Documentation Index
Fetch the complete documentation index at: https://docs.mira.network/llms.txt
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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" |
| 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 |
| Type | Description |
|---|
| string | Text-based input values |
| 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.
