renamed files

midterm-app

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-Subproject commit a6492b2481dbd143f30a0d5ebf707b3b070e7f54

midterm_app.py

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-# Import Required Libraries
-import os
-from dotenv import load_dotenv
-
-import openai
-import fitz  # PyMuPDF
-import pandas as pd
-from transformers import pipeline
-from qdrant_client import QdrantClient
-from qdrant_client.http import models as qdrant_models
-import chainlit as cl
-import tiktoken
-
-# Specific imports from the libraries
-from langchain.document_loaders import PyMuPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import OpenAIEmbeddings
-#old import from langchain_openai import OpenAIEmbeddings
-from langchain_community.vectorstores import Qdrant
-from langchain.prompts import ChatPromptTemplate
-from langchain.chat_models import ChatOpenAI
-#old import from langchain_openai import ChatOpenAI
-from operator import itemgetter
-from langchain.schema.output_parser import StrOutputParser
-from langchain.schema.runnable import RunnablePassthrough
-
-# Set Environment Variables
-load_dotenv()
-
-# Load environment variables
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-
-# Initialize OpenAI client after loading the environment variables
-openai.api_key = OPENAI_API_KEY
-
-# Load and split documents
-loader = PyMuPDFLoader("/home/user/app/data/airbnb_q1_2024.pdf")
-#old file path is loader = PyMuPDFLoader("/Users/sampazar/AIE3-Midterm/data/airbnb_q1_2024.pdf")
-documents = loader.load()
-
-def tiktoken_len(text):
-    tokens = tiktoken.encoding_for_model("gpt-4o").encode(text)
-    return len(tokens)
-
-text_splitter = RecursiveCharacterTextSplitter(
-    chunk_size=150, 
-    chunk_overlap=100,
-    length_function = tiktoken_len
-)
-
-split_chunks = text_splitter.split_documents(documents)
-
-
-# Load OpenAI Embeddings Model
-embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
-
-# Creating a Qdrant Vector Store
-qdrant_vector_store = Qdrant.from_documents(
-    split_chunks,
-    embeddings,
-    location=":memory:",
-    collection_name="Airbnb_Q1_2024",
-)
-
-# Create a Retriever
-retriever = qdrant_vector_store.as_retriever()
-
-# Create a prompt template
-template = """Answer the question based only on the following context. If you cannot answer the question with the context, please respond with 'I don't know':
-
-Context:
-{context}
-
-Question:
-{question}
-"""
-
-prompt = ChatPromptTemplate.from_template(template)
-
-# Define the primary LLM
-primary_llm = ChatOpenAI(model_name="gpt-4o", temperature=0)
-
-# Creating a Retrieval Augmented Generation (RAG) Chain
-retrieval_augmented_qa_chain = (
-    # INVOKE CHAIN WITH: {"question" : "<>"}
-    # "question" : populated by getting the value of the "question" key
-    # "context"  : populated by getting the value of the "question" key and chaining it into the base_retriever
-    {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
-    # "context"  : is assigned to a RunnablePassthrough object (will not be called or considered in the next step)
-    #              by getting the value of the "context" key from the previous step
-    | RunnablePassthrough.assign(context=itemgetter("context"))
-    # "response" : the "context" and "question" values are used to format our prompt object and then piped
-    #              into the LLM and stored in a key called "response"
-    # "context"  : populated by getting the value of the "context" key from the previous step
-    | {"response": prompt | primary_llm, "context": itemgetter("context")}
-)
-
-# Chainlit integration for deployment
-@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-4o",
-        "temperature": 0,
-        "max_tokens": 500,
-        "top_p": 1,
-        "frequency_penalty": 0,
-        "presence_penalty": 0,
-    }
-    cl.user_session.set("settings", settings)
-
-@cl.on_message  # marks a function that should be run each time the chatbot receives a message from a user
-async def handle_message(message: cl.Message):
-    settings = cl.user_session.get("settings")
-
-    response = retrieval_augmented_qa_chain.invoke({"question": message.content})
-    
-    #msg = cl.Message(content=response["response"])
-    #await msg.send()
-
-    # Extracting and sending just the content
-    content = response["response"].content
-    pretty_content = content.strip()  # Remove any leading/trailing whitespace
-
-    await cl.Message(content=pretty_content).send()