refactor: Replace PromptTemplates with AgentPromptTemplates across agents and remove obsolete templates module

src/sql_optimizer_team/agents/conservative_analysis_agent.py

@@ -1,7 +1,7 @@
 from agno.agent import Agent
 from agno.db.sqlite import SqliteDb
 from sql_optimizer_team.tools.engine.model_selector import get_model
-from sql_optimizer_team.tools.engine.prompt_tools.templates import PromptTemplates
+from sql_optimizer_team.agents.prompts import AgentPromptTemplates
 from sql_optimizer_team.tools.prompt_tools import supported_databases
 from sql_optimizer_team.tools.sql_tools import load_sql_from_file, ensure_non_empty
 import os
@@ -31,7 +31,7 @@ conservative_analysis_agent = Agent(
         "- Solicite banco e SQL se não estiverem presentes.",
         "- Se o usuário fornecer um caminho de arquivo, use load_sql_from_file().",
         "- Use a template oficial abaixo para a análise conservadora (sem reescrever a SQL).",
-        PromptTemplates.CONSERVATIVE_ANALYSIS.template.strip(),
+        AgentPromptTemplates.CONSERVATIVE_ANALYSIS.template.strip(),
         "- NÃO reescreva a SQL em hipótese alguma.",
     ],
 )

src/sql_optimizer_team/agents/prompts.py

@@ -1,18 +1,14 @@
-"""Prompt templates for SQL optimization.
+"""Prompt templates used by Agno agents.
 
-This module provides reusable prompt templates that can be customized
-for different database types.
+This module keeps the canonical prompts alongside the agents to ensure
+all prompt content is owned and maintained by the agent layer.
 """
 
 from string import Template
 
 
-class PromptTemplates:
-	"""Collection of prompt templates for SQL optimization.
-
-	These templates use Python's string.Template for safe string substitution.
-	Templates can be customized per database type while maintaining consistency.
-	"""
+class AgentPromptTemplates:
+	"""Collection of prompt templates for SQL optimization agents."""
 
 	SQL_TO_NATURAL = Template("""
 		You are an expert $database_name database analyst and performance specialist. Your task is to translate the SQL query below into a detailed, precise natural-language description that another agent will later use to reconstruct and optimize the query.
@@ -76,7 +72,6 @@ class PromptTemplates:
 		Explanation:
 	""")
 
- 
 	NATURAL_TO_SQL = Template("""
 		You are an expert $database_name SQL developer and query performance specialist.
 		Your task is to write an optimized SQL query based exclusively on the natural-language description provided below.
@@ -138,7 +133,6 @@ class PromptTemplates:
 		Optimized SQL Query:
 	""")
 
-
 	CONSERVATIVE_ANALYSIS = Template("""
 		You are an expert $database_name database analyst and performance specialist.
 
@@ -190,17 +184,6 @@ class PromptTemplates:
 	def render_sql_to_natural(
 		cls, database_name: str, query: str, specific_features: str = "", analysis_requirements: str = ""
 	) -> str:
-		"""Render SQL-to-natural language prompt.
-
-		Args:
-			database_name: Name of database system
-			query: SQL query to explain
-			specific_features: Additional database-specific features to mention
-			analysis_requirements: Additional performance analysis requirements
-
-		Returns:
-			Rendered prompt string
-		"""
 		return cls.SQL_TO_NATURAL.substitute(
 			database_name=database_name,
 			query=query,
@@ -212,17 +195,6 @@ class PromptTemplates:
 	def render_natural_to_sql(
 		cls, database_name: str, explanation: str, specific_requirements: str
 	) -> str:
-		"""Render natural-to-SQL prompt.
-
-		Args:
-			database_name: Name of database system
-			explanation: Natural language description
-			specific_requirements: Database-specific optimization requirements
-
-		Returns:
-			Rendered prompt string
-		"""
-		# Format requirements as bullet points
 		return cls.NATURAL_TO_SQL.substitute(
 			database_name=database_name,
 			explanation=explanation,
@@ -243,21 +215,6 @@ class PromptTemplates:
 		join_count: int = 0,
 		complexity_level: str = "unknown",
 	) -> str:
-		"""Render conservative analysis prompt (no query rewriting).
-
-		Args:
-			database_name: Name of database system
-			query: SQL query to analyze
-			column_count: Number of columns in query
-			table_count: Number of tables in query
-			subquery_count: Number of subqueries
-			case_count: Number of CASE statements
-			join_count: Number of JOINs
-			complexity_level: Detected complexity level
-
-		Returns:
-			Rendered prompt string
-		"""
 		return cls.CONSERVATIVE_ANALYSIS.substitute(
 			database_name=database_name,
 			query=query,

