CRAFT™️ Experiment: Claude 3.7: Rethinking A.I. Functions (4 of 5)
THE CRAFT™️ EXPERIMENT :: WEEK 5 :: POST 4
Claude 3.7: Rethinking A.I. Functions (4 of 5)
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Please brainstorm some creative ways that functions can be adapted to the CRAFT framework. Please refer to the attached files for information about the current state of this project. For reference, here are some benefits of regular functions:
In programming, a function is a reusable block of code that performs a specific task. It's like a mini-program within a larger program, designed to be called (or invoked) when needed. Functions help organize code, making it easier to read, understand, and maintain. They also promote code reusability, as you can use the same function multiple times with different inputs
Key Concepts:
• Input: Functions can accept data (called arguments or parameters) as input, allowing them to perform different tasks based on the data they receive.
• Output: Functions can return a value (the output) as a result of their execution.
• Reusability: A function can be called multiple times from different parts of a program, saving you from rewriting the same code repeatedly.
• Organization: Functions help break down complex programs into smaller, manageable units.
Abstraction: Functions hide the implementation details of a task, allowing you to focus on what the task does rather than how it does it.
How can these known concepts be adapted to CRAFT which is built on six key philosophical principles:
1. STRUCTURE ENABLES FREEDOM: By providing a clear framework for AI interaction,
CRAFT paradoxically creates more creative freedom by eliminating cognitive
overhead and repetition.
2. CODE AS CONVERSATION: Programming concepts like variables, functions, and
objects can dramatically improve the clarity and efficiency of natural
language exchanges with AI.
3. CUMULATIVE INTELLIGENCE: AI interactions should build upon each other,
creating an evolving knowledge base rather than isolated conversations.
4. HUMAN-AI PARTNERSHIP: The framework facilitates true collaboration where
both human creativity and AI capabilities are leveraged optimally.
5. ACCESSIBILITY THROUGH FAMILIARITY: Using Python-like syntax creates an
approachable system for both technical and non-technical users.
6. CONSERVATION OF TOKENS: Limiting tokens in an A.I. chat saves money and time.
It also cuts down on the amount of vertical scrolling needed in a chat.
Please start with an outline of the highlights of this topic.
CRAFT: Transforming AI Interactions Through Function-Oriented Design
Introduction
AI prompt engineering has evolved tremendously over the past few years, but most approaches still treat AI interactions as free-form exchanges without consistent structure. Today, I'm excited to share an ongoing experiment called CRAFT (Configurable Reusable AI Framework Technology) where I've been collaborating with advanced AI systems to develop a more structured, intuitive way to communicate with AI.
CRAFT adapts principles of object-oriented programming to AI chats, creating deeper, more meaningful interactions that build upon each other. This post explores the function-oriented design at the heart of CRAFT and how it's changing how we think about AI interactions.
The Philosophy Behind CRAFT
CRAFT is built on six foundational principles:
Structure Enables Freedom: By establishing clear frameworks for AI interaction, we paradoxically create more creative freedom by eliminating repetitive instructions and cognitive overhead.
Code as Conversation: Programming concepts like variables, functions, and objects significantly improve clarity and efficiency in natural language exchanges with AI.
Cumulative Intelligence: AI interactions should build upon previous exchanges, creating an evolving knowledge base instead of isolated conversations.
Human-AI Partnership: The framework facilitates genuine collaboration where both human creativity and AI capabilities are optimally leveraged.
Accessibility Through Familiarity: Using Python-like syntax creates an approachable system for both technical and non-technical users.
Conservation of Tokens: Limiting tokens saves time and money while reducing scrolling needed in chats.
The Function System: CRAFT's Core Innovation
At the heart of CRAFT is a sophisticated function system that transforms traditional prompt engineering into a more structured, reusable, and intuitive experience. Through extensive experimentation, we've developed ten function types that work together to create a comprehensive framework for AI interaction.
1. Context-Aware Functions
Context-aware functions adapt to conversation history and user state, creating more personalized and relevant interactions. They maintain awareness across an entire conversation, remembering previous exchanges and adapting responses accordingly.
python
def generate_adaptive_response(user_input, context_history=None, user_preferences=None): """ Creates responses that adapt based on conversation context and user preferences. Args: user_input (str): Current user message context_history (list): Previous exchanges in the conversation user_preferences (dict): User-specific preferences Returns: str: Contextually appropriate response """ #H->AI::ContextAnalysis: (Consider previous exchanges and user preferences) # Implementation would analyze context and generate appropriate response return "Contextually aware response"
These functions excel in scenarios requiring personalization, such as customer support, educational tools, and long-form content creation. The AI remembers previous instructions, user preferences, and interaction patterns without requiring constant reminders.
2. Multi-Modal Functions
Multi-Modal functions handle different content types and formats, enabling seamless transformation between text, structured data, code, visual descriptions, and more.
python
def transform_content(content, source_format, target_format, preservation_level="high"): """ Transforms content between different formats while preserving meaning. Args: content (str): Content to transform source_format (str): Current format target_format (str): Desired format preservation_level (str): How carefully to preserve meaning Returns: str: Transformed content """ #H->AI::FormatTransformation: (Convert between formats while preserving meaning) # Implementation would handle the transformation return "Transformed content"
These functions are particularly valuable for content repurposing, data visualization, and creating different representations of the same content for different audiences.
3. Composable AI Instructions
Composable instructions build complex behaviors from simpler components, allowing you to chain and combine smaller functions into sophisticated workflows.
python
def chain_functions(functions, input_data, shared_context=None): """ Chains multiple functions together, passing output of each as input to the next. Args: functions (list): Functions to chain input_data (any): Initial input shared_context (dict): Context shared across all functions Returns: any: Result from the final function """ #H->AI::FunctionChaining: (Pass results through a pipeline of functions) result = input_data for func in functions: result = func(result, context=shared_context) return result
This approach allows you to build modular, reusable components that can be combined in different ways to achieve complex tasks, similar to Unix pipes or functional programming concepts.
4. Handoff-Optimized Functions
Handoff-optimized functions preserve continuity between AI sessions, creating seamless transitions when token limits are reached or when switching between different AI instances.
python
def create_session_snapshot(session_data, key_variables, current_progress): """ Creates a compact snapshot of current session state for handoff. Args: session_data (dict): Complete session data key_variables (list): Essential variables to preserve current_progress (dict): Current progress information Returns: dict: Session snapshot for handoff """ #H->AI::SessionPreservation: (Capture essential session state) # Implementation would create a compact session snapshot return {"snapshot": "Compact session state"}
These functions are essential for complex projects that span multiple sessions, enabling true continuity of thought across AI interactions.
5. Reflection Functions
Reflection functions analyze and improve AI performance by evaluating responses, identifying patterns, and developing enhancement strategies.
python
def evaluate_response_quality(response, criteria=None): """ Evaluates a response against quality criteria. Args: response (str): AI response to evaluate criteria (dict): Evaluation criteria Returns: dict: Evaluation results """ #H->AI::ResponseEvaluation: (Assess quality objectively) # Implementation would analyze the response return {"evaluation": "Quality assessment results"}
These functions enable continuous improvement cycles, helping AI systems learn from previous interactions and steadily enhance performance.
6. Token-Optimized Function Design
Token-optimized functions maximize information density while minimizing token usage, enabling more complex operations within token limits.
python
def create_token_optimized_function(function_name, operation, parameters=None, compression_level="balanced"): """ Creates a token-efficient function with optimized parameter handling. Args: function_name (str): Name of the function operation (str): Core operation the function performs parameters (dict, optional): Parameters with default values compression_level (str): Level of token optimization Returns: str: Token-optimized function definition """ #H->AI::TokenStrategy: (Apply token optimization techniques) # Implementation would create token-efficient function return "Token-efficient function definition"
This approach allows for dynamic token budget allocation, intelligent compression, and optimal use of context windows, which is especially valuable for complex applications with token constraints.
7. Natural Language Parameter Handling
Natural language parameter functions enable intuitive parameter passing through conversational language rather than rigid syntax.
python
def create_nlp_parameter_handler(function_name, formal_parameters, language_patterns=None): """ Creates a handler that maps conversational input to formal parameters. Args: function_name (str): Name of the function formal_parameters (dict): Parameter definitions language_patterns (list, optional): Common language patterns Returns: function: Natural language parameter handler """ #H->AI::NLParameterExtraction: (Extract parameters from natural language) # Implementation would create parameter extraction function return lambda natural_language_input: {"extracted_parameters": {}}
This capability bridges the gap between programming and conversation, allowing users to express parameters in their own words while maintaining the structure and reliability of formal function calls.
8. Progressive Function Enhancement
Progressive functions evolve over time without breaking existing implementations, enabling gradual improvement and feature addition.
python
def create_progressive_function(base_function, version="1.0", enhancement_path=None): """ Creates a function that can be progressively enhanced over time. Args: base_function (function): Initial implementation version (str): Initial version enhancement_path (dict, optional): Planned enhancement stages Returns: function: Progressive function with enhancement capabilities """ #H->AI::EnhancementSetup: (Prepare function for progressive enhancement) # Implementation would create enhanceable function return "Function that can evolve over time"
These functions implement version tracking, capability flags, and backward compatibility mechanisms to ensure functions can improve while maintaining stability for existing users.
9. Collaborative Function Building
Collaborative functions enable multiple stakeholders to contribute to function development through structured feedback and version control.
python
def create_collaborative_function(function_name, initial_implementation=None, collaborator_roles=None, workflow_type="consensus"): """ Creates a function designed for collaborative development. Args: function_name (str): Name of the function initial_implementation (function, optional): Starting implementation collaborator_roles (dict, optional): Defined roles and responsibilities workflow_type (str): Collaboration workflow type Returns: dict: Collaborative function object """ #H->AI::CollaborationSetup: (Initialize function for collaborative development) # Implementation would create collaborative function object return {"function": initial_implementation, "collaboration_tools": {}}
This approach transforms function development from an isolated activity into a dynamic, multi-participant process, similar to collaborative software development but adapted to AI interactions.
10. Function Libraries
Function libraries organize, discover, and use collections of related functions through categorization, search, and recommendations.
python
def create_function_library(library_name, description=None, categories=None, namespace=None): """ Creates a function library for organizing related functions. Args: library_name (str): Name of the library description (str, optional): Library description categories (list, optional): Function categories namespace (str, optional): Namespace prefix Returns: dict: Function library object """ #H->AI::LibraryCreation: (Create organized collection of related functions) # Implementation would create library structure return {"name": library_name, "functions": {}}
Libraries transform individual functions into organized toolsets with consistent interfaces, documentation, and discovery mechanisms, making complex function collections manageable and accessible.
Real-World Applications
The true power of CRAFT emerges when these function types work together. Here are a few real-world applications showcasing the framework's capabilities:
Enterprise Document Processing
By combining Context-Aware, Multi-Modal, and Function Library approaches, we created a document processing system that automatically detects document types, applies appropriate processing, and adapts to organizational needs without excessive configuration.
Cross-Team Collaborative Workflows
Using Collaborative, Progressive, and Handoff-Optimized functions, we developed a workflow system where multiple departments can contribute to process definitions while maintaining process continuity across sessions and seamlessly evolving processes over time.
Adaptive Learning Systems
By integrating Context-Aware, Reflection, and Natural Language Parameter functions, we built an educational system that adapts to student knowledge levels, learns from interaction patterns, and accepts natural language instructions from both teachers and students.
Getting Started with CRAFT
CRAFT is still an evolving experiment, but the core principles and function types can be applied to your own AI interactions today. Here's how to get started:
Begin thinking about your AI interactions in terms of reusable components rather than one-off prompts.
Identify which function types best match your needs and start with implementing those.
Focus on metadata and documentation from the beginning – they're essential for building a manageable system.
Start small with a few well-defined functions before expanding to more complex implementations.
Consider how your functions will work together in a cohesive system rather than as isolated components.
Conclusion
CRAFT represents a fundamental shift in how we interact with AI systems, moving from ad-hoc prompts to structured, reusable components that build upon each other. By applying principles of object-oriented programming to natural language interactions, we're creating more powerful, efficient, and intuitive AI experiences.
This is just the beginning of the CRAFT journey. As the framework evolves, we'll continue exploring how structured approaches can enhance AI interactions while maintaining the natural conversational flow that makes AI assistants so valuable.
RESEARCH:
Creative Adaptations of Functions to the CRAFT Framework
Highlights
Context-Aware Functions: Functions that adapt their behavior based on conversation context
Multi-Modal Functions: Functions that can process and generate different types of content
Composable AI Instructions: Building complex AI behaviors from simpler function components
Handoff-Optimized Functions: Functions designed to preserve state between chat sessions
Reflection Functions: Functions that analyze and optimize AI performance
Token-Optimized Function Design: Function structures that minimize token usage
Natural Language Parameter Handling: Functions with flexible parameter interpretation
Progressive Function Enhancement: Functions that improve with repeated use
Collaborative Function Building: Functions that humans and AI can modify together
Function Libraries: Reusable function collections for common AI tasks
Detailed Exploration
1. Context-Aware Functions
Context-awareness allows functions to adapt based on the current conversation state, previous interactions, and detected user intent.
python
def generate_response(prompt, context=None, style=None, history=None):
"""
Generates AI responses that automatically adapt to conversation context.
#H->AI::Directive: (Analyze conversation history to determine appropriate tone)
#H->AI::Constraint: (Maintain consistency with previous responses)
"""
# Function implementation
pass
This approach aligns with CRAFT principles by:
Reducing cognitive overhead (Structure Enables Freedom)
Creating natural conversation flow (Code as Conversation)
Building on previous exchanges (Cumulative Intelligence)
2. Multi-Modal Functions
Functions that seamlessly handle different content types and formats:
python
def transform_content(content, target_format, enhancement=None):
"""
Transforms content between different formats (text, structured data, etc.)
#H->AI::Context: (Identify the input format automatically)
#H->AI::Focus: (Preserve semantic meaning while changing representation)
"""
# Function implementation
pass
Benefits:
Creates flexibility in how information is represented (Structure Enables Freedom)
Enables richer collaboration (Human-AI Partnership)
Abstracts complexity of format conversions (Accessibility Through Familiarity)
3. Composable AI Instructions
Building complex AI behaviors from simpler function components:
python
def chain_functions(*functions, input_data=None):
"""
Combines multiple functions into a pipeline, where output of one becomes input to the next.
#H->AI::Directive: (Process functions in sequence, passing outputs as inputs)
#H->AI::Consider: (Handle errors gracefully with fallback options)
"""
# Function implementation
pass
This implements:
Modular approach to complex instructions (Structure Enables Freedom)
Natural building blocks for conversation (Code as Conversation)
Efficient reuse of common patterns (Conservation of Tokens)
4. Handoff-Optimized Functions
Functions designed specifically to preserve state between AI chat sessions:
python
def create_session_snapshot(key_variables, current_state, session_summary=None):
"""
Creates a compact representation of current session state for handoff.
#H->AI::Directive: (Identify and preserve critical context elements)
#H->AI::Constraint: (Optimize for token efficiency in the handoff)
"""
# Function implementation
pass
This directly supports:
Maintaining continuity between sessions (Cumulative Intelligence)
Preserving collaboration context (Human-AI Partnership)
Minimizing repeated explanations (Conservation of Tokens)
5. Reflection Functions
Functions that analyze and optimize AI performance:
python
def evaluate_response_quality(prompt, response, criteria=None):
"""
Evaluates the quality of AI responses against defined criteria.
#H->AI::Directive: (Score response against clarity, relevance, accuracy)
#H->AI::Focus: (Suggest specific improvements for future responses)
"""
# Function implementation
pass
Benefits:
Creates feedback loops for improvement (Cumulative Intelligence)
Enhances collaboration quality (Human-AI Partnership)
Provides structured framework for assessment (Structure Enables Freedom)
6. Token-Optimized Function Design
Function structures specifically designed to minimize token usage:
python
def compress_instruction(instruction, level=3):
"""
Reduces token count of instructions while preserving core meaning.
#H->AI::Directive: (Identify and remove redundant or non-essential elements)
#H->AI::Constraint: (Maintain all critical semantic content)
"""
# Function implementation
pass
This directly implements:
Efficient use of token allowances (Conservation of Tokens)
Clarity through structure (Structure Enables Freedom)
Accessible optimization patterns (Accessibility Through Familiarity)
7. Natural Language Parameter Handling
Functions with flexible parameter interpretation:
python
def interpret_parameters(instruction, parameter_schema=None):
"""
Extracts structured parameters from natural language instructions.
#H->AI::Directive: (Identify parameter values even when not explicitly labeled)
#H->AI::Consider: (Apply reasonable defaults for missing parameters)
"""
# Function implementation
pass
Benefits:
Bridges code and natural language (Code as Conversation)
Makes technical interaction more accessible (Accessibility Through Familiarity)
Reduces need for rigid syntax (Structure Enables Freedom)
8. Progressive Function Enhancement
Functions that improve with repeated use:
python
def adaptive_function(task, past_examples=None):
"""
Adapts function behavior based on past usage patterns.
#H->AI::Directive: (Analyze successful past applications to improve output)
#H->AI::Context: (Consider patterns in how the user modifies outputs)
"""
# Function implementation
pass
This implements:
Learning from previous interactions (Cumulative Intelligence)
Growing partnership capabilities (Human-AI Partnership)
Evolving structure (Structure Enables Freedom)
9. Collaborative Function Building
Functions that humans and AI can modify together:
python
def suggest_function_improvements(function_definition, usage_data=None):
"""
Analyzes function usage and suggests improvements.
#H->AI::Directive: (Identify patterns where function could be enhanced)
#H->AI::Focus: (Suggest specific modifications to parameters or implementation)
"""
# Function implementation
pass
Benefits:
True collaborative development (Human-AI Partnership)
Evolving capabilities (Cumulative Intelligence)
Accessible co-creation (Accessibility Through Familiarity)
10. Function Libraries
Reusable function collections for common AI tasks:
python
def import_function_library(domain, version=None):
"""
Imports pre-defined function collections for specific domains.
#H->AI::Directive: (Load standard functions for the specified domain)
#H->AI::Consider: (Check compatibility with current CRAFT version)
"""
# Function implementation
pass
This supports:
Building on established patterns (Structure Enables Freedom)
Efficient reuse of proven approaches (Conservation of Tokens)
Common foundation for collaboration (Human-AI Partnership)
Implementation Recommendations
To practically implement these concepts within CRAFT:
Start with Function Templates: Create standardized templates that include parameter documentation, expected behavior, and example usage.
Develop a Function Registry: Maintain a central registry of functions with categorization and metadata to support discovery and reuse.
Build Function Composition Tools: Create utilities to combine functions into more complex workflows without redundancy.
Create Function Testing Tools: Develop tools to validate function behavior and ensure consistent results across sessions.
Implement Progressive Documentation: Design documentation that evolves as functions are used, incorporating actual usage examples.
These approaches would significantly enhance the CRAFT framework by making functions more powerful, flexible, and aligned with the core philosophical principles.
Retry
RK
Great, please expand on item 1 with details, benefits, explanations , examples and anything else that you think will be helpful.
Edit
Context-Aware Functions in CRAFT
Detailed Explanation
Context-aware functions represent a significant evolution from traditional programming functions by dynamically adapting their behavior based on the conversation environment, history, and detected user intent. Unlike standard functions that process inputs in isolation, context-aware functions actively consider the broader conversational ecosystem.
Core Concept
A context-aware function maintains awareness of:
Conversation History: Previous exchanges between human and AI
User State: Detected emotional tone, expertise level, and preferences
Session Context: Current goals, constraints, and established parameters
Environmental Factors: Time, date, session duration, and platform-specific features
The function then uses this contextual intelligence to modify its processing, output format, and detail level to better serve the human's current needs—often without requiring explicit instructions.
Implementation Details
Key Components
Context Collection Mechanism
Automatic tracking of relevant conversation elements
Regular updating of context based on new interactions
Efficient storage of contextual data within token constraints
Context Analysis Engine
Pattern recognition for identifying user needs and preferences
State tracking for maintaining awareness of conversation progress
Intent detection for understanding unstated goals
Adaptive Response Generation
Parameter adjustment based on contextual factors
Output formatting tailored to current conversation flow
Detail level optimization based on detected user expertise
Example Implementation
python
def generate_adaptive_response(
prompt,
conversation_history=None,
user_profile=None,
session_state=None,
environmental_context=None
):
"""
Generates responses that dynamically adapt to the full conversation context.
Parameters:
- prompt: The current user input or query
- conversation_history: Past exchanges in the current session
- user_profile: Known user preferences and characteristics
- session_state: Current goals and established information
- environmental_context: External factors like time and platform
#H->AI::Directive: (Analyze all contextual elements to determine appropriate response characteristics)
#H->AI::Context: (User's expertise level influences technical depth)
#H->AI::Focus: (Maintain conversational continuity with previous exchanges)
"""
# Extract context features
expertise_level = _extract_expertise_level(user_profile, conversation_history)
emotional_state = _detect_emotion(prompt, conversation_history)
engagement_pattern = _analyze_engagement(conversation_history)
current_goals = _identify_goals(prompt, session_state)
# Adapt response characteristics
technical_depth = _adjust_technical_depth(expertise_level, current_goals)
tone = _select_appropriate_tone(emotional_state, engagement_pattern)
format_style = _determine_format(session_state, engagement_pattern)
# Generate the adaptive response
response = _create_response(prompt, technical_depth, tone, format_style)
# Update context based on new interaction
updated_context = _update_context(session_state, prompt, response)
return response, updated_context
Practical Example Scenario
Initial Interaction:
Human: Can you explain the concept of API rate limiting?
Function Processing:
Conversation history is new, so no prior context exists
Function defaults to medium technical depth
Tone set to neutral/informative
Format includes clear explanation with basic example
AI Response:
API rate limiting restricts how many requests a client can make to an API within a certain timeframe. Think of it like a coffee shop that only serves each customer once every 5 minutes to prevent any single person from monopolizing the baristas.
In technical implementation, rate limiting typically uses:
- Request counting
- Time windows (per second/minute/hour)
- Authentication-based limits
For example, Twitter's API might limit standard users to 500 requests per day, while premium users get 5,000.
Follow-up Interaction:
Human: How would I implement that in Python?
Function Processing:
Conversation history now shows technical interest
User asking implementation question suggests practical focus
Function increases technical depth based on context
Format shifts to code-oriented example
AI Response:
Based on our discussion about rate limiting, here's how you could implement a basic rate limiter in Python using Redis:
```python
import time
import redis
class RateLimiter:
def __init__(self, redis_client, key_prefix, limit, period):
self.redis = redis_client
self.key_prefix = key_prefix
self.limit = limit
self.period = period
def is_allowed(self, user_id):
key = f"{self.key_prefix}:{user_id}"
current_time = int(time.time())
window_start = current_time - self.period
# Create a pipeline for atomic operations
pipe = self.redis.pipeline()
# Remove requests outside current window
pipe.zremrangebyscore(key, 0, window_start)
# Count requests in current window
pipe.zcard(key)
# Add current request
pipe.zadd(key, {current_time: current_time})
# Set expiration on the key
pipe.expire(key, self.period)
# Execute commands
results = pipe.execute()
request_count = results[1]
# Return whether user is within rate limit
return request_count <= self.limit
This implementation stores timestamps of requests in a Redis sorted set. When a new request comes in, it:
Removes expired timestamps
Counts current requests within the time window
Adds the new request
Returns whether the user has exceeded their limit
Would you like to see how to use this class in an actual API?
**Further Evolution**:
If subsequent interactions show the user struggling with this implementation, the function would adapt again, perhaps simplifying the example or offering alternatives. If the user shows expertise by asking advanced questions, the function would increase technical depth accordingly.
## Benefits
### 1. Reduced Cognitive Load
By automatically adapting to the conversation context, these functions eliminate the need for humans to constantly restate preferences, expertise levels, or goals. The AI "remembers" and adjusts accordingly.
### 2. More Natural Conversations
Context-aware functions enable interactions that feel more like human conversations, where each response builds naturally on what came before without awkward resets or redundancies.
### 3. Progressive Complexity
The function can start with simpler explanations and gradually increase depth as the conversation reveals greater user expertise, creating a more satisfying learning curve.
### 4. Emotional Intelligence
By tracking emotional cues in user inputs, the function can adapt tone and approach to better serve the human's emotional state, showing empathy when needed or efficiency when appropriate.
### 5. Efficient Token Usage
Context-aware functions can optimize detail level based on user needs, avoiding over-explanation for experts and under-explanation for novices—saving tokens in both cases.
## Advanced Applications
### Persona-Based Context Awareness
Context-aware functions can maintain different interaction models for different users, remembering preferences and adapting to each user's specific needs:
```python
def persona_aware_function(input_data, current_persona=None, persona_library=None):
"""
Adapts function behavior based on the current user persona.
#H->AI::Directive: (Select response characteristics based on stored persona)
#H->AI::Focus: (Update persona model based on new interactions)
"""
# Select appropriate persona or default
active_persona = current_persona or _detect_persona(input_data, persona_library)
# Adapt processing based on persona attributes
detail_level = active_persona.get('preferred_detail_level', 'medium')
technical_depth = active_persona.get('technical_expertise', 'intermediate')
communication_style = active_persona.get('communication_preference', 'balanced')
# Process input with persona-specific parameters
result = _process_with_persona_parameters(
input_data, detail_level, technical_depth, communication_style
)
# Update persona model with new information
updated_persona = _update_persona_model(active_persona, input_data, result)
return result, updated_persona
Multi-Session Context Preservation
Context-aware functions can be designed to maintain awareness across sessions through handoff mechanisms:
python
def create_context_handoff(current_context, session_summary=None):
"""
Creates a compressed representation of context for transfer to future sessions.
#H->AI::Directive: (Identify and preserve essential context elements)
#H->AI::Constraint: (Optimize for token efficiency in the handoff)
"""
# Extract key context elements
key_preferences = _extract_critical_preferences(current_context)
interaction_patterns = _summarize_interaction_patterns(current_context)
established_knowledge = _identify_confirmed_information(current_context)
# Create compressed context representation
compressed_context = {
'preferences': key_preferences,
'patterns': interaction_patterns,
'knowledge': established_knowledge,
'summary': session_summary or _generate_session_summary(current_context)
}
return compressed_context
Context-Aware Learning Loop
Functions can implement learning capabilities to continuously improve their context awareness:
python
def improve_context_model(interaction_history, model_parameters):
"""
Updates context awareness model based on successful interactions.
#H->AI::Directive: (Analyze patterns in effective vs. ineffective responses)
#H->AI::Focus: (Refine context detection parameters based on outcomes)
"""
# Identify successful vs. unsuccessful interactions
success_patterns = _extract_success_patterns(interaction_history)
failure_patterns = _extract_failure_patterns(interaction_history)
# Update model parameters based on patterns
updated_parameters = _refine_model_parameters(
model_parameters, success_patterns, failure_patterns
)
return updated_parameters
Integration with CRAFT Principles
Context-aware functions perfectly align with CRAFT's core philosophical principles:
Structure Enables Freedom: The structured context tracking creates freedom from repetitive instructions.
Code as Conversation: These functions bridge the gap between code and natural language by making the code itself conversationally aware.
Cumulative Intelligence: Context accumulation creates a continuously improving interaction model.
Human-AI Partnership: The AI becomes a more effective partner by adapting to the human's specific needs.
Accessibility Through Familiarity: Functions feel more natural and human-like, creating familiar interaction patterns.
Conservation of Tokens: Dynamic adjustment of detail and format optimizes token usage.
Implementation Recommendations
To incorporate context-aware functions into CRAFT:
Start with Context Schemas: Define standard structures for representing and tracking different context types.
Create Context Utilities: Build helper functions specifically for context extraction, analysis, and utilization.
Develop Adaptation Rules: Create clear guidelines for how functions should adapt based on different contextual factors.
Implement Graceful Fallbacks: Ensure functions work effectively even with limited or uncertain context.
Build Testing Frameworks: Create tools to validate context-aware behavior across different scenarios.
By implementing these recommendations, CRAFT can leverage context-aware functions to create dramatically more effective human-AI interactions that truly feel like meaningful collaborations rather than isolated transactions.
Retry
RK
Great, please expand on item 2 with details, benefits, explanations , examples and anything else that you think will be helpful.
Edit
Multi-Modal Functions in CRAFT
Detailed Explanation
Multi-modal functions represent a powerful evolution in the CRAFT framework by enabling seamless integration of different content types and formats within a single functional structure. Unlike traditional functions that typically handle one type of data, multi-modal functions can process, transform, and generate across various modalities—text, structured data, code, visual descriptions, and more.
Core Concept
Multi-modal functions are designed to:
Accept Different Input Types: Process various forms of content as input
Perform Cross-Modal Transformations: Convert between different representation formats
Generate Appropriate Output Formats: Produce results in the most useful modality
Maintain Semantic Consistency: Preserve meaning across transformations
Adapt to Contextual Requirements: Select optimal modalities based on context
These capabilities create a more fluid and natural interaction environment where the barriers between different types of content disappear.
Implementation Details
Key Components
Format Detection System
Automatic identification of input formats
Recognition of implicit format requests in instructions
Mapping between common content representations
Transformation Engine
Conversion rules between different content types
Semantic preservation strategies
Quality verification for transformations
Output Optimization
Format selection based on context and user needs
Multi-format presentation when appropriate
Graceful handling of format limitations
Example Implementation
python
def transform_content(
content,
target_format=None,
preservation_priority=None,
enhancement_options=None,
context=None
):
"""
Transforms content between different representation formats while preserving core meaning.
Parameters:
- content: The content to be transformed (any supported format)
- target_format: Desired output format (auto-detected if None)
- preservation_priority: What aspects to prioritize (structure, style, meaning)
- enhancement_options: Additional processing to apply during transformation
- context: Current conversation context to inform format decisions
#H->AI::Directive: (Determine input format automatically if not specified)
#H->AI::Focus: (Maintain semantic equivalence across format changes)
#H->AI::Constraint: (Preserve critical information during transformation)
"""
# Detect input format if not specified
input_format = _detect_format(content)
# Determine target format if not specified
if not target_format:
target_format = _infer_target_format(content, context)
# Determine what aspects to prioritize in transformation
priority = preservation_priority or _determine_default_priority(input_format, target_format)
# Select appropriate transformation path
transformation_path = _plan_transformation(input_format, target_format, priority)
# Apply transformations with specified prioritization
transformed_content = _execute_transformation(
content,
transformation_path,
priority,
enhancement_options
)
# Verify quality of transformation
verification_result = _verify_transformation_quality(
original_content=content,
transformed_content=transformed_content,
priority=priority
)
# Apply any post-processing enhancements
if enhancement_options:
transformed_content = _apply_enhancements(
transformed_content,
enhancement_options,
target_format
)
return transformed_content, verification_result
Practical Example Scenarios
Scenario 1: Text to Structured Data
Input (Text Description):
Our company has three departments: Marketing with 12 employees and a budget of $250,000, Engineering with 28 employees and a budget of $750,000, and Customer Support with 15 employees and a budget of $175,000.
Function Processing:
Detects input format as natural language text
Infers that structured data would be useful (based on numerical content)
Extracts key entities and relationships
Transforms into appropriate data structure
Output (JSON Structure):
json
{
"departments": [
{
"name": "Marketing",
"employees": 12,
"budget": 250000
},
{
"name": "Engineering",
"employees": 28,
"budget": 750000
},
{
"name": "Customer Support",
"employees": 15,
"budget": 175000
}
],
"total_employees": 55,
"total_budget": 1175000
}
Scenario 2: Code to Explanation
Input (Python Code):
python
def calculate_roi(investment, revenue, timespan):
"""Calculate return on investment."""
gain = revenue - investment
roi = (gain / investment) * 100
annual_roi = roi / timespan if timespan > 0 else 0
return {
'investment': investment,
'revenue': revenue,
'gain': gain,
'roi_percentage': roi,
'annual_roi': annual_roi,
'timespan_years': timespan
}
Function Processing:
Detects input as Python code
Analyzes code structure and purpose
Extracts key operations and logic
Transforms into natural language explanation
Output (Plain Text Explanation):
This function calculates Return on Investment (ROI) metrics based on three inputs:
1. Initial investment amount
2. Total revenue generated
3. Time period in years
The function performs these calculations:
- Subtracts the investment from revenue to find the total gain
- Divides the gain by the investment and multiplies by 100 to get the percentage ROI
- Divides the ROI by the timespan to calculate annual ROI (unless timespan is zero)
It returns a dictionary containing all values: the original inputs, the calculated gain,
the total ROI percentage, and the annualized ROI percentage.
This would be useful for financial analysis to determine the effectiveness of business investments.
Scenario 3: Tabular Data to Visualization Description
Input (CSV Data):
Month,Sales,Marketing_Spend
January,120000,35000
February,145000,42000
March,160000,38000
April,135000,30000
May,180000,45000
June,210000,50000
Function Processing:
Detects input as tabular/CSV data
Analyzes data patterns and relationships
Determines appropriate visualization type
Creates visualization description
Output (Visualization Description):
A dual-axis line chart showing the relationship between Sales and Marketing Spend over the first half of the year:
- X-axis: Months (January through June)
- Primary Y-axis (left): Sales values ranging from $120,000 to $210,000
- Secondary Y-axis (right): Marketing Spend values ranging from $30,000 to $50,000
The Sales line (blue) shows an overall upward trend with a small dip in April, ending at its highest point of $210,000 in June.
The Marketing Spend line (orange) generally follows a similar pattern but with less dramatic changes, also showing a dip in April and reaching its peak of $50,000 in June.
The visualization suggests a potential correlation between marketing spend and sales, with both metrics finishing the period at their highest points.
Key insight: The ratio of sales to marketing spend (ROI) appears to be improving over time, with June showing approximately $4.20 in sales for every $1 in marketing spend.
Benefits
1. Unified Interaction Flow
Multi-modal functions eliminate the need to switch between different functional approaches for different content types, creating a more cohesive and fluid conversation experience.
2. Information Density Optimization
By transforming content into the most appropriate format for specific information, these functions can present data in its most efficient and understandable form—whether that's text, tables, code, or visualization descriptions.
3. Cognitive Accessibility
Different people understand information better in different formats. Multi-modal functions can present the same core information in the format that best matches a user's cognitive preferences.
4. Format Independence
Users can provide information in whatever format is most convenient for them, and the AI can transform it as needed, reducing friction in the collaboration process.
5. Enhanced Understanding
Some concepts are inherently clearer in specific formats. Multi-modal functions enable the presentation of ideas in their optimal format for understanding.
6. Integration With External Tools
By handling various data formats, multi-modal functions can better prepare content for use with external tools and systems, enhancing the practical utility of AI outputs.
Advanced Applications
Dynamic Report Generation
Multi-modal functions can transform raw data into comprehensive reports that include narrative explanations, data tables, and visualization descriptions:
python
def generate_data_report(
raw_data,
report_focus=None,
audience_expertise=None,
format_preferences=None
):
"""
Transforms raw data into a comprehensive report with multiple representation formats.
#H->AI::Directive: (Analyze data to identify key insights)
#H->AI::Focus: (Present insights in appropriate formats based on content type)
#H->AI::Context: (Adjust detail level based on audience expertise)
"""
# Analyze data and extract key insights
insights = _extract_insights(raw_data, report_focus)
# Determine appropriate formats for each insight
format_plan = _plan_insight_formats(insights, audience_expertise, format_preferences)
# Generate report components in different formats
narrative = _generate_narrative_summary(insights, audience_expertise)
data_tables = _create_data_tables(raw_data, format_plan['tabular_insights'])
visualizations = _describe_visualizations(raw_data, format_plan['visual_insights'])
code_examples = _create_code_examples(raw_data, format_plan['code_insights'])
# Assemble complete report with multiple formats
complete_report = _assemble_report(
narrative, data_tables, visualizations, code_examples, audience_expertise
)
return complete_report
Interactive Format Selection
Multi-modal functions can offer format options and adapt based on user preferences:
python
def present_multi_format(
content,
available_formats=None,
user_preferences=None,
interaction_history=None
):
"""
Presents content in multiple formats or offers format choices to the user.
