Elemental Flows

An Elemental Flow is a self-contained, autonomous unit designed to perform specific, unique actions. These flows serve as the foundational building blocks within the Mira Flows ecosystem, enabling precise and reliable AI-powered operations.

Elemental Flow Attributes

Version

Component	Description	Required	Example
version	Flow specification version using semantic versioning	Yes	`"0.0.1"`

Metadata

Component	Description	Required	Example
name	Unique identifier for the flow	Yes	`"your-flow-name"`
description	Explanation of flow’s purpose	Yes	`"A brief description of your flow"`
author	Creator’s username	Yes	`"your-username"`
tags	Keywords for categorization	No	`[tag1, tag2, tag3]`
private	Access control setting	Yes	`false`

Input Configuration

Supported Input Types:

Type	Description
string	Text-based input values

Input Structure:

Component	Description	Required	Example
inputs	Map of input parameters	Yes	Collection of input definitions
type	Data type of input (currently only string)	Yes	`"string"`
description	Purpose of the input	Yes	`"Description of input1"`
required	Whether input is mandatory	Yes	`true` or `false`
example	Sample input value	No	`"Example value for input1"`

Model Configuration

Component	Description	Required	Example
provider	AI service provider	Yes	`"provider-name"`
name	Specific model identifier	Yes	`"model-name"`

Dataset Configuration (Optional)

Component	Description	Required	Example
source	Reference to dataset	No	`"author_name/dataset_name"`

Prompt and Readme Configuration

Component	Description	Required	Example
prompt	Instructions for model behavior	Yes	”Generate a tweet on the given topic: “
readme	Usage documentation	Yes	Markdown-formatted guide

Basic Flow Structure

The official YAML structure for a basic Mira Flow:

version: "your.version.here"

metadata:
  name: "your-flow-name"
  description: "A brief description of your flow"
  author: "your-username"
  tags: [tag1, tag2, tag3]
  private: false

inputs:
  input1:
    type: string
    description: "Description of input1"
    required: true
    example: "Example value for input1"
  input2:
    type: string
    description: "Description of input2"
    required: true
    example: "Example value for input2"

model:
  provider: "provider-name"
  name: "model-name"

prompt: |
  Your flow's primary instruction or role...
  You can use {input1} and {input2} placeholders to reference inputs.

readme: |
  Your flow's readme...
  You can use raw text or markdown here.

RAG Integration 🔍

To integrate RAG capabilities, you’ll need to:

Create a dataset
Add data sources to your dataset
Link the dataset to your flow

The following file formats are supported for datasets:

File Type	Processing Method
PDF (.pdf)	Text extraction from document
Markdown (.md)	Text extraction from document
URL	Web content scraping
CSV (.csv)	URL extraction and content scraping
Text (.txt)	Direct text extraction
Zip (.zip)	Processing of contained supported files

Creating a Dataset 🗃️

from mira_sdk import MiraClient

client = MiraClient(config={"API_KEY": "YOUR_API_KEY"})

# Create dataset
client.dataset.create("author/dataset_name", "Optional description")

Adding Data Sources

# Add URL to your dataset
client.dataset.add_source("author/dataset_name", url="example.com")

# Add file to your dataset
client.dataset.add_source("author/dataset_name", file_path="path/to/my/file.csv")

Linking Dataset with Flow

Add the following configuration to your flow.yaml file:

dataset:
  source: "author/dataset_name"

Flow Structure with RAG

The official YAML structure for a flow with RAG capabilities:

version: "your.version.here"

metadata:
  name: "your-flow-name"
  description: "A brief description of your flow"
  author: "your-username"
  tags: [tag1, tag2, tag3]
  private: false

inputs:
  input1:
    type: string
    description: "Description of input1"
    required: true
    example: "Example value for input1"

model:
  provider: "provider-name"
  name: "model-name"

dataset:
  source: "author_name/dataset_name"

prompt: |
  Your flow's primary instruction or role...
  You can use {input1} placeholders to reference inputs.

readme: |
  Your flow's readme...
  You can use raw text or markdown here.

Get Started

Core Concepts

How To Guides

Elemental Flows

Elemental Flow Attributes

Version

Metadata

Input Configuration

Model Configuration

Dataset Configuration (Optional)

Prompt and Readme Configuration

Basic Flow Structure

RAG Integration 🔍

Creating a Dataset 🗃️

Adding Data Sources

Linking Dataset with Flow

Flow Structure with RAG

​Elemental Flow Attributes

​Version

​Metadata

​Input Configuration

​Model Configuration

​Dataset Configuration (Optional)

​Prompt and Readme Configuration

​Basic Flow Structure

​RAG Integration 🔍

​Creating a Dataset 🗃️

​Adding Data Sources

​Linking Dataset with Flow

​Flow Structure with RAG

Elemental Flow Attributes

Version

Metadata

Input Configuration

Model Configuration

Dataset Configuration (Optional)

Prompt and Readme Configuration

Basic Flow Structure

RAG Integration 🔍

Creating a Dataset 🗃️

Adding Data Sources

Linking Dataset with Flow

Flow Structure with RAG