# Prompt Assembly

Utilities for packaging database context into a single LLM prompt.

## Files
- `prompt_builder.py`: Loads schema/glossary/value hints/example artifacts and produces a structured prompt for SQL generation.
- `__init__.py`: Exposes `load_context` and `build_prompt` helpers.

## Quick Start
1. Ensure context artifacts exist under `db_agent/context/` (run the extractor, curate glossary/value hints/examples).
2. In Python, build the prompt:
   ```python
   from db_agent.prompting import load_context, build_prompt

   ctx = load_context()
   prompt = build_prompt(
       question="Which vehicles hauled more than 30 tons last week?",
       context=ctx,
       table_hints=["dbo.SugarLoadData", "dbo.SugarVehicles"],
   )
   print(prompt)
   ```
3. Send the prompt to your LLM provider (OpenAI, Azure OpenAI, Anthropic, etc.) and parse the JSON response with the `sql` and `summary` fields.

## Configuration
- Customize file paths by instantiating `PromptConfig` with alternate filenames or directories.
- Use `table_hints` to restrict the schema section to relevant tables (helps stay within context limits).
- Adjust `max_columns` to control how many column definitions are emitted per table.

## Next Steps
- Integrate the prompt builder into a service layer that calls the LLM and executes the generated SQL with validation.
- Add retrieval heuristics to automatically pick tables based on keyword matching instead of manually passing `table_hints`.
- Extend the format to include recent query logs or execution feedback for reinforcement.