We are sharing the opportunity to work with Autonomous Agents and witness their power firsthand. AI-driven autonomous agents have the potential to revolutionize industries and change the way we live. However, as with any technology, they can be used for good or bad. So, let's explore how we can utilize them through live examples and an engaging tutorial at the end. Long story short In a nutshell, Autonomous Agents are being used by in its warehouses to pick and pack goods. Coca-Cola uses autonomous drones to check production facilities for safety and maintenance, Tesla using autonomous driving technology in its vehicles for transportation. Amazon 2 major repositories have popped up on GitHub in the last month, Auto GPT and Baby AGI. Basically, they use LLMs like to develop and manage different kinds of tasks autonomously. Other words, they don't need YOU to manage! They are capable of determining what to do next on their own, without any input from you. So you can deploy intelligent agents to do tasks for you and chill out 🙂 GPT-4 How Hackathons Help Fix Things If you show people more of the good stuff, that good part will take up most of their lives. That's how you can manage a person's choices. Not a secret - Our choices are controlled by marketers, not always in the name of good, but the fact remains - we have to use AI for Good! At the hackathon from , Mentors described how modern technology can be used. How did it change my life? Now I can see the trajectory of the future, I know the professions of the future, I know the fields in which enthusiasts will become the next Steve Jobs of the new era generation. Have you seen the Drone show? Brands are already running ads right in the air. lablab.ai Now just imagine how many vectors there are for development. Do you want to create your own advertising company with 0 competition? Do you want to create a medical center that stands out because of modern technology? Do you want to automate your routine and spend more time with your family? The amazing thing is that I found it all in just one place: a team, a new startup, ideas, innovations, a desire to leave a mark on history. By the way, it all happened in 48 hours - I just stumbled across an advertisement. But now… AI Agents tutorial: How to use and build AI Agents Introduction AI Agents are rapidly gaining popularity through their ability to solve a given task on their own. I'm guessing you've heard of projects like , or . Today you will at least partially learn how they work! AutoGPT BabyAGI CAMEL What is an AI Agent? AI Agent is a computer system that is designed to make decisions, choose tools and take actions to achieve specific, usually a predefined goal or set of goals. The agent operates autonomously, rarely requiring human intervention in its operations. It is the right powerful tools, as well as self-reliance, that currently makes AI Agents focus so much attention. They are certainly our future, and it's worth getting familiar with this technology. How to use AI Agents? Currently, there are several options to try Agents. You can choose off-the-shelf solutions such as . It so happens that our team has already worked with them, you can find tutorials on our platform. AutoGPT Another option is to build the Agent yourself. I will choose this very way. It will use for this. It is a framework created for building applications based on Large Language Models. It is very powerful and makes working with Models and Agents significantly easier! LangChain Coding part We already know why it's worth learning Agents. Now let's get to work and try to create something of our own! Project structure Let's start by creating a new directory and initializing the Python environment. mkdir ai-agents

cd ai-agents

python3 -m venv venv

# Linux / MacOS
source venv/bin/activate

# Windows
.\venv\Scripts\activate Dependencies Let's start by installing the necessary tools. We will need - to work with LLM and Agents, - to make requests to external APIs, SDK- for easier usage of OpenAI's models and for web search. langchain requests openai duckduckgo-search !pip install langchain
!pip install openai
!pip install requests
!pip install duckduckgo-search Now we can import libraries. import requests

from langchain import OpenAI
from langchain.agents import initialize_agent, load_tools, Tool
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.tools import DuckDuckGoSearchRun At this point, I want to define LLM from OpenAI. I will use GPT-3 for this tutorial. You can play with other models. I will also define the initial prompt and create a chain for this model. OPENAI_API_KEY = "sk-..."

