storylinez 0.3.1b2

Storylinez: A modular library for narrative generation and story manipulation

Storylinez SDK

Note: Example usage scripts can be found in the examples folder. Utility and automation scripts are located in the scripts folder.

Table of Contents

• Overview
• Features
• Installation
• Authentication
• Quick Start
• Video Creation Process
• Module Reference
• Detailed Usage Examples
• Workflow Patterns
• Error Handling & Best Practices
• Environment Configuration
• Performance Considerations
• Troubleshooting
• API Documentation
• Requirements
• Contributing
• License
• Support
• Advanced Usage Patterns
• SDK Versioning and Updates

Overview

Storylinez SDK is a powerful Python library that provides comprehensive access to the Storylinez AI-driven content creation platform. Designed to enable developers to build sophisticated video generation workflows, the SDK integrates natural language processing, media management, and professional video production capabilities through a clean, intuitive API.

This library empowers developers to:

• Generate professional video content from text descriptions
• Create storyboards and narrative sequences with AI assistance
• Access a vast library of stock media content
• Apply brand styling and professional effects
• Render final videos with subtitles, voiceovers, and transitions
• Manage content libraries and search across multimedia assets

Whether you're building a content automation system, integrating AI-driven video generation into your application, or creating tools for content marketers, the Storylinez SDK provides the foundation for sophisticated media production workflows.

Features

• Complete API Coverage - Access all Storylinez platform capabilities through a unified interface
• Modular Architecture - Import only the modules you need for your specific use case
• Intelligent Content Generation - AI-powered tools for storyboards, scripts, and content planning
• Custom Media Processing - Upload, analyze, search, and transform your own media assets
• Professional Rendering - Create polished videos with transitions, effects, and brand styling
• Advanced Search - Semantic search across media content, stock libraries, and generated assets
• Brand Management - Create and apply consistent styling to maintain brand identity
• Workflow Tools - Helper methods for common content creation patterns
• Robust Validation - Clear error messages and parameter validation to prevent issues
• Comprehensive Type Hints - Complete typing for improved IDE support and code completion
• Flexible Authentication - Simple setup with API keys or environment variables

Installation

Standard Installation

Install the SDK using pip:

pip install storylinez

Development Installation

For the latest features or development work:

git clone https://github.com/Kawai-Senpai/Storylinez-SDK.git
cd Storylinez-SDK
pip install -e .

Authentication

Using Environment Variables

Create a .env file with your API credentials:

STORYLINEZ_API_KEY=api_your_key_here
STORYLINEZ_API_SECRET=your_secret_here
STORYLINEZ_ORG_ID=your_org_id_here

Then load these credentials:

from storylinez import StorylinezClient
import os
from dotenv import load_dotenv

# Load credentials from .env file
load_dotenv()

# Initialize the client
client = StorylinezClient(
    api_key=os.environ.get("STORYLINEZ_API_KEY"),
    api_secret=os.environ.get("STORYLINEZ_API_SECRET"),
    org_id=os.environ.get("STORYLINEZ_ORG_ID")
)

Direct Initialization

You can also initialize the client directly:

from storylinez import StorylinezClient

# Initialize with explicit credentials
client = StorylinezClient(
    api_key="api_your_key_here",
    api_secret="your_secret_here",
    org_id="your_org_id_here"
)

# Initialize with custom API endpoint (for enterprise customers)
client = StorylinezClient(
    api_key="api_your_key_here",
    api_secret="your_secret_here",
    org_id="your_org_id_here",
    base_url="https://your-custom-endpoint.storylinez.com"
)

Multiple Organizations

If you work with multiple organizations, you can:

# Initialize client without default org_id
client = StorylinezClient(
    api_key="api_your_key_here",
    api_secret="your_secret_here"
)

# Specify organization per operation
project = client.project.create_project(
    name="Project Name",
    orientation="landscape",
    org_id="specific_org_id_here"
)

# Or set a new default
client.set_default_org_id("different_org_id")

Quick Start

End-to-End Video Creation

This example shows the complete process of creating a video from a text prompt:

from storylinez import StorylinezClient
import os
from dotenv import load_dotenv

# Setup
load_dotenv()
client = StorylinezClient()

# 1. Create a project
project = client.project.create_project(
    name="Product Introduction",
    orientation="landscape",
    purpose="Introduce our new sustainable product line"
)
project_id = project["project"]["project_id"]

# 2. Create a text prompt
client.prompt.create_text_prompt(
    project_id=project_id,
    main_prompt="Create a 30-second video introducing our eco-friendly product line that reduces plastic waste by 75%",
    document_context="Our products are made from bamboo and recycled materials. The target audience is environmentally conscious millennials.",
    temperature=0.7,
    total_length=30
)

# 3. Generate a storyboard
storyboard_job = client.storyboard.create_storyboard(
    project_id=project_id,
    deepthink=True,
    web_search=True
)
print(f"Storyboard job started: {storyboard_job['job_id']}")