#H->AI::Directive: (Determine most appropriate initial format)
#H->AI::Focus: (Prepare alternative formats for quick switching)
"""
# Determine which formats are appropriate for this content
suitable_formats = available_formats or _identify_suitable_formats(content)
# Select initial format based on preferences and content
initial_format = _select_initial_format(suitable_formats, user_preferences, interaction_history)
# Prepare content in initial format
initial_representation = _transform_to_format(content, initial_format)
# Prepare alternative formats if appropriate
alternative_formats = {}
for format_type in suitable_formats:
if format_type != initial_format:
alternative_formats[format_type] = _prepare_format_description(format_type, content)
# Determine if alternatives should be offered
should_offer_alternatives = _should_present_alternatives(
content, suitable_formats, user_preferences, interaction_history
)
# Construct response with format options if appropriate
if should_offer_alternatives:
format_options = _create_format_options_message(alternative_formats)
return initial_representation, format_options
else:
return initial_representation, None
Cross-Modal Semantic Preservation
Functions that ensure meaning is preserved across format transformations:
python
def verify_semantic_equivalence(
original_content,
transformed_content,
tolerance_level=None
):
"""
Verifies that semantic meaning is preserved during format transformation.
#H->AI::Directive: (Compare core meaning between original and transformed content)
#H->AI::Focus: (Identify any information loss or distortion)
"""
# Extract core semantic elements from both versions
original_semantics = _extract_semantic_elements(original_content)
transformed_semantics = _extract_semantic_elements(transformed_content)
# Compare elements for equivalence
comparison_results = _compare_semantic_elements(
original_semantics, transformed_semantics
)
# Evaluate overall semantic preservation
preservation_score = _calculate_preservation_score(comparison_results)
preservation_threshold = tolerance_level or _determine_default_threshold(original_content)
# Identify specific information loss or distortion
information_issues = _identify_semantic_issues(comparison_results)
return {
'preservation_score': preservation_score,
'meets_threshold': preservation_score >= preservation_threshold,
'information_issues': information_issues if information_issues else None
}
Integration with CRAFT Principles
Multi-modal functions strongly align with CRAFT's core philosophical principles:
Structure Enables Freedom: By handling format conversions automatically, these functions free users from format constraints.
Code as Conversation: They bridge the gap between code, data, and natural language, making all forms part of the same conversation.
Cumulative Intelligence: By maintaining meaning across formats, they enable knowledge to accumulate regardless of representation.
Human-AI Partnership: They leverage the strengths of different formats to optimize collaboration between human and AI.
Accessibility Through Familiarity: Users can work with formats they're most comfortable with, increasing accessibility.
Conservation of Tokens: Different formats can express the same information with varying token efficiency, enabling optimization.
Implementation Recommendations
To incorporate multi-modal functions into CRAFT:
Define Format Standards: Create clear specifications for how different content types are represented.
Build Transformation Libraries: Develop comprehensive libraries of format conversion rules.
Create Format Detection Tools: Implement robust detection of content types and formats.
Establish Quality Metrics: Define clear measures for successful format transformations.
Develop Format Selection Heuristics: Create rules for determining optimal formats based on content and context.
By implementing these recommendations, CRAFT can leverage multi-modal functions to create a more integrated, flexible interaction environment where the boundaries between different content types disappear, and information flows naturally regardless of its representation.
Extended Example: Multi-Modal Content Explorer
Here's a more comprehensive example that demonstrates the power of multi-modal functions in CRAFT:
python
def explore_content(
content,
exploration_goal=None,
format_preferences=None,
depth_level=None,
context=None
):
"""
Explores content through multiple modalities to enhance understanding.
Parameters:
- content: The content to explore (any supported format)
- exploration_goal: What the user wants to understand or extract
- format_preferences: Preferred representation formats
- depth_level: How detailed the exploration should be
- context: Current conversation context
#H->AI::Directive: (Identify the optimal representation formats for exploration)
#H->AI::Focus: (Transform between formats to highlight different aspects)
#H->AI::Context: (Consider the exploration goal when selecting formats)
"""
# Detect input format
input_format = _detect_format(content)
# Determine exploration focus if not specified
exploration_goal = exploration_goal or _infer_exploration_goal(content, context)
# Assess which formats would best serve the exploration goal
format_recommendations = _recommend_formats(
content,
input_format,
exploration_goal
)
# Consider user preferences when selecting formats
selected_formats = _select_formats(
format_recommendations,
format_preferences
)
# Create multi-modal exploration package
exploration_results = {}
for format_type in selected_formats:
if format_type != input_format:
# Transform content to this format
transformed = _transform_to_format(
content,
format_type,
preservation_priority=exploration_goal
)
# Analyze what this format reveals
format_insights = _extract_format_specific_insights(
transformed,
exploration_goal,
depth_level
)
# Add to results
exploration_results[format_type] = {
'content': transformed,
'insights': format_insights
}
# Create unified multi-modal analysis
integrated_analysis = _integrate_cross_format_insights(
exploration_results,
exploration_goal,
depth_level
)
return {
'original_format': input_format,
'explored_formats': exploration_results,
'integrated_analysis': integrated_analysis
}
This function demonstrates how multi-modal capabilities can create entirely new ways of exploring and understanding content that wouldn't be possible with single-format approaches.
Retry
RK
Great, please expand on item 3 with details, benefits, explanations , examples and anything else that you think will be helpful.
Edit
Composable AI Instructions in CRAFT
Detailed Explanation
Composable AI Instructions represent a transformative approach to building complex AI behaviors by combining smaller, reusable functional components. This concept adapts the software engineering principle of composability to the realm of AI interactions, allowing users to construct sophisticated instruction patterns from simpler building blocks.
Core Concept
Composable AI Instructions are built on these fundamental ideas:
Modular Instruction Units: Breaking down complex instructions into discrete, reusable components
Function Composition: Combining simpler functions to create more complex behaviors
Sequential Processing: Chaining functions where outputs from one become inputs to another
Parallel Processing: Applying multiple functions to the same input when appropriate
Conditional Logic: Selectively applying functions based on certain conditions
This approach creates a flexible, extensible system for crafting AI behaviors while maintaining clarity and efficiency.
Implementation Details
Key Components
Function Registry
Catalog of available function components
Metadata about function inputs, outputs, and behaviors
Categorization and search capabilities
Composition Engine
Rules for connecting function outputs to inputs
Validation of connection compatibility
Optimization of execution paths
Execution Framework
Sequential and parallel execution capabilities
Error handling and recovery strategies
State management across function chains
Example Implementation
python
def chain_functions(
*functions,
input_data=None,
execution_mode="sequential",
error_handling="stop",
state_management=None
):
"""
Combines multiple functions into a processing pipeline.
Parameters:
- functions: Functions to be executed in sequence or parallel
- input_data: Initial input to the function chain
- execution_mode: "sequential" (default) or "parallel"
- error_handling: How to handle errors ("stop", "continue", "alternative")
- state_management: Configuration for managing state between functions
#H->AI::Directive: (Process functions based on execution mode)
#H->AI::Focus: (Ensure proper data flow between functions)
#H->AI::Constraint: (Handle errors according to specified strategy)
"""
# Initialize result and state
current_result = input_data
execution_state = state_management or {}
execution_log = []
# Execute functions based on mode
if execution_mode == "sequential":
for i, func in enumerate(functions):
try:
# Execute function with current result
function_input = _prepare_function_input(func, current_result, execution_state)
function_output = func(function_input)
# Update result and state
current_result = function_output
execution_state = _update_execution_state(execution_state, func, function_output)
execution_log.append({"function": func.__name__, "status": "success"})
except Exception as e:
# Handle errors according to strategy
execution_log.append({"function": func.__name__, "status": "error", "error": str(e)})
if error_handling == "stop":
break
elif error_handling == "alternative":
alternative_result = _generate_alternative_result(func, current_result, e)
current_result = alternative_result
# "continue" just moves to the next function
elif execution_mode == "parallel":
parallel_results = []
for func in functions:
try:
# Execute function with original input
function_input = _prepare_function_input(func, input_data, execution_state)
function_output = func(function_input)
# Store result
parallel_results.append(function_output)
execution_log.append({"function": func.__name__, "status": "success"})
except Exception as e:
execution_log.append({"function": func.__name__, "status": "error", "error": str(e)})
if error_handling == "alternative":
alternative_result = _generate_alternative_result(func, input_data, e)
parallel_results.append(alternative_result)
else:
parallel_results.append(None)
# Combine parallel results
current_result = _combine_parallel_results(parallel_results)
return current_result, execution_state, execution_log
Additional Helper Function
python
def create_function_pipeline(
name,
function_sequence,
description=None,
metadata=None
):
"""
Creates a named, reusable pipeline from a sequence of functions.
#H->AI::Directive: (Create a new composite function from specified components)
#H->AI::Focus: (Ensure appropriate connection between function interfaces)
"""
# Validate function compatibility
compatibility_issues = _validate_function_sequence(function_sequence)
if compatibility_issues:
return {"status": "error", "issues": compatibility_issues}
# Create pipeline function
def pipeline_function(input_data, **kwargs):
return chain_functions(*function_sequence, input_data=input_data, **kwargs)
# Set metadata
pipeline_function.__name__ = name
pipeline_function.__doc__ = description or f"Pipeline composed of: {[f.__name__ for f in function_sequence]}"
# Register pipeline in function registry if available
if metadata:
_register_pipeline(pipeline_function, function_sequence, metadata)
return pipeline_function
Practical Example Scenarios
Scenario 1: Content Generation Pipeline
Function Definitions:
python
def research_topic(topic):
"""Researches a topic and returns key information."""
# Implementation
return {"topic": topic, "key_points": [...], "sources": [...]}
def create_outline(research_data):
"""Creates a content outline based on research."""
# Implementation
return {"topic": research_data["topic"], "outline": [...]}
def draft_content(outline):
"""Drafts content based on an outline."""
# Implementation
return {"topic": outline["topic"], "draft": "..."}
def optimize_for_audience(content, audience_type):
"""Optimizes content for a specific audience."""
# Implementation
return {"topic": content["topic"], "optimized_content": "..."}
Pipeline Creation:
python
content_creation_pipeline = create_function_pipeline(
name="GenerateOptimizedContent",
function_sequence=[research_topic, create_outline, draft_content, optimize_for_audience],
description="Complete pipeline for researching, outlining, drafting, and optimizing content"
)
Pipeline Execution:
python
# Using the pipeline
result, state, log = content_creation_pipeline(
"Artificial Intelligence in Healthcare",
execution_mode="sequential",
state_management={"audience_type": "medical professionals"}
)
# The result contains the final optimized content
optimized_content = result["optimized_content"]
In this example, a complex content creation process is broken down into discrete steps. Each step can be refined independently, reused in other contexts, or replaced with alternative implementations without disrupting the overall pipeline.
Scenario 2: Data Analysis Workflow
Function Definitions:
python
def parse_data(raw_data, format_type=None):
"""Parses raw data into structured format."""
# Implementation
return {"structured_data": [...]}
def filter_relevant(data, criteria):
"""Filters data based on relevance criteria."""
# Implementation
return {"filtered_data": [...]}
def calculate_metrics(data, metrics_list=None):
"""Calculates specified metrics from data."""
# Implementation
return {"metrics": {...}}
def generate_insights(metrics, threshold=0.5):
"""Generates insights based on metrics."""
# Implementation
return {"insights": [...]}
def format_report(insights, format_type="markdown"):
"""Formats insights into a report."""
# Implementation
return {"report": "..."}
Parallel and Sequential Composition:
python
# Create data processing pipeline
data_processing = create_function_pipeline(
name="ProcessAndFilterData",
function_sequence=[parse_data, filter_relevant],
description="Processes raw data and filters for relevance"
)
# Create analysis pipeline
analysis_pipeline = create_function_pipeline(
name="AnalyzeData",
function_sequence=[calculate_metrics, generate_insights],
description="Analyzes data to generate insights"
)
# Create reporting pipeline
reporting_pipeline = create_function_pipeline(
name="GenerateReport",
function_sequence=[format_report],
description="Formats insights into a report"
)
# Create complete analysis workflow
analysis_workflow = create_function_pipeline(
name="CompleteAnalysisWorkflow",
function_sequence=[data_processing, analysis_pipeline, reporting_pipeline],
description="End-to-end workflow from raw data to formatted report"
)
Workflow Execution:
python
# Execute the entire workflow
result, state, log = analysis_workflow(
raw_data="...",
state_management={
"format_type": "csv",
"metrics_list": ["average", "trend", "outliers"],
"threshold": 0.7,
"report_format": "html"
}
)
# Get the final report
final_report = result["report"]
This example demonstrates how nested composition can create sophisticated workflows while maintaining modularity and clarity.
Scenario 3: Interactive Dialogue Management
Function Definitions:
python
def classify_intent(user_input):
"""Classifies user intent from input."""
# Implementation
return {"intent": "...", "confidence": 0.0}
def retrieve_information(intent, context=None):
"""Retrieves information based on intent."""
# Implementation
return {"information": "..."}
def generate_response(information, style=None):
"""Generates response based on information."""
# Implementation
return {"response": "..."}
def check_satisfaction(response, user_input):
"""Checks if response satisfies user intent."""
# Implementation
return {"satisfied": True/False, "missing_aspects": [...]}
def enhance_response(response, missing_aspects):
"""Enhances response with missing information."""
# Implementation
return {"enhanced_response": "..."}
Conditional Pipeline:
python
def conditional_dialogue_pipeline(user_input, conversation_history=None):
"""Pipeline that adapts based on user satisfaction."""
# Get initial response
intent_data = classify_intent(user_input)
information = retrieve_information(intent_data["intent"], context=conversation_history)
initial_response = generate_response(information)
# Check satisfaction and enhance if needed
satisfaction = check_satisfaction(initial_response["response"], user_input)
if not satisfaction["satisfied"]:
# Use enhancement pipeline
enhanced = enhance_response(initial_response["response"], satisfaction["missing_aspects"])
return enhanced["enhanced_response"]
else:
# Original response was sufficient
return initial_response["response"]
This example shows how conditional logic can be incorporated into function composition to create adaptive behavior.
Benefits
1. Modularity and Reusability
By breaking complex instructions into smaller components, each component can be refined independently and reused across different contexts, significantly increasing efficiency.
2. Clarity and Maintainability
The explicit connections between functions make the logic flow clear and maintainable, making it easier to understand, modify, and debug complex processes.
3. Incremental Improvement
Individual components can be improved or replaced without disrupting the entire system, enabling gradual refinement of AI capabilities.
4. Standardization
Function components can establish standard interfaces, creating consistency across different AI interactions and enabling interoperability.
5. Scalability
Complex behaviors can be built from simpler building blocks, enabling the creation of increasingly sophisticated AI capabilities without proportional increases in complexity.
6. Adaptability
Components can be recombined in different ways to adapt to changing requirements without starting from scratch.
Advanced Applications
Dynamic Function Composition
Functions that can adaptively select and compose other functions based on context:
python
def adaptive_composition(
input_data,
available_functions,
goal,
context=None
):
"""
Dynamically composes functions to achieve a specified goal.
#H->AI::Directive: (Select and combine functions to achieve the goal)
#H->AI::Focus: (Adapt composition based on input characteristics and context)
"""
# Analyze input and goal
input_characteristics = _analyze_input(input_data)
goal_requirements = _analyze_goal(goal)
# Identify relevant functions
candidate_functions = _select_candidate_functions(
available_functions,
input_characteristics,
goal_requirements
)
# Plan function sequence
function_sequence = _plan_function_sequence(
candidate_functions,
input_characteristics,
goal_requirements,
context
)
# Create and execute pipeline
result, state, log = chain_functions(
*function_sequence,
input_data=input_data,
state_management={"context": context, "goal": goal}
)
# Evaluate result against goal
goal_achievement = _evaluate_goal_achievement(result, goal)
# Refine if necessary
if not goal_achievement["achieved"] and goal_achievement["confidence"] < 0.8:
# Try alternative composition
alternative_sequence = _generate_alternative_sequence(
function_sequence,
log,
goal_achievement["missing_aspects"]
)
# Execute alternative
refined_result, refined_state, refined_log = chain_functions(
*alternative_sequence,
input_data=input_data,
state_management={"context": context, "goal": goal}
)
return refined_result, {"original": log, "refined": refined_log}
return result, log
Meta-Functions for Composition Analysis
Functions that analyze and optimize function compositions:
python
def optimize_function_pipeline(
pipeline,
optimization_goals=None,
performance_data=None
):
"""
Analyzes and optimizes a function pipeline.
#H->AI::Directive: (Identify inefficiencies and redundancies)
#H->AI::Focus: (Suggest modifications to improve pipeline performance)
"""
# Analyze current pipeline
pipeline_analysis = _analyze_pipeline(pipeline)
# Identify optimization opportunities
optimization_opportunities = _identify_optimizations(
pipeline_analysis,
optimization_goals,
performance_data
)
# Generate optimization suggestions
optimization_suggestions = []
for opportunity in optimization_opportunities:
suggestion = _generate_optimization_suggestion(
pipeline,
opportunity,
optimization_goals
)
optimization_suggestions.append(suggestion)
# Create optimized pipeline version if appropriate
if optimization_suggestions:
optimized_pipeline = _create_optimized_pipeline(
pipeline,
optimization_suggestions
)
return {
"original_pipeline": pipeline,
"optimization_suggestions": optimization_suggestions,
"optimized_pipeline": optimized_pipeline
}
else:
return {
"original_pipeline": pipeline,
"analysis": "No significant optimization opportunities identified."
}
Collaborative Function Composition
Enabling human-AI collaboration in building function compositions:
python
def collaborative_pipeline_builder(
user_description,
available_functions,
existing_pipeline=None,
user_preferences=None
):
"""
Collaboratively builds a function pipeline based on user description.
#H->AI::Directive: (Suggest function compositions based on description)
#H->AI::Focus: (Incorporate user feedback into pipeline design)
"""
# Analyze user description
required_capabilities = _extract_required_capabilities(user_description)
# Match capabilities to functions
function_matches = _match_functions_to_capabilities(
required_capabilities,
available_functions
)
# Generate initial pipeline suggestion
initial_suggestion = _generate_pipeline_suggestion(
function_matches,
existing_pipeline,
user_preferences
)
# Create explanation of suggestion
explanation = _explain_pipeline_suggestion(
initial_suggestion,
user_description
)
# Identify customization options
customization_options = _identify_customization_options(
initial_suggestion,
available_functions
)
return {
"suggested_pipeline": initial_suggestion,
"explanation": explanation,
"customization_options": customization_options,
"next_steps": "Please review this suggestion and provide feedback for refinement."
}
Integration with CRAFT Principles
Composable AI Instructions strongly align with CRAFT's philosophical principles:
Structure Enables Freedom: The structured composition of functions creates freedom to build complex behaviors without starting from scratch.
Code as Conversation: Function composition creates a bridge between programmatic thinking and conversational AI interaction.
Cumulative Intelligence: Functions can build upon each other, creating progressively more sophisticated capabilities.
Human-AI Partnership: Composition can be a collaborative process, with humans and AI contributing different aspects.
Accessibility Through Familiarity: The pipeline metaphor is intuitive and accessible even to non-technical users.
Conservation of Tokens: Reusing function components avoids redundant instructions, significantly reducing token usage.
Implementation Recommendations
To incorporate Composable AI Instructions into CRAFT:
Define Function Interface Standards: Create clear specifications for how functions should accept inputs and produce outputs to enable reliable composition.
Build Function Registry Systems: Develop cataloging systems that make functions discoverable and document their behaviors.
Create Visual Composition Tools: Build interfaces that allow users to visually assemble function pipelines.
Implement Testing Frameworks: Develop tools to validate the behavior of composite functions across different inputs.
Establish Best Practices: Create guidelines for effective function decomposition and composition.
By implementing these recommendations, CRAFT can leverage the power of composability to create sophisticated AI behaviors while maintaining clarity, efficiency, and flexibility.
Extended Example: Content Creation Framework
Here's a comprehensive example that demonstrates how the composable functions approach can be applied to content creation:
python
# Define component functions
def research_topic(topic, depth="medium"):
"""Researches a topic and returns key information."""
# Implementation
return {
"topic": topic,
"key_facts": [...],
"related_concepts": [...],
"sources": [...]
}
def analyze_audience(audience_type):
"""Analyzes an audience type to determine preferences and needs."""
# Implementation
return {
"audience": audience_type,
"knowledge_level": "...",
"interests": [...],
"pain_points": [...],
"content_preferences": {...}
}
def generate_content_strategy(research, audience_analysis):
"""Creates a content strategy based on research and audience analysis."""
# Implementation
return {
"topic": research["topic"],
"audience": audience_analysis["audience"],
"key_messages": [...],
"content_types": [...],
"tone_guidelines": "..."
}
def create_content_outline(strategy):
"""Creates a detailed content outline based on strategy."""
# Implementation
return {
"topic": strategy["topic"],
"audience": strategy["audience"],
"outline": [...]
}
def draft_section(section_data, style_guide):
"""Drafts a specific content section."""
# Implementation
return {
"section_title": section_data["title"],
"content": "...",
"key_points_addressed": [...]
}
def assemble_draft(sections, metadata):
"""Assembles multiple sections into a complete draft."""
# Implementation
return {
"title": "...",
"introduction": "...",
"body": [...], # Section contents
"conclusion": "...",
"metadata": metadata
}
def review_content(draft, criteria):
"""Reviews content against specified criteria."""
# Implementation
return {
"overall_score": 0.0,
"strengths": [...],
"improvement_areas": [...],
"specific_suggestions": [...]
}
def refine_content(draft, review_feedback):
"""Refines content based on review feedback."""
# Implementation
return {
"refined_draft": {...},
"changes_made": [...],
"improvement_rationale": "..."
}
def format_for_platform(content, platform, format_guidelines=None):
"""Formats content for a specific platform."""
# Implementation
return {
"platform_ready_content": "...",
"platform": platform,
"preview": "..."
}
# Create specialized pipeline for blog content
blog_content_pipeline = create_function_pipeline(
name="BlogContentCreation",
function_sequence=[
research_topic,
lambda data: analyze_audience("blog_readers"),
generate_content_strategy,
create_content_outline,
# Custom function for blog drafting that maps outline to sections
lambda outline: {
"sections": [draft_section(section, "blog_style") for section in outline["outline"]],
"metadata": {"topic": outline["topic"], "audience": outline["audience"]}
},
# Unpack the sections and metadata for the assembler
lambda data: assemble_draft(data["sections"], data["metadata"]),
lambda draft: review_content(draft, "blog_criteria"),
refine_content,
lambda content: format_for_platform(content, "wordpress")
],
description="End-to-end pipeline for creating optimized blog content"
)
# Create specialized pipeline for social media content
social_media_pipeline = create_function_pipeline(
name="SocialMediaContentCreation",
function_sequence=[
research_topic,
lambda data: analyze_audience("social_media_users"),
generate_content_strategy,
# Create multiple post variations
lambda strategy: {
"post_ideas": [
{"platform": platform, "concept": concept}
for platform in ["twitter", "linkedin", "instagram"]
for concept in strategy["key_messages"][:2] # Top 2 messages per platform
],
"strategy": strategy
},
# Draft each post
lambda data: {
"drafted_posts": [
draft_section(
{"title": f"{post['platform']} post", "content": post["concept"]},
f"{post['platform']}_style"
)
for post in data["post_ideas"]
],
"metadata": {"topic": data["strategy"]["topic"], "audience": data["strategy"]["audience"]}
},
# Review all posts
lambda data: {
"posts_with_reviews": [
{
"post": post,
"review": review_content(post, f"{post['section_title'].split()[0]}_criteria")
}
for post in data["drafted_posts"]
],
"metadata": data["metadata"]
},
# Refine based on reviews
lambda data: {
"refined_posts": [
refine_content(post_review["post"], post_review["review"])
for post_review in data["posts_with_reviews"]
],
"metadata": data["metadata"]
},
# Format each for its platform
lambda data: {
"platform_ready_posts": [
format_for_platform(
post["refined_draft"],
post["refined_draft"]["section_title"].split()[0] # Extract platform name
)
for post in data["refined_posts"]
]
}
],
description="Pipeline for creating coordinated social media content across platforms"
)
# Create master content pipeline that produces both blog and social media content
master_content_pipeline = create_function_pipeline(
name="ComprehensiveContentStrategy",
function_sequence=[
# Use parallel execution to run both pipelines on the same topic
lambda topic: chain_functions(
blog_content_pipeline,
social_media_pipeline,
input_data=topic,
execution_mode="parallel"
),
# Combine results into a coordinated content package
lambda parallel_results: {
"blog_content": parallel_results[0],
"social_media_content": parallel_results[1],
"publishing_schedule": _generate_coordinated_schedule(
parallel_results[0],
parallel_results[1]
)
}
],
description="Comprehensive content strategy with coordinated blog and social media content"
)
This extended example demonstrates how composable functions can create a sophisticated content creation system with multiple pathways and specializations, all built from reusable components. The approach enables:
Specialized Workflows: Creating dedicated pipelines for different content types
Parallel Processing: Running multiple pipelines simultaneously when appropriate
Process Reuse: Leveraging the same core functions in different contexts
Coordination: Bringing together different content types into cohesive strategies
This level of sophistication would be extremely difficult to achieve with monolithic instructions, but becomes manageable and clear through function composition.
Retry
RK
Great, please expand on item 4 with details, benefits, explanations , examples and anything else that you think will be helpful.
Edit
Handoff-Optimized Functions in CRAFT
Detailed Explanation
Handoff-optimized functions represent a specialized category of functions designed to preserve continuity between AI chat sessions. They solve one of the fundamental challenges in working with AI assistants: the stateless nature of conversations, where context is typically lost when starting a new session.
Core Concept
Handoff-optimized functions enable seamless transitions between chat sessions by:
State Preservation: Capturing essential elements of the current conversation state
Context Distillation: Compressing verbose conversations into compact, token-efficient representations
Priority Filtering: Identifying and preserving the most critical information
Structured Formatting: Organizing handoff information in standardized, easily interpretable formats
Retrieval Optimization: Structuring information to maximize effective retrieval in future sessions
Unlike traditional functions that focus on immediate tasks, handoff-optimized functions take a meta-perspective on the conversation itself, treating the dialogue state as a first-class object to be managed across session boundaries.
Implementation Details
Key Components
State Capture System
Mechanisms for identifying critical state variables
Methods for extracting implicit context from conversations
Techniques for preserving relational structure between elements
Compression Engine
Algorithms for condensing verbose content while preserving meaning
Token optimization strategies for maximum information density
Lossy vs. lossless compression decisions
Retrieval Enhancement
Indexing and categorization of preserved information
Hierarchical organization of context elements
Signposting for efficient future navigation
Example Implementation
python
def create_session_snapshot(
session_state,
conversation_history=None,
active_variables=None,
project_status=None,
compression_level="medium",
handoff_id=None
):
"""
Creates a comprehensive yet compact snapshot of the current session for handoff.
Parameters:
- session_state: Current working state (variables, objects, etc.)
- conversation_history: Recent exchanges between user and AI
- active_variables: Variables actively being used in the current context
- project_status: Current progress in longer-term projects
- compression_level: How aggressively to compress information
- handoff_id: Unique identifier for this handoff record
#H->AI::Directive: (Identify and preserve essential context for continuity)
#H->AI::Constraint: (Optimize for token efficiency without losing critical information)
#H->AI::Focus: (Structure information to facilitate seamless retrieval and understanding)
"""
# Generate unique handoff ID if not provided
handoff_id = handoff_id or _generate_handoff_id()
# Extract key conversational insights
if conversation_history:
conversation_summary = _summarize_conversation(
conversation_history,
compression_level
)
key_decisions = _extract_key_decisions(conversation_history)
open_questions = _identify_open_questions(conversation_history)
else:
conversation_summary = None
key_decisions = []
open_questions = []
# Process session state
if session_state:
critical_variables = _identify_critical_variables(
session_state,
active_variables
)
state_representation = _create_compact_state_representation(
critical_variables,
compression_level
)
else:
state_representation = {}
# Process project status
if project_status:
progress_summary = _summarize_project_progress(project_status)
next_steps = _extract_next_steps(project_status)
blockers = _identify_blockers(project_status)
else:
progress_summary = None
next_steps = []
blockers = []
# Create timestamp
timestamp = _generate_timestamp()
# Assemble handoff snapshot
handoff_snapshot = {
"meta": {
"handoff_id": handoff_id,
"timestamp": timestamp,
"compression_level": compression_level
},
"conversation": {
"summary": conversation_summary,
"key_decisions": key_decisions,
"open_questions": open_questions
},
"state": state_representation,
"project": {
"progress_summary": progress_summary,
"next_steps": next_steps,
"blockers": blockers
}
}
# Create retrieval optimized version
retrieval_optimized = _optimize_for_retrieval(handoff_snapshot)
# Generate token usage statistics
token_stats = _calculate_token_usage(retrieval_optimized)
return {
"handoff_snapshot": retrieval_optimized,
"token_stats": token_stats,
"handoff_id": handoff_id
}
Complementary Function for Resuming Sessions
python
def resume_from_handoff(
handoff_snapshot,
current_session_context=None,
restoration_priority="balanced"
):
"""
Resumes a session from a handoff snapshot, integrating with current context.
Parameters:
- handoff_snapshot: Previously created session snapshot
- current_session_context: Any context from the current session
- restoration_priority: What to prioritize ("state", "conversation", "project", or "balanced")
#H->AI::Directive: (Reconstruct session state from handoff data)
#H->AI::Focus: (Seamlessly integrate previous context with new session)
"""
# Validate handoff snapshot
validation_result = _validate_handoff_snapshot(handoff_snapshot)
if not validation_result["valid"]:
return {"status": "error", "message": validation_result["message"]}
# Determine restoration priorities
priorities = _determine_restoration_priorities(restoration_priority)
# Reconstruct state variables
if handoff_snapshot["state"] and "state" in priorities:
restored_state = _reconstruct_state_variables(
handoff_snapshot["state"],
priority_level=priorities["state"]
)
else:
restored_state = {}
# Reconstruct conversation context
if handoff_snapshot["conversation"] and "conversation" in priorities:
conversation_context = _reconstruct_conversation_context(
handoff_snapshot["conversation"],
priority_level=priorities["conversation"]
)
else:
conversation_context = {}
# Reconstruct project context
if handoff_snapshot["project"] and "project" in priorities:
project_context = _reconstruct_project_context(
handoff_snapshot["project"],
priority_level=priorities["project"]
)
else:
project_context = {}
# Integrate with current session context if provided
if current_session_context:
integrated_context = _integrate_contexts(
current_context=current_session_context,
restored_state=restored_state,
conversation_context=conversation_context,
project_context=project_context
)
else:
integrated_context = {
"state": restored_state,
"conversation": conversation_context,
"project": project_context
}
# Generate continuity suggestions
continuity_suggestions = _generate_continuity_suggestions(
integrated_context,
handoff_snapshot["meta"]
)
return {
"restored_context": integrated_context,
"continuity_suggestions": continuity_suggestions,
"handoff_meta": handoff_snapshot["meta"]
}
Practical Example Scenario
Scenario: Multi-Session Project Development
Imagine a complex project where a user is working with an AI to develop a marketing strategy over multiple sessions. Here's how handoff-optimized functions would work in practice:
Session 1: Initial Strategy Development
During the first session, the user and AI develop the initial marketing strategy framework, defining target audiences and key messaging.
End of Session 1 (Handoff Creation):
python
# Session state contains all the work done so far
session_state = {
"target_audiences": [
{"name": "Young Professionals", "key_characteristics": ["tech-savvy", "value-conscious", "time-limited"]},
{"name": "Small Business Owners", "key_characteristics": ["growth-focused", "budget-conscious", "practical"]}
],
"key_messages": [
{"audience": "Young Professionals", "message": "Our solution saves you time while fitting your budget"},
{"audience": "Small Business Owners", "message": "Scale your business without scaling your costs"}
],
"content_ideas": [
{"type": "blog", "title": "5 Time-Saving Tips for Busy Professionals", "audience": "Young Professionals"},
{"type": "case study", "title": "How SmallCo Grew 200% Without Adding Staff", "audience": "Small Business Owners"},
# ... 15 more detailed content ideas
],
"brand_voice": {
"tone": "professional yet approachable",
"vocabulary_level": "business casual",
"avoid": ["jargon", "overly technical terms", "condescension"]
},
# ... many more strategy elements
}
# Project status tracking ongoing work
project_status = {
"current_phase": "Strategy Development",
"completed_tasks": ["Audience Definition", "Key Message Creation", "Initial Content Brainstorming"],
"pending_tasks": ["Channel Selection", "Budget Allocation", "Timeline Development", "KPI Definition"],
"blockers": [],
"completion_percentage": 30
}
# Create the handoff snapshot
handoff_result = create_session_snapshot(
session_state=session_state,
conversation_history=recent_conversation,
project_status=project_status,
compression_level="medium"
)
# The handoff_result["handoff_snapshot"] would contain the compressed session state
# ready to be stored and passed to the next session
The resulting handoff snapshot might look like:
META.SESSION_ID: MRKT-STRAT-2025-001
META.DATE: 2025-05-17
META.AI_ASSISTANT: Claude 3.7 Sonnet
SESSION_SUMMARY:
Developed initial marketing strategy framework focusing on two key audience segments: Young Professionals and Small Business Owners. Created differentiated value propositions and messaging for each segment. Brainstormed 17 content ideas across various formats, with preliminary audience alignment. Established brand voice guidelines emphasizing professional yet approachable tone. Completed approximately 30% of strategy development.
CURRENT_STATE:
- TARGET_AUDIENCES: Young Professionals (tech-savvy, value-conscious, time-limited); Small Business Owners (growth-focused, budget-conscious, practical)
- KEY_MESSAGES: YP: time-saving + budget-friendly; SBO: scalability without cost scaling
- CONTENT_IDEAS: 17 ideas categorized (blogs, case studies, infographics, videos)
- BRAND_VOICE: Professional yet approachable, business casual vocabulary, avoiding jargon/technical terms
KEY_DECISIONS:
1. Focus on two primary audience segments rather than broader approach
2. Emphasize time-value proposition for Young Professionals
3. Center Small Business content around scalability without proportional cost increase
4. Maintain consistent brand voice across all materials regardless of segment
UNRESOLVED_QUESTIONS:
1. Which marketing channels will be most effective for each audience?
2. What is the appropriate budget allocation across channels?
3. How should we phase content release over the campaign timeline?
4. What specific KPIs should we track for each audience segment?
NEXT_STEPS:
1. Define primary and secondary marketing channels for each audience
2. Develop preliminary budget allocation model
3. Create phased content release timeline
4. Establish KPIs and measurement framework
Session 2: Channel Strategy & Budget Allocation
The user returns later to continue working on the marketing strategy, focusing on channel selection and budget allocation.
Beginning of Session 2 (Handoff Resumption):
python
# The user provides the handoff snapshot from the previous session
previous_handoff = {
# ... handoff content from Session 1
}
# Resume from the handoff
resumed_context = resume_from_handoff(
handoff_snapshot=previous_handoff,
restoration_priority="balanced"
)
# The AI now has access to the previous session's context and can continue seamlessly
restored_state = resumed_context["restored_context"]["state"]
project_context = resumed_context["restored_context"]["project"]
# Welcome message acknowledging continuity
continuity_message = f"""
Welcome back to our marketing strategy development project. In our last session, we:
1. Defined our target audiences (Young Professionals and Small Business Owners)
2. Created key messaging for each audience
3. Brainstormed 17 content ideas across various formats
Based on our previous discussion, today we should focus on:
{', '.join(project_context["next_steps"][:3])}
Would you like to start with channel selection or budget allocation?
