Piyush Garg

Piyush Garg - Building AI Agent from Scratch

The discussion revolves around building an AI agent from scratch without using any frameworks or libraries. The video explains the difference between an LLM (Large Language Model) and an AI agent, emphasizing that while LLMs can understand and process natural language, they cannot perform tasks autonomously. The main challenge is to enable LLMs to access real-world data and perform tasks on behalf of the user. This is achieved by creating a framework where LLMs are provided with tools, which are essentially developer-defined functions that allow the AI to interact with databases and perform operations. The video demonstrates this by building a simple weather application using JavaScript, where the AI agent can access real-time weather data by calling specific functions. The process involves setting up a system prompt, defining available tools, and using auto-prompting to guide the AI in performing tasks. The video concludes by highlighting the potential of AI agents to perform complex operations when integrated with multiple tools and frameworks.

Key Points:

AI agents are built by integrating LLMs with tools to perform tasks.
LLMs can understand natural language but need tools to access real-world data.
Developers define functions as tools for AI to interact with databases.
Auto-prompting helps guide AI in executing tasks based on user input.
Building AI agents involves setting up system prompts and defining tool access.

Details:

1. 🔍 Introduction to AI Agent Creation

The introduction section lacks specific insights or actionable steps, focusing instead on setting the context for understanding AI agent creation.
To improve, the section should be divided into smaller, thematic subsections to enhance readability and understanding.
Adding detailed explanations, examples, and actionable insights will provide a more comprehensive understanding of AI agent creation.
The title should accurately reflect the depth of content, potentially by including a subtitle that indicates a high-level overview or foundational concepts.
Contextual information should be expanded to highlight the strategic importance and practical applications of AI agents.
Incorporating metrics, data points, and strategic instructions would enhance the relevance and practical value of the section.

2. 🛠️ Understanding AI Agents and LLMs

AI agents are systems that perceive their environment and take actions to maximize their chance of success.
Building an AI agent from scratch involves understanding the fundamental components, such as perception, decision-making, and action execution.
Constructing an AI agent without frameworks encourages a deeper understanding of underlying principles and allows for customization according to specific needs.
The process includes defining the agent's goals, designing its environment, and implementing algorithms for perception, decision-making, and learning.
Practical examples include creating agents for specific tasks like game playing, robotic control, or data analysis, which can help solidify understanding.

3. 🤖 Designing AI Agents from Scratch

The video demystifies the difference between large language models (LLMs) and AI agents, emphasizing the unique capabilities of AI agents in autonomous decision-making and task execution.
It outlines a step-by-step guide to designing AI agents from scratch, focusing on defining objectives, integrating data processing capabilities, and implementing decision-making algorithms.
Key insights include the importance of clear objective setting, robust data integration, and iterative testing to refine agent performance.
The content, though brief, serves as a foundational guide for beginners in AI agent design.

4. 🧠 Capabilities and Limitations of LLMs

AI agents are defined as systems or programs capable of autonomously performing tasks on behalf of a user.
These agents are important because they can function independently, making decisions and executing tasks without constant user intervention.
AI agents utilize available tools effectively, optimizing task performance and resource allocation.
Examples of AI agents include virtual assistants like Siri and Alexa, which manage tasks based on user commands.
The capabilities of AI agents extend to learning from interactions, improving their performance over time.
However, limitations exist, such as dependency on predefined algorithms and the inability to handle tasks outside their programmed scope.

5. 🔗 Integrating AI Agents with Real-World Data

AI models like GPT-4 and GPT-3.5 leverage diverse real-world data, enhancing their application capabilities.
Extensive training on vast datasets enables AI models to possess a broad knowledge of the world, facilitating accurate and relevant responses.
The natural language interaction capability of these models makes them user-friendly and accessible for various applications.
Specific data contexts in training improve the models' ability to deliver precise and contextually relevant responses.
Successful AI integration examples include personalized customer service systems and predictive analytics in finance, showing significant improvements in efficiency and customer satisfaction.

6. 🗃️ Data Access Challenges for LLMs

LLMs (Large Language Models) have advanced natural language processing capabilities, allowing them to understand and process sentiment, akin to a brain that can comprehend human-like prompts.
Despite their advanced capabilities, LLMs cannot independently perform tasks such as writing or decision-making without human intervention, highlighting a significant limitation in their practical application.
The inability of LLMs to autonomously execute tasks underscores a challenge in leveraging their full potential in real-world applications, where human input remains crucial.

