feat(ml): prompt registry + per-request variant selection
Replaces the hardcoded "v1" label with a real prompt registry:
ml/serving/prompts.py — keyed by version: v1 (baseline),
v2-mentor (calm/specific persona),
v3-few-shot (v1 persona + curated examples)
ml/serving/main.py — POST /generate accepts optional prompt_version,
422 on unknown, echoes the version actually used
back in the response
services/api/src/config.ts — TIP_PROMPT_VERSION: empty / single / comma-list
(uniform random per request)
services/api/src/routes/recommender.ts
— pickPromptVersion() drives selection; the
response's prompt_version (not a stale TS
constant) is what lands in tip_scores so the
#92 reward-analytics dashboard shows real
per-variant reaction rates
Closes #84.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
@@ -37,3 +37,11 @@ TODOIST_CLIENT_SECRET=
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NATS_URL=
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# How often the background scheduler refreshes Todoist tasks per active user (ms).
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TODOIST_SYNC_INTERVAL_MS=900000
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# Tip prompt selection — empty = use ml/serving default (v1).
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# Pin a single variant: "v2-mentor"
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# Rotate uniformly across variants: "v1,v2-mentor,v3-few-shot"
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# Buckets show up in the admin reward-analytics dashboard (#92).
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TIP_PROMPT_VERSION=
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# Default version on the Python side when the API doesn't specify one.
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DEFAULT_PROMPT_VERSION=v1
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@@ -4,7 +4,7 @@ Python. Owns models, features, training, online scoring.
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| Dir | Role | Phase |
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|---|---|---|
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| `serving/` | FastAPI online scorer (`/score`, `/generate`) + LiteLLM gateway, called by `recommender` | 1–2 |
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| `serving/` | FastAPI online scorer (`/score`, `/generate`) + LiteLLM gateway + prompt registry (`prompts.py`), called by `recommender` | 1–2 |
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| `features/` | context assembler (`context.py`): signals → `PromptContext`; Feast adapter later | 2 |
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| `pipelines/` | batch feature + training DAGs (Prefect/Airflow) | 4 |
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| `registry/` | MLflow-backed model registry integration | 4 |
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@@ -17,3 +17,7 @@ Python. Owns models, features, training, online scoring.
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- Online inference must be stateless and < 50ms p99.
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- Training reads from the offline feature store; serving reads from the online feature store; definitions are shared (no train/serve skew).
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- Shadow deploys before any policy change that affects real users.
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## Prompt registry
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`serving/prompts.py` keys tip-generation prompts by stable version string. Adding a new variant means adding an entry — no caller changes. Selection precedence: `POST /generate` body's `prompt_version` field → env `DEFAULT_PROMPT_VERSION` → `"v1"`. The TypeScript recommender drives selection via `TIP_PROMPT_VERSION` (single value or comma-separated rotation); the version actually used flows back in the response and is persisted to `tip_scores.prompt_version` so the admin reward-analytics dashboard can bucket reactions per variant.
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@@ -31,6 +31,8 @@ import numpy as np
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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from prompts import get_prompt
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app = FastAPI(title="oO ML Serving", version="1.0.0")
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LITELLM_URL = os.getenv("LITELLM_URL", "http://localhost:4000")
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@@ -181,6 +183,7 @@ class GenerateRequest(BaseModel):
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user_id: str
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context: PromptContext = PromptContext()
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n: int = 3
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prompt_version: Optional[str] = None # None → server default (env DEFAULT_PROMPT_VERSION)
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class TipCandidate(BaseModel):
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@@ -193,33 +196,11 @@ class TipCandidate(BaseModel):
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class GenerateResponse(BaseModel):
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candidates: list[TipCandidate]
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model: str
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prompt_version: str
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prompt_tokens: int = 0
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completion_tokens: int = 0
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_GENERATE_SYSTEM = (
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"You are a personal productivity coach. "
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"Given the user's current context, generate actionable, specific tips. "
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"Respond ONLY with a JSON array of objects, each with keys: "
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'"id" (short slug), "content" (the tip, ≤2 sentences), "rationale" (why now, ≤1 sentence). '
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"No markdown, no prose outside the JSON array."
