854396680f
- Recipes: Now content-addressed only (cache + IPFS), removed Redis storage - Runs: Completed runs stored in PostgreSQL, Redis only for task_id mapping - Add list_runs_by_actor() to database.py for paginated run queries - Add list_by_type() to cache_manager for filtering by node_type - Fix upload endpoint to return size and filename fields - Fix recipe run endpoint with proper DAG input binding - Fix get_run_service() dependency to pass database module Storage architecture: - Redis: Ephemeral only (sessions, task mappings with TTL) - PostgreSQL: Permanent records (completed runs, metadata) - Cache: Content-addressed files (recipes, media, outputs) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
412 lines
15 KiB
Python
412 lines
15 KiB
Python
"""
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Run Service - business logic for run management.
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Runs are content-addressed (run_id computed from inputs + recipe).
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Completed runs are stored in PostgreSQL, not Redis.
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In-progress runs are tracked via Celery task state.
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"""
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import hashlib
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import json
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import os
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Optional, List, Dict, Any, Tuple
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def compute_run_id(input_hashes: list, recipe: str, recipe_hash: str = None) -> str:
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"""
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Compute a deterministic run_id from inputs and recipe.
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The run_id is a SHA3-256 hash of:
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- Sorted input content hashes
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- Recipe identifier (recipe_hash if provided, else "effect:{recipe}")
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This makes runs content-addressable: same inputs + recipe = same run_id.
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"""
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# Handle both list and dict inputs
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if isinstance(input_hashes, dict):
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sorted_inputs = sorted(input_hashes.values())
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else:
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sorted_inputs = sorted(input_hashes)
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data = {
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"inputs": sorted_inputs,
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"recipe": recipe_hash or f"effect:{recipe}",
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"version": "1",
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}
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json_str = json.dumps(data, sort_keys=True, separators=(",", ":"))
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return hashlib.sha3_256(json_str.encode()).hexdigest()
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def detect_media_type(cache_path: Path) -> str:
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"""Detect if file is image, video, or audio based on magic bytes."""
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try:
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with open(cache_path, "rb") as f:
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header = f.read(32)
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except Exception:
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return "unknown"
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# Video signatures
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if header[:4] == b'\x1a\x45\xdf\xa3': # WebM/MKV
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return "video"
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if len(header) > 8 and header[4:8] == b'ftyp': # MP4/MOV
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return "video"
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if header[:4] == b'RIFF' and len(header) > 12 and header[8:12] == b'AVI ': # AVI
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return "video"
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# Image signatures
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if header[:8] == b'\x89PNG\r\n\x1a\n': # PNG
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return "image"
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if header[:2] == b'\xff\xd8': # JPEG
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return "image"
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if header[:6] in (b'GIF87a', b'GIF89a'): # GIF
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return "image"
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if header[:4] == b'RIFF' and len(header) > 12 and header[8:12] == b'WEBP': # WebP
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return "image"
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return "unknown"
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class RunService:
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"""
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Service for managing recipe runs.
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Uses PostgreSQL for completed runs, Celery for task state.
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Redis is only used for task_id mapping (ephemeral).
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"""
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def __init__(self, database, redis, cache):
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self.db = database
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self.redis = redis # Only for task_id mapping
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self.cache = cache
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self.task_key_prefix = "artdag:task:" # run_id -> task_id mapping only
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self.cache_dir = Path(os.environ.get("CACHE_DIR", "/tmp/artdag-cache"))
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async def get_run(self, run_id: str) -> Optional[Dict[str, Any]]:
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"""Get a run by ID. Checks database first, then Celery task state."""
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# Check database for completed run
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cached = await self.db.get_run_cache(run_id)
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if cached:
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return {
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"run_id": run_id,
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"status": "completed",
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"recipe": cached.get("recipe"),
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"inputs": cached.get("inputs", []),
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"output_hash": cached.get("output_hash"),
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"ipfs_cid": cached.get("ipfs_cid"),
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"provenance_cid": cached.get("provenance_cid"),
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"actor_id": cached.get("actor_id"),
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"created_at": cached.get("created_at"),
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"completed_at": cached.get("created_at"),
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}
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# Check if there's a running task
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task_id = self.redis.get(f"{self.task_key_prefix}{run_id}")
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if task_id:
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if isinstance(task_id, bytes):
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task_id = task_id.decode()
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# Get task state from Celery
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from celery.result import AsyncResult
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from celery_app import app as celery_app
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result = AsyncResult(task_id, app=celery_app)
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status = result.status.lower()
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run_data = {
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"run_id": run_id,
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"status": status if status != "pending" else "pending",
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"celery_task_id": task_id,
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}
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# If task completed, get result
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if result.ready():
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if result.successful():
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run_data["status"] = "completed"
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task_result = result.result
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if isinstance(task_result, dict):
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run_data["output_hash"] = task_result.get("output_hash")
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else:
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run_data["status"] = "failed"
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run_data["error"] = str(result.result)
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return run_data
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return None
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async def list_runs(self, actor_id: str, offset: int = 0, limit: int = 20) -> list:
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"""List runs for a user. Returns completed runs from database."""
