omnigen-usf 0.0.1.post3

Enterprise-Grade Synthetic Data Generation

OmniGen 🚀

Generate synthetic data at scale using an enterprise-ready framework with full customizable configuration, security, and ease of use

Built by Ultrasafe AI for production environments.

---

What is OmniGen?

OmniGen is an enterprise-grade framework for generating synthetic datasets at scale—from scratch or from base data. Generate trillions of tokens and billions of samples across multiple modalities:

🎯 Data Types Supported

• 💬 Conversational Data - Single-turn to multi-turn dialogues
• 🤖 Agentic Datasets - Tool use, function calling, multi-step reasoning
• 🎨 Multimodal Datasets - Text, images, audio, video combinations
• 🖼️ Images - Synthetic image generation and editing
• 🎵 Audio - Speech, music, sound effects
• 🎬 Video - Synthetic video sequences

🎓 Use Cases

• Fine-Tuning - Instruction following, task-specific models
• Supervised Fine-Tuning (SFT) - High-quality labeled datasets
• Offline Reinforcement Learning - Preference datasets with rewards
• Online Reinforcement Learning - Ground truth with reward checking scripts
• Pre-Training - Large-scale corpus generation
• Machine Learning - Training data for any ML task

🏗️ Why OmniGen?

• ✅ Enterprise-Ready - Built for production at scale
• ✅ Fully Customizable - Configure every aspect of generation
• ✅ Secure - Complete isolation, no data mixing
• ✅ Easy - Simple API, clear examples
• ✅ Modular - Independent pipelines for different data types

---

🚀 Currently Available Pipeline

conversation_extension - Extend Single-Turn to Multi-Turn Conversations

Turn your base questions into rich multi-turn dialogues. This is just the first pipeline—more coming soon!

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Why OmniGen?

✅ Simple - One command to generate thousands of conversations
✅ Scalable - Parallel processing for fast generation
✅ Flexible - Mix different AI providers (OpenAI, Anthropic, Ultrasafe AI)
✅ Production Ready - Built for SaaS platforms with multi-tenant support

---

Quick Start

1. Install

pip install omnigen

2. Prepare Base Data

Create a file base_data.jsonl with your starting questions:

{"conversations": [{"role": "user", "content": "How do I learn Python?"}]}
{"conversations": [{"role": "user", "content": "What is machine learning?"}]}
{"conversations": [{"role": "user", "content": "Explain neural networks"}]}

3. Generate Conversations

You can configure the pipeline in two ways:

Option A: Using YAML Configuration File

Create a config.yaml file:

providers:
  user_followup:
    name: ultrasafe
    api_key: ${ULTRASAFE_API_KEY}
    model: usf-mini
  assistant_response:
    name: ultrasafe
    api_key: ${ULTRASAFE_API_KEY}
    model: usf-mini

generation:
  num_conversations: 100
  turn_range: {min: 3, max: 8}

base_data:
  source_type: file
  file_path: base_data.jsonl

storage:
  type: jsonl
  output_file: output.jsonl

Then load and run:

from omnigen.pipelines.conversation_extension import (
    ConversationExtensionConfig,
    ConversationExtensionPipeline
)

# Load configuration from YAML file
config = ConversationExtensionConfig.from_yaml('config.yaml')

# Run the pipeline
pipeline = ConversationExtensionPipeline(config)
pipeline.run()

Option B: Using Programmatic Configuration (Python)

from omnigen.pipelines.conversation_extension import (
    ConversationExtensionConfigBuilder,
    ConversationExtensionPipeline
)

# Configure the pipeline programmatically
config = (ConversationExtensionConfigBuilder()
    # User followup generator
    .add_provider(
        role='user_followup',
        name='ultrasafe',
        api_key='your-api-key',
        model='usf-mini'
    )
    # Assistant response generator
    .add_provider(
        role='assistant_response',
        name='ultrasafe',
        api_key='your-api-key',
        model='usf-mini'
    )
    # Generation settings
    .set_generation(
        num_conversations=100,
        turn_range=(3, 8)  # 3-8 turns per conversation
    )
    # Input data
    .set_data_source(
        source_type='file',
        file_path='base_data.jsonl'
    )
    # Output
    .set_storage(
        type='jsonl',
        output_file='output.jsonl'
    )
    .build()
)

# Run the pipeline
pipeline = ConversationExtensionPipeline(config)
pipeline.run()

4. Get Results

Your generated conversations will be in output.jsonl:

{
  "id": 0,
  "conversations": [
    {"role": "user", "content": "How do I learn Python?"},
    {"role": "assistant", "content": "Great choice! Start with the basics..."},
    {"role": "user", "content": "What resources do you recommend?"},
    {"role": "assistant", "content": "I recommend these resources..."},
    {"role": "user", "content": "How long will it take?"},
    {"role": "assistant", "content": "With consistent practice..."}
  ],
  "num_turns": 3,
  "success": true
}