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)
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def _build_prompt(ctx: PromptContext, n: int) -> str:
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lines = [f"Time: {ctx.hour_of_day:02d}:00, day_of_week={ctx.day_of_week}"]
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if ctx.tasks:
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overdue = [t for t in ctx.tasks if t.get("is_overdue")]
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lines.append(f"Tasks: {len(ctx.tasks)} total, {len(overdue)} overdue")
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for t in ctx.tasks[:5]:
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due = t.get("due_date", "no due date")
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lines.append(f" - [{t.get('priority','?')}] {t.get('content','?')} (due: {due})")
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for k, v in ctx.extra.items():
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lines.append(f"{k}: {v}")
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lines.append(f"\nGenerate {n} tips as a JSON array.")
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return "\n".join(lines)
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# ── Endpoints ──────────────────────────────────────────────────────────────
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@app.get("/health")
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@@ -253,10 +234,14 @@ async def generate(req: GenerateRequest) -> GenerateResponse:
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Retries up to _MAX_GENERATE_RETRIES times on malformed JSON, appending
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a correction hint to the conversation so the model can self-correct.
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"""
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prompt = _build_prompt(req.context, req.n)
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try:
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prompt_template = get_prompt(req.prompt_version)
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except KeyError as e:
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raise HTTPException(status_code=422, detail=f"Unknown prompt_version: {e.args[0]}")
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user_msg = prompt_template.build_user(req.context, req.n)
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messages: list[dict] = [
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{"role": "system", "content": _GENERATE_SYSTEM},
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{"role": "user", "content": prompt},
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{"role": "system", "content": prompt_template.system},
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{"role": "user", "content": user_msg},
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]
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headers = {"Authorization": f"Bearer {LITELLM_MASTER_KEY}"}
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last_parse_error: str = ""
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@@ -313,6 +298,7 @@ async def generate(req: GenerateRequest) -> GenerateResponse:
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return GenerateResponse(
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candidates=candidates,
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model=model_used,
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prompt_version=prompt_template.version,
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prompt_tokens=total_usage["prompt_tokens"],
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completion_tokens=total_usage["completion_tokens"],
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)
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105
ml/serving/prompts.py
Normal file
105
ml/serving/prompts.py
Normal file
@@ -0,0 +1,105 @@
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"""Prompt registry for tip generation (#84).
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Each entry is an immutable (system, build_user) pair keyed by a stable version
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string. Adding a new version here makes it selectable via the ``prompt_version``
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field on ``POST /generate`` — the selected version flows back in the response
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and is persisted to ``tip_scores.prompt_version`` so the admin reward-analytics
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dashboard can bucket reactions per variant.
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Versions:
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v1 — neutral "productivity coach" baseline (unchanged from ffdf707).
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v2-mentor — calm/specific mentor persona; same structural prompt as v1.
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v3-few-shot — v1 persona plus two curated example tips inside the system prompt.
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"""
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from __future__ import annotations
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import os
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from dataclasses import dataclass
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from typing import Callable, Protocol
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class _Ctx(Protocol):
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tasks: list[dict]
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hour_of_day: int
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day_of_week: int
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extra: dict
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@dataclass(frozen=True)
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class Prompt:
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version: str
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system: str
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build_user: Callable[["_Ctx", int], str]
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def _base_user_lines(ctx: "_Ctx") -> list[str]:
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lines = [f"Time: {ctx.hour_of_day:02d}:00, day_of_week={ctx.day_of_week}"]
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if ctx.tasks:
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overdue = [t for t in ctx.tasks if t.get("is_overdue")]
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lines.append(f"Tasks: {len(ctx.tasks)} total, {len(overdue)} overdue")
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for t in ctx.tasks[:5]:
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due = t.get("due_date", "no due date")
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lines.append(f" - [{t.get('priority','?')}] {t.get('content','?')} (due: {due})")
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for k, v in ctx.extra.items():
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lines.append(f"{k}: {v}")
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return lines
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def _build_user_v1(ctx: "_Ctx", n: int) -> str:
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return "\n".join([*_base_user_lines(ctx), f"\nGenerate {n} tips as a JSON array."])
