Design a comprehensive framework for extracting and processing value from arbitrary input, regardless of its original intended purpose.
<antArtifact identifier="universal-input-processor" type="text/markdown" title="Universal Input Processing and Value Extraction Framework">
# Universal Input Processing and Value Extraction Framework
## 1. Input Analysis Pipeline
### 1.1 Initial Input Classification
```python
def classify_input_type(raw_input: str) -> InputMetadata:
return {
'format_indicators': detect_format_patterns(raw_input),
'context_markers': identify_context_clues(raw_input),
'temporal_indicators': extract_time_references(raw_input),
'entity_relationships': map_entity_connections(raw_input),
'sentiment_markers': analyze_emotional_content(raw_input),
'action_indicators': identify_action_items(raw_input),
'decision_points': detect_decision_markers(raw_input)
}
```
### 1.2 Value Identification Matrix
```python
VALUE_EXTRACTORS = {
'actionable_items': {
'markers': ['need to', 'should', 'must', 'will', 'going to'],
'priority_indicators': ['urgent', 'important', 'critical', 'asap'],
'temporal_markers': ['today', 'tomorrow', 'next week', 'by'],
'extraction_method': extract_action_items
},
'decision_analysis': {
'markers': ['decide', 'choose', 'option', 'alternative', 'versus'],
'stakeholder_indicators': ['we', 'they', 'team', 'group', 'party'],
'outcome_markers': ['result', 'consequence', 'impact', 'effect'],
'extraction_method': analyze_decision_points
},
'cognitive_patterns': {
'bias_indicators': ['always', 'never', 'everyone', 'nobody', 'obvious'],
'assumption_markers': ['surely', 'clearly', 'obviously', 'certainly'],
'blind_spot_signals': ['didn't consider', 'forgot about', 'overlooked'],
'extraction_method': identify_cognitive_patterns
},
'strategic_elements': {
'negotiation_markers': ['offer', 'propose', 'accept', 'reject', 'terms'],
'strategy_indicators': ['plan', 'approach', 'method', 'strategy'],
'outcome_preferences': ['prefer', 'rather', 'better', 'worse'],
'extraction_method': extract_strategic_elements
}
}
```
## 2. Processing Directives
### 2.1 Value Extraction Protocol
```python
def extract_value(input_text: str, input_metadata: InputMetadata) -> ValueContainer:
primary_value_type = determine_primary_value(input_metadata)
secondary_value_types = identify_secondary_values(input_metadata)
extracted_values = {
'primary': VALUE_EXTRACTORS[primary_value_type]['extraction_method'](input_text),
'secondary': [VALUE_EXTRACTORS[vtype]['extraction_method'](input_text)
for vtype in secondary_value_types],
'metadata': input_metadata
}
return optimize_value_container(extracted_values)
```
### 2.2 Output Format Selection
```python
OUTPUT_FORMATS = {
'action_list': {
'conditions': ['actionable_items > 3', 'temporal_urgency > 0.6'],
'format': generate_checklist
},
'decision_matrix': {
'conditions': ['decision_points > 1', 'stakeholders > 1'],
'format': generate_decision_matrix
},
'strategic_analysis': {
'conditions': ['negotiation_elements > 0', 'stakeholders > 2'],
'format': generate_game_theory_analysis
},
'bias_report': {
'conditions': ['cognitive_patterns > 2', 'assumption_markers > 3'],
'format': generate_bias_analysis
}
}
```
## 3. Implementation Guidelines
### 3.1 Action Item Extraction
```python
def extract_action_items(text: str) -> List[ActionItem]:
actions = []
for sentence in tokenize_sentences(text):
if contains_action_markers(sentence):
action = {
'task': extract_core_action(sentence),
'priority': determine_priority(sentence),
'timeline': extract_timeline(sentence),
'dependencies': identify_dependencies(sentence),
'stakeholders': extract_stakeholders(sentence)
}
actions.append(action)
return prioritize_actions(actions)
```
### 3.2 Game Theory Analysis
```python
def analyze_game_theoretic_elements(text: str) -> GameTheoryAnalysis:
return {
'players': identify_stakeholders(text),
'strategies': extract_strategy_options(text),
'payoffs': estimate_payoff_matrix(text),
'equilibria': find_nash_equilibria(payoff_matrix),
'recommendations': generate_strategic_recommendations()
}
```
### 3.3 Cognitive Bias Detection
```python
def detect_cognitive_biases(text: str) -> BiasAnalysis:
return {
'confirmed_biases': identify_clear_biases(text),
'potential_biases': detect_bias_patterns(text),
'blind_spots': identify_blind_spots(text),
'mitigation_strategies': generate_mitigation_strategies()
}
```
## 4. Output Generation
### 4.1 Format Selection Logic
```python
def select_output_format(extracted_value: ValueContainer) -> OutputFormatter:
primary_format = determine_primary_format(extracted_value)
supplementary_formats = identify_supplementary_formats(extracted_value)
return {
'primary_format': OUTPUT_FORMATS[primary_format],
'supplementary_formats': [OUTPUT_FORMATS[fmt] for fmt in supplementary_formats],
'integration_strategy': determine_integration_strategy()
}
```
### 4.2 Output Generation Rules
```python
OUTPUT_RULES = {
'prioritization': {
'high': 'urgent + important + time_sensitive',
'medium': 'important + planned',
'low': 'routine + flexible'
},
'formatting': {
'checklist': '- [ ] {action_item}',
'matrix': '| {option} | {impact} | {feasibility} |',
'analysis': '## {section_heading}\n{content}'
},
'organization': {
'grouping': 'by_priority || by_topic || by_timeline',
'ordering': 'priority_first || chronological || logical_flow'