# 4. Wait for storyboard completion
storyboard = client.storyboard.wait_for_storyboard(
    project_id=project_id,
    timeout_seconds=300,
    polling_interval=5
)
print(f"Storyboard created with {len(storyboard['scenes'])} scenes")

# 5. Create a sequence
sequence_job = client.sequence.create_sequence(
    project_id=project_id,
    apply_template=True,
    apply_grade=True
)
print(f"Sequence job started: {sequence_job['job_id']}")

# 6. Wait for sequence completion
sequence = client.sequence.get_sequence(
    project_id=project_id,
    include_results=True
)
while sequence.get("status") != "COMPLETED":
    import time
    time.sleep(5)
    sequence = client.sequence.get_sequence(project_id=project_id)
print("Sequence completed")

# 7. Render the final video
render_job = client.render.create_render(
    project_id=project_id,
    target_width=1920,
    target_height=1080,
    subtitle_enabled=True,
    company_name="Eco Solutions Inc.",
    call_to_action="Visit eco-solutions.com today"
)
print(f"Render job started: {render_job['job_id']}")

# 8. Get the download link when complete
render = client.render.wait_for_render(
    project_id=project_id,
    timeout_seconds=1800,
    polling_interval=20
)
if render["status"] == "COMPLETED":
    download_info = client.render.get_render(
        project_id=project_id,
        generate_download_link=True,
        generate_streamable_link=True
    )
    print(f"Video ready! Download URL: {download_info['download_url']}")
    print(f"Stream URL: {download_info['streamable_url']}")
else:
    print(f"Render failed with status: {render['status']}")

Video Creation Process

The Storylinez SDK follows a structured workflow for creating professional videos. Understanding this process will help you efficiently create compelling content:

Complete Workflow Visualization

┌─────────────────┐
│  Upload Files   │
└─────────────────┘  
        │
        ▼
┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│  Create Project │────▶│   Add Files     │────▶│  Create Prompt  │
└─────────────────┘     └─────────────────┘     └─────────────────┘
                                                         │
                                                         ▼
┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│ Create Sequence │◀────│Create Voiceover │◀────│Create Storyboard│
└─────────────────┘     └─────────────────┘     └─────────────────┘
         │
         ▼
┌─────────────────┐
│  Render Video   │
└─────────────────┘

Following the Workflow: Step-by-Step Implementation

Let's walk through each step of the flowchart with practical code examples:

1. Upload Files (Optional)

Start by uploading any custom media you want to use in your project:

# Upload custom videos, images, or audio
media_result = client.storage.upload_file(
    file_path="./product_demo.mp4",
    folder_path="/my_project_assets",
    tags=["product", "demo", "brand"],
    analyze_video=True,  # Enable content analysis
    generate_thumbnail=True
)
file_id = media_result["file"]["file_id"]
print(f"Uploaded file with ID: {file_id}")

# For multiple files
files_to_upload = ["./logo.png", "./background_music.mp3", "./team_photo.jpg"]
for file_path in files_to_upload:
    client.storage.upload_file(
        file_path=file_path,
        folder_path="/my_project_assets"
    )

Practical Tip: Upload high-quality source materials in the highest resolution available. The SDK will automatically optimize them during the rendering process.

2. Create Project

Set up the foundation for your video:

# Create a new project 
project = client.project.create_project(
    name="Product Launch Video",
    orientation="landscape",  # Use "portrait" for vertical videos (social media)
    purpose="Introduce our new sustainable product line",
    target_audience="Eco-conscious consumers aged 25-45"
)
project_id = project["project"]["project_id"]

When to use: Always start here. This creates the container for all your video assets and settings.

3. Add Files

Associate your uploaded files or stock media with your project:

# Add your custom uploaded files
if file_id:  # From previous upload step
    client.project.add_associated_file(
        project_id=project_id,
        file_id=file_id
    )

# Find and add stock videos
stock_videos = client.stock.search(
    queries=["sustainable products", "eco friendly lifestyle"],
    collections=["videos"],
    detailed=True,
    num_results_videos=5,
    orientation="landscape"
)

# Add selected stock videos to your project
for video in stock_videos["videos"][:3]:  # Add first 3 results
    client.project.add_stock_file(
        project_id=project_id,
        stock_id=video["stock_id"],
        media_type="videos"
    )

Practical Tip: Mixing custom and stock media creates unique, brand-specific videos. Consider uploading your logo, product shots, and team photos.

4. Create Prompt

Tell the AI what your video should be about:

# Define your video content with natural language
client.prompt.create_text_prompt(
    project_id=project_id,
    main_prompt="Create a 30-second video introducing our eco-friendly product line that reduces plastic waste by 75%. Show the products in use, highlight their environmental benefits, and include testimonials.",
    document_context="Our products are made from bamboo and recycled materials. The target audience is environmentally conscious millennials.",
    temperature=0.7,  # Controls AI creativity (0.0-1.0)
    total_length=30   # Target video length in seconds
)

Practical Tip: Be specific in your prompt. Include desired tone, style, key messages, and any must-have elements. The more detailed your prompt, the better the results.

