alvis
430804e9a5
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>
543 lines
18 KiB
Python
543 lines
18 KiB
Python
"""
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oO ML Serving — Phase 1: LinUCB contextual bandit.
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Contract:
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POST /score { user_id, candidates, context } → { tip_id, score, policy }
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POST /reward { user_id, tip_id, reward, features } → { ok }
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POST /reset/{user_id} → { ok }
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GET /stats/{user_id} → { pulls, cumulative_reward, estimated_mean, last_updated }
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GET /features/{user_id} → { history: [{ ts, features, score }] }
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GET /health → { ok }
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Features (d=5):
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hour_sin, hour_cos — cyclical time-of-day encoding
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is_overdue — 0 or 1
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task_age_days — days since due date (clipped 0–30, normalised 0–1)
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priority_norm — Todoist priority 1–4, normalised to 0–1
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"""
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from __future__ import annotations
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import json
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import math
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import os
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import time
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from collections import deque
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from pathlib import Path
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from typing import Optional, Deque
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import httpx
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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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LITELLM_MASTER_KEY = os.getenv("LITELLM_MASTER_KEY", "sk-oo-dev")
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STATE_DIR = Path(os.getenv("STATE_DIR", "/tmp/oo-bandit-state"))
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STATE_DIR.mkdir(parents=True, exist_ok=True)
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ALPHA = 1.0 # LinUCB exploration coefficient
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D = 5 # LinUCB feature dimension
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D7 = 7 # ε-greedy feature dimension (adds day-of-week cyclical encoding)
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EPSILON = 0.1 # ε-greedy exploration rate
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FEATURE_HISTORY_SIZE = 100 # per-user ring buffer
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# ── Per-user in-memory feature history ────────────────────────────────────
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_feature_history: dict[str, deque] = {}
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def get_feature_history(user_id: str) -> deque:
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if user_id not in _feature_history:
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_feature_history[user_id] = deque(maxlen=FEATURE_HISTORY_SIZE)
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return _feature_history[user_id]
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# ── Feature helpers ────────────────────────────────────────────────────────
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def build_feature_vector(features: dict) -> np.ndarray:
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hour = features.get("hour_of_day", 12)
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hour_sin = math.sin(2 * math.pi * hour / 24)
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hour_cos = math.cos(2 * math.pi * hour / 24)
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is_overdue = float(bool(features.get("is_overdue", False)))
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age = min(float(features.get("task_age_days", 0)), 30.0) / 30.0
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priority = (float(features.get("priority", 1)) - 1.0) / 3.0
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return np.array([hour_sin, hour_cos, is_overdue, age, priority], dtype=np.float64)
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# ── Per-user bandit state (disjoint LinUCB, global arm) ───────────────────
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# ── LinUCB state helpers ───────────────────────────────────────────────────
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def state_path(user_id: str) -> Path:
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safe = "".join(c if c.isalnum() else "_" for c in user_id)
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return STATE_DIR / f"{safe}.json"
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def load_state(user_id: str) -> tuple[np.ndarray, np.ndarray, dict]:
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"""Returns (A, b, meta). A is DxD, b is D-vector."""
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p = state_path(user_id)
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if p.exists():
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raw = json.loads(p.read_text())
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A = np.array(raw["A"], dtype=np.float64)
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b = np.array(raw["b"], dtype=np.float64)
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meta = raw.get("meta", {})
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return A, b, meta
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return np.identity(D, dtype=np.float64), np.zeros(D, dtype=np.float64), {}
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def save_state(user_id: str, A: np.ndarray, b: np.ndarray, meta: dict) -> None:
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p = state_path(user_id)
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p.write_text(json.dumps({"A": A.tolist(), "b": b.tolist(), "meta": meta}))
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# ── ε-greedy state helpers (d=7, extended features) ───────────────────────
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def build_feature_vector_7(features: dict, day_of_week: int = 0) -> np.ndarray:
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"""d=7: base 5 features + day-of-week cyclical encoding."""
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base = build_feature_vector(features)
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dow_sin = math.sin(2 * math.pi * day_of_week / 7)
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dow_cos = math.cos(2 * math.pi * day_of_week / 7)
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return np.append(base, [dow_sin, dow_cos])
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def state7_path(user_id: str) -> Path:
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safe = "".join(c if c.isalnum() else "_" for c in user_id)
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return STATE_DIR / f"{safe}_egreedy.json"
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def load_state7(user_id: str) -> tuple[np.ndarray, np.ndarray, dict]:
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"""Returns (A, b, meta) for ε-greedy d=7 policy."""
