CASQE - Context-Aware Search Query Enrichment

A generic, domain-agnostic search query enrichment system that automatically generates comprehensive search variations using contextual knowledge and AI-powered insights.

Note: This module does not perform the search itself. It enriches queries to be used with search engines or search APIs later on.

🎯 What Problem Does CASQE Solve?

Effective search requires keyword-enhanced queries with proper context—without them, search results are often poor or incomplete. SERP (Search Engine Results Page) search is typically the first critical step in information gathering operations, but it's also a major bottleneck.

CASQE addresses this challenge by automatically generating keyword-enhanced query variations based on your specific search job context. Instead of manually brainstorming different keyword combinations, CASQE systematically produces targeted search variations that improve your search coverage and result quality.

✨ Key Features

• Universal Applicability: Works for any search target (products, companies, people, technologies, research topics)
• Dual Enrichment Strategies:
  • Basic Enrichment: Combinatorial approach using identifiers, platforms, and entities
  • Advanced Enrichment: AI-powered natural language query generation
• Contextual Intelligence: Uses provided context to generate relevant, high-value search variations
• Rich Operation Results: Returns detailed metadata including timing, usage, and generation details
• Async Support: Concurrent execution for improved performance
• Scoring System: Each enriched query gets a relevance score and explanation
• Flexible Configuration: Fine-grained control over basic/advanced limits and total results

🏗️ Architecture

CASQE uses a clean Engine + Orchestrator pattern:

• EnrichmentEngine: Core enrichment logic and LLM interactions
• SearchQueryEnricher: Workflow orchestration, metadata management, and public API

🚀 Quick Start

Installation

pip install -r requirements.txt

Basic Usage

from casqe.search_query_enricher import SearchQueryEnricher
from casqe.schemes import SearchQueryEnrichmentRequestObject

# Initialize the enricher
enricher = SearchQueryEnricher()

# Create a request object with fine-grained control
request = SearchQueryEnrichmentRequestObject(
    query="Tesla Model Y",
    identifier_context="Electric SUV manufactured by Tesla, competitor to BMW iX",
    search_reason_context="Find detailed technical specifications and reviews",
    how_many_basic=7,        # Generate 7 basic enriched queries
    how_many_advanced=5,     # Generate 5 advanced enriched queries  
    how_many_total=10,       # Return top 10 from merged results
    use_basic_enrichment=True,
    use_advanced_enrichment=True
)

# Generate enriched queries
operation = enricher.enrich(request)

# Access results and metadata
print(f"Generated {len(operation)} queries in {operation.elapsed_time:.2f}s")
print(f"Usage: {operation.usage}")

# Iterate over detailed results
for query_obj in operation:
    print(f"{query_obj.query} (score: {query_obj.score:.3f}) - {query_obj.explanation}")

# Or just get the query strings
queries = operation.all_queries()

Async Usage for Better Performance

import asyncio

async def main():
    # Async version runs basic + advanced enrichment concurrently
    operation = await enricher.async_enrich(request)
    print(f"Async: Generated {len(operation)} queries in {operation.elapsed_time:.2f}s")

asyncio.run(main())

📖 Usage Examples

Product Research

request = SearchQueryEnrichmentRequestObject(
    query="iPhone 15 Pro",
    identifier_context="Apple's flagship smartphone, A17 Pro chip, titanium build",
    search_reason_context="Compare with competitors and find detailed reviews",
    how_many_basic=5,
    how_many_advanced=8,
    use_advanced_enrichment=True
)

Sample Output:

• [advanced] iPhone 15 Pro vs Samsung Galaxy S24 comparison (score: 0.92)
• [basic] iPhone 15 Pro titanium durability test (score: 0.87)
• [advanced] A17 Pro chip benchmarks performance (score: 0.85)

Company Research

request = SearchQueryEnrichmentRequestObject(
    query="Anthropic",
    identifier_context="AI safety company founded 2021, creates Claude AI assistant",
    search_reason_context="Research business model and competitive landscape",
    how_many_basic=10,
    how_many_advanced=5,
    how_many_total=12,
    use_basic_enrichment=True,
    use_advanced_enrichment=True
)

Sample Output:

