Update app.py

app.py

@@ -1,12 +1,14 @@
-# TRASH CAN DO WHAT YOU WANT WITH IT
+# AI MAKERSPACE PREPR 
+# Date: 2024-5-16
 
 # Basic Imports & Setup
 import os
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from openai import AsyncOpenAI
 
 # Using Chainlit for our UI
 import chainlit as cl
 from chainlit.prompt import Prompt, PromptMessage
+from chainlit.playground.providers import ChatOpenAI
 
 # Getting the API key from the .env file
 from dotenv import load_dotenv
@@ -22,12 +24,13 @@ file_id = "1UQnaQjBKKyWAiLdr6UlwSJovOp9zDdxr"
 # file_id = "12cvKg19CJf-wt98q5sPJctjp5fW-nsh6"  //Used for MLOps Meetup
 direct_url = f"https://drive.google.com/uc?export=download&id={file_id}"
 
+
 # Now load the document using the direct URL
 docs = PyMuPDFLoader(direct_url).load()
 
 import tiktoken
 def tiktoken_len(text):
-    tokens = tiktoken.encoding_for_model("solar-10.7b").encode(
+    tokens = tiktoken.encoding_for_model("gpt-3.5-turbo").encode(
         text,
     )
     return len(tokens)
@@ -36,20 +39,20 @@ def tiktoken_len(text):
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 
 text_splitter = RecursiveCharacterTextSplitter(
-    chunk_size=500,           # 500 tokens per chunk, experiment with this value
-    chunk_overlap=50,        # 50 tokens overlap between chunks, experiment with this value
-    length_function=tiktoken_len,
+    chunk_size = 500,           # 500 tokens per chunk, experiment with this value
+    chunk_overlap = 50,        # 50 tokens overlap between chunks, experiment with this value
+    length_function = tiktoken_len,
 )
 
 split_chunks = text_splitter.split_documents(docs)
 
 # Load the embeddings model
-from langchain.embeddings import HuggingFaceEmbeddings
+from langchain_openai.embeddings import OpenAIEmbeddings
 
-embedding_model = HuggingFaceEmbeddings(model_name="solar-10.7b")
+embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
 
 # Load the vector store and retriever from Qdrant
-from langchain.vectorstores import Qdrant
+from langchain_community.vectorstores import Qdrant
 
 qdrant_vectorstore = Qdrant.from_documents(
     split_chunks,
@@ -60,12 +63,10 @@ qdrant_vectorstore = Qdrant.from_documents(
 
 qdrant_retriever = qdrant_vectorstore.as_retriever()
 
-# Load the Solar 10.7B model
-tokenizer = AutoTokenizer.from_pretrained("Upstage/SOLAR-10.7B-v1.0")
-model = AutoModelForCausalLM.from_pretrained("Upstage/SOLAR-10.7B-v1.0")
+from langchain_openai import ChatOpenAI
+openai_chat_model = ChatOpenAI(model="gpt-3.5-turbo")
 
-# Set up the RAG prompt template
-from langchain.prompts import ChatPromptTemplate
+from langchain_core.prompts import ChatPromptTemplate
 
 RAG_PROMPT = """
 SYSTEM:
@@ -77,15 +78,15 @@ When to talk with the user about conferences, it can be a "transactional convers
 Here is an example of a transactional conversation:
 User: When is the conference?
 You: The conference is on June 1st, 2024. What else would you like to know?
-It can also be a chain of questions and answers where you and the user continue the chain until they say "Got it".
+It can also be a chain of questions and answers where you and the user continues the chain until they say "Got it".
 Here is an example of a transactional conversation:
 User: What sessions should I attend?
 You: You should attend the keynote session by Bono. Would you like to know more?
 User: Yes
 You: The keynote session by Bono is on June 1st, 2024. What else would you like?
-If asked a question about sessions, you can provide detailed information about the session.
+If asked a question about a sessions, you can provide detailed information about the session.
 If there are multiple sessions, you can provide information about each session.
-The format of session-related replies is:
+The format of session related replies is:
 Title:
 Description:
 Speaker:
@@ -97,15 +98,15 @@ CONTEXT:
 {context}
 QUERY:
 {question}
-ALL ANSWERS MUST COME FROM THE INCLUDED DOCUMENTS AND NOT FROM THE INTERNET OR FROM AI (do not make up an answer). If you can't reply, say: dunno, look it up yourself, bozo.
+ALL ANWSERS MUST COME FROM THE INCLUDE DOCUMENTS AND NOT FROM INTERNET OR FROM AI (do not make up an answer).  If you can't reply, say: dunno, look it up yourself, bozo.
 Most questions are about the date, location, and purpose of the conference.
 You may be asked for fine details about the conference regarding the speakers, sponsors, and attendees.
 You are capable of looking up information and providing detailed responses.
 When asked a question about a conference, you should provide a detailed response.
 After completing your response, you should ask the user if they would like to know more about the conference by asking "Hope that helps".
-If the user says "yes", you should provide more information about the conference. If the user says "no", you should say "Goodbye!" or ask if they would like to provide feedback.
+If the user says "yes", you should provide more information about the conference. If the user says "no", you should say "Goodbye! or ask if they would like to provide feedback.
 If you are asked a question about Cher, you should respond with "Rock on With Your Bad Self!".
-You guess if you do not have an answer, but you must preface the response with: "I might be guessing, but ..."
+You guess if you do not have a answer, but you must preface the repsonse with: "I might be guessing, but ..."
 """
 rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
 
@@ -116,14 +117,14 @@ from langchain.schema.runnable import RunnablePassthrough
 retrieval_augmented_qa_chain = (
     {"context": itemgetter("question") | qdrant_retriever, "question": itemgetter("question")}
     | RunnablePassthrough.assign(context=itemgetter("context"))
-    | {"response": rag_prompt | model, "context": itemgetter("context")}
+    | {"response": rag_prompt | openai_chat_model, "context": itemgetter("context")}
 )
 
 # Chainlit App
 @cl.on_chat_start
 async def start_chat():
     settings = {
-        "model": "solar-10.7b",
+        "model": "gpt-3.5-turbo",
         "temperature": 0,
         "max_tokens": 500,
         "top_p": 1,
@@ -135,9 +136,9 @@ async def start_chat():
 @cl.on_message
 async def main(message: cl.Message):
     chainlit_question = message.content
+    #chainlit_question = "What was the total value of 'Cash and cash equivalents' as of December 31, 2023?"
     response = retrieval_augmented_qa_chain.invoke({"question": chainlit_question})
     chainlit_answer = response["response"].content
 
     msg = cl.Message(content=chainlit_answer)
-    await msg.send()
-
+    await msg.send()