7. 🔍 Exploring LLMs' Closed Nature

LLMs like GPT are inherently closed systems, meaning they cannot access external data sources like the internet or databases by default.
To enable LLMs to interact with external databases or access the internet, developers must implement additional solutions, such as integrating APIs or utilizing plugins that bridge the gap between the LLM and external data sources.
Without these integrations, LLMs are limited to the data they were trained on and cannot retrieve real-time information or updates.

8. 🌐 Making AI Agents Access Real-Time Data

AI models lack the ability to perform CRUD operations on databases, which restricts them from creating, reading, updating, or deleting data directly.
They are trained on static data available at the time of training and cannot access or process real-time data.
AI models are inherently behind real-time data by at least 24 hours due to the time required for updates.
Despite advanced capabilities like sentiment analysis and NLP, AI models cannot match human intelligence in real-time decision-making tasks.
Example: An AI model trained on last year’s market data cannot predict stock market changes happening in real-time today.
Scenario: AI used in customer support might not reflect the latest product updates if it lacks real-time data access.

9. 🛠️ Building an AI Agent Framework

The framework should enable AI models to autonomously access and utilize real-world data and databases, enhancing their ability to perform tasks on behalf of users.
Integration with various LLMs, such as OpenAI's models, Meta's LLaMA, or Anthropic's Claude, is crucial to boost functionality.
A primary objective is to allow these models to browse the internet and execute database operations without direct human intervention.
AI agent workflows are integral to providing these models with the necessary data access and task execution capabilities.
Examples of successful integration include AI models autonomously retrieving data from online sources to update user databases or execute specific user commands.
Case studies highlight frameworks where AI agents have improved efficiency by automating routine data retrieval and processing tasks, showcasing the potential for significant productivity gains.

10. 🖥️ Implementing Developer-Defined Functions

Developers can create a 'black box' system where the internal workings are abstracted from the user, allowing interaction through a smart LLM (Large Language Model).
The LLM is enhanced to interact with this 'black box' by utilizing a set of tools, which are essentially developer-defined functions.
Users benefit by being able to access databases through NLP (Natural Language Processing) and GPT capabilities, without needing to understand the underlying complexity.
The black box contains developer-defined functions that allow for various operations, such as database access, under developer control.
These functions enable the execution of complex queries and data retrieval tasks efficiently, improving user experience.
Examples include executing data analytics processes or fetching specific data sets based on user queries.
The approach allows developers to customize the interaction model, ensuring security and tailored usability.
By abstracting the complexity, developers allow non-technical users to leverage advanced computational functionalities seamlessly.

11. 🗂️ Creating a Simple Weather Application

Implement functions in JavaScript or any language to read/write to the database, using them as tools for building applications.
Provide GPT with context about available functions and their descriptions to enhance its interaction capabilities.
Develop an 'auto-prompt' mechanism, feeding context into GPT based on user requests, to facilitate user interaction during sessions.
Address the challenge of real-time data unavailability in GPT by integrating it with real-time data sources, such as creating a real-time weather application.

12. 🔑 Setting Up OpenAI API and Keys

12.1. 🔧 Setting Up the Node.js Project

12.2. 🔌 Integrating OpenAI SDK

13. 📜 Writing Basic JavaScript for AI Agent

Initialize an OpenAI client using an API key to enable interaction with OpenAI's LLM model, setting the foundation for AI integration.
Develop a 'getWeatherDetails' function, illustrating how to handle weather data without real API calls by using hardcoded data as a placeholder.
The function requires a city name input, returning '10°C' for 'Patiala' as an example, demonstrating the function's structure and potential future API integration.
Highlight the function's role as a placeholder for actual API calls, emphasizing the forward-looking aspect of real-time data retrieval.
This approach allows developers to simulate real-world scenarios and test their implementation before integrating with live APIs.

14. 📊 Handling User Queries and Responses

Implemented a method to simulate API call results by using hardcoded temperature data for cities: Mohali (14°C), Bangalore (20°C), Chandigarh (8°C), Delhi (12°C).
Utilized a structured approach to handle user queries, such as 'What is the weather of Patiala?', by setting up prompts for input.
Created a message structure with defined roles to process user queries, indicating the user and the content.
Simulated interaction with a language model (like ChatGPT) for query processing, without actual API calls.

