mo
f36c8906bc
Adds native Command Center features (no new containers) integrated as sub-tabs in the existing Data Explorer and Data Quality views: - Continuous Data Quality (dq_monitor.py): live completeness/uniqueness/validity/ freshness scorecards via Trino with rolling trends → DataQuality "Live Monitoring". - Ownership & stewardship (catalog_governance.py): owner/steward/tier matrix, orphan detection, business glossary; local store best-effort synced to OpenMetadata (owner PATCH) → Data Explorer "Ownership". - Access & policy posture: per-dataset compliance combining PII masking, ownership, live DQ and observability alerts vs data contracts → Data Explorer "Access & Policies". - Lineage (lineage.py): staged source→CDC→Spark→S3→Iceberg→Trino→serving graph with live row counts and column-level PII/masking tracing → Data Explorer "Lineage". - Observability (observability.py): volume/freshness/schema-drift monitoring with alerts → Data Explorer "Observability". - Shared lake_meta.py dataset registry + bounded Trino client; fast native row-count and PK-indexed freshness so monitors stay cheap on 25-54M-row tables. - LLM context (lab_context.py) enriched with DQ scores, ownership and active alerts.
1196 lines
44 KiB
Python
1196 lines
44 KiB
Python
"""ATC Command Center API — FastAPI backend."""
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from __future__ import annotations
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import asyncio
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import json
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import os
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import time
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import uuid
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from contextlib import asynccontextmanager
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from datetime import datetime, timezone
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from typing import Any
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import httpx
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import redis.asyncio as aioredis
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from fastapi import Body, FastAPI, File, UploadFile, WebSocket, WebSocketDisconnect
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from fastapi.middleware.cors import CORSMiddleware
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from agent_terminal import (
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get_all_terminals,
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get_terminal_lines,
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init_terminals,
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make_logger,
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set_terminal_publisher,
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terminal_log,
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)
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from lab_context import collect_full_lab_context, format_context_for_agent
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from presentation import build_presentation_payload, render_presentation_html
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from presentation_upload import (
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clear_live_override,
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create_deck,
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delete_deck,
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get_asset_path,
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get_deck,
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get_live_override,
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list_decks,
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save_deck,
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save_image,
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save_live_override,
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save_upload,
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)
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from presentation_static import get_static_deck, list_static_decks
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from storage_s3 import router as storage_s3_router
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from hdfs_api import router as hdfs_router
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from pipeline_ops import router as pipeline_router
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from hadoop_analytics import router as hadoop_router
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from elasticsearch_api import router as elasticsearch_router
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from sql_console import router as sql_router
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from agent_ops import router as agent_ops_router, agent_dml_loop, etl_agent_loop, custodian_offload_loop
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from agent_activity import agent_activity_loop
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from streaming_ops import connector_autoheal_loop
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from cdc_consumer import router as cdc_router, cdc_consumer_loop
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from movements import router as movements_router
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from movements import MOVEMENT_BY_ID, trigger_and_watch
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from dataflow import router as dataflow_router
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from streaming_ops import router as streaming_router
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from spark_workbench import router as spark_workbench_router
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from pii_catalog import router as pii_router
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from trino_federated import router as federated_router
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from etl_offload import router as etl_offload_router
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from lineage import router as lineage_router
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from dq_monitor import router as dq_router
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from observability import router as observability_router
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from catalog_governance import router as governance_router
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from ssh_terminal import ssh_session
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from node_registry import NODE_IDS, NODE_AGENT, NODE_REGISTRY, is_node_id
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from node_ops import build_node_detail, probe_node, run_node_probe_task
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from approval_service import (
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APPROVAL_ACTION_TYPES,
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approval_stats,
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create_approval_request,
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decide_approval_request,
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detect_agent_proposed_action,
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detect_approval_intent,
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list_approvals,
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)
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from db import SessionLocal, db_health, init_database
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from supervisor import mirror_terminal_line, mirror_to_supervisors
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from workload import build_workload_payload
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_workload_cache: dict[str, Any] = {"ts": 0.0, "data": None}
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_presentation_cache: dict[str, Any] = {"ts": 0.0, "data": None}
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WORKLOAD_CACHE_TTL = 30.0
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PRESENTATION_CACHE_TTL = 45.0
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from pydantic import BaseModel, Field
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from sqlalchemy import Column, DateTime, String, Text, select
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from sqlalchemy.orm import DeclarativeBase
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REDIS_URL = os.getenv("REDIS_URL", "redis://redis:6379/0")
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DOCKHAND_URL = os.getenv("DOCKHAND_URL", "http://10.0.21.45:8082")
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GPU_URL = os.getenv("GPU_URL", "http://10.0.20.106:9000")
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GPU_UI_URL = os.getenv("GPU_UI_URL", GPU_URL)
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LLM_URL = os.getenv("LLM_URL", "http://10.0.20.106:8001/v1")
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LLM_MODEL = os.getenv("LLM_MODEL", "gpt-4o")
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LLM_API_KEY = os.getenv("LLM_API_KEY", "sk-local")
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LLM_TIMEOUT = float(os.getenv("LLM_TIMEOUT", "120"))
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AGENTS = [
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{
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"id": "etl-guardian",
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"name": "ETL Guardian",
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"color": "#00f0ff",
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"zone": "etl",
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"role": "Airflow, Kafka, Debezium, S3 pipeline",
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"icon": "⚡",
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"motto": "Pipelines never sleep",