• [advanced] Anthropic funding Series C investment (score: 0.89)
• [basic] Claude AI ChatGPT (score: 0.86)
• [advanced] Anthropic constitutional AI research papers (score: 0.83)

Technology Research

request = SearchQueryEnrichmentRequestObject(
    query="Kubernetes",
    identifier_context="Container orchestration platform, originally by Google, CNCF project",
    search_reason_context="Find best practices for production deployment",
    how_many_advanced=12,
    use_advanced_enrichment=True
)

Sample Output:

• [advanced] Kubernetes production best practices 2024 (score: 0.91)
• [advanced] Google Kubernetes Engine deployment guide (score: 0.88)
• [advanced] CNCF Kubernetes security checklist (score: 0.85)

🔧 API Reference

SearchQueryEnrichmentRequestObject

Parameter	Type	Description	Default
query	str	The base search query	Required
identifier_context	str	Additional context about the search target	None
search_reason_context	str	What you're trying to accomplish with the search	None
text_rules	str	Custom rules for query generation	None
how_many_basic	int	Maximum basic enriched queries to generate	10
how_many_advanced	int	Maximum advanced enriched queries to generate	10
how_many_total	int	Final limit after merging both types	None
use_basic_enrichment	bool	Enable combinatorial enrichment	False
use_advanced_enrichment	bool	Enable AI-powered enrichment	False
use_thinking	bool	Use advanced reasoning model (o3)	False

SearchQueryEnrichmentOperation

The enrich() method returns a rich operation object with:

Property	Type	Description
objects	List[UnifiedQueryCandidate]	All enriched query candidates
generation_results	List[GenerationResult]	Raw LLM call results
elapsed_time	float	Total execution time in seconds
usage	Dict[str, Any]	Aggregated usage statistics (tokens, costs, etc.)

Utility Methods:

• all_queries() → List[str]: Just the query strings
• basic_queries() → List[str]: Only basic enrichment results
• advanced_queries() → List[str]: Only advanced enrichment results
• len(operation): Number of total results
• for query in operation:: Iterate over UnifiedQueryCandidate objects

UnifiedQueryCandidate

Each enriched query contains:

• query: The enriched search string
• score: Relevance score (0.0-1.0)
• explanation: Description of what information this query targets
• origin: Whether from "basic" or "advanced" enrichment

🏗️ Architecture Details

Basic Enrichment

1. Extract Components: AI identifies identifiers, platforms, and entities from context
2. Generate Combinations: Creates systematic combinations (identifier + platform, identifier + entity, etc.)
3. Score & Filter: Assigns relevance scores, prevents duplicates, applies limits

Advanced Enrichment

1. Context Analysis: AI analyzes the search goal and available context
2. Natural Query Generation: Creates human-like search variations
3. Quality Assessment: Each query gets explanation and relevance scoring

Merge & Rank

• Combines results from both enrichment strategies
• Deduplicates similar queries (keeping highest scored)
• Returns top N results sorted by relevance score

⚙️ Configuration

Environment Setup

Create a .env file with your LLM service configuration:

OPENAI_API_KEY=your_api_key_here
# Add other model provider keys as needed

Custom Models

from casqe.myllmservice import MyLLMService

# Use specific model
llm_service = MyLLMService()
enricher = SearchQueryEnricher(llm=llm_service)

# For requests requiring advanced reasoning
request.use_thinking = True  # Uses o3 model

🎯 Use Cases

• Competitive Intelligence: Comprehensive competitor research
• Product Research: Technical specifications, reviews, comparisons
• Academic Research: Literature reviews, expert finding
• OSINT: Open source intelligence gathering
• Market Research: Industry analysis, trend identification
• Due Diligence: Background research for business decisions
• Content Research: Finding diverse sources for content creation

📁 Module Structure

casqe/
├── __init__.py
├── schemes.py                    # Data classes and schemas
├── enrichment_engine.py          # Core enrichment logic
├── search_query_enricher.py      # Public API and orchestration
├── myllmservice.py              # LLM service integration
└── query_enricher.py            # Legacy module (deprecated)

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests for new functionality
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

📝 License

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

🙋‍♀️ Support

If you encounter any issues or have questions:

• Open an issue on GitHub
• Check the documentation for detailed guides
• Review the example scripts in the examples/ directory

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CASQE - Making comprehensive research accessible to everyone.