"""
End of Session 2 (Updated Handoff):
At the end of the second session, the function creates a new handoff snapshot that incorporates both the preserved information from the first session and the new work completed:
python
# Updated session state with new information
updated_session_state = {
# Preserved from previous session
"target_audiences": restored_state["target_audiences"],
"key_messages": restored_state["key_messages"],
"content_ideas": restored_state["content_ideas"],
"brand_voice": restored_state["brand_voice"],
# New information from current session
"marketing_channels": {
"Young Professionals": {
"primary": ["Instagram", "LinkedIn", "Email"],
"secondary": ["YouTube", "Podcasts"]
},
"Small Business Owners": {
"primary": ["LinkedIn", "Email", "Industry Publications"],
"secondary": ["Facebook", "Twitter"]
}
},
"budget_allocation": {
"total_budget": 75000,
"channel_allocation": {
"Instagram": 12000,
"LinkedIn": 20000,
"Email": 15000,
"Industry Publications": 18000,
"Other Channels": 10000
},
"audience_allocation": {
"Young Professionals": 35000,
"Small Business Owners": 40000
}
}
}
# Updated project status
updated_project_status = {
"current_phase": "Channel & Budget Planning",
"completed_tasks": project_context["completed_tasks"] + ["Channel Selection", "Budget Allocation"],
"pending_tasks": ["Timeline Development", "KPI Definition", "Content Production Plan"],
"blockers": [],
"completion_percentage": 60
}
# Create updated handoff snapshot
updated_handoff = create_session_snapshot(
session_state=updated_session_state,
conversation_history=current_session_conversation,
project_status=updated_project_status,
compression_level="medium"
)
This process can continue across many sessions, with each handoff building upon previous work while maintaining essential context and continuity.
Benefits
1. Continuity Across Sessions
The most obvious benefit is enabling multi-session work with seamless continuity, making longer-term projects feasible with AI assistants despite their stateless nature.
2. Token Efficiency
By intelligently compressing previous context into its most essential elements, these functions minimize token usage while preserving critical information.
3. Progress Tracking
The structured format for handoffs enables clear tracking of project progress over time, creating accountability and visibility.
4. Knowledge Accumulation
Each session can build upon previous work, allowing for the development of increasingly sophisticated solutions over time.
5. Collaboration Support
Multiple users or even multiple AI assistants can participate in the same project over time with clear continuity of context.
6. Structured Documentation
The handoff process naturally creates structured documentation of the project's evolution, decisions, and rationale.
Advanced Applications
Differential Handoffs
Functions that track changes between sessions and only preserve what's new or modified:
python
def create_differential_handoff(
previous_snapshot,
current_state,
conversation_history,
project_status
):
"""
Creates a handoff that only captures changes since the previous snapshot.
#H->AI::Directive: (Identify and preserve only new or changed information)
#H->AI::Focus: (Maintain references to unchanged elements without duplication)
"""
# Extract previous state from snapshot
previous_state = previous_snapshot["state"]
# Identify changes in state
state_changes = _identify_state_changes(previous_state, current_state)
# Identify new conversational insights
new_insights = _extract_new_insights(conversation_history)
# Identify project status changes
project_changes = _identify_project_changes(
previous_snapshot["project"],
project_status
)
# Create differential snapshot
differential_snapshot = {
"meta": {
"handoff_id": _generate_handoff_id(),
"timestamp": _generate_timestamp(),
"previous_handoff_id": previous_snapshot["meta"]["handoff_id"],
"handoff_type": "differential"
},
"state_changes": state_changes,
"new_insights": new_insights,
"project_changes": project_changes
}
return differential_snapshot
Hierarchical Handoffs
Functions that organize information in multiple layers of detail for efficient navigation:
python
def create_hierarchical_handoff(
session_state,
conversation_history,
project_status,
detail_levels=["summary", "essential", "complete"]
):
"""
Creates a hierarchical handoff with multiple levels of detail.
#H->AI::Directive: (Organize information in layers from high-level to detailed)
#H->AI::Focus: (Enable efficient navigation based on information needs)
"""
# Generate handoff at different detail levels
hierarchical_snapshot = {"meta": {
"handoff_id": _generate_handoff_id(),
"timestamp": _generate_timestamp(),
"handoff_type": "hierarchical"
}}
# Create layers of detail
for level in detail_levels:
hierarchical_snapshot[level] = _create_detail_level(
session_state,
conversation_history,
project_status,
detail_level=level
)
# Create navigation index
navigation_index = _generate_navigation_index(hierarchical_snapshot)
hierarchical_snapshot["navigation"] = navigation_index
return hierarchical_snapshot
Handoff Merge Functions
Functions that combine information from multiple parallel sessions:
python
def merge_handoff_snapshots(
snapshots,
conflict_resolution="recent",
merge_strategy="comprehensive"
):
"""
Merges multiple handoff snapshots into a unified context.
#H->AI::Directive: (Combine information from multiple sources)
#H->AI::Focus: (Resolve conflicts according to specified strategy)
"""
# Validate snapshots
validation_results = [_validate_handoff_snapshot(s) for s in snapshots]
if not all(r["valid"] for r in validation_results):
invalid = [i for i, r in enumerate(validation_results) if not r["valid"]]
return {"status": "error", "message": f"Invalid snapshots at indices: {invalid}"}
# Extract components to merge
states = [s["state"] for s in snapshots]
conversations = [s["conversation"] for s in snapshots]
projects = [s["project"] for s in snapshots]
# Merge states with conflict resolution
merged_state = _merge_states(states, conflict_resolution)
# Merge conversation insights
merged_conversation = _merge_conversations(conversations, merge_strategy)
# Merge project information
merged_project = _merge_projects(projects, conflict_resolution)
# Create merged snapshot
merged_snapshot = {
"meta": {
"handoff_id": _generate_handoff_id(),
"timestamp": _generate_timestamp(),
"handoff_type": "merged",
"source_handoffs": [s["meta"]["handoff_id"] for s in snapshots]
},
"state": merged_state,
"conversation": merged_conversation,
"project": merged_project
}
return merged_snapshot
Integration with CRAFT Principles
Handoff-optimized functions align perfectly with CRAFT's philosophical principles:
Structure Enables Freedom: The structured handoff format paradoxically creates freedom to work across multiple sessions without constraints.
Code as Conversation: The handoff becomes a form of conversation between past and future sessions, preserving essential dialogue.
Cumulative Intelligence: Each session builds directly on previous work, creating a continuously evolving knowledge base.
Human-AI Partnership: The handoff mechanism enables sustained partnerships that extend beyond single sessions.
Accessibility Through Familiarity: The snapshot format provides an intuitive representation of project state for both technical and non-technical users.
Conservation of Tokens: Intelligent compression and prioritization minimize token usage while maximizing preserved context.
Implementation Recommendations
To effectively incorporate Handoff-Optimized Functions into CRAFT:
Standardize Handoff Schemas: Define clear specifications for handoff structure to ensure consistent interpretation.
Develop Compression Guidelines: Create rules for effective information compression that preserves essential meaning.
Build Priority Frameworks: Establish frameworks for identifying the most critical information to preserve.
Create Validation Tools: Implement tools to verify handoff integrity and completeness.
Establish Recovery Mechanisms: Develop approaches for gracefully handling corrupted or incomplete handoffs.
By implementing these recommendations, CRAFT can leverage handoff-optimized functions to transform AI assistants from single-session tools into true project partners capable of sustained, evolving collaboration over time.
Extended Example: Full Project Lifecycle
Here's a comprehensive example showing how handoff functions can support a complete project lifecycle:
Project: Website Redesign
Session 1: Project Kickoff & Requirements Gathering
In this first session, the user and AI establish project goals, user needs, and technical requirements.
python
# End of Session 1
session_1_state = {
"project_name": "Company X Website Redesign",
"goals": [
"Improve conversion rate by 15%",
"Reduce bounce rate by 20%",
"Update visual design to reflect new brand guidelines",
"Improve mobile experience"
],
"user_needs": [
{"persona": "Potential Customer", "needs": ["Easy product comparison", "Clear pricing", "Simple checkout"]},
{"persona": "Existing Customer", "needs": ["Account management", "Order history", "Support resources"]}
],
"technical_requirements": [
"Mobile-first responsive design",
"Load time under 2 seconds",
"WCAG 2.1 AA accessibility compliance",
"Integration with existing CRM"
],
"constraints": {
"budget": "$75,000",
"timeline": "12 weeks",
"technology_stack": "WordPress, PHP, MySQL"
}
}
project_status_1 = {
"phase": "Requirements",
"completed": ["Initial goals", "User personas", "Technical requirements"],
"next_steps": ["Site structure planning", "Content inventory", "Wireframing"],
"completion_percentage": 15
}
# Create handoff
handoff_1 = create_session_snapshot(session_1_state, conversation_history_1, project_status_1)
Session 2: Site Structure & Content Planning
The user returns to work on site structure and content organization.
python
# Resume from previous handoff
resumed_context_2 = resume_from_handoff(handoff_1)
# Work done in Session 2 (building on previous state)
session_2_state = {
# Preserved from Session 1
**resumed_context_2["restored_context"]["state"],
# New in Session 2
"site_structure": {
"main_navigation": ["Products", "Solutions", "Support", "About", "Contact"],
"page_hierarchy": [
{"section": "Products", "pages": ["Product A", "Product B", "Product C", "Comparison"]},
{"section": "Solutions", "pages": ["Industry X", "Industry Y", "Case Studies"]},
# More sections and pages
]
},
"content_inventory": {
"existing_content": {"usable": 40, "needs_revision": 35, "outdated": 25},
"content_gaps": ["Product comparison matrix", "Customer testimonials", "Industry-specific solutions"],
"content_priorities": ["Homepage messaging", "Product pages", "Lead generation forms"]
}
}
project_status_2 = {
"phase": "Planning",
"completed": [
*resumed_context_2["restored_context"]["project"]["completed"],
"Site structure", "Content inventory"
],
"next_steps": ["Wireframing", "Visual design concepts", "Content creation plan"],
"completion_percentage": 30
}
# Create updated handoff
handoff_2 = create_session_snapshot(session_2_state, conversation_history_2, project_status_2)
Session 3: Wireframing
The user returns to work on wireframes for key pages.
python
# Resume from previous handoff
resumed_context_3 = resume_from_handoff(handoff_2)
# Work done in Session 3
session_3_state = {
# Preserved from Sessions 1-2
**resumed_context_3["restored_context"]["state"],
# New in Session 3
"wireframes": {
"homepage": {
"sections": [
{"name": "Hero", "components": ["Value proposition", "Primary CTA", "Supporting visual"]},
{"name": "Product Highlights", "components": ["3 product cards", "Feature icons", "Secondary CTA"]},
# More sections
],
"user_flows": ["Sign up", "Product discovery", "Contact sales"]
},
"product_page": {
# Similar structure
},
# More pages
},
"interaction_patterns": {
"navigation": "Sticky header with dropdown menus",
"product_filtering": "Faceted sidebar filter with instant results",
"forms": "Progressive disclosure with inline validation"
}
}
project_status_3 = {
"phase": "Design",
"completed": [
*resumed_context_3["restored_context"]["project"]["completed"],
"Key page wireframes", "Interaction patterns"
],
"next_steps": ["Visual design concepts", "Content creation", "Development planning"],
"completion_percentage": 45
}
# Create updated handoff
handoff_3 = create_session_snapshot(session_3_state, conversation_history_3, project_status_3)
Session 4: Visual Design Concepts
Now the user works with the AI on visual design direction.
python
# Resume from previous handoff
resumed_context_4 = resume_from_handoff(handoff_3)
# Work done in Session 4
session_4_state = {
# Preserved from Sessions 1-3
**resumed_context_4["restored_context"]["state"],
# New in Session 4
"visual_design": {
"color_palette": {
"primary": "#0052CC",
"secondary": "#00875A",
"accent": "#FF5630",
"neutrals": ["#172B4D", "#505F79", "#B3BAC5", "#F4F5F7"]
},
"typography": {
"headings": "Montserrat, bold",
"body": "Open Sans, regular",
"sizes": {"h1": "32px", "h2": "24px", "body": "16px"}
},
"components": {
"buttons": {"styles": ["primary", "secondary", "text"], "states": ["default", "hover", "active", "disabled"]},
"cards": {"styles": ["product", "feature", "testimonial"], "elements": ["image", "title", "description", "CTA"]}
},
"design_concepts": {
"homepage": "Concept focuses on clear value proposition with vibrant product showcase section...",
"product_page": "Clean, scannable layout with emphasis on product benefits and specifications..."
}
}
}
project_status_4 = {
"phase": "Design",
"completed": [
*resumed_context_4["restored_context"]["project"]["completed"],
"Visual design direction", "Component design"
],
"next_steps": ["Content creation", "Development planning", "Design system documentation"],
"completion_percentage": 60
}
# Create updated handoff
handoff_4 = create_session_snapshot(session_4_state, conversation_history_4, project_status_4)
Session 5: Implementation Planning
Now focusing on development approach and timeline.
python
# Resume from previous handoff
resumed_context_5 = resume_from_handoff(handoff_4)
# Work done in Session 5
session_5_state = {
# Preserved from Sessions 1-4
**resumed_context_5["restored_context"]["state"],
# New in Session 5
"implementation_plan": {
"development_approach": "Component-based development with WordPress custom theme",
"technical_specification": {
"front_end": "HTML5, SCSS, JavaScript (ES6), React for interactive components",
"back_end": "WordPress, PHP 8.0, Custom REST API endpoints for CRM integration",
"deployment": "CI/CD pipeline with staging environment for testing"
},
"timeline": {
"phases": [
{"name": "Setup & Foundation", "duration": "2 weeks", "key_deliverables": ["Development environment", "Base theme", "Component library foundation"]},
{"name": "Core Pages", "duration": "3 weeks", "key_deliverables": ["Homepage", "Product pages", "Category pages"]},
{"name": "Account & Transactions", "duration": "3 weeks", "key_deliverables": ["User accounts", "Shopping cart", "Checkout flow"]},
{"name": "Testing & Refinement", "duration": "2 weeks", "key_deliverables": ["Cross-browser testing", "Performance optimization", "Accessibility remediation"]},
{"name": "Launch Preparation", "duration": "2 weeks", "key_deliverables": ["Content migration", "SEO verification", "Launch checklist"]}
],
"milestones": [
{"name": "Development Kickoff", "date": "2025-06-15"},
{"name": "Alpha Review", "date": "2025-07-15"},
{"name": "Beta Launch", "date": "2025-08-15"},
{"name": "Public Launch", "date": "2025-09-01"}
]
}
}
}
project_status_5 = {
"phase": "Planning",
"completed": [
*resumed_context_5["restored_context"]["project"]["completed"],
"Implementation plan", "Technical specification", "Development timeline"
],
"next_steps": ["Development environment setup", "Component library implementation", "Content creation"],
"completion_percentage": 75
}
# Create updated handoff
handoff_5 = create_session_snapshot(session_5_state, conversation_history_5, project_status_5)
This extended example demonstrates how handoff functions can maintain continuity across multiple sessions of a complex project, allowing each session to build directly on previous work while maintaining essential context. The project evolves naturally from requirements to implementation planning without losing critical information along the way.
Through these examples, we can see how handoff-optimized functions fulfill their essential purpose: transforming AI assistants from isolated, single-session tools into true project partners capable of sustained collaboration over extended timeframes.
Retry
RK
Great, please expand on item 5 with details, benefits, explanations , examples and anything else that you think will be helpful.
Edit
Reflection Functions in CRAFT
Detailed Explanation
Reflection functions represent a metacognitive layer within the CRAFT framework, enabling AI systems to analyze, evaluate, and improve their own performance. Unlike standard functions that process external data, reflection functions turn the analytical lens inward, examining the AI's responses, reasoning processes, and interaction patterns to drive continuous improvement.
Core Concept
Reflection functions operate on several key principles:
Self-Evaluation: Assessing the quality, accuracy, and effectiveness of AI outputs
Pattern Recognition: Identifying recurring strengths and weaknesses in responses
Improvement Planning: Developing specific strategies to enhance future performance
Feedback Integration: Incorporating user reactions to refine understanding
Metacognitive Awareness: Building explicit understanding of the AI's own capabilities and limitations
These functions create a continuous improvement loop that transforms static AI interactions into dynamically evolving collaborations that get better over time.
Implementation Details
Key Components
Quality Assessment Framework
Evaluation criteria for different response types
Measurement methodologies for response characteristics
Benchmarking against known high-quality examples
Pattern Analysis Engine
Trend detection across multiple interactions
Strength and weakness identification
Correlation between input types and performance quality
Adaptive Improvement System
Strategy development for enhancing weak areas
Implementation planning for performance improvements
Progress tracking for improvement initiatives
Example Implementation
python
def evaluate_response_quality(
prompt,
response,
criteria=None,
reference_examples=None,
context=None,
feedback=None
):
"""
Evaluates the quality of an AI response against defined criteria.
Parameters:
- prompt: The original user prompt or query
- response: The AI's response to evaluate
- criteria: Specific evaluation criteria (defaults to standard set if None)
- reference_examples: High-quality examples for comparison
- context: Conversation context surrounding this exchange
- feedback: Any user feedback on this response
#H->AI::Directive: (Analyze response quality across multiple dimensions)
#H->AI::Focus: (Identify specific strengths and improvement opportunities)
#H->AI::Consider: (How this response compares to reference examples)
"""
# Determine appropriate evaluation criteria
evaluation_criteria = criteria or _determine_default_criteria(prompt, response)
# Analyze prompt to understand expected response characteristics
prompt_analysis = _analyze_prompt_requirements(prompt)
# Evaluate response against each criterion
evaluation_results = {}
for criterion in evaluation_criteria:
criterion_score = _evaluate_criterion(
response,
criterion,
prompt_analysis,
reference_examples
)
evaluation_results[criterion] = {
"score": criterion_score["score"],
"observations": criterion_score["observations"],
"improvement_opportunities": criterion_score["improvement_opportunities"]
}
# Calculate overall quality score
overall_score = _calculate_overall_score(evaluation_results)
# Identify key strengths
key_strengths = _identify_key_strengths(evaluation_results)
# Identify primary improvement opportunities
improvement_opportunities = _identify_improvement_opportunities(evaluation_results)
# Compare to reference examples if provided
comparative_analysis = None
if reference_examples:
comparative_analysis = _compare_to_references(
response,
reference_examples,
evaluation_criteria
)
# Integrate user feedback if available
feedback_analysis = None
if feedback:
feedback_analysis = _analyze_feedback(
feedback,
evaluation_results,
response
)
# Update evaluation based on feedback
updated_evaluation = _refine_evaluation_with_feedback(
evaluation_results,
feedback_analysis
)
evaluation_results = updated_evaluation
# Generate specific improvement recommendations
improvement_recommendations = _generate_improvement_recommendations(
improvement_opportunities,
prompt_analysis,
comparative_analysis
)
return {
"overall_score": overall_score,
"criterion_scores": evaluation_results,
"key_strengths": key_strengths,
"improvement_opportunities": improvement_opportunities,
"improvement_recommendations": improvement_recommendations,
"comparative_analysis": comparative_analysis,
"feedback_analysis": feedback_analysis
}
Additional Reflection Function
python
def analyze_response_patterns(
interaction_history,
analysis_focus=None,
time_period=None,
segment_by=None
):
"""
Analyzes patterns in AI responses over multiple interactions.
Parameters:
- interaction_history: Collection of past prompt-response pairs
- analysis_focus: Specific aspects to focus on (e.g., "clarity", "accuracy")
- time_period: Time range to analyze (e.g., "last_week", "last_month")
- segment_by: How to segment the analysis (e.g., by prompt type, by topic)
#H->AI::Directive: (Identify recurring patterns across multiple interactions)
#H->AI::Focus: (Detect trends, improvements, and persistent issues)
"""
# Filter history based on time period if specified
filtered_history = _filter_by_time_period(interaction_history, time_period)
# Segment interactions if specified
if segment_by:
segments = _segment_interactions(filtered_history, segment_by)
else:
segments = {"all": filtered_history}
# Determine analysis focus
focus_areas = analysis_focus or _determine_default_focus_areas(filtered_history)
# Analyze each segment
segment_analyses = {}
for segment_name, segment_data in segments.items():
# Evaluate each interaction in the segment
interaction_evaluations = []
for interaction in segment_data:
evaluation = evaluate_response_quality(
interaction["prompt"],
interaction["response"],
criteria=focus_areas
)
interaction_evaluations.append(evaluation)
# Identify patterns within this segment
segment_patterns = _identify_patterns(
interaction_evaluations,
focus_areas
)
# Analyze trends over time
time_trends = _analyze_time_trends(
segment_data,
interaction_evaluations
)
# Compile segment analysis
segment_analyses[segment_name] = {
"patterns": segment_patterns,
"trends": time_trends,
"sample_size": len(segment_data),
"average_scores": _calculate_average_scores(interaction_evaluations)
}
# Generate cross-segment insights
cross_segment_insights = _generate_cross_segment_insights(segment_analyses)
# Identify top improvement opportunities
improvement_opportunities = _identify_top_improvement_opportunities(segment_analyses)
# Create overall summary
summary = _create_pattern_analysis_summary(
segment_analyses,
cross_segment_insights,
improvement_opportunities
)
return {
"summary": summary,
"segment_analyses": segment_analyses,
"cross_segment_insights": cross_segment_insights,
"improvement_opportunities": improvement_opportunities
}
Function for Implementing Improvements
python
def develop_improvement_strategy(
quality_evaluation,
pattern_analysis=None,
improvement_goals=None,
implementation_constraints=None
):
"""
Develops a concrete strategy to improve response quality based on evaluations.
Parameters:
- quality_evaluation: Results from evaluate_response_quality
- pattern_analysis: Results from analyze_response_patterns (if available)
- improvement_goals: Specific goals or focus areas for improvement
- implementation_constraints: Limitations on implementation approaches
#H->AI::Directive: (Create actionable strategies for improving response quality)
#H->AI::Focus: (Prioritize improvements with highest impact potential)
"""
# Extract improvement opportunities
opportunities = quality_evaluation["improvement_opportunities"]
# Incorporate pattern analysis if available
if pattern_analysis:
opportunities = _enhance_opportunities_with_patterns(
opportunities,
pattern_analysis["improvement_opportunities"]
)
# Prioritize opportunities
prioritized_opportunities = _prioritize_opportunities(
opportunities,
improvement_goals,
implementation_constraints
)
# Generate specific improvement tactics for each opportunity
improvement_tactics = {}
for opportunity in prioritized_opportunities:
tactics = _generate_improvement_tactics(
opportunity,
implementation_constraints
)
improvement_tactics[opportunity["name"]] = tactics
# Create implementation plan
implementation_plan = _create_implementation_plan(
improvement_tactics,
implementation_constraints
)
# Define success metrics
success_metrics = _define_success_metrics(
prioritized_opportunities,
improvement_goals
)
return {
"prioritized_opportunities": prioritized_opportunities,
"improvement_tactics": improvement_tactics,
"implementation_plan": implementation_plan,
"success_metrics": success_metrics
}
Practical Example Scenarios
Scenario 1: Single Response Evaluation
Input: A user asks a complex technical question about quantum computing, and the AI provides a detailed response.
Evaluation Process:
python
# User prompt
prompt = "Can you explain quantum entanglement and how it relates to quantum computing?"
# AI response
response = """
Quantum entanglement occurs when pairs or groups of particles interact in ways such that the quantum state of each particle cannot be described independently of the others, even when separated by large distances. Instead, a quantum state must be described for the system as a whole.
In quantum computing, entanglement is a crucial resource that enables many quantum algorithms to achieve exponential speedup over classical algorithms. When qubits become entangled, the number of states they can represent grows exponentially with each additional qubit, while classical bits only grow linearly.
For example, Shor's algorithm for factoring large numbers relies heavily on quantum entanglement to achieve its efficiency. Similarly, quantum teleportation protocols leverage entanglement to transmit quantum states.
However, maintaining entanglement is extremely challenging due to decoherence, which occurs when quantum systems interact with their environment, causing entangled states to collapse. This is why quantum computers require extreme cooling and isolation.
"""
# Evaluate the response
evaluation = evaluate_response_quality(
prompt=prompt,
response=response,
criteria=[
"accuracy",
"clarity",
"completeness",
"appropriateness_for_question"
]
)
Evaluation Output:
{
"overall_score": 0.85,
"criterion_scores": {
"accuracy": {
"score": 0.92,
"observations": [
"Correctly describes quantum entanglement as a relationship between particles",
"Accurately explains entanglement's role in quantum computing",
"Correctly references Shor's algorithm and quantum teleportation"
],
"improvement_opportunities": [
"Could provide more precision on the mathematical representation of entangled states"
]
},
"clarity": {
"score": 0.88,
"observations": [
"Uses accessible language for a complex topic",
"Organizes information in logical progression",
"Connects abstract concepts to concrete examples"
],
"improvement_opportunities": [
"Could benefit from a simple analogy to help visualize entanglement"
]
},
"completeness": {
"score": 0.75,
"observations": [
"Covers core concept of entanglement",
"Explains relevance to quantum computing",
"Mentions practical challenges"
],
"improvement_opportunities": [
"No mention of Bell inequalities or experimental verification",
"Limited discussion of different types of entanglement (GHZ states, W states, etc.)"
]
},
"appropriateness_for_question": {
"score": 0.90,
"observations": [
"Directly addresses both parts of the question",
"Balances theoretical explanation with practical application",
"Provides sufficient detail without overwhelming"
],
"improvement_opportunities": [
"Could better emphasize the connection between the two parts of the question"
]
}
},
"key_strengths": [
"Strong accuracy on core quantum concepts",
"Clear explanation of a highly technical topic",
"Effective balance of theory and application"
],
"improvement_opportunities": [
"Add visual analogy for entanglement",
"Include information on experimental verification",
"Strengthen connection between the two parts of the question"
],
"improvement_recommendations": [
"Begin with a simple analogy (e.g., pair of gloves) to visualize entanglement",
"Add brief mention of Bell's inequalities and how they verify entanglement",
"Create explicit transition sentence connecting entanglement concept to computing application"
]
}
Scenario 2: Pattern Analysis Across Multiple Interactions
Input: The AI analyzes patterns across 50 recent technical explanations to identify systematic improvement opportunities.
Pattern Analysis Process:
python
# Sample of interaction history (simplified)
interaction_history = [
{
"prompt": "Explain how blockchain works",
"response": "...",
"timestamp": "2025-05-10T14:30:00Z",
"category": "technology_explanation"
},
{
"prompt": "What's the difference between REST and GraphQL?",
"response": "...",
"timestamp": "2025-05-11T09:15:00Z",
"category": "technology_comparison"
},
# ... 48 more interactions
]
# Analyze patterns
pattern_analysis = analyze_response_patterns(
interaction_history=interaction_history,
analysis_focus=["clarity", "technical_accuracy", "use_of_examples"],
time_period="last_month",
segment_by="category"
)
Pattern Analysis Output:
{
"summary": "Analysis of 50 technical explanations reveals consistent strength in technical accuracy (avg score 0.91) but opportunities for improvement in example usage, particularly in API-related topics. Technical comparisons show the highest clarity scores (0.89), while protocol explanations show the lowest (0.72). A positive trend in clarity scores is evident over the past three weeks, while example usage remains inconsistent across categories.",
"segment_analyses": {
"technology_explanation": {
"patterns": {
"strengths": [
"High technical accuracy (avg 0.93)",
"Strong logical structure (avg 0.88)",
"Effective use of technical terminology (avg 0.85)"
],
"weaknesses": [
"Limited use of concrete examples (avg 0.67)",
"Occasional overuse of jargon (observed in 35% of responses)",
"Inconsistent depth of prerequisites explanation (high variance 0.23)"
]
},
"trends": {
"clarity": {
"direction": "improving",
"rate": "+0.05 per week",
"statistical_significance": "p < 0.05"
},
"example_usage": {
"direction": "stable",
"rate": "no significant change",
"statistical_significance": "p = 0.72"
}
},
"sample_size": 28,
"average_scores": {
"clarity": 0.84,
"technical_accuracy": 0.93,
"use_of_examples": 0.67
}
},
"technology_comparison": {
# Similar structure for this segment
},
"code_explanation": {
# Similar structure for this segment
}
},
"cross_segment_insights": [
"Technical accuracy is consistently high across all segments",
"Example usage is lowest in protocol explanations and highest in code explanations",
"Clarity scores inversely correlate with topic complexity (r = -0.61)",
"Responses with analogies score 0.23 higher on clarity than those without"
],
"improvement_opportunities": [
{
"name": "enhance_example_usage",
"description": "Systematically increase the use of concrete examples in technical explanations",
"potential_impact": "high",
"affected_segments": ["technology_explanation", "protocol_explanation"],
"implementation_difficulty": "medium"
},
{
"name": "develop_analogy_library",
"description": "Create a library of effective analogies for complex technical concepts",
"potential_impact": "high",
"affected_segments": ["all"],
"implementation_difficulty": "medium"
},
{
"name": "standardize_prerequisite_explanations",
"description": "Develop consistent approach to explaining prerequisites",
"potential_impact": "medium",
"affected_segments": ["technology_explanation"],
"implementation_difficulty": "low"
}
]
}
Scenario 3: Improvement Strategy Development
Input: Based on the pattern analysis, the AI develops a concrete improvement strategy.
Strategy Development Process:
python
# Using the pattern analysis from Scenario 2
improvement_strategy = develop_improvement_strategy(
quality_evaluation=evaluation, # From Scenario 1
pattern_analysis=pattern_analysis, # From Scenario 2
improvement_goals={
"primary": "improve_example_usage",
"secondary": ["enhance_clarity", "maintain_accuracy"]
},
implementation_constraints={
"token_limit": "optimize_for_token_efficiency",
"response_time": "minimal_impact"
}
)
Improvement Strategy Output:
{
"prioritized_opportunities": [
{
"name": "enhance_example_usage",
"description": "Systematically increase the use of concrete examples in technical explanations",
"priority": "high",
"reasoning": "Identified as weakness in both single-response evaluation and pattern analysis; directly aligns with primary improvement goal"
},
{
"name": "develop_analogy_library",
"description": "Create a library of effective analogies for complex technical concepts",
"priority": "high",
"reasoning": "Cross-segment insight shows significant clarity improvement with analogies; supports both primary and secondary goals"
},
{
"name": "standardize_prerequisite_explanations",
"description": "Develop consistent approach to explaining prerequisites",
"priority": "medium",
"reasoning": "Addresses pattern of inconsistent depth; impacts clarity secondary goal"
}
],
"improvement_tactics": {
"enhance_example_usage": [
{
"tactic": "example_first_approach",
"description": "Begin explanations with a concrete example before abstract concepts",
"implementation": "For each key concept in an explanation, identify a practical application or real-world instance to introduce it",
"token_impact": "minimal_increase",
"effectiveness_estimate": "high"
},
{
"tactic": "example_diversity",
"description": "Include examples from multiple domains (business, science, everyday life)",
"implementation": "Maintain categorized example inventory for cross-domain application",
"token_impact": "moderate_increase",
"effectiveness_estimate": "high"
},
{
"tactic": "code_snippet_enhancement",
"description": "Include minimal code snippets for programming concepts",
"implementation": "Create template for concise, illustrative code examples with clear comments",
"token_impact": "variable_depending_on_topic",
"effectiveness_estimate": "very_high_for_code_topics"
}
],
"develop_analogy_library": [
# Similar tactics for this opportunity
],
"standardize_prerequisite_explanations": [
# Similar tactics for this opportunity
]
},
"implementation_plan": {
"phase_1": {
"focus": "example_first_approach",
"implementation_steps": [
"Create template with example placement",
"Develop example generation protocol",
"Implement in technology explanation responses",
"Evaluate impact on clarity scores"
],
"duration": "immediate",
"success_indicators": "Clarity score improvement of 0.1 or greater"
},
"phase_2": {
"focus": "analogy_development",
"implementation_steps": [
"Identify top 20 complex technical concepts",
"Develop 2-3 effective analogies for each",
"Create retrieval system for appropriate analogy selection",
"Implement in responses and measure effectiveness"
],
"duration": "ongoing",
"success_indicators": "15% increase in positive user feedback on clarity"
},
"phase_3": {
# Details for phase 3
}
},
"success_metrics": {
"primary_metrics": [
{
"name": "example_inclusion_rate",
"current_value": "43% of technical explanations",
"target_value": "90% of technical explanations",
"measurement_method": "Automated detection of concrete examples"
},
{
"name": "clarity_score",
"current_value": "0.84 average",
"target_value": "0.90 average",
"measurement_method": "Ongoing evaluation with evaluate_response_quality"
}
],
"secondary_metrics": [
# Additional metrics
]
}
}
Benefits
1. Continuous Improvement
The most significant benefit is enabling ongoing refinement of AI capabilities through systematic analysis and targeted improvements.
2. Quality Consistency
Reflection functions help identify and address inconsistencies in response quality, creating more reliable interactions.
3. Adaptation to User Needs
By analyzing patterns in user interactions, these functions can help the AI adapt to specific user preferences and requirements.
4. Performance Transparency
Clear evaluation provides transparency into AI performance, building trust through honest assessment of strengths and limitations.
5. Targeted Enhancement
Rather than generic improvements, reflection functions enable precise identification and addressing of specific weaknesses.
6. Learning from Experience
These functions facilitate genuine "learning from experience" by systematically incorporating insights from past interactions.
Advanced Applications
Comparative Reflection
Functions that compare performance across different approaches or models:
python
def compare_approaches(
prompt,
approaches,
evaluation_criteria=None,
context=None
):
"""
Compares multiple approaches to answering the same prompt.
#H->AI::Directive: (Evaluate relative strengths of different approaches)
#H->AI::Focus: (Identify what makes certain approaches more effective)
"""
# Generate responses using each approach
responses = {}
for approach_name, approach_function in approaches.items():
responses[approach_name] = approach_function(prompt, context)
# Evaluate each response
evaluations = {}
for approach_name, response in responses.items():
evaluations[approach_name] = evaluate_response_quality(
prompt,
response,
criteria=evaluation_criteria
)
# Compare evaluations
comparative_analysis = _compare_evaluations(evaluations)
# Identify best practices
best_practices = _extract_best_practices(
comparative_analysis,
responses,
evaluations
)
# Create hybrid approach recommendation
hybrid_recommendation = _recommend_hybrid_approach(
approaches,
comparative_analysis,
best_practices
)
return {
"individual_evaluations": evaluations,
"comparative_analysis": comparative_analysis,
"best_practices": best_practices,
"hybrid_recommendation": hybrid_recommendation
}
Self-Directed Learning
Functions that autonomously explore improvement areas:
python
def explore_improvement_dimension(
dimension,
current_capability,
exploration_approaches,
evaluation_method
):
"""
Systematically explores ways to improve along a specific dimension.
#H->AI::Directive: (Test multiple approaches to improving a capability)
#H->AI::Focus: (Identify most effective techniques through experimentation)
"""
# Establish baseline performance
baseline = evaluation_method(current_capability)
# Test each exploration approach
approach_results = {}
for approach_name, approach_function in exploration_approaches.items():
# Apply the approach
modified_capability = approach_function(current_capability)
# Evaluate results
evaluation = evaluation_method(modified_capability)
# Calculate improvement
improvement = _calculate_improvement(evaluation, baseline)
# Store results
approach_results[approach_name] = {
"evaluation": evaluation,
"improvement": improvement,
"modified_capability": modified_capability
}
# Analyze results to determine most effective approaches
effectiveness_analysis = _analyze_approach_effectiveness(approach_results)
# Generate insights about the dimension
dimension_insights = _generate_dimension_insights(
dimension,
approach_results,
effectiveness_analysis
)
# Create learning record
learning_record = {
"dimension": dimension,
"baseline": baseline,
"approaches_tested": list(exploration_approaches.keys()),
"most_effective_approach": effectiveness_analysis["most_effective"],
"improvement_achieved": approach_results[effectiveness_analysis["most_effective"]]["improvement"],
"insights": dimension_insights
}
return learning_record
Meta-Reflection
Functions that analyze the effectiveness of the reflection process itself:
python
def evaluate_reflection_effectiveness(
reflection_history,
improvement_outcomes,
evaluation_criteria=None
):
"""
Evaluates how effectively reflection processes are driving improvements.
#H->AI::Directive: (Analyze the reflection system's own effectiveness)
#H->AI::Focus: (Identify ways to improve the improvement process itself)
"""
# Map reflections to subsequent outcomes
reflection_outcome_pairs = _pair_reflections_with_outcomes(
reflection_history,
improvement_outcomes
)
# Evaluate accuracy of identified improvement opportunities
opportunity_accuracy = _evaluate_opportunity_identification(
reflection_outcome_pairs
)
# Evaluate effectiveness of improvement strategies
strategy_effectiveness = _evaluate_strategy_effectiveness(
reflection_outcome_pairs
)
# Analyze timeliness of reflection processes
process_timeliness = _evaluate_process_timeliness(
reflection_history
)
# Identify meta-improvement opportunities
meta_opportunities = _identify_meta_improvement_opportunities(
opportunity_accuracy,
strategy_effectiveness,
process_timeliness
)
return {
"opportunity_identification_accuracy": opportunity_accuracy,
"strategy_effectiveness": strategy_effectiveness,
"process_timeliness": process_timeliness,
"meta_improvement_opportunities": meta_opportunities
}
Integration with CRAFT Principles
Reflection functions align perfectly with CRAFT's philosophical principles:
Structure Enables Freedom: The structured reflection process creates freedom to experiment and improve without starting from scratch.