src/sql_optimizer_team/agents/sql_analyst_agent.py

@@ -2,7 +2,7 @@ from agno.agent import Agent
 from agno.db.sqlite import SqliteDb
 from sql_optimizer_team.tools.engine.model_selector import get_model
 from sql_optimizer_team.tools.core_tools import explain_query_core
-from sql_optimizer_team.tools.engine.prompt_tools.templates import PromptTemplates
+from sql_optimizer_team.agents.prompts import AgentPromptTemplates
 from sql_optimizer_team.tools.prompt_tools import supported_databases
 from sql_optimizer_team.tools.sql_tools import load_sql_from_file, ensure_non_empty
 import os
@@ -33,7 +33,7 @@ sql_analyst_agent = Agent(
         "- Se o usuário fornecer um caminho de arquivo, use load_sql_from_file().",
         "- Preferência: use explain_query_core(database_type, sql) para gerar a explicação via core de negócio.",
         "- Use a template oficial abaixo para estruturar a explicação (SQL → natural).",
-        PromptTemplates.SQL_TO_NATURAL.template.strip(),
+        AgentPromptTemplates.SQL_TO_NATURAL.template.strip(),
         "- Entregue apenas a explicação natural estruturada conforme a prompt; não reescreva a SQL.",
         "- Identifique problemas críticos de performance conforme a prompt.",
     ],

src/sql_optimizer_team/agents/sql_optimizer_agent.py

@@ -2,7 +2,7 @@ from agno.agent import Agent
 from agno.db.sqlite import SqliteDb
 from sql_optimizer_team.tools.engine.model_selector import get_model
 from sql_optimizer_team.tools.core_tools import optimize_query_core
-from sql_optimizer_team.tools.engine.prompt_tools.templates import PromptTemplates
+from sql_optimizer_team.agents.prompts import AgentPromptTemplates
 from sql_optimizer_team.tools.prompt_tools import supported_databases
 from sql_optimizer_team.tools.sql_tools import load_sql_from_file, ensure_non_empty
 import os
@@ -32,7 +32,7 @@ sql_optimizer_agent = Agent(
         "- Exija banco alvo e SQL antes de otimizar.",
         "- Use optimize_query_core(database_type, sql) para executar o core de negócio.",
         "- Use a template oficial abaixo para reescrever (natural → SQL) mantendo 100% da lógica.",
-        PromptTemplates.NATURAL_TO_SQL.template.strip(),
+        AgentPromptTemplates.NATURAL_TO_SQL.template.strip(),
         "- Extraia e devolva SOMENTE optimized_query (sem explicações, sem markdown).",
         "- Preserve 100% da lógica, colunas, aliases, filtros, joins e subqueries.",
     ],

src/sql_optimizer_team/tools/engine/prompt_tools/base_generator.py

@@ -7,7 +7,7 @@ reducing code duplication and ensuring consistency.
 from abc import ABC, abstractmethod
 
 from sql_optimizer_team.tools.engine.tools_api.prompt_tool import PromptGeneratorTool
-from sql_optimizer_team.tools.engine.prompt_tools.templates import PromptTemplates
+from sql_optimizer_team.agents.prompts import AgentPromptTemplates
 
 
 class BasePromptGenerator(PromptGeneratorTool, ABC):
@@ -70,7 +70,7 @@ class BasePromptGenerator(PromptGeneratorTool, ABC):
 		Returns:
 			Formatted prompt for LLM
 		"""
-		return PromptTemplates.render_sql_to_natural(
+		return AgentPromptTemplates.render_sql_to_natural(
 			database_name=self.get_database_name(),
 			query=query,
 			specific_features=self.get_specific_features(),
@@ -86,7 +86,7 @@ class BasePromptGenerator(PromptGeneratorTool, ABC):
 		Returns:
 			Formatted prompt for LLM
 		"""
-		return PromptTemplates.render_natural_to_sql(
+		return AgentPromptTemplates.render_natural_to_sql(
 			database_name=self.get_database_name(),
 			explanation=explanation,
 			specific_requirements=self.get_specific_requirements(),