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- Added support for the new planning exercise expectation profile slug in the AI prompt runtime. - Refactored SQL parameter handling in the planning exercise retrieval process to ensure correct binding for full-text search. - Updated the planning exercise suggestion logic to incorporate LLM expectation handling, improving the accuracy of exercise recommendations. - Introduced new functions to determine when to run the LLM expectation pipeline, enhancing the decision-making process for exercise suggestions. - Incremented version to 0.8.176 and updated changelog to reflect these enhancements in planning AI capabilities.
70 lines
2.7 KiB
Python
70 lines
2.7 KiB
Python
"""
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Preset „Nächste aus Kontext“: LLM leitet Erwartungsprofil aus Planungskontext ab.
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Prompt: planning_exercise_expectation_profile (Migration 074)
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"""
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from __future__ import annotations
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import logging
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from typing import Any, Dict, Mapping, Optional, Tuple
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from planning_exercise_intent import (
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PlanningQueryIntentParsed,
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_compact_json,
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_load_compact_catalog,
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_load_skills_catalog_compact,
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parse_planning_query_intent_response,
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)
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from ai_prompt_runtime import AiPromptUnavailableError, load_and_render_ai_prompt
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from openrouter_chat import (
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effective_openrouter_model_for_prompt_row,
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normalize_openrouter_env,
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openrouter_chat_completion,
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)
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_logger = logging.getLogger("shinkan.planning_exercise_expectation")
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def try_build_planning_expectation_from_context(
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cur,
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*,
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heuristic_intent: str,
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context_summary: Mapping[str, Any],
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target_profile_summary: Mapping[str, Any],
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) -> Tuple[Optional[PlanningQueryIntentParsed], bool]:
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"""
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LLM-Erwartungsprofil für preset_next / leere Anfrage mit Planungsbezug.
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Returns (parsed overlay, applied).
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"""
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api_key, _ = normalize_openrouter_env()
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if not api_key:
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return None, False
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variables = {
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"heuristic_intent": heuristic_intent or "suggest_next",
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"planning_context_json": _compact_json(dict(context_summary or {})),
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"target_profile_json": _compact_json(dict(target_profile_summary or {})),
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"skills_catalog_json": _compact_json(_load_skills_catalog_compact(cur)),
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"focus_areas_catalog_json": _compact_json(_load_compact_catalog(cur, "focus_areas", "id")),
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"training_types_catalog_json": _compact_json(_load_compact_catalog(cur, "training_types", "id")),
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"style_directions_catalog_json": _compact_json(_load_compact_catalog(cur, "style_directions", "id")),
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"target_groups_catalog_json": _compact_json(_load_compact_catalog(cur, "target_groups", "id")),
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}
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try:
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prow, rendered = load_and_render_ai_prompt(cur, "planning_exercise_expectation_profile", variables)
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model = effective_openrouter_model_for_prompt_row(prow)
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raw = openrouter_chat_completion(api_key=api_key, model=model, user_content=rendered.text)
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parsed = parse_planning_query_intent_response(raw)
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if parsed.scenario not in ("preset_next", "continue_plan", "free_search"):
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parsed = parsed.model_copy(update={"scenario": "preset_next"})
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return parsed, True
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except AiPromptUnavailableError:
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return None, False
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except Exception as exc:
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_logger.warning("Planungs-Erwartungsprofil-LLM fehlgeschlagen: %s", exc)
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return None, False
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__all__ = ["try_build_planning_expectation_from_context"]
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