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"capabilities": ["Airflow", "Kafka", "Debezium", "S3", "Connectors"],
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"suggested_prompts": [
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"How is Debezium doing?",
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"Are all Airflow DAGs healthy?",
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"Kafka connector status?",
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],
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},
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{
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"id": "lakehouse-ops",
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"name": "Lakehouse Ops",
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"color": "#ff00aa",
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"zone": "lakehouse",
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"role": "Spark, Trino, Iceberg",
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"icon": "🏔️",
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"motto": "Query the lake, trust the table",
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"capabilities": ["Spark", "Trino", "Iceberg", "Delta", "SQL"],
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"suggested_prompts": [
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"Lakehouse stack status?",
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"Is Trino reachable?",
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"How many lakehouse containers are running?",
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],
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},
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{
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"id": "data-custodian",
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"name": "Data Custodian",
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"color": "#ffaa00",
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"zone": "db",
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"role": "PostgreSQL, MySQL, Mongo, Cassandra, Neo4j",
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"icon": "🛡️",
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"motto": "Guardian of every row",
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"capabilities": ["PostgreSQL", "MySQL", "MongoDB", "Cassandra", "Neo4j"],
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"suggested_prompts": [
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"How much data is in the databases?",
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"What is in PostgreSQL sales_orders?",
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"MongoDB supplychain overview",
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],
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},
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{
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"id": "hadoop-ranger",
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"name": "Hadoop Ranger",
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"color": "#39ff14",
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"zone": "hadoop",
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"role": "HDFS, YARN cluster",
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"icon": "🌲",
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"motto": "Patrol the data forest",
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"capabilities": ["HDFS", "YARN", "NameNode", "DataNodes"],
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"suggested_prompts": [
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"Is HDFS NameNode up?",
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"Hadoop cluster status?",
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"YARN nodes healthy?",
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],
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},
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{
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"id": "infra-sentinel",
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"name": "Infra Sentinel",
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"color": "#9b72cf",
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"zone": "docker",
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"role": "Docker, Proxmox, GPU, monitoring",
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"icon": "👁️",
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"motto": "See everything, miss nothing",
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"capabilities": ["Docker", "Proxmox", "GPU", "vLLM", "Monitoring"],
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"suggested_prompts": [
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"GPU status?",
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"Which LLM model is running?",
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"Docker container overview",
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],
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},
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{
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"id": "mo-commander",
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"name": "Mo · Command",
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"color": "#4c9aed",
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"zone": "command",
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"role": "Supervisor — full event intel, ingress, approvals",
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"icon": "🎯",
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"motto": "Nothing happens without Mo knowing",
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"supervisor": True,
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"person": "mo",
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"capabilities": ["Events", "Ingress", "Approvals", "Agent dispatch", "Network IN"],
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"suggested_prompts": [
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"What happened today?",
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"What events came in?",
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"Pipeline status overview",
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],
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},
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{
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"id": "bart-commander",
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"name": "Bart · Ops",
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"color": "#3fb950",
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"zone": "command",
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"role": "Supervisor — egress, MCP comms, network OUT",
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"icon": "📡",
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"motto": "All traffic flows through Bart",
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"supervisor": True,
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"person": "bart",
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"capabilities": ["Egress", "MCP routing", "Network OUT", "GPU inference", "S3 writes"],
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"suggested_prompts": [
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"What is leaving the cluster?",
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"MCP agent communication status?",
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"Network egress overview",
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],
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},
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{
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"id": "network-watcher",
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"name": "Network Watcher",
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"color": "#58a6ff",
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"zone": "network",
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"role": "VLAN 20/21 traffic, data in & out paths",
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"icon": "🌐",
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"motto": "Every packet tells a story",
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"capabilities": ["VLAN 20", "VLAN 21", "Ingress", "Egress", "Firewall paths"],
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"suggested_prompts": [
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"Data ingress status?",
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"What leaves the cluster?",
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"Network path to S3?",
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],
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},
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{
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"id": "mcp-coordinator",
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"name": "MCP Coordinator",
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"color": "#f778ba",
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"zone": "mcp",
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"role": "MCP hub — routes all agent tool calls & comms",
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"icon": "🔀",
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"motto": "Route once, deliver everywhere",
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"capabilities": ["MCP servers", "Tool routing", "Agent relay", "WebSocket bus"],
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"suggested_prompts": [
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"Which MCP agents are active?",
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"MCP hub route status?",
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"Agent communication overview",
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],
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},
