Key Building Blocks of Agents (Langchain Overview)
Summary
In this video, I'll be discussing the core components of Torque Agents, which are based on the LangChain framework. LangChain is a powerful software framework that leverages large language models like ChatGPT. I'll explain the role of agents, prompts, tools, memory, and chains in the framework. Agents are context-aware tools that use language models as reasoning engines. Prompts and system messages provide additional context to agents. Tools enhance the reliability of agent outputs. Memory allows agents to remember context from interactions. Chains enable consistent and reliable language model outputs. Watch this video to understand how Torque Agents work and how they leverage the LangChain framework.
Key Building Blocks of Agents Video
Transcript
0:01 The Torque Agent module is based on an open source software framework called LangChain. LangChain, in a nutshell, is a framework for developing applications based on large language models, like the ones that power tools like ChatGPT.
0:15 LangChain provides the basis for each of the core components of Torque Agents, a few of which I'll be going over in detail in this video.
0:22 For now, let's focus on Agents, Prompts, Tools, Memory, and Chains. So at the core of this framework are agents. An agent is a tool which uses a language model, for example, as we can see here, GPT-4, as a reasoning engine to determine which actions to take and in which order.
0:44 Agents are context aware, which means that they can reason based on background information, instructions, examples, and other forms of context.
0:53 The most obvious way to provide context to an agent is in the chatbox itself when you prompt it, but you can also use a system message to provide additional context or connect a prompt to help the agent contextualize its responses.
1:06 So in the case of the report agent that I'm using here, I'm using a system message to define a report format.
1:14 And I'm also using a report topic, in this case, to identify specific indicators. And both of those provide valuable context that is going to ensure that the output that this agent gives me is as reliable as possible.
1:32 Now, beyond that, the most obvious way to get an agent to produce a more reliable output is a tool. Now a tool, as its name suggests, is a tool or more accurately a function that an agent can invoke.
1:48 Now in a separate video we've gone over what these various tools do, but under the hood each tool consists of an input schema which tells the language model what parameters are needed to use the tool, and a function or code to run.
2:11 Memory is what allows agents to remember context from their interactions with you. So, the basic example. Here is buffer memory, which is the most standard option for most of our agents.
2:24 Each agent requires memory either as a standalone node or in a few cases as a feature built directly into the node.
2:31 So, as you can see here with the report agent, there's actually no separate memory node, but with Torque AGI, which is the standard agent that we use to build most of our templates, there is a separate buffer memory node.
2:46 Finally, a chain is a method of hard-coding a sequence of actions to make language model outputs more consistent and reliable, and this can be particularly helpful with multi-step processes where consistent formatting or structure is paramount.
3:02 So here's an example of why you might want to do something like that. So in this case, with a prompt chain, we are trying to ensure that rather than an agent that can choose which actions to take and when, we're using language models to perform a precise set of actions in a precise order.
3:21 So in this case, which is fairly straightforward, we're using the LLM chain in a two-stage sequence, first it's going to identify a task to complete based on a given objective stated by the user, and then create tasks to complete that based on the previous results.
In this video, I'll be discussing the core components of Torque Agents, which are based on the LangChain framework. LangChain is a powerful software framework that leverages large language models like ChatGPT. I'll explain the role of agents, prompts, tools, memory, and chains in the framework. Agents are context-aware tools that use language models as reasoning engines. Prompts and system messages provide additional context to agents. Tools enhance the reliability of agent outputs. Memory allows agents to remember context from interactions. Chains enable consistent and reliable language model outputs. Watch this video to understand how Torque Agents work and how they leverage the LangChain framework.
Key Building Blocks of Agents Video
Transcript
0:01 The Torque Agent module is based on an open source software framework called LangChain. LangChain, in a nutshell, is a framework for developing applications based on large language models, like the ones that power tools like ChatGPT.
0:15 LangChain provides the basis for each of the core components of Torque Agents, a few of which I'll be going over in detail in this video.
0:22 For now, let's focus on Agents, Prompts, Tools, Memory, and Chains. So at the core of this framework are agents. An agent is a tool which uses a language model, for example, as we can see here, GPT-4, as a reasoning engine to determine which actions to take and in which order.
0:44 Agents are context aware, which means that they can reason based on background information, instructions, examples, and other forms of context.
0:53 The most obvious way to provide context to an agent is in the chatbox itself when you prompt it, but you can also use a system message to provide additional context or connect a prompt to help the agent contextualize its responses.
1:06 So in the case of the report agent that I'm using here, I'm using a system message to define a report format.
1:14 And I'm also using a report topic, in this case, to identify specific indicators. And both of those provide valuable context that is going to ensure that the output that this agent gives me is as reliable as possible.
1:32 Now, beyond that, the most obvious way to get an agent to produce a more reliable output is a tool. Now a tool, as its name suggests, is a tool or more accurately a function that an agent can invoke.
1:48 Now in a separate video we've gone over what these various tools do, but under the hood each tool consists of an input schema which tells the language model what parameters are needed to use the tool, and a function or code to run.
2:11 Memory is what allows agents to remember context from their interactions with you. So, the basic example. Here is buffer memory, which is the most standard option for most of our agents.
2:24 Each agent requires memory either as a standalone node or in a few cases as a feature built directly into the node.
2:31 So, as you can see here with the report agent, there's actually no separate memory node, but with Torque AGI, which is the standard agent that we use to build most of our templates, there is a separate buffer memory node.
2:46 Finally, a chain is a method of hard-coding a sequence of actions to make language model outputs more consistent and reliable, and this can be particularly helpful with multi-step processes where consistent formatting or structure is paramount.
3:02 So here's an example of why you might want to do something like that. So in this case, with a prompt chain, we are trying to ensure that rather than an agent that can choose which actions to take and when, we're using language models to perform a precise set of actions in a precise order.
3:21 So in this case, which is fairly straightforward, we're using the LLM chain in a two-stage sequence, first it's going to identify a task to complete based on a given objective stated by the user, and then create tasks to complete that based on the previous results.
Updated on: 16/04/2024
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