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Text to Query
Explore and evaluate different approaches to natural language query conversion using cutting-edge AI techniques.
Approaches Explored
Prompt Chaining
- Sequential prompt engineering for complex query generation
- Structured decomposition of natural language queries
- Context preservation across the chain
Retrieval Augmented Generation (RAG)
- Integration with database schema and metadata
- Dynamic context retrieval for accurate query generation
- Semantic matching with historical queries
Advanced Techniques
Graph-Based Approach
Schema representation as knowledge graphs with intelligent traversal for query construction.
RAG + Graph Hybrid
Combined benefits of both approaches for enhanced context understanding and accuracy.
Agent + RAG Implementation
Autonomous query refinement and optimization with interactive feedback loops.
Technical Implementation
Benchmarks
Comprehensive performance comparisons across different approaches and scenarios.
View Benchmarks