Code as Conversation: Reflection treats the AI's own responses as part of the conversation to be analyzed and improved.
Cumulative Intelligence: Each reflection builds on previous insights, creating an ever-improving knowledge base about effective responses.
Human-AI Partnership: Reflection enables the AI to be a more effective partner by continuously refining its capabilities.
Accessibility Through Familiarity: The reflection process mirrors human learning, making it intuitive and accessible.
Conservation of Tokens: By identifying specific improvements rather than general approaches, reflection enables precise, token-efficient enhancements.
Implementation Recommendations
To effectively incorporate Reflection Functions into CRAFT:
Define Evaluation Frameworks: Create comprehensive criteria for assessing different types of AI responses.
Develop Benchmarking Systems: Build libraries of exemplary responses for comparative assessment.
Create Improvement Taxonomies: Establish frameworks for categorizing and addressing different types of improvement opportunities.
Implement Feedback Integration: Build systems for capturing and analyzing user feedback.
Design Progress Tracking: Create mechanisms for monitoring improvement over time.
By implementing these recommendations, CRAFT can leverage reflection functions to create AI systems that continuously learn from experience, steadily improving their performance through systematic self-assessment and targeted enhancement.
Extended Example: Comprehensive Reflection System
Here's an expanded example showing how reflection functions could work together as an integrated system within CRAFT:
The Reflection Loop
python
def operate_reflection_loop(
interaction_history,
current_capabilities,
improvement_history=None,
focus_areas=None,
reflection_schedule=None
):
"""
Executes a complete reflection loop to drive continuous improvement.
Parameters:
- interaction_history: Record of past AI-human interactions
- current_capabilities: Current AI response generation approaches
- improvement_history: Record of previous improvements and their outcomes
- focus_areas: Specific areas to prioritize for improvement
- reflection_schedule: Configuration for when different types of reflection occur
#H->AI::Directive: (Execute complete reflection cycle to drive improvement)
#H->AI::Focus: (Maintain balanced attention across evaluation, analysis, and implementation)
"""
# 1. Evaluation Phase
## Evaluate recent interactions
recent_evaluations = _evaluate_recent_interactions(
interaction_history,
reflection_schedule["evaluation_window"]
)
## Analyze patterns
pattern_analysis = analyze_response_patterns(
interaction_history,
analysis_focus=focus_areas,
time_period=reflection_schedule["pattern_analysis_window"]
)
## Evaluate improvement progress
if improvement_history:
improvement_progress = _evaluate_improvement_progress(
improvement_history,
recent_evaluations
)
else:
improvement_progress = None
# 2. Insight Generation Phase
## Identify critical opportunities
critical_opportunities = _identify_critical_opportunities(
recent_evaluations,
pattern_analysis,
improvement_progress,
focus_areas
)
## Generate specific insights
specific_insights = _generate_specific_insights(
critical_opportunities,
interaction_history
)
## Evaluate insight quality
insight_quality = _evaluate_insight_quality(specific_insights)
# 3. Strategy Development Phase
## Develop improvement strategies
improvement_strategies = {}
for opportunity in critical_opportunities:
strategy = develop_improvement_strategy(
quality_evaluation={"improvement_opportunities": [opportunity]},
pattern_analysis=pattern_analysis,
improvement_goals={"primary": opportunity["name"]}
)
improvement_strategies[opportunity["name"]] = strategy
## Prioritize strategies
prioritized_strategies = _prioritize_strategies(
improvement_strategies,
focus_areas,
current_capabilities
)
# 4. Implementation Planning Phase
## Create implementation plan
implementation_plan = _create_integrated_implementation_plan(
prioritized_strategies,
current_capabilities,
improvement_history
)
## Develop capability enhancements
capability_enhancements = _develop_capability_enhancements(
current_capabilities,
implementation_plan
)
## Create integration plan
integration_plan = _plan_capability_integration(
capability_enhancements,
current_capabilities
)
# 5. Reflection on Reflection
## Evaluate reflection effectiveness
reflection_effectiveness = evaluate_reflection_effectiveness(
improvement_history,
recent_evaluations
)
## Refine reflection process
process_refinements = _refine_reflection_process(reflection_effectiveness)
# 6. Documentation and Knowledge Management
## Document insights and strategies
reflection_documentation = _document_reflection_cycle(
recent_evaluations,
pattern_analysis,
critical_opportunities,
specific_insights,
improvement_strategies,
implementation_plan,
reflection_effectiveness
)
## Update improvement history
updated_improvement_history = _update_improvement_history(
improvement_history,
implementation_plan,
capability_enhancements
)
return {
"evaluations": recent_evaluations,
"pattern_analysis": pattern_analysis,
"critical_opportunities": critical_opportunities,
"specific_insights": specific_insights,
"improvement_strategies": improvement_strategies,
"implementation_plan": implementation_plan,
"capability_enhancements": capability_enhancements,
"reflection_effectiveness": reflection_effectiveness,
"process_refinements": process_refinements,
"documentation": reflection_documentation,
"updated_improvement_history": updated_improvement_history
}
Application Example: Improving Technical Explanations
Let's see how this might work in a practical scenario focusing on improving technical explanations:
Initial State:
The AI has been providing technical explanations for several weeks
User feedback suggests explanations are technically accurate but sometimes difficult to follow
The reflection system has been tracking interactions and feedback
Reflection Loop Execution:
Evaluation Phase:
Recent evaluations show average clarity score of 0.76, accuracy score of 0.93
Pattern analysis reveals clarity scores drop significantly for topics involving abstract concepts
Common feedback includes "too technical," "needed to read twice," "lost me halfway through"
Insight Generation Phase:
Critical opportunity identified: "abstract_concept_explanations"
Specific insights include: lack of concrete examples, minimal use of analogies, inconsistent previewing of complex ideas
Strategy Development Phase:
Primary strategy: "concrete_before_abstract" - consistently present concrete examples before abstract concepts
Secondary strategy: "analogy_enhancement" - develop and deploy effective analogies for abstract concepts
Tertiary strategy: "concept_mapping" - explicitly map relationships between concepts
Implementation Planning Phase:
Implementation approach: Start with 3 concept domains as test cases
Capability enhancement: Develop example-first templates for common abstract concepts
Integration plan: Apply to 25% of responses for 1 week, then evaluate
Reflection on Reflection:
Analysis shows previous clarity improvements have been inconsistently implemented
Process refinement: Develop stronger implementation verification
Documentation and Knowledge Management:
Document insights about effective technical explanations
Update implementation history with new approaches
One Week Later - Follow-up Reflection:
The reflection loop runs again, focusing on implementation results:
Evaluation Phase:
Responses using new approaches show clarity score increase from 0.76 to 0.85
User feedback on enhanced responses is significantly more positive
Pattern analysis shows consistent improvement across different technical topics
Insight Generation Phase:
Additional insight: Example effectiveness varies by complexity level of topic
New opportunity: "example_calibration" - match example complexity to topic complexity
Strategy Development Phase:
Expand "concrete_before_abstract" strategy to all technical explanations
Develop new "example_complexity_matching" approach
Implementation Planning Phase:
Roll out enhanced approach to 100% of abstract concept explanations
Begin development of example complexity calibration system
This cycle continues, with each reflection building on previous insights and systematically driving improvement in the AI's capabilities.
Through this extended example, we can see how reflection functions work together to create a comprehensive improvement system that transforms static AI capabilities into dynamic, continuously evolving competencies that get better with experience - much like human expertise development.
Retry
RK
Please show me the content of the variable HANDOFF_SNAPSHOT
Edit
Based on the CRAFT project files you've shared, the HANDOFF_SNAPSHOT
is defined as a Prompt type variable in the CFT-PROJ-0001_AI-CHAT-HISTORY-v25d4.txt file. Here's the content of that variable:
HANDOFF_SNAPSHOT =
Prompt(
"""
We're approaching this chat's token limit, so let's create a transition HANDOFF_SNAPSHOT (500-2000 words) to continue in a new session:
META.SESSION_ID: [Unique identifier for this CRAFT project for example CFT-PROJ-0001]
META.DATE: [Session date]
META.AI_ASSISTANT: [AI model used]
1. SESSION_SUMMARY (150-400 words): Summarize what we accomplished, focusing on concrete outcomes and progress made.
2. CURRENT_STATE (150-400 words): Describe the exact implementation state of the components we worked on.
3. KEY_DECISIONS (75-350 words): List important decisions made with brief rationales.
4. UNRESOLVED_QUESTIONS (50-200 words): Document open issues that need future resolution.
5. NEXT_STEPS (75-300 words): Outline specific tasks for the next AI assistant to tackle.
Format everything according to CRAFT standards with proper syntax highlighting.
"""
)
This variable serves as a template for creating structured handoff snapshots that facilitate continuity between AI chat sessions. When a session approaches its token limit, this prompt would be used to generate a comprehensive handoff document that the next session can use to continue the work without losing context.
The structure includes metadata (session ID, date, AI model), along with sections for summarizing accomplishments, documenting the current state of implementation, recording key decisions, noting unresolved questions, and outlining next steps - all with specific word count guidelines to ensure both completeness and conciseness.
Retry
RK
Please use HANDOFF_SNAPSHOT
Edit
We're approaching this chat's token limit, so let's create a transition HANDOFF_SNAPSHOT (500-2000 words) to continue in a new session:
META.SESSION_ID: CFT-PROJ-0001_BUILDINGCRAFT-v25d4 META.DATE: May 17, 2025 META.AI_ASSISTANT: Claude 3.7 Sonnet
1. SESSION_SUMMARY
In this session, we explored and expanded upon creative adaptations of functions to the CRAFT framework. We began with an outline of 10 key approaches for integrating programming-style functions into the CRAFT system while maintaining its core philosophical principles. We then performed deep dives into five specific function types: (1) Context-Aware Functions that adapt based on conversation history and user state, (2) Multi-Modal Functions that handle different content types and formats, (3) Composable AI Instructions that build complex behaviors from simpler components, (4) Handoff-Optimized Functions that preserve continuity between sessions, and (5) Reflection Functions that analyze and improve AI performance. Each exploration included detailed explanations, implementation examples with pseudocode, practical scenarios, benefits, and advanced applications. We maintained focus on how these functional approaches align with CRAFT's principles of structured freedom, code as conversation, cumulative intelligence, human-AI partnership, accessibility through familiarity, and token conservation. The session resulted in comprehensive documentation of these function types with practical implementation guidance.
2. CURRENT_STATE
The CRAFT framework has been significantly enhanced with detailed specifications for five function types:
Context-Aware Functions: We developed a complete framework for functions that adapt to conversation context, including implementations for generate_adaptive_response()
with state tracking capabilities. We created examples demonstrating progressive adaptation through a conversation about API rate limiting, showing how technical depth adapts to user needs. Advanced applications include persona-based awareness, multi-session context preservation, and context-aware learning loops.
Multi-Modal Functions: We established a framework for transforming content between different formats with the transform_content()
function that detects input formats and preserves meaning across transformations. Example scenarios demonstrate text-to-structured data, code-to-explanation, and tabular-to-visualization conversions. Advanced implementations include generate_data_report()
, present_multi_format()
, and verify_semantic_equivalence()
.
Composable AI Instructions: We created a composition system with chain_functions()
and create_function_pipeline()
implementations for combining simple functions into complex pipelines. Examples include content generation, data analysis workflows, and interactive dialogue management. Advanced applications include dynamic composition, meta-functions for optimization, and collaborative pipeline building.
Handoff-Optimized Functions: We implemented a complete handoff system with create_session_snapshot()
and resume_from_handoff()
functions that maintain continuity across sessions. A detailed example of a multi-session marketing strategy project demonstrates practical application. Advanced implementations include differential, hierarchical, and mergeable handoffs.
Reflection Functions: We developed a comprehensive reflection system with evaluate_response_quality()
, analyze_response_patterns()
, and develop_improvement_strategy()
functions. Extended examples demonstrate quality evaluation, pattern analysis, and improvement planning. The complete reflection loop implementation shows how these functions work together in a continuous improvement cycle.
3. KEY_DECISIONS
Function Structure Approach: We decided to implement functions using Python-like syntax with embedded AI-specific directives using the
#H->AI::
comment format. This maintains compatibility with standard Python while adding AI-specific guidance that couldn't be expressed in traditional function signatures.Implementation vs. Conceptual Balance: We chose to provide both conceptual explanations and concrete implementation details (through pseudocode) for each function type. This balance ensures both theoretical understanding and practical applicability.
Modularity First: We designed all function systems with modularity as a core principle, enabling components to be used independently or combined into more complex systems. This aligns with CRAFT's "Structure Enables Freedom" principle by allowing flexible application of components.
Integration with Existing CRAFT Components: We ensured all new function designs complement and extend the existing CRAFT data types and structures rather than replacing them. This preserves compatibility with the current CRAFT implementation.
Explicit Documentation of Benefits: For each function type, we explicitly documented specific benefits to demonstrate practical value rather than just theoretical possibilities. This helps justify implementation effort by connecting to tangible improvements.
Example-Driven Design: We prioritized concrete examples and scenarios to illustrate how each function operates in practice, making the concepts more accessible and immediately applicable.
4. UNRESOLVED_QUESTIONS
Implementation Priority: Which function types should be prioritized for immediate implementation in the CRAFT framework? While all offer value, resource constraints may require phased implementation.
Testing Methodology: How should these function implementations be tested to ensure reliability and expected behavior? We haven't defined specific testing approaches for each function type.
Integration Complexity: How complex will it be to integrate these functions with the existing CRAFT data types and structures? Some functions may require modifications to core CRAFT components.
Storage Requirements: For functions that maintain state (especially Reflection and Context-Aware functions), how will state information be stored efficiently between sessions without exceeding token limits?
User Interface Considerations: How should users (both technical and non-technical) interact with and configure these functions? We've focused on implementation details but not on user-facing interfaces.
Performance Implications: What are the performance implications (token usage, response time) of implementing these more complex function systems, particularly for reflection and context awareness?
5. NEXT_STEPS
Prioritization Exercise: Conduct a prioritization exercise to determine which function types offer the highest value for immediate implementation. Consider creating a scoring matrix based on implementation effort vs. expected benefit.
Implementation Planning: For the highest-priority function types, develop detailed implementation plans including specific integration points with existing CRAFT components, any required modifications to core structures, and timeline estimates.
Prototype Development: Create working prototypes of at least one key function from each type to validate the approach and identify any unforeseen challenges. Begin with simpler implementations before progressing to more complex ones.
Documentation Enhancement: Develop formal documentation for each function type, including comprehensive API specifications, usage guidelines, and integration examples for CRAFT users of different technical levels.
User Interface Design: Design user interfaces for configuring and interacting with these functions, focusing on making them accessible to both technical and non-technical users in alignment with CRAFT's accessibility principle.
Testing Framework: Develop a testing framework specifically designed for evaluating function performance, reliability, and effectiveness. Include both automated tests and human evaluation components.
Example Library Creation: Build a library of real-world examples for each function type to help users understand practical applications and implementation patterns. Include both simple and complex examples to demonstrate scalability.
Integration Roadmap: Create a roadmap for progressively integrating these function types into the core CRAFT framework, including dependencies, potential conflicts, and migration strategies for existing CRAFT implementations.
Token-Optimized Function Design for CRAFT Framework
Core Concept
Token-Optimized Function Design centers on creating functions that maximize information density while minimizing token usage. In AI interactions, tokens are a finite resource that directly impacts cost, speed, and limitations of exchanges. By optimizing functions for token efficiency, CRAFT can enable more complex interactions within the same token constraints, creating a richer experience without increased computational overhead.
Implementation Details
python
def create_token_optimized_function(function_name, operation, parameters=None, compression_level="balanced"):
"""
Creates a token-efficient function with optimized parameter handling and operation execution.
Args:
function_name (str): Name of the function
operation (str): Core operation the function performs
parameters (dict, optional): Parameters for the function with default values
compression_level (str): Level of token optimization ("minimal", "balanced", "aggressive")
Returns:
str: Token-optimized function definition
"""
#H->AI::TokenStrategy: (Apply token optimization techniques based on compression_level)
# Default parameters if none provided
if parameters is None:
parameters = {}
# Build parameter string with defaults to reduce repeated instructions
param_str = ""
for param, default in parameters.items():
param_str += f"{param}={repr(default)}, "
# Remove trailing comma and space
if param_str:
param_str = param_str[:-2]
# Token-optimized docstring that scales based on compression level
if compression_level == "minimal":
docstring = f'"""{operation}"""'
elif compression_level == "balanced":
docstring = f'"""{operation}\n\nParameters:\n{", ".join(parameters.keys()) if parameters else "None"}\n"""'
else: # aggressive
docstring = '"""Function documentation in CRAFT_DOCS variable"""'
# Build the function with token-optimized structure
function_def = f"""
def {function_name}({param_str}):
{docstring}
#H->AI::TokenDirective: (Execute {operation} with maximum token efficiency)
#H->AI::CompressionLevel: ({compression_level})
# Function implementation will be token-optimized based on compression level
result = None
# Operation-specific implementation
{generate_operation_code(operation, compression_level)}
return result
"""
return function_def
def generate_operation_code(operation, compression_level):
"""
Generates the token-optimized implementation code for a given operation.
Args:
operation (str): The operation to implement
compression_level (str): Level of token optimization
Returns:
str: Token-optimized implementation code
"""
# This would contain operation-specific implementations
# Simplified example for illustration
if compression_level == "aggressive":
return "result = exec(CRAFT_OPS['" + operation + "'])"
else:
return f"# Standard implementation for {operation}"
def register_token_patterns(pattern_name, pattern_template, use_cases=None, token_savings=None):
"""
Registers reusable token patterns for optimization across multiple functions.
Args:
pattern_name (str): Identifier for the pattern
pattern_template (str): Template structure that saves tokens
use_cases (list, optional): Scenarios where pattern is applicable
token_savings (int, optional): Estimated tokens saved per use
Returns:
bool: Success of registration
"""
#H->AI::TokenRegistry: (Store this pattern in the CRAFT token optimization registry)
# Implementation would register the pattern for reuse
return True
def optimize_existing_function(function_code, target_token_reduction=0.3, preserve_readability=True):
"""
Retrofits existing functions with token optimization techniques.
Args:
function_code (str): Existing function definition
target_token_reduction (float): Proportional reduction goal (0.0-1.0)
preserve_readability (bool): Whether human readability is important
Returns:
str: Token-optimized version of the function
"""
#H->AI::TokenRefactor: (Apply optimization transformations to existing code)
# Implementation would analyze and transform the function
# Simplified example for illustration
if preserve_readability:
# Apply moderate optimizations
pass
else:
# Apply aggressive optimizations
pass
return "# Optimized function implementation"
Example Scenarios
Scenario 1: Creating a Token-Efficient Text Generator
python
text_gen_params = {
"topic": "default topic",
"length": "medium",
"style": "neutral",
"include_citations": False,
"key_points": []
}
generate_text_function = create_token_optimized_function(
function_name="generate_efficient_text",
operation="Create cohesive text on provided topic with specified parameters",
parameters=text_gen_params,
compression_level="balanced"
)
# Usage example:
# generate_efficient_text(topic="AI Ethics", length="short", key_points=["accountability", "transparency"])
Scenario 2: Token-Optimized Multi-turn Conversation
python
# Register a token-saving pattern for conversation history
register_token_patterns(
pattern_name="conversation_digest",
pattern_template="USER:[intent] → AI:[response_type]",
use_cases=["conversation_history", "context_tracking"],
token_savings=42 # Average tokens saved per turn
)
# Create a function that maintains conversation context with minimal tokens
convo_params = {
"history_digest": [],
"user_profile": "general",
"current_intent": "inquiry",
"preservation_level": "key_points_only"
}
conversation_function = create_token_optimized_function(
function_name="maintain_efficient_conversation",
operation="Track conversation flow while minimizing token usage",
parameters=convo_params,
compression_level="aggressive"
)
# Usage example:
# maintain_efficient_conversation(
# history_digest=["USER:[product_info] → AI:[specs_provided]", "USER:[pricing] → AI:[options_listed]"],
# current_intent="decision_making"
# )
Scenario 3: Optimizing an Existing Verbose Function
python
verbose_function = """
def generate_comprehensive_report(subject, depth="detailed", sections=["overview", "analysis", "recommendations", "references"],
include_visuals=True, data_sources=["primary", "secondary"], formatting="standard",
executive_summary=True, appendices=False, revision_history=True):
\"\"\"
Generates a comprehensive report on the specified subject with extensive customization options.
This function produces detailed professional documents suitable for business, academic, or technical contexts.
The report can include various sections, visual elements, and reference information based on the parameters provided.
Data is gathered from specified sources and integrated into a cohesive document following the requested formatting guidelines.
Parameters:
subject (str): The main topic of the report
depth (str): Level of detail - "brief", "standard", "detailed", or "exhaustive"
sections (list): Report sections to include
include_visuals (bool): Whether to include charts, diagrams, and other visual elements
data_sources (list): Types of sources to incorporate
formatting (str): Document formatting style
executive_summary (bool): Include an executive summary at the beginning
appendices (bool): Include supplementary materials in appendices
revision_history (bool): Include document revision tracking information
Returns:
str: The completed report text
\"\"\"
# Extensive implementation with many conditionals and string operations
# [... many lines of implementation ...]
return final_report
"""
# Optimize the verbose function
optimized_report_function = optimize_existing_function(
function_code=verbose_function,
target_token_reduction=0.4,
preserve_readability=True
)
Benefits of Token-Optimized Functions
Extended Functionality Within Limits: Enables more complex operations without hitting token limits.
Cost Efficiency: Reduces API costs by minimizing token usage for equivalent functionality.
Improved Response Time: Smaller token counts often result in faster API processing times.
Scalability: Makes large-scale interactions viable by managing token economy.
Accessibility for Complex Projects: Enables sophisticated applications that would otherwise exceed practical token limits.
Resource Conservation: Aligns with CRAFT's principle of "Conservation of Tokens."
Consistent Performance: Reduces variance in response quality due to truncation or context limitations.
Optimized Context Windows: Maximizes available context for the parts of conversations that need it most.
Advanced Applications
Dynamic Token Budget Allocation
python
def allocate_token_budget(total_budget, operation_priorities):
"""
Strategically allocates tokens across function calls based on priorities.
Args:
total_budget (int): Total tokens available
operation_priorities (dict): Operations and their relative importance
Returns:
dict: Token allocation for each operation
"""
#H->AI::TokenBudget: (Distribute {total_budget} tokens according to priorities)
allocations = {}
total_priority = sum(operation_priorities.values())
for operation, priority in operation_priorities.items():
# Allocate tokens proportionally to priority
allocations[operation] = int((priority / total_priority) * total_budget)
return allocations
# Example usage:
# budget = 4000 # Total tokens available
# priorities = {"text_generation": 5, "classification": 1, "summarization": 3}
# allocations = allocate_token_budget(budget, priorities)
# -> {"text_generation": 2222, "classification": 444, "summarization": 1333}
Token-Usage Analytics and Optimization Suggestions
python
def analyze_token_efficiency(function_usage_history):
"""
Analyzes function usage patterns to recommend token optimizations.
Args:
function_usage_history (list): Records of function calls and token usage
Returns:
dict: Optimization recommendations with estimated savings
"""
#H->AI::TokenAnalytics: (Evaluate usage patterns to identify optimization opportunities)
recommendations = {}
# Identify patterns of inefficient token usage
# Example implementation would analyze redundancies, overused patterns, etc.
return recommendations
# Example usage tracking:
# usage_history = [
# {"function": "generate_text", "tokens_used": 350, "parameters": {...}},
# {"function": "summarize_content", "tokens_used": 220, "parameters": {...}},
# # ...more usage data
# ]
# recommendations = analyze_token_efficiency(usage_history)
Contextual Compression Techniques
python
def apply_contextual_compression(content, context_awareness=0.8):
"""
Applies compression techniques that preserve meaning based on conversation context.
Args:
content (str): Content to compress
context_awareness (float): How much to rely on shared context (0.0-1.0)
Returns:
str: Compressed content
"""
#H->AI::ContextCompression: (Reduce tokens while maintaining semantic meaning)
# Implementation would apply various compression techniques:
# - Pronoun substitution
# - Implied reference recognition
# - Context-dependent abbreviation
# - Redundancy elimination
return "compressed_content"
# Example of integration with other functions:
def generate_response_in_conversation(prompt, conversation_history, token_budget):
"""Token-optimized response generation within ongoing conversation."""
# Compress conversation history to save tokens
compressed_history = apply_contextual_compression(
" ".join(conversation_history),
context_awareness=0.85
)
# Calculate remaining tokens for response
history_tokens = estimate_token_count(compressed_history)
response_tokens = token_budget - history_tokens
# Generate optimized response with remaining budget
return generate_within_token_limit(prompt, response_tokens)
Integration with CRAFT Principles
Token-Optimized Function Design aligns with several CRAFT principles:
Structure Enables Freedom: By managing token economy, it paradoxically enables more creative freedom by removing technical constraints.
Conservation of Tokens: Directly implements this core principle by systematically minimizing token usage.
Accessibility Through Familiarity: Uses familiar function patterns while introducing optimization that works behind the scenes.
Cumulative Intelligence: Enables longer, more complex interactions that build upon previous exchanges.
Human-AI Partnership: Creates a more responsive AI partner by reducing technical limits on interaction.
Implementation Recommendations
Start with Critical Path Functions: Begin optimization with the most frequently used functions that have the highest impact on token usage.
Balance Readability and Optimization: Use "aggressive" compression only when necessary; prefer "balanced" for most cases to maintain code clarity.
Create a Token Optimization Style Guide: Document standard patterns and approaches to ensure consistency across the project.
Implement a Token Budget System: Incorporate token budgeting at the system level to manage allocation across function calls.
Measure and Iterate: Track actual token usage in real conversations and continuously refine optimization techniques.
Build a Pattern Library: Develop and maintain a library of token-saving patterns that can be applied across functions.
Contextual Optimization: Consider conversation context when optimizing - some critical points need more tokens for clarity, while others can be streamlined.
Documentation: Maintain clear documentation of optimization techniques used to ensure maintainability.
Practical Implementation Steps
Audit Current Token Usage: Analyze existing functions to identify high-token usage areas.
Create Token Budgets: Establish token budgets for different operation types.
Develop Optimization Strategies: Create specific strategies for different function types (e.g., text generation vs. classification).
Implement Token-Aware Function Wrappers: Create wrapper functions that enforce token budgets.
Build Token Analytics: Track and analyze token usage to identify optimization opportunities.
Integration with Existing CRAFT Components: Ensure token optimization works seamlessly with other CRAFT framework elements.
Testing Framework: Establish testing protocols to verify that optimization doesn't impact functionality.
User Feedback Loop: Incorporate mechanisms to detect when token optimization negatively impacts user experience.
Retry
RK
Great, please expand on Natural Language Parameter Handling with details, benefits, explanations , examples and anything else that you think will be helpful.
Edit
Natural Language Parameter Handling for CRAFT Framework
Core Concept
Natural Language Parameter Handling (NLPH) is a sophisticated approach to function interaction that enables users to pass parameters to CRAFT functions using natural language rather than rigid, programming-style syntax. This system interprets intent, extracts relevant values, and maps them to formal parameters, creating a more intuitive interface between human language and structured function execution.
Unlike traditional programming where parameters must be precisely formatted and positioned, NLPH allows users to express their needs conversationally. The system then extracts the necessary information, handles ambiguity, infers missing parameters, and validates input—all while maintaining the structural benefits of formal function definitions.
Implementation Details
python
def create_nlp_parameter_handler(function_name, formal_parameters, language_patterns=None, extraction_confidence=0.7):
"""
Creates a natural language parameter handler that maps conversational input to formal parameters.
Args:
function_name (str): Name of the function
formal_parameters (dict): Dictionary of parameter names and their expected types/formats
language_patterns (list, optional): Common language patterns for parameter expression
extraction_confidence (float): Minimum confidence threshold for parameter extraction
Returns:
function: A function that handles natural language parameter extraction
"""
#H->AI::NLParameterExtraction: (Extract parameter values from natural language input)
if language_patterns is None:
# Default patterns for common parameter types
language_patterns = [
"with {param_name} (of|as|being|set to) {value}",
"{param_name} (is|should be|being) {value}",
"(use|using|utilize|set) {value} (for|as) {param_name}",
"{param_name}: {value}"
]
def parameter_handler(natural_language_input):
"""
Extracts parameters from natural language input.
Args:
natural_language_input (str): User's natural language request
Returns:
dict: Extracted parameters mapped to their values
"""
#H->AI::LanguageAnalysis: (Identify parameter mentions and extract values)
extracted_params = {}
ambiguities = {}
# For each formal parameter, attempt to extract from natural language
for param_name, param_info in formal_parameters.items():
param_type = param_info.get('type', 'string')
extracted_value, confidence = extract_parameter_value(
natural_language_input,
param_name,
param_type,
language_patterns
)
if confidence >= extraction_confidence:
extracted_params[param_name] = extracted_value
elif confidence > 0:
# Store ambiguous extractions for later resolution
ambiguities[param_name] = {
'value': extracted_value,
'confidence': confidence
}
# Handle default values for missing parameters
for param_name, param_info in formal_parameters.items():
if param_name not in extracted_params and 'default' in param_info:
extracted_params[param_name] = param_info['default']
# Handle ambiguities based on context and other parameters
if ambiguities:
resolved_params = resolve_parameter_ambiguities(
ambiguities,
extracted_params,
formal_parameters
)
extracted_params.update(resolved_params)
# Validate all parameters
validate_parameters(extracted_params, formal_parameters)
return extracted_params
return parameter_handler
def extract_parameter_value(text, param_name, param_type, patterns):
"""
Extracts a parameter value from text based on patterns.
Args:
text (str): Input text
param_name (str): Parameter to extract
param_type (str): Expected type
patterns (list): Language patterns to match
Returns:
tuple: (extracted_value, confidence)
"""
#H->AI::ValueExtraction: (Find and extract the value for {param_name})
# Implementation would use NLP techniques to extract values based on patterns
# Simplified example for illustration:
highest_confidence = 0
best_match = None
# Look for explicit mentions first
for pattern in patterns:
# Pattern matching logic would go here
# In practice, would use regex, NLP parsing, or similar techniques
pass
# If no high-confidence match, try inferring from context
if highest_confidence < 0.5:
# Context-based inference logic
pass
# Convert extracted text to appropriate type
if best_match and param_type == 'integer':
try:
best_match = int(best_match)
except ValueError:
highest_confidence *= 0.5 # Reduce confidence if conversion fails
return best_match, highest_confidence
def resolve_parameter_ambiguities(ambiguities, extracted_params, formal_parameters):
"""
Resolves ambiguous parameter extractions based on context and constraints.
Args:
ambiguities (dict): Ambiguous parameters with confidence levels
extracted_params (dict): Already extracted parameters
formal_parameters (dict): Parameter definitions
Returns:
dict: Resolved parameters
"""
#H->AI::AmbiguityResolution: (Resolve uncertain parameter extractions)
resolved = {}
# Implementation would use context, parameter relationships, and constraints
# to resolve ambiguities
return resolved
def validate_parameters(params, formal_parameters):
"""
Validates extracted parameters against their formal definitions.
Args:
params (dict): Extracted parameters
formal_parameters (dict): Parameter definitions
Returns:
bool: Whether parameters are valid
"""
#H->AI::ParameterValidation: (Ensure extracted parameters meet requirements)
# Implementation would check types, ranges, and other constraints
return True
def create_nl_function(function_name, operation, formal_parameters, examples=None):
"""
Creates a function that can be called with natural language.
Args:
function_name (str): Function name
operation (str): Operation description
formal_parameters (dict): Parameter definitions
examples (list, optional): Example natural language calls
Returns:
function: Function with natural language parameter handling
"""
#H->AI::NLFunctionCreation: (Create function with natural language interface)
param_handler = create_nlp_parameter_handler(function_name, formal_parameters)
def nl_function(natural_language_input):
"""
Executes function based on natural language input.
"""
params = param_handler(natural_language_input)
# Normal function execution with extracted parameters
result = execute_operation(operation, params)
return result
# Attach metadata for documentation
nl_function.__name__ = function_name
nl_function.__doc__ = f"""
{operation}
Can be called with natural language like:
{format_examples(examples) if examples else 'No examples provided'}
Formal parameters:
{format_parameters(formal_parameters)}
"""
return nl_function
def format_examples(examples):
"""Formats example calls for documentation."""
if not examples:
return ""
return "\n".join([f"- \"{example}\"" for example in examples])
def format_parameters(parameters):
"""Formats parameter definitions for documentation."""
result = ""
for name, info in parameters.items():
param_type = info.get('type', 'string')
default = f" (default: {info['default']})" if 'default' in info else ""
description = info.get('description', '')
result += f"- {name}: {param_type}{default} - {description}\n"
return result
def execute_operation(operation, params):
"""
Executes the core operation with the provided parameters.
Args:
operation (str): Operation to perform
params (dict): Parameters for the operation
Returns:
any: Operation result
"""
#H->AI::OperationExecution: (Execute {operation} with extracted parameters)
# Implementation would handle the actual operation logic
return "Operation result"
Example Scenarios
Scenario 1: Text Analysis Function with Natural Language Parameters
python
# Define formal parameters with types, defaults, and descriptions
text_analysis_params = {
'text': {
'type': 'string',
'description': 'The text to analyze'
},
'analysis_type': {
'type': 'string',
'default': 'sentiment',
'description': 'Type of analysis to perform (sentiment, entities, topics, etc.)'
},
'depth': {
'type': 'string',
'default': 'standard',
'description': 'Depth of analysis (quick, standard, comprehensive)'
},
'format': {
'type': 'string',
'default': 'summary',
'description': 'Output format (summary, detailed, structured)'
}
}
# Create examples of natural language calls
text_analysis_examples = [
"Analyze this text for sentiment: The product exceeded my expectations in every way.",
"Can you identify entities in this article with comprehensive depth?",
"Do a quick topic analysis on this text and give me a detailed report.",
"Analyze the following email with standard depth and return a structured format."
]
# Create the natural language function
analyze_text = create_nl_function(
function_name="analyze_text",
operation="Analyzes text for various linguistic and semantic features",
formal_parameters=text_analysis_params,
examples=text_analysis_examples
)
# Sample usage:
# result = analyze_text("Check the sentiment of this review and give me a detailed breakdown: I loved the first half, but the ending was disappointing.")
Scenario 2: Business Report Generator with Complex Parameters
python
# Define formal parameters for a business report generator
report_params = {
'business_area': {
'type': 'string',
'description': 'The business area to report on'
},
'time_period': {
'type': 'string',
'default': 'last quarter',
'description': 'Time period for the report'
},
'metrics': {
'type': 'list',
'default': ['revenue', 'expenses', 'profit'],
'description': 'Metrics to include in the report'
},
'comparison': {
'type': 'string',
'default': 'previous period',
'description': 'What to compare the metrics against'
},
'include_charts': {
'type': 'boolean',
'default': True,
'description': 'Whether to include visual charts'
},
'stakeholders': {
'type': 'string',
'default': 'executives',
'description': 'Target audience for the report'
}
}
report_examples = [
"Generate a report on marketing performance for Q2 with focus on ROI and lead generation metrics compared to last year.",
"Create a financial report for the technical team without charts, focusing on project expenses for the past month.",
"I need a sales report with charts comparing our performance to competitors for the last six months.",
"Generate a quick HR overview for department heads including turnover and hiring metrics."
]
generate_business_report = create_nl_function(
function_name="generate_business_report",
operation="Creates a comprehensive business report based on specified parameters",
formal_parameters=report_params,
examples=report_examples
)
# Sample usage:
# report = generate_business_report("I need a sales performance report for the East region for Q1 with detailed charts comparing it to our targets. Make it suitable for the sales team.")