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p = state7_path(user_id)
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if p.exists():
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raw = json.loads(p.read_text())
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A = np.array(raw["A"], dtype=np.float64)
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b = np.array(raw["b"], dtype=np.float64)
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return A, b, raw.get("meta", {})
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return np.identity(D7, dtype=np.float64), np.zeros(D7, dtype=np.float64), {}
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def save_state7(user_id: str, A: np.ndarray, b: np.ndarray, meta: dict) -> None:
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p = state7_path(user_id)
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p.write_text(json.dumps({"A": A.tolist(), "b": b.tolist(), "meta": meta}))
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# ── API models ─────────────────────────────────────────────────────────────
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class CandidateFeatures(BaseModel):
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hour_of_day: int = 12
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is_overdue: bool = False
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task_age_days: float = 0.0
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priority: int = 1
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class Candidate(BaseModel):
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id: str
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content: str
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source: str
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source_id: Optional[str] = None
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features: CandidateFeatures = CandidateFeatures()
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class Context(BaseModel):
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hour_of_day: int = 12
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day_of_week: int = 0
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class ScoreRequest(BaseModel):
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user_id: str
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candidates: list[Candidate]
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context: Context = Context()
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class ScoreResponse(BaseModel):
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tip_id: str
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score: float
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policy: str
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class RewardRequest(BaseModel):
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user_id: str
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tip_id: str
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reward: float # +1 done, +0.5 helpful, 0 snooze, -0.5 not_helpful, -1 dismiss
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features: CandidateFeatures
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day_of_week: int = 0 # included so egreedy can train dow features correctly
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class RewardResponse(BaseModel):
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ok: bool
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class PromptContext(BaseModel):
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tasks: list[dict] = []
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hour_of_day: int = 12
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day_of_week: int = 0
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extra: dict = {}
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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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id: str
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content: str
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source: str = "llm"
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rationale: Optional[str] = None
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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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# ── Endpoints ──────────────────────────────────────────────────────────────
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@app.get("/health")
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def health():
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return {"ok": True}
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_RETRY_SUFFIX = (
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"\n\nYour previous response was not valid JSON. "
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"Reply ONLY with the JSON array — no prose, no markdown fences."
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)
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_MAX_GENERATE_RETRIES = 2
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def _parse_llm_json(raw: str) -> list[dict]:
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"""Strip markdown fences and parse JSON array. Raises ValueError on failure."""
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text = raw.strip()
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if text.startswith("```"):
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parts = text.split("```")
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text = parts[1] if len(parts) > 1 else text
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if text.startswith("json"):
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text = text[4:]
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return json.loads(text)
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@app.post("/generate", response_model=GenerateResponse)
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async def generate(req: GenerateRequest) -> GenerateResponse:
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"""Generate tip candidates via LiteLLM → tip-generator alias.
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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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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": 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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last_raw: str = ""
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total_usage: dict = {"prompt_tokens": 0, "completion_tokens": 0}
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model_used = "tip-generator"
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async with httpx.AsyncClient(timeout=30.0) as client:
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for attempt in range(1 + _MAX_GENERATE_RETRIES):
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payload = {"model": "tip-generator", "messages": messages, "temperature": 0.7}
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try:
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resp = await client.post(
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f"{LITELLM_URL}/chat/completions",
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json=payload,
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headers=headers,
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)
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resp.raise_for_status()
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except httpx.HTTPStatusError as e:
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raise HTTPException(status_code=502, detail=f"LiteLLM error: {e.response.text}")
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except httpx.RequestError as e:
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raise HTTPException(status_code=503, detail=f"LiteLLM unreachable: {e}")
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data = resp.json()
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usage = data.get("usage", {})
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total_usage["prompt_tokens"] += usage.get("prompt_tokens", 0)
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total_usage["completion_tokens"] += usage.get("completion_tokens", 0)
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model_used = data.get("model", "tip-generator")
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last_raw = data["choices"][0]["message"]["content"]
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try:
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items = _parse_llm_json(last_raw)
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break
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except (json.JSONDecodeError, ValueError) as e:
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last_parse_error = str(e)
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# Feed the bad reply back so the model can self-correct
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messages.append({"role": "assistant", "content": last_raw})
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messages.append({"role": "user", "content": _RETRY_SUFFIX})
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else:
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raise HTTPException(
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status_code=502,
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detail=f"LLM returned invalid JSON after {_MAX_GENERATE_RETRIES} retries: "
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f"{last_parse_error}\n{last_raw[:200]}",
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)
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candidates = [
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TipCandidate(
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id=item.get("id", f"tip-{i}"),
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content=item.get("content", ""),
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rationale=item.get("rationale"),
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)
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for i, item in enumerate(items)
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]
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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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@app.post("/score", response_model=ScoreResponse)
