Tools in LangChain

The need for tools

Imagine you’re having a conversation with an advanced AI system.

It can discuss complex topics, create creative text, and even sense emotions. But what if you ask something like, “What’s the weather like in New York right now?” It might generate a reasonable guess based on its training data, but it won’t have live access to current weather information.

This is where the concept of tools becomes essential.

Tools are like superpowers for your AI. They allow it to interact with the outside world, including databases, APIs, web search engines, etc. With the right tools, your AI can go beyond just answering questions and perform actions, making it a more versatile and practical assistant.

This is where tool calling comes in, an incredibly important concept. Tool calling allows the AI to decide when and how to use these tools. This isn’t about just having access but about smart, contextual usage.

What exactly is a tool?

At its core, a tool is a Python function designed to perform a specific task. In LangChain, it extends beyond a basic function. A tool is a function enriched with metadata, enabling the AI to understand its purpose, usage, and required inputs. It’s like a well-structured instruction manual for your AI assistant.

The BaseTool interface

LangChain uses a class called BaseTool as the blueprint for all tools. It defines the core attributes and methods that all tools should have:

• name: A descriptive name for the tool (e.g., “weather_lookup”, “multiply_numbers”). ...