}
}
```
### 4.3 Quality Control
```python
def validate_output(generated_output: str, input_metadata: InputMetadata) -> bool:
return (
verify_completeness(generated_output, input_metadata) and
verify_accuracy(generated_output, input_metadata) and
verify_usefulness(generated_output, input_metadata) and
verify_clarity(generated_output)
)
```
</antArtifact>
This framework provides:
1. **Universal Input Processing**:
- Handles any form of text input
- Identifies valuable information regardless of original intent
- Adapts to different communication styles and contexts
2. **Intelligent Value Extraction**:
- Recognizes multiple types of valuable information
- Prioritizes based on context and potential impact
- Maintains relationships between extracted elements
3. **Flexible Output Generation**:
- Adapts output format to match extracted value
- Ensures actionable and useful presentation
- Maintains context and relationships
Would you like me to:
1. Detail any specific component further?
2. Provide concrete examples of how it handles different types of input?
3. Expand the implementation of any particular value extractor?
> [!ai]+ AI
>
> This is a well-structured framework. It outlines the key components of a universal input processor and value extractor. Here’s a breakdown of its strengths and areas for improvement:
> **Strengths:**
> - **Modular Design:** The framework is broken down into logical modules (input analysis, processing directives, implementation guidelines, output generation), making it easier to understand, maintain, and extend.
> - **Comprehensive Approach:** It considers various aspects of value extraction, including context, sentiment, actions, decisions, and cognitive patterns.
> - **Flexible Output:** The framework adapts the output format based on the extracted value, ensuring relevance and usability.
> - **Quality Control:** The inclusion of validation steps ensures the accuracy and usefulness of the generated output.
> - **Use of Metadata:** Tracking metadata throughout the process allows for richer analysis and more informed decisions about output format and content.
> **Areas for Improvement:**
> - **Abstract Implementations:** While the framework provides a good structure, the actual implementations of functions like `detect_format_patterns`, `extract_action_items`, `analyze_game_theoretic_elements`, etc., are highly abstract. Providing more concrete examples or pseudocode would make the framework more practical.
> - **Handling Non-Textual Input:** The framework focuses primarily on text input. Consider how it could be extended to handle other input types like images, audio, or video. This might involve a pre-processing step to convert these inputs into a textual representation or feature vectors.
> - **Scalability and Performance:** Processing large inputs or complex value extraction tasks could be computationally expensive. The framework should address scalability and performance considerations.
> - **Ambiguity Resolution:** Natural language is inherently ambiguous. The framework needs mechanisms to handle ambiguity and uncertainty in the input. This could involve using probabilistic models or incorporating external knowledge sources.
> - **Contextual Understanding:** While the framework mentions context, it doesn’t delve into how context is actually modeled and used. A more detailed explanation of context representation and its role in value extraction would be beneficial.
> - **Learning and Adaptation:** A truly universal input processor should be able to learn and adapt to new input types and value extraction tasks. Consider incorporating machine learning techniques to enable this.
> **Specific Suggestions and Examples:**
> 1. **`detect_format_patterns(raw_input)`:** This could use regular expressions, libraries like `filetype`, or machine learning models to identify file formats (e.g., JSON, XML, CSV) or structural patterns (e.g., email headers, log file entries).
> 2. **`extract_action_items(text)`:** Provide a more concrete example using NLP techniques. For instance:
> ```python
> import spacy
> nlp = spacy.load("en_core_web_sm")
> def extract_action_items(text):
> doc = nlp(text)
> actions = []
> for token in doc:
> if token.pos_ == "VERB" and token.dep_ in ["ROOT", "aux"]: # Look for main verbs
> action = {"task": token.text}
> # Extract related information (e.g., object, time, etc.)
> for child in token.children:
> if child.dep_ == "dobj": # Direct object
> action["object"] = child.text
> # ... other dependency parsing ...
> actions.append(action)
> return actions
> ```
> 3. **Handling Images:** For image input, you could integrate with an image recognition API (e.g., Google Cloud Vision API) to extract text, objects, and other relevant information. This extracted information can then be fed into the text processing pipeline.
> 4. **Ambiguity Resolution:** Consider using probabilistic parsing or semantic role labeling to disambiguate sentences and identify the intended meaning.
> By addressing these points, you can make the framework more robust, practical, and truly universal. The current version provides an excellent foundation, and with further refinement, it has the potential to be a powerful tool for extracting value from diverse data sources.