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backend/src/services/llm_prompt_templates.py
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94
backend/src/services/llm_prompt_templates.py
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# [DEF:backend.src.services.llm_prompt_templates:Module]
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# @TIER: STANDARD
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# @SEMANTICS: llm, prompts, templates, settings
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# @PURPOSE: Provide default LLM prompt templates and normalization helpers for runtime usage.
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# @LAYER: Domain
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# @RELATION: DEPENDS_ON -> backend.src.core.config_manager
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# @INVARIANT: All required prompt template keys are always present after normalization.
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from __future__ import annotations
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from copy import deepcopy
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from typing import Dict, Any
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# [DEF:DEFAULT_LLM_PROMPTS:Constant]
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# @TIER: STANDARD
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# @PURPOSE: Default prompt templates used by documentation, dashboard validation, and git commit generation.
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DEFAULT_LLM_PROMPTS: Dict[str, str] = {
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"dashboard_validation_prompt": (
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"Analyze the attached dashboard screenshot and the following execution logs for health and visual issues.\n\n"
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"Logs:\n"
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"{logs}\n\n"
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"Provide the analysis in JSON format with the following structure:\n"
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"{\n"
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' "status": "PASS" | "WARN" | "FAIL",\n'
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' "summary": "Short summary of findings",\n'
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' "issues": [\n'
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" {\n"
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' "severity": "WARN" | "FAIL",\n'
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' "message": "Description of the issue",\n'
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' "location": "Optional location info (e.g. chart name)"\n'
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" }\n"
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" ]\n"
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"}"
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),
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"documentation_prompt": (
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"Generate professional documentation for the following dataset and its columns.\n"
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"Dataset: {dataset_name}\n"
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"Columns: {columns_json}\n\n"
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"Provide the documentation in JSON format:\n"
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"{\n"
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' "dataset_description": "General description of the dataset",\n'
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' "column_descriptions": [\n'
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" {\n"
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' "name": "column_name",\n'
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' "description": "Generated description"\n'
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" }\n"
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" ]\n"
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"}"
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),
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"git_commit_prompt": (
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"Generate a concise and professional git commit message based on the following diff and recent history.\n"
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"Use Conventional Commits format (e.g., feat: ..., fix: ..., docs: ...).\n\n"
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"Recent History:\n"
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"{history}\n\n"
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"Diff:\n"
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"{diff}\n\n"
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"Commit Message:"
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),
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}
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# [/DEF:DEFAULT_LLM_PROMPTS:Constant]
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# [DEF:normalize_llm_settings:Function]
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# @TIER: STANDARD
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# @PURPOSE: Ensure llm settings contain stable schema with prompts section and default templates.
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# @PRE: llm_settings is dictionary-like value or None.
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# @POST: Returned dict contains prompts with all required template keys.
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def normalize_llm_settings(llm_settings: Any) -> Dict[str, Any]:
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normalized: Dict[str, Any] = {"providers": [], "default_provider": "", "prompts": {}}
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if isinstance(llm_settings, dict):
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normalized.update({k: v for k, v in llm_settings.items() if k in ("providers", "default_provider", "prompts")})
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prompts = normalized.get("prompts") if isinstance(normalized.get("prompts"), dict) else {}
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merged_prompts = deepcopy(DEFAULT_LLM_PROMPTS)
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merged_prompts.update({k: v for k, v in prompts.items() if isinstance(v, str) and v.strip()})
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normalized["prompts"] = merged_prompts
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return normalized
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# [/DEF:normalize_llm_settings:Function]
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# [DEF:render_prompt:Function]
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# @TIER: STANDARD
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# @PURPOSE: Render prompt template using deterministic placeholder replacement with graceful fallback.
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# @PRE: template is a string and variables values are already stringifiable.
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# @POST: Returns rendered prompt text with known placeholders substituted.
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def render_prompt(template: str, variables: Dict[str, Any]) -> str:
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rendered = template
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for key, value in variables.items():
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rendered = rendered.replace("{" + key + "}", str(value))
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return rendered
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# [/DEF:render_prompt:Function]
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# [/DEF:backend.src.services.llm_prompt_templates:Module]
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