompt(RAQA_PROMPT_TEMPLATE)
 
-USER_PROMPT_TEMPLATE = """
-User Query:
-{user_query}
-"""
+@cl.on_chat_start  # marks a function that will be executed at the start of a user session
+async def start_chat():
+    settings = {
+        "model": "gpt-3.5-turbo",
+        "temperature": 0,
+        "max_tokens": 500,
+        "top_p": 1,
+        "frequency_penalty": 0,
+        "presence_penalty": 0,
+    }
 
-user_prompt = UserRolePrompt(USER_PROMPT_TEMPLATE)
+    cl.user_session.set("settings", settings)
 
-class RetrievalAugmentedQAPipeline:
-    def __init__(self, llm: ChatOpenAI(), vector_db_retriever: VectorDatabase) -> None:
-        self.llm = llm
-        self.vector_db_retriever = vector_db_retriever
 
-    def run_pipeline(self, user_query: str) -> str:
-        context_list = self.vector_db_retriever.search_by_text(user_query, k=4)
-        
-        context_prompt = ""
-        for context in context_list:
-            context_prompt += context[0] + "\n"
+@cl.on_message  # marks a function that should be run each time the chatbot receives a message from a user
+async def main(message: cl.Message):
+    settings = cl.user_session.get("settings")
 
-        formatted_system_prompt = raqa_prompt.create_message(context=context_prompt)
+    client = AsyncOpenAI()
 
-        formatted_user_prompt = user_prompt.create_message(user_query=user_query)
-        
-        return self.llm.run([formatted_system_prompt, formatted_user_prompt])
-    
-    
+    # Do some raqa stuff
+    msg = cl.Message(content=message.content)
 
-retrieval_augmented_qa_pipeline = RetrievalAugmentedQAPipeline(
-    vector_db_retriever=vector_db,
-    llm=chat_openai
-)
+    
+    # Send and close the message stream
+    await msg.send()