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william.dias 5719fdbe18 feat: Add Agno tool interfaces for LLM, prompt generation, and storage operations 
- Introduced LLMTool interface for LLM providers, defining methods for text generation and health checks.
- Created PromptGeneratorTool interface for generating database-specific prompts, including SQL to natural language and vice versa.
- Implemented FileStorageTool and MetadataStoreTool interfaces for file operations and metadata persistence, respectively.
- Defined core types including DatabaseType, QueryHash, and QueryMetric to support optimization flows.
- Established exception handling with specific error classes for query validation, LLM provider issues, and optimization errors.
- Added data models for optimization metadata and results, supporting serialization and versioning.
2026-01-23 09:26:57 -03:00
docs feat: Add Agno tool interfaces for LLM, prompt generation, and storage operations 2026-01-23 09:26:57 -03:00
scripts feat: Implement SQL prompt generators for PostgreSQL, SQLite, and SQL Server 2026-01-21 13:51:03 -03:00
src feat: Add Agno tool interfaces for LLM, prompt generation, and storage operations 2026-01-23 09:26:57 -03:00
.gitignore feat: Implement SQL prompt generators for PostgreSQL, SQLite, and SQL Server 2026-01-21 13:51:03 -03:00
README.md feat: Add Agno tool interfaces for LLM, prompt generation, and storage operations 2026-01-23 09:26:57 -03:00
requirements.txt feat: Implement SQL prompt generators for PostgreSQL, SQLite, and SQL Server 2026-01-21 13:51:03 -03:00
sample.env feat: Implement SQL prompt generators for PostgreSQL, SQLite, and SQL Server 2026-01-21 13:51:03 -03:00

README.md

SQL Optimizer Team (Agno)

POC de um time de agentes usando o framework Agno para reproduzir o fluxo do projeto oracle-sql-query-optimizer.

Objetivo

  • Receber uma SQL e o banco alvo (oracle/sqlserver/postgresql/mysql/sqlite).
  • Gerar explicação detalhada (SQL → linguagem natural).
  • Gerar SQL otimizada (linguagem natural → SQL), preservando 100% da lógica de negócio.
  • (Opcional) Gerar análise conservadora (sem reescrever a query).

As prompts são mantidas idênticas às do projeto oracle-sql-query-optimizer.

Estrutura

src/
  sql_optimizer_team/
    team_app.py
    agents/
    tools/

Configuração rápida

  1. Crie o ambiente e instale dependências:
    • pip install -r requirements.txt
  2. Configure variáveis de ambiente (exemplo em sample.env).
  3. Execute o servidor:
    • PYTHONPATH=src python -m main

Acesse:

  • http://localhost:8204/docs (Swagger UI)
  • http://localhost:8204 (informações básicas da API)

Fluxo do time

  1. Gestor recebe a requisição e valida o contexto (banco + SQL).
  2. SQL Analyst gera a explicação detalhada usando a prompt original.
  3. SQL Optimizer gera a query otimizada preservando toda a lógica.
  4. SQL Quality Reviewer valida fidelidade e checklist.
  5. Conservative Analyst (se solicitado) gera análise sem reescrever a query.
  6. Gestor consolida e entrega.

Observações

  • Use o modelo configurado em variáveis de ambiente (ex.: OpenAI, Gemini, Groq, etc.).
  • O time é colaborativo e mantém histórico em SQLite (configurável via env).