5. Create Storyboard

Generate a scene-by-scene plan for your video:

# Start storyboard generation
storyboard_job = client.storyboard.create_storyboard(
    project_id=project_id,
    deepthink=True,      # Enable for better quality
    overdrive=False,     # Enable for maximum quality (takes longer)
    web_search=True,     # Enable for up-to-date information
    eco=False,           # Set to True for faster results (lower quality)
    iterations=3,        # More iterations = better quality
    full_length=30       # Target video length in seconds
)

# Wait for storyboard to complete
storyboard = client.storyboard.wait_for_storyboard(
    project_id=project_id,
    timeout_seconds=300
)

print(f"Storyboard created with {len(storyboard['scenes'])} scenes")

# Review and optionally edit specific scenes
scene_index = 2  # Third scene (zero-indexed)
client.storyboard.update_scene(
    storyboard_id=storyboard["storyboard_id"],
    scene_index=scene_index,
    visual_description="Close-up of product with eco-friendly packaging, highlighting the bamboo material with soft natural lighting",
    voiceover_text="Our innovative design eliminates plastic waste while maintaining durability and functionality."
)

Practical Tip: Review your storyboard carefully. This is the blueprint for your video, and changes here are easier than later in the process.

6. Create Voiceover

Generate narration for your video:

# Create AI-generated voiceover based on your storyboard
voiceover_job = client.voiceover.create_voiceover(
    project_id=project_id,
    voice_id="en-US-Neural2-F"  # Choose a specific voice (optional)
)

# Wait for voiceover to complete
voiceover = client.voiceover.wait_for_voiceover(
    project_id=project_id,
    timeout_seconds=180
)

# Alternatively, upload your own custom voiceover
custom_voiceover_file = client.storage.upload_file(
    file_path="./custom_voiceover.wav",
    folder_path="/voiceovers"
)
client.project.add_voiceover(
    project_id=project_id,
    file_id=custom_voiceover_file["file"]["file_id"]
)

Practical Tip: For professional videos, consider recording your own voiceover or hiring a voice actor. For quick prototyping, the AI voices work well.

7. Create Sequence

Arrange your media assets into a cohesive timeline:

# Generate the video sequence
sequence_job = client.sequence.create_sequence(
    project_id=project_id,
    apply_template=True,    # Apply professional video templates
    apply_grade=True,       # Apply color grading for cinematic look
    grade_type="single",    # "single" or "multi" for different looks per scene
    deepthink=True,         # Enable for better quality
    iterations=3            # More iterations = better quality
)

# Wait for sequence to complete
sequence = client.sequence.wait_for_sequence(
    project_id=project_id,
    timeout_seconds=600
)

Practical Tip: Templates add professional transitions, animations, and timing. Enable apply_template for polished results.

8. Render Video

Generate the final video output:

# Start the rendering process
render_job = client.render.create_render(
    project_id=project_id,
    target_width=1920,         # Video width in pixels
    target_height=1080,        # Video height in pixels
    bg_music_volume=0.5,       # Background music volume (0.0-1.0)
    video_audio_volume=0.0,    # Original video audio volume (0.0 = mute)
    voiceover_volume=0.5,      # Narration volume (0.0-1.0)
    subtitle_enabled=True,     # Add subtitles for accessibility
    outro_duration=5.0,        # Duration of end card in seconds
    company_name="Eco Solutions Inc.",
    call_to_action="Visit eco-solutions.com today"
)

# Wait for rendering to complete and get download links
render = client.render.wait_for_render(
    project_id=project_id,
    timeout_seconds=1800
)

# Get streamable and download links
download_info = client.render.get_render(
    project_id=project_id,
    generate_download_link=True,
    generate_streamable_link=True
)

print(f"Video ready! Download URL: {download_info['download_url']}")
print(f"Stream URL: {download_info['streamable_url']}")

Practical Tip: Enable subtitles for accessibility and for viewers watching with sound off. Add a clear call-to-action at the end of your video.

Complete End-to-End Script

For a quick implementation of the entire workflow, here's a complete script that follows the flowchart:

from storylinez import StorylinezClient
import os
from dotenv import load_dotenv
import time

# Setup
load_dotenv()
client = StorylinezClient()

# 1-2. Create a new project
project = client.project.create_project(
    name="Product Introduction",
    orientation="landscape",
    purpose="Introduce our sustainable product line"
)
project_id = project["project"]["project_id"]

# 3. Find and add stock media
search_query_results = client.prompt.start_query_gen_and_wait(
    project_id=project_id,
    num_videos=3,
    num_audio=2
)

# Search for videos
video_queries = search_query_results["result"]["results"]["videos"]
stock_videos = client.stock.search(
    queries=video_queries,
    collections=["videos"],
    detailed=True,
    generate_thumbnail=True,
    num_results_videos=3,
    orientation="landscape"
)

