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>
62 lines
2.6 KiB
Python
62 lines
2.6 KiB
Python
"""Base class and shared data structures for all recommendation sub-agents."""
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from dataclasses import dataclass, field
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from datetime import datetime, timedelta, timezone
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from typing import ClassVar
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@dataclass
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class AgentInput:
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"""Everything an agent may need to produce its prompt snippet."""
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user_id: str
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tasks: list[dict] # task signal dicts (content, priority, is_overdue, …)
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profile: dict[str, float | None] # profile feature values keyed by feature name
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feedback_history: list[dict] = field(default_factory=list) # [{action, dwell_ms, created_at}, …]
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now: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
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# Per-agent inferred/user prefs loaded from user_preferences (ADR-0014 §3).
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# Keys match the agent's pref_schema + inferred_params. 'user' source takes
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# precedence over 'inferred' source; the caller resolves priority before
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# passing this dict in.
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agent_prefs: dict = field(default_factory=dict)
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# Pre-fetched enrichment cache: {content_hash -> description}. Populated by
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# the TS caller from the task_enrichments DB table to avoid redundant LLM calls.
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enrichment_cache: dict = field(default_factory=dict)
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@dataclass
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class AgentOutput:
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"""Result produced by an agent; persisted to agent_outputs table."""
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user_id: str
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agent_id: str
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prompt_text: str # snippet passed to the orchestrator
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signals_snapshot: dict # inputs consumed (for explainability / debugging)
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computed_at: str # ISO 8601
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expires_at: str # ISO 8601
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agent_version: str
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class BaseAgent(ABC):
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agent_id: ClassVar[str]
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ttl_seconds: ClassVar[int]
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version: ClassVar[str]
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@abstractmethod
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def compute(self, inp: AgentInput) -> AgentOutput:
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"""Analyse inp and return a prompt snippet describing what was found."""
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...
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def _make_output(self, inp: AgentInput, prompt_text: str, snapshot: dict) -> AgentOutput:
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computed_at = inp.now.astimezone(timezone.utc).isoformat()
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expires_at = (inp.now.astimezone(timezone.utc) + timedelta(seconds=self.ttl_seconds)).isoformat()
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return AgentOutput(
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user_id=inp.user_id,
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agent_id=self.agent_id,
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prompt_text=prompt_text,
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signals_snapshot=snapshot,
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computed_at=computed_at,
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expires_at=expires_at,
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agent_version=self.version,
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)
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