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_SYS_V1 = (
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"You are a personal productivity coach. "
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"Given the user's current context, generate actionable, specific tips. "
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"Respond ONLY with a JSON array of objects, each with keys: "
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'"id" (short slug), "content" (the tip, ≤2 sentences), "rationale" (why now, ≤1 sentence). '
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"No markdown, no prose outside the JSON array."
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)
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_SYS_V2_MENTOR = (
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"You are a calm, wise mentor — the kind who has seen a thousand people get stuck on "
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"the same thing and knows when a small, concrete step unblocks them. Your tips are "
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"earned, never generic; they reference the user's specific context and respect that "
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"their time is short. Speak plainly. Prefer one precise action over vague encouragement. "
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"Respond ONLY with a JSON array of objects, each with keys: "
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'"id" (short slug), "content" (the tip, ≤2 sentences), "rationale" (why now, ≤1 sentence). '
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"No markdown, no prose outside the JSON array."
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)
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# Two curated examples illustrate the shape we want: (1) a precise micro-action
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# for an overdue item, and (2) a time-aware tip that trades tiny effort now for
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# reduced friction later. Kept inside the system prompt so token cost is paid
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# once per conversation and not per user turn.
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_SYS_V3_FEW_SHOT = _SYS_V1 + (
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"\n\nExamples of the shape and tone to aim for:\n"
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'[{"id":"overdue-anchor",'
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'"content":"Spend the next 12 minutes on \\"Call dentist\\" — set a timer and stop '
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'when it rings, done or not.",'
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'"rationale":"Overdue 6 days; a fixed micro-session breaks the avoidance loop."},'
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'{"id":"evening-wind-down",'
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'"content":"Pick one task from tomorrow\'s list and write its first line now while '
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'context is fresh.",'
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'"rationale":"It is 21:00; tomorrow-you will thank present-you for not starting cold."}]'
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)
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PROMPTS: dict[str, Prompt] = {
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"v1": Prompt("v1", _SYS_V1, _build_user_v1),
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"v2-mentor": Prompt("v2-mentor", _SYS_V2_MENTOR, _build_user_v1),
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"v3-few-shot": Prompt("v3-few-shot", _SYS_V3_FEW_SHOT, _build_user_v1),
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}
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def default_version() -> str:
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return os.getenv("DEFAULT_PROMPT_VERSION", "v1")
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def get_prompt(version: str | None) -> Prompt:
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"""Look up a prompt by version. Falls back to ``DEFAULT_PROMPT_VERSION`` when
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``version`` is ``None``; raises :class:`KeyError` for unknown versions so
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callers can surface a 422 to clients."""
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v = version or default_version()
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if v not in PROMPTS:
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raise KeyError(v)
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return PROMPTS[v]
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@@ -8,7 +8,10 @@ import httpx
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from unittest.mock import AsyncMock, patch
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from httpx import AsyncClient, ASGITransport, Response
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from main import app, _build_prompt, PromptContext
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from main import app, PromptContext
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from prompts import PROMPTS, get_prompt
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_build_user_v1 = PROMPTS["v1"].build_user
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def _litellm_response(candidates: list[dict]) -> Response:
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@@ -96,7 +99,7 @@ def test_build_prompt_includes_tasks():
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hour_of_day=9,
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day_of_week=2,
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)
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prompt = _build_prompt(ctx, n=3)
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prompt = _build_user_v1(ctx, n=3)
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assert "Write report" in prompt
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assert "09:00" in prompt
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assert "Generate 3 tips" in prompt
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@@ -105,21 +108,21 @@ def test_build_prompt_includes_tasks():
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def test_build_prompt_truncates_at_five():
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tasks = [{"content": f"Task {i}", "priority": 1, "is_overdue": False, "due_date": None} for i in range(8)]
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ctx = PromptContext(tasks=tasks, hour_of_day=12)
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prompt = _build_prompt(ctx, n=2)
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prompt = _build_user_v1(ctx, n=2)
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assert "Task 4" in prompt
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assert "Task 5" not in prompt
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def test_build_prompt_extra_fields():
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ctx = PromptContext(tasks=[], hour_of_day=8, extra={"mood": "focused", "energy": "high"})
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prompt = _build_prompt(ctx, n=1)
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prompt = _build_user_v1(ctx, n=1)
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assert "mood: focused" in prompt
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assert "energy: high" in prompt
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def test_build_prompt_empty_tasks_no_task_line():
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ctx = PromptContext(tasks=[], hour_of_day=10)
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prompt = _build_prompt(ctx, n=2)
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prompt = _build_user_v1(ctx, n=2)
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assert "Tasks:" not in prompt
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assert "Generate 2 tips" in prompt
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@@ -223,3 +226,57 @@ def test_parse_llm_json_raises_on_invalid():
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from main import _parse_llm_json
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with pytest.raises((ValueError, Exception)):
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_parse_llm_json("this is not json")
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# ── Prompt registry / selection (#84) ──────────────────────────────────────
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def test_prompt_registry_contains_expected_versions():
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assert set(PROMPTS.keys()) >= {"v1", "v2-mentor", "v3-few-shot"}
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# v2-mentor must differ from v1 in tone — easiest assertion: different system prompt.