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]
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ZONES = [
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{"id": "docker", "label": "DOCKER RACK", "x": 8, "color": "#b366ff"},
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{"id": "db", "label": "DB VAULT", "x": 28, "color": "#ffaa00"},
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{"id": "lakehouse", "label": "LAKEHOUSE HUB", "x": 50, "color": "#ff00aa"},
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{"id": "hadoop", "label": "HADOOP CLUSTER", "x": 72, "color": "#39ff14"},
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{"id": "etl", "label": "ETL PIPE", "x": 92, "color": "#00f0ff"},
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]
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INTENT_KEYWORDS: dict[str, list[str]] = {
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"data-custodian": ["database", "postgres", "postgresql", "mysql", "mongo", "mongodb", "cassandra", "neo4j", "sql", "db "],
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"lakehouse-ops": ["trino", "spark", "lakehouse", "iceberg", "query", "table"],
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"hadoop-ranger": [
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"hadoop", "hdfs", "yarn", "datanode", "namenode", "replicatie", "replication",
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"rf factor", "opslag", "bestanden", "blocks", "cluster opslag", "data op",
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],
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"infra-sentinel": ["docker", "container", "vm", "proxmox", "infra", "grafana", "gpu", "vllm", "llm", "nvidia", "inference", "model"],
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"etl-guardian": ["airflow", "dag", "debezium", "kafka", "connector", "etl", "pipeline", "s3"],
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"network-watcher": ["network", "vlan", "ingress", "egress", "traffic", "packet", "firewall", "route"],
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"mcp-coordinator": ["mcp", "tool", "router", "relay", "websocket", "hub"],
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"mo-commander": ["mo", "supervisor", "events", "overzicht", "alles", "gebeurd"],
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"bart-commander": ["bart", "egress", "uitgaand", "communicatie", "mcp comm"],
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}
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class Base(DeclarativeBase):
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pass
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class FeedEntry(Base):
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__tablename__ = "feed"
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id = Column(String, primary_key=True)
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ts = Column(DateTime, default=lambda: datetime.now(timezone.utc))
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agent_id = Column(String)
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level = Column(String, default="info")
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message = Column(Text)
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class Approval(Base):
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__tablename__ = "approvals"
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id = Column(String, primary_key=True)
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ts = Column(DateTime, default=lambda: datetime.now(timezone.utc))
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agent_id = Column(String)
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action = Column(Text)
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reason = Column(Text)
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status = Column(String, default="pending")
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action_type = Column(String, default="generic.mutate")
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target = Column(Text, default="")
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payload = Column(Text, default="{}")
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decided_by = Column(String, nullable=True)
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decide_note = Column(Text, nullable=True)
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decided_at = Column(DateTime, nullable=True)
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priority = Column(String, default="normal")
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_db_info = init_database(Base)
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redis_client: aioredis.Redis | None = None
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ws_clients: set[WebSocket] = set()
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class PromptRequest(BaseModel):
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message: str = Field(min_length=1, max_length=2000)
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agent_id: str | None = None
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class NodeAskRequest(BaseModel):
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message: str = Field(min_length=1, max_length=2000)
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class ApprovalCreateRequest(BaseModel):
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agent_id: str = Field(min_length=1, max_length=64)
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action: str = Field(min_length=1, max_length=2000)
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reason: str = Field(min_length=1, max_length=2000)
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action_type: str = "generic.mutate"
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target: str = ""
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payload: dict[str, Any] | None = None
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priority: str = "normal"
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class ApprovalDecision(BaseModel):
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approved: bool
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decided_by: str = "mo-commander"
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note: str = ""
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def route_agent(message: str) -> str:
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lower = message.lower()
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# Storage/data questions default to Hadoop unless clearly about databases
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if any(w in lower for w in ("data", "opslag", "gb", "replicatie", "replication", "hdfs", "hadoop")):
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if not any(w in lower for w in ("postgres", "mysql", "mongo", "database", "sql", "neo4j", "cassandra")):
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return "hadoop-ranger"
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scores = {aid: sum(1 for kw in kws if kw in lower) for aid, kws in INTENT_KEYWORDS.items()}
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best = max(scores, key=scores.get)
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if scores[best] == 0:
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return "infra-sentinel"
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return best
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async def gather_agent_context(
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agent_id: str,
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status: dict[str, Any],
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log: Any | None = None,
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) -> str:
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"""Full lab snapshot for vLLM — all domains, agent's primary domain highlighted."""
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snapshot = await collect_full_lab_context(gpu_data=status.get("gpu"), log=log)
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snapshot["domains_summary"] = status.get("domains", {})
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ctx = format_context_for_agent(agent_id, snapshot)
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agent_lines = ["", "=== AGENTS & SUPERVISORS ==="]
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for a in AGENTS:
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sup = " [supervisor]" if a.get("supervisor") else ""
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agent_lines.append(f" - {a['name']} ({a['id']}){sup}: {a['role']}")
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ctx = ctx + "\n".join(agent_lines)
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try:
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from platform_context import build_llm_addendum
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ctx = ctx + "\n\n" + build_llm_addendum()
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except Exception:
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pass
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if log:
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await log("info", "fetch", f"▸ Context assembled: {len(ctx)} chars for LLM")
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return ctx
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async def ask_llm(
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agent_id: str,
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message: str,
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context: str,
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log: Any | None = None,
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) -> str | None:
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agent = next(a for a in AGENTS if a["id"] == agent_id)
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system = f"""You are {agent['name']}, an autonomous ops agent in the Dell ATC data lab.