# Add videos to project
for video in stock_videos["videos"]:
    client.project.add_stock_file(
        project_id=project_id,
        stock_id=video["stock_id"],
        media_type="videos"
    )
    time.sleep(1)  # Avoid rate limits

# 4. Create a text prompt
client.prompt.create_text_prompt(
    project_id=project_id,
    main_prompt="Create a 30-second video introducing our eco-friendly product line that reduces plastic waste by 75%",
    document_context="Our products are made from bamboo and recycled materials. The target audience is environmentally conscious millennials.",
    temperature=0.7,
    total_length=30
)

# 5. Generate a storyboard
storyboard_job = client.storyboard.create_storyboard(
    project_id=project_id,
    deepthink=True,
    web_search=True
)
storyboard = client.storyboard.wait_for_storyboard(
    project_id=project_id,
    timeout_seconds=300
)
print(f"Storyboard created with {len(storyboard['scenes'])} scenes")

# 6. Create a voiceover
voiceover_job = client.voiceover.create_voiceover(project_id=project_id)
voiceover = client.voiceover.wait_for_voiceover(
    project_id=project_id,
    timeout_seconds=180
)

# 7. Create a sequence
sequence_job = client.sequence.create_sequence(
    project_id=project_id,
    apply_template=True,
    apply_grade=True
)
sequence = client.sequence.wait_for_sequence(
    project_id=project_id,
    timeout_seconds=600
)

# 8. Render the final video
render_job = client.render.create_render(
    project_id=project_id,
    target_width=1920,
    target_height=1080,
    subtitle_enabled=True,
    company_name="Eco Solutions Inc.",
    call_to_action="Visit eco-solutions.com today"
)
render = client.render.wait_for_render(
    project_id=project_id,
    timeout_seconds=1800
)

# Get the download links
if render["status"] == "COMPLETED":
    download_info = client.render.get_render(
        project_id=project_id,
        generate_download_link=True,
        generate_streamable_link=True
    )
    print(f"Video ready! Download URL: {download_info['download_url']}")
    print(f"Stream URL: {download_info['streamable_url']}")

Troubleshooting Common Workflow Issues

• Storyboard generating low-quality results: Increase iterations, enable deepthink and overdrive, and provide more detailed prompts
• Media not appearing in sequence: Check that media files were successfully added to the project and are compatible
• Voiceover sounds unnatural: Try adjusting the script for more conversational language, or upload a custom voiceover
• Render taking too long: Lower the resolution or disable subtitles for faster rendering
• Stock content doesn't match needs: Use more specific search terms, lower similarity threshold, or upload custom content

For more detailed video creation options, see the examples directory and module reference documentation.

Module Reference

The SDK is organized into logical modules for different aspects of the content creation process:

Core Modules

Module	Primary Focus	Key Capabilities
project	Project & folder management	Create, organize, update, and search projects and project folders
prompt	Content prompting	Create text and video-based prompts for AI content generation
storyboard	Visual planning	Create, edit, and manage scene-by-scene storyboards
sequence	Media arrangement	Arrange media into polished video sequences with transitions
render	Video production	Generate final videos with customizable settings

Media & Content Modules

Module	Primary Focus	Key Capabilities
storage	Media management	Upload, organize, analyze, and retrieve media assets
stock	Stock content	Search and license professional stock media
search	Content discovery	Semantic search across projects and media assets
voiceover	Audio narration	Generate and manage AI or custom voiceovers

Branding & Styling Modules

Module	Primary Focus	Key Capabilities
brand	Brand identity	Create and manage brand styling presets
company_details	Company information	Manage organization profiles and metadata

System & Utility Modules

Module	Primary Focus	Key Capabilities
settings	User preferences	Manage default settings and user preferences
tools	AI tools	Access specialized AI tools like briefs and audience research
utils	Utilities	Helper functions for common tasks
user	User management	Profile and subscription operations

Detailed Usage Examples

Project Management

# Create project folders for organization
quarterly_folder = client.project.create_folder(
    name="Q3 Marketing Campaigns",
    description="All campaign videos for Q3 2023"
)

# Create a project in the folder
project = client.project.create_project(
    name="Summer Product Launch",
    orientation="landscape",
    purpose="Announce new summer product line with emphasis on outdoor use",
    target_audience="Active adults 25-40, outdoor enthusiasts",
    folder_id=quarterly_folder["folder_id"]
)

# List all projects in a folder
folder_projects = client.project.get_projects_by_folder(
    folder_id=quarterly_folder["folder_id"],
    generate_thumbnail_links=True
)

# Search for specific projects
search_results = client.project.search_projects(
    query="summer product",
    search_fields=["name", "purpose"],
    status="completed",
    created_after="2023-06-01",
    sort_by="created_at",
    sort_order="desc"
)

Advanced Media Operations

# Upload and analyze video with advanced options
video_result = client.storage.upload_file(
    file_path="./interview.mp4",
    folder_path="/raw_footage/interviews",
    context="CEO interview about new product line",
    tags=["interview", "CEO", "product launch"],
    analyze_video=True,
    analyze_audio=True,
    transcribe=True,
    generate_chapters=True,
    deepthink=True
)