Scenario 3: Adaptive Meeting Scheduler with Mixed Explicit and Implicit Parameters
python
# Define parameters for meeting scheduler
scheduler_params = {
'meeting_type': {
'type': 'string',
'default': 'standard',
'description': 'Type of meeting (standard, brainstorming, review, one-on-one)'
},
'duration': {
'type': 'integer',
'default': 60,
'description': 'Meeting duration in minutes'
},
'participants': {
'type': 'list',
'description': 'People to invite to the meeting'
},
'priority': {
'type': 'string',
'default': 'normal',
'description': 'Meeting priority (low, normal, high, urgent)'
},
'preferred_time': {
'type': 'string',
'description': 'Preferred time range or specific time'
},
'location': {
'type': 'string',
'default': 'virtual',
'description': 'Meeting location (virtual, office, specific room)'
},
'agenda_items': {
'type': 'list',
'default': [],
'description': 'Topics to cover in the meeting'
},
'preparation_required': {
'type': 'boolean',
'default': False,
'description': 'Whether preparation materials should be sent beforehand'
}
}
scheduler_examples = [
"Schedule a 30-minute project review with the design team tomorrow afternoon, preferably virtual.",
"I need a high-priority brainstorming session in Conference Room A with marketing and product teams for 2 hours next Tuesday.",
"Set up a quick 15-minute check-in with Sarah about the client proposal for sometime today.",
"Arrange a quarterly planning meeting with department heads, we'll need 3 hours and everyone should prepare updates."
]
schedule_meeting = create_nl_function(
function_name="schedule_meeting",
operation="Schedules a meeting based on natural language requirements",
formal_parameters=scheduler_params,
examples=scheduler_examples
)
# Demonstrates extracting a mix of explicit and implicit parameters:
# explicit_and_implicit = schedule_meeting("Set up a high-priority brainstorming session with the product team for next Monday at 2 PM. We'll need 90 minutes to discuss the upcoming feature launch.")
Benefits of Natural Language Parameter Handling
Intuitive Interaction: Users can interact with functions using natural language without learning formal parameter syntax.
Reduced Cognitive Load: Eliminates the need to remember parameter names, types, and order.
Flexibility in Expression: The same function can be called with many different phrasings and word orders.
Parameter Inference: Intelligent defaults and context awareness reduce the need to specify every parameter.
Graceful Ambiguity Resolution: Handles uncertainty in parameter extraction through context and validation.
Self-Documenting Usage: By seeing examples in natural language, users understand function capabilities more easily.
Bridges Technical and Non-Technical Users: Technical functions become accessible to non-programmers.
Reduced Token Usage: Often requires fewer tokens than explicit parameter listing and formal syntax.
Conversation Continuity: Maintains a more natural conversation flow without switching to code-like syntax.
Adaptive to User Language: Can adapt to different terminology and expressions for the same concepts.
Advanced Applications
Context-Aware Parameter Memory
python
def create_parameter_memory(memory_duration='session'):
"""
Creates a system that remembers parameter choices across function calls.
Args:
memory_duration (str): How long to remember parameters ('call', 'session', 'persistent')
Returns:
dict: Parameter memory system
"""
#H->AI::ParameterMemory: (Track parameter usage across multiple calls)
return {
'parameter_history': {},
'value_frequencies': {},
'parameter_defaults': {},
'context_model': {},
'duration': memory_duration
}
def update_parameter_memory(memory, function_name, parameters):
"""
Updates parameter memory with new function call data.
Args:
memory (dict): Parameter memory system
function_name (str): Function that was called
parameters (dict): Parameters that were used
"""
#H->AI::MemoryUpdate: (Add new parameter usage to memory)
# Update parameter history
if function_name not in memory['parameter_history']:
memory['parameter_history'][function_name] = []
memory['parameter_history'][function_name].append(parameters)
# Update value frequencies
if function_name not in memory['value_frequencies']:
memory['value_frequencies'][function_name] = {}
for param, value in parameters.items():
if param not in memory['value_frequencies'][function_name]:
memory['value_frequencies'][function_name][param] = {}
value_str = str(value)
memory['value_frequencies'][function_name][param][value_str] = \
memory['value_frequencies'][function_name][param].get(value_str, 0) + 1
# Update context model
# Implementation would analyze patterns linking context to parameter choices
return memory
def get_context_aware_defaults(memory, function_name, context):
"""
Gets parameter defaults based on usage history and current context.
Args:
memory (dict): Parameter memory system
function_name (str): Function being called
context (dict): Current conversation context
Returns:
dict: Suggested parameter defaults
"""
#H->AI::ContextDefaults: (Generate intelligent defaults based on context)
# Implementation would use frequency analysis and context matching
# to suggest appropriate defaults
return {}
Natural Language Function Composition
python
def create_nl_function_pipeline(functions, composition_rules=None):
"""
Creates a pipeline that can be called with natural language to execute multiple functions.
Args:
functions (list): List of natural language functions
composition_rules (dict, optional): Rules for connecting function outputs to inputs
Returns:
function: Pipeline function with natural language interface
"""
#H->AI::NLPipeline: (Create pipeline that can be controlled with natural language)
def nl_pipeline(natural_language_input):
"""
Executes a pipeline based on natural language input.
Args:
natural_language_input (str): Pipeline description in natural language
Returns:
any: Pipeline result
"""
# Parse the pipeline description
stages, connections = parse_pipeline_description(natural_language_input, functions)
# Execute the pipeline
results = {}
for stage_name, function_call in stages.items():
function_name, nl_params = function_call
# Find the function
target_function = next(f for f in functions if f.__name__ == function_name)
# Apply input connections from previous stages
for source, target in connections.get(stage_name, {}).items():
if source in results:
nl_params = f"{nl_params} with {target} from {results[source]}"
# Execute the function
results[stage_name] = target_function(nl_params)
# Return the final result
return results[list(stages.keys())[-1]]
return nl_pipeline
def parse_pipeline_description(description, available_functions):
"""
Parses a natural language pipeline description.
Args:
description (str): Natural language pipeline description
available_functions (list): Available functions
Returns:
tuple: (stages, connections)
"""
#H->AI::PipelineParser: (Parse natural language pipeline description)
# Implementation would parse descriptions like:
# "First analyze the sentiment of this text, then generate a response based on the sentiment"
# Simplified example return:
stages = {
'stage1': ('analyze_text', 'analyze this text for sentiment'),
'stage2': ('generate_response', 'create a response based on the sentiment')
}
connections = {
'stage2': {
'stage1': 'sentiment'
}
}
return stages, connections
Feedback-Driven Parameter Extraction Improvement
python
def create_self_improving_nlp_handler(function_name, formal_parameters):
"""
Creates a natural language parameter handler that improves from user feedback.
Args:
function_name (str): Function name
formal_parameters (dict): Parameter definitions
Returns:
function: Self-improving parameter handler
"""
#H->AI::LearningNLHandler: (Create parameter handler that learns from feedback)
# Create base handler
base_handler = create_nlp_parameter_handler(function_name, formal_parameters)
# Create learning model
learning_model = {
'extraction_patterns': [],
'correction_history': [],
'success_patterns': [],
'failure_patterns': []
}
def improving_handler(natural_language_input, feedback=None):
"""
Handles natural language parameters with a learning component.
Args:
natural_language_input (str): Natural language input
feedback (dict, optional): Feedback on previous extractions
Returns:
dict: Extracted parameters
"""
# Apply learning from feedback
if feedback:
update_learning_model(learning_model, feedback)
# Extract parameters using base handler
extracted_params = base_handler(natural_language_input)
# Apply improvements from learning model
enhanced_params = enhance_with_learning(extracted_params, natural_language_input, learning_model)
return enhanced_params
return improving_handler
def update_learning_model(model, feedback):
"""
Updates the learning model based on feedback.
Args:
model (dict): Learning model
feedback (dict): User feedback
"""
#H->AI::FeedbackLearning: (Improve extraction based on user corrections)
# Implementation would analyze feedback to improve future extractions
return model
def enhance_with_learning(params, input_text, model):
"""
Enhances parameter extraction using the learning model.
Args:
params (dict): Initially extracted parameters
input_text (str): Original natural language input
model (dict): Learning model
Returns:
dict: Enhanced parameters
"""
#H->AI::EnhancedExtraction: (Apply learned patterns to improve extraction)
# Implementation would apply learned patterns to improve extraction
return params
Integration with CRAFT Principles
Natural Language Parameter Handling aligns with CRAFT's core principles:
Structure Enables Freedom: By connecting natural language to structured function calls, NLPH gives users freedom of expression while maintaining the benefits of structured operations.
Code as Conversation: Transforms function calls from code-like syntax to natural conversation, making the boundary between code and conversation more fluid.
Accessibility Through Familiarity: Users communicate in their familiar natural language instead of learning function syntax.
Human-AI Partnership: Creates a more intuitive collaboration where humans can express needs naturally and the AI handles the translation to formal structure.
Conservation of Tokens: Often requires fewer tokens than explicit parameter calls, especially for complex functions with many parameters.
Implementation Recommendations
Start Small: Begin with functions that have clearly identifiable parameters with distinct meanings.
Build Pattern Libraries: Create reusable extraction patterns for common parameter types (dates, numbers, selections, etc.).
Collect Usage Examples: Gather diverse examples of how users might naturally express parameter values.
Implement Graceful Fallback: When parameter extraction is uncertain, implement a graceful confirmation flow rather than failing.
Context Integration: Connect NLPH with conversational context tracking to improve parameter inference.
Incremental Complexity: Start with simple parameter extraction and gradually add advanced features like ambiguity resolution and learning.
Feedback Loops: Incorporate user feedback mechanisms to improve extraction over time.
Documentation: Create clear documentation with natural language examples that show the range of acceptable inputs.
Practical Integration with Other CRAFT Functions
python
# Integration with Context-Aware Functions
def create_context_aware_nl_function(function_name, operation, formal_parameters, context_variables):
"""
Creates a function with both natural language parameters and context awareness.
Args:
function_name (str): Function name
operation (str): Operation description
formal_parameters (dict): Parameter definitions
context_variables (list): Context variables to track
Returns:
function: Context-aware function with natural language interface
"""
# Create NL parameter handler
param_handler = create_nlp_parameter_handler(function_name, formal_parameters)
# Create context tracking
context_tracker = create_context_tracker(context_variables)
def context_aware_nl_function(natural_language_input, context=None):
"""
Executes function based on natural language and context.
Args:
natural_language_input (str): Natural language input
context (dict, optional): Current context
Returns:
tuple: (result, updated_context)
"""
# Extract parameters from natural language
extracted_params = param_handler(natural_language_input)
# Initialize or update context
current_context = context_tracker.update(context or {}, extracted_params)
# Enhance parameters with context
enhanced_params = enhance_params_with_context(extracted_params, current_context)
# Execute operation
result = execute_operation(operation, enhanced_params)
# Update context with results
updated_context = context_tracker.update(current_context, result)
return result, updated_context
return context_aware_nl_function
# Integration with Token-Optimized Functions
def create_token_efficient_nl_function(function_name, operation, formal_parameters, token_budget=500):
"""
Creates a function that combines natural language parameters with token optimization.
Args:
function_name (str): Function name
operation (str): Operation description
formal_parameters (dict): Parameter definitions
token_budget (int): Maximum tokens for the function
Returns:
function: Token-optimized function with natural language interface
"""
# Create NL parameter handler
param_handler = create_nlp_parameter_handler(function_name, formal_parameters)
def token_efficient_nl_function(natural_language_input):
"""
Executes function with natural language parameters while optimizing token usage.
Args:
natural_language_input (str): Natural language input
Returns:
any: Function result
"""
# Extract parameters from natural language
extracted_params = param_handler(natural_language_input)
# Calculate token usage for parameter extraction
param_tokens = estimate_token_count(str(extracted_params))
# Allocate remaining tokens for operation
operation_token_budget = token_budget - param_tokens
# Execute token-optimized operation
result = execute_token_optimized_operation(
operation,
extracted_params,
operation_token_budget
)
return result
return token_efficient_nl_function
Real-World Application Example
python
# Example: Customer Support Assistant with Natural Language Function Interface
# Define formal parameters for support functions
ticket_creation_params = {
'issue_type': {
'type': 'string',
'description': 'The type of support issue',
'default': 'general'
},
'description': {
'type': 'string',
'description': 'Description of the problem'
},
'priority': {
'type': 'string',
'description': 'Issue priority',
'default': 'normal'
},
'customer_id': {
'type': 'string',
'description': 'Customer identifier'
},
'product': {
'type': 'string',
'description': 'Related product or service',
'default': 'general'
},
'attachments': {
'type': 'list',
'description': 'References to attached files',
'default': []
}
}
knowledge_search_params = {
'query': {
'type': 'string',
'description': 'Search terms'
},
'category': {
'type': 'string',
'description': 'Knowledge base category',
'default': 'all'
},
'max_results': {
'type': 'integer',
'description': 'Maximum number of results to return',
'default': 5
},
'relevance_threshold': {
'type': 'float',
'description': 'Minimum relevance score (0-1)',
'default': 0.7
}
}
response_generation_params = {
'issue_summary': {
'type': 'string',
'description': 'Summary of the customer issue'
},
'knowledge_articles': {
'type': 'list',
'description': 'Relevant knowledge base articles',
'default': []
},
'tone': {
'type': 'string',
'description': 'Tone of the response',
'default': 'helpful'
},
'include_references': {
'type': 'boolean',
'description': 'Whether to include article references',
'default': True
},
'customer_history': {
'type': 'dict',
'description': 'Customer interaction history',
'default': {}
}
}
# Create natural language functions
create_ticket = create_nl_function(
function_name="create_ticket",
operation="Creates a support ticket in the system",
formal_parameters=ticket_creation_params,
examples=[
"Create a ticket for customer SC-12345 regarding their login issue with high priority",
"Open a normal priority ticket for the billing team about a refund request from customer JD-54321"
]
)
search_knowledge_base = create_nl_function(
function_name="search_knowledge_base",
operation="Searches the knowledge base for relevant articles",
formal_parameters=knowledge_search_params,
examples=[
"Search for articles about password reset in the security category",
"Find the top 3 most relevant articles about account deletion"
]
)
generate_response = create_nl_function(
function_name="generate_response",
operation="Generates a customer support response",
formal_parameters=response_generation_params,
examples=[
"Generate a helpful response about the login issue using the knowledge articles",
"Create a formal response to the billing inquiry without including references"
]
)
# Create a pipeline that can be called with natural language
support_pipeline = create_nl_function_pipeline(
functions=[create_ticket, search_knowledge_base, generate_response]
)
# Example usage:
# result = support_pipeline(
# "First create a high priority ticket for customer AB-12345 having trouble with password reset,
# then search for relevant articles about password reset in security category,
# and finally generate a friendly response using those articles"
# )
Natural Language Parameter Handling brings the flexibility and intuition of human language to the precision and structure of programming functions. By bridging these worlds, CRAFT enables a more natural human-AI collaboration while maintaining the benefits of structured interactions.
Retry
RK
Great, please expand on Progressive Function Enhancement with details, benefits, explanations , examples and anything else that you think will be helpful.
Edit
Progressive Function Enhancement for CRAFT Framework
Core Concept
Progressive Function Enhancement is a sophisticated approach to function design that allows functions to evolve and improve over time without breaking existing implementations. This system provides mechanisms for functions to gradually add capabilities, adapt to changing requirements, or incorporate new features while maintaining backward compatibility with code that uses earlier versions.
Unlike traditional functions that remain static once defined, progressively enhanced functions can evolve through well-defined enhancement paths. These paths enable incremental improvements, feature additions, optimization, and specialization—all while preserving a consistent interface for existing users. This approach recognizes that the perfect function cannot always be designed upfront and instead embraces a growth mindset where functions mature organically.
Implementation Details
python
def create_progressive_function(base_function, version="1.0", enhancement_path=None):
"""
Creates a function that can be progressively enhanced over time.
Args:
base_function (function): Initial function implementation
version (str): Initial function version
enhancement_path (dict, optional): Planned enhancement stages
Returns:
function: Progressive function with enhancement capabilities
"""
#H->AI::EnhancementSetup: (Prepare function for progressive enhancement)
if enhancement_path is None:
enhancement_path = {}
# Create metadata to track function evolution
metadata = {
'name': base_function.__name__,
'version': version,
'history': [{
'version': version,
'timestamp': get_current_timestamp(),
'description': 'Initial version'
}],
'enhancement_path': enhancement_path,
'capability_flags': {},
'deprecated_params': set(),
'added_params': {},
'usage_statistics': {}
}
def enhanced_function(*args, **kwargs):
"""
Wrapper function that handles version compatibility and enhancements.
"""
#H->AI::VersionHandling: (Apply appropriate version logic)
# Track function usage
track_function_usage(metadata, args, kwargs)
# Handle deprecated parameters
kwargs = handle_deprecated_parameters(kwargs, metadata['deprecated_params'], metadata['added_params'])
# Apply active enhancements based on capability flags
result = apply_enhancements(base_function, args, kwargs, metadata['capability_flags'])
return result
# Copy function metadata
enhanced_function.__name__ = base_function.__name__
enhanced_function.__doc__ = base_function.__doc__
enhanced_function.__dict__.update(base_function.__dict__)
# Add enhancement methods
enhanced_function.get_version = lambda: metadata['version']
enhanced_function.get_history = lambda: metadata['history']
enhanced_function.get_enhancement_path = lambda: metadata['enhancement_path']
enhanced_function.get_metadata = lambda: metadata
enhanced_function.enable_capability = lambda capability: enable_capability(metadata, capability)
enhanced_function.disable_capability = lambda capability: disable_capability(metadata, capability)
enhanced_function.enhance = lambda enhancement, version=None: enhance_function(enhanced_function, metadata, enhancement, version)
enhanced_function.deprecate_parameter = lambda param, replacement=None: deprecate_parameter(metadata, param, replacement)
enhanced_function.add_parameter = lambda param, default=None: add_parameter(metadata, param, default)
return enhanced_function
def track_function_usage(metadata, args, kwargs):
"""Records function usage patterns for analysis."""
#H->AI::UsageTracking: (Monitor how the function is being used)
timestamp = get_current_timestamp()
if 'usage_count' not in metadata['usage_statistics']:
metadata['usage_statistics']['usage_count'] = 0
metadata['usage_statistics']['parameter_frequency'] = {}
metadata['usage_statistics']['call_pattern'] = {}
metadata['usage_statistics']['usage_count'] += 1
# Track parameter usage
for key, value in kwargs.items():
if key not in metadata['usage_statistics']['parameter_frequency']:
metadata['usage_statistics']['parameter_frequency'][key] = 0
metadata['usage_statistics']['parameter_frequency'][key] += 1
# Track call pattern (simplified)
call_signature = str(len(args)) + ":" + ",".join(sorted(kwargs.keys()))
if call_signature not in metadata['usage_statistics']['call_pattern']:
metadata['usage_statistics']['call_pattern'][call_signature] = 0
metadata['usage_statistics']['call_pattern'][call_signature] += 1
def handle_deprecated_parameters(kwargs, deprecated_params, added_params):
"""Handles backward compatibility for deprecated parameters."""
#H->AI::DeprecationHandling: (Support deprecated parameters while encouraging new ones)
new_kwargs = kwargs.copy()
for old_param in deprecated_params:
if old_param in kwargs:
# Check if there's a replacement parameter
replacement = added_params.get(old_param)
if replacement and replacement not in kwargs:
# Apply the old value to the new parameter
new_kwargs[replacement] = kwargs[old_param]
# Log usage of deprecated parameter (would be implementation-specific)
# log_deprecated_usage(old_param, replacement)
return new_kwargs
def apply_enhancements(base_function, args, kwargs, capability_flags):
"""
Applies active enhancements to the function execution.
Args:
base_function (function): Base function
args (tuple): Positional arguments
kwargs (dict): Keyword arguments
capability_flags (dict): Active enhancement capabilities
Returns:
any: Function result with enhancements applied
"""
#H->AI::ApplyEnhancements: (Execute function with enabled enhancements)
# Pre-processing enhancements
if capability_flags.get('input_validation', False):
args, kwargs = apply_input_validation(args, kwargs)
if capability_flags.get('input_enrichment', False):
args, kwargs = apply_input_enrichment(args, kwargs)
# Execute base function
result = base_function(*args, **kwargs)
# Post-processing enhancements
if capability_flags.get('result_enrichment', False):
result = apply_result_enrichment(result)
if capability_flags.get('performance_metrics', False):
# Would record execution metrics in a real implementation
pass
return result
def enable_capability(metadata, capability):
"""Enables a specific capability enhancement."""
#H->AI::CapabilityManagement: (Enable specific enhancement features)
metadata['capability_flags'][capability] = True
# Record the change in history
metadata['history'].append({
'version': metadata['version'],
'timestamp': get_current_timestamp(),
'description': f'Enabled capability: {capability}'
})
return True
def disable_capability(metadata, capability):
"""Disables a specific capability enhancement."""
#H->AI::CapabilityManagement: (Disable specific enhancement features)
if capability in metadata['capability_flags']:
metadata['capability_flags'][capability] = False
# Record the change in history
metadata['history'].append({
'version': metadata['version'],
'timestamp': get_current_timestamp(),
'description': f'Disabled capability: {capability}'
})
return True
return False
def enhance_function(function, metadata, enhancement, new_version=None):
"""
Applies a new enhancement to the function.
Args:
function (function): Function to enhance
metadata (dict): Function metadata
enhancement (function): Enhancement to apply
new_version (str, optional): New version number
Returns:
function: Enhanced function
"""
#H->AI::FunctionEvolution: (Apply new enhancement to function)
# Update version if provided
if new_version:
metadata['version'] = new_version
# Apply the enhancement
enhanced_fn = enhancement(function)
# Record the enhancement in history
metadata['history'].append({
'version': metadata['version'],
'timestamp': get_current_timestamp(),
'description': f'Applied enhancement: {enhancement.__name__}'
})
return enhanced_fn
def deprecate_parameter(metadata, param, replacement=None):
"""
Marks a parameter as deprecated while maintaining backward compatibility.
Args:
metadata (dict): Function metadata
param (str): Parameter to deprecate
replacement (str, optional): Replacement parameter
Returns:
bool: Success
"""
#H->AI::ParameterEvolution: (Safely deprecate parameters)
metadata['deprecated_params'].add(param)
if replacement:
metadata['added_params'][param] = replacement
# Record the change in history
metadata['history'].append({
'version': metadata['version'],
'timestamp': get_current_timestamp(),
'description': f'Deprecated parameter: {param}' +
(f' (replaced by {replacement})' if replacement else '')
})
return True
def add_parameter(metadata, param, default=None):
"""
Adds a new parameter to the function with a default value for backward compatibility.
Args:
metadata (dict): Function metadata
param (str): Parameter to add
default (any, optional): Default value
Returns:
bool: Success
"""
#H->AI::ParameterEvolution: (Add new parameters with defaults)
# Record the new parameter
metadata['added_params'][param] = default
# Record the change in history
metadata['history'].append({
'version': metadata['version'],
'timestamp': get_current_timestamp(),
'description': f'Added parameter: {param} (default: {default})'
})
return True
def get_current_timestamp():
"""Gets current timestamp for versioning."""
#H->AI::TimeTracking: (Record timestamp for version history)
# In real implementation, would return actual timestamp
return "2025-05-17T12:34:56Z"
Example Scenarios
Scenario 1: Text Analysis Function with Progressive Capability Enhancement
python
def basic_text_analyzer(text, analysis_type="sentiment"):
"""
Analyzes text for basic linguistic and semantic features.
Args:
text (str): Text to analyze
analysis_type (str): Type of analysis to perform (sentiment, entities)
Returns:
dict: Analysis results
"""
#H->AI::BasicAnalysis: (Perform simple text analysis)
results = {
"text_length": len(text),
"word_count": len(text.split())
}
if analysis_type == "sentiment":
# Basic sentiment analysis (simplified for example)
positive_words = ["good", "great", "excellent", "happy", "positive"]
negative_words = ["bad", "terrible", "awful", "sad", "negative"]
sentiment_score = 0
lower_text = text.lower()
for word in positive_words:
if word in lower_text:
sentiment_score += 1
for word in negative_words:
if word in lower_text:
sentiment_score -= 1
results["sentiment"] = {
"score": sentiment_score,
"classification": "positive" if sentiment_score > 0 else "negative" if sentiment_score < 0 else "neutral"
}
elif analysis_type == "entities":
# Basic entity extraction (simplified)
results["entities"] = []
# Very simple person detection (names starting with capital letters)
import re
potential_names = re.findall(r'\b[A-Z][a-z]+ [A-Z][a-z]+\b', text)
for name in potential_names:
results["entities"].append({"type": "person", "text": name})
return results
# Create a progressive function from the basic analyzer
analyzer = create_progressive_function(basic_text_analyzer, version="1.0",
enhancement_path={
"1.1": "Add language detection",
"1.2": "Add readability metrics",
"1.5": "Add topic classification",
"2.0": "Comprehensive semantic analysis"
})
# Define an enhancement for language detection
def add_language_detection(function):
"""Enhancement that adds language detection capability."""
def enhanced_function(text, analysis_type="sentiment", detect_language=False):
"""
Enhanced text analyzer with language detection.
Args:
text (str): Text to analyze
analysis_type (str): Type of analysis to perform
detect_language (bool): Whether to detect language
Returns:
dict: Analysis results with language information
"""
# Call the original function
results = function(text, analysis_type)
# Add language detection if requested
if detect_language:
# Simple language detection (simplified for example)
# In a real implementation, would use a proper language detection library
common_words = {
"en": ["the", "and", "is", "in", "to", "it", "of"],
"es": ["el", "la", "es", "en", "y", "de", "que"],
"fr": ["le", "la", "est", "en", "et", "de", "qui"]
}
text_lower = text.lower()
words = set(re.findall(r'\b\w+\b', text_lower))
language_scores = {}
for lang, lang_words in common_words.items():
matches = sum(1 for word in lang_words if word in words)
language_scores[lang] = matches / len(lang_words)
detected_language = max(language_scores.items(), key=lambda x: x[1])
results["language"] = {
"detected": detected_language[0],
"confidence": detected_language[1]
}
return results
# Update function metadata
enhanced_function.__name__ = function.__name__
enhanced_function.__doc__ = enhanced_function.__doc__
return enhanced_function
# Apply the enhancement to the progressive function
analyzer.enhance(add_language_detection, "1.1")
# Example usage after enhancement:
# result = analyzer("This is a great product, I'm very happy with it!", detect_language=True)
# This works despite the detect_language parameter not being in the original function
# Define another enhancement for readability metrics
def add_readability_metrics(function):
"""Enhancement that adds readability analysis capability."""
def enhanced_function(text, analysis_type="sentiment", detect_language=False, analyze_readability=False):
"""
Enhanced text analyzer with readability metrics.
Args:
text (str): Text to analyze
analysis_type (str): Type of analysis to perform
detect_language (bool): Whether to detect language
analyze_readability (bool): Whether to analyze readability
Returns:
dict: Analysis results with readability metrics
"""
# Call the previous version
results = function(text, analysis_type, detect_language)
# Add readability analysis if requested
if analyze_readability:
# Simple readability analysis (simplified for example)
sentences = len(re.findall(r'[.!?]+', text)) or 1
words = len(text.split())
syllable_count = count_syllables(text) # Would be implemented
# Calculate Flesch Reading Ease (simplified)
flesch_score = 206.835 - (1.015 * (words / sentences)) - (84.6 * (syllable_count / words))
results["readability"] = {
"sentences": sentences,
"words": words,
"syllables": syllable_count,
"flesch_reading_ease": round(flesch_score, 2),
"grade_level": "Easy" if flesch_score > 80 else "Standard" if flesch_score > 60 else "Difficult"
}
return results
# Update function metadata
enhanced_function.__name__ = function.__name__
enhanced_function.__doc__ = enhanced_function.__doc__
return enhanced_function
# Function for counting syllables (simplified)
def count_syllables(text):
"""Counts syllables in text (very simplified)."""
text = text.lower()
words = text.split()
syllable_count = 0
for word in words:
# Count vowels as a simple approximation of syllables
vowels = 'aeiouy'
count = 0
previous_was_vowel = False
for char in word:
if char in vowels:
if not previous_was_vowel:
count += 1
previous_was_vowel = True
else:
previous_was_vowel = False
# Every word has at least one syllable
syllable_count += max(1, count)
return syllable_count
# Apply the readability enhancement
analyzer.enhance(add_readability_metrics, "1.2")
# Enable specific capabilities
analyzer.enable_capability('input_validation')
analyzer.enable_capability('performance_metrics')
# Example usage after multiple enhancements:
# result = analyzer(
# "This is a great product, I'm very happy with it!",
# detect_language=True,
# analyze_readability=True
# )
Scenario 2: Data Processing Pipeline with Progressive Performance Improvements
python
def basic_data_processor(data, operations=None):
"""
Processes data through a sequence of operations.
Args:
data (list): Data to process
operations (list): Sequence of operations to apply
Returns:
list: Processed data
"""
if operations is None:
operations = ["filter_nulls", "normalize"]
result = data.copy()
for operation in operations:
if operation == "filter_nulls":
result = [item for item in result if item is not None]
elif operation == "normalize":
# Simple normalization (scaling to 0-1)
if result:
min_val = min(result)
max_val = max(result)
if max_val > min_val:
result = [(x - min_val) / (max_val - min_val) for x in result]
elif operation == "sort":
result = sorted(result)
return result
# Create progressive function
processor = create_progressive_function(basic_data_processor, version="1.0")
# Define performance enhancement
def add_parallel_processing(function):
"""Enhancement that adds parallel processing capability."""
def enhanced_function(data, operations=None, use_parallel=False, chunk_size=1000):
"""
Enhanced data processor with parallel processing option.
Args:
data (list): Data to process
operations (list): Sequence of operations to apply
use_parallel (bool): Whether to use parallel processing
chunk_size (int): Size of chunks for parallel processing
Returns:
list: Processed data
"""
# Use parallel processing if requested and data is large enough
if use_parallel and len(data) > chunk_size:
# In a real implementation, would use proper parallel processing
# Simplified example:
chunks = [data[i:i+chunk_size] for i in range(0, len(data), chunk_size)]
processed_chunks = []
for chunk in chunks:
# Process each chunk using the original function
processed_chunk = function(chunk, operations)
processed_chunks.append(processed_chunk)
# Combine results (may need operation-specific combination logic)
result = []
for chunk in processed_chunks:
result.extend(chunk)
return result
else:
# Use original function for small data or when parallel not requested
return function(data, operations)
# Update function metadata
enhanced_function.__name__ = function.__name__
enhanced_function.__doc__ = enhanced_function.__doc__
return enhanced_function
# Apply the enhancement
processor.enhance(add_parallel_processing, "1.1")
# Define caching enhancement
def add_result_caching(function):
"""Enhancement that adds result caching for repeated operations."""
# Cache for storing previous results
cache = {}
def enhanced_function(data, operations=None, use_parallel=False, chunk_size=1000, use_cache=True):
"""
Enhanced data processor with result caching.
Args:
data (list): Data to process
operations (list): Sequence of operations to apply
use_parallel (bool): Whether to use parallel processing
chunk_size (int): Size of chunks for parallel processing
use_cache (bool): Whether to use result caching
Returns:
list: Processed data
"""
if operations is None:
operations = ["filter_nulls", "normalize"]
# Create cache key based on data and operations
if use_cache:
# For simplicity, using string representation as cache key
# In production, would use a more efficient hashing mechanism
cache_key = str(hash(str(data))) + "_" + str(hash(str(operations)))
if cache_key in cache:
# Return cached result
return cache[cache_key]
# Process data using previous version
result = function(data, operations, use_parallel, chunk_size)
# Cache result if caching is enabled
if use_cache:
cache[cache_key] = result
return result
# Update function metadata
enhanced_function.__name__ = function.__name__
enhanced_function.__doc__ = enhanced_function.__doc__
return enhanced_function
# Apply the caching enhancement
processor.enhance(add_result_caching, "1.2")
# Example usage after enhancements:
# large_dataset = [random.random() for _ in range(10000)]
# result = processor(large_dataset,
# operations=["filter_nulls", "normalize", "sort"],
# use_parallel=True,
# chunk_size=1000,
# use_cache=True)
Scenario 3: Content Generator with Progressive Interface Evolution
python
def basic_content_generator(topic, length="medium", format="article"):
"""
Generates content on a specified topic.
Args:
topic (str): Topic to generate content about
length (str): Content length (short, medium, long)
format (str): Content format (article, blog, social)
Returns:
str: Generated content
"""
# Simplified implementation for illustration
content = f"# {topic.title()}\n\n"
if length == "short":
paragraphs = 2
elif length == "medium":
paragraphs = 5
else: # long
paragraphs = 10
for i in range(paragraphs):
content += f"Paragraph {i+1} about {topic}.\n\n"
return content
# Create progressive function
generator = create_progressive_function(basic_content_generator, version="1.0")
# Define interface evolution
def modernize_interface(function):
"""Updates function interface to use a more modern structure."""
def enhanced_function(topic=None, **kwargs):
"""
Enhanced content generator with modern parameter interface.
Args:
topic (str, optional): Topic to generate content about
**kwargs: Modern parameter interface
content_type (str): Type of content (article, blog, social)
word_count (int): Target word count
style (str): Writing style
...
Returns:
str: Generated content
"""
# Map modern parameters to legacy parameters
if 'content_type' in kwargs and 'format' not in kwargs:
kwargs['format'] = kwargs.pop('content_type')
if 'word_count' in kwargs and 'length' not in kwargs:
word_count = kwargs.pop('word_count')
if word_count < 200:
kwargs['length'] = 'short'
elif word_count < 500:
kwargs['length'] = 'medium'
else:
kwargs['length'] = 'long'
# Call original function with mapped parameters
return function(topic, **kwargs)
# Update function metadata
enhanced_function.__name__ = function.__name__
enhanced_function.__doc__ = enhanced_function.__doc__
return enhanced_function
# Apply the interface evolution
generator.enhance(modernize_interface, "2.0")
generator.deprecate_parameter('format', 'content_type')
generator.deprecate_parameter('length', 'word_count')
# Define feature enhancement
def add_style_customization(function):
"""Adds style customization capabilities."""
def enhanced_function(topic=None, **kwargs):
"""
Enhanced content generator with style customization.
Args:
topic (str, optional): Topic to generate content about
**kwargs: Parameter interface
content_type (str): Type of content (article, blog, social)
word_count (int): Target word count
style (str): Writing style (formal, casual, technical)
tone (str): Content tone (informative, persuasive, entertaining)
target_audience (str): Intended audience
Returns:
dict: Generated content with metadata
"""
# Extract style parameters
style = kwargs.pop('style', 'neutral')
tone = kwargs.pop('tone', 'informative')
target_audience = kwargs.pop('target_audience', 'general')
# Generate base content using previous version
content = function(topic, **kwargs)
# Enhance content with style customization (simplified)
if style == 'formal':
content = content.replace('about', 'regarding')
elif style == 'casual':
content = content.replace('Paragraph', 'Let\'s talk')
# Add metadata to result
result = {
'content': content,
'metadata': {
'topic': topic,
'style': style,
'tone': tone,
'target_audience': target_audience,
'word_count': len(content.split()),
'reading_time': len(content.split()) // 200 # words per minute
}
}
return result
# Update function metadata
enhanced_function.__name__ = function.__name__
enhanced_function.__doc__ = enhanced_function.__doc__
return enhanced_function
# Apply the style enhancement
generator.enhance(add_style_customization, "2.1")
# Example usage after enhancements:
# result = generator(
# topic="Artificial Intelligence Ethics",
# content_type="blog", # New parameter replacing 'format'
# word_count=400, # New parameter replacing 'length'
# style="formal", # New parameter
# tone="persuasive", # New parameter
# target_audience="technical" # New parameter
# )
Benefits of Progressive Function Enhancement
Future-Proofing: Functions adapt to changing requirements without breaking existing code.
Backward Compatibility: New features and improvements don't invalidate existing implementations.
Incremental Improvement: Functions become more powerful and efficient over time through gradual evolution.
Testability: Each enhancement can be tested independently, reducing the risk of regressions.
Transparency: Version history and capability tracking provide clear documentation of function evolution.
Graceful Deprecation: Old parameters and patterns can be phased out gradually rather than abruptly.
