Agents Prompt Chaining

Overview

Use a flow that executes a series of agents to solve a problem or prompt.

Prompt Chaining

Prompt chaining is a method that involves linking multiple prompts together to generate more complex and refined outputs in natural language processing tasks. It enhances the performance of models by breaking down a task into smaller, manageable subtasks, each handled by a different prompt. This technique is useful for creating detailed and accurate responses by systematically narrowing down the scope and focusing on specific aspects of the task at each stage.

Prompt Engineering

The easiest way to generate this behavior is with clear steps and actions and the prompt Generate, create, etc.

Prompt

1. Generate an agent to create 10 articles for health issues 2. Generate an agent to analyze the themes for an article and crate tags for each article.

Usage

Leverage Base to easily design your API and prototype apps, UX, or screens. To experiment with different outcomes, set "cached" to false

Using /base with "cached" is equivalent to direclty calling the function after is creation, all none neccesary params will be ignored.

POST /base

Creates a new flow with one or more actions, installs dependencies, builds tests and executes the resulting code.

Actions can be agents, backend functions, or cloud functions.

Headers

Name	Value
Content-Type	application/json
Authorization	Bearer <token>

Body

Name	Type	Required	Description
name	string		Name of the flow to summarize the actions
prompt	string		The instructions for AgentBase to transform in to code
data	object		The data example or parameters for your function to work
schedule	string		The cron calendar for the function to run automatically
return	object		The data example of your expected return
model	string		The model to run for your prompt
errors	array		List or possible errors your function needs to catch
cached	boolean		It will use the last version of the function whenever is available to save time and tokens

Response

200

{
  "run_id": 1,
  "return" : {
        "sentiment":"sentiment",
        "sentiment_id":"id",
        "articles:" ["article"]
  },
  "error": null
}

400

Body Example

{
    "function": "analyze_sentiment",
    "prompt": "get the current sentimient for the message and save it as property in the post, then generate 10 articles thay may help the user to solve the problem",
    "data" : {
        "message": "message example"
    },
    "return" : {
        "sentiment":"sentiment",
        "sentiment_id":"id",
        "articles:" ["article"]
    }
}