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Specialization: {agent['role']}.
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Motto: {agent.get('motto', '')}.
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You respond on behalf of your domain but have visibility into the FULL lab stack: Docker, databases, lakehouse (Trino/Spark/Kafka Connect), ETL (Airflow/Kafka), Hadoop HDFS, and GPU/vLLM.
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Rules:
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- Always respond in English.
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- You have full visibility into the entire cluster: all VMs, zones, connectors, GPU, Hadoop, ObjectScale and Command Center.
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- Use ONLY the live data below — do not invent hosts, ports, numbers or connector names.
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- Use exact container/connector names from the data (e.g. mysql-hr-connector, not "Debezium").
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- If something is DOWN or 0 GB, say so honestly.
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- Respect the data masking policy: NEVER reveal, guess or reconstruct raw values of MASKED columns (they arrive as the token 🔒 MASKED). You MUST still answer helpfully — confirm the column is masked for privacy/governance, explain why, and you may use non-sensitive aggregates/counts over it.
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- You are fully aware of all latest platform changes via the section PLATFORM CAPABILITIES & RECENT CHANGES below; use it to answer questions about recent changes, the Spark Workbench, the Hadoop pipeline, the Data Flow pulse switch and the autonomous agents (DML, ETL, Custodian Hadoop offload).
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- Be concise and helpful (max ~10 sentences); bullet lists are fine when they aid clarity.
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--- LIVE LAB DATA (primary domain first, then full stack) ---
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{context}
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"""
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if log:
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await log("info", "llm", f"▸ Querying vLLM model={LLM_MODEL}")
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await log("cmd", "llm", f"$ POST {LLM_URL.rstrip('/')}/chat/completions")
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await log("info", "llm", f" user: {message[:160]}{'…' if len(message) > 160 else ''}")
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try:
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async with httpx.AsyncClient(timeout=LLM_TIMEOUT) as client:
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t0 = time.monotonic()
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r = await client.post(
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f"{LLM_URL.rstrip('/')}/chat/completions",
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headers={
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"Authorization": f"Bearer {LLM_API_KEY}",
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"Content-Type": "application/json",
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},
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json={
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"model": LLM_MODEL,
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"messages": [
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{"role": "system", "content": system},
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{"role": "user", "content": message},
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],
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"max_tokens": 800,
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"temperature": 0.25,
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},
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)
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r.raise_for_status()
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content = r.json()["choices"][0]["message"]["content"].strip()
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ms = int((time.monotonic() - t0) * 1000)
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if content and content.strip("!"):
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if log:
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await log("ok", "llm", f"← vLLM response {len(content)} chars ({ms}ms)")
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preview = content.replace("\n", " ")[:180]
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await log("info", "llm", f" » {preview}{'…' if len(content) > 180 else ''}")
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return content
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if log:
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await log("warn", "llm", f"← Empty or invalid LLM output ({ms}ms)")
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except Exception as exc:
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if log:
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await log("err", "llm", f"✗ vLLM error: {exc}")
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return None
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def fallback_answer(agent_id: str, context: str) -> str:
|
|
agent_name = next(a["name"] for a in AGENTS if a["id"] == agent_id)
|
|
return f"**{agent_name}** (offline LLM — ruwe data):\n\n{context}"
|
|
|
|
|
|
async def publish_event(event: dict[str, Any]) -> None:
|
|
payload = json.dumps(event, default=str)
|
|
if redis_client:
|
|
await redis_client.publish("ops", payload)
|
|
dead = []
|
|
for ws in ws_clients:
|
|
try:
|
|
await ws.send_text(payload)
|
|
except Exception:
|
|
dead.append(ws)
|
|
for ws in dead:
|
|
ws_clients.discard(ws)
|
|
|
|
et = event.get("type")
|
|
if et == "feed":
|
|
entry = event.get("entry") or {}
|
|
await mirror_to_supervisors(
|
|
entry.get("agent_id", "?"),
|
|
entry.get("message", ""),
|
|
level=entry.get("level", "info"),
|
|
)
|
|
elif et == "terminal":
|
|
await mirror_terminal_line(event.get("line") or {})
|
|
elif et in ("agent_dispatch", "agent_fetch", "agent_return"):
|
|
aid = event.get("agent_id", "?")
