app.py

import gradio as gr
import openai, os, time, wandb

from langchain.chains import LLMChain, RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import PyPDFLoader, WebBaseLoader
from langchain.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader
from langchain.document_loaders.generic import GenericLoader
from langchain.document_loaders.parsers import OpenAIWhisperParser

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.prompts import PromptTemplate
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.vectorstores import MongoDBAtlasVectorSearch

from pymongo import MongoClient

from wandb.sdk.data_types.trace_tree import Trace

from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())

WANDB_API_KEY = os.environ["WANDB_API_KEY"]

MONGODB_URI = os.environ["MONGODB_ATLAS_CLUSTER_URI"]
client = MongoClient(MONGODB_URI)
MONGODB_DB_NAME = "langchain_db"
MONGODB_COLLECTION_NAME = "gpt-4"
MONGODB_COLLECTION = client[MONGODB_DB_NAME][MONGODB_COLLECTION_NAME]
MONGODB_INDEX_NAME = "default"

description = os.environ["DESCRIPTION"]

config = {
    "chunk_overlap": 150,
    "chunk_size": 1500,
    "k": 3,
    "model": "gpt-4",
    "temperature": 0,
    "verbose": True
}

template = """If you don't know the answer, just say that you don't know, don't try to make up an answer. Keep the answer as concise as possible. Always say 
              "🧠 Thanks for using the app - Bernd" at the end of the answer. """

llm_template = "Answer the question at the end. " + template + "Question: {question} Helpful Answer: "
rag_template = "Use the following pieces of context to answer the question at the end. " + template + "{context} Question: {question} Helpful Answer: "

LLM_CHAIN_PROMPT = PromptTemplate(input_variables = ["question"], 
                                  template = llm_template)
RAG_CHAIN_PROMPT = PromptTemplate(input_variables = ["context", "question"], 
                                  template = rag_template)

CHROMA_DIR  = "/data/chroma"
YOUTUBE_DIR = "/data/youtube"

PDF_URL       = "https://arxiv.org/pdf/2303.08774.pdf"
WEB_URL       = "https://openai.com/research/gpt-4"
YOUTUBE_URL_1 = "https://www.youtube.com/watch?v=--khbXchTeE"
YOUTUBE_URL_2 = "https://www.youtube.com/watch?v=hdhZwyf24mE"
YOUTUBE_URL_3 = "https://www.youtube.com/watch?v=vw-KWfKwvTQ"

def document_loading_splitting():
    # Document loading
    docs = []
    # Load PDF
    loader = PyPDFLoader(PDF_URL)
    docs.extend(loader.load())
    # Load Web
    loader = WebBaseLoader(WEB_URL)
    docs.extend(loader.load())
    # Load YouTube
    loader = GenericLoader(YoutubeAudioLoader([YOUTUBE_URL_1,
                                               YOUTUBE_URL_2,
                                               YOUTUBE_URL_3], YOUTUBE_DIR), 
                           OpenAIWhisperParser())
    docs.extend(loader.load())
    # Document splitting
    text_splitter = RecursiveCharacterTextSplitter(chunk_overlap = config["chunk_overlap"],
                                                   chunk_size = config["chunk_size"])
    splits = text_splitter.split_documents(docs)
    return splits

def document_storage_chroma(splits):
    Chroma.from_documents(documents = splits, 
                          embedding = OpenAIEmbeddings(disallowed_special = ()), 
                          persist_directory = CHROMA_DIR)

def document_storage_mongodb(splits):
    MongoDBAtlasVectorSearch.from_documents(documents = splits,
                                            embedding = OpenAIEmbeddings(disallowed_special = ()),
                                            collection = MONGODB_COLLECTION,
                                            index_name = MONGODB_INDEX_NAME)

def document_retrieval_chroma(llm, prompt):
    db = Chroma(embedding_function = OpenAIEmbeddings(),
                persist_directory = CHROMA_DIR)
    return db