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assert PROMPTS["v1"].system != PROMPTS["v2-mentor"].system
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# v3-few-shot must include curated example content in its system prompt.
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assert "Examples" in PROMPTS["v3-few-shot"].system
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def test_get_prompt_unknown_raises_keyerror():
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with pytest.raises(KeyError):
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get_prompt("does-not-exist")
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def test_get_prompt_default_when_none():
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p = get_prompt(None)
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assert p.version == "v1" # current DEFAULT_PROMPT_VERSION
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@pytest.mark.anyio
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async def test_generate_echoes_selected_prompt_version():
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"""Server should report back which prompt_version it actually used."""
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fake_items = [{"id": "tip-1", "content": "x", "rationale": "y"}]
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mock_resp = _litellm_response(fake_items)
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with patch("main.httpx.AsyncClient") as MockClient:
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instance = AsyncMock()
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instance.post = AsyncMock(return_value=mock_resp)
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instance.__aenter__ = AsyncMock(return_value=instance)
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instance.__aexit__ = AsyncMock(return_value=False)
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MockClient.return_value = instance
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async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
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resp = await client.post(
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"/generate",
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json={"user_id": "u1", "n": 1, "prompt_version": "v2-mentor"},
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)
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assert resp.status_code == 200
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assert resp.json()["prompt_version"] == "v2-mentor"
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@pytest.mark.anyio
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async def test_generate_422_on_unknown_prompt_version():
|
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async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
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resp = await client.post(
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"/generate",
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json={"user_id": "u1", "n": 1, "prompt_version": "nonsense"},
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)
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assert resp.status_code == 422
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assert "Unknown prompt_version" in resp.json()["detail"]
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@@ -43,4 +43,12 @@ export const config = {
|
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/** How often to proactively sync Todoist tasks in the background (ms) */
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TODOIST_SYNC_INTERVAL_MS: parseInt(optional('TODOIST_SYNC_INTERVAL_MS', String(15 * 60 * 1000)), 10),
|
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|
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/**
|
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* Tip prompt version selection. Single value (e.g. "v2-mentor") pins one
|
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* variant; comma-separated list (e.g. "v1,v2-mentor,v3-few-shot") rotates
|
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* uniformly per request so #92's reward-analytics dashboard accumulates
|
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* comparable buckets. Empty → ml/serving's own default ("v1").
|
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*/
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TIP_PROMPT_VERSION: optional('TIP_PROMPT_VERSION', ''),
|
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};
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@@ -134,6 +134,7 @@ describe('POST /recommend integration', () => {
|
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json: async () => ({
|
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candidates: [{ id: 'adv-1', content: 'Take a break.', rationale: 'You deserve it.' }],
|
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model: 'tip-generator',
|
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prompt_version: 'v1',
|
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}),
|
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} as any);
|
||||
}
|
||||
|
||||
@@ -2,8 +2,9 @@
|
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* Pure-function unit tests for recommender logic — no DB, no HTTP.
|
||||
* These can import directly from the module without any mocking.
