Upload app.py

app.py

@@ -0,0 +1,157 @@
+import gradio as gr
+import os
+import pinecone
+import openai
+
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.chat_models import ChatOpenAI
+from langchain.vectorstores import Pinecone
+
+from langchain.agents.openai_functions_agent.agent_token_buffer_memory import AgentTokenBufferMemory
+from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
+from langchain.schema.messages import SystemMessage
+from langchain.prompts import MessagesPlaceholder
+from langchain.agents import AgentExecutor
+from langchain.agents.agent_toolkits import create_retriever_tool
+
+from langchain.callbacks.base import BaseCallbackHandler
+
+from queue import Queue
+from threading import Thread
+
+print("CHECK - Pinecone vector db setup")
+
+# set up OpenAI environment vars and embeddings
+openai.api_key = os.environ.get("OPENAI_API_KEY")
+embeddings = OpenAIEmbeddings()
+
+# initialize pinecone db
+index_name = "kellogg-markstrat"
+
+pinecone.init(
+    api_key=os.getenv("PINECONE_API_KEY"),  # find at app.pinecone.io
+	environment=os.getenv("PINECONE_ENV"),  # next to api key in console
+)
+
+# load existing index
+vectorsearch = Pinecone.from_existing_index(index_name, embeddings)
+retriever = vectorsearch.as_retriever()
+
+print("CHECK - setting up conversational retrieval agent")
+
+# callback handler for streaming
+class QueueCallback(BaseCallbackHandler):
+    """Callback handler for streaming LLM responses to a queue."""
+
+    def __init__(self, q):
+        self.q = q
+
+    def on_llm_new_token(self, token: str, **kwargs: any) -> None:
+        self.q.put(token)
+
+    def on_llm_end(self, *args, **kwargs: any) -> None:
+        return self.q.empty()
+
+# create retrieval tool
+tool = create_retriever_tool(
+    retriever, 
+    "search_markstrat",
+    "Searches and returns information about the MarkStrat simulation program."
+)
+tools = [tool]
+
+system_message = SystemMessage(
+        content=(
+			"You are an AI bot marketing professor at a business school helping students understand how to play the markstrat simulation. For every question or comment compose a well-structured response to the user's question, using context and conversation information. Your tone of voice will be conversational and engaging, while still being to the point and direct. "
+			"Use the MarkStrat search tool to generate helpful answers for the user question. "
+            "If its a simple question that asks for a quantitative answer, then provide a much more succinct response. "
+			"Respond to questions that are at least 80% similar to the content within the specified context being sent to you, if the question has nothing to do with the additional information supplied to you, then reply with 'I can only answer questions related to MarkStrat."
+        )
+)
+
+print("CHECK - setting up gradio chatbot UI")
+
+# build Gradio selectable options in Chat UI
+model_type=gr.Dropdown(choices=["gpt-4 + rag", 
+								"gpt-3.5-turbo + rag"], 
+								value="gpt-4 + rag",
+								type="index",
+								label="LLM Models"
+)
+
+# RAG agent function
+def predict(message, model_type):
+	# Create a Queue
+	q = Queue()
+	job_done = object()
+	
+	# conversational retrieval agent component construction - memory, prompt template, agent, agent executor
+	# specifying LLM to use
+	if (model_type==1):
+		llm =  ChatOpenAI(temperature = 0.1, model_name="gpt-3.5-turbo-16k", streaming=True, callbacks=[QueueCallback(q)])
+	else:
+		llm =  ChatOpenAI(temperature = 0.1, model_name="gpt-4-1106-preview", streaming=True, callbacks=[QueueCallback(q)])
+
+	# This is needed for both the memory and the prompt
+	memory_key = "history"
+	memory = AgentTokenBufferMemory(memory_key=memory_key, llm=llm)
+
+	prompt = OpenAIFunctionsAgent.create_prompt(
+        	system_message=system_message,
+        	extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)]
+	)
+
+	agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)
+	agent_executor = AgentExecutor(agent=agent, tools=tools, memory=memory, verbose=False, return_intermediate_steps=True)	
+
+	# Create a funciton to call - this will run in a thread
+	def task():
+		resp = agent_executor({"input":message})
+		q.put(job_done)
+
+	# Create a thread and start the function
+	t = Thread(target=task)
+	t.start()
+
+	content = ""
+
+	# Get each new token from the queue and yield for our generator
+	while True:
+		try:
+			next_token = q.get(True, timeout=1)
+			if next_token is job_done:
+				break
+			content += next_token
+			yield next_token, content
+		except:
+			pass
+
+def ask_llm(message, history, model_type):
+    for next_token, content in predict(message, model_type):
+        yield(content)
+
+# set up and run chat interface
+kellogg_agent = gr.ChatInterface(
+	fn=ask_llm,
+	chatbot=gr.Chatbot(height=500),
+	textbox=gr.Textbox(placeholder="Ask me a question", container=False, scale=7),
+	title="Kellogg MarkStrat AI Assistant",
+	description="Please provide your questions about MarkStrat.",
+	# additional_inputs=[model_type],
+	# additional_inputs_accordion_name="AI Assistant Options:",
+	examples=[["What is MarkStrat?"]],
+#    cache_examples=True,
+#    retry_btn=None,
+#	undo_btn="Delete Previous",
+	clear_btn="Clear",
+)
+
+user_cred = os.environ.get("USER_CRED")
+pass_cred = os.environ.get("PASS_CRED")
+
+def main():
+	kellogg_agent.queue().launch(auth=(user_cred, pass_cred))
+
+# start UI
+if __name__ == "__main__":
+	main()