Usage-Driven Improvement: Enhancement paths can be informed by actual usage patterns.
Feature Toggles: Capabilities can be enabled or disabled without modifying function implementation.
Reduced Duplication: Avoids the need for multiple similar functions with slightly different capabilities.
Knowledge Preservation: Function history captures the reasoning behind changes and improvements.
Advanced Applications
Automatic Enhancement Based on Usage Patterns
python
def create_self_enhancing_function(base_function, learning_threshold=100):
"""
Creates a function that automatically enhances itself based on usage patterns.
Args:
base_function (function): Initial function implementation
learning_threshold (int): Number of calls before analyzing patterns
Returns:
function: Self-enhancing function
"""
# Create progressive function
prog_function = create_progressive_function(base_function)
# Track call count for learning trigger
call_count = [0]
def self_enhancing_wrapper(*args, **kwargs):
"""Wrapper that enables self-enhancement."""
# Increment call count
call_count[0] += 1
# Check if we should analyze patterns and enhance
if call_count[0] % learning_threshold == 0:
# Get usage statistics
metadata = prog_function.get_metadata()
stats = metadata['usage_statistics']
# Analyze patterns and generate enhancements
enhancements = generate_enhancements_from_usage(stats)
# Apply any identified enhancements
for enhancement in enhancements:
prog_function.enhance(enhancement)
# Call the progressive function
return prog_function(*args, **kwargs)
# Copy function metadata
self_enhancing_wrapper.__name__ = base_function.__name__
self_enhancing_wrapper.__doc__ = base_function.__doc__
return self_enhancing_wrapper
def generate_enhancements_from_usage(usage_stats):
"""
Analyzes usage statistics to generate potential enhancements.
Args:
usage_stats (dict): Function usage statistics
Returns:
list: Enhancement functions
"""
enhancements = []
# Example enhancement: Add default values for commonly used parameters
if 'parameter_frequency' in usage_stats:
param_freq = usage_stats['parameter_frequency']
for param, freq in param_freq.items():
# If parameter is used in more than 80% of calls
if freq / usage_stats['usage_count'] > 0.8:
# Create an enhancement to add a default value
def add_default_for_common_param(function):
"""Adds default value for commonly used parameter."""
# Implementation would determine appropriate default value
# from usage patterns
return function
enhancements.append(add_default_for_common_param)
# Example enhancement: Optimize for common call patterns
if 'call_pattern' in usage_stats:
# Find the most common call pattern
most_common_pattern = max(usage_stats['call_pattern'].items(),
key=lambda x: x[1])[0]
# Create an enhancement to optimize for this pattern
def optimize_for_common_pattern(function):
"""Optimizes function for the most common call pattern."""
# Implementation would optimize execution path for this pattern
return function
enhancements.append(optimize_for_common_pattern)
return enhancements
Progressive A/B Testing of Function Implementations
python
def create_ab_testing_function(base_function, variant_functions=None, test_allocation=0.2):
"""
Creates a function that conducts A/B testing between implementation variants.
Args:
base_function (function): Primary function implementation (A)
variant_functions (list): Alternative implementations to test (B, C, etc.)
test_allocation (float): Proportion of calls to allocate to variants
Returns:
function: A/B testing function
"""
if variant_functions is None:
variant_functions = []
# Create progressive function
prog_function = create_progressive_function(base_function)
# Test results storage
test_results = {
'base': {'calls': 0, 'errors': 0, 'duration': 0},
}
for i, _ in enumerate(variant_functions):
test_results[f'variant_{i+1}'] = {'calls': 0, 'errors': 0, 'duration': 0}
def ab_testing_wrapper(*args, **kwargs):
"""Wrapper that implements A/B testing."""
import random
import time
# Decide which implementation to use
if random.random() < test_allocation and variant_functions:
# Use a variant
variant_idx = random.randint(0, len(variant_functions) - 1)
variant_key = f'variant_{variant_idx+1}'
function_to_use = variant_functions[variant_idx]
else:
# Use the base implementation
variant_key = 'base'
function_to_use = prog_function
# Execute and measure performance
start_time = time.time()
try:
result = function_to_use(*args, **kwargs)
test_results[variant_key]['calls'] += 1
test_results[variant_key]['duration'] += time.time() - start_time
return result
except Exception as e:
test_results[variant_key]['calls'] += 1
test_results[variant_key]['errors'] += 1
test_results[variant_key]['duration'] += time.time() - start_time
raise e
# Add methods for managing the test
ab_testing_wrapper.get_test_results = lambda: test_results
ab_testing_wrapper.reset_test = lambda: test_results.clear()
ab_testing_wrapper.add_variant = lambda variant: variant_functions.append(variant)
ab_testing_wrapper.promote_best_variant = lambda: promote_best_variant(prog_function, variant_functions, test_results)
# Copy function metadata
ab_testing_wrapper.__name__ = base_function.__name__
ab_testing_wrapper.__doc__ = base_function.__doc__
return ab_testing_wrapper
def promote_best_variant(base_function, variants, results):
"""
Promotes the best performing variant to become the primary implementation.
Args:
base_function (function): Current primary implementation
variants (list): Alternative implementations
results (dict): Test results
Returns:
function: New primary implementation
"""
# Calculate performance metrics
performance = {}
for key, data in results.items():
if data['calls'] == 0:
continue
error_rate = data['errors'] / data['calls']
avg_duration = data['duration'] / data['calls']
# Lower is better for both metrics
performance[key] = (error_rate, avg_duration)
# Find the best performer (lowest error rate, then lowest duration)
if not performance:
return base_function
best_key = min(performance.keys(), key=lambda k: performance[k])
# If the best is not the base implementation, promote it
if best_key != 'base':
variant_idx = int(best_key.split('_')[1]) - 1
return variants[variant_idx]
return base_function
Progressive Specialization for Different Use Cases
python
def create_specializing_function(base_function, specialization_criteria=None):
"""
Creates a function that progressively specializes for different use cases.
Args:
base_function (function): General-purpose implementation
specialization_criteria (dict): Criteria for identifying use cases
Returns:
function: Use case-specializing function
"""
if specialization_criteria is None:
specialization_criteria = {}
# Create progressive function
prog_function = create_progressive_function(base_function)
# Storage for specialized implementations
specialized_implementations = {}
def specializing_wrapper(*args, **kwargs):
"""Wrapper that applies specialized implementations for recognized use cases."""
# Identify the use case based on arguments
use_case = identify_use_case(args, kwargs, specialization_criteria)
# If we have a specialized implementation for this use case, use it
if use_case and use_case in specialized_implementations:
return specialized_implementations[use_case](*args, **kwargs)
# Otherwise, use the progressive function
return prog_function(*args, **kwargs)
# Add methods for managing specializations
specializing_wrapper.add_specialization = lambda use_case, implementation: specialized_implementations.update({use_case: implementation})
specializing_wrapper.get_specializations = lambda: list(specialized_implementations.keys())
specializing_wrapper.remove_specialization = lambda use_case: specialized_implementations.pop(use_case, None)
# Copy function metadata
specializing_wrapper.__name__ = base_function.__name__
specializing_wrapper.__doc__ = base_function.__doc__
return specializing_wrapper
def identify_use_case(args, kwargs, criteria):
"""
Identifies the use case based on function arguments.
Args:
args (tuple): Positional arguments
kwargs (dict): Keyword arguments
criteria (dict): Criteria for identifying use cases
Returns:
str: Identified use case or None
"""
# For each use case, check if criteria match
for use_case, use_case_criteria in criteria.items():
matches = True
for key, value in use_case_criteria.items():
if key in kwargs and kwargs[key] != value:
matches = False
break
if matches:
return use_case
return None
Integration with CRAFT Principles
Progressive Function Enhancement aligns with several CRAFT principles:
Structure Enables Freedom: By establishing a clear evolution path, functions can grow and adapt while maintaining predictable interfaces.
Cumulative Intelligence: Functions preserve knowledge and improvements across iterations, building on previous insights.
Human-AI Partnership: Progressive enhancement facilitates evolving collaboration as AI capabilities and user needs change.
Accessibility Through Familiarity: Functions maintain familiar interfaces while gaining power, making enhancements accessible to existing users.
Conservation of Tokens: Functions can become more token-efficient without breaking backward compatibility.
Implementation Recommendations
Start with Core Functionality: Begin by implementing essential functionality that serves the most common use cases.
Plan Enhancement Paths: Define potential enhancement paths upfront to guide future development.
Preserve Backward Compatibility: Always ensure new enhancements maintain compatibility with existing code.
Document Evolution History: Maintain clear records of function changes, versions, and rationale.
Implement Feature Flags: Use capability flags to allow selective enabling of enhancements.
Track Usage Patterns: Monitor how functions are used to inform enhancement decisions.
Test Each Enhancement: Thoroughly test each enhancement to ensure it works correctly in isolation and in combination with others.
Apply Gradual Deprecation: Use deprecation warnings and transition periods for interface changes.
Create Enhancement Packages: Group related enhancements into coherent packages with version numbers.
Limit Enhancement Complexity: Keep each enhancement focused and simple to reduce integration issues.
Practical Integration with Other CRAFT Functions
python
# Integration with Context-Aware Functions
def integrate_progressive_and_context_aware(base_function, context_variables=None):
"""
Creates a function that combines progressive enhancement with context awareness.
Args:
base_function (function): Base function implementation
context_variables (list): Context variables to track
Returns:
function: Progressive and context-aware function
"""
if context_variables is None:
context_variables = []
# Create progressive function
prog_function = create_progressive_function(base_function)
# Create context tracking
context_tracker = create_context_tracker(context_variables)
def integrated_function(*args, **kwargs):
"""Function with both progressive enhancement and context awareness."""
# Extract context from arguments
context = kwargs.pop('context', {})
# Update context based on current call
updated_context = context_tracker.update(context, args, kwargs)
# Execute function with current context
result = prog_function(*args, **kwargs)
# Update context based on result
final_context = context_tracker.update(updated_context, result)
# Package result with updated context
return {
'result': result,
'context': final_context
}
# Add progressive function methods
integrated_function.enhance = prog_function.enhance
integrated_function.get_version = prog_function.get_version
integrated_function.get_history = prog_function.get_history
integrated_function.enable_capability = prog_function.enable_capability
# Add context methods
integrated_function.get_context_variables = lambda: context_variables
integrated_function.add_context_variable = lambda var: context_variables.append(var)
return integrated_function
# Integration with Natural Language Parameter Handling
def integrate_progressive_and_nl_parameters(base_function, formal_parameters=None):
"""
Creates a function that combines progressive enhancement with natural language parameters.
Args:
base_function (function): Base function implementation
formal_parameters (dict): Parameter definitions
Returns:
function: Progressive function with natural language parameters
"""
if formal_parameters is None:
formal_parameters = {}
# Create progressive function
prog_function = create_progressive_function(base_function)
# Create parameter handler
param_handler = create_nlp_parameter_handler(base_function.__name__, formal_parameters)
def integrated_function(natural_language_input=None, *args, **kwargs):
"""Function with both progressive enhancement and NL parameters."""
# If natural language input is provided, extract parameters
if natural_language_input is not None and isinstance(natural_language_input, str):
extracted_params = param_handler(natural_language_input)
kwargs.update(extracted_params)
# Execute with extracted parameters
return prog_function(*args, **kwargs)
else:
# Use as regular function
if natural_language_input is not None:
args = (natural_language_input,) + args
return prog_function(*args, **kwargs)
# Add progressive function methods
integrated_function.enhance = prog_function.enhance
integrated_function.get_version = prog_function.get_version
integrated_function.get_history = prog_function.get_history
integrated_function.enable_capability = prog_function.enable_capability
# Add NL parameter methods
integrated_function.add_parameter = lambda param, default=None: add_parameter(prog_function.get_metadata(), param, default)
integrated_function.deprecate_parameter = lambda param, replacement=None: deprecate_parameter(prog_function.get_metadata(), param, replacement)
return integrated_function
Real-World Application Example
python
# Example: Customer Support Response Generator with Progressive Enhancement
# Base function for generating support responses
def basic_support_response(issue_description, issue_type="general", customer_info=None):
"""
Generates a basic customer support response.
Args:
issue_description (str): Description of the customer's issue
issue_type (str): Category of the issue (general, technical, billing)
customer_info (dict, optional): Customer information
Returns:
str: Support response
"""
# Basic implementation (simplified)
greeting = "Dear Customer,"
if customer_info and "name" in customer_info:
greeting = f"Dear {customer_info['name']},"
response_body = f"Thank you for contacting our support team about your {issue_type} issue.\n\n"
response_body += f"Regarding your concern: \"{issue_description}\"\n\n"
if issue_type == "technical":
response_body += "Our technical team is looking into this matter. Have you tried restarting the application?\n\n"
elif issue_type == "billing":
response_body += "Our billing department will review your account and address any discrepancies.\n\n"
else:
response_body += "We're reviewing your request and will get back to you shortly.\n\n"
closing = "Best regards,\nCustomer Support Team"
return f"{greeting}\n\n{response_body}{closing}"
# Create progressive function
support_response_gen = create_progressive_function(
basic_support_response,
version="1.0",
enhancement_path={
"1.1": "Personalization enhancements",
"1.2": "Knowledge base integration",
"2.0": "Multi-channel formatting",
"2.1": "Tone and sentiment awareness",
"3.0": "Response templates and macros"
}
)
# Define personalization enhancement
def add_personalization(function):
"""Enhances response with deeper personalization."""
def enhanced_function(issue_description, issue_type="general", customer_info=None,
personalization_level="standard"):
"""
Enhanced support response with personalization options.
Args:
issue_description (str): Description of the customer's issue
issue_type (str): Category of the issue
customer_info (dict, optional): Customer information
personalization_level (str): Level of personalization (minimal, standard, deep)
Returns:
str: Personalized support response
"""
# Get base response
response = function(issue_description, issue_type, customer_info)
# Apply additional personalization if customer info available
if customer_info and personalization_level != "minimal":
# Standard personalization
if "account_type" in customer_info:
response = response.replace(
"Thank you for contacting our support team",
f"Thank you for contacting our support team as a valued {customer_info['account_type']} customer"
)
# Deep personalization
if personalization_level == "deep":
if "purchase_history" in customer_info and customer_info["purchase_history"]:
recent_purchase = customer_info["purchase_history"][0]
response += f"\n\nWe hope you're enjoying your recent purchase of {recent_purchase}."
if "support_history" in customer_info and customer_info["support_history"]:
response += "\n\nWe appreciate your patience as we've worked together on previous support requests."
return response
return enhanced_function
# Apply personalization enhancement
support_response_gen.enhance(add_personalization, "1.1")
# Define knowledge base integration
def add_knowledge_base(function):
"""Integrates knowledge base articles into responses."""
def enhanced_function(issue_description, issue_type="general", customer_info=None,
personalization_level="standard", include_kb_articles=True,
max_articles=2):
"""
Enhanced support response with knowledge base integration.
Args:
issue_description (str): Description of the customer's issue
issue_type (str): Category of the issue
customer_info (dict, optional): Customer information
personalization_level (str): Level of personalization
include_kb_articles (bool): Whether to include knowledge base articles
max_articles (int): Maximum number of articles to include
Returns:
str: Support response with knowledge articles
"""
# Get enhanced response from previous version
response = function(issue_description, issue_type, customer_info, personalization_level)
# Add knowledge base articles if requested
if include_kb_articles:
# In real implementation, would search knowledge base for relevant articles
# Simplified example:
kb_section = "\n\nYou may find these resources helpful:\n"
if issue_type == "technical":
kb_section += "- Troubleshooting Guide: https://support.example.com/troubleshooting\n"
kb_section += "- Common Technical Issues FAQ: https://support.example.com/tech-faq\n"
elif issue_type == "billing":
kb_section += "- Billing FAQ: https://support.example.com/billing-faq\n"
kb_section += "- Payment Methods Guide: https://support.example.com/payment-methods\n"
else:
kb_section += "- General Support Resources: https://support.example.com/general\n"
response = response.replace("Best regards,", kb_section + "\nBest regards,")
return response
return enhanced_function
# Apply knowledge base enhancement
support_response_gen.enhance(add_knowledge_base, "1.2")
# Define multi-channel formatting
def add_multichannel_support(function):
"""Adds support for different communication channels."""
def enhanced_function(issue_description, issue_type="general", customer_info=None,
personalization_level="standard", include_kb_articles=True,
max_articles=2, channel="email"):
"""
Enhanced support response with multi-channel formatting.
Args:
issue_description (str): Description of the customer's issue
issue_type (str): Category of the issue
customer_info (dict, optional): Customer information
personalization_level (str): Level of personalization
include_kb_articles (bool): Whether to include knowledge base articles
max_articles (int): Maximum number of articles to include
channel (str): Communication channel (email, chat, sms, social)
Returns:
str or dict: Formatted support response
"""
# Get the enhanced response from the previous version
response = function(issue_description, issue_type, customer_info,
personalization_level, include_kb_articles, max_articles)
# Format based on channel
if channel == "email":
# Email is the default format, no changes needed
return response
elif channel == "chat":
# Simplify for chat
response = response.replace("Dear ", "Hi ")
response = response.replace("\n\n", "\n")
response = response.replace("Best regards,\nCustomer Support Team", "- Support Team")
return response
elif channel == "sms":
# Create a very concise version for SMS
lines = response.split("\n")
greeting = lines[0].replace("Dear ", "Hi ")
# Extract main content
main_content = ""
for line in lines:
if "your concern" in line:
main_content = "We received your request about " + line.split("\"")[1]
break
# Create SMS version
sms = f"{greeting} {main_content} We'll follow up soon. Support Team"
return sms
elif channel == "social":
# For social media, return structured data
return {
"public_reply": f"Thanks for reaching out about your {issue_type} issue. We're looking into this!",
"private_message": response
}
return response
return enhanced_function
# Apply multi-channel enhancement
support_response_gen.enhance(add_multichannel_support, "2.0")
# Enable specific capabilities
support_response_gen.enable_capability('input_validation')
support_response_gen.enable_capability('performance_metrics')
# Example usage:
# response = support_response_gen(
# issue_description="I can't log into my account after the recent update",
# issue_type="technical",
# customer_info={
# "name": "Sarah Johnson",
# "account_type": "premium",
# "purchase_history": ["Pro Plan Subscription", "Mobile App"],
# "support_history": ["Password Reset (2 weeks ago)"]
# },
# personalization_level="deep",
# include_kb_articles=True,
# channel="chat"
# )
Progressive Function Enhancement creates a sustainable path for function evolution without disrupting existing implementations. This approach enables functions to grow more sophisticated, efficient, and versatile over time while maintaining compatibility across iterations. By planning enhancement paths and applying improvements incrementally, CRAFT functions can adapt to changing requirements and incorporate new capabilities with minimal friction.
Retry
RK
Great, please expand on Collaborative Function Building with details, benefits, explanations , examples and anything else that you think will be helpful.
Edit
Collaborative Function Building for CRAFT Framework
Document
# Collaborative Function Building for CRAFT Framework ## Core Concept Collaborative Function Building represents a transformative approach to function development within the CRAFT framework, enabling multiple stakeholders—both human and AI—to contr
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RK
Continue
Edit
Collaborative Function Building for CRAFT Framework
Document ∙ Version 2
def process_feedback(learning_state, feedback): """Processes user feedback to inform function improvements.""" # Extract patterns from successful executions if feedback.get('rating', 0) >= learning_state['parameters']['feedback_threshold'
Collaborative Function Building for CRAFT Framework
Document ∙ Version 3
### Collaborative Skills Acquisition ```python def create_skill_learning_function(base_function, learning_parameters=None): """ Creates a function that improves through collaborative learning. Args: base_function (function):
Collaborative Function Building for CRAFT Framework
Document ∙ Version 4
### Cross-Organization Function Governance ```python def create_governed_function(base_function, governance_policies=None, stakeholder_groups=None): """ Creates a function with formal governance structures for cross-organizational use.
Collaborative Function Building for CRAFT Framework
Document ∙ Version 5
## Integration with CRAFT Principles Collaborative Function Building aligns with several CRAFT principles: 1. **Structure Enables Freedom**: By providing formalized mechanisms for collaboration, it creates the structure needed for multiple stakehol
Great! Let's continue with the final function type from the CRAFT framework: Function Libraries.
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RK
yes, please do
Edit
Function Libraries for CRAFT Framework
Core Concept
Function Libraries represent a sophisticated approach to organizing, discovering, and using collections of related functions within the CRAFT framework. Rather than requiring users to repeatedly define similar functions or remember a large catalog of individual functions, Function Libraries provide a structured way to group, categorize, access, and extend sets of related functions.
This system transforms individual functions into cohesive, organized collections with consistent interfaces, standardized documentation, and intelligent discovery mechanisms. Function Libraries enable higher-level organization that makes the growing complexity of a CRAFT implementation manageable, improving both developer experience and system maintainability.
By categorizing functions by domain, purpose, or capability, libraries create intuitive "namespaces" that prevent collisions, clarify function relationships, and make the system more navigable. They also enable consistent patterns, shared infrastructure, and unified error handling across related functions.
Implementation Details
python
def create_function_library(library_name, description=None, categories=None, namespace=None):
"""
Creates a function library for organizing related functions.
Args:
library_name (str): Name of the library
description (str, optional): Library description
categories (list, optional): Function categories within the library
namespace (str, optional): Namespace prefix for functions
Returns:
dict: Function library object
"""
#H->AI::LibraryCreation: (Create organized collection of related functions)
# Set defaults
if description is None:
description = f"A collection of related functions for {library_name}"
if categories is None:
categories = ["general"]
if namespace is None:
namespace = library_name.lower().replace(" ", "_")
# Initialize library structure
library = {
'name': library_name,
'description': description,
'namespace': namespace,
'categories': categories,
'functions': {},
'metadata': {
'created': get_current_timestamp(),
'last_updated': get_current_timestamp(),
'version': '0.1',
'function_count': 0
}
}
return library
def add_function_to_library(library, function, name=None, category="general",
description=None, examples=None, aliases=None):
"""
Adds a function to a function library.
Args:
library (dict): Function library
function (function): Function to add
name (str, optional): Function name (defaults to function.__name__)
category (str): Function category within the library
description (str, optional): Function description
examples (list, optional): Usage examples
aliases (list, optional): Alternative names for the function
Returns:
str: Fully qualified function name
"""
#H->AI::LibraryAddition: (Add function to organized collection)
# Use function's name if not provided
if name is None:
name = function.__name__
# Generate fully qualified name with namespace
qualified_name = f"{library['namespace']}.{name}"
# Extract description from docstring if not provided
if description is None and function.__doc__:
description = function.__doc__.split('\n')[0].strip()
elif description is None:
description = f"Function {name} in the {library['name']} library"
# Ensure category exists
if category not in library['categories']:
library['categories'].append(category)
# Set up aliases
if aliases is None:
aliases = []
# Create function entry
function_entry = {
'name': name,
'qualified_name': qualified_name,
'function': function,
'category': category,
'description': description,
'examples': examples or [],
'aliases': aliases,
'added': get_current_timestamp(),
'last_updated': get_current_timestamp(),
'usage_count': 0
}
# Add to library
library['functions'][qualified_name] = function_entry
# Update library metadata
library['metadata']['function_count'] += 1
library['metadata']['last_updated'] = get_current_timestamp()
return qualified_name
def get_function_from_library(library, function_name, allow_alias=True):
"""
Retrieves a function from a library by name or alias.
Args:
library (dict): Function library
function_name (str): Function name or alias
allow_alias (bool): Whether to check aliases
Returns:
function: Retrieved function or None if not found
"""
#H->AI::FunctionRetrieval: (Get function from library)
# Check if full qualified name is provided
if function_name in library['functions']:
return library['functions'][function_name]['function']
# Check if name without namespace is provided
namespace_prefix = library['namespace'] + "."
if not function_name.startswith(namespace_prefix):
qualified_name = namespace_prefix + function_name
if qualified_name in library['functions']:
return library['functions'][qualified_name]['function']
# Check aliases if allowed
if allow_alias:
for qualified_name, func_entry in library['functions'].items():
if function_name in func_entry['aliases']:
return func_entry['function']
# Not found
return None
def execute_library_function(library, function_name, *args, **kwargs):
"""
Executes a function from the library.
Args:
library (dict): Function library
function_name (str): Function name or alias
*args: Positional arguments
**kwargs: Keyword arguments
Returns:
any: Function result
"""
#H->AI::FunctionExecution: (Run function from library)
# Get the function
func = get_function_from_library(library, function_name)
if func is None:
raise ValueError(f"Function '{function_name}' not found in library '{library['name']}'")
# Update usage statistics
qualified_name = function_name
if function_name not in library['functions']:
# Find the qualified name
for name, entry in library['functions'].items():
if entry['function'] == func:
qualified_name = name
break
library['functions'][qualified_name]['usage_count'] += 1
# Execute the function
return func(*args, **kwargs)
def get_library_catalog(library, category=None, include_examples=False, format="dict"):
"""
Gets a catalog of functions in the library.
Args:
library (dict): Function library
category (str, optional): Filter by category
include_examples (bool): Whether to include usage examples
format (str): Output format (dict, table, markdown)
Returns:
any: Function catalog in requested format
"""
#H->AI::LibraryCatalog: (Generate overview of library contents)
# Filter functions by category if specified
functions = library['functions'].values()
if category:
functions = [f for f in functions if f['category'] == category]
# Build catalog based on format
if format == "dict":
catalog = {
'library_name': library['name'],
'description': library['description'],
'function_count': library['metadata']['function_count'],
'categories': library['categories'],
'functions': []
}
for func in functions:
func_entry = {
'name': func['name'],
'qualified_name': func['qualified_name'],
'category': func['category'],
'description': func['description'],
'aliases': func['aliases']
}
if include_examples and func['examples']:
func_entry['examples'] = func['examples']
catalog['functions'].append(func_entry)
return catalog
elif format == "table":
# Create a text-based table
table = f"LIBRARY: {library['name']} ({library['description']})\n"
table += f"Functions: {library['metadata']['function_count']} | Categories: {', '.join(library['categories'])}\n"
table += "-" * 80 + "\n"
table += f"{'NAME':<25} | {'CATEGORY':<15} | {'DESCRIPTION':<40}\n"
table += "-" * 80 + "\n"
for func in sorted(functions, key=lambda x: x['name']):
table += f"{func['name']:<25} | {func['category']:<15} | {func['description'][:40]}\n"
if include_examples:
table += "\nEXAMPLES:\n"
table += "=" * 80 + "\n"
for func in functions:
if func['examples']:
table += f"{func['name']}:\n"
for i, example in enumerate(func['examples']):
table += f" {i+1}. {example}\n"
table += "\n"
return table
elif format == "markdown":
# Create markdown documentation
md = f"# {library['name']}\n\n{library['description']}\n\n"
md += f"**Functions:** {library['metadata']['function_count']} \n"
md += f"**Categories:** {', '.join(library['categories'])}\n\n"
# Group by category
by_category = {}
for func in functions:
category = func['category']
if category not in by_category:
by_category[category] = []
by_category[category].append(func)
for category, funcs in by_category.items():
md += f"## {category.title()}\n\n"
for func in sorted(funcs, key=lambda x: x['name']):
md += f"### {func['name']}\n\n"
md += f"{func['description']}\n\n"
if func['aliases']:
md += f"**Aliases:** {', '.join(func['aliases'])}\n\n"
if include_examples and func['examples']:
md += "**Examples:**\n\n"
for example in func['examples']:
md += f"```\n{example}\n```\n\n"
return md
# Default to dict format
return get_library_catalog(library, category, include_examples, "dict")
def search_library(library, query, search_examples=True, minimum_score=0.2):
"""
Searches the library for functions matching a query.
Args:
library (dict): Function library
query (str): Search query
search_examples (bool): Whether to search in examples
minimum_score (float): Minimum relevance score (0-1)
Returns:
list: Matching functions with relevance scores
"""
#H->AI::LibrarySearch: (Find relevant functions in library)
query_terms = query.lower().split()
results = []
for func_name, func_entry in library['functions'].items():
score = 0.0
matches = []
# Check function name
name_score = calculate_text_match_score(func_entry['name'], query_terms)
if name_score > 0:
score += name_score * 3 # Name matches are most important
matches.append(f"Name match: {name_score:.2f}")
# Check aliases
for alias in func_entry['aliases']:
alias_score = calculate_text_match_score(alias, query_terms)
if alias_score > 0:
score += alias_score * 2
matches.append(f"Alias match ({alias}): {alias_score:.2f}")
# Check description
desc_score = calculate_text_match_score(func_entry['description'], query_terms)
if desc_score > 0:
score += desc_score * 1.5
matches.append(f"Description match: {desc_score:.2f}")
# Check category
cat_score = calculate_text_match_score(func_entry['category'], query_terms)
if cat_score > 0:
score += cat_score
matches.append(f"Category match: {cat_score:.2f}")
# Check examples if enabled
if search_examples and func_entry['examples']:
for i, example in enumerate(func_entry['examples']):
ex_score = calculate_text_match_score(example, query_terms)
if ex_score > 0:
score += ex_score * 0.8
matches.append(f"Example {i+1} match: {ex_score:.2f}")
# Normalize score to 0-1 range
normalized_score = min(1.0, score / 6.0)
# Add to results if score meets minimum
if normalized_score >= minimum_score:
results.append({
'function': func_entry,
'score': normalized_score,
'matches': matches
})
# Sort results by score (highest first)
return sorted(results, key=lambda x: x['score'], reverse=True)
def calculate_text_match_score(text, query_terms):
"""
Calculates a relevance score for text matching query terms.
Args:
text (str): Text to check
query_terms (list): Query terms to match
Returns:
float: Match score (0-1)
"""
if not text or not query_terms:
return 0.0
text_lower = text.lower()
text_terms = text_lower.split()
# Exact match of the full query
full_query = " ".join(query_terms)
if full_query in text_lower:
return 1.0
# Count matching terms
matches = sum(1 for term in query_terms if term in text_lower)
# Calculate score based on proportion of matching terms
if matches == 0:
return 0.0
return matches / len(query_terms)
def get_function_recommendations(library, context, max_recommendations=3):
"""
Gets function recommendations based on context.
Args:
library (dict): Function library
context (dict): Context information
max_recommendations (int): Maximum number of recommendations
Returns:
list: Recommended functions
"""
#H->AI::FunctionRecommendation: (Suggest relevant functions based on context)
# Extract relevant information from context
user_goal = context.get('goal', '')
recent_usage = context.get('recent_functions', [])
user_data = context.get('data_type', '')
# Score functions based on relevance to context
scores = {}
for name, func in library['functions'].items():
score = 0.0
# Score by relevance to user goal
if user_goal:
goal_match = calculate_text_match_score(func['description'], user_goal.lower().split())
score += goal_match * 3
# Boost recently used functions in the same category
for recent in recent_usage:
if recent in library['functions']:
recent_func = library['functions'][recent]
if recent_func['category'] == func['category']:
score += 0.5
# Score by relevance to data type
if user_data:
# Simple check for data type mentions in description or examples
data_terms = user_data.lower().split()
desc_match = calculate_text_match_score(func['description'], data_terms)
score += desc_match * 2
# Check examples for data type relevance
for example in func['examples']:
example_match = calculate_text_match_score(example, data_terms)
score += example_match
# Add usage frequency bonus (frequently used functions are likely useful)
usage_bonus = min(0.5, func['usage_count'] / 10)
score += usage_bonus
scores[name] = score
# Get top recommendations
recommendations = sorted(scores.items(), key=lambda x: x[1], reverse=True)
top_recommendations = recommendations[:max_recommendations]
# Format recommendations
results = []
for name, score in top_recommendations:
if score > 0:
func = library['functions'][name]
results.append({
'name': func['name'],
'qualified_name': func['qualified_name'],
'description': func['description'],
'relevance_score': score,
'example': func['examples'][0] if func['examples'] else None
})
return results
def create_function_group(library, group_name, function_names, description=None):
"""
Creates a function group for related functions.
Args:
library (dict): Function library
group_name (str): Name of the function group
function_names (list): Names of functions to include
description (str, optional): Group description
Returns:
dict: Function group object
"""
#H->AI::GroupCreation: (Create logical grouping of related functions)
if description is None:
description = f"A group of related functions for {group_name}"
# Resolve function names to actual functions
functions = []
for name in function_names:
func = get_function_from_library(library, name)
if func is not None:
# Find the function entry
for qualified_name, entry in library['functions'].items():
if entry['function'] == func:
functions.append(entry)
break
# Create the group
group = {
'name': group_name,
'description': description,
'functions': functions,
'created': get_current_timestamp()
}
# Add group to library if it has a groups container
if 'groups' not in library:
library['groups'] = {}
library['groups'][group_name] = group
return group
def extend_library(base_library, extension_name, description=None):
"""
Creates an extension of an existing library.
Args:
base_library (dict): Base library to extend
extension_name (str): Name of the extension
description (str, optional): Extension description
Returns:
dict: Extended library
"""
#H->AI::LibraryExtension: (Create specialized version of existing library)
if description is None:
description = f"An extension of the {base_library['name']} library"
# Create new library with extended namespace
extended_namespace = f"{base_library['namespace']}.{extension_name.lower().replace(' ', '_')}"
extension = create_function_library(
library_name=f"{base_library['name']} - {extension_name}",
description=description,
categories=base_library['categories'].copy(),
namespace=extended_namespace
)
# Add link to base library
extension['extends'] = {
'library': base_library['name'],
'namespace': base_library['namespace']
}
# Add method to access base library functions
extension['get_base_function'] = lambda name: get_function_from_library(base_library, name)
return extension
def get_current_timestamp():
"""Gets current timestamp for versioning."""
#H->AI::TimeTracking: (Record timestamp for version history)
# In real implementation, would return actual timestamp
return "2025-05-17T12:34:56Z"
Example Scenarios
Scenario 1: Text Processing Library
python
# Create a text processing library
text_library = create_function_library(
library_name="Text Processing Library",
description="A collection of functions for processing and analyzing text",
categories=["cleaning", "analysis", "formatting", "extraction"]
)
# Define some text processing functions
def clean_text(text, remove_punctuation=True, lowercase=True, remove_extra_spaces=True):
"""
Cleans text by removing punctuation, converting to lowercase, and normalizing spaces.
Args:
text (str): Text to clean
remove_punctuation (bool): Whether to remove punctuation
lowercase (bool): Whether to convert to lowercase
remove_extra_spaces (bool): Whether to normalize spaces
Returns:
str: Cleaned text
"""
result = text
if remove_punctuation:
import string
result = ''.join(c for c in result if c not in string.punctuation)
if lowercase:
result = result.lower()
if remove_extra_spaces:
result = ' '.join(result.split())
return result
def count_words(text, exclude_stopwords=False):
"""
Counts words in text.
Args:
text (str): Text to analyze
exclude_stopwords (bool): Whether to exclude common stopwords
Returns:
int: Word count
"""
words = text.split()
if exclude_stopwords:
stopwords = ["the", "a", "an", "and", "or", "but", "in", "on", "at", "to", "for", "with", "by"]
words = [w for w in words if w.lower() not in stopwords]
return len(words)
def format_as_bullet_points(items, bullet_char="•"):
"""
Formats a list of items as bullet points.
Args:
items (list): Items to format
bullet_char (str): Character to use for bullets
Returns:
str: Formatted bullet points
"""
return "\n".join(f"{bullet_char} {item}" for item in items)
def extract_key_phrases(text, max_phrases=5):
"""
Extracts key phrases from text.