|
||||
*/
|
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import { describe, it, expect } from 'vitest';
|
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import { inferReward, dueAgeDays } from '../recommender.js';
|
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import { describe, it, expect, beforeEach, afterEach, vi } from 'vitest';
|
||||
import { inferReward, dueAgeDays, pickPromptVersion } from '../recommender.js';
|
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import { config } from '../../config.js';
|
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|
||||
describe('inferReward', () => {
|
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it('dismiss → -1', () => expect(inferReward('dismiss', null)).toBe(-1.0));
|
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@@ -37,3 +38,45 @@ describe('dueAgeDays', () => {
|
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expect(dueAgeDays({ date: yesterday })).toBeGreaterThan(0);
|
||||
});
|
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});
|
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|
||||
describe('pickPromptVersion', () => {
|
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// Save + restore the original env-driven config field across tests.
|
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let original: string;
|
||||
beforeEach(() => { original = config.TIP_PROMPT_VERSION; });
|
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afterEach(() => { (config as { TIP_PROMPT_VERSION: string }).TIP_PROMPT_VERSION = original; });
|
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|
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it('empty config → null (let ml/serving pick its default)', () => {
|
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(config as { TIP_PROMPT_VERSION: string }).TIP_PROMPT_VERSION = '';
|
||||
expect(pickPromptVersion()).toBeNull();
|
||||
});
|
||||
|
||||
it('whitespace-only config → null', () => {
|
||||
(config as { TIP_PROMPT_VERSION: string }).TIP_PROMPT_VERSION = ' ';
|
||||
expect(pickPromptVersion()).toBeNull();
|
||||
});
|
||||
|
||||
it('single value → that value', () => {
|
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(config as { TIP_PROMPT_VERSION: string }).TIP_PROMPT_VERSION = 'v2-mentor';
|
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expect(pickPromptVersion()).toBe('v2-mentor');
|
||||
});
|
||||
|
||||
it('comma-separated → uniformly samples from the set', () => {
|
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(config as { TIP_PROMPT_VERSION: string }).TIP_PROMPT_VERSION = 'v1,v2-mentor,v3-few-shot';
|
||||
const seen = new Set<string>();
|
||||
// With 100 trials, the chance of missing any of 3 buckets is (2/3)^100 ≈ 0 — test is reliable.
|
||||
for (let i = 0; i < 100; i++) {
|
||||
const picked = pickPromptVersion();
|
||||
expect(picked).not.toBeNull();
|
||||
seen.add(picked!);
|
||||
}
|
||||
expect(seen).toEqual(new Set(['v1', 'v2-mentor', 'v3-few-shot']));
|
||||
});
|
||||
|
||||
it('trims whitespace around comma-separated entries', () => {
|
||||
(config as { TIP_PROMPT_VERSION: string }).TIP_PROMPT_VERSION = ' v1 , v2-mentor ';
|
||||
for (let i = 0; i < 20; i++) {
|
||||
const picked = pickPromptVersion()!;
|
||||
expect(['v1', 'v2-mentor']).toContain(picked);
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
@@ -13,7 +13,19 @@ import { SignalAggregator } from '../signals/aggregator.js';
|
||||
|
||||
const router: ExpressRouter = Router();
|
||||
|
||||
const PROMPT_VERSION = 'v1';
|
||||
/**
|
||||
* Pick a prompt version for this request. `config.TIP_PROMPT_VERSION` is either
|
||||
* empty (let ml/serving pick its default), a single version, or a comma-separated
|
||||
* list to rotate uniformly across requests so the #92 dashboard accumulates
|
||||
* comparable buckets per variant. Exported for testing.