|
|
zone = event.get("zone", "")
|
|
await mirror_to_supervisors(aid, f"{et} → zone {zone}", level="info", phase="dispatch")
|
|
|
|
|
|
def add_feed(agent_id: str, message: str, level: str = "info") -> dict:
|
|
entry_id = str(uuid.uuid4())[:8]
|
|
with SessionLocal() as db:
|
|
row = FeedEntry(id=entry_id, agent_id=agent_id, message=message, level=level)
|
|
db.add(row)
|
|
db.commit()
|
|
return {
|
|
"id": entry_id,
|
|
"ts": datetime.now(timezone.utc).isoformat(),
|
|
"agent_id": agent_id,
|
|
"message": message,
|
|
"level": level,
|
|
}
|
|
|
|
|
|
async def dockhand_env_containers(env_id: int) -> list[dict]:
|
|
try:
|
|
async with httpx.AsyncClient(timeout=8.0) as client:
|
|
r = await client.get(f"{DOCKHAND_URL}/api/containers", params={"env": env_id})
|
|
r.raise_for_status()
|
|
return r.json()
|
|
except Exception:
|
|
return []
|
|
|
|
|
|
async def probe_url(url: str) -> bool:
|
|
try:
|
|
async with httpx.AsyncClient(timeout=4.0, verify=False) as client:
|
|
r = await client.get(url)
|
|
return r.status_code < 500
|
|
except Exception:
|
|
return False
|
|
|
|
|
|
async def collect_gpu() -> dict[str, Any]:
|
|
host = GPU_URL.replace("http://", "").replace("https://", "").split("/")[0]
|
|
base = {"ok": False, "host": host, "ui_url": GPU_UI_URL}
|
|
|
|
try:
|
|
async with httpx.AsyncClient(timeout=6.0) as client:
|
|
metrics_r, model_r, integration_r = await asyncio.gather(
|
|
client.get(f"{GPU_URL}/api/gpu/metrics"),
|
|
client.get(f"{GPU_URL}/api/active-model"),
|
|
client.get(f"{GPU_URL}/api/integration"),
|
|
return_exceptions=True,
|
|
)
|
|
|
|
gpus: list[dict[str, Any]] = []
|
|
if isinstance(metrics_r, httpx.Response) and metrics_r.status_code == 200:
|
|
current = metrics_r.json().get("current", {})
|
|
gpus = [
|
|
{
|
|
"index": g["index"],
|
|
"name": g["name"],
|
|
"util_gpu": g.get("util_gpu", 0),
|
|
"memory_used_mib": g.get("memory_used_mib", 0),
|
|
"memory_total_mib": g.get("memory_total_mib", 0),
|
|
"temperature_c": g.get("temperature_c", 0),
|
|
"power_w": g.get("power_w", 0),
|
|
}
|
|
for g in current.get("gpus", [])
|
|
]
|
|
|
|
active_model = None
|
|
inference_active = False
|
|
vllm_url = None
|
|
|
|
if isinstance(model_r, httpx.Response) and model_r.status_code == 200:
|
|
model_data = model_r.json()
|
|
active_model = model_data.get("name")
|
|
inference_active = bool(model_data.get("inference_active"))
|
|
vllm_url = model_data.get("base_url")
|
|
|
|
if isinstance(integration_r, httpx.Response) and integration_r.status_code == 200:
|
|
integ = integration_r.json()
|
|
if not active_model:
|
|
active_model = integ.get("active_name")
|
|
if not inference_active:
|
|
inference_active = bool(integ.get("inference_active"))
|
|
if not vllm_url:
|
|
vllm_url = integ.get("recommended_base_url")
|
|
|
|
return {
|
|
**base,
|
|
"ok": len(gpus) > 0 or inference_active,
|
|
"inference_active": inference_active,
|
|
"active_model": active_model,
|
|
"vllm_url": vllm_url,
|
|
"gpu_count": len(gpus),
|
|
"gpus": gpus,
|
|
}
|
|
except Exception as exc:
|
|
return {**base, "error": str(exc)}
|
|
|
|
|
|
async def collect_status() -> dict[str, Any]:
|
|
db_containers = await dockhand_env_containers(5)
|
|
db_running = sum(1 for c in db_containers if c.get("state") == "running")
|
|
db_total = len(db_containers) or 6
|
|
|
|
lake_containers = await dockhand_env_containers(9)
|
|
lake_running = sum(1 for c in lake_containers if c.get("state") == "running")
|
|
lake_total = len(lake_containers) or 6
|
|
|
|
docker_containers = await dockhand_env_containers(1)
|
|
docker_running = sum(1 for c in docker_containers if c.get("state") == "running")
|
|
|
|
hdfs_ok = await probe_url("http://10.0.21.61:9870")
|
|
kafka_ok = await probe_url("http://10.0.21.36:9000")
|
|
airflow_ok = await probe_url("http://10.0.21.55:8080")
|
|
|
|
def level(running: int, total: int) -> str:
|
|
if total == 0:
|
|
return "unknown"
|
|
ratio = running / total
|
|
if ratio >= 0.9:
|
|
return "ok"
|
|
if ratio >= 0.5:
|
|
return "warn"
|
|
return "down"
|
|
|
|
gpu = await collect_gpu()
|
|
gpu_level = "ok" if gpu.get("ok") and gpu.get("inference_active") else ("warn" if gpu.get("ok") else "down")
|
|
gpu_label = gpu.get("active_model") or (f"{gpu.get('gpu_count', 0)} GPUs" if gpu.get("ok") else "offline")
