Prompt Templates

Prompt templates help to translate user input and parameters into instructions for a language model. This can be used to guide a model's response, helping it understand the context and generate relevant and coherent language-based output.

Prompt Templates take as input an object, where each key represents a variable in the prompt template to fill in.

Prompt Templates output a PromptValue. This PromptValue can be passed to an LLM or a ChatModel, and can also be cast to a string or a list of messages. The reason this PromptValue exists is to make it easy to switch between strings and messages.

There are a few different types of prompt templates:

String PromptTemplates

These prompt templates are used to format a single string, and generally are used for simpler inputs. For example, a common way to construct and use a PromptTemplate is as follows:

import { PromptTemplate } from "@langchain/core/prompts";

const promptTemplate = PromptTemplate.fromTemplate(
  "Tell me a joke about {topic}"
);

await promptTemplate.invoke({ topic: "cats" });

StringPromptValue {
  value: 'Tell me a joke about cats'
}

ChatPromptTemplates

These prompt templates are used to format a list of messages. These "templates" consist of a list of templates themselves. For example, a common way to construct and use a ChatPromptTemplate is as follows:

import { ChatPromptTemplate } from "@langchain/core/prompts";

const promptTemplate = ChatPromptTemplate.fromMessages([
  ["system", "You are a helpful assistant"],
  ["user", "Tell me a joke about {topic}"],
]);

await promptTemplate.invoke({ topic: "cats" });

ChatPromptValue {
  messages: [
    SystemMessage {
      "content": "You are a helpful assistant",
      "additional_kwargs": {},
      "response_metadata": {}
    },
    HumanMessage {
      "content": "Tell me a joke about cats",
      "additional_kwargs": {},
      "response_metadata": {}
    }
  ]
}

In the above example, this ChatPromptTemplate will construct two messages when called. The first is a system message, that has no variables to format. The second is a HumanMessage, and will be formatted by the topic variable the user passes in.

MessagesPlaceholder

This prompt template is responsible for adding a list of messages in a particular place. In the above ChatPromptTemplate, we saw how we could format two messages, each one a string. But what if we wanted the user to pass in a list of messages that we would slot into a particular spot? This is how you use MessagesPlaceholder.

import {
  ChatPromptTemplate,
  MessagesPlaceholder,
} from "@langchain/core/prompts";
import { HumanMessage } from "@langchain/core/messages";

const promptTemplate = ChatPromptTemplate.fromMessages([
  ["system", "You are a helpful assistant"],
  new MessagesPlaceholder("msgs"),
]);

await promptTemplate.invoke({ msgs: [new HumanMessage("hi!")] });

ChatPromptValue {
  messages: [
    SystemMessage {
      "content": "You are a helpful assistant",
      "additional_kwargs": {},
      "response_metadata": {}
    },
    HumanMessage {
      "content": "hi!",
      "additional_kwargs": {},
      "response_metadata": {}
    }
  ]
}

This will produce a list of two messages, the first one being a system message, and the second one being the HumanMessage we passed in. If we had passed in 5 messages, then it would have produced 6 messages in total (the system message plus the 5 passed in). This is useful for letting a list of messages be slotted into a particular spot.

An alternative way to accomplish the same thing without using the MessagesPlaceholder class explicitly is:

const promptTemplate = ChatPromptTemplate.fromMessages([
  ["system", "You are a helpful assistant"],
  ["placeholder", "{msgs}"], // <-- This is the changed part
]);

For specifics on how to use prompt templates, see the relevant how-to guides here.