damien-kaiser/taskloft
Taskloft — team task boards (owner / member / viewer), card history, my plate, overdue, blockers, AI checklists and summaries, usable from Claude and ChatGPT over MCP.
A remix template · 0 remixes · Remix damien-kaiser/taskloft
app/boards/infrastructure/dodil.py
"""The Dodil side: a service-account token, DataK3 on the Postgres wire, and Dodil Models.
The remix generated the service account and put its id and secret into the repo's CI secrets;
.dodil/deploy.yaml hands them to the app as environment variables.
"""
import os
import threading
import time
import httpx
import psycopg
TOKEN_URL = "https://id.dodil.io/realms/dodil/protocol/openid-connect/token"
PG_HOST = os.environ.get("PG_HOST", "pg.uk-lon-1.dodil.io")
MODELS_BASE = os.environ.get("MODELS_BASE", "https://api.dodil.io/v1").rstrip("/")
CHAT_MODEL = os.environ.get("CHAT_MODEL", "kimi-k2.6")
EMBED_MODEL = os.environ.get("EMBED_MODEL", "jina-embeddings-v4") # 2048 dimensions
_lock, _tok = threading.Lock(), {"v": None, "exp": 0.0}
def token() -> str:
"""A short-lived service-account token (client_credentials), cached until near expiry.
It is the DataK3 password AND the Models bearer."""
if os.environ.get("DODIL_TOKEN"): # local development only: a token from your own Dodil login
return os.environ["DODIL_TOKEN"]
with _lock:
if _tok["v"] and time.time() < _tok["exp"] - 60:
return _tok["v"]
r = httpx.post(TOKEN_URL, timeout=15, data={
"grant_type": "client_credentials",
"client_id": os.environ["DODIL_SERVICE_ACCOUNT_ID"],
"client_secret": os.environ["DODIL_SERVICE_ACCOUNT_SECRET"]})
r.raise_for_status()
d = r.json()
_tok.update(v=d["access_token"], exp=time.time() + float(d.get("expires_in", 300)))
return _tok["v"]
def connect() -> psycopg.Connection:
"""DataK3 speaks Postgres: the database is the bucket, TLS is required."""
return psycopg.connect(host=PG_HOST, port=5432, dbname=os.environ["BUCKET"], user="token",
password=token(), sslmode="require", autocommit=True)
def chat(prompt: str) -> str:
"""One chat call to Dodil Models (OpenAI-compatible). The gateway wraps the OpenAI body as
{"status": ..., "data": {...}}, so unwrap it before reading choices."""
r = httpx.post(f"{MODELS_BASE}/chat/completions", timeout=120,
headers={"Authorization": f"Bearer {token()}"},
json={"model": CHAT_MODEL, "messages": [{"role": "user", "content": prompt}]})
r.raise_for_status()
body = r.json()
inner = body.get("data")
if isinstance(inner, dict) and "choices" in inner:
body = inner
return body["choices"][0]["message"]["content"] or ""
def embed(texts: list) -> list:
"""Dodil Models embeddings (OpenAI-compatible /embeddings), unwrapped like chat."""
r = httpx.post(f"{MODELS_BASE}/embeddings", timeout=120, headers={"Authorization": f"Bearer {token()}"},
json={"model": EMBED_MODEL, "input": list(texts)})
r.raise_for_status()
body = r.json()
inner = body.get("data")
if isinstance(inner, dict) and "data" in inner:
body = inner
rows = sorted(body["data"], key=lambda x: x.get("index", 0))
return [row["embedding"] for row in rows]
class ModelsAssistant:
def checklist(self, card_title: str, description: str = "") -> list:
text = chat("Break this task into a short checklist of 3 to 6 concrete steps. "
"One step per line, no numbering, nothing else.\n\nTask: " + card_title +
(("\nDetails: " + description[:1500]) if description else ""))
return [line.strip("-*• ").strip() for line in text.splitlines() if line.strip()][:8]
def summary(self, board_title: str, facts: str) -> str:
return chat("You write the daily summary of a team task board, for the team, in 3 to 6 short lines: "
"what got done recently, what is overdue or blocked (name the card numbers), and the one "
"thing that most needs attention today. Use only the facts below; invent nothing.\n\n"
f"Board: {board_title}\n{facts}").strip()
def embed(self, texts: list) -> list:
return embed(texts)