---

Supported AI Providers

Provider	Model Examples
Ultrasafe AI	usf-mini, usf-max
OpenAI	gpt-4-turbo, gpt-3.5-turbo
Anthropic	claude-3-5-sonnet, claude-3-opus
OpenRouter	Various models

Mix Different Providers

config = (ConversationExtensionConfigBuilder()
    .add_provider('user_followup', 'openai', api_key, 'gpt-4-turbo')
    .add_provider('assistant_response', 'anthropic', api_key, 'claude-3-5-sonnet')
    # ... rest of config
    .build()
)

---

Advanced Features

Multi-Tenant SaaS Support

Perfect for platforms serving multiple users concurrently:

# Each user gets isolated workspace
workspace_id = f"user_{user_id}_session_{session_id}"

config = (ConversationExtensionConfigBuilder(workspace_id=workspace_id)
    .add_provider('user_followup', 'ultrasafe', shared_api_key, 'usf-mini')
    .add_provider('assistant_response', 'ultrasafe', shared_api_key, 'usf-mini')
    .set_storage('jsonl', output_file='output.jsonl')  # Auto-isolated
    .build()
)

# Storage automatically goes to: workspaces/{workspace_id}/output.jsonl

Parallel Dataset Generation

from concurrent.futures import ProcessPoolExecutor

def process_dataset(input_file, output_file):
    config = (ConversationExtensionConfigBuilder()
        .add_provider('user_followup', 'ultrasafe', api_key, 'usf-mini')
        .add_provider('assistant_response', 'ultrasafe', api_key, 'usf-mini')
        .set_data_source('file', file_path=input_file)
        .set_storage('jsonl', output_file=output_file)
        .build()
    )
    ConversationExtensionPipeline(config).run()

# Process 3 datasets in parallel
with ProcessPoolExecutor(max_workers=3) as executor:
    executor.submit(process_dataset, 'data1.jsonl', 'out1.jsonl')
    executor.submit(process_dataset, 'data2.jsonl', 'out2.jsonl')
    executor.submit(process_dataset, 'data3.jsonl', 'out3.jsonl')

📝 Configuration Methods

OmniGen supports multiple ways to configure your pipeline. Choose the method that best fits your workflow:

Method 1: YAML Configuration File ⭐ (Recommended)

The most flexible and maintainable approach:

Step 1: Create config.yaml

providers:
  user_followup:
    name: ultrasafe
    api_key: ${ULTRASAFE_API_KEY}  # Environment variable
    model: usf-mini
  assistant_response:
    name: ultrasafe
    api_key: ${ULTRASAFE_API_KEY}
    model: usf-mini

generation:
  num_conversations: 100
  turn_range: {min: 3, max: 8}

base_data:
  source_type: file
  file_path: base_data.jsonl

storage:
  type: jsonl
  output_file: output.jsonl

Step 2: Load and run

from omnigen.pipelines.conversation_extension import (
    ConversationExtensionConfig,
    ConversationExtensionPipeline
)

# Load from YAML file
config = ConversationExtensionConfig.from_yaml('config.yaml')

# Run pipeline
pipeline = ConversationExtensionPipeline(config)
pipeline.run()

✅ Benefits:

• Easy to version control and share
• Environment variable support with ${VAR_NAME}
• No code changes needed for config updates
• Clear, readable configuration

Method 2: Programmatic Configuration (Python)

Build configuration directly in code:

from omnigen.pipelines.conversation_extension import (
    ConversationExtensionConfigBuilder,
    ConversationExtensionPipeline
)

config = (ConversationExtensionConfigBuilder()
    .add_provider('user_followup', 'ultrasafe', 'api-key', 'usf-mini')
    .add_provider('assistant_response', 'ultrasafe', 'api-key', 'usf-mini')
    .set_generation(num_conversations=100, turn_range=(3, 8))
    .set_data_source('file', file_path='base_data.jsonl')
    .set_storage('jsonl', output_file='output.jsonl')
    .build()
)

pipeline = ConversationExtensionPipeline(config)
pipeline.run()

✅ Benefits:

• Type safety and IDE autocomplete
• Dynamic configuration based on runtime
• No external files needed
• Easy application integration

Method 3: Dictionary Configuration

Create from Python dictionary:

from omnigen.pipelines.conversation_extension import (
    ConversationExtensionConfig,
    ConversationExtensionPipeline
)

config_dict = {
    'providers': {
        'user_followup': {'name': 'ultrasafe', 'api_key': 'key', 'model': 'usf-mini'},
        'assistant_response': {'name': 'ultrasafe', 'api_key': 'key', 'model': 'usf-mini'}
    },
    'generation': {'num_conversations': 100, 'turn_range': {'min': 3, 'max': 8}},
    'base_data': {'source_type': 'file', 'file_path': 'base_data.jsonl'},
    'storage': {'type': 'jsonl', 'output_file': 'output.jsonl'}
}

config = ConversationExtensionConfig.from_dict(config_dict)
pipeline = ConversationExtensionPipeline(config)
pipeline.run()

✅ Benefits:

• Load from JSON files or APIs
• Easy programmatic modification
• Flexible for dynamic scenarios

