EASILY Create GPT 4.5 AI Agents using No Code AI Tool

GPT 4.5 AI Agents: Meal Planner & Blog Agent with Vector Shift

Key Concepts:

• GPT 4.5 AI Agents
• No-code solution
• Vector Shift (pipeline automation platform)
• Meal Creator Agent
• Blog Creator Agent
• Pipelines (data flow through agents)
• LLMs (Large Language Models)
• System Instructions (agent behavior definition)
• Chatbot integration
• WordPress integration
• iFrame embed code

1. Introduction:

The video demonstrates how to create two AI agents, a Meal Planner and a Blog Agent, using Vector Shift, a no-code platform. The process involves setting up a pipeline where input is processed by the Meal Planner agent, then passed to the Blog Agent, and finally outputted as a blog post. The final product is integrated into a WordPress website as a chatbot.

2. Vector Shift Overview:

• Vector Shift is a no-code solution for building pipelines and automating tasks using multiple large language models (LLMs).
• It allows users to create chatbots or workflow automations.
• The platform offers pre-built templates, but the video focuses on creating a pipeline from scratch.
• Coupon Code: "mvin praison" for 20% off.

3. Pipeline Creation (Step-by-Step):

• Step 1: Input and Output: Start by defining the input and output nodes in the Vector Shift interface.
• Step 2: Agent Creation: Create two agents using GPT 4.5 LLMs. This is done by dragging and dropping LLM components onto the canvas.
• Step 3: Meal Creator Agent Configuration:
  • Connect the input node to the first agent (Meal Creator).
  • Define the system instruction for the agent: "You are a meal Creator agent."
• Step 4: Blog Creator Agent Configuration:
  • Connect the output of the Meal Creator agent to the input of the second agent (Blog Creator).
  • Define the system instruction for the agent: "You are a Blog Creator agent."
• Step 5: Output Configuration: Connect the output of the Blog Creator agent to the output node.
• Step 6: Testing: Click the "Run" icon to test the pipeline. Provide an input (e.g., "chicken") and observe the status of each agent. The output is displayed as a blog post.

4. Deployment and Integration:

• Step 1: Deploy Changes: Click "Deploy Changes" to save the pipeline configuration.
• Step 2: Export as Chatbot: Go to "Export" and create a chatbot.
• Step 3: Chatbot Configuration: Configure the chatbot's title, appearance, and other settings.
• Step 4: Embed Code: Obtain the iFrame embed code for the chatbot.
• Step 5: WordPress Integration:
  • In WordPress, navigate to "Appearance" -> "Editor" -> "Patterns".
  • Add a new block as HTML and paste the iFrame embed code.
  • Save the changes.

5. Example and Application:

• The example used is creating a blog post about chicken recipes.
• The user inputs "chicken," and the AI agents generate a relevant blog post.
• The integrated chatbot allows users to ask questions or request recipes directly on the website.

6. Technical Details:

• LLMs: Large Language Models, such as GPT 4.5, are used to power the agents.
• System Instructions: These instructions define the role and behavior of each agent.
• iFrame: An HTML element used to embed the chatbot into a website.

7. Conclusion:

The video demonstrates a simple yet powerful way to create AI agents and integrate them into a website using a no-code platform. By leveraging Vector Shift and GPT 4.5, even beginners can automate content creation and provide interactive experiences for their website visitors. The key takeaway is the ease of use and the potential for customization and integration with various applications.