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# Get completed runs from database
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runs = await self.db.list_runs_by_actor(actor_id, offset=offset, limit=limit)
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# Also check for any pending tasks in Redis
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pending = []
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cursor = 0
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while True:
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cursor, keys = self.redis.scan(
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cursor=cursor,
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match=f"{self.task_key_prefix}*",
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count=100
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)
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for key in keys:
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run_id = key.decode().replace(self.task_key_prefix, "") if isinstance(key, bytes) else key.replace(self.task_key_prefix, "")
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# Check if this run belongs to the user and isn't already in results
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if not any(r.get("run_id") == run_id for r in runs):
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run = await self.get_run(run_id)
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if run and run.get("status") in ("pending", "running"):
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pending.append(run)
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if cursor == 0:
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break
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# Combine and sort
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all_runs = pending + runs
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all_runs.sort(key=lambda r: r.get("created_at", ""), reverse=True)
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return all_runs[offset:offset + limit]
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async def create_run(
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self,
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recipe: str,
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inputs: list,
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output_name: str = None,
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use_dag: bool = True,
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dag_json: str = None,
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actor_id: str = None,
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l2_server: str = None,
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) -> Tuple[Optional[Dict[str, Any]], Optional[str]]:
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"""
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Create a new rendering run. Checks cache before executing.
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Returns (run_dict, error_message).
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"""
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import httpx
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try:
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from legacy_tasks import render_effect, execute_dag, build_effect_dag
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except ImportError as e:
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return None, f"Celery tasks not available: {e}"
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# Handle both list and dict inputs
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if isinstance(inputs, dict):
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input_list = list(inputs.values())
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else:
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input_list = inputs
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# Compute content-addressable run_id
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run_id = compute_run_id(input_list, recipe)
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# Generate output name if not provided
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if not output_name:
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output_name = f"{recipe}-{run_id[:8]}"
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# Check database cache first (completed runs)
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cached_run = await self.db.get_run_cache(run_id)
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if cached_run:
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output_hash = cached_run.get("output_hash")
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if output_hash and self.cache.has_content(output_hash):
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return {
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"run_id": run_id,
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"status": "completed",
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"recipe": recipe,
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"inputs": input_list,
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"output_name": output_name,
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"output_hash": output_hash,
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"ipfs_cid": cached_run.get("ipfs_cid"),
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"provenance_cid": cached_run.get("provenance_cid"),
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"created_at": cached_run.get("created_at"),
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"completed_at": cached_run.get("created_at"),
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"actor_id": actor_id,
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}, None
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# Check L2 if not in local cache
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if l2_server:
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try:
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async with httpx.AsyncClient(timeout=10) as client:
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l2_resp = await client.get(f"{l2_server}/assets/by-run-id/{run_id}")
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if l2_resp.status_code == 200:
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l2_data = l2_resp.json()
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output_hash = l2_data.get("output_hash")
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ipfs_cid = l2_data.get("ipfs_cid")
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if output_hash and ipfs_cid:
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# Pull from IPFS to local cache
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try:
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import ipfs_client
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legacy_dir = self.cache_dir / "legacy"
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legacy_dir.mkdir(parents=True, exist_ok=True)
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recovery_path = legacy_dir / output_hash
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if ipfs_client.get_file(ipfs_cid, str(recovery_path)):
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# Save to database cache
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await self.db.save_run_cache(
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run_id=run_id,
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output_hash=output_hash,
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recipe=recipe,
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inputs=input_list,
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ipfs_cid=ipfs_cid,
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provenance_cid=l2_data.get("provenance_cid"),
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actor_id=actor_id,
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)
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return {
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"run_id": run_id,
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"status": "completed",
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"recipe": recipe,
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"inputs": input_list,
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"output_hash": output_hash,
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"ipfs_cid": ipfs_cid,
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"provenance_cid": l2_data.get("provenance_cid"),
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"created_at": datetime.now(timezone.utc).isoformat(),
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"actor_id": actor_id,
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}, None
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except Exception:
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pass # IPFS recovery failed, continue to run
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except Exception:
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pass # L2 lookup failed, continue to run
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# Not cached - submit to Celery
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try:
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if use_dag or recipe == "dag":
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if dag_json:
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dag_data = dag_json
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else:
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dag = build_effect_dag(input_list, recipe)
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dag_data = dag.to_json()
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task = execute_dag.delay(dag_data, run_id)
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else:
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if len(input_list) != 1:
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return None, "Legacy mode only supports single-input recipes. Use use_dag=true for multi-input."
