alvis
9ddeea6cac
Each unique task title is now enriched by LiteLLM once and cached in the DB. Subsequent agent compute cycles (every 12h) fetch the cache before calling ml-serving; only new titles hit the tip-generator. - DB: task_enrichments(content_hash PK, description, model, created_at) - TS: fetchEnrichmentCache / persistEnrichments helpers in agent-outputs.ts; enrichment_cache passed in compute request, new_enrichments persisted from response - Python: AgentComputeRequest.enrichment_cache / AgentComputeResponse.new_enrichments; AgentInput.enrichment_cache; _enrich_batch returns (descriptions, new_entries); cluster_tasks returns (clusters, new_enrichments) - FocusAreaAgent stashes new_enrichments in signals_snapshot under _new_enrichments; compute_agent endpoint pops it before storing the snapshot Closes part of #129 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
116 lines
4.3 KiB
Python
116 lines
4.3 KiB
Python
from __future__ import annotations
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from collections import Counter
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from typing import ClassVar
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from .base import BaseAgent, AgentInput, AgentOutput
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from .clustering import cluster_tasks
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from .inference.history import UserHistory
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from .manifest import AgentManifest, InferredParam
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def _infer_preferred_areas(history: UserHistory) -> list[str]:
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"""Top-2 project IDs by completed task count (last 90 days worth of data)."""
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counts: Counter[str] = Counter()
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for tc in history.task_completions:
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if tc.project_id:
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counts[tc.project_id] += 1
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return [pid for pid, _ in counts.most_common(2)]
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MANIFEST = AgentManifest(
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id="focus-area",
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version="2.0.0", # semantic clustering via nomic-embed-text (#97, #113)
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description="Identifies the most congested semantic focus area in the user's task list.",
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pref_schema={
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"type": "object",
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"additionalProperties": False,
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"properties": {
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"preferred_areas": {
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"type": "array",
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"items": {"type": "string"},
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"default": [],
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"description": "Project IDs or label names to prioritise when multiple areas tie.",
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},
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},
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},
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context_schema=["todoist.tasks"],
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required_consents=["data:core", "data:todoist"],
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output_contract={"type": "snippet", "format": "free_text"},
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ttl_sec=43_200,
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inferred_params=[
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InferredParam(
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key="preferred_areas",
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ttl_sec=86_400,
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cold_start_default=[],
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min_history=0, # use task_completions, not feedback events; handle empty inside
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infer=_infer_preferred_areas,
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),
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],
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)
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class FocusAreaAgent(BaseAgent):
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"""Identifies the most congested semantic focus area in the user's task list."""
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agent_id: ClassVar[str] = MANIFEST.id
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ttl_seconds: ClassVar[int] = MANIFEST.ttl_sec
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version: ClassVar[str] = MANIFEST.version
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def compute(self, inp: AgentInput) -> AgentOutput:
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preferred: list[str] = inp.agent_prefs.get("preferred_areas", [])
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if not inp.tasks:
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return self._make_output(
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inp,
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"No tasks available to identify a focus area.",
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{"cluster_count": 0, "strategy": "none"},
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)
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clusters, new_enrichments = cluster_tasks(inp.tasks, enrichment_cache=inp.enrichment_cache)
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if not clusters:
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return self._make_output(
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inp,
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"No tasks available to identify a focus area.",
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{"cluster_count": 0, "strategy": "none"},
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)
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strategy = "semantic" if len(clusters) > 1 or len(inp.tasks) > 1 else "fallback"
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def score(cluster) -> float:
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base = sum(2.0 if t.get("is_overdue") else 1.0 for t in cluster.tasks)
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boosted = any(p in cluster.label for p in preferred) if preferred else False
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return base + (0.5 if boosted else 0.0)
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top = max(clusters, key=score)
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boosted = bool(preferred) and any(p in top.label for p in preferred)
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parts = [
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f'The user\'s most active focus area is "{top.label}" '
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f"({top.task_count} task{'s' if top.task_count != 1 else ''}, "
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f"{top.overdue_count} overdue). "
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f"(Note: task titles may be in any language — always write the tip in English.)"
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]
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if boosted:
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parts.append("This area matches the user's stated focus preferences.")
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if top.overdue_count >= 3:
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parts.append("Consider surfacing an action from this area.")
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if len(clusters) > 1:
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other_total = sum(c.task_count for c in clusters if c is not top)
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parts.append(
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f"{len(clusters) - 1} other area{'s' if len(clusters) > 2 else ''} "
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f"contain {other_total} task{'s' if other_total != 1 else ''}."
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)
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snapshot = {
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"top_cluster_label": top.label,
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"top_task_count": top.task_count,
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"top_overdue_count": top.overdue_count,
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"cluster_count": len(clusters),
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"strategy": strategy,
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"preferred_areas": preferred,
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# Consumed by compute_agent endpoint; stripped before storing the snapshot.
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"_new_enrichments": new_enrichments,
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}
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return self._make_output(inp, " ".join(parts), snapshot)
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