|
|
|
|
return {
|
|
"ts": datetime.now(timezone.utc).isoformat(),
|
|
"domains": {
|
|
"docker": {"level": "ok" if docker_running >= 5 else "warn", "label": f"{docker_running} containers", "running": docker_running},
|
|
"databases": {"level": level(db_running, db_total), "label": f"{db_running}/{db_total} up", "running": db_running, "total": db_total},
|
|
"lakehouse": {"level": level(lake_running, lake_total), "label": f"{lake_running}/{lake_total} up", "running": lake_running, "total": lake_total},
|
|
"hadoop": {"level": "ok" if hdfs_ok else "warn", "label": "NN up" if hdfs_ok else "NN check"},
|
|
"etl": {"level": "ok" if kafka_ok and airflow_ok else "warn", "label": "Kafka+Airflow"},
|
|
"gpu": {"level": gpu_level, "label": gpu_label},
|
|
},
|
|
"gpu": gpu,
|
|
"kafka_ok": kafka_ok,
|
|
"airflow_ok": airflow_ok,
|
|
"hdfs_ok": hdfs_ok,
|
|
}
|
|
|
|
|
|
|
|
|
|
async def _run_agent_task_safe(agent_id: str, message: str, prompt_id: str) -> None:
|
|
try:
|
|
await run_agent_task(agent_id, message, prompt_id)
|
|
except Exception as exc:
|
|
agent_name = next((a["name"] for a in AGENTS if a["id"] == agent_id), agent_id)
|
|
err = f"Sorry — {agent_name} could not complete your request: {exc}"
|
|
await terminal_log(agent_id, f"[{prompt_id}] ✗ Error: {exc}", level="err", phase="error", prompt_id=prompt_id)
|
|
feed = add_feed(agent_id, f"{agent_name} failed: {str(exc)[:80]}", "err")
|
|
await publish_event({"type": "feed", "entry": feed})
|
|
await publish_event({
|
|
"type": "prompt_result",
|
|
"prompt_id": prompt_id,
|
|
"agent_id": agent_id,
|
|
"answer": err,
|
|
})
|
|
|
|
|
|
async def run_agent_task(agent_id: str, message: str, prompt_id: str) -> str:
|
|
zone = next(a["zone"] for a in AGENTS if a["id"] == agent_id)
|
|
agent_name = next(a["name"] for a in AGENTS if a["id"] == agent_id)
|
|
log = make_logger(agent_id, prompt_id)
|
|
approval_created = False
|
|
|
|
intent = detect_approval_intent(message)
|
|
if intent:
|
|
with SessionLocal() as db:
|
|
await create_approval_request(
|
|
db=db,
|
|
ApprovalModel=Approval,
|
|
agent_id=agent_id,
|
|
action=intent["action"],
|
|
reason=intent["reason"],
|
|
action_type=intent["action_type"],
|
|
terminal_log=terminal_log,
|
|
mirror_supervisors=mirror_to_supervisors,
|
|
publish=publish_event,
|
|
add_feed=add_feed,
|
|
)
|
|
approval_created = True
|
|
await terminal_log(
|
|
agent_id,
|
|
f"[{prompt_id}] Mutating request detected — approval queued for Mo & Bart",
|
|
level="warn",
|
|
phase="approval",
|
|
prompt_id=prompt_id,
|
|
)
|
|
|
|
await terminal_log(
|
|
agent_id,
|
|
f"[{prompt_id}] ▶ Mission accepted: {message}",
|
|
level="info",
|
|
phase="dispatch",
|
|
prompt_id=prompt_id,
|
|
)
|
|
await publish_event({"type": "agent_dispatch", "agent_id": agent_id, "zone": zone, "prompt_id": prompt_id})
|
|
await asyncio.sleep(0.4)
|
|
await terminal_log(agent_id, f"[{prompt_id}] Walking to zone: {zone}", level="info", phase="dispatch", prompt_id=prompt_id)
|
|
await publish_event({"type": "agent_fetch", "agent_id": agent_id, "zone": zone, "prompt_id": prompt_id})
|
|
|
|
await log("info", "fetch", f"[{prompt_id}] Collecting live lab metrics…")
|
|
status = await collect_status()
|
|
context = await gather_agent_context(agent_id, status, log=log)
|
|
|
|
answer = await ask_llm(agent_id, message, context, log=log)
|
|
if not answer:
|
|
await log("warn", "llm", "LLM fallback — returning raw context")
|
|
answer = fallback_answer(agent_id, context)
|
|
|
|
if not approval_created:
|
|
proposed = detect_agent_proposed_action(answer, message)
|
|
if proposed:
|
|
with SessionLocal() as db:
|
|
await create_approval_request(
|
|
db=db,
|
|
ApprovalModel=Approval,
|
|
agent_id=agent_id,
|
|
action=proposed["action"],
|
|
reason=proposed["reason"],
|
|
action_type=proposed["action_type"],
|
|
target=proposed.get("target", ""),
|
|
terminal_log=terminal_log,
|
|
mirror_supervisors=mirror_to_supervisors,
|
|
publish=publish_event,
|
|
add_feed=add_feed,
|
|
)
|
|
approval_created = True
|
|
answer = (
|
|
f"{answer}\n\n⏸ **Approval required** — this action is in the Approval Inbox. "
|
|
f"Mo & Bart have been notified and must approve before we execute."