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task = render_effect.delay(input_list[0], recipe, output_name)
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# Store task_id mapping in Redis (ephemeral)
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self.redis.setex(
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f"{self.task_key_prefix}{run_id}",
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3600 * 24, # 24 hour TTL
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task.id
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)
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return {
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"run_id": run_id,
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"status": "running",
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"recipe": recipe,
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"inputs": input_list,
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"output_name": output_name,
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"celery_task_id": task.id,
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"created_at": datetime.now(timezone.utc).isoformat(),
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"actor_id": actor_id,
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}, None
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except Exception as e:
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return None, f"Failed to submit task: {e}"
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async def discard_run(
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self,
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run_id: str,
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actor_id: str,
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username: str,
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) -> Tuple[bool, Optional[str]]:
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"""
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Discard (delete) a run record.
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Note: This removes the run record but not the output content.
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"""
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run = await self.get_run(run_id)
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if not run:
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return False, f"Run {run_id} not found"
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# Check ownership
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run_owner = run.get("actor_id")
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if run_owner and run_owner not in (username, actor_id):
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return False, "Access denied"
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# Remove task_id mapping from Redis
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self.redis.delete(f"{self.task_key_prefix}{run_id}")
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# Note: We don't delete from run_cache as that's a permanent record
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# of completed work. The content itself remains in cache.
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return True, None
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async def get_run_plan(self, run_id: str) -> Optional[Dict[str, Any]]:
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"""Get execution plan for a run."""
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plan_path = self.cache_dir / "plans" / f"{run_id}.json"
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if plan_path.exists():
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with open(plan_path) as f:
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return json.load(f)
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return None
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async def get_run_artifacts(self, run_id: str) -> List[Dict[str, Any]]:
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"""Get all artifacts (inputs + outputs) for a run."""
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run = await self.get_run(run_id)
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if not run:
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return []
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artifacts = []
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def get_artifact_info(content_hash: str, role: str, name: str) -> Optional[Dict]:
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if self.cache.has_content(content_hash):
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path = self.cache.get_by_content_hash(content_hash)
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if path and path.exists():
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return {
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"hash": content_hash,
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"size_bytes": path.stat().st_size,
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"media_type": detect_media_type(path),
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"role": role,
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"step_name": name,
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}
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return None
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# Add inputs
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inputs = run.get("inputs", [])
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if isinstance(inputs, dict):
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inputs = list(inputs.values())
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for i, h in enumerate(inputs):
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info = get_artifact_info(h, "input", f"Input {i + 1}")
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if info:
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artifacts.append(info)
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# Add output
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if run.get("output_hash"):
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info = get_artifact_info(run["output_hash"], "output", "Output")
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if info:
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artifacts.append(info)
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return artifacts
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async def get_run_analysis(self, run_id: str) -> List[Dict[str, Any]]:
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"""Get analysis data for each input in a run."""
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run = await self.get_run(run_id)
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if not run:
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return []
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analysis_dir = self.cache_dir / "analysis"
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results = []
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inputs = run.get("inputs", [])
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if isinstance(inputs, dict):
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inputs = list(inputs.values())
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for i, input_hash in enumerate(inputs):
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analysis_path = analysis_dir / f"{input_hash}.json"
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analysis_data = None
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if analysis_path.exists():
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try:
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with open(analysis_path) as f:
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analysis_data = json.load(f)
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except (json.JSONDecodeError, IOError):
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pass
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results.append({
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"input_hash": input_hash,
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"input_name": f"Input {i + 1}",
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"has_analysis": analysis_data is not None,
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"tempo": analysis_data.get("tempo") if analysis_data else None,
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"beat_times": analysis_data.get("beat_times", []) if analysis_data else [],
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"raw": analysis_data,
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})
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return results
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def detect_media_type(self, path: Path) -> str:
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"""Detect media type for a file path."""
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return detect_media_type(path)
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