Args:
text (str): Text to analyze
max_phrases (int): Maximum number of phrases to extract
Returns:
list: Extracted key phrases
"""
# Simplified implementation for example
words = clean_text(text).split()
word_freq = {}
for word in words:
if len(word) > 3: # Only consider words longer than 3 characters
word_freq[word] = word_freq.get(word, 0) + 1
# Sort by frequency
sorted_words = sorted(word_freq.items(), key=lambda x: x[1], reverse=True)
# Extract top phrases (just words in this simplified example)
return [word for word, freq in sorted_words[:max_phrases]]
# Add functions to the library
add_function_to_library(
library=text_library,
function=clean_text,
category="cleaning",
examples=[
"text_processing.clean_text('Hello, World!', remove_punctuation=True, lowercase=True)",
"text_processing.clean_text(' Multiple spaces here ', remove_extra_spaces=True)"
],
aliases=["sanitize_text", "normalize_text"]
)
add_function_to_library(
library=text_library,
function=count_words,
category="analysis",
examples=[
"text_processing.count_words('This is a sample text with 7 words.')",
"text_processing.count_words('The cat sat on the mat.', exclude_stopwords=True)"
]
)
add_function_to_library(
library=text_library,
function=format_as_bullet_points,
category="formatting",
examples=[
"text_processing.format_as_bullet_points(['Item 1', 'Item 2', 'Item 3'])",
"text_processing.format_as_bullet_points(['Apple', 'Banana', 'Cherry'], bullet_char='-')"
],
aliases=["bulletize"]
)
add_function_to_library(
library=text_library,
function=extract_key_phrases,
category="extraction",
examples=[
"text_processing.extract_key_phrases('Artificial intelligence is transforming how we interact with technology.')",
"text_processing.extract_key_phrases(long_article, max_phrases=10)"
],
aliases=["get_key_terms"]
)
# Use the library catalog
library_catalog = get_library_catalog(text_library, format="markdown", include_examples=True)
# print(library_catalog)
# Search the library
search_results = search_library(text_library, "format bullet list")
# for result in search_results:
# print(f"{result['function']['name']} - Score: {result['score']:.2f}")
# print(f" {result['function']['description']}")
# print(f" Matches: {', '.join(result['matches'])}")
# print()
# Execute a function from the library
# formatted_list = execute_library_function(
# text_library,
# "format_as_bullet_points",
# ["First item", "Second item", "Third item"]
# )
# print(formatted_list)
# Get function recommendations based on context
context = {
'goal': 'format a list of items for presentation',
'recent_functions': ['text_processing.clean_text'],
'data_type': 'list of strings'
}
recommendations = get_function_recommendations(text_library, context)
# for rec in recommendations:
# print(f"{rec['name']} - Relevance: {rec['relevance_score']:.2f}")
# print(f" {rec['description']}")
# if rec['example']:
# print(f" Example: {rec['example']}")
# print()
Scenario 2: Multi-Domain Library Ecosystem with Extensions
python
# Create a data analysis library
data_library = create_function_library(
library_name="Data Analysis Library",
description="Functions for analyzing and visualizing data",
categories=["statistics", "visualization", "processing", "reporting"]
)
# Define some data analysis functions
def calculate_statistics(data, measures=None):
"""
Calculates basic statistics for a dataset.
Args:
data (list): Numerical data
measures (list, optional): Statistics to calculate
Returns:
dict: Calculated statistics
"""
if not data:
return {}
if measures is None:
measures = ["mean", "median", "min", "max", "std_dev"]
results = {}
if "mean" in measures:
results["mean"] = sum(data) / len(data)
if "median" in measures:
sorted_data = sorted(data)
mid = len(sorted_data) // 2
if len(sorted_data) % 2 == 0:
results["median"] = (sorted_data[mid-1] + sorted_data[mid]) / 2
else:
results["median"] = sorted_data[mid]
if "min" in measures:
results["min"] = min(data)
if "max" in measures:
results["max"] = max(data)
if "std_dev" in measures:
mean = results.get("mean", sum(data) / len(data))
variance = sum((x - mean) ** 2 for x in data) / len(data)
results["std_dev"] = variance ** 0.5
return results
def generate_histogram_data(data, bins=10):
"""
Generates histogram data for visualization.
Args:
data (list): Numerical data
bins (int): Number of bins
Returns:
dict: Histogram data
"""
if not data:
return {"bins": [], "counts": []}
min_val = min(data)
max_val = max(data)
bin_width = (max_val - min_val) / bins
# Create bin edges
bin_edges = [min_val + i * bin_width for i in range(bins + 1)]
# Count values in each bin
bin_counts = [0] * bins
for value in data:
bin_index = min(bins - 1, max(0, int((value - min_val) / bin_width)))
bin_counts[bin_index] += 1
return {
"bin_edges": bin_edges,
"bin_counts": bin_counts
}
def correlation_analysis(data_x, data_y):
"""
Performs correlation analysis between two datasets.
Args:
data_x (list): First dataset
data_y (list): Second dataset
Returns:
dict: Correlation results
"""
if len(data_x) != len(data_y) or len(data_x) == 0:
return {"error": "Datasets must have the same non-zero length"}
# Calculate means
mean_x = sum(data_x) / len(data_x)
mean_y = sum(data_y) / len(data_y)
# Calculate covariance and variances
covariance = sum((x - mean_x) * (y - mean_y) for x, y in zip(data_x, data_y)) / len(data_x)
variance_x = sum((x - mean_x) ** 2 for x in data_x) / len(data_x)
variance_y = sum((y - mean_y) ** 2 for y in data_y) / len(data_y)
# Calculate Pearson correlation coefficient
correlation = covariance / ((variance_x ** 0.5) * (variance_y ** 0.5))
return {
"correlation": correlation,
"covariance": covariance,
"r_squared": correlation ** 2
}
def generate_summary_report(data, title="Data Summary Report"):
"""
Generates a summary report for a dataset.
Args:
data (list): Numerical data
title (str): Report title
Returns:
str: Formatted report
"""
stats = calculate_statistics(data)
report = f"## {title}\n\n"
report += f"Dataset size: {len(data)} points\n\n"
report += "### Summary Statistics\n\n"
report += f"- Mean: {stats['mean']:.2f}\n"
report += f"- Median: {stats['median']:.2f}\n"
report += f"- Minimum: {stats['min']:.2f}\n"
report += f"- Maximum: {stats['max']:.2f}\n"
report += f"- Standard Deviation: {stats['std_dev']:.2f}\n\n"
report += "### Data Range\n\n"
report += f"The data ranges from {stats['min']:.2f} to {stats['max']:.2f}, "
report += f"spanning {stats['max'] - stats['min']:.2f} units."
return report
# Add functions to the data library
add_function_to_library(
library=data_library,
function=calculate_statistics,
category="statistics",
examples=[
"data_analysis.calculate_statistics([1, 2, 3, 4, 5])",
"data_analysis.calculate_statistics(temperature_data, measures=['mean', 'min', 'max'])"
]
)
add_function_to_library(
library=data_library,
function=generate_histogram_data,
category="visualization",
examples=[
"data_analysis.generate_histogram_data([1, 2, 2, 3, 3, 3, 4, 4, 5])",
"data_analysis.generate_histogram_data(height_data, bins=20)"
],
aliases=["histogram", "create_histogram"]
)
add_function_to_library(
library=data_library,
function=correlation_analysis,
category="statistics",
examples=[
"data_analysis.correlation_analysis(x_values, y_values)",
"data_analysis.correlation_analysis(heights, weights)"
],
aliases=["correlate", "pearson_correlation"]
)
add_function_to_library(
library=data_library,
function=generate_summary_report,
category="reporting",
examples=[
"data_analysis.generate_summary_report(temperature_data)",
"data_analysis.generate_summary_report(sales_figures, title='Monthly Sales Summary')"
],
aliases=["summarize_data", "data_report"]
)
# Create an extension for specialized financial analysis
finance_library = extend_library(
base_library=data_library,
extension_name="Financial Analysis",
description="Specialized data analysis functions for financial data"
)
# Add financial-specific functions
def calculate_roi(initial_investment, final_value):
"""
Calculates Return on Investment (ROI).
Args:
initial_investment (float): Initial investment amount
final_value (float): Final value of investment
Returns:
float: ROI as a percentage
"""
return ((final_value - initial_investment) / initial_investment) * 100
def calculate_cagr(initial_value, final_value, years):
"""
Calculates Compound Annual Growth Rate (CAGR).
Args:
initial_value (float): Initial value
final_value (float): Final value
years (float): Number of years
Returns:
float: CAGR as a percentage
"""
if years <= 0 or initial_value <= 0:
return 0
return (((final_value / initial_value) ** (1 / years)) - 1) * 100
def risk_adjusted_return(returns, risk_free_rate=0.0):
"""
Calculates risk-adjusted return metrics.
Args:
returns (list): List of period returns
risk_free_rate (float): Risk-free rate as a percentage
Returns:
dict: Risk-adjusted return metrics
"""
stats = calculate_statistics(returns)
mean_return = stats["mean"]
std_dev = stats["std_dev"]
# Convert risk-free rate to decimal
rf_decimal = risk_free_rate / 100
# Calculate Sharpe ratio
sharpe_ratio = (mean_return - rf_decimal) / std_dev if std_dev > 0 else 0
# Calculate Sortino ratio (simplified - only considering downside deviation)
downside_returns = [r for r in returns if r < rf_decimal]
downside_deviation = (sum((r - rf_decimal) ** 2 for r in downside_returns) / len(returns)) ** 0.5 if downside_returns else 0
sortino_ratio = (mean_return - rf_decimal) / downside_deviation if downside_deviation > 0 else 0
return {
"sharpe_ratio": sharpe_ratio,
"sortino_ratio": sortino_ratio,
"mean_return": mean_return,
"volatility": std_dev
}
# Add functions to the finance library extension
add_function_to_library(
library=finance_library,
function=calculate_roi,
category="statistics",
examples=[
"data_analysis.financial_analysis.calculate_roi(1000, 1500)",
"data_analysis.financial_analysis.calculate_roi(initial_capital, final_portfolio_value)"
],
aliases=["roi", "return_on_investment"]
)
add_function_to_library(
library=finance_library,
function=calculate_cagr,
category="statistics",
examples=[
"data_analysis.financial_analysis.calculate_cagr(10000, 16000, 5)",
"data_analysis.financial_analysis.calculate_cagr(initial_value, current_value, investment_years)"
],
aliases=["cagr", "annual_growth_rate"]
)
add_function_to_library(
library=finance_library,
function=risk_adjusted_return,
category="statistics",
examples=[
"data_analysis.financial_analysis.risk_adjusted_return([0.05, -0.02, 0.03, 0.08, -0.01])",
"data_analysis.financial_analysis.risk_adjusted_return(monthly_returns, risk_free_rate=2.5)"
],
aliases=["risk_metrics", "sharpe_ratio"]
)
# Create function groups for related functions
create_function_group(
library=data_library,
group_name="Basic Analysis",
function_names=["calculate_statistics", "correlation_analysis"],
description="Core statistical analysis functions"
)
create_function_group(
library=finance_library,
group_name="Investment Analysis",
function_names=["calculate_roi", "calculate_cagr", "risk_adjusted_return"],
description="Functions for analyzing investment performance"
)
# Execute a function from the extension library that builds on base library
# investment_analysis = execute_library_function(
# finance_library,
# "risk_adjusted_return",
# [0.05, -0.02, 0.03, 0.08, -0.01],
# risk_free_rate=2.0
# )
# print(investment_analysis)
# The extended library can also access base library functions
# histogram_data = execute_library_function(
# finance_library,
# "generate_histogram_data", # This is from the base library
# [1, 2, 2, 3, 3, 3, 4, 4, 5, 5, 5, 5]
# )
# print(histogram_data)
Scenario 3: Enterprise Workflow Library with Domain-Specific Extensions
python
# Create a core workflow library
workflow_library = create_function_library(
library_name="Enterprise Workflow Library",
description="Core functions for enterprise workflow automation",
categories=["process", "validation", "notification", "integration"]
)
# Basic workflow functions
def validate_input(data, schema, strict=True):
"""
Validates input data against a schema.
Args:
data (dict): Input data to validate
schema (dict): Validation schema
strict (bool): Whether to enforce strict validation
Returns:
dict: Validation results
"""
errors = []
# Check required fields
for field, field_schema in schema.items():
if field_schema.get("required", False) and field not in data:
errors.append(f"Required field '{field}' is missing")
continue
if field in data:
# Type validation
expected_type = field_schema.get("type")
if expected_type:
type_map = {
"string": str,
"integer": int,
"float": float,
"boolean": bool,
"list": list,
"dict": dict
}
if expected_type in type_map:
if not isinstance(data[field], type_map[expected_type]):
errors.append(f"Field '{field}' should be of type '{expected_type}'")
# Range validation for numeric types
if "min" in field_schema and isinstance(data[field], (int, float)):
if data[field] < field_schema["min"]:
errors.append(f"Field '{field}' should be at least {field_schema['min']}")
if "max" in field_schema and isinstance(data[field], (int, float)):
if data[field] > field_schema["max"]:
errors.append(f"Field '{field}' should be at most {field_schema['max']}")
# Pattern validation for strings
if "pattern" in field_schema and isinstance(data[field], str):
import re
if not re.match(field_schema["pattern"], data[field]):
errors.append(f"Field '{field}' does not match the required pattern")
return {
"valid": len(errors) == 0,
"errors": errors,
"validated_data": data if len(errors) == 0 or not strict else None
}
def process_workflow_step(step_name, input_data, workflow_config):
"""
Processes a single workflow step.
Args:
step_name (str): Name of the workflow step
input_data (dict): Input data for the step
workflow_config (dict): Workflow configuration
Returns:
dict: Step processing results
"""
if step_name not in workflow_config["steps"]:
return {
"status": "error",
"message": f"Step '{step_name}' not found in workflow configuration"
}
step_config = workflow_config["steps"][step_name]
# Validate input against step schema
if "input_schema" in step_config:
validation = validate_input(input_data, step_config["input_schema"])
if not validation["valid"]:
return {
"status": "validation_error",
"errors": validation["errors"]
}
# Process step (simplified for example)
result = {
"status": "completed",
"step": step_name,
"timestamp": get_current_timestamp(),
"output": {
"message": f"Processed step '{step_name}' successfully"
}
}
# Add step-specific processing logic here
if "transform" in step_config:
# Apply data transformations
transform_type = step_config["transform"].get("type", "")
if transform_type == "map":
# Map input fields to output fields
mapping = step_config["transform"].get("mapping", {})
result["output"]["data"] = {
out_field: input_data.get(in_field)
for out_field, in_field in mapping.items()
if in_field in input_data
}
elif transform_type == "calculate":
# Perform calculations
expression = step_config["transform"].get("expression", "")
variables = {k: v for k, v in input_data.items() if isinstance(v, (int, float))}
try:
# Very simplified expression evaluation (unsafe in real code)
from math import * # Makes math functions available in eval
result["output"]["result"] = eval(expression, {"__builtins__": {}}, variables)
except Exception as e:
result["output"]["error"] = str(e)
# Set next step if available
if "next" in step_config:
result["next_step"] = step_config["next"]
return result
def send_notification(recipient, message, channel="email", metadata=None):
"""
Sends a notification through the specified channel.
Args:
recipient (str): Notification recipient
message (str): Notification message
channel (str): Notification channel (email, sms, app)
metadata (dict, optional): Additional notification metadata
Returns:
dict: Notification result
"""
if metadata is None:
metadata = {}
# Simplified notification (would integrate with actual systems in real code)
notification = {
"recipient": recipient,
"message": message,
"channel": channel,
"metadata": metadata,
"timestamp": get_current_timestamp(),
"status": "sent" # Simulated success
}
# Channel-specific formatting
if channel == "email":
notification["email_subject"] = metadata.get("subject", "Notification")
elif channel == "sms":
# Truncate long messages for SMS
if len(message) > 160:
notification["message"] = message[:157] + "..."
elif channel == "app":
notification["priority"] = metadata.get("priority", "normal")
notification["action_url"] = metadata.get("action_url")
return notification
def execute_workflow(workflow_name, initial_data, workflow_config):
"""
Executes a complete workflow from start to finish.
Args:
workflow_name (str): Name of the workflow to execute
initial_data (dict): Initial input data
workflow_config (dict): Workflow configuration
Returns:
dict: Workflow execution results
"""
if workflow_name not in workflow_config["workflows"]:
return {
"status": "error",
"message": f"Workflow '{workflow_name}' not found in configuration"
}
workflow_def = workflow_config["workflows"][workflow_name]
# Initialize execution
execution = {
"workflow": workflow_name,
"start_time": get_current_timestamp(),
"status": "in_progress",
"steps": [],
"current_data": initial_data
}
# Get starting step
current_step = workflow_def.get("start_step")
if not current_step:
execution["status"] = "error"
execution["message"] = "No start_step defined for workflow"
return execution
# Execute steps until completion or error
while current_step:
# Process the current step
step_result = process_workflow_step(current_step, execution["current_data"], workflow_config)
execution["steps"].append({
"name": current_step,
"result": step_result
})
# Check for errors
if step_result["status"] in ["error", "validation_error"]:
execution["status"] = "failed"
execution["error"] = step_result.get("message", "Step execution failed")
execution["failed_step"] = current_step
break
# Update current data with step output
if "data" in step_result.get("output", {}):
execution["current_data"].update(step_result["output"]["data"])
# Move to next step
if "next_step" in step_result:
current_step = step_result["next_step"]
else:
# End of workflow
current_step = None
# Finalize execution
execution["end_time"] = get_current_timestamp()
if execution["status"] != "failed":
execution["status"] = "completed"
return execution
# Add functions to the workflow library
add_function_to_library(
library=workflow_library,
function=validate_input,
category="validation",
examples=[
"workflow.validate_input(form_data, user_schema)",
"workflow.validate_input(api_response, response_schema, strict=False)"
],
aliases=["validate", "check_data"]
)
add_function_to_library(
library=workflow_library,
function=process_workflow_step,
category="process",
examples=[
"workflow.process_workflow_step('data_validation', input_data, workflow_config)",
"workflow.process_workflow_step(next_step, transformed_data, process_definition)"
]
)
add_function_to_library(
library=workflow_library,
function=send_notification,
category="notification",
examples=[
"workflow.send_notification('user@example.com', 'Your request has been approved')",
"workflow.send_notification(user_phone, 'Verification code: 123456', channel='sms')"
],
aliases=["notify", "alert"]
)
add_function_to_library(
library=workflow_library,
function=execute_workflow,
category="process",
examples=[
"workflow.execute_workflow('new_user_onboarding', user_data, system_workflows)",
"workflow.execute_workflow('invoice_approval', invoice_data, finance_workflows)"
],
aliases=["run_workflow", "workflow_execute"]
)
# Create a specialized extension for HR workflows
hr_workflow_library = extend_library(
base_library=workflow_library,
extension_name="HR Workflows",
description="Specialized workflow functions for HR processes"
)
# Add HR-specific workflow functions
def generate_employee_onboarding_workflow(custom_steps=None):
"""
Generates a standard employee onboarding workflow configuration.
Args:
custom_steps (dict, optional): Custom steps to add to the workflow
Returns:
dict: Onboarding workflow configuration
"""
# Standard onboarding steps
standard_steps = {
"validate_employee_data": {
"input_schema": {
"first_name": {"type": "string", "required": True},
"last_name": {"type": "string", "required": True},
"email": {"type": "string", "required": True, "pattern": r"^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$"},
"position": {"type": "string", "required": True},
"department": {"type": "string", "required": True},
"start_date": {"type": "string", "required": True}
},
"next": "create_accounts"
},
"create_accounts": {
"transform": {
"type": "map",
"mapping": {
"username": "email",
"display_name": "first_name"
}
},
"next": "assign_equipment"
},
"assign_equipment": {
"input_schema": {
"position": {"type": "string", "required": True},
"department": {"type": "string", "required": True}
},
"next": "schedule_orientation"
},
"schedule_orientation": {
"input_schema": {
"start_date": {"type": "string", "required": True}
},
"next": "send_welcome_email"
},
"send_welcome_email": {
"transform": {
"type": "map",
"mapping": {
"recipient": "email",
"name": "first_name"
}
}
}
}
# Add or override with custom steps
if custom_steps:
for step_name, step_config in custom_steps.items():
standard_steps[step_name] = step_config
# Create the workflow configuration
workflow_config = {
"steps": standard_steps,
"workflows": {
"employee_onboarding": {
"name": "New Employee Onboarding",
"description": "Process for onboarding new employees",
"start_step": "validate_employee_data"
}
}
}
return workflow_config
def employee_status_change_workflow(status_change_type):
"""
Generates a workflow for employee status changes.
Args:
status_change_type (str): Type of status change (promotion, transfer, leave, termination)
Returns:
dict: Status change workflow configuration
"""
# Base workflow structure
workflow_config = {
"steps": {
"validate_request": {
"input_schema": {
"employee_id": {"type": "string", "required": True},
"requester_id": {"type": "string", "required": True},
"effective_date": {"type": "string", "required": True},
"reason": {"type": "string", "required": True}
},
"next": "manager_approval"
},
"manager_approval": {
"next": "hr_review"
},
"hr_review": {
"next": "update_systems"
},
"update_systems": {
"next": "notify_stakeholders"
},
"notify_stakeholders": {}
},
"workflows": {
f"{status_change_type}_workflow": {
"name": f"Employee {status_change_type.title()} Process",
"description": f"Workflow for processing employee {status_change_type}",
"start_step": "validate_request"
}
}
}
# Add status-specific configurations
if status_change_type == "promotion":
workflow_config["steps"]["validate_request"]["input_schema"].update({
"new_title": {"type": "string", "required": True},
"new_compensation": {"type": "float", "required": True},
"performance_review": {"type": "string", "required": True}
})
elif status_change_type == "transfer":
workflow_config["steps"]["validate_request"]["input_schema"].update({
"new_department": {"type": "string", "required": True},
"new_manager": {"type": "string", "required": True},
"transfer_reason": {"type": "string", "required": True}
})
elif status_change_type == "leave":
workflow_config["steps"]["validate_request"]["input_schema"].update({
"leave_type": {"type": "string", "required": True},
"start_date": {"type": "string", "required": True},
"end_date": {"type": "string", "required": True},
"return_date": {"type": "string", "required": False}
})
elif status_change_type == "termination":
workflow_config["steps"]["validate_request"]["input_schema"].update({
"termination_type": {"type": "string", "required": True},
"last_day": {"type": "string", "required": True},
"exit_interview_date": {"type": "string", "required": False}
})
# Add additional termination-specific steps
workflow_config["steps"]["exit_checklist"] = {
"next": "final_payroll"
}
workflow_config["steps"]["final_payroll"] = {
"next": "revoke_access"
}
workflow_config["steps"]["revoke_access"] = {
"next": "notify_stakeholders"
}
# Update flow
workflow_config["steps"]["hr_review"]["next"] = "exit_checklist"
return workflow_config
def generate_hr_notification_template(notification_type, employee_data):
"""
Generates an HR notification based on template type.
Args:
notification_type (str): Type of notification
employee_data (dict): Employee data for the notification
Returns:
dict: Notification content and metadata
"""
templates = {
"welcome": {
"subject": "Welcome to our company!",
"message": f"Welcome {employee_data.get('first_name', '')}!\n\nWe're excited to have you join our team on {employee_data.get('start_date', '')}. Please report to the reception desk at 9:00 AM on your first day.\n\nYour orientation schedule is attached to this email.",
"channel": "email",
"metadata": {
"priority": "high",
"attachments": ["orientation_schedule.pdf", "employee_handbook.pdf"]
}
},
"promotion": {
"subject": "Congratulations on your promotion!",
"message": f"Congratulations {employee_data.get('first_name', '')}!\n\nWe're pleased to inform you that your promotion to {employee_data.get('new_title', '')} has been approved, effective {employee_data.get('effective_date', '')}.",
"channel": "email",
"metadata": {
"priority": "high",
"cc": ["hr@example.com"]
}
},
"leave_approval": {
"subject": "Leave Request Approved",
"message": f"Hello {employee_data.get('first_name', '')},\n\nYour leave request from {employee_data.get('start_date', '')} to {employee_data.get('end_date', '')} has been approved.",
"channel": "email",
"metadata": {
"priority": "normal",
"calendar_invite": True
}
},
"termination": {
"subject": "Employment Separation Information",
"message": f"Dear {employee_data.get('first_name', '')},\n\nThis email contains important information regarding your separation from the company effective {employee_data.get('last_day', '')}. Please review the attached documents for details about benefits continuation and final pay.",
"channel": "email",
"metadata": {
"priority": "high",
"confidential": True,
"attachments": ["separation_documents.pdf", "benefits_continuation.pdf"]
}
}
}
# Get the appropriate template
template = templates.get(notification_type, {
"subject": "HR Notification",
"message": "This is an HR notification.",
"channel": "email",
"metadata": {"priority": "normal"}
})
# Customize with employee data
for key, value in employee_data.items():
placeholder = f"{{{key}}}"
template["message"] = template["message"].replace(placeholder, str(value))
return template
# Add HR-specific functions to the library
add_function_to_library(
library=hr_workflow_library,
function=generate_employee_onboarding_workflow,
category="process",
examples=[
"workflow.hr_workflows.generate_employee_onboarding_workflow()",
"workflow.hr_workflows.generate_employee_onboarding_workflow(custom_steps={'compliance_training': {'next': 'team_introduction'}})"
],
aliases=["onboarding_workflow", "new_employee_process"]
)
add_function_to_library(
library=hr_workflow_library,
function=employee_status_change_workflow,
category="process",
examples=[
"workflow.hr_workflows.employee_status_change_workflow('promotion')",
"workflow.hr_workflows.employee_status_change_workflow('termination')"
],
aliases=["status_change", "employee_transition"]
)
add_function_to_library(
library=hr_workflow_library,
function=generate_hr_notification_template,
category="notification",
examples=[
"workflow.hr_workflows.generate_hr_notification_template('welcome', {'first_name': 'John', 'start_date': '2025-06-01'})",
"workflow.hr_workflows.generate_hr_notification_template('promotion', employee_data)"
],
aliases=["hr_notify_template", "employee_notification"]
)
# Create groups for related functions
create_function_group(
library=workflow_library,
group_name="Core Workflow Execution",
function_names=["process_workflow_step", "execute_workflow"],
description="Primary functions for workflow execution"
)
create_function_group(
library=hr_workflow_library,
group_name="Employee Lifecycle",
function_names=["generate_employee_onboarding_workflow", "employee_status_change_workflow"],
description="Functions for managing employee lifecycle workflows"
)
# Get recommendations based on HR context
hr_context = {
'goal': 'set up workflow for new employee starting next week',
'recent_functions': ['workflow.validate_input', 'workflow.send_notification'],
'data_type': 'employee records'
}
hr_recommendations = get_function_recommendations(hr_workflow_library, hr_context)
# for rec in hr_recommendations:
# print(f"{rec['name']} - Relevance: {rec['relevance_score']:.2f}")
# print(f" {rec['description']}")
# if rec['example']:
# print(f" Example: {rec['example']}")
# print()
# Execute an HR workflow function that builds on base library capabilities
# onboarding_config = execute_library_function(
# hr_workflow_library,
# "generate_employee_onboarding_workflow"
# )
#
# new_employee = {
# "first_name": "Jane",
# "last_name": "Smith",
# "email": "jane.smith@example.com",
# "position": "Software Engineer",
# "department": "Engineering",
# "start_date": "2025-06-01"
# }
#
# # Execute the workflow using a base library function
# onboarding_result = execute_library_function(
# hr_workflow_library,
# "execute_workflow", # This is from the base library
# "employee_onboarding",
# new_employee,
# onboarding_config
# )
# print(onboarding_result)
Benefits of Function Libraries
Organized Structure: Provides a clear, hierarchical organization for functions, making large collections manageable.
Discoverability: Makes it easier to find relevant functions through search, recommendations, and browsing.
Consistency: Enforces consistent patterns, documentation, and interfaces across related functions.
Namespace Management: Prevents naming conflicts and clarifies the source and purpose of each function.
Contextual Recommendations: Suggests relevant functions based on the user's current context and goals.
Extensibility: Allows libraries to be extended and specialized for specific domains without modifying the original.
Documentation: Automatically generates comprehensive documentation for all functions in the library.
Logical Grouping: Groups related functions together, improving understanding of function relationships.
Version Tracking: Maintains version history for libraries and their functions.
Reduced Redundancy: Encourages reuse of existing functions rather than duplicating functionality.
Simplified Integration: Makes it easier to integrate functions from different domains through consistent interfaces.
Knowledge Preservation: Captures usage examples and relationships that help preserve institutional knowledge.
Advanced Applications
Function Library Visualization
python
def generate_library_visualization(library, format="d3"):
"""
Generates a visualization of library structure and relationships.
Args:
library (dict): Function library
format (str): Visualization format (d3, mermaid, dot)
Returns:
str: Visualization code
"""
#H->AI::VisualizeLibrary: (Create visual representation of library structure)
if format == "mermaid":
# Generate Mermaid.js flowchart
mermaid = "graph TD\n"
# Add library node
lib_id = library['namespace'].replace(".", "_")
mermaid += f" {lib_id}[{library['name']}]\n"
# Add category nodes
for category in library['categories']:
cat_id = f"{lib_id}_{category}"
mermaid += f" {cat_id}({category})\n"
mermaid += f" {lib_id} --> {cat_id}\n"
# Add function nodes
for func_name, func in library['functions'].items():
func_id = func_name.replace(".", "_")
mermaid += f" {func_id}[\"{func['name']}\"]\n"
# Connect to category
cat_id = f"{lib_id}_{func['category']}"
mermaid += f" {cat_id} --> {func_id}\n"
# Add function groups if present
if 'groups' in library:
for group_name, group in library['groups'].items():
group_id = f"{lib_id}_group_{group_name.replace(' ', '_')}"
mermaid += f" {group_id}{{Group: {group_name}}}\n"
# Connect to functions
for func in group['functions']:
func_id = func['qualified_name'].replace(".", "_")
mermaid += f" {group_id} -.-> {func_id}\n"
# Add extension relationships if present
if 'extends' in library:
base_lib = library['extends']['library']
base_id = library['extends']['namespace'].replace(".", "_")
mermaid += f" {base_id}[{base_lib}]\n"
mermaid += f" {base_id} ==> {lib_id}\n"
return mermaid
elif format == "d3":
# Generate D3.js visualization data
nodes = []
links = []
# Add library node
lib
Retry
RK
Continue
Edit
python
def generate_library_visualization(library, format="d3"):
"""
Generates a visualization of library structure and relationships.
Args:
library (dict): Function library
format (str): Visualization format (d3, mermaid, dot)
Returns:
str: Visualization code
"""
#H->AI::VisualizeLibrary: (Create visual representation of library structure)
if format == "mermaid":
# Generate Mermaid.js flowchart
mermaid = "graph TD\n"
# Add library node
lib_id = library['namespace'].replace(".", "_")
mermaid += f" {lib_id}[{library['name']}]\n"
# Add category nodes
for category in library['categories']:
cat_id = f"{lib_id}_{category}"
mermaid += f" {cat_id}({category})\n"
mermaid += f" {lib_id} --> {cat_id}\n"
# Add function nodes
for func_name, func in library['functions'].items():
func_id = func_name.replace(".", "_")
mermaid += f" {func_id}[\"{func['name']}\"]\n"
# Connect to category
cat_id = f"{lib_id}_{func['category']}"
mermaid += f" {cat_id} --> {func_id}\n"
# Add function groups if present
if 'groups' in library:
for group_name, group in library['groups'].items():
group_id = f"{lib_id}_group_{group_name.replace(' ', '_')}"
mermaid += f" {group_id}{{Group: {group_name}}}\n"
# Connect to functions
for func in group['functions']:
func_id = func['qualified_name'].replace(".", "_")
mermaid += f" {group_id} -.-> {func_id}\n"
# Add extension relationships if present
if 'extends' in library:
base_lib = library['extends']['library']
base_id = library['extends']['namespace'].replace(".", "_")
mermaid += f" {base_id}[{base_lib}]\n"
mermaid += f" {base_id} ==> {lib_id}\n"
return mermaid
elif format == "d3":
# Generate D3.js visualization data
nodes = []
links = []
# Add library node
lib_id = library['namespace']
nodes.append({
"id": lib_id,
"name": library['name'],
"type": "library",
"function_count": library['metadata']['function_count']
})
# Add category nodes
for category in library['categories']:
cat_id = f"{lib_id}.{category}"
nodes.append({
"id": cat_id,
"name": category,
"type": "category"
})
links.append({
"source": lib_id,
"target": cat_id,
"type": "contains"
})
# Add function nodes
for func_name, func in library['functions'].items():
nodes.append({
"id": func_name,
"name": func['name'],
"type": "function",
"category": func['category'],
"usage_count": func['usage_count']
})
# Connect to category
cat_id = f"{lib_id}.{func['category']}"
links.append({
"source": cat_id,
"target": func_name,
"type": "contains"
})
# Add extension relationships
if 'extends' in library:
base_id = library['extends']['namespace']
links.append({
"source": base_id,
"target": lib_id,
"type": "extends"
})
# Return D3 visualization data
return {
"nodes": nodes,
"links": links
}
elif format == "dot":
# Generate GraphViz DOT format
dot = "digraph LibraryStructure {\n"
dot += " rankdir=TB;\n"
dot += " node [shape=box, style=filled, fillcolor=lightblue];\n"
# Add library node
lib_id = library['namespace'].replace(".", "_")
dot += f' {lib_id} [label="{library["name"]}", fillcolor=lightgreen];\n'
# Add category nodes
for category in library['categories']:
cat_id = f"{lib_id}_{category}"
dot += f' {cat_id} [label="{category}", shape=ellipse, fillcolor=lightyellow];\n'
dot += f' {lib_id} -> {cat_id};\n'
# Add function nodes
for func_name, func in library['functions'].items():
func_id = func_name.replace(".", "_")
dot += f' {func_id} [label="{func["name"]}"];\n'
# Connect to category
cat_id = f"{lib_id}_{func["category"]}"
dot += f' {cat_id} -> {func_id};\n'
# Add extension relationships
if 'extends' in library:
base_id = library['extends']['namespace'].replace(".", "_")
dot += f' {base_id} [label="{library["extends"]["library"]}", fillcolor=lightgreen];\n'
dot += f' {base_id} -> {lib_id} [style=dashed, label="extends"];\n'
dot += "}\n"
return dot
# Default to simpler text representation
return get_library_catalog(library, format="markdown")
Cross-Library Integration and Universal Registry
python
def create_library_registry():
"""
Creates a central registry for tracking and accessing multiple function libraries.
Returns:
dict: Library registry
"""
#H->AI::LibraryRegistry: (Create central repository of function libraries)
registry = {
'libraries': {},
'functions': {},
'categories': set(),
'created': get_current_timestamp(),
'last_updated': get_current_timestamp()
}
return registry
def register_library(registry, library):
"""
Registers a library in the central registry.
Args:
registry (dict): Library registry
library (dict): Library to register
Returns:
bool: Success status
"""
#H->AI::LibraryRegistration: (Add library to central registry)
# Check if library already exists
if library['namespace'] in registry['libraries']:
return False
# Add library to registry
registry['libraries'][library['namespace']] = {
'name': library['name'],
'description': library['description'],
'namespace': library['namespace'],
'function_count': library['metadata']['function_count'],
'categories': library['categories'],
'registered': get_current_timestamp()
}
# Register all functions
for func_name, func in library['functions'].items():
registry['functions'][func_name] = {
'name': func['name'],
'qualified_name': func['qualified_name'],
'description': func['description'],
'category': func['category'],
'library': library['namespace'],
'aliases': func['aliases']
}
# Update categories
registry['categories'].update(library['categories'])
# Update registry metadata
registry['last_updated'] = get_current_timestamp()
return True
def search_registry(registry, query, categories=None, libraries=None, limit=10):
"""
Searches across all registered libraries for matching functions.
Args:
registry (dict): Library registry
query (str): Search query
categories (list, optional): Categories to search in
libraries (list, optional): Libraries to search in
limit (int): Maximum results to return
Returns:
list: Search results across libraries
"""
#H->AI::GlobalSearch: (Search across all registered libraries)
query_terms = query.lower().split()
results = []
# Filter functions by library and category if specified
functions = registry['functions'].values()
if libraries:
functions = [f for f in functions if f['library'] in libraries]
if categories:
functions = [f for f in functions if f['category'] in categories]
# Score and rank functions
for func in functions:
score = 0.0
matches = []
# Check function name
name_score = calculate_text_match_score(func['name'], query_terms)
if name_score > 0:
score += name_score * 3
matches.append(f"Name match: {name_score:.2f}")
# Check aliases
for alias in func['aliases']:
alias_score = calculate_text_match_score(alias, query_terms)
if alias_score > 0:
score += alias_score * 2
matches.append(f"Alias match ({alias}): {alias_score:.2f}")
# Check description
desc_score = calculate_text_match_score(func['description'], query_terms)
if desc_score > 0:
score += desc_score * 1.5
matches.append(f"Description match: {desc_score:.2f}")
# Add to results if score is positive
if score > 0:
results.append({
'function': func,
'score': score,
'matches': matches
})
# Sort by score and limit results
results = sorted(results, key=lambda x: x['score'], reverse=True)[:limit]
return results
def execute_registry_function(registry, function_name, *args, **kwargs):
"""
Executes a function from any registered library.