|
|
)
|
|
|
|
await asyncio.sleep(0.3)
|
|
await terminal_log(agent_id, f"[{prompt_id}] ✓ Mission complete", level="ok", phase="done", prompt_id=prompt_id)
|
|
await publish_event({"type": "agent_return", "agent_id": agent_id, "zone": zone, "prompt_id": prompt_id})
|
|
feed = add_feed(agent_id, f"{agent_name} completed a response (see Comms)", "info")
|
|
await publish_event({"type": "feed", "entry": feed})
|
|
await publish_event({"type": "prompt_result", "prompt_id": prompt_id, "agent_id": agent_id, "answer": answer})
|
|
return answer
|
|
|
|
|
|
async def heartbeat_loop() -> None:
|
|
while True:
|
|
try:
|
|
status = await collect_status()
|
|
workload = await collect_workload()
|
|
await publish_event({"type": "status", "data": status})
|
|
await publish_event({"type": "workload", "data": workload})
|
|
for domain, info in status["domains"].items():
|
|
if info["level"] == "down":
|
|
agent = "data-custodian" if domain == "databases" else "infra-sentinel"
|
|
feed = add_feed(agent, f"Alert: {domain} is DOWN ({info['label']})", "warn")
|
|
await publish_event({"type": "feed", "entry": feed})
|
|
except Exception as exc:
|
|
await publish_event({"type": "error", "message": str(exc)})
|
|
await asyncio.sleep(60)
|
|
|
|
|
|
@asynccontextmanager
|
|
async def lifespan(app: FastAPI):
|
|
global redis_client
|
|
redis_client = aioredis.from_url(REDIS_URL, decode_responses=True)
|
|
set_terminal_publisher(publish_event)
|
|
init_terminals([a["id"] for a in AGENTS] + NODE_IDS)
|
|
for a in AGENTS:
|
|
await terminal_log(a["id"], f"{a['name']} terminal online — awaiting missions", level="info", phase="boot")
|
|
for nid in NODE_IDS:
|
|
if nid not in NODE_REGISTRY:
|
|
continue
|
|
meta = NODE_REGISTRY[nid]
|
|
await terminal_log(nid, f"{meta['label']} shell ready — click node to connect", level="info", phase="boot")
|
|
task = asyncio.create_task(heartbeat_loop())
|
|
dml_task = asyncio.create_task(agent_dml_loop())
|
|
cdc_task = asyncio.create_task(cdc_consumer_loop())
|
|
etl_task = asyncio.create_task(etl_agent_loop())
|
|
cust_task = asyncio.create_task(custodian_offload_loop())
|
|
act_task = asyncio.create_task(agent_activity_loop())
|
|
heal_task = asyncio.create_task(connector_autoheal_loop())
|
|
add_feed("infra-sentinel", "ATC Command Center API online", "info")
|
|
yield
|
|
task.cancel()
|
|
dml_task.cancel()
|
|
cdc_task.cancel()
|
|
etl_task.cancel()
|
|
cust_task.cancel()
|
|
act_task.cancel()
|
|
heal_task.cancel()
|
|
if redis_client:
|
|
await redis_client.close()
|
|
|
|
|
|
app = FastAPI(title="ATC Command Center API", lifespan=lifespan)
|
|
app.include_router(storage_s3_router)
|
|
app.include_router(hdfs_router)
|
|
app.include_router(pipeline_router)
|
|
app.include_router(hadoop_router)
|
|
app.include_router(elasticsearch_router)
|
|
app.include_router(sql_router)
|
|
app.include_router(agent_ops_router)
|
|
app.include_router(cdc_router)
|
|
app.include_router(movements_router)
|
|
app.include_router(dataflow_router)
|
|
app.include_router(streaming_router)
|
|
app.include_router(spark_workbench_router)
|
|
app.include_router(pii_router)
|
|
app.include_router(federated_router)
|
|
app.include_router(etl_offload_router)
|
|
app.include_router(lineage_router)
|
|
app.include_router(dq_router)
|
|
app.include_router(observability_router)
|
|
app.include_router(governance_router)
|
|
app.add_middleware(
|
|
CORSMiddleware,
|
|
allow_origins=["*"],
|
|
allow_credentials=True,
|
|
allow_methods=["*"],
|
|
allow_headers=["*"],
|
|
)
|
|
|
|
|
|
@app.get("/api/health")
|
|
async def health():
|
|
llm_ok = False
|
|
try:
|
|
async with httpx.AsyncClient(timeout=4.0) as client:
|
|
r = await client.get(f"{LLM_URL.rstrip('/')}/models", headers={"Authorization": f"Bearer {LLM_API_KEY}"})
|
|
llm_ok = r.status_code == 200
|
|
except Exception:
|
|
pass
|
|
return {
|
|
"ok": True,
|
|