Method 4: Hybrid Approach

Combine YAML with programmatic overrides:

from omnigen.pipelines.conversation_extension import (
    ConversationExtensionConfig,
    ConversationExtensionPipeline
)

# Load base config from YAML
config = ConversationExtensionConfig.from_yaml('base_config.yaml')

# Modify specific settings
config_dict = config.to_dict()
config_dict['generation']['num_conversations'] = 500  # Override
config_dict['storage']['output_file'] = f'output_{user_id}.jsonl'  # Dynamic

# Rebuild and run
config = ConversationExtensionConfig.from_dict(config_dict)
pipeline = ConversationExtensionPipeline(config)
pipeline.run()

✅ Benefits:

• Base configuration in YAML
• Runtime customization in code
• Best of both worlds

Environment Variables in YAML

Use ${VARIABLE_NAME} syntax:

providers:
  user_followup:
    name: ${PROVIDER_NAME}           # From environment
    api_key: ${ULTRASAFE_API_KEY}    # From environment
    model: ${USER_MODEL}             # From environment

base_data:
  file_path: ${INPUT_PATH}           # From environment

storage:
  output_file: ${OUTPUT_PATH}        # From environment

Set variables:

export PROVIDER_NAME="ultrasafe"
export ULTRASAFE_API_KEY="your-key"
export USER_MODEL="usf-mini"
export INPUT_PATH="data/input.jsonl"
export OUTPUT_PATH="data/output.jsonl"

# Then run
python your_script.py

CLI Usage with Config File

Run directly from command line:

# Using YAML config
omnigen conversation-extension --config config.yaml

# With overrides
omnigen conversation-extension \
  --config config.yaml \
  --num-conversations 500 \
  --output custom_output.jsonl

---

---

📖 Complete Configuration Reference

All Configuration Options Explained

Below is a comprehensive YAML configuration showing ALL available options with detailed explanations:

# ==============================================================================
# WORKSPACE ISOLATION (Optional)
# ==============================================================================
# Unique ID for multi-tenant environments - auto-isolates all output files
workspace_id: "user_123_session_abc"

# ==============================================================================
# PROVIDERS - AI Model Configuration
# ==============================================================================
# Configure different AI providers for each role
# Each role can use a different provider/model combination

providers:
  # Provider for generating user follow-up questions
  user_followup:
    name: ultrasafe              # Options: ultrasafe, openai, anthropic, openrouter
    api_key: ${API_KEY}          # Use env var: ${VAR_NAME} or direct key
    model: usf-mini              # Model identifier
    temperature: 0.7             # Randomness (0.0-1.0): higher = more creative
    max_tokens: 2048             # Max tokens in response
    timeout: 300                 # Request timeout in seconds
    max_retries: 5               # Number of retry attempts on failure
    retry_delay: 2               # Delay between retries in seconds
  
  # Provider for generating assistant responses
  assistant_response:
    name: ultrasafe              # Can use different provider than user_followup
    api_key: ${API_KEY}
    model: usf-mini
    temperature: 0.7
    max_tokens: 8192             # Larger for detailed responses
    timeout: 300
    max_retries: 5
    retry_delay: 2

# PROVIDER OPTIONS:
# ----------------
# ultrasafe:
#   models: usf-mini, usf-max
#
# openai:
#   models: gpt-4-turbo, gpt-4, gpt-3.5-turbo, gpt-4o, gpt-4o-mini
#
# anthropic:
#   models: claude-3-5-sonnet-20241022, claude-3-opus-20240229,
#           claude-3-sonnet-20240229, claude-3-haiku-20240307
#
# openrouter:
#   models: Any OpenRouter supported model
#   base_url: https://openrouter.ai/api/v1 (optional)

# ==============================================================================
# GENERATION SETTINGS
# ==============================================================================
generation:
  num_conversations: 100           # Total conversations to generate
  
  turn_range:                      # Number of turns per conversation
    min: 3                         # Minimum turns
    max: 8                         # Maximum turns
  
  parallel_workers: 10             # Concurrent workers (balance speed vs rate limits)
  
  # Extension behavior for multi-turn input
  extension_mode: "smart"          # Options: "smart" | "legacy"
  # - smart: Intelligently handle multi-turn conversations
  # - legacy: Always extract first user message only
  
  skip_invalid: true               # Skip invalid patterns (recommended: true)
  
  # Turn calculation method
  turn_calculation: "additional"   # Options: "additional" | "total"
  # - additional: Add NEW turns on top of existing (default)
  # - total: Keep total turns within range (never removes existing)

# ==============================================================================
# DATA SOURCE CONFIGURATION
# ==============================================================================
base_data:
  enabled: true                    # Enable base data loading
  
  # OPTION 1: Local File
  source_type: file                # Use local JSONL/JSON file
  file_path: data/input.jsonl      # Path to file
  format: conversations            # JSON key containing conversation array
  shuffle: false                   # Shuffle data before processing
  
