description: >- Store prompt template with name & description to be later queried by MultiPromptChain.

Prompt Retriever

The Prompt Retriever node is designed to store prompt templates with associated names and descriptions. These stored prompts can later be queried and used by a MultiPromptChain. This node is particularly useful for organizing and managing multiple prompts that can be dynamically selected based on the context or requirements of a conversation or task.

Input Parameters

1. Prompt Name

   • Type: string

   • Description: A unique identifier for the prompt template

   • Example: "physics-qa"

2. Prompt Description

   • Type: string

   • Description: A brief explanation of what the prompt does and when it should be used

   • Example: "Good for answering questions about physics"

3. Prompt System Message

   • Type: string

   • Description: The actual prompt template or system message that guides the AI's behavior

   • Example: "You are a very smart physics professor. You are great at answering questions about physics in a concise and easy to understand manner. When you don't know the answer to a question you admit that you don't know."

Output

The node initializes and returns a PromptRetriever object, which encapsulates the provided prompt information (name, description, and system message).

Usage

This node is typically used as part of a larger system where multiple specialized prompts are needed. By storing prompts with metadata, it allows for:

1. Organized Prompt Management: Keeping track of multiple prompts for different purposes.

2. Dynamic Prompt Selection: Enabling systems to choose the most appropriate prompt based on the current context or user query.

3. Improved Maintainability: Centralizing prompt storage and making it easier to update or modify prompts without changing the underlying code.

Integration

The Prompt Retriever is often used in conjunction with a MultiPromptChain, which can dynamically select and use the most appropriate prompt based on the input or context. This allows for creating more flexible and adaptive AI systems that can handle a wide range of queries or tasks by selecting the most suitable prompt template.

Example Use Case

In a multi-purpose AI assistant, you might have several Prompt Retriever nodes set up:

• One for physics questions

• One for literature analysis

• One for coding help

• One for general conversation

The system could then use a MultiPromptChain to analyze the user's input and select the most appropriate prompt, allowing the AI to seamlessly switch between different areas of expertise or conversation styles.

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