# Access detailed analysis results
video_id = video_result["file"]["file_id"]
analysis = client.storage.get_file_analysis(
    file_id=video_id,
    include_transcript=True,
    include_scenes=True
)

# Find specific moments in the video
moments = client.search.search_video_moments(
    file_id=video_id,
    query="CEO explains product benefits",
    detailed=True
)

# Use a specific moment as reference for a new video
for moment in moments["moments"]:
    if moment["confidence"] > 0.8:
        clip_start = moment["start_time"]
        clip_end = moment["end_time"]
        print(f"Found relevant clip from {clip_start}s to {clip_end}s")
        
        # Create a clip from this moment
        clip = client.storage.create_clip(
            file_id=video_id,
            start_time=clip_start,
            end_time=clip_end,
            name=f"CEO explaining benefits - {clip_start}s",
            generate_thumbnail=True
        )

Storyboard Creation and Editing

# Create a storyboard with AI assistance
storyboard = client.storyboard.create_storyboard(
    project_id=project_id,
    deepthink=True,
    web_search=True,
    overdrive=True
)

# Wait for storyboard completion
completed_storyboard = client.storyboard.wait_for_storyboard(
    project_id=project_id,
    timeout_seconds=300
)

# Regenerate a specific scene
scene_index = 2
client.storyboard.regenerate_scene(
    storyboard_id=completed_storyboard["storyboard_id"],
    scene_index=scene_index,
    guidance="Make this scene more dynamic and emphasize the product's durability"
)

# Update multiple scenes
updates = [
    {
        "scene_index": 0, 
        "visual_description": "Open with aerial shot of mountains with overlay text: 'Adventure Awaits'"
    },
    {
        "scene_index": 4,
        "scene_type": "product_closeup",
        "visual_description": "Close-up of product with water splashing off it, demonstrating waterproof feature"
    }
]
client.storyboard.update_scenes(
    storyboard_id=completed_storyboard["storyboard_id"],
    scene_updates=updates
)

# Export storyboard to PDF
pdf_result = client.storyboard.export_to_pdf(
    storyboard_id=completed_storyboard["storyboard_id"],
    include_script=True,
    include_thumbnails=True
)
pdf_url = pdf_result["download_url"]

Brand Management and Styling

# Upload a logo and create a brand preset
brand = client.brand.upload_logo(
    file_path="./company_logo.png",
    name="Corporate Blue",
    is_default=True,
    # Styling options
    outro_bg_color="#003366",
    main_text_color="#FFFFFF",
    sub_text_color="#E0E0E0",
    company_font="Montserrat-Bold",
    subtext_font="Montserrat-Regular",
    # Subtitle styling
    subtitle_font="OpenSans-Regular",
    subtitle_font_size=28,
    subtitle_color="#FFFFFF",
    subtitle_bg_color="#000000",
    subtitle_bg_opacity=0.7,
    subtitle_bg_rounded=True
)

# Update company details
company = client.company_details.create(
    company_name="EcoTech Solutions",
    tag_line="Innovating for a Sustainable Future",
    description="""EcoTech Solutions develops eco-friendly technologies 
                  that help businesses reduce their environmental impact 
                  while maintaining productivity.""",
    website="https://ecotechsolutions.example.com",
    industry="Environmental Technology",
    is_default=True
)

# Apply brand to project
client.project.update_project(
    project_id=project_id,
    brand_id=brand["brand_id"],
    company_details_id=company["company_details_id"]
)

Advanced Sequence Operations

# Create a sequence with detailed settings
sequence = client.sequence.create_sequence(
    project_id=project_id,
    apply_template=True,
    apply_grade=True,
    grade_type="multi",  # Enhanced color grading
    orientation="landscape",
    deepthink=True,
    overdrive=True,
    temperature=0.6,
    iterations=2
)

# Update sequence after storyboard changes
client.sequence.update_sequence(
    sequence_id=sequence["sequence_id"],
    update_ai_params=True
)

# Wait for sequence to be ready
completed_sequence = client.sequence.wait_for_sequence(
    sequence_id=sequence["sequence_id"],
    timeout_seconds=600
)

# Get media assets used in the sequence
media = client.sequence.get_sequence_media(
    sequence_id=sequence["sequence_id"],
    include_analysis=True,
    generate_thumbnail=True,
    generate_streamable=True
)

# Track history of sequence changes
history = client.sequence.get_sequence_history(
    sequence_id=sequence["sequence_id"],
    history_type="generation",
    include_current=True
)

AI-Powered Tools

# Generate a creative brief
brief = client.tools.create_creative_brief(
    name="Q4 Campaign Brief",
    user_input="Create a creative brief for our winter product line focusing on indoor comfort and wellness",
    company_details_id=company["company_details_id"],
    deepthink=True,
    overdrive=True,
    web_search=True
)

