Calling Custom OCI Gen AI Agent using Python

Posted on February 24, 2025 Updated on February 22, 2025

In a previous post, I demonstrated how to create a custom Generative AI Agent on OCI. This GenAI Agent was built using some of Shakespeare’s works. Using the OCI GenAI Agent interface is an easy way to test the Agent and to see how it behaves. Beyond that, it doesn’t have any use as you’ll need to call it using some other language or tool. The most common of these is using Python.

The code below calls my GenAI Agent, which I’ve called BOCAS (Brendan’s Oracle Chat Agent for Shakespeare).

import oci
from oci import generative_ai_agent_runtime
import json
from colorama import Fore, Back, Style


CONFIG_PROFILE = "DEFAULT"
config = oci.config.from_file('~/.oci/config', CONFIG_PROFILE)

#AI Agent service endpoint
SERVICE_EP = <add your Service Endpoint> 
AGENT_EP_ID = <add your GenAI Agent Endpoint>

welcome_msg = "This is Brendan's Oracle Chatbot Agent for Shakespeare. Ask questions about the works of Shakespeare."
    
def gen_Agent_Client():
    #Initiate AI Agent runtime client
    genai_agent_runtime_client = generative_ai_agent_runtime.GenerativeAiAgentRuntimeClient(config, service_endpoint=SERVICE_EP, retry_strategy=oci.retry.NoneRetryStrategy())

    create_session_details = generative_ai_agent_runtime.models.CreateSessionDetails()
    create_session_details.display_name = "Welcome to BOCAS"
    create_session_details.idle_timeout_in_seconds = 20
    create_session_details.description = welcome_msg

    return create_session_details, genai_agent_runtime_client

def Quest_Answer(user_question, create_session_details, genai_agent_runtime_client):
    #Create a Chat Session for AI Agent
    try:
        create_session_response = genai_agent_runtime_client.create_session(create_session_details, AGENT_EP_ID)
    except:
        create_session_details, genai_agent_runtime_client = gen_Agent_Client()
        create_session_response = genai_agent_runtime_client.create_session(create_session_details, AGENT_EP_ID)
    
    #Define Chat details and input message/question
    session_details = generative_ai_agent_runtime.models.ChatDetails()
    session_details.session_id = create_session_response.data.id
    session_details.should_stream = False
    session_details.user_message = user_question

    #Get AI Agent Respose
    session_response = genai_agent_runtime_client.chat(agent_endpoint_id=AGENT_EP_ID, chat_details=session_details)
    return session_response

print(Style.BRIGHT + Fore.RED + welcome_msg + Style.RESET_ALL)

ses_details, genai_client = gen_Agent_Client()

while True:
    question = input("Enter text (or Enter to quit): ")
    if not question:
        break
    chat_response = Quest_Answer(question, ses_details, genai_client)
    print(Style.DIM +'********** Question for BOCAS **********')
    print(Style.BRIGHT + Fore.RED + question + Style.RESET_ALL)
    print(Style.DIM + '********** Answer from BOCAS **********' + Style.RESET_ALL)
    print(Fore.MAGENTA + chat_response.data.message.content.text + Style.RESET_ALL)

print("*** The End - Exiting BOCAS ***")

When the above code is run, it will loop, asking for questions, until no question is added and the ‘Enter’ key is pressed. Here is the output of the BOCAS running for some of the questions I asked in my previous post, along with a few others. These questions are based on the Irish Leaving Certificate English Examination.

Using a Gen AI Agent to answer Leaving Certificate English papers

Posted on February 8, 2025

In a previous post, I walked through the steps needed to create a Gen AI Agent on a data set of documents containing the works of Shakespeare. In this post, I’ll look at how this Gen AI Agent can be used to answer questions from the Irish Leaving Certificate Higher Level English examination papers from the past few years.

For this evaluation, I will start with some basic questions before moving on to questions from the Higher Level English examination from 2022, 2023 and 2024. I’ve pasted the output generated below from chatting with the AI Agent.

The main texts we will examine will be Othello, McBeth and Hamlett. Let’s start with some basic questions about Hamlet.

We can look at the sources used by the AI Agent to generate their answer, by clicking on View citations or Sources retrieved on the right-hand side panel.

Let’s have a look at the 2022 English examination question on Othello. Students typically have the option of answering one out of two questions.

In 2023, the Shakespeare text was McBeth.

In 2024, the Shakespeare text was Hamlet.

We can see from the above questions, that the AI Agent was able to generate possible answers. As a learning and study resource, it can be difficult to determine the correctness of these answers. Currently, there does seem to be evidence that students typically believe what the AI is generating. But the real question is, should they? Why the AI Agent can give a believable answer for students to memorise, but how good are the answers really? How many marks would they get for these answers? What kind of details are missing from these answers?

To help me answer these questions I enlisted the help of some previous Students who took these English examinations, along with two English teachers who teach higher-level English classes. The students all achieved a H1 grade for English. This is the highest grade possible, where a H1 means they achieved between 90-100%. The feedback from the students and teachers was largely positive. One teacher remarked the answers, to some of the questions, were surprisingly good. When asked about what grade or what percentage range these answers would achieve, again the students and teachers were largely in agreement, with a range between 60-75%. The students tended to give slightly higher marks than the teachers. They were then asked about what was missing from these answers, as in what was needed to get more marks. Again the responses from both the students and teachers were similar, with details of higher-level reasoning, understanding of interpersonal themes, irony, imagery, symbolism, etc were missing.