# Use Cases Configuration
use_cases:
  sql_generation:
    name: "SQL Generation"
    description: "Natural language to SQL query generation"
    input_type: "natural_language"
    output_type: "sql_query"
    metrics:
      - correctness_exact
      - exec_success
      - result_match_f1
      - dialect_ok
      - readability
      - latency
    weights:
      correctness_exact: 0.40
      exec_success: 0.25
      result_match_f1: 0.15
      dialect_ok: 0.10
      readability: 0.05
      latency: 0.05
    datasets:
      - nyc_taxi_small
    dialects:
      - presto
      - bigquery
      - snowflake
    
  code_generation:
    name: "Code Generation"
    description: "Natural language to source code generation"
    input_type: "natural_language"
    output_type: "source_code"
    metrics:
      - syntax_correctness
      - compilation_success
      - execution_success
      - code_quality
      - performance
      - latency
    weights:
      syntax_correctness: 0.30
      compilation_success: 0.25
      execution_success: 0.20
      code_quality: 0.15
      performance: 0.05
      latency: 0.05
    languages:
      - python
      - go
      - javascript
      - java
    datasets:
      - python_algorithms
      - go_algorithms
    
  documentation:
    name: "Documentation Generation"
    description: "Natural language to technical documentation"
    input_type: "natural_language"
    output_type: "documentation"
    metrics:
      - accuracy
      - completeness
      - clarity
      - format_compliance
      - technical_correctness
      - latency
    weights:
      accuracy: 0.25
      completeness: 0.25
      clarity: 0.20
      format_compliance: 0.15
      technical_correctness: 0.10
      latency: 0.05
    formats:
      - markdown
      - html
      - json
      - yaml
    datasets:
      - technical_docs
      - api_documentation

# Evaluation frameworks for each use case
evaluation_frameworks:
  sql_generation:
    executor: "SQLExecutor"
    metrics_computer: "SQLMetricsComputer"
    validator: "SQLValidator"
    
  code_generation:
    executor: "CodeExecutor"
    metrics_computer: "CodeMetricsComputer"
    validator: "CodeValidator"
    
  documentation:
    executor: "DocProcessor"
    metrics_computer: "DocMetricsComputer"
    validator: "DocValidator"

# Model configurations for each use case
model_configs:
  sql_generation:
    models:
      - "SQLCoder-7B"
      - "SQLCoder2-7B"
      - "CodeT5-Base"
      - "GPT-4"
    
  code_generation:
    models:
      - "CodeT5-Base"
      - "CodeGen-6B"
      - "GPT-4"
      - "Claude-3"
    
  documentation:
    models:
      - "GPT-4"
      - "Claude-3"
      - "Llama-2"
      - "PaLM-2"