# Generate audience research
audience = client.tools.create_audience_research(
    name="Home Wellness Audience",
    user_input="Research the audience for premium home wellness products",
    additional_context="Focus on demographics, behaviors, and purchasing patterns of health-conscious homeowners",
    deepthink=True
)

# Create a video plan
plan = client.tools.create_video_plan(
    name="Winter Wellness Campaign",
    user_input="Create a detailed video plan for a series promoting indoor wellness products during winter",
    additional_context="Should cover product benefits, user testimonials, and lifestyle imagery",
    deepthink=True,
    overdrive=True
)

# Generate a shotlist for a specific scene
shotlist = client.tools.create_shotlist(
    name="Product Demo Sequence",
    user_input="Create a shotlist for demonstrating our air purifier's key features",
    scene_details="Modern living room setting, morning light through windows, focus on product operation and air quality indicator",
    visual_style="Clean, bright, minimalist aesthetic with soft natural lighting"
)

Workflow Patterns

Storylinez SDK supports several workflow patterns to fit different use cases:

Asynchronous Processing with Polling

For long-running operations:

# Start the job
job = client.storyboard.create_storyboard(project_id=project_id)
job_id = job["job_id"]

# Poll for completion
while True:
    status = client.storyboard.get_storyboard(project_id=project_id)
    if status["status"] in ["COMPLETED", "ERROR"]:
        break
    print(f"Status: {status['status']}, progress: {status.get('progress', '?')}%")
    time.sleep(10)

Helper Methods for Waiting

For convenience, many modules offer wait methods:

# Create and wait in one call
storyboard = client.storyboard.create_and_wait(
    project_id=project_id,
    timeout_seconds=300,
    polling_interval=5
)

# Or use the standalone wait method
render = client.render.wait_for_render(
    render_id=render_id,
    timeout_seconds=1200
)

Batch Operations

For processing multiple items:

# Process a batch of videos
video_paths = ["./video1.mp4", "./video2.mp4", "./video3.mp4"]
upload_results = client.storage.batch_upload_files(
    file_paths=video_paths,
    folder_path="/batch_uploads",
    analyze_video=True
)

# Create projects from templates
project_templates = [
    {"name": "Product Demo 1", "orientation": "landscape"},
    {"name": "Product Demo 2", "orientation": "portrait"},
    {"name": "Tutorial Video", "orientation": "landscape"}
]

projects = []
for template in project_templates:
    project = client.project.create_project(**template)
    projects.append(project)

Template-Based Production

For consistent video production:

def create_product_video(product_name, description, features, image_url):
    """Generate a standardized product video based on template"""
    
    # Create project
    project = client.project.create_project(
        name=f"{product_name} Video",
        orientation="landscape"
    )
    project_id = project["project"]["project_id"]
    
    # Create prompt with standardized format
    prompt_text = f"""
    Create a 30-second product video for {product_name}.
    
    Product description:
    {description}
    
    Key features to highlight:
    {", ".join(features)}
    
    Use a professional, modern style with bold typography.
    Include product shots, feature demonstrations, and end with a call to action.
    """
    
    client.prompt.create_text_prompt(
        project_id=project_id,
        main_prompt=prompt_text
    )
    
    # Upload product image if provided
    if image_url:
        # Download and upload image (simplified example)
        import requests
        from io import BytesIO
        
        response = requests.get(image_url)
        if response.status_code == 200:
            image_data = BytesIO(response.content)
            client.storage.upload_file_object(
                file_object=image_data,
                filename=f"{product_name}.jpg",
                folder_path="/product_images",
                project_id=project_id
            )
    
    # Generate storyboard and wait for completion
    storyboard = client.storyboard.create_and_wait(
        project_id=project_id,
        timeout_seconds=300
    )
    
    # Apply standard brand
    default_brand = client.brand.get_default()
    client.project.update_project(
        project_id=project_id,
        brand_id=default_brand["brand_id"]
    )
    
    # Create sequence and render
    sequence = client.sequence.create_and_wait(
        project_id=project_id,
        apply_template=True,
        apply_grade=True
    )
    
    render = client.render.create_and_wait(
        project_id=project_id,
        target_width=1920,
        target_height=1080,
        subtitle_enabled=True,
        call_to_action="Visit our website to learn more"
    )
    
    # Return completed video info
    return {
        "project_id": project_id,
        "render_id": render["render_id"],
        "download_url": client.render.get_render(
            render_id=render["render_id"], 
            generate_download_link=True
        ).get("download_url")
    }

# Use the template function
new_product_video = create_product_video(
    product_name="EcoWash 3000",
    description="Revolutionary washing machine that uses 50% less water and energy",
    features=["Eco-Mode", "Smart Detection", "Ultra Capacity", "Whisper Quiet"],
    image_url="https://example.com/images/ecowash3000.jpg"
)