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def score(req: ScoreRequest) -> ScoreResponse:
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if not req.candidates:
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raise HTTPException(status_code=422, detail="No candidates")
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A, b, meta = load_state(req.user_id)
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try:
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A_inv = np.linalg.inv(A)
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except np.linalg.LinAlgError:
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A_inv = np.identity(D, dtype=np.float64)
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theta = A_inv @ b
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best_id = None
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best_score = -float("inf")
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best_features: dict = {}
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for candidate in req.candidates:
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feat_dict = {
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"hour_of_day": req.context.hour_of_day,
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"is_overdue": candidate.features.is_overdue,
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"task_age_days": candidate.features.task_age_days,
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"priority": candidate.features.priority,
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}
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x = build_feature_vector(feat_dict)
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exploit = float(theta @ x)
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explore = ALPHA * math.sqrt(float(x @ A_inv @ x))
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ucb = exploit + explore
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if ucb > best_score:
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best_score = ucb
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best_id = candidate.id
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best_features = feat_dict
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# Log to feature history ring buffer
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history = get_feature_history(req.user_id)
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history.append({
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"ts": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
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"features": best_features,
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"score": best_score,
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"tip_id": best_id,
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})
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# Update meta stats
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meta["pulls"] = meta.get("pulls", 0) + 1
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meta["last_updated"] = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
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save_state(req.user_id, A, b, meta)
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return ScoreResponse(tip_id=best_id, score=best_score, policy="linucb-v1")
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@app.post("/reward", response_model=RewardResponse)
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def reward(req: RewardRequest) -> RewardResponse:
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A, b, meta = load_state(req.user_id)
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feat_dict = {
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"hour_of_day": req.features.hour_of_day,
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"is_overdue": req.features.is_overdue,
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"task_age_days": req.features.task_age_days,
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"priority": req.features.priority,
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}
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x = build_feature_vector(feat_dict)
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A += np.outer(x, x)
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b += req.reward * x
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# Track cumulative reward in meta
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meta["cumulative_reward"] = meta.get("cumulative_reward", 0.0) + req.reward
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meta["reward_count"] = meta.get("reward_count", 0) + 1
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meta["last_updated"] = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
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save_state(req.user_id, A, b, meta)
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return RewardResponse(ok=True)
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@app.post("/score/egreedy", response_model=ScoreResponse)
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def score_egreedy(req: ScoreRequest) -> ScoreResponse:
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"""ε-greedy policy with d=7 features (adds day-of-week encoding).
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Exploration: pick uniformly at random with probability ε.
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Exploitation: pick argmax of linear payoff estimate θ·x.
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Differs from LinUCB in: no UCB bonus, richer feature space.
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"""
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if not req.candidates:
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raise HTTPException(status_code=422, detail="No candidates")
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A, b, meta = load_state7(req.user_id)
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try:
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A_inv = np.linalg.inv(A)
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except np.linalg.LinAlgError:
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A_inv = np.identity(D7, dtype=np.float64)
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theta = A_inv @ b
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dow = req.context.day_of_week
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exploring = np.random.random() < EPSILON
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if exploring:
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chosen = req.candidates[np.random.randint(len(req.candidates))]
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feat_dict = {
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"hour_of_day": req.context.hour_of_day,
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"is_overdue": chosen.features.is_overdue,
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"task_age_days": chosen.features.task_age_days,
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"priority": chosen.features.priority,
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}
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x = build_feature_vector_7(feat_dict, dow)
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best_score = float(theta @ x)
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best_id = chosen.id
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else:
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best_id = None
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best_score = -float("inf")
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feat_dict = {}
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for candidate in req.candidates:
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fd = {
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"hour_of_day": req.context.hour_of_day,
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"is_overdue": candidate.features.is_overdue,
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"task_age_days": candidate.features.task_age_days,
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"priority": candidate.features.priority,
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}
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x = build_feature_vector_7(fd, dow)
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s = float(theta @ x)
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if s > best_score:
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best_score = s
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best_id = candidate.id
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feat_dict = fd
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history = get_feature_history(req.user_id)
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history.append({
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"ts": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
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"features": {**feat_dict, "day_of_week": dow, "exploring": exploring},
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"score": best_score,
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"tip_id": best_id,
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"policy": "egreedy-v1",
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})
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meta["pulls"] = meta.get("pulls", 0) + 1
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meta["explore_count"] = meta.get("explore_count", 0) + int(exploring)
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meta["last_updated"] = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
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save_state7(req.user_id, A, b, meta)
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return ScoreResponse(tip_id=best_id, score=best_score, policy="egreedy-v1")
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@app.post("/reward/egreedy", response_model=RewardResponse)
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def reward_egreedy(req: RewardRequest) -> RewardResponse:
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"""Update ε-greedy ridge estimator with observed reward."""
