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| import gradio as gr | |
| import logging, os, sys, time | |
| from dotenv import load_dotenv, find_dotenv | |
| from rag_langchain import LangChainRAG | |
| from rag_llamaindex import LlamaIndexRAG | |
| from trace import trace_wandb | |
| _ = load_dotenv(find_dotenv()) | |
| RAG_INGESTION = False # load, split, embed, and store documents | |
| RAG_OFF = "Off" | |
| RAG_LANGCHAIN = "LangChain" | |
| RAG_LLAMAINDEX = "LlamaIndex" | |
| config = { | |
| "chunk_overlap": 100, # split documents | |
| "chunk_size": 2000, # split documents | |
| "k": 3, # retrieve documents | |
| "model_name": "gpt-4-0314", # llm | |
| "temperature": 0 # llm | |
| } | |
| logging.basicConfig(stream = sys.stdout, level = logging.INFO) | |
| logging.getLogger().addHandler(logging.StreamHandler(stream = sys.stdout)) | |
| def invoke(openai_api_key, prompt, rag_option): | |
| if (openai_api_key == ""): | |
| raise gr.Error("OpenAI API Key is required.") | |
| if (prompt == ""): | |
| raise gr.Error("Prompt is required.") | |
| if (rag_option is None): | |
| raise gr.Error("Retrieval Augmented Generation is required.") | |
| os.environ["OPENAI_API_KEY"] = openai_api_key | |
| if (RAG_INGESTION): | |
| if (rag_option == RAG_LANGCHAIN): | |
| rag = LangChainRAG() | |
| rag.ingestion(config) | |
| elif (rag_option == RAG_LLAMAINDEX): | |
| rag = LlamaIndexRAG() | |
| rag.ingestion(config) | |
| completion = "" | |
| result = "" | |
| callback = "" | |
| err_msg = "" | |
| try: | |
| start_time_ms = round(time.time() * 1000) | |
| if (rag_option == RAG_LANGCHAIN): | |
| rag = LangChainRAG() | |
| completion, callback = rag.rag_chain(config, prompt) | |
| result = completion["result"] | |
| elif (rag_option == RAG_LLAMAINDEX): | |
| rag = LlamaIndexRAG() | |
| result, callback = rag.retrieval(config, prompt) | |
| else: | |
| rag = LangChainRAG() | |
| completion, callback = rag.llm_chain(config, prompt) | |
| result = completion.generations[0][0].text | |
| except Exception as e: | |
| err_msg = e | |
| raise gr.Error(e) | |
| finally: | |
| end_time_ms = round(time.time() * 1000) | |
| trace_wandb( | |
| config, | |
| rag_option, | |
| prompt, | |
| completion, | |
| result, | |
| callback, | |
| err_msg, | |
| start_time_ms, | |
| end_time_ms | |
| ) | |
| return result | |
| gr.close_all() | |
| demo = gr.Interface( | |
| fn = invoke, | |
| inputs = [gr.Textbox(label = "OpenAI API Key", type = "password", lines = 1), | |
| gr.Textbox(label = "Prompt", value = "What are GPT-4's media capabilities in 5 emojis and 1 sentence?", lines = 1), | |
| gr.Radio([RAG_OFF, RAG_LANGCHAIN, RAG_LLAMAINDEX], label = "Retrieval-Augmented Generation", value = RAG_LANGCHAIN)], | |
| outputs = [gr.Textbox(label = "Completion", lines = 1)], | |
| title = "Context-Aware Reasoning Application", | |
| description = os.environ["DESCRIPTION"], | |
| examples = [["", "What are GPT-4's media capabilities in 5 emojis and 1 sentence?", RAG_LLAMAINDEX], | |
| ["", "List GPT-4's exam scores and benchmark results.", RAG_LANGCHAIN], | |
| ["", "Compare GPT-4 to GPT-3.5 in markdown table format.", RAG_LLAMAINDEX], | |
| ["", "Write a Python program that calls the GPT-4 API.", RAG_LANGCHAIN], | |
| ["", "What is the GPT-4 API's cost and rate limit? Answer in English, Arabic, Chinese, Hindi, and Russian in JSON format.", RAG_LLAMAINDEX]], | |
| cache_examples = False | |
| ) | |
| demo.launch() |