  # OPTION 2: HuggingFace Dataset
  # source_type: huggingface       # Use HuggingFace dataset
  # hf_dataset: username/dataset   # HuggingFace dataset path
  # hf_split: train                # Dataset split: train, test, validation
  # hf_token: ${HF_TOKEN}          # HuggingFace API token (if private)
  # hf_streaming: false            # Stream dataset (for large datasets)
  # format: conversations          # Field name in dataset
  # shuffle: true                  # Shuffle after loading

# ==============================================================================
# STORAGE CONFIGURATION
# ==============================================================================
storage:
  type: jsonl                      # Options: jsonl | mongodb
  
  # JSONL Storage (Default)
  output_file: output.jsonl        # Successful conversations
  partial_file: partial.jsonl      # Partial/incomplete conversations
  failed_file: failed.jsonl        # Failed conversations
  
  # MongoDB Storage (Alternative)
  # type: mongodb
  # mongodb:
  #   connection_string: mongodb://localhost:27017
  #   database: omnigen
  #   collection: conversations
  #   output_collection: output          # Successful
  #   partial_collection: partial        # Partial
  #   failed_collection: failed          # Failed

# ==============================================================================
# DATETIME CONFIGURATION (Optional)
# ==============================================================================
datetime_config:
  enabled: true                    # Enable datetime generation
  mode: random_from_range          # Options: random_from_range | current | fixed
  timezone: UTC                    # Timezone (UTC, America/New_York, Asia/Dubai, etc.)
  format: "%Y-%m-%d %H:%M:%S"      # Python strftime format
  
  # For random_from_range mode
  range:
    start: "2024-01-01 00:00:00"   # Start datetime
    end: "2024-12-31 23:59:59"     # End datetime
  
  # For fixed mode
  # fixed_datetime: "2024-06-15 12:00:00"

# ==============================================================================
# SYSTEM MESSAGES (Optional)
# ==============================================================================
system_messages:
  # Prepend system message to every conversation
  prepend_always:
    enabled: true
    content: "You are a helpful AI assistant. Current time: {current_datetime} ({timezone})."
  
  # Append system message to every conversation
  append_always:
    enabled: false
    content: "Remember to be concise and helpful."
  
  # Add system message only if none exists
  add_if_missing:
    enabled: false
    content: "You are an AI assistant."

# Available variables in system messages:
# - {current_datetime}: Generated datetime
# - {timezone}: Configured timezone
# - {workspace_id}: Current workspace ID

# ==============================================================================
# CUSTOM PROMPTS (Optional)
# ==============================================================================
prompts:
  # Custom prompt for user follow-up generation
  followup_question: |
    ## Your Task
    Generate an intelligent follow-up user question based on conversation history.
    
    ### CONVERSATION HISTORY:
    {history}
    
    ### INSTRUCTIONS:
    - Generate a meaningful follow-up question
    - Be conversational and natural
    - Vary your phrasing and tone
    - Build on the assistant's last response
    
    Return your follow-up question wrapped in XML tags:
    <user>Your follow-up question here</user>
  
  # Custom prompt for assistant response generation
  # assistant_response: |
  #   Your custom assistant response prompt here...

# ==============================================================================
# DEBUG OPTIONS (Optional)
# ==============================================================================
debug:
  log_api_timing: true             # Log API call timings
  log_parallel_status: true        # Log parallel worker status
  verbose: false                   # Verbose logging

Quick Configuration Examples

Example 1: Local File Input

providers:
  user_followup:
    name: ultrasafe
    api_key: ${ULTRASAFE_API_KEY}
    model: usf-mini
  assistant_response:
    name: ultrasafe
    api_key: ${ULTRASAFE_API_KEY}
    model: usf-mini

generation:
  num_conversations: 100
  turn_range: {min: 3, max: 8}

base_data:
  source_type: file
  file_path: input.jsonl

storage:
  type: jsonl
  output_file: output.jsonl

Example 2: HuggingFace Dataset Input

providers:
  user_followup:
    name: openai
    api_key: ${OPENAI_API_KEY}
    model: gpt-4-turbo
  assistant_response:
    name: anthropic
    api_key: ${ANTHROPIC_API_KEY}
    model: claude-3-5-sonnet-20241022

generation:
  num_conversations: 1000
  turn_range: {min: 5, max: 10}
  parallel_workers: 20

base_data:
  source_type: huggingface
  hf_dataset: username/my-dataset
  hf_split: train
  hf_token: ${HF_TOKEN}
  format: conversations
  shuffle: true

storage:
  type: jsonl
  output_file: output.jsonl

Example 3: Mixed Providers with MongoDB

providers:
  user_followup:
    name: openai
    api_key: ${OPENAI_API_KEY}
    model: gpt-3.5-turbo
    temperature: 0.8
  assistant_response:
    name: anthropic
    api_key: ${ANTHROPIC_API_KEY}
    model: claude-3-5-sonnet-20241022
    temperature: 0.7

generation:
  num_conversations: 500
  turn_range: {min: 3, max: 8}

base_data:
  source_type: file
  file_path: questions.jsonl

storage:
  type: mongodb
  mongodb:
    connection_string: mongodb://localhost:27017
    database: omnigen
    collection: conversations