|
||||
*/
|
||||
export function pickPromptVersion(): string | null {
|
||||
const raw = config.TIP_PROMPT_VERSION.trim();
|
||||
if (!raw) return null;
|
||||
const versions = raw.split(',').map((v) => v.trim()).filter(Boolean);
|
||||
if (!versions.length) return null;
|
||||
return versions[Math.floor(Math.random() * versions.length)] ?? null;
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Signal aggregator — register sources here as new integrations are added
|
||||
@@ -117,12 +129,19 @@ interface LlmCandidate {
|
||||
rationale?: string;
|
||||
}
|
||||
|
||||
interface LlmGenerateResult {
|
||||
candidates: TipCandidate[];
|
||||
promptVersion: string | null;
|
||||
model: string | null;
|
||||
}
|
||||
|
||||
async function fetchLlmCandidates(
|
||||
userId: string,
|
||||
signals: Signal[],
|
||||
hour: number,
|
||||
dayOfWeek: number,
|
||||
): Promise<TipCandidate[]> {
|
||||
promptVersion: string | null,
|
||||
): Promise<LlmGenerateResult> {
|
||||
try {
|
||||
const tasks = signals.slice(0, 10).map((s) => ({
|
||||
content: s.content,
|
||||
@@ -137,13 +156,18 @@ async function fetchLlmCandidates(
|
||||
user_id: userId,
|
||||
context: { tasks, hour_of_day: hour, day_of_week: dayOfWeek },
|
||||
n: 3,
|
||||
...(promptVersion ? { prompt_version: promptVersion } : {}),
|
||||
}),
|
||||
signal: AbortSignal.timeout(15_000),
|
||||
});
|
||||
if (!res.ok) return [];
|
||||
const data = (await res.json()) as { candidates: LlmCandidate[]; model?: string };
|
||||
if (!res.ok) return { candidates: [], promptVersion: null, model: null };
|
||||
const data = (await res.json()) as {
|
||||
candidates: LlmCandidate[];
|
||||
model?: string;
|
||||
prompt_version?: string;
|
||||
};
|
||||
const now = new Date().toISOString();
|
||||
return data.candidates.map((c) => ({
|
||||
const candidates: TipCandidate[] = data.candidates.map((c) => ({
|
||||
id: `llm:${c.id}`,
|
||||
content: c.content,
|
||||
source: 'llm' as const,
|
||||
@@ -152,8 +176,13 @@ async function fetchLlmCandidates(
|
||||
createdAt: now,
|
||||
features: { is_overdue: false, task_age_days: 0, priority: 1 },
|
||||
}));
|
||||
return {
|
||||
candidates,
|
||||
promptVersion: data.prompt_version ?? null,
|
||||
model: data.model ?? null,
|
||||
};
|
||||
} catch {
|
||||
return [];
|
||||
return { candidates: [], promptVersion: null, model: null };
|
||||
}
|
||||
}
|
||||
|
||||
@@ -181,9 +210,16 @@ router.post('/recommend', requireAuth, async (req: AuthenticatedRequest, res: Re
|
||||
const signals = await aggregator.fetchAll(req.userId!);
|
||||
|
||||
const signalCandidates = signals.map(signalToCandidate);
|
||||
const llmCandidates = await fetchLlmCandidates(req.userId!, signals, hour, dayOfWeek);
|
||||
const requestedPromptVersion = pickPromptVersion();
|
||||
const llmResult = await fetchLlmCandidates(
|
||||
req.userId!,
|
||||
signals,
|
||||
hour,
|
||||
dayOfWeek,
|
||||
requestedPromptVersion,
|
||||
);
|
||||
|
||||
const allCandidates: TipCandidate[] = [...signalCandidates, ...llmCandidates];
|
||||
const allCandidates: TipCandidate[] = [...signalCandidates, ...llmResult.candidates];
|
||||
if (!allCandidates.length) {
|
||||
res.status(204).end();
|
||||
return;
|
||||
@@ -227,8 +263,10 @@ router.post('/recommend', requireAuth, async (req: AuthenticatedRequest, res: Re
|
||||
candidateCount: allCandidates.length,
|
||||
latencyMs,
|
||||
servedAt,
|
||||
promptVersion: isLlmTip ? PROMPT_VERSION : null,
|
||||
llmModel: isLlmTip ? 'tip-generator' : null,
|
||||
// Trust the version/model the generator reports; falls back to whatever
|
||||
// we asked for so the bucket isn't mislabeled if /generate omits it.
|
||||
promptVersion: isLlmTip ? (llmResult.promptVersion ?? requestedPromptVersion ?? null) : null,
|
||||
llmModel: isLlmTip ? (llmResult.model ?? 'tip-generator') : null,
|
||||
tipKind: tip.kind ?? null,
|
||||
});
|
||||
|
||||
|
||||
Reference in New Issue
Block a user