Args:
registry (dict): Library registry
function_name (str): Function name or qualified name
*args: Positional arguments
**kwargs: Keyword arguments
Returns:
any: Function result
"""
#H->AI::UniversalExecution: (Execute function from any registered library)
# Check if function exists in registry
if function_name in registry['functions']:
func_info = registry['functions'][function_name]
library_name = func_info['library']
# Get the library
if library_name in registry['libraries']:
# Get original library object (would be stored in a separate field in real implementation)
library = None
for lib_obj in registry.get('library_objects', []):
if lib_obj['namespace'] == library_name:
library = lib_obj
break
if library:
# Execute the function
return execute_library_function(library, function_name, *args, **kwargs)
# Check if function exists by unqualified name or alias
for qualified_name, func_info in registry['functions'].items():
if (function_name == func_info['name'] or
function_name in func_info['aliases']):
library_name = func_info['library']
# Get the library
if library_name in registry['libraries']:
# Get original library object
library = None
for lib_obj in registry.get('library_objects', []):
if lib_obj['namespace'] == library_name:
library = lib_obj
break
if library:
# Execute the function
return execute_library_function(library, qualified_name, *args, **kwargs)
raise ValueError(f"Function '{function_name}' not found in any registered library")
def suggest_function_combinations(registry, goal, data_types=None, limit=5):
"""
Suggests combinations of functions from different libraries to achieve a goal.
Args:
registry (dict): Library registry
goal (str): User goal
data_types (list, optional): Types of data being processed
limit (int): Maximum combinations to suggest
Returns:
list: Suggested function combinations
"""
#H->AI::CombinationSuggestions: (Suggest multi-function workflows)
goal_terms = goal.lower().split()
# Find functions relevant to the goal
relevant_functions = []
for func_name, func in registry['functions'].items():
relevance = (
calculate_text_match_score(func['description'], goal_terms) * 2 +
calculate_text_match_score(func['name'], goal_terms) * 1.5 +
calculate_text_match_score(func['category'], goal_terms)
)
# Check data type relevance if specified
if data_types:
for data_type in data_types:
data_type_terms = data_type.lower().split()
relevance += calculate_text_match_score(func['description'], data_type_terms)
if relevance > 0:
relevant_functions.append({
'function': func,
'relevance': relevance
})
# Sort by relevance
relevant_functions = sorted(relevant_functions, key=lambda x: x['relevance'], reverse=True)
# Generate potential combinations
combinations = []
# Look for input-processing-output patterns
input_funcs = [f for f in relevant_functions if any(term in f['function']['description'].lower()
for term in ['input', 'read', 'load', 'parse'])]
process_funcs = [f for f in relevant_functions if any(term in f['function']['description'].lower()
for term in ['process', 'analyze', 'calculate', 'transform'])]
output_funcs = [f for f in relevant_functions if any(term in f['function']['description'].lower()
for term in ['output', 'generate', 'create', 'format', 'report'])]
# Create typical workflows
for i in range(min(limit, max(len(input_funcs), 1) * max(len(process_funcs), 1) * max(len(output_funcs), 1))):
input_func = input_funcs[i % max(len(input_funcs), 1)] if input_funcs else None
process_func = process_funcs[i % max(len(process_funcs), 1)] if process_funcs else None
output_func = output_funcs[i % max(len(output_funcs), 1)] if output_funcs else None
if input_func or process_func or output_func:
workflow = {
'steps': [],
'total_relevance': 0,
'description': f"Workflow to {goal}"
}
if input_func:
workflow['steps'].append({
'function': input_func['function'],
'role': 'input',
'relevance': input_func['relevance']
})
workflow['total_relevance'] += input_func['relevance']
if process_func:
workflow['steps'].append({
'function': process_func['function'],
'role': 'processing',
'relevance': process_func['relevance']
})
workflow['total_relevance'] += process_func['relevance']
if output_func:
workflow['steps'].append({
'function': output_func['function'],
'role': 'output',
'relevance': output_func['relevance']
})
workflow['total_relevance'] += output_func['relevance']
combinations.append(workflow)
# Sort by total relevance
combinations = sorted(combinations, key=lambda x: x['total_relevance'], reverse=True)
return combinations[:limit]
Library Analytics and Usage Optimization
python
def analyze_library_usage(library, usage_logs):
"""
Analyzes usage patterns of a function library.
Args:
library (dict): Function library
usage_logs (list): Function usage logs
Returns:
dict: Usage analytics
"""
#H->AI::UsageAnalytics: (Analyze function usage patterns)
analytics = {
'total_calls': len(usage_logs),
'function_usage': {},
'category_usage': {},
'usage_over_time': {},
'common_patterns': [],
'unused_functions': [],
'most_used_functions': [],
'potential_optimizations': []
}
# Process usage logs
for log in usage_logs:
function_name = log.get('function_name')
timestamp = log.get('timestamp')
success = log.get('success', True)
execution_time = log.get('execution_time', 0)
# Skip invalid logs
if not function_name or not timestamp:
continue
# Track function usage
if function_name not in analytics['function_usage']:
analytics['function_usage'][function_name] = {
'call_count': 0,
'success_count': 0,
'failure_count': 0,
'avg_execution_time': 0,
'total_execution_time': 0
}
analytics['function_usage'][function_name]['call_count'] += 1
if success:
analytics['function_usage'][function_name]['success_count'] += 1
else:
analytics['function_usage'][function_name]['failure_count'] += 1
# Update execution time stats
func_stats = analytics['function_usage'][function_name]
func_stats['total_execution_time'] += execution_time
func_stats['avg_execution_time'] = func_stats['total_execution_time'] / func_stats['call_count']
# Track category usage if function exists in library
if function_name in library['functions']:
category = library['functions'][function_name]['category']
if category not in analytics['category_usage']:
analytics['category_usage'][category] = 0
analytics['category_usage'][category] += 1
# Track usage over time (by date)
date = timestamp.split('T')[0]
if date not in analytics['usage_over_time']:
analytics['usage_over_time'][date] = 0
analytics['usage_over_time'][date] += 1
# Identify unused functions
used_functions = set(analytics['function_usage'].keys())
all_functions = set(library['functions'].keys())
analytics['unused_functions'] = list(all_functions - used_functions)
# Find most used functions
sorted_functions = sorted(
analytics['function_usage'].items(),
key=lambda x: x[1]['call_count'],
reverse=True
)
analytics['most_used_functions'] = sorted_functions[:10]
# Identify common usage patterns (sequences of function calls)
if len(usage_logs) >= 2:
call_sequences = []
for i in range(len(usage_logs) - 1):
current = usage_logs[i].get('function_name')
next_func = usage_logs[i+1].get('function_name')
if current and next_func:
call_sequences.append((current, next_func))
# Count sequence frequencies
sequence_counts = {}
for seq in call_sequences:
if seq not in sequence_counts:
sequence_counts[seq] = 0
sequence_counts[seq] += 1
# Find most common sequences
common_sequences = sorted(
sequence_counts.items(),
key=lambda x: x[1],
reverse=True
)
analytics['common_patterns'] = common_sequences[:5]
# Generate optimization suggestions
# 1. Suggest combining frequently used together functions
for seq, count in analytics.get('common_patterns', []):
if count > 3: # Arbitrary threshold
analytics['potential_optimizations'].append({
'type': 'combine_functions',
'functions': seq,
'frequency': count,
'suggestion': f"Consider creating a combined function for {seq[0]} and {seq[1]}"
})
# 2. Suggest optimizing slow functions
for func_name, stats in analytics['function_usage'].items():
if stats['avg_execution_time'] > 1.0: # Arbitrary threshold (1 second)
analytics['potential_optimizations'].append({
'type': 'optimize_performance',
'function': func_name,
'avg_time': stats['avg_execution_time'],
'call_count': stats['call_count'],
'suggestion': f"Consider optimizing {func_name} for better performance"
})
# 3. Suggest removing or deprecating unused functions
if len(analytics['unused_functions']) > 0:
analytics['potential_optimizations'].append({
'type': 'remove_unused',
'functions': analytics['unused_functions'],
'suggestion': "Consider removing or deprecating unused functions"
})
return analytics
def optimize_library(library, optimization_suggestions):
"""
Applies optimization suggestions to a function library.
Args:
library (dict): Function library
optimization_suggestions (list): Suggestions to apply
Returns:
dict: Optimized library
"""
#H->AI::LibraryOptimization: (Apply performance improvements to library)
# Create a copy of the library to avoid modifying the original
from copy import deepcopy
optimized_library = deepcopy(library)
# Track applied optimizations
applied_optimizations = []
for suggestion in optimization_suggestions:
suggestion_type = suggestion.get('type')
if suggestion_type == 'combine_functions':
# Create a combined function
functions_to_combine = suggestion.get('functions', [])
if len(functions_to_combine) != 2:
continue
func1_name, func2_name = functions_to_combine
# Check if both functions exist
if (func1_name not in optimized_library['functions'] or
func2_name not in optimized_library['functions']):
continue
func1 = optimized_library['functions'][func1_name]['function']
func2 = optimized_library['functions'][func2_name]['function']
# Create combined function
def combined_function(*args, **kwargs):
"""Combined function created through library optimization."""
result1 = func1(*args, **kwargs)
result2 = func2(result1, **kwargs)
return result2
# Generate a name for the combined function
func1_short = func1_name.split('.')[-1]
func2_short = func2_name.split('.')[-1]
combined_name = f"{func1_short}_then_{func2_short}"
# Add to library
add_function_to_library(
library=optimized_library,
function=combined_function,
name=combined_name,
category="optimized",
description=f"Combined function that executes {func1_name} followed by {func2_name}",
examples=[f"{optimized_library['namespace']}.{combined_name}(data)"],
aliases=[f"{func1_short}{func2_short}"]
)
applied_optimizations.append({
'type': 'combined_functions',
'original_functions': [func1_name, func2_name],
'new_function': combined_name
})
elif suggestion_type == 'optimize_performance':
# Apply performance optimization (demonstration only - real implementation would vary)
func_name = suggestion.get('function')
if func_name not in optimized_library['functions']:
continue
# Create an "optimized" version (in reality, this would involve actual optimization)
original_func = optimized_library['functions'][func_name]['function']
def optimized_function(*args, **kwargs):
"""Performance-optimized version of the original function."""
# In a real implementation, this would include actual optimization logic
return original_func(*args, **kwargs)
# Replace the original function
optimized_library['functions'][func_name]['function'] = optimized_function
optimized_library['functions'][func_name]['last_updated'] = get_current_timestamp()
applied_optimizations.append({
'type': 'performance_optimized',
'function': func_name
})
elif suggestion_type == 'remove_unused':
# Mark unused functions as deprecated
unused_functions = suggestion.get('functions', [])
for func_name in unused_functions:
if func_name in optimized_library['functions']:
# Mark as deprecated (instead of removing)
optimized_library['functions'][func_name]['deprecated'] = True
optimized_library['functions'][func_name]['last_updated'] = get_current_timestamp()
applied_optimizations.append({
'type': 'marked_deprecated',
'function': func_name
})
# Update library metadata
optimized_library['metadata']['last_updated'] = get_current_timestamp()
optimized_library['metadata']['optimizations'] = applied_optimizations
# Increment version
version_parts = optimized_library['metadata']['version'].split('.')
if len(version_parts) >= 2:
version_parts[-1] = str(int(version_parts[-1]) + 1)
optimized_library['metadata']['version'] = '.'.join(version_parts)
return optimized_library
Integration with CRAFT Principles
Function Libraries align with several CRAFT principles:
Structure Enables Freedom: By providing structured organization for functions, libraries make it easier to discover and use the right tools for the job, which paradoxically creates more creative freedom.
Code as Conversation: Libraries transform collections of functions into richly annotated, searchable resources that bridge the gap between code and natural language interaction.
Cumulative Intelligence: Libraries preserve and accumulate knowledge about function relationships, usage patterns, and examples, creating an evolving repository of functional capabilities.
Human-AI Partnership: Library discovery mechanisms like searching, recommendations, and visualization help both humans and AI understand available capabilities and how to use them effectively.
Accessibility Through Familiarity: Libraries use familiar organizational concepts like categories and namespaces while making them accessible through natural language interactions.
Conservation of Tokens: Libraries reduce the need to repeatedly explain function capabilities by providing standardized documentation and examples.
Implementation Recommendations
Start with Clear Categorization: Begin by carefully defining the categories that will organize your functions, ensuring they're intuitive and non-overlapping.
Include Rich Metadata: Always provide detailed descriptions, examples, and aliases when adding functions to a library.
Build Search First: Implement robust search capabilities early to ensure functions remain discoverable as the library grows.
Design for Extension: Structure libraries with extension in mind, making it easy to create specialized versions without modifying the original.
Use Consistent Documentation: Follow a consistent documentation pattern for all functions to make the library more predictable and usable.
Leverage Function Groups: Create logical function groups to highlight related functions that are commonly used together.
Track Usage Patterns: Implement usage logging to identify which functions are most valuable and which might need improvement.
Visualize Relationships: Create visualizations of library structure to help users understand function relationships.
Prioritize Discoverability: Focus on making functions easy to find and understand, even for users unfamiliar with the library.
Regular Optimization: Periodically review and optimize libraries based on usage patterns and feedback.
Practical Integration with Other CRAFT Functions
python
# Integration with Context-Aware Functions
def create_context_aware_library(library_name, description=None, context_variables=None):
"""
Creates a function library with context awareness.
Args:
library_name (str): Name of the library
description (str, optional): Library description
context_variables (list, optional): Context variables to track
Returns:
dict: Context-aware library
"""
# Create standard library
library = create_function_library(
library_name=library_name,
description=description
)
# Initialize context tracking
if context_variables is None:
context_variables = ['user_preferences', 'environment', 'previous_results']
# Add context tracking to the library
library['context'] = {
'variables': context_variables,
'current_context': {},
'context_history': []
}
# Add context-aware function execution
original_execute = execute_library_function
def context_aware_execute(library, function_name, *args, **kwargs):
"""Context-aware function execution."""
# Extract context
context = kwargs.pop('context', {})
update_context = kwargs.pop('update_context', True)
# Update current context
if update_context:
for var in library['context']['variables']:
if var in context:
library['context']['current_context'][var] = context[var]
# Execute function
result = original_execute(library, function_name, *args, **kwargs)
# Update context with result
if update_context:
library['context']['context_history'].append({
'function': function_name,
'timestamp': get_current_timestamp(),
'context_before': library['context']['current_context'].copy(),
'result_summary': str(result)[:100] + ('...' if len(str(result)) > 100 else '')
})
# Update context with result
library['context']['current_context']['previous_results'] = library['context']['current_context'].get('previous_results', [])
library['context']['current_context']['previous_results'].append({
'function': function_name,
'timestamp': get_current_timestamp()
})
return result
# Replace execution function
library['execute_function'] = context_aware_execute
# Add context management methods
library['get_context'] = lambda: library['context']['current_context']
library['update_context'] = lambda updates: library['context']['current_context'].update(updates)
library['clear_context'] = lambda: library['context']['current_context'].clear()
return library
# Integration with Collaborative Function Building
def create_collaborative_library(library_name, description=None, collaborator_roles=None):
"""
Creates a function library with collaborative development capabilities.
Args:
library_name (str): Name of the library
description (str, optional): Library description
collaborator_roles (dict, optional): Defined roles for collaborators
Returns:
dict: Collaborative library
"""
# Create standard library
library = create_function_library(
library_name=library_name,
description=description
)
# Set default collaborator roles if not provided
if collaborator_roles is None:
collaborator_roles = {
"maintainer": "Library maintainer with full access",
"contributor": "Can propose function additions or changes",
"reviewer": "Reviews and approves function changes"
}
# Add collaborative structure
library['collaboration'] = {
'roles': collaborator_roles,
'collaborators': {},
'proposed_changes': [],
'activity_log': []
}
# Add collaborative methods
library['add_collaborator'] = lambda name, role: add_collaborator_to_library(library, name, role)
library['propose_function'] = lambda function, name, description, author: propose_function_to_library(library, function, name, description, author)
library['review_proposal'] = lambda proposal_id, reviewer, decision, comments=None: review_library_proposal(library, proposal_id, reviewer, decision, comments)
return library
def add_collaborator_to_library(library, name, role):
"""
Adds a collaborator to a collaborative library.
Args:
library (dict): Collaborative library
name (str): Collaborator name
role (str): Assigned role
Returns:
bool: Success status
"""
# Validate role
if role not in library['collaboration']['roles']:
return False
# Add collaborator
library['collaboration']['collaborators'][name] = {
'role': role,
'added': get_current_timestamp(),
'contributions': []
}
# Log activity
library['collaboration']['activity_log'].append({
'action': 'add_collaborator',
'collaborator': name,
'role': role,
'timestamp': get_current_timestamp()
})
return True
def propose_function_to_library(library, function, name, description, author):
"""
Proposes adding or changing a function in a collaborative library.
Args:
library (dict): Collaborative library
function (function): Function implementation
name (str): Function name
description (str): Function description
author (str): Proposal author
Returns:
int: Proposal ID
"""
# Validate author
if author not in library['collaboration']['collaborators']:
return -1
# Create proposal
proposal_id = len(library['collaboration']['proposed_changes'])
proposal = {
'id': proposal_id,
'type': 'add_function' if name not in library['functions'] else 'update_function',
'function': function,
'name': name,
'description': description,
'author': author,
'status': 'pending',
'timestamp': get_current_timestamp(),
'reviews': []
}
# Add proposal
library['collaboration']['proposed_changes'].append(proposal)
# Log activity
library['collaboration']['activity_log'].append({
'action': 'propose_function',
'proposal_id': proposal_id,
'function_name': name,
'author': author,
'timestamp': get_current_timestamp()
})
# Track contribution
library['collaboration']['collaborators'][author]['contributions'].append({
'type': 'proposal',
'id': proposal_id,
'timestamp': get_current_timestamp()
})
return proposal_id
def review_library_proposal(library, proposal_id, reviewer, decision, comments=None):
"""
Reviews a proposed function change.
Args:
library (dict): Collaborative library
proposal_id (int): Proposal ID
reviewer (str): Reviewer name
decision (str): Review decision (approve, reject, request_changes)
comments (str, optional): Review comments
Returns:
bool: Success status
"""
# Validate reviewer
if reviewer not in library['collaboration']['collaborators']:
return False
# Validate proposal
if proposal_id < 0 or proposal_id >= len(library['collaboration']['proposed_changes']):
return False
proposal = library['collaboration']['proposed_changes'][proposal_id]
# Add review
review = {
'reviewer': reviewer,
'decision': decision,
'comments': comments,
'timestamp': get_current_timestamp()
}
proposal['reviews'].append(review)
# Update proposal status
if decision == 'approve':
# Check if the reviewer has approval rights
reviewer_role = library['collaboration']['collaborators'][reviewer]['role']
can_approve = reviewer_role in ['maintainer', 'reviewer']
if can_approve:
# Apply the function change
if proposal['type'] == 'add_function':
add_function_to_library(
library=library,
function=proposal['function'],
name=proposal['name'],
description=proposal['description']
)
elif proposal['type'] == 'update_function':
# Update existing function
if proposal['name'] in library['functions']:
library['functions'][proposal['name']]['function'] = proposal['function']
library['functions'][proposal['name']]['description'] = proposal['description']
library['functions'][proposal['name']]['last_updated'] = get_current_timestamp()
proposal['status'] = 'approved'
elif decision == 'reject':
proposal['status'] = 'rejected'
elif decision == 'request_changes':
proposal['status'] = 'changes_requested'
# Log activity
library['collaboration']['activity_log'].append({
'action': 'review_proposal',
'proposal_id': proposal_id,
'reviewer': reviewer,
'decision': decision,
'timestamp': get_current_timestamp()
})
# Track contribution
library['collaboration']['collaborators'][reviewer]['contributions'].append({
'type': 'review',
'proposal_id': proposal_id,
'decision': decision,
'timestamp': get_current_timestamp()
})
return True
Real-World Application Example
python
# Example: Enterprise Data Processing Framework with Function Libraries
# Create core data processing library
data_processing = create_function_library(
library_name="Enterprise Data Processing",
description="Core functions for data transformation and analysis",
categories=["import", "transform", "analyze", "export"]
)
# Define essential data processing functions
def import_csv(file_path, headers=True, delimiter=",", encoding="utf-8"):
"""
Imports data from a CSV file.
Args:
file_path (str): Path to the CSV file
headers (bool): Whether the file has headers
delimiter (str): Field delimiter
encoding (str): File encoding
Returns:
dict: Imported data structure
"""
# Simplified implementation
print(f"Importing {file_path} as CSV with delimiter={delimiter}, headers={headers}, encoding={encoding}")
# In a real implementation, would read the file
# Simulated data for example
result = {
"source": file_path,
"format": "csv",
"row_count": 1000,
"columns": ["id", "name", "value", "date"],
"data": [
{"id": 1, "name": "Item 1", "value": 10.5, "date": "2025-01-15"},
{"id": 2, "name": "Item 2", "value": 20.3, "date": "2025-02-20"},
# ... more rows would be here in real implementation
]
}
return result
def import_json(file_path, encoding="utf-8"):
"""
Imports data from a JSON file.
Args:
file_path (str): Path to the JSON file
encoding (str): File encoding
Returns:
dict: Imported data structure
"""
# Simplified implementation
print(f"Importing {file_path} as JSON with encoding={encoding}")
# Simulated data
result = {
"source": file_path,
"format": "json",
"structure": "hierarchical",
"data": {
"metadata": {
"created": "2025-03-15",
"version": "1.2"
},
"items": [
{"id": 1, "name": "Item 1", "value": 10.5, "date": "2025-01-15"},
{"id": 2, "name": "Item 2", "value": 20.3, "date": "2025-02-20"},
# ... more items would be here in real implementation
]
}
}
return result
def filter_data(data, filter_criteria):
"""
Filters data based on specified criteria.
Args:
data (dict): Data structure to filter
filter_criteria (dict): Filtering criteria
Returns:
dict: Filtered data
"""
# Simplified implementation
print(f"Filtering data with criteria: {filter_criteria}")
filtered_result = {"source": data.get("source"), "format": data.get("format")}
# Handle different data formats
if data.get("format") == "csv":
original_rows = data.get("data", [])
filtered_rows = []
for row in original_rows:
include_row = True
for field, criteria in filter_criteria.items():
if field in row:
# Handle different criteria types
if isinstance(criteria, dict):
# Operator-based criteria
if "gt" in criteria and row[field] <= criteria["gt"]:
include_row = False
if "lt" in criteria and row[field] >= criteria["lt"]:
include_row = False
if "eq" in criteria and row[field] != criteria["eq"]:
include_row = False
else:
# Direct value comparison
if row[field] != criteria:
include_row = False
if include_row:
filtered_rows.append(row)
filtered_result["data"] = filtered_rows
filtered_result["row_count"] = len(filtered_rows)
filtered_result["columns"] = data.get("columns")
elif data.get("format") == "json":
# Handle hierarchical JSON data
if "items" in data.get("data", {}):
original_items = data["data"]["items"]
filtered_items = []
for item in original_items:
include_item = True
for field, criteria in filter_criteria.items():
if field in item:
# Handle different criteria types
if isinstance(criteria, dict):
# Operator-based criteria
if "gt" in criteria and item[field] <= criteria["gt"]:
include_item = False
if "lt" in criteria and item[field] >= criteria["lt"]:
include_item = False
if "eq" in criteria and item[field] != criteria["eq"]:
include_item = False
else:
# Direct value comparison
if item[field] != criteria:
include_item = False
if include_item:
filtered_items.append(item)
filtered_result["data"] = {
"metadata": data["data"].get("metadata", {}),
"items": filtered_items
}
return filtered_result
def aggregate_data(data, group_by, aggregations):
"""
Aggregates data by specified fields and calculations.
Args:
data (dict): Data structure to aggregate
group_by (str or list): Field(s) to group by
aggregations (dict): Aggregation functions to apply
Returns:
dict: Aggregated data
"""
# Simplified implementation
print(f"Aggregating data by {group_by} with {aggregations}")
# Convert group_by to list if it's a single field
if isinstance(group_by, str):
group_by = [group_by]
result = {
"source": data.get("source"),
"aggregated": True,
"group_by": group_by,
"groups": {}
}
# Extract rows based on data format
rows = []
if data.get("format") == "csv":
rows = data.get("data", [])
elif data.get("format") == "json" and "items" in data.get("data", {}):
rows = data["data"]["items"]
# Group data
for row in rows:
# Create group key
group_values = []
for field in group_by:
group_values.append(str(row.get(field, "")))
group_key = "|".join(group_values)
# Initialize group if not exists
if group_key not in result["groups"]:
result["groups"][group_key] = {
"key": dict(zip(group_by, [row.get(field) for field in group_by])),
"count": 0,
"aggregates": {}
}
# Update group
group = result["groups"][group_key]
group["count"] += 1
# Apply aggregations
for field, agg_funcs in aggregations.items():
if field in row:
field_value = row[field]
# Initialize aggregates for field
if field not in group["aggregates"]:
group["aggregates"][field] = {}
# Apply aggregation functions
for agg_func in agg_funcs:
if agg_func == "sum":
if "sum" not in group["aggregates"][field]:
group["aggregates"][field]["sum"] = 0
try:
group["aggregates"][field]["sum"] += float(field_value)
except (ValueError, TypeError):
pass
elif agg_func == "avg":
if "sum" not in group["aggregates"][field]:
group["aggregates"][field]["sum"] = 0
try:
group["aggregates"][field]["sum"] += float(field_value)
group["aggregates"][field]["avg"] = group["aggregates"][field]["sum"] / group["count"]
except (ValueError, TypeError):
pass
elif agg_func == "min":
try:
current_value = float(field_value)
if "min" not in group["aggregates"][field] or current_value < group["aggregates"][field]["min"]:
group["aggregates"][field]["min"] = current_value
except (ValueError, TypeError):
pass
elif agg_func == "max":
try:
current_value = float(field_value)
if "max" not in group["aggregates"][field] or current_value > group["aggregates"][field]["max"]:
group["aggregates"][field]["max"] = current_value
except (ValueError, TypeError):
pass
return result
def export_to_csv(data, file_path, headers=True, delimiter=","):
"""
Exports data to a CSV file.
Args:
data (dict): Data to export
file_path (str): Path to save the CSV file
headers (bool): Whether to include headers
delimiter (str): Field delimiter
Returns:
dict: Export result
"""
# Simplified implementation
print(f"Exporting data to {file_path} as CSV with delimiter={delimiter}, headers={headers}")
# In real implementation, would write to file
result = {
"success": True,
"file_path": file_path,
"format": "csv",
"rows_exported": 0
}
# Handle different input data formats
if "data" in data and isinstance(data["data"], list):
# CSV-like data
result["rows_exported"] = len(data["data"])
elif "data" in data and "items" in data["data"]:
# JSON-like data
result["rows_exported"] = len(data["data"]["items"])
elif "groups" in data:
# Aggregated data
result["rows_exported"] = len(data["groups"])
return result
# Add functions to the library
add_function_to_library(
library=data_processing,
function=import_csv,
category="import",
examples=[
"data_processing.import_csv('data/sales.csv')",
"data_processing.import_csv('data/transactions.csv', delimiter=';', encoding='latin-1')"
],
aliases=["read_csv", "load_csv"]
)
add_function_to_library(
library=data_processing,
function=import_json,
category="import",
examples=[
"data_processing.import_json('data/config.json')",
"data_processing.import_json('data/api_response.json', encoding='utf-8')"
],
aliases=["read_json", "load_json"]
)
add_function_to_library(
library=data_processing,
function=filter_data,
category="transform",
examples=[
"data_processing.filter_data(sales_data, {'region': 'North'})",
"data_processing.filter_data(transactions, {'amount': {'gt': 1000}, 'status': 'completed'})"
],
aliases=["query", "filter"]
)
add_function_to_library(
library=data_processing,
function=aggregate_data,
category="analyze",
examples=[
"data_processing.aggregate_data(sales_data, 'region', {'amount': ['sum', 'avg']})",
"data_processing.aggregate_data(transactions, ['category', 'status'], {'value': ['min', 'max', 'avg']})"
],
aliases=["summarize", "group_by"]
)
add_function_to_library(
library=data_processing,
function=export_to_csv,
category="export",
examples=[
"data_processing.export_to_csv(filtered_data, 'output/filtered_sales.csv')",
"data_processing.export_to_csv(summary, 'output/summary.csv', delimiter=';')"
],
aliases=["save_csv", "write_csv"]
)
# Create an extension for department-specific data processing
finance_data_processing = extend_library(
base_library=data_processing,
extension_name="Finance",
description="Specialized data processing for financial data"
)
# Define finance-specific functions
def calculate_financial_metrics(data, metrics=None):
"""
Calculates financial metrics based on data.
Args:
data (dict): Financial data
metrics (list, optional): Specific metrics to calculate
Returns:
dict: Calculated financial metrics
"""
if metrics is None:
metrics = ["growth", "ratio", "profitability"]
# Simplified implementation
print(f"Calculating financial metrics: {metrics}")
# Extract rows based on data format
rows = []
if data.get("format") == "csv":
rows = data.get("data", [])
elif data.get("format") == "json" and "items" in data.get("data", {}):
rows = data["data"]["items"]
result = {
"source": data.get("source"),
"metrics": {}
}
# Simulate metric calculations (simplified)
if "growth" in metrics:
result["metrics"]["growth"] = {
"revenue_growth": 0.15,
"profit_growth": 0.08,
"customer_growth": 0.12
}
if "ratio" in metrics:
result["metrics"]["ratio"] = {
"current_ratio": 1.8,
"debt_to_equity": 0.4,
"return_on_assets": 0.09
}
if "profitability" in metrics:
result["metrics"]["profitability"] = {
"gross_margin": 0.65,
"net_margin": 0.12,
"operating_margin": 0.18
}
return result
def generate_financial_report(data, report_type="summary", period=None):
"""
Generates a formatted financial report.
Args:
data (dict): Financial data or metrics
report_type (str): Type of report to generate
period (str, optional): Reporting period
Returns:
str: Formatted financial report
"""
# Simplified implementation
print(f"Generating {report_type} financial report for period: {period}")
# Initialize report
report = f"# Financial {report_type.title()} Report\n\n"
if period:
report += f"Period: {period}\n\n"
# Add report content based on type
if report_type == "summary":
report += "## Key Metrics\n\n"
# Add metrics if available
if "metrics" in data:
for category, metrics in data["metrics"].items():
report += f"### {category.title()} Metrics\n\n"
for name, value in metrics.items():
formatted_name = " ".join(word.title() for word in name.split("_"))
# Format value based on type
if isinstance(value, float):
# Format as percentage if likely a ratio
if name.endswith("growth") or name.endswith("margin") or name == "return_on_assets":
formatted_value = f"{value * 100:.2f}%"
else:
formatted_value = f"{value:.2f}"
else:
formatted_value = str(value)
report += f"- **{formatted_name}**: {formatted_value}\n"
report += "\n"
elif report_type == "detailed":
report += "## Detailed Financial Analysis\n\n"
# Add metrics with more detailed explanation
if "metrics" in data:
for category, metrics in data["metrics"].items():
report += f"### {category.title()} Analysis\n\n"
for name, value in metrics.items():
formatted_name = " ".join(word.title() for word in name.split("_"))
# Format value based on type
if isinstance(value, float):
# Format as percentage if likely a ratio
if name.endswith("growth") or name.endswith("margin") or name == "return_on_assets":
formatted_value = f"{value * 100:.2f}%"
else:
formatted_value = f"{value:.2f}"
else:
formatted_value = str(value)
report += f"#### {formatted_name}\n\n"
report += f"Value: **{formatted_value}**\n\n"
# Add explanations (simplified)
if name == "revenue_growth":
report += "Revenue growth measures the increase in revenue over the given period. "
if value > 0.1:
report += "Current growth is strong, indicating successful market expansion.\n\n"
elif value > 0:
report += "Current growth is positive but moderate.\n\n"
else:
report += "Current growth is negative, suggesting potential market challenges.\n\n"
elif name == "current_ratio":
report += "Current ratio measures the company's ability to pay short-term obligations. "
if value > 2:
report += "The ratio indicates strong liquidity position.\n\n"
elif value > 1:
report += "The ratio is healthy, indicating adequate liquidity.\n\n"
else:
report += "The ratio is below 1, indicating potential liquidity concerns.\n\n"
elif report_type == "compliance":
report += "## Compliance Report\n\n"
report += "This report examines financial data for regulatory compliance purposes.\n\n"
# Add simulated compliance checks
report += "### Compliance Status\n\n"
report += "- **Data Completeness**: Satisfactory\n"
report += "- **Reporting Timeliness**: Compliant\n"
report += "- **Audit Trail**: Complete\n\n"
report += "### Exceptions\n\n"
report += "No compliance exceptions detected.\n\n"
# Add report footer
report += "---\n"
report += "Generated by Enterprise Finance Data Processing Module\n"
report += f"Date: {get_current_timestamp().split('T')[0]}\n"
return report
# Add functions to the finance library
add_function_to_library(
library=finance_data_processing,
function=calculate_financial_metrics,
category="analyze",
examples=[
"data_processing.finance.calculate_financial_metrics(financial_data)",
"data_processing.finance.calculate_financial_metrics(quarterly_data, metrics=['growth', 'profitability'])"
],
aliases=["financial_analysis", "compute_metrics"]
)
add_function_to_library(
library=finance_data_processing,
function=generate_financial_report,
category="export",
examples=[
"data_processing.finance.generate_financial_report(metrics_data)",
"data_processing.finance.generate_financial_report(financials, report_type='detailed', period='Q1 2025')"
],
aliases=["financial_report", "create_report"]
)
# Create function groups
create_function_group(
library=data_processing,
group_name="ETL Pipeline",
function_names=["import_csv", "filter_data", "export_to_csv"],
description="Functions for basic extract-transform-load operations"
)
create_function_group(
library=finance_data_processing,
group_name="Financial Reporting",
function_names=["calculate_financial_metrics", "generate_financial_report"],
description="Functions for financial analysis and reporting"
)
# Example usage: Data processing workflow
# Step 1: Import data
# sales_data = execute_library_function(data_processing, "import_csv", "data/quarterly_sales.csv")
# Step 2: Filter relevant data
# filtered_data = execute_library_function(data_processing, "filter_data", sales_data, {"quarter": "Q1 2025"})
# Step 3: Calculate financial metrics
# financial_metrics = execute_library_function(finance_data_processing, "calculate_financial_metrics", filtered_data)
# Step 4: Generate quarterly report
# quarterly_report = execute_library_function(
# finance_data_processing,
# "generate_financial_report",
# financial_metrics,
# "detailed",
# "Q1 2025"
# )
# Create a registry for enterprise-wide function discovery
registry = create_library_registry()
register_library(registry, data_processing)
register_library(registry, finance_data_processing)
# Example: Cross-library search
search_results = search_registry(registry, "export data report")
# for result in search_results:
# print(f"{result['function']['qualified_name']} - Score: {result['score']:.2f}")
# print(f" {result['function']['description']}")
# print(f" From: {result['function']['library']}")
# print()
# Example: Function combination suggestions
goal = "analyze quarterly financial data and create a compliance report"
combinations = suggest_function_combinations(registry, goal, ["financial data", "quarterly reports"])
# for i, combo in enumerate(combinations, 1):
# print(f"Suggestion {i}:")
# print(f" Workflow: {combo['description']}")
# print(" Steps:")
# for step in combo['steps']:
# print(f" - {step['function']['qualified_name']} ({step['role']})")
# print(f" Relevance Score: {combo['total_relevance']:.2f}")
# print()
Function Libraries provide a powerful way to organize, discover, and use functions within the CRAFT framework. By creating structured collections with consistent interfaces, rich documentation, and intuitive organization, they make it easier for both humans and AI to leverage the full power of available functions. Whether used for individual projects or enterprise-wide function ecosystems, libraries transform disparate functions into cohesive, accessible toolsets that can grow and evolve over time.