Example 4: Programmatic Configuration (Python)

from omnigen.pipelines.conversation_extension import (
    ConversationExtensionConfigBuilder,
    ConversationExtensionPipeline
)

# Build configuration programmatically
config = (ConversationExtensionConfigBuilder()
    # Workspace isolation
    .set_workspace_id("user_123_session_abc")
    
    # Providers
    .add_provider(
        role='user_followup',
        name='ultrasafe',
        api_key='your-api-key',
        model='usf-mini',
        temperature=0.7,
        max_tokens=2048
    )
    .add_provider(
        role='assistant_response',
        name='ultrasafe',
        api_key='your-api-key',
        model='usf-mini',
        temperature=0.7,
        max_tokens=8192
    )
    
    # Generation settings
    .set_generation(
        num_conversations=100,
        turn_range=(3, 8),
        parallel_workers=10,
        extension_mode='smart',
        skip_invalid=True,
        turn_calculation='additional'
    )
    
    # Data source - Local file
    .set_data_source(
        source_type='file',
        file_path='input.jsonl',
        format='conversations',
        shuffle=False
    )
    
    # Data source - HuggingFace (alternative)
    # .set_data_source(
    #     source_type='huggingface',
    #     hf_dataset='username/dataset',
    #     hf_split='train',
    #     hf_token='your-token',
    #     format='conversations',
    #     shuffle=True
    # )
    
    # Storage
    .set_storage(
        type='jsonl',
        output_file='output.jsonl',
        partial_file='partial.jsonl',
        failed_file='failed.jsonl'
    )
    
    # Custom prompts (optional)
    .set_prompts(
        followup_question="Your custom prompt here with {history}"
    )
    
    .build()
)

# Run pipeline
pipeline = ConversationExtensionPipeline(config)
pipeline.run()

---

🔧 Advanced Configuration Options - Deep Dive

System Messages Configuration

System messages allow you to inject context or instructions into conversations. You have three modes of operation:

1. Prepend Always

Add a system message at the start of every conversation:

system_messages:
  prepend_always:
    enabled: true
    content: "You are a helpful AI assistant. Current time: {current_datetime} ({timezone})."

Use Cases:

• Set assistant personality/role
• Provide real-time context (date, time)
• Add global instructions

2. Append Always

Add a system message at the end of every conversation:

system_messages:
  append_always:
    enabled: true
    content: "Remember to be concise and provide sources when possible."

Use Cases:

• Add final reminders
• Set response style constraints
• Add quality guidelines

3. Add If Missing

Add a system message only if the conversation doesn't already have one:

system_messages:
  add_if_missing:
    enabled: true
    content: "You are an AI assistant."

Use Cases:

• Ensure all conversations have baseline instructions
• Fallback when base data lacks system messages

Available Template Variables

You can use these variables in your system message content:

Variable	Description	Example Output
{current_datetime}	Generated datetime	2024-06-15 14:30:00
{timezone}	Configured timezone	UTC or America/New_York
{workspace_id}	Current workspace ID	user_123_session_abc

Complete Example - All Three Modes

system_messages:
  prepend_always:
    enabled: true
    content: |
      You are a knowledgeable AI assistant.
      Current datetime: {current_datetime} ({timezone})
      Workspace: {workspace_id}
  
  append_always:
    enabled: true
    content: |
      IMPORTANT REMINDERS:
      - Always cite sources when providing facts
      - Be concise but thorough
      - Ask clarifying questions when needed
  
  add_if_missing:
    enabled: true
    content: "You are a helpful assistant."

Note: If both prepend_always and add_if_missing are enabled, prepend_always takes precedence.

Programmatic Usage

from omnigen.pipelines.conversation_extension import ConversationExtensionConfigBuilder

config = (ConversationExtensionConfigBuilder()
    # ... other config ...
    .build()
)

# Add system messages to config dict
config_dict = config.to_dict()
config_dict['system_messages'] = {
    'prepend_always': {
        'enabled': True,
        'content': 'You are a helpful assistant. Time: {current_datetime}'
    }
}

---

DateTime Configuration

Control how datetime values are generated and injected into system messages and conversations.

Mode 1: Random from Range (Default)

Generate random datetimes within a specified range:

datetime_config:
  enabled: true
  mode: random_from_range
  timezone: UTC                        # Any valid timezone
  format: "%Y-%m-%d %H:%M:%S"          # Python strftime format
  range:
    start: "2024-01-01 00:00:00"
    end: "2024-12-31 23:59:59"

Use Cases:

• Training data with temporal diversity
• Simulating historical conversations
• Creating time-aware datasets

Common Timezones:

• UTC - Coordinated Universal Time
• America/New_York - Eastern Time
• Europe/London - British Time
• Asia/Dubai - Gulf Standard Time
• Asia/Tokyo - Japan Standard Time

Mode 2: Current Time

Use actual current time when generating:

datetime_config:
  enabled: true
  mode: current
  timezone: America/New_York
  format: "%B %d, %Y at %I:%M %p"      # December 15, 2024 at 02:30 PM