def document_retrieval_mongodb(llm, prompt):
    db = MongoDBAtlasVectorSearch.from_connection_string(MONGODB_URI,
                                                         MONGODB_DB_NAME + "." + MONGODB_COLLECTION_NAME,
                                                         OpenAIEmbeddings(disallowed_special = ()),
                                                         index_name = MONGODB_INDEX_NAME)
    return db

def llm_chain(llm, prompt):
    llm_chain = LLMChain(llm = llm, prompt = LLM_CHAIN_PROMPT)
    completion = llm_chain.run({"question": prompt})
    return completion

def rag_chain(llm, prompt, db):
    rag_chain = RetrievalQA.from_chain_type(llm, 
                                            chain_type_kwargs = {"prompt": RAG_CHAIN_PROMPT}, 
                                            retriever = db.as_retriever(search_kwargs = {"k": config["k"]}), 
                                            return_source_documents = True)
    completion = rag_chain({"query": prompt})
    return completion

def wandb_trace(rag_option, prompt, prompt_template, result, completion, chain_name, status_msg, start_time_ms, end_time_ms):
    wandb.init(project = "openai-llm-rag")
    trace = Trace(
        name = chain_name,
        kind = "chain",
        status_code = "SUCCESS" if (str(status_msg) == "") else "ERROR",
        status_message = str(status_msg),
        metadata={
            "chunk_overlap": "" if (rag_option == "Off") else config["chunk_overlap"],
            "chunk_size": "" if (rag_option == "Off") else config["chunk_size"],
            "k": "" if (rag_option == "Off") else config["k"],
            "model": config["model"],
            "temperature": config["temperature"],
            "verbose": config["verbose"],
        },
        start_time_ms = start_time_ms,
        end_time_ms = end_time_ms,
        inputs = {"rag_option": rag_option, "prompt": str(prompt), "prompt_template": str(prompt_template)},
        outputs = {"result": str(result), "completion": str(completion)},
        #model_dict = {"llm": str(llm)}
    )
    trace.log("test")
    wandb.finish()

def invoke(openai_api_key, rag_option, prompt):
    if (openai_api_key == ""):
        raise gr.Error("OpenAI API Key is required.")
    if (rag_option is None):
        raise gr.Error("Retrieval Augmented Generation is required.")
    if (prompt == ""):
        raise gr.Error("Prompt is required.")
    completion = ""
    result = ""
    prompt_template = ""
    chain_name = ""
    status_msg = ""
    try:
        start_time_ms = round(time.time() * 1000)
        llm = ChatOpenAI(model_name = config["model"], 
                         openai_api_key = openai_api_key, 
                         temperature = config["temperature"],
                         verbose = config["verbose"])
        if (rag_option == "Chroma"):
            #splits = document_loading_splitting()
            #document_storage_chroma(splits)
            db = document_retrieval_chroma(llm, prompt)
            completion = rag_chain(llm, prompt, db)
            result = completion["result"]
            prompt_template = rag_template
            chain_name = "RetrievalQA"
        elif (rag_option == "MongoDB"):
            #splits = document_loading_splitting()
            #document_storage_mongodb(splits)
            db = document_retrieval_mongodb(llm, prompt)
            completion = rag_chain(llm, prompt, db)
            result = completion["result"]
            prompt_template = rag_template
            chain_name = "RetrievalQA"
        else:
            result = llm_chain(llm, prompt)
            prompt_template = llm_template
            chain_name = "LLMChain"
    except Exception as e:
        status_msg = e
        raise gr.Error(e)
    finally:
        end_time_ms = round(time.time() * 1000)
        wandb_trace(rag_option, prompt, prompt_template, result, completion, chain_name, status_msg, start_time_ms, end_time_ms)
    return result

gr.close_all()
demo = gr.Interface(fn=invoke, 
                    inputs = [gr.Textbox(label = "OpenAI API Key", value = "sk-", lines = 1), 
                              gr.Radio(["Off", "Chroma", "MongoDB"], label="Retrieval Augmented Generation", value = "Off"),
                              gr.Textbox(label = "Prompt", value = "What is GPT-4?", lines = 1)],
                    outputs = [gr.Textbox(label = "Completion", lines = 1)],
                    title = "Generative AI - LLM & RAG",
                    description = description)
demo.launch()