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
+
+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")
+
+# create LLM
+llm4 = ChatOpenAI(temperature = 0.1, model_name="gpt-4")
+llm35 = ChatOpenAI(temperature = 0.1, model_name="gpt-3.5-turbo-16k")
+llm = llm4
+
+# create retrieval tool
+tool = create_retriever_tool(
+    retriever, 
+    "search_kellogg_site",
+    "Searches and returns content from within the Kellogg website."
+)
+tools = [tool]
+
+# conversational retrieval agent component construction - memory, prompt template, agent, agent executor
+# This is needed for both the memory and the prompt
+memory_key = "history"
+memory = AgentTokenBufferMemory(memory_key=memory_key, llm=llm)
+# memory = AgentTokenBufferMemory(memory_key=memory_key, llm=llm, max_history=0, max_token_limit= 4000)
+
+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."
+        )
+)
+
+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=True,return_intermediate_steps=True)
+
+# query = "I first noticed that data wasn’t flowing from splunk and received the following error message - No errors were recorded for the customer’s Splunk input"
+# result = agent_executor({"input":query})
+
+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"
+)
+
+# gradio chatbot UI
+def predict(message, history, model_type):
+	# clearing RAG memory
+	memory.clear()
+	
+	# specifying LLM to use
+	if (model_type==0):
+		llm = llm4
+	elif (model_type==1):
+		llm = llm35
+	else:
+		llm = llm4
+		
+	llm_response = agent_executor({"input":message})
+	
+	return llm_response["output"]
+
+# set up and run chat interface
+kellogg_agent = gr.ChatInterface(
+	fn=predict,
+	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",
+)
+
+# start UI
+if __name__ == "__main__":
+	kellogg_agent.queue().launch()