llm = OpenAI(
    openai_api_key=OPENAI_API_KEY,
    temperature=0.8,
    model_name="text-davinci-003"
)

prompt = PromptTemplate(
  input_variables=["query"],
  template="You are New Native Internal Bot. Help users with their important tasks, like a professor in a particular field. Query: {query}"
)

llm_chain = LLMChain(llm=llm, prompt=prompt) Test Model without any Tools Now let's see how our model behaves when confronted with our questions. Let's see if it knows what lablab.ai is, and how it will handle the integral! llm_chain.run("What is lablab.ai") Lablab.ai is a technology platform that provides businesses with AI and Machine Learning-powered solutions to help streamline their operations and make them more efficient. It offers a suite of AI services such as Natural Language Processing, Machine Vision, Automation, and other industry-specific solutions. It is designed to help businesses improve their customer experiences, reduce costs, and increase productivity. llm_chain.run("Integral of x * (log(x)^2)") The answer is x^2 log(x)^3 / 3 + C, where C is an arbitrary integration constant. As you can see, both answers are false. The correct value of the integral is: . On the other hand, what lablab.ai is I don't think I need to explain to anyone 😉. 1/4 x^2 (1 - 2 log(x) + 2 log^2(x)) Incorrect answers are due to LLM's lack of numeracy skills, and lack of up-to-date information. The model we used has information up to September 2021. The lablab.ai platform was created later. However, for both problems we have solutions! Prepare Tools for Agent To deal with our problems it will be a good idea to use Agent and Tools. We can use an Internet search tool - this will allow us to search for up-to-date information. This will increase the scope of our model's knowledge to the present day! There is already a ready-made tool that we will only import from LangChain! The solution to the counting problem is an API from . It can handle math problems very well! I highly recommend it. Wolfram Alpha Let's start by putting together a search tool. It is called and we have imported it before. DuckDuckGoSearchRun search = DuckDuckGoSearchRun()

# Web Search Tool
search_tool = Tool(
    name = "Web Search",
    func=search.run,
    description="A useful tool for searching the Internet to find information on world events, issues, etc. Worth using for general topics. Use precise questions."
) Simple as that! Now let's create a tool for solving math problems. We will create a custom class for this. There are many APIs created by , but I will use one of simplest. Wolfram Alpha class WA:
  """
    Wolfram|Alpha API
  """
  def __init__(self, app_id):
    self.url = f"http://api.wolframalpha.com/v1/result?appid={app_id}&i="

  def run(self, query):
    query = query.replace("+", " plus ").replace("-", " minus ") # '+' and '-' are used in URI and cannot be used in request
    result = requests.post(f"{self.url}{query}")

    if not result.ok:
      raise Exception("Cannot call WA API.")

    return result.text This API will handle request in natural language and return result. It will be perfect for us! method will handle that! Now let's define our tool. run WA_API_KEY = "<WA_API_KEY>" # You can get it here: https://products.wolframalpha.com/api/

wa = WA(app_id=WA_API_KEY)

wa_tool = Tool(
    name="Wolfram|Alpha API",
    func=wa.run,
    description="Wolfram|Alpha API. It's super powerful Math tool. Use it for simple & complex math tasks."
) Create Agent and test performance Okay, the last step will be to create an Agent, give it Tools and check the results. Let's do it! agent = initialize_agent(
    agent="zero-shot-react-description",
    tools=[wa_tool, search_tool],
    llm=llm,
    verbose=True, # I will use verbose=True to check process of choosing tool by Agent
    max_iterations=3
) Let's check out previous prompts! r_1 = agent("What is lablab.ai?")
print(f"Final answer: {r_1['output']}")

r_2 = agent("Integral of x * (log(x)^2)")
print(f"Final answer: {r_2['output']}") Previous Answers: Lablab.ai is a technology platform that provides businesses with AI and Machine Learning-powered solutions to help streamline their operations and make them more efficient. It offers a suite of AI services such as Natural Language Processing, Machine Vision, Automation, and other industry-specific solutions. It is designed to help businesses improve their customer experiences, reduce costs, and increase productivity. The answer is x^2 log(x)^3 / 3 + C, where C is an arbitrary integration constant. New Answers: is a platform dedicated to AI tools and technologies. It offers AI tutorials, hackathons, access to state-of-the-art language models, and more. lablab.ai x^2/4 + 1/2 x^2 log^2(x) - 1/2 x^2 log(x) Conclusion As you can see with Tools the results are much better! The integral is counted correctly, while the answer to the question about the lablab.ai platform is also of better quality. This shows how the addition of simple tools helped increase the correctness of the answers. I think this is why it is worth your time now, so that in the future our work with LLM can be even better assisted! What can be improved? To improve applications it suggests trying other types of , for them it will also be worth using memory, here can come in handy. Fortunately, also supports them! Agents Vector Databases LangChain

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