# Annotation Based Prompt Template

Annotation provides a simple way to define prompt templates with variables using annotations. This library has two main parts:

1. `@Prompt` annotation: Used to define a prompt template with variables, examples, and an optional name for the prompt.
2. `PromptProcessor` class: Responsible for processing the `@Prompt` and `@Prompts` annotations on a class and creating a `PromptTemplate` based on the annotations and the fields of the annotated class.

### Usage:

#### Example

Here's an example of using the `@Prompt` annotation:

```java
@Prompt(
    template = "Hello, {{$name}}! Your age is {{$age}}.",
    variables = {"name", "age"})
class HelloAgePrompt {
  public String name = "Jane";
  public String age = "30";
}

HelloAgePrompt helloAgePrompt = new HelloAgePrompt();
PromptTemplate helloAgePromptTemplate =
    PromptProcessor.createPromptTemplate(HelloAgePrompt.class, helloAgePrompt);
String result = helloAgePromptTemplate.format();
System.out.println(result); // Output: "Hello, Jane! Your age is 30."

```

### Using multiple prompts

When you have multiple prompt templates in a single class, use the `@Prompts` annotation as a container for multiple `@Prompt` annotations. In this case, provide a unique name for each prompt:<br>

```java
@Prompt(
    template = "My favourite subject in school is {{$subject}}.",
    variables = {"subject"},
    name = "favoriteSubjectPrompt")
@Prompt(
    template = "I really love {{$subject}} because {{$reason}}.",
    name = "reasonPrompt",
    variables = {"reason", "subject"})
private static class SubjectRelatedPrompt {
  public String subject = "Math";
  public String reason = "I love numbers";
}

SubjectRelatedPrompt subjectRelatedPrompt = new SubjectRelatedPrompt();
PromptTemplate textPromptTemplate =
    PromptProcessor.createPromptTemplate(
        SubjectRelatedPrompt.class, subjectRelatedPrompt, "favoriteSubjectPrompt");
String result = textPromptTemplate.format();
System.out.println(result); // Output: "My favourite subject in school is Math."

```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://knowly-ai.gitbook.io/langtorch/prompt-template/annotation-based-prompt-template.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.