Use Cases:

• Real-time conversation simulation
• Current events discussions
• Live system demonstrations

Mode 3: Fixed DateTime

Use the same datetime for all conversations:

datetime_config:
  enabled: true
  mode: fixed
  timezone: UTC
  format: "%Y-%m-%d %H:%M:%S"
  fixed_datetime: "2024-06-15 12:00:00"

Use Cases:

• Consistent training data
• Specific time period simulation
• Testing and debugging

Format String Examples

Common datetime formats using Python's strftime:

# ISO 8601 Format
format: "%Y-%m-%d %H:%M:%S"           # 2024-12-15 14:30:00

# Human-Readable
format: "%B %d, %Y at %I:%M %p"       # December 15, 2024 at 02:30 PM

# Date Only
format: "%Y-%m-%d"                     # 2024-12-15

# Time Only
format: "%H:%M:%S"                     # 14:30:00

# Custom Format
format: "%A, %B %d, %Y"               # Monday, December 15, 2024

Complete DateTime Example

datetime_config:
  enabled: true
  mode: random_from_range
  timezone: America/New_York
  format: "%A, %B %d, %Y at %I:%M %p %Z"
  range:
    start: "2024-01-01 09:00:00"      # Business hours only
    end: "2024-12-31 17:00:00"

# Then use in system messages:
system_messages:
  prepend_always:
    enabled: true
    content: |
      You are a business assistant.
      Current datetime: {current_datetime}
      Timezone: {timezone}

Output Example:

You are a business assistant.
Current datetime: Wednesday, June 15, 2024 at 02:30 PM EST
Timezone: America/New_York

---

Custom Prompts Configuration

Override default prompts to control how the AI generates follow-up questions and responses.

Default Prompts

OmniGen uses optimized default prompts, but you can customize them:

1. Follow-up Question Prompt (for user_followup role):

prompts:
  followup_question: |
    ## Your Task
    Generate an intelligent follow-up user question based on conversation history.
    
    ### CONVERSATION HISTORY:
    {history}
    
    ### INSTRUCTIONS:
    - Generate a meaningful follow-up question
    - Be conversational and natural
    - Vary your phrasing and tone
    - Build on the assistant's last response
    - Make the question specific to the conversation context
    
    Return your follow-up question wrapped in XML tags:
    <user>Your follow-up question here</user>

2. Assistant Response Prompt (for assistant_response role):

prompts:
  assistant_response: |
    ## Your Task
    Generate a helpful assistant response based on the conversation history.
    
    ### CONVERSATION HISTORY:
    {history}
    
    ### INSTRUCTIONS:
    - Provide accurate and helpful information
    - Be conversational and friendly
    - Reference previous context when relevant
    - Keep responses focused and concise
    
    Return your response wrapped in XML tags:
    <assistant>Your response here</assistant>

Available Template Variables

Variable	Description	Content
{history}	Full conversation history	All previous messages formatted as text

Custom Prompt Examples

Example 1: Technical Documentation Assistant

prompts:
  followup_question: |
    Generate a technical follow-up question from a developer's perspective.
    
    CONVERSATION:
    {history}
    
    Create a question that:
    - Asks about implementation details
    - Seeks code examples or best practices
    - Explores edge cases or potential issues
    
    <user>Your technical question</user>
  
  assistant_response: |
    Provide a detailed technical response with code examples.
    
    CONVERSATION:
    {history}
    
    Your response should:
    - Include code snippets when helpful
    - Explain technical concepts clearly
    - Provide links to documentation
    - Mention potential pitfalls
    
    <assistant>Your technical response</assistant>

Example 2: Customer Support Simulation

prompts:
  followup_question: |
    Simulate a customer asking for help.
    
    CONVERSATION:
    {history}
    
    Generate a question that:
    - Shows frustration or confusion (realistic)
    - Asks for clarification on previous response
    - Requests specific solutions or workarounds
    
    <user>Customer question</user>
  
  assistant_response: |
    Provide empathetic customer support.
    
    CONVERSATION:
    {history}
    
    Your response must:
    - Show empathy and understanding
    - Provide clear step-by-step solutions
    - Offer alternatives if applicable
    - End with "Is there anything else I can help with?"
    
    <assistant>Support response</assistant>

Example 3: Educational Tutor

prompts:
  followup_question: |
    Generate a student's follow-up question showing learning progression.
    
    CONTEXT:
    {history}
    
    The question should:
    - Build on what was just explained
    - Show either understanding or confusion
    - Ask for examples or clarification
    - Demonstrate curiosity about the topic
    
    <user>Student question</user>
  
  assistant_response: |
    Respond as a patient, knowledgeable tutor.
    