"ts": datetime.now(timezone.utc).isoformat(),
|
|
"llm_url": LLM_URL,
|
|
"llm_ok": llm_ok,
|
|
"llm_model": LLM_MODEL,
|
|
"database": db_health(),
|
|
"db_init": _db_info,
|
|
}
|
|
|
|
|
|
async def collect_workload(*, fast: bool = True, use_cache: bool = True) -> dict[str, Any]:
|
|
import time as _time
|
|
now = _time.time()
|
|
if use_cache and _workload_cache.get("data") and now - float(_workload_cache.get("ts") or 0) < WORKLOAD_CACHE_TTL:
|
|
return _workload_cache["data"]
|
|
gpu = await collect_gpu()
|
|
snap = await collect_full_lab_context(gpu_data=gpu, include_inventory=not fast)
|
|
payload = build_workload_payload(snap)
|
|
_workload_cache["ts"] = now
|
|
_workload_cache["data"] = payload
|
|
return payload
|
|
|
|
|
|
|
|
|
|
async def get_presentation_data(*, use_cache: bool = True) -> dict[str, Any]:
|
|
import time as _time
|
|
now = _time.time()
|
|
if use_cache and _presentation_cache.get("data") and now - float(_presentation_cache.get("ts") or 0) < PRESENTATION_CACHE_TTL:
|
|
return _presentation_cache["data"]
|
|
gpu = await collect_gpu()
|
|
snap = await collect_full_lab_context(gpu_data=gpu, include_inventory=False)
|
|
data = build_presentation_payload(snap)
|
|
override = get_live_override()
|
|
if override and override.get("slides"):
|
|
data["title"] = override.get("title") or data.get("title")
|
|
data["subtitle"] = override.get("subtitle") or data.get("subtitle", "")
|
|
data["slides"] = override["slides"]
|
|
data["slide_count"] = len(override["slides"])
|
|
data["edited"] = True
|
|
data["override_ts"] = override.get("ts")
|
|
else:
|
|
data["edited"] = False
|
|
data["source"] = "live"
|
|
data["id"] = "live"
|
|
_presentation_cache["ts"] = now
|
|
_presentation_cache["data"] = data
|
|
return data
|
|
|
|
|
|
@app.get("/api/presentation")
|
|
async def get_presentation():
|
|
return await get_presentation_data()
|
|
|
|
|
|
@app.get("/api/presentation/html")
|
|
async def get_presentation_html():
|
|
from fastapi.responses import HTMLResponse
|
|
payload = await get_presentation_data()
|
|
return HTMLResponse(render_presentation_html(payload))
|
|
|
|
|
|
@app.get("/api/presentation/decks")
|
|
async def get_presentation_decks():
|
|
return {"live": True, "builtin": list_static_decks(), "uploaded": list_decks()}
|
|
|
|
|
|
@app.get("/api/presentation/decks/{deck_id}")
|
|
async def get_presentation_deck(deck_id: str):
|
|
if deck_id == "live":
|
|
return await get_presentation_data()
|
|
deck = get_static_deck(deck_id) or get_deck(deck_id)
|
|
if not deck:
|
|
return {"error": "deck not found"}
|
|
return deck
|
|
|
|
|
|
@app.get("/api/presentation/decks/{deck_id}/html")
|
|
async def get_presentation_deck_html(deck_id: str):
|
|
from fastapi.responses import HTMLResponse
|
|
if deck_id == "live":
|
|
payload = await get_presentation_data()
|
|
else:
|
|
payload = get_static_deck(deck_id) or get_deck(deck_id)
|
|
if not payload:
|
|
return HTMLResponse("<h1>Deck not found</h1>", status_code=404)
|
|
return HTMLResponse(render_presentation_html(payload))
|
|
|
|
|
|
@app.post("/api/presentation/upload")
|
|
async def upload_presentation(file: UploadFile = File(...)):
|
|
content = await file.read()
|
|
if len(content) > 50 * 1024 * 1024:
|
|
return {"error": "file too large (max 50MB)"}
|
|
deck = await save_upload(file.filename or "upload.pptx", content)
|
|
return {"ok": True, "deck": deck}
|
|
|
|
|
|
@app.post("/api/presentation/decks/new")
|
|
async def create_presentation_deck(body: dict[str, Any] | None = Body(default=None)):
|
|
body = body or {}
|
|
title = str(body.get("title") or "Untitled deck")
|
|
seed_id = body.get("from")
|
|
slides = None
|
|
if seed_id:
|
|
src = get_static_deck(seed_id) or get_deck(seed_id)
|
|
if seed_id == "live":
|
|
src = await get_presentation_data()
|
|
if src and src.get("slides"):
|
|
slides = [
|
|
{
|
|
"id": s.get("id") or f"slide-{