Day 19 - Agents: Giving your LLM ability to run workflows
Welcome to Day 19 of our #75DaysofGenerativeAI series. As we continue to explore the vast possibilities of generative AI, today we're diving into an exciting topic that's gaining significant attention in the LLM ecosystem: Agents in Large Language Models (LLMs).
Why Agents in LLMs?
Before we dive into the specifics, let's address the elephant in the room: why agents are becoming increasingly important in the LLM ecosystem. Agents act as intelligent intermediaries between LLMs and the external world, enhancing the models' capabilities to perform complex, multi-step tasks. They bring autonomy and goal-oriented behavior that pure language models often lack.
Imagine having an AI assistant that doesn't just answer questions, but can actively research, plan, and execute tasks on your behalf. That's the power of agents in LLMs.
What are Agents in LLMs?
At their core, agents are software entities that use LLMs to perceive, reason, and act in an environment to achieve specific goals. They can interact with external tools, databases, and APIs, making decisions based on the task and available information.
Let's explore some common types of agents and see how they work in practice.
Zero-Shot React Agent
Zero-Shot React agents are designed to handle tasks without any specific training examples. They rely on the LLM's general knowledge and ability to understand and follow instructions.
Example: Task Planning
Let's create a simple Zero-Shot React agent to help plan a weekend trip ( with simulated data ):
from langchain_groq import ChatGroq
from langchain.agents import initialize_agent, Tool
llm = ChatGroq(temperature=0, model_name="llama-3.1-70b-versatile")
tools = [
Tool(
name="Weather Checker",
func=lambda x: "Sunny and warm",
description="Checks the weather forecast"
),
Tool(
name="Hotel Finder",
func=lambda x: "Seaside Resort available",
description="Finds available hotels"
)
]
agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)
agent.run("Plan a weekend trip to the beach")Output looks like something like this
> Entering new AgentExecutor chain...
Thought: To plan a weekend trip to the beach, I need to consider the location and the weather. I should start by finding a suitable beach location and then check the weather forecast for that location.
Action: Hotel Finder
Action Input: Beach hotels
Observation: Seaside Resort available
Thought:Thought: I've found a potential beach location, Seaside Resort. Now I need to check the weather forecast for that location to ensure it's suitable for a weekend trip.
Action: Weather Checker
Action Input: Seaside Resort
Observation: Sunny and warm
Thought:Thought: I now know the weather forecast for Seaside Resort, and it's sunny and warm, which is perfect for a weekend trip to the beach. I can now plan the trip.
Final Answer: Plan a weekend trip to Seaside Resort, where you can enjoy sunny and warm weather at the beach.
> Finished chain.This agent can use the provided tools to check the weather and find a hotel, demonstrating how it can break down a complex task into smaller, actionable steps.
React Docstore Agent
React Docstore agents can access and utilize external knowledge sources, making them excellent for tasks requiring specific domain knowledge.
Example: Product Information Retrieval
Few-Shot Agent
Few-shot agents use a small number of examples to adapt to specific tasks. They're particularly useful when you want to guide the agent's behavior for particular types of queries.
Example: Customer Service Bot
Here's a simple Few-Shot agent for handling customer inquiries:
from langchain import PromptTemplate, LLMChain
template = """
You are a customer service agent. Here are some example interactions:
Customer: How can I return an item?
Agent: To return an item, please log into your account, go to 'Order History', select the item you wish to return, and follow the prompts. Make sure the item is in its original condition and packaging.
Customer: What's your refund policy?
Agent: Our refund policy allows for full refunds within 30 days of purchase for items in their original condition. After 30 days, we offer store credit for returns.
Now, please respond to the following customer inquiry:
Customer: {customer_inquiry}
Agent:
"""
prompt = PromptTemplate(template=template, input_variables=["customer_inquiry"])
llm_chain = LLMChain(prompt=prompt, llm=llm)
response = llm_chain.run("Do you offer free shipping?")
print(response)The output looks like this
We offer free standard shipping on all orders over $50. For orders under $50, a flat rate shipping fee of $8 applies. Additionally, we also offer expedited shipping options for an extra fee, which can be selected during the checkout process.This Few-Shot agent uses example interactions to guide its responses to customer inquiries, demonstrating how a small amount of task-specific context can improve performance.
Challenges and Future Directions
While agents in LLMs offer exciting possibilities, they also face challenges. Ensuring consistency in responses, managing complex multi-step tasks, and maintaining ethical behavior are ongoing areas of research and development.
Looking ahead, we can expect to see more sophisticated agents that can handle increasingly complex tasks, better integration with external tools and APIs, and improved decision-making capabilities.
Conclusion
Agents in LLMs represent a significant step forward in making AI systems more capable and useful in real-world scenarios. By combining the language understanding capabilities of LLMs with the ability to interact with external tools and information sources, agents open up a world of possibilities for AI-assisted task completion and problem-solving.
As we continue our #75DaysofGenerativeAI journey, I encourage you to experiment with these agent types and share your experiences. The field is rapidly evolving, and your insights could help shape the future of AI agents!
Stay tuned for Day 20, where we'll build a full backend of an app using different custom agents. Until then, happy coding!