Upload app.py

app.py

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+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-course-assistant"
+
+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_kellogg_site",
+    "Searches and returns content from within the Kellogg website."
+)
+tools = [tool]
+
+system_message = SystemMessage(
+        content=(
+            "You are a helpful educational expert providing advice to students of the Northwestern business school Kellogg. "
+            "Use both your knowledge and the Kellogg site search tool to generate helpful answers for questions about courses and providing a list of suggested web course articles for more information. "
+            "Format your answer with distinct <h3>titles</h3> and <h3>subtitles</h3>, <b>emphasis</b>, <b>bold</b>, <i>italic<i>, <li>lists</li>, and tables *use html code*.  For lists, or bullet points, always start them by having a topic in <b>emphasis</b> before going into the description. Ensure to frequently take concepts and break them down into bullet points or lists following the emphasis directions that were just laid out."
+            "Do not include details of your intermediate steps in the final response. "
+            "At the end of your response, provide links to relevant web course articles returned by the retriever."
+        )
+)
+
+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):
+	# clearing RAG memory
+	# memory.clear()
+	
+	# 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-1106", 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 Course AI Assistant",
+	description="Please provide your questions about courses offered by Kellogg.",
+	additional_inputs=[model_type],
+	additional_inputs_accordion_name="AI Assistant Options:",
+	examples=[["Can you tell me about a marketing major? What would I want from my career if I went that way instead of say strategy?"],
+		["I'm interested in strategy. Can you give me a recommendation of courses I should consider over the next year?"],
+		["I'm wanting to know more about advertising. Can you recommend some courses on that subject?"],
+		["How many credits do I need to graduate?"],
+		["I loved the Competitive Strategy and industrial structure class. Can you tell me others like that one?"]],
+#    cache_examples=True,
+#    retry_btn=None,
+#	undo_btn="Delete Previous",
+	clear_btn="Clear",
+)
+
+def main():
+	kellogg_agent.queue().launch()
+
+# start UI
+if __name__ == "__main__":
+	main()