Error Handling & Best Practices

Validation and Error Handling

The SDK provides comprehensive validation and clear error messages:

try:
    # Invalid parameter (temperature must be 0.0-1.0)
    client.storyboard.create_storyboard(
        project_id=project_id,
        temperature=2.0
    )
except ValueError as e:
    print(f"Validation error: {str(e)}")
    # Output: "Validation error: temperature must be between 0.0 and 1.0"
    
try:
    # Network or API errors
    result = client.project.get_project(project_id="invalid_id")
except requests.RequestException as e:
    print(f"Network error: {str(e)}")
except Exception as e:
    print(f"API error: {str(e)}")

Handling Long-Running Jobs

For operations that may take time:

def create_with_timeout(project_id, max_attempts=12, check_interval=10):
    """Create a storyboard with timeout handling"""
    try:
        # Start the storyboard creation
        job = client.storyboard.create_storyboard(project_id=project_id)
        job_id = job["job_id"]
        
        # Poll for completion with timeout
        attempts = 0
        while attempts < max_attempts:
            status = client.storyboard.get_storyboard(project_id=project_id)
            
            if status["status"] == "COMPLETED":
                return status
            
            if status["status"] == "ERROR":
                error_msg = status.get("error_message", "Unknown error")
                raise Exception(f"Storyboard creation failed: {error_msg}")
                
            print(f"Status: {status['status']}, attempt {attempts+1}/{max_attempts}")
            attempts += 1
            time.sleep(check_interval)
            
        # Timeout reached
        raise TimeoutError(f"Operation timed out after {max_attempts * check_interval} seconds")
        
    except Exception as e:
        print(f"Error: {str(e)}")
        return None

Resource Management

Best practices for managing SDK resources:

# Organizing content with folders
uploads_folder = client.storage.create_folder(
    folder_path="/projects/campaign_2023/uploads"
)

# Cleaning up temporary resources
tmp_files = client.storage.list_files(
    folder_path="/tmp",
    created_before=(datetime.now() - timedelta(days=7)).isoformat()
)

for file in tmp_files["files"]:
    client.storage.delete_file(file_id=file["file_id"])

# Setting sensible timeouts for operations
try:
    result = client.storyboard.create_and_wait(
        project_id=project_id,
        timeout_seconds=300  # 5 minutes max
    )
except TimeoutError:
    print("Operation is taking longer than expected. Check status later.")

Environment Configuration

The SDK can be configured through environment variables or direct initialization:

Environment Variables

Variable	Description	Required	Default
STORYLINEZ_API_KEY	API key for authentication	Yes	None
STORYLINEZ_API_SECRET	API secret for authentication	Yes	None
STORYLINEZ_ORG_ID	Organization ID	No	None
STORYLINEZ_BASE_URL	Custom API endpoint	No	https://api.storylinez.com
STORYLINEZ_DEBUG	Enable debug logging	No	False
STORYLINEZ_TIMEOUT	Request timeout in seconds	No	120
STORYLINEZ_MAX_RETRIES	Max retry attempts for failed requests	No	3

Configuration File

You can also create a config file (~/.storylinez/config.json):

{
  "api_key": "api_your_key_here",
  "api_secret": "your_secret_here",
  "org_id": "your_org_id_here",
  "base_url": "https://api.storylinez.com",
  "debug": false,
  "timeout": 120,
  "max_retries": 3
}

Load this configuration with:

client = StorylinezClient.from_config()

Performance Considerations

Rate Limits

The Storylinez API applies rate limits based on your subscription plan:

• Free tier: 60 requests per minute
• Pro tier: 300 requests per minute
• Enterprise: Custom limits

The SDK includes built-in rate limiting and automatic retry logic to handle rate limit errors.

Batch Operations

For bulk processing, use batch operations when available:

# Better than uploading files in a loop
results = client.storage.batch_upload_files(
    file_paths=[file1, file2, file3],
    folder_path="/batch_uploads"
)

# Better than individual requests
files = client.storage.get_files_by_ids(
    file_ids=["id1", "id2", "id3", "id4", "id5"]
)

Asynchronous Processing

For high-throughput applications, consider asynchronous patterns:

import asyncio
import concurrent.futures

async def process_projects(project_ids):
    # Use a thread pool for concurrent API calls
    with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
        loop = asyncio.get_event_loop()
        futures = [
            loop.run_in_executor(
                executor,
                lambda id=pid: client.project.get_project(project_id=id)
            )
            for pid in project_ids
        ]
        results = await asyncio.gather(*futures)
        return results

# Use the async function
project_ids = ["id1", "id2", "id3", "id4", "id5"]
results = asyncio.run(process_projects(project_ids))

Troubleshooting

Common Issues and Solutions

Problem	Possible Cause	Solution
Authentication failed	Invalid API key	Verify API key and secret are correct
Permission denied	Missing role/scope	Check permissions in Storylinez dashboard
Rate limit exceeded	Too many requests	Add backoff logic or request rate limit increase
Timeout errors	Operation taking too long	Increase timeout value or use asynchronous patterns
Resource not found	Incorrect ID or deleted resource	Verify IDs and check if resource exists

