Create app.py

app.py

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+import gradio as gr
+import logging, os, sys, threading, time
+
+from dotenv import load_dotenv, find_dotenv
+
+from rag_langchain import LangChainRAG
+from rag_llamaindex import LlamaIndexRAG
+from trace import trace_wandb
+
+lock = threading.Lock()
+
+_ = 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": 2,                     # 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 not openai_api_key:
+        raise gr.Error("OpenAI API Key is required.")
+    if not prompt:
+        raise gr.Error("Prompt is required.")
+    if not rag_option:
+        raise gr.Error("Retrieval-Augmented Generation is required.")
+
+    with lock:
+        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
+            )
+
+            del os.environ["OPENAI_API_KEY"]
+    
+        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 = "List GPT-4's exam scores and benchmark results.", lines = 1),
+              gr.Radio([RAG_OFF, RAG_LANGCHAIN, RAG_LLAMAINDEX], label = "Retrieval-Augmented Generation", value = RAG_LANGCHAIN)],
+    outputs = [gr.Textbox(label = "Completion")],
+    title = "Context-Aware Reasoning Application",
+    description = os.environ["DESCRIPTION"],
+    examples = [["sk-<BringYourOwn>", "What are GPT-4's media capabilities in 5 emojis and 1 sentence?", RAG_LLAMAINDEX],
+                ["sk-<BringYourOwn>", "List GPT-4's exam scores and benchmark results.", RAG_LANGCHAIN],
+                ["sk-<BringYourOwn>", "Compare GPT-4 to GPT-3.5 in markdown table format.", RAG_LLAMAINDEX],
+                ["sk-<BringYourOwn>", "Write a Python program that calls the GPT-4 API.", RAG_LANGCHAIN],
+                ["sk-<BringYourOwn>", "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()