15. 🔄 Automating Prompt Responses

To improve automation, always specify the correct model; for instance, using GPT-4 when needed to ensure task accuracy.
Recognize the limitation of LLMs, such as the inability to provide real-time data like current weather updates, and develop workarounds such as integrating APIs for real-time data.
Correct common errors like incorrect import statements that can disrupt workflows, by verifying syntax and dependencies.
Address the AI model's failure to provide results by refining prompts and ensuring the model's capabilities align with task requirements.

16. 🧩 Designing System Prompts and Examples

Create a system prompt that explicitly defines the AI's role as an assistant to ensure clarity in interactions.
Incorporate actions like 'start plan action' and 'observation' to guide the AI's response, ensuring a structured and strategic approach to interaction.
Emphasize the importance of the AI waiting for user prompts before planning and action to maintain a controlled response process.
Provide examples or case studies to illustrate successful implementation of these principles in real-world scenarios.

17. 🗂️ Structured Examples for AI Understanding

Begin by receiving input from the user and proceed with planning.
After planning, decide on actionable steps, such as which tool to call.
Wait for an observation after calling a tool to inform further actions.
Example: If a user asks for the sum of the weather of Patiala and Mohali, and the AI lacks direct weather access, it should first plan to call a function to retrieve weather details for Patiala.
Perform the action by calling the relevant function with the necessary input parameter, such as 'Patiala'.
Developer inputs the observation, e.g., '10°C' for Patiala.
Plan to repeat the process to obtain weather details for Mohali.
Perform the action and receive the observation, e.g., '14°C' for Mohali.
Calculate the final output as the sum of both observations, resulting in '24°C'.

18. 🔄 Enhancing AI with Auto-Prompting

Auto-prompting involves a structured process where the AI takes user input, plans necessary actions, executes them, and observes outcomes. This process is primarily a developer's responsibility.
A practical example of auto-prompting is using a function to fetch weather details by city name, which requires the AI to understand and utilize available tools effectively.
Developers should enhance AI's understanding by providing clear prompts and examples of available functions, enabling the AI to respond accurately to user queries such as 'What is the weather in Patiala?'
Incorporating models like GPT-4, developers can simulate user prompts and responses, allowing the AI to execute asynchronous functions to fetch results, which must be properly awaited.
Effective implementation requires developers to define input prompts clearly, including system prompts and user roles, to ensure the AI can plan actions and retrieve necessary details accurately.

19. 🌐 Interactive User Input and Response

19.1. Weather Information Retrieval

19.2. User Input Handling and Processing

20. 🎛️ Auto Prompting Logic Implementation

20.1. Query Creation and Message Insertion

20.2. Auto Prompting and Chat Call

20.3. Response Handling and Output Format

20.4. Result Extraction and Function Implementation

20.5. Output and Logging

21. 🔄 Dynamic Function Calling

Dynamic function calling involves efficiently ending processes in code by breaking out of loops once the desired output is obtained.
Action types are dynamically mapped to specific function calls, which are performed using 'call.function' and 'call.input', with observations recorded as outputs.
The system iteratively manages inputs and outputs through auto-prompting until the correct action or function output is achieved, ensuring user queries are resolved.
A practical example includes dynamically checking weather conditions for various locations by calling specific functions, demonstrating real-time application.
This method emphasizes tracking and managing function call outcomes, crucial for effective dynamic function execution.

22. 📊 Demonstrating AI's Real-Time Processing

The AI tool demonstrated real-time processing by fetching and displaying weather information efficiently.
When asked for weather details of multiple cities sequentially, the AI remembered previously fetched data and only retrieved new data when necessary.
For instance, it fetched weather details for Patiala and then only retrieved new data for Mohali when requested for the sum of weather details of Patiala and Mohali.
This process was further demonstrated when AI fetched data for Delhi, utilizing existing data for Patiala and Mohali, showcasing its ability to avoid redundant data fetching.
The AI automatically converted temperature units, demonstrating its capability to handle complex queries efficiently.
The demonstration emphasized how AI can integrate multiple tools and databases, allowing for seamless and efficient data operations.

23. 🎬 Conclusion and Future Prospects

23.1. Conclusion

23.2. Future Prospects

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