theaio/theaio-boards

Laneway, team task boards you can run from Claude or ChatGPT. FastAPI + DataK3 + appid + Models + MCP.

• dodil-challenge
• dodil-challenge-boards
• boards
• kanban
• mcp
• fastapi

A remix template · 0 remixes · Remix theaio/theaio-boards

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")
# Short enough that a slow model never holds a web request past the gateway; callers fall back to plain facts.
MODELS_TIMEOUT = float(os.environ.get("MODELS_TIMEOUT", "25"))
EMBED_MODEL = os.environ.get("EMBED_MODEL", "jina-embeddings-v4")  # 2048 numbers: VECTOR(2048)

_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."""
    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 _models(path: str, payload: dict) -> dict:
    """One call to Dodil Models (OpenAI-compatible). The gateway wraps the OpenAI body as
    {"status": ..., "data": {...}}, so unwrap it before reading choices / embeddings."""
    r = httpx.post(f"{MODELS_BASE}{path}", timeout=MODELS_TIMEOUT,
                   headers={"Authorization": f"Bearer {token()}"}, json=payload)
    r.raise_for_status()
    body = r.json()
    inner = body.get("data")
    return inner if isinstance(inner, dict) else body


def chat(prompt: str) -> str:
    body = _models("/chat/completions",
                   {"model": CHAT_MODEL, "messages": [{"role": "user", "content": prompt}]})
    return body["choices"][0]["message"]["content"] or ""


def embed(texts: list) -> list:
    """One vector per text, in order (the OpenAI shape: data[i].embedding, data[i].index)."""
    body = _models("/embeddings", {"model": EMBED_MODEL, "input": list(texts)})
    return [item["embedding"] for item in sorted(body["data"], key=lambda d: d.get("index", 0))]


class ModelsAssistant:
    def checklist(self, card_title: 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)
        return [line.strip("-*• ").strip() for line in text.splitlines() if line.strip()][:8]

    def embed(self, texts: list) -> list:
        return embed(texts)

    def summarize(self, facts: str) -> str:
        return chat("You write the daily note for a team task board. From the facts below, "
                    "write 3 to 5 plain sentences: what moved, what is late or blocked and who "
                    "should look at it today. Use card numbers. No headings, no lists, no "
                    "markdown, and nothing that is not in the facts.\n\n" + facts).strip()