    CONTEXT:
    {history}
    
    Your response should:
    - Use simple, clear language
    - Provide concrete examples
    - Check for understanding
    - Encourage further questions
    
    <assistant>Tutor response</assistant>

Programmatic Prompt Configuration

from omnigen.pipelines.conversation_extension import ConversationExtensionConfigBuilder

custom_prompts = {
    'followup_question': """
        Generate a follow-up based on: {history}
        Make it specific and contextual.
        <user>question</user>
    """,
    'assistant_response': """
        Respond helpfully to: {history}
        Be clear and concise.
        <assistant>response</assistant>
    """
}

config = (ConversationExtensionConfigBuilder()
    # ... other config ...
    .set_prompts(**custom_prompts)
    .build()
)

---

Debug Configuration

Enable detailed logging and monitoring for troubleshooting and optimization.

Debug Options

debug:
  log_api_timing: true          # Log API call duration and performance
  log_parallel_status: true     # Log parallel worker status and progress
  verbose: false                # Enable verbose logging (all operations)

Option Details

1. API Timing Logs (log_api_timing: true)

Tracks API performance for each call:

[API TIMING] user_followup request took 1.23s
[API TIMING] assistant_response request took 2.45s
[API TIMING] Average response time: 1.84s

Use Cases:

• Identify slow API providers
• Optimize provider selection
• Monitor rate limits
• Debug timeout issues

2. Parallel Status Logs (log_parallel_status: true)

Shows worker activity in real-time:

[PARALLEL] Worker 1/10: Processing conversation 5
[PARALLEL] Worker 2/10: Processing conversation 6
[PARALLEL] Worker 3/10: Waiting for task...
[PARALLEL] Progress: 45/100 conversations complete (45%)
[PARALLEL] Active workers: 8/10 | Queue: 12 remaining

Use Cases:

• Monitor parallel processing
• Identify bottlenecks
• Optimize worker count
• Track progress in real-time

3. Verbose Logging (verbose: true)

Enables comprehensive logging of all operations:

[DEBUG] Loading base data from: data/input.jsonl
[DEBUG] Loaded 100 base conversations
[DEBUG] Initializing provider: ultrasafe (usf-mini)
[DEBUG] Starting parallel generation with 10 workers
[DEBUG] Conversation 1: Processing...
[DEBUG] Conversation 1: Generated 5 turns
[DEBUG] Conversation 1: Saved to output.jsonl
[DEBUG] Final stats: 95 success, 3 partial, 2 failed

Use Cases:

• Development and testing
• Debugging issues
• Understanding pipeline flow
• Troubleshooting data problems

Complete Debug Configuration

# Full debug mode for development
debug:
  log_api_timing: true
  log_parallel_status: true
  verbose: true

# Production mode (minimal logging)
debug:
  log_api_timing: false
  log_parallel_status: false
  verbose: false

# Performance monitoring mode
debug:
  log_api_timing: true      # Track API performance
  log_parallel_status: true # Monitor workers
  verbose: false            # Don't flood logs

Programmatic Debug Configuration

from omnigen.pipelines.conversation_extension import ConversationExtensionConfigBuilder

config = (ConversationExtensionConfigBuilder()
    # ... other config ...
    .build()
)

# Add debug settings
config_dict = config.to_dict()
config_dict['debug'] = {
    'log_api_timing': True,
    'log_parallel_status': True,
    'verbose': False
}

Debug Output Examples

Scenario 1: API Performance Issue

debug:
  log_api_timing: true

Output helps identify slow providers:

[API TIMING] openai/gpt-4-turbo: 0.8s
[API TIMING] anthropic/claude-3-5-sonnet: 3.2s  ← SLOW!
[API TIMING] ultrasafe/usf-mini: 0.5s

Scenario 2: Parallel Processing Bottleneck

debug:
  log_parallel_status: true

Output shows worker utilization:

[PARALLEL] Active: 3/10 workers  ← Only 30% utilized!
[PARALLEL] Queue: 0 tasks remaining
[PARALLEL] Suggestion: Reduce worker count to 5

Scenario 3: Data Loading Issues

debug:
  verbose: true

Output reveals the problem:

[DEBUG] Loading: data/input.jsonl
[DEBUG] Line 1: Valid ✓
[DEBUG] Line 2: Valid ✓
[DEBUG] Line 3: ERROR - First message not from user
[DEBUG] Line 4: Valid ✓
[DEBUG] Loaded: 3 valid, 1 invalid

---

📖 Conversation Extension Pipeline - Complete Guide

Overview

The Conversation Extension Pipeline intelligently transforms base conversations into rich multi-turn dialogues. It can handle both single-turn questions and extend existing multi-turn conversations.

