AI Assistant Agent Builder

[BrandonFrazier007]

Discussion

I was working on an AI Assistant and saw your video. 🏗️
I have one that appears to work. 🤷🏻‍♂️
I have an example output, 🔽 plain text instructions, and used Open AI docs for file attachment.
I do not come from a programmer background but I do work for a AI company building AIs. 💡
I like your project, it may be a little robust for me, but I always start with simple things. 🔢

Output example:

User
I want to build and AI Agent to check my Google Calendar. How do I get started?

AI Assistant Agent Builder
Building an AI agent to interact with your Google Calendar involves several steps. Here is a high-level outline to get you started:

Define Your Requirements: Clearly specify what you want the AI agent to achieve. Do you want it to only read calendar events, or also create and modify them? Should it handle reminders or send notifications as well?

Project Setup:

Create a new Google Cloud Platform (GCP) project if you don't have one.
Enable the Google Calendar API through the GCP console.
Set up OAuth 2.0 to authenticate and authorize your AI agent to access the Google Calendar.
Develop Your AI Agent:

Choose a programming language, such as Python, which has good support for both Google Calendar API and AI development.
Implement Natural Language Processing (NLP) using libraries like NLTK, spaCy, or a platform like Dialogflow to process user inputs.
Develop the core logic to interpret user commands and make appropriate requests to the Google Calendar API.
Google Calendar API Integration:

Use the official Google API Client Library for your chosen programming language to interface with Google Calendar.
Implement functions to list, create, update, and delete calendar events based on user requests.
Testing:

Write unit tests to ensure your code interacts with the Google Calendar API correctly.
Perform integration testing to make sure the AI agent correctly understands and processes user requests.
Run user acceptance tests to validate the AI agent's functionality with real users.
Deployment:

Deploy your AI agent on a suitable hosting environment. Cloud services like Google Cloud, AWS, or Azure are common choices.
Make sure the environment is secure and has the necessary credentials to interact with the Google Calendar API.
Monitoring and Maintenance:

Implement logging and error tracking to catch and fix issues quickly.
Regularly update the AI agent to handle any changes in the Google Calendar API or to add new features.
Legal and Ethical Considerations:

Ensure you are handling user data securely and in compliance with applicable laws like GDPR.
Implement clear consent and privacy policies for users.
Performance Tuning and Optimization:

Monitor the AI agent's performance and optimize its NLP capabilities for better understanding and responding to user requests.
User Education and Support:

Create documentation to help users interact with your AI agent.
Provide support channels for users who need assistance or encounter problems.
Before you start coding, you might need to gain familiarity with Google's API usage and NLP concepts if they're new to you. There are many resources and tutorials online to help you with these aspects.

Would you like to delve into any of these steps in more detail?

User
How can I use Open AI Assistants to create the AI Agent to Check my Google Calendar?

AI Assistant Agent Builder
OpenAI offers powerful language models such as GPT-3 and more recently GPT-4, which you can use as the basis for creating an AI agent capable of interacting with Google Calendar. Here’s a high-level workflow to do this:

Access OpenAI API:

Sign up for OpenAI and get access to the API.
Obtain your API key, which is needed to authenticate requests to OpenAI services.
Set Up Google Calendar API:

Create a new project in the Google Cloud Platform (GCP).
Enable the Google Calendar API within the GCP project.
Configure OAuth 2.0 in GCP to allow your application to access a user's Google Calendar with their consent.
Obtain the necessary OAuth credentials, such as client_id and client_secret.
Build the AI Agent:

Choose a backend framework (e.g., Flask, Django for Python) to create an application that can communicate with OpenAI's API and Google Calendar API.
Write code to authenticate users using Google's OAuth system and obtain their consent to access their calendar.
Use OpenAI's API to process natural language inputs from the user. Send user queries to the OpenAI API and interpret the responses.
Develop logic to translate the interpreted user intent into actions on the Google Calendar API (listing events, creating new events, etc.).
Integrate Natural Language Understanding:

Once you receive a user's request, send it to OpenAI's API endpoint using your API key.
Process the AI's response to determine the user's intent i.e., whether they want to read, create, or update a calendar entry.
With GPT-3 and GPT-4, you can fine-tune models for specific tasks or employ prompts that guide the model to give responses in a format that's easier to translate into API calls.
Handle Google Calendar Interactions:

Make requests to the Google Calendar API to perform operations like querying for upcoming events or adding new ones based on the processed user commands.
Testing and Debugging:

