LangChain Agents: A Simple, Fast-Paced Guide

Summary

This video delves into the concept of agents and Lang chain, exploring how they combine large language models with practical tools to create powerful virtual assistants. It discusses the thought-action-observation pattern agents follow, the role of prompts and output parsers, and how agents utilize tools to complete tasks. The video also covers different types of agents, such as react doc store and self-ask with search, and introduces new frameworks like plan and execute for handling complex use cases. With examples and code snippets hosted on GitHub, viewers are guided through the process of implementing these agents, highlighting their potential in automating tasks and decision-making.

Takeaways

• 🧠 Large language models (LLMs) are capable of reasoning and acting in external worlds, which can be combined to create 'agents' that can think and perform tasks.
• 🛠️ Agents follow a 'thought-action-observation' pattern, where they think about what to do next, perform actions using tools, and observe the output to inform their next thought.
• 📝 Agents use prompts to instruct the LLM to format its response according to a specified output schema, which includes tools available to the agent and chat history.
• 🔧 The agent's output parser translates the LLM's output into a Python object, which can be an 'agent action' with tools and inputs or an 'agent finish' with results.
• 🔄 The agent executor runs the tool with the input received from the thought step, abstracting away the thought-action-observation loop from the user.
• 🏢 The script discusses various types of agents, including 'react' agents that follow the same loop and 'self-ask with search' agents that ask themselves sub-questions to find answers.
• 📈 The 'plan and execute' agent framework was created to address the need for stability and complexity in enterprise use cases, separating planning and acting for more reliability.
• 💡 Baby AGI and the 'plan and solve' paper inspired the creation of autonomous agents that can devise plans and maintain memory between model calls to achieve objectives.
• 🎉 The 'generative agent' framework allows characters to plan, interact, form relationships, and celebrate events in a simulated universe, showcasing the potential for agent-based AI interactions.
• 🛑 Autonomous agents can be expensive to run due to the number of calls they make and the risk of infinite loops, making them more of fun projects than practical solutions currently.
• 🔗 Tools and toolkits in LangChain can be explored and created using specific functions and decorators, allowing for structured and efficient agent interactions.

Q & A

What is the concept of 'Lang Chain' and how does it relate to large language models?

Lang Chain is a framework that combines large language models with agents to perform tasks. It allows agents to reason and act in external worlds, utilizing the reasoning capabilities of large language models to think through actions and execute them using provided tools.

How does an agent in Lang Chain understand the instructions given to it?

An agent in Lang Chain understands instructions through prompts and output parsers. The prompt instructs the language model to format its response according to a specified output schema, which includes a list of tools the agent can use, chat history, and examples. The output parser then translates the language model's output into a Python object that the agent can act upon.

What is the 'thought-action-observation' pattern mentioned in the script?

The 'thought-action-observation' pattern is a cycle followed by agents in Lang Chain. It starts with a thought, where the agent decides what to do next using the language model. Then it moves to the action step, where the agent executes the decided task using available tools. Finally, it observes the outcome, which informs the next thought process.

Can you explain the role of 'agent executor' in the Lang Chain framework?

The agent executor in Lang Chain is responsible for running the tool with the input received from the thought step. It abstracts away the thought-action-observation loop from the user, providing an interface for interaction and executing the agent's decisions.

What are some of the early stopping conditions that can be applied in the Lang Chain framework?

Early stopping conditions in Lang Chain can include a limit on the number of iterations or a time limit that has passed. These conditions prevent the agent from running indefinitely and ensure that a final answer is reached within a reasonable timeframe.

How is the 'react doc store' agent different from other agents in Lang Chain?

The 'react doc store' agent uses the React framework to search and lookup documents within a document store. This is different from the more commonly used Vector store abstraction and allows for efficient document retrieval.

What is the 'self-ask with search' agent and how does it work?

The 'self-ask with search' agent is capable of asking itself a series of sub-questions to arrive at an answer. For example, if asked for the hometown of the reigning men's U.S. Open champion, it would first find out who the champion is, then ask where he is from, and use these intermediate answers to provide the final response.

What are the benefits and drawbacks of using the 'plan and execute' agent framework?

The 'plan and execute' framework offers reliability by separating planning and acting concerns, which can also allow for the use of smaller, cheaper, fine-tuned models for specific tasks. However, it requires more language model calls, which can increase latency for user-facing applications.

Can you provide an example of how the 'plan and execute' agent framework is used?

An example of using the 'plan and execute' framework is creating and sending a sales report to an email using data only available in an SQL database. The agent plans the steps needed, such as querying the database and composing the email, then executes each step to achieve the final goal.

What is the 'generative agent' framework and how does it differ from other agent frameworks?

The 'generative agent' framework is inspired by the idea of creating a simulated universe where characters plan, interact, and form relationships. It differs from other frameworks by focusing on the generation of content and interactions within a simulated environment rather than task execution.

How can one create custom tools and toolkits in Lang Chain?

Custom tools can be created using the 'tool_from_function_factory' method, which requires a function that accepts a string input and returns a string output, along with a name and description. Toolkits are arrays of related tools, and can be defined using a decorator to structure tool classes for multi-argument tools.