Key Features

• ✅ Smart Extension - Continues from existing conversations based on last role
• ✅ Flexible Input - Handles single-turn or multi-turn base data
• ✅ Provider Mix - Use different AI providers for user and assistant
• ✅ Multi-Tenant - Complete workspace isolation
• ✅ Configurable - Full control over generation behavior

Configuration Options

Extension Modes

Smart Mode (Default)

generation:
  extension_mode: "smart"

• Single-turn input → Generate new conversation from scratch
• Multi-turn (user last) → Add 1 assistant response, then continue
• Multi-turn (assistant last) → Add user + assistant, then continue
• Invalid patterns → Skip row entirely

Legacy Mode

generation:
  extension_mode: "legacy"

• Always extracts first user message only (original behavior)

Turn Calculation

Additional Mode (Default) - Add NEW turns on top of existing

generation:
  turn_calculation: "additional"  # Add 3-8 NEW turns

Total Mode - Keep total within range (never removes existing)

generation:
  turn_calculation: "total"  # Total should be 3-8 turns

Complete Configuration

# Workspace isolation (optional)
workspace_id: "user_123"

# AI Providers
providers:
  user_followup:
    name: "ultrasafe"
    api_key: "${ULTRASAFE_API_KEY}"
    model: "usf-mini"
    temperature: 0.7
    max_tokens: 2048
  
  assistant_response:
    name: "ultrasafe"
    api_key: "${ULTRASAFE_API_KEY}"
    model: "usf-mini"
    temperature: 0.7
    max_tokens: 8192

# Generation Settings
generation:
  num_conversations: 100
  turn_range:
    min: 3
    max: 8
  parallel_workers: 10
  
  # Extension behavior
  extension_mode: "smart"        # "smart" | "legacy"
  skip_invalid: true             # Skip invalid patterns
  turn_calculation: "additional" # "additional" | "total"

# Input Data
base_data:
  enabled: true
  source_type: "file"
  file_path: "base_data.jsonl"
  format: "conversations"
  shuffle: false

# Output Storage
storage:
  type: "jsonl"
  output_file: "output.jsonl"
  partial_file: "partial.jsonl"
  failed_file: "failed.jsonl"

# System Messages (optional)
system_messages:
  add_if_missing:
    enabled: true
    content: "You are a helpful assistant. Current datetime: {current_datetime}"

# DateTime (optional)
datetime_config:
  enabled: true
  timezone: "UTC"
  format: "%Y-%m-%d %H:%M:%S"
  range:
    start_date: "2024-01-01"
    end_date: "2024-12-31"

Input Data Formats

Valid Patterns

Single-turn ✅

{"conversations": [{"role": "user", "content": "How do I learn Python?"}]}

Multi-turn (user last) ✅

{
  "conversations": [
    {"role": "user", "content": "How do I learn Python?"},
    {"role": "assistant", "content": "Start with basics..."},
    {"role": "user", "content": "What resources?"}
  ]
}

Multi-turn (assistant last) ✅

{
  "conversations": [
    {"role": "user", "content": "How do I learn Python?"},
    {"role": "assistant", "content": "Start with basics..."}
  ]
}

Invalid Patterns (Skipped)

❌ First message not user

{"conversations": [{"role": "assistant", "content": "Hello"}]}

❌ Empty conversations

{"conversations": []}

Programmatic Usage

from omnigen.pipelines.conversation_extension import (
    ConversationExtensionConfigBuilder,
    ConversationExtensionPipeline
)

config = (ConversationExtensionConfigBuilder()
    .add_provider('user_followup', 'ultrasafe', 'api-key', 'usf-mini')
    .add_provider('assistant_response', 'ultrasafe', 'api-key', 'usf-mini')
    .set_generation(
        num_conversations=100,
        turn_range=(3, 8),
        parallel_workers=10,
        extension_mode='smart',      # Handle multi-turn intelligently
        skip_invalid=True,            # Skip invalid patterns
        turn_calculation='additional' # Add new turns (default)
    )
    .set_data_source('file', file_path='base_data.jsonl')
    .set_storage('jsonl', output_file='output.jsonl')
    .build()
)

pipeline = ConversationExtensionPipeline(config)
pipeline.run()

Turn Calculation Examples

Additional Mode (Default)

Existing: 2 turns
Config: turn_range = (3, 8)
Result: Add 3-8 NEW turns → Total: 5-10 turns

Total Mode

Existing: 2 turns
Config: turn_range = (3, 8)
Result: Add 1-6 turns → Total: 3-8 turns

Existing: 10 turns (already > max)
Config: turn_range = (3, 8)
Result: Add 0 turns → Keep 10 turns (never remove)

Best Practices

Provider Selection

• Use better models for assistant (claude-3-5-sonnet, gpt-4-turbo)
• Use cheaper models for user followups (usf-mini, gpt-3.5-turbo)

Turn Range

• Quick exchanges: (2, 4)
• In-depth: (5, 10)
• Balanced: (3, 8) ✅

Parallel Workers

• Conservative: 5 (avoid rate limits)
• Balanced: 10 ✅
• Aggressive: 20 (watch for rate limits)

Troubleshooting

Issue: Empty output

• Check input data format (first message must be user)
• Set skip_invalid: false to see errors

Issue: Rate limits

• Reduce parallel_workers
• Check provider API limits

Issue: Low quality

• Increase temperature (0.8-0.9)
• Use better models
• Add custom prompts and system messages

---

License

MIT License - Ultrasafe AI © 2024

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About Ultrasafe AI

Enterprise-grade AI tools with focus on safety and performance.

• 🌐 Website: us.inc
• 📧 Email: [e-mail]

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