Thoroughly test the AI agent's ability to understand various natural language inputs and its correctness in making Google Calendar API requests.
Ensure that the flow of obtaining consent and tokens from Google works flawlessly.
Deployment:

Deploy your application to a server or cloud platform, taking care to protect user data and credentials.
Monitor the application's usage and performance to handle scalability and potential issues.
Compliance and Privacy:

Clearly inform users how their data will be used and ensure that you are compliant with relevant data protection laws (GDPR, CCPA, etc.)
Implement proper security measures to keep user data and access tokens secure.
This is a simplified overview, and each step can be quite involved. Integrating OpenAI's language models will greatly enhance the AI agent's natural language understanding capabilities, but you will also need to handle aspects such as state management, session handling, and possibly integrating a database for storing tokens and user preferences.

If you do not have a programming background, or if your experience in API integrations is limited, you may need to partner with someone who has these skills or consider hiring a developer. If you have some experience in these areas, you might want to start experimenting with the APIs (both OpenAI's and Google Calendar's) in a controlled environment to get a sense of how they work before diving into full-scale development.

User
Build the AI Agent in python

AI Assistant Agent Builder
Creating a basic AI agent in Python to interact with Google Calendar using OpenAI's language model for natural language understanding is a multi-step process. Below is a very simplified code example that would serve as a starting point. This example assumes that you have set up Google Cloud Platform and obtained necessary credentials for both Google Calendar API and OpenAI API.

Before you start, make sure you have:

Installed the google-auth, google-auth-oauthlib, google-auth-httplib2, google-api-python-client, and openai Python packages.
Set up the Google Cloud project, enabled the Google Calendar API, and obtained the credentials.json for OAuth2 authentication.
Obtained OpenAI API key for using GPT-3 or GPT-4.
Let's start by authenticating with Google Calendar and setting up a way to send queries to the OpenAI API:

import datetime
import openai

from google_auth_oauthlib.flow import InstalledAppFlow
from googleapiclient.discovery import build

# Initialize the OpenAI API client with your API key
openai.api_key = 'your-openai-api-key'

# Function to authenticate with Google and create a service object
def authenticate_google_calendar():
    # The scope tells Google what level of access to grant
    scopes = ['https://www.googleapis.com/auth/calendar']
    
    # Set up the flow using the client_secret file
    flow = InstalledAppFlow.from_client_secrets_file('credentials.json', scopes)
    creds = flow.run_local_server(port=0)
    
    # Build the service object for the Google Calendar API
    service = build('calendar', 'v3', credentials=creds)
    return service

# Function to use OpenAI completions to process natural language and extract intent
def get_intent_from_query(query):
    response = openai.Completion.create(
        engine="text-davinci-003",  # or the latest model
        prompt=query,
        max_tokens=150
    )
    return response.choices[0].text.strip()

# Example function that interprets intent and performs an action on Calendar
def process_query(service, query):
    intent = get_intent_from_query(query)
    
    # Basic keyword matching for the intent - this is very simplistic
    if "schedule" in intent or "event" in intent:
        # Here, you would include the code to create an event using Google Calendar API
        print("An event creation intent was found.")
    elif "what" in intent and "upcoming" in intent:
        # Here, you would include code to list upcoming events using Google Calendar API
        now = datetime.datetime.utcnow().isoformat() + 'Z'
        print('Getting the upcoming events')
        events_result = service.events().list(calendarId='primary', timeMin=now,
                                              maxResults=10, singleEvents=True,
                                              orderBy='startTime').execute()
        events = events_result.get('items', [])
        if not events:
            print('No upcoming events found.')
        for event in events:
            start = event['start'].get('dateTime', event['start'].get('date'))
            print(start, event['summary'])
    else:
        print("No recognized intent.")

# Authenticate and create a Google Calendar service object
google_service = authenticate_google_calendar()

# Sample user query
user_query = "I have a meeting with Bob tomorrow at 10 AM, can you schedule that?"

# Process the user query
process_query(google_service, user_query)

Remember to place your own OpenAI API key where it's indicated and ensure that 'credentials.json' is in your working directory. This code does not handle errors, edge cases, or complexities in natural language understanding. Real-world use would require a much more robust implementation, like handling time zones, event details extraction, or managing user sessions and state.

For a production system, you'll need to write additional code to ensure security, manage state, extract detailed user intent (including event participants, locations, etc.), and handle other detailed aspects of Google Calendar event management. Additionally, perform thorough testing to ensure its reliability and usability.

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[daveshap]

Copypasting example outputs is not a good use of this space. Please just provide a link to a ShareGPT or your own repo. We need to keep the discussions space clean and tidy.