alvis
ffdf70733f
Issues closed: #86, #87, #88, #89, #90, #91, #79, #80, #82 infra: - docker-compose `ai` profile: Ollama + LiteLLM services - infra/litellm/litellm_config.yaml: tip-generator / embedder / judge aliases - .env.example: LITELLM_URL, LITELLM_MASTER_KEY, OLLAMA_URL ml/serving: - POST /generate: calls LiteLLM tip-generator alias, returns TipCandidate[] - JSON retry loop (2 retries with correction prompt on malformed response) - _parse_llm_json strips markdown fences ml/features: - context.py: build_context() assembles user signals → PromptContext (sorts overdue/high-priority tasks first for LLM prompt quality) shared-types: - TipKind, TipSource, TipCandidate types - Tip gains kind + rationale fields services/api: - recommender: 3-stage pipeline (assemble → score → serve) Stage 1: Todoist tasks + LLM candidates fetched in parallel Stage 2: egreedy bandit scores merged candidate pool Stage 3: serve + log with prompt_version, llm_model, tip_kind - tip_scores: prompt_version, llm_model, tip_kind columns + migrations - config: LITELLM_URL added - integrations: surface token_status in /integrations response tests: - ml/serving/tests/test_generate.py: 13 tests (retry, 502/503, fence variants) - ml/features/test_context.py: 9 tests (sorting, edge cases) - services/api recommender.unit.test.ts: 16 pure-function tests (inferReward, dueAgeDays) - services/api recommender.test.ts: 4 integration tests (tip_scores columns, LLM fallback) - shared-types: TipCandidate, rationale, full TipFeedback action set docs: - ADR-0008: LiteLLM AI gateway decision - overview.md: M2 pipeline description updated - ml/README.md: serving + features roles updated Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
64 lines
1.6 KiB
Python
64 lines
1.6 KiB
Python
"""
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Context assembler — converts raw user signals into a PromptContext for LLM tip generation.
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Usage:
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from ml.features.context import build_context
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ctx = build_context(tasks, hour_of_day=9, day_of_week=2)
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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@dataclass
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class TaskSignal:
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id: str
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content: str
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priority: int = 1 # 1–4 (Todoist scale)
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is_overdue: bool = False
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task_age_days: float = 0.0
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due_date: str | None = None
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@dataclass
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class PromptContext:
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tasks: list[dict] = field(default_factory=list)
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hour_of_day: int = 12
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day_of_week: int = 0
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extra: dict = field(default_factory=dict)
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def build_context(
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tasks: list[TaskSignal],
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hour_of_day: int = 12,
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day_of_week: int = 0,
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extra: dict | None = None,
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) -> PromptContext:
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"""
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Assemble user signals into a PromptContext.
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Signals are sorted so overdue + high-priority tasks appear first,
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giving the LLM the most actionable context at the top of the prompt.
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"""
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sorted_tasks = sorted(
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tasks,
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key=lambda t: (not t.is_overdue, -t.priority, -t.task_age_days),
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)
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task_dicts = [
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{
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"id": t.id,
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"content": t.content,
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"priority": t.priority,
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"is_overdue": t.is_overdue,
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"task_age_days": round(t.task_age_days, 1),
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"due_date": t.due_date,
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}
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for t in sorted_tasks
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]
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return PromptContext(
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tasks=task_dicts,
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hour_of_day=hour_of_day,
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day_of_week=day_of_week,
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extra=extra or {},
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)
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