📖 Usage Examples

Product Research

request = SearchQueryEnrichmentRequestObject(
    query="iPhone 15 Pro",
    identifier_context="Apple's flagship smartphone, A17 Pro chip, titanium build",
    search_reason_context="Compare with competitors and find detailed reviews",
    how_many=8,
    use_advanced_enrichment=True
)

Sample Output:

• iPhone 15 Pro vs Samsung Galaxy S24 comparison (score: 0.92)
• iPhone 15 Pro titanium durability test (score: 0.87)
• A17 Pro chip benchmarks performance (score: 0.85)

Company Research

request = SearchQueryEnrichmentRequestObject(
    query="Anthropic",
    identifier_context="AI safety company founded 2021, creates Claude AI assistant",
    search_reason_context="Research business model and competitive landscape",
    how_many=10,
    use_basic_enrichment=True,
    use_advanced_enrichment=True
)

Sample Output:

• Anthropic funding Series C investment (score: 0.89)
• Claude AI vs ChatGPT comparison (score: 0.86)
• Anthropic constitutional AI research papers (score: 0.83)

Technology Research

request = SearchQueryEnrichmentRequestObject(
    query="Kubernetes",
    identifier_context="Container orchestration platform, originally by Google, CNCF project",
    search_reason_context="Find best practices for production deployment",
    how_many=12,
    use_advanced_enrichment=True
)

Sample Output:

• Kubernetes production best practices 2024 (score: 0.91)
• Google Kubernetes Engine deployment guide (score: 0.88)
• CNCF Kubernetes security checklist (score: 0.85)

🔧 API Reference

SearchQueryEnrichmentRequestObject

Parameter	Type	Description	Default
query	str	The base search query	Required
identifier_context	str	Additional context about the search target	None
search_reason_context	str	What you're trying to accomplish with the search	None
text_rules	str	Custom rules for query generation	None
how_many	int	Maximum number of enriched queries to return	10
use_basic_enrichment	bool	Enable combinatorial enrichment	False
use_advanced_enrichment	bool	Enable AI-powered enrichment	False
use_thinking	bool	Use advanced reasoning model (o3)	False

UnifiedQueryCandidate

Each enriched query returns:

• query: The enriched search string
• score: Relevance score (0.0-1.0)
• explanation: Description of what information this query targets
• origin: Whether from "basic" or "advanced" enrichment

🏗️ Architecture

Basic Enrichment

1. Extract Components: AI identifies identifiers, platforms, and entities from context
2. Generate Combinations: Creates systematic combinations (identifier + platform, identifier + entity, etc.)
3. Score & Filter: Assigns relevance scores and filters low-quality combinations

Advanced Enrichment

1. Context Analysis: AI analyzes the search goal and available context
2. Natural Query Generation: Creates human-like search variations
3. Quality Assessment: Each query gets explanation and relevance scoring

Merge & Rank

• Combines results from both enrichment strategies
• Deduplicates similar queries (keeping highest scored)
• Returns top N results sorted by relevance score

⚙️ Configuration

Environment Setup

Create a .env file with your LLM service configuration:

OPENAI_API_KEY=your_api_key_here
# Add other model provider keys as needed

Custom Models

from casqe.myllmservice import MyLLMService

# Use specific model
llm_service = MyLLMService()
enricher = SearchQueryEnricher(llm=llm_service)

# For requests requiring advanced reasoning
request.use_thinking = True  # Uses o3 model

🎯 Use Cases

• Competitive Intelligence: Comprehensive competitor research
• Product Research: Technical specifications, reviews, comparisons
• Academic Research: Literature reviews, expert finding
• OSINT: Open source intelligence gathering
• Market Research: Industry analysis, trend identification
• Due Diligence: Background research for business decisions
• Content Research: Finding diverse sources for content creation

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests for new functionality
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

📝 License

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

🙋‍♀️ Support

If you encounter any issues or have questions:

• Open an issue on GitHub
• Check the documentation for detailed guides
• Review the example scripts in the examples/ directory

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