Prompt templates are a way to store, version and collaborate on prompts. Developers use prompt templates to:
  • clean up their source code
  • make edits to prompts without re-deploying code
  • collaborate with non-technical teammates
  • A/B test prompts

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Creating a template

You can create a prompt template by clicking on the “Create prompt template” button in the Prompts section of the dashboard.

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Usage with OpenAI

You can use templates seamlessly with OpenAI’s API with our SDKs. This will make sure the tracking of the prompt is done automatically. 
import OpenAI from "openai";
import lunary from "lunary"
import { monitorOpenAI } from "lunary/openai";

// Make sure your OpenAI instance is wrapped with `monitorOpenAI`
const openai = monitorOpenAI(new OpenAI())

const template = await lunary.renderTemplate("template-slug", {
name: "John", // Inject variables
})

const result = await openai.chat.completions.create(template)

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Usage with LangChain’s templates

You can pull templates in the LangChain format and use them directly as PromptTemplate and ChatPromptTemplate classes. Example with simple text template:
The getLangChainTemplate method returns a PromptTemplate object for simple templates, which can be used directly in chains or to format prompts.
import { getLangChainTemplate } from "lunary/langchain";

const prompt = await getLangChainTemplate("icecream-prompt");

const promptValue = await prompt.invoke({ topic: "ice cream" });

console.log(promptValue);
Example with a Chat template (ChatPromptTemplate):
The getLangChainTemplate function directly returns a ChatPromptTemplate object for chat messages templates, which can be used to format messages.
import { getLangChainTemplate } from "lunary/langchain";

const prompt = await getLangChainTemplate("context-prompt");

const promptValue = await prompt.invoke({ topic: "ice cream" });

console.log(promptValue);
/**
ChatPromptValue {
  messages: [
    HumanMessage {
      content: 'Tell me a short joke about ice cream',
      name: undefined,
      additional_kwargs: {}
    }
  ]
}
*/

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Manual LangChain Usage with LLM Classes

Using with LangChain LLM Classes is similar to using with OpenAI, but requires you to format the messages in the LangChain format as well as pass the template id in the metadata. 
Coming soon

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Manual usage

You can also use templates manually with any LLM API by accessing the relevant fields (returned in OpenAI’s format). 
import lunary from "lunary"

const {
  messages,
  model,
  temperature,
  max_tokens
} = await lunary.renderTemplate("template-slug", {
  name: "John", // Inject variables
})


// ... use the fields like you want

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Testing Prompts

The Prompt Playground provides a powerful interactive environment for testing and refining your prompts. You can experiment with different models, parameters, and even test against custom API endpoints. Learn more about the Prompt Playground →