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A, b, meta = load_state7(req.user_id)
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feat_dict = {
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"hour_of_day": req.features.hour_of_day,
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"is_overdue": req.features.is_overdue,
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"task_age_days": req.features.task_age_days,
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"priority": req.features.priority,
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}
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x = build_feature_vector_7(feat_dict, day_of_week=req.day_of_week)
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A += np.outer(x, x)
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b += req.reward * x
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|
||
meta["cumulative_reward"] = meta.get("cumulative_reward", 0.0) + req.reward
|
||
meta["reward_count"] = meta.get("reward_count", 0) + 1
|
||
meta["last_updated"] = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
|
||
save_state7(req.user_id, A, b, meta)
|
||
return RewardResponse(ok=True)
|
||
|
||
|
||
@app.get("/stats/egreedy/{user_id}")
|
||
def stats_egreedy(user_id: str):
|
||
"""ε-greedy policy stats — pulls, cumulative reward, θ vector."""
|
||
A, b, meta = load_state7(user_id)
|
||
try:
|
||
theta = (np.linalg.inv(A) @ b).tolist()
|
||
except np.linalg.LinAlgError:
|
||
theta = [0.0] * D7
|
||
|
||
pulls = meta.get("pulls", 0)
|
||
cumulative_reward = meta.get("cumulative_reward", 0.0)
|
||
reward_count = meta.get("reward_count", 0)
|
||
explore_count = meta.get("explore_count", 0)
|
||
|
||
return {
|
||
"user_id": user_id,
|
||
"policy": "egreedy-v1",
|
||
"pulls": pulls,
|
||
"reward_count": reward_count,
|
||
"cumulative_reward": cumulative_reward,
|
||
"estimated_mean_reward": cumulative_reward / reward_count if reward_count > 0 else 0.0,
|
||
"exploration_rate": explore_count / pulls if pulls > 0 else 0.0,
|
||
"theta": theta,
|
||
"feature_labels": ["hour_sin", "hour_cos", "is_overdue", "task_age", "priority", "dow_sin", "dow_cos"],
|
||
"last_updated": meta.get("last_updated"),
|
||
}
|
||
|
||
|
||
@app.post("/reset/{user_id}", response_model=RewardResponse)
|
||
def reset(user_id: str) -> RewardResponse:
|
||
"""Reset per-user bandit state (admin action)."""
|
||
p = state_path(user_id)
|
||
if p.exists():
|
||
p.unlink()
|
||
p7 = state7_path(user_id)
|
||
if p7.exists():
|
||
p7.unlink()
|
||
if user_id in _feature_history:
|
||
_feature_history[user_id].clear()
|
||
return RewardResponse(ok=True)
|
||
|
||
|
||
@app.get("/stats/{user_id}")
|
||
def stats(user_id: str):
|
||
"""Return current LinUCB state summary for a user."""
|
||
A, b, meta = load_state(user_id)
|
||
try:
|
||
A_inv = np.linalg.inv(A)
|
||
theta = (A_inv @ b).tolist()
|
||
except np.linalg.LinAlgError:
|
||
theta = [0.0] * D
|
||
|
||
pulls = meta.get("pulls", 0)
|
||
cumulative_reward = meta.get("cumulative_reward", 0.0)
|
||
reward_count = meta.get("reward_count", 0)
|
||
estimated_mean = cumulative_reward / reward_count if reward_count > 0 else 0.0
|
||
|
||
return {
|
||
"user_id": user_id,
|
||
"pulls": pulls,
|
||
"reward_count": reward_count,
|
||
"cumulative_reward": cumulative_reward,
|
||
"estimated_mean_reward": estimated_mean,
|
||
"theta": theta,
|
||
"last_updated": meta.get("last_updated"),
|
||
}
|
||
|
||
|
||
@app.get("/features/{user_id}")
|
||
def features(user_id: str):
|
||
"""Return recent feature vectors logged at scoring time."""
|
||
history = get_feature_history(user_id)
|
||
return {
|
||
"user_id": user_id,
|
||
"history": list(history),
|
||
}
|