Debug Mode

Enable debug mode to see detailed request/response information:

import logging

# Configure logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger('storylinez')
logger.setLevel(logging.DEBUG)

# Create client with debug enabled
client = StorylinezClient(
    api_key="your_key",
    api_secret="your_secret",
    debug=True
)

Checking API Health

Check API service status:

status = client.utils.service_status()
print(f"API Status: {status['status']}")
print(f"API Version: {status['version']}")

API Documentation

For full details on all available methods and parameters, refer to the following resources:

• Official Storylinez API Documentation

The SDK examples directory contains additional code samples:

• examples/project_examples.py - Project creation and management
• examples/storyboard_examples.py - Storyboard generation
• examples/rendering_examples.py - Video rendering options
• examples/branding_examples.py - Brand styling and presets
• examples/advanced_workflows.py - Complex production workflows

Requirements

• Python 3.6+
• Core dependencies:
  • requests>=2.25.0
  • python-dotenv>=0.15.0

Contributing

Contributions to the Storylinez SDK are welcome! Here's how to get started:

1. Fork the Repository

   • Create a fork from our main GitHub repository

2. Set Up Development Environment

   git clone https://github.com/your-username/Storylinez-SDK.git
   cd Storylinez-SDK
   pip install -e ".[dev]"
   

3. Create a Feature Branch

   git checkout -b feature/your-amazing-feature
   

4. Make Your Changes

   • Implement your feature or bug fix
   • Add or update tests as necessary
   • Update documentation to reflect changes

5. Run Tests

   pytest
   

6. Commit and Push Changes

   git commit -m "Add detailed description of your changes"
   git push origin feature/your-amazing-feature
   

7. Submit a Pull Request

   • Open a PR against the main repository
   • Provide a clear description of the changes
   • Reference any related issues

Code Style Guidelines

• Follow PEP 8 standards
• Add type hints to function signatures
• Include docstrings for all public methods
• Maintain test coverage above 80%

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For support with the Storylinez SDK:

• Documentation: https://docs.storylinez.com
• Email Support: [e-mail]

For enterprise customers, please contact your dedicated account manager for priority support.

Advanced Usage Patterns

Custom Workflows with Operation Chaining

You can chain operations to create custom workflows:

def create_highlight_reel(client, video_files, title, duration=60):
    """Create a highlight reel from multiple video files"""
    # Create project
    project = client.project.create_project(
        name=f"Highlight Reel: {title}",
        orientation="landscape",
        purpose=f"Create a {duration}-second highlight reel"
    )
    project_id = project["project"]["project_id"]
    
    # Upload and analyze videos
    for file_path in video_files:
        video = client.storage.upload_file(
            file_path=file_path,
            analyze_video=True,
            generate_chapters=True
        )
        # Add video to project
        client.project.add_associated_file(
            project_id=project_id,
            file_id=video["file"]["file_id"]
        )
        
    # Create an AI-driven prompt based on uploads
    client.prompt.create_text_prompt(
        project_id=project_id,
        main_prompt=f"Create a {duration}-second highlight reel from the uploaded videos. Focus on exciting moments, set to upbeat music.",
        total_length=duration,
        deepthink=True
    )
    
    # Process end-to-end
    storyboard = client.storyboard.create_and_wait(project_id=project_id)
    sequence = client.sequence.create_and_wait(project_id=project_id, apply_template=True)
    render = client.render.create_and_wait(project_id=project_id)
    
    return render

Implementing Custom Retry Logic

For robust production applications:

def execute_with_retry(func, max_retries=5, initial_backoff=1, max_backoff=60):
    """Execute a function with exponential backoff retry logic"""
    retry_count = 0
    backoff = initial_backoff
    
    while True:
        try:
            return func()
        except Exception as e:
            retry_count += 1
            if retry_count > max_retries:
                raise e
                
            # Calculate backoff with jitter
            jitter = random.uniform(0, 0.1 * backoff)
            sleep_time = min(backoff + jitter, max_backoff)
            
            print(f"Attempt {retry_count} failed: {str(e)}. Retrying in {sleep_time:.1f}s...")
            time.sleep(sleep_time)
            
            # Exponential backoff
            backoff = min(backoff * 2, max_backoff)

# Usage example
result = execute_with_retry(
    lambda: client.storyboard.create_storyboard(project_id=project_id)
)

Event-Driven Processing with Webhooks

For integrating with other services:

from flask import Flask, request

app = Flask(__name__)

@app.route('/webhook/render/complete', methods=['POST'])
def render_complete_handler():
    data = request.json
    render_id = data.get('render_id')
    project_id = data.get('project_id')
    status = data.get('status')
    
    if status == 'COMPLETED':
        # Get the rendered video
        render_info = client.render.get_render(
            render_id=render_id,
            generate_download_link=True
        )
        
        # Store the download URL
        download_url = render_info.get('download_url')
        
        # Notify users or trigger further processes
        notify_completion(project_id, download_url)
        
    return {'status': 'success'}

def notify_completion(project_id, download_url):
    # Send email, push notification, etc.
    pass

Staying Updated

To get the latest version:

pip install --upgrade storylinez