haive.agents.rag.db_rag.sql_rag.prompts
=======================================

.. py:module:: haive.agents.rag.db_rag.sql_rag.prompts

.. autoapi-nested-parse::

   Prompt templates for SQL RAG Agent.

   This module contains all the prompt templates used throughout the SQL RAG
   workflow. Each prompt is carefully crafted to guide the LLM through specific
   tasks while maintaining consistency and accuracy.

   The prompts follow a structured format with clear system instructions and
   user templates that include necessary context variables.

   .. rubric:: Example

   Using prompts with LangChain::

       >>> from langchain_core.prompts import ChatPromptTemplate
       >>> from haive.agents.rag.db_rag.sql_rag.prompts import GENERATE_SQL_PROMPT
       >>>
       >>> # Format prompt with variables
       >>> messages = GENERATE_SQL_PROMPT.format_messages(
       ...     schema=db_schema,
       ...     dialect="postgresql",
       ...     question="Show top products",
       ...     query_analysis=analysis_result
       ... )
       >>>
       >>> # Use with LLM
       >>> llm = ChatOpenAI()
       >>> response = llm.invoke(messages)



Attributes
----------

.. autoapisummary::

   haive.agents.rag.db_rag.sql_rag.prompts.ANALYZE_QUERY_PROMPT
   haive.agents.rag.db_rag.sql_rag.prompts.ANSWER_GRADING_PROMPT
   haive.agents.rag.db_rag.sql_rag.prompts.GENERATE_FINAL_ANSWER_PROMPT
   haive.agents.rag.db_rag.sql_rag.prompts.GENERATE_SQL_PROMPT
   haive.agents.rag.db_rag.sql_rag.prompts.GUARDRAILS_PROMPT
   haive.agents.rag.db_rag.sql_rag.prompts.HALLUCINATION_CHECK_PROMPT
   haive.agents.rag.db_rag.sql_rag.prompts.VALIDATE_SQL_PROMPT


Module Contents
---------------

.. py:data:: ANALYZE_QUERY_PROMPT

   Prompt for analyzing natural language queries.

   This prompt guides the LLM to break down user questions into SQL components
   without generating the actual query. It helps identify:
   - Required tables and columns
   - JOIN operations needed
   - WHERE clause constraints
   - Aggregation functions
   - Query complexity

   Variables:
       schema: Database schema information
       dialect: SQL dialect (postgresql, mysql, etc.)
       question: User's natural language question

   :returns: SQLAnalysisOutput with structured analysis

.. py:data:: ANSWER_GRADING_PROMPT

   Prompt for grading answer relevance.

   This prompt evaluates whether the generated answer actually
   addresses what the user asked, ensuring quality responses.

   Variables:
       question: Original question
       answer: Generated answer

   :returns: GradeAnswer with binary score

.. py:data:: GENERATE_FINAL_ANSWER_PROMPT

   Prompt for generating natural language answers.

   This prompt converts raw SQL query results into user-friendly
   answers that directly address the original question.

   Variables:
       question: Original user question
       sql_query: Executed SQL query
       query_result: Raw query results

   :returns: String with natural language answer

.. py:data:: GENERATE_SQL_PROMPT

   Prompt for generating SQL queries.

   This prompt converts natural language questions into executable SQL queries
   based on the analysis and schema information. It ensures:
   - Correct syntax for the specific dialect
   - Safe queries (SELECT only)
   - Proper use of JOINs and aggregations
   - Result limiting for safety
   - Meaningful result ordering

   Variables:
       schema: Database schema information
       dialect: SQL dialect
       question: User's question
       query_analysis: Analysis from previous step

   :returns: SQLQueryOutput with generated query

.. py:data:: GUARDRAILS_PROMPT

   Prompt for domain relevance checking (guardrails).

   This prompt filters out irrelevant questions that aren't about
   database queries, preventing the agent from attempting to answer
   questions outside its scope.

   Variables:
       schema: Database schema
       tables: List of table names
       question: User's question

   :returns: GuardrailsOutput with decision and reason

.. py:data:: HALLUCINATION_CHECK_PROMPT

   Prompt for detecting hallucinations in answers.

   This prompt ensures answers are grounded in actual query results
   and don't include fabricated information.

   Variables:
       question: Original question
       query_result: Actual data returned
       answer: Generated answer to check

   :returns: GradeHallucinations with binary score

.. py:data:: VALIDATE_SQL_PROMPT

   Prompt for validating SQL queries.

   This prompt performs comprehensive validation including:
   - Syntax correctness
   - Schema compliance
   - JOIN validity
   - Aggregation rules
   - Performance considerations
   - Security checks

   Variables:
       schema: Database schema
       dialect: SQL dialect
       question: Original question
       sql_query: Query to validate

   :returns: SQLValidationOutput with errors and suggestions