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| import gradio as gr | |
| import langchain, 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, | |
| } | |
| langchain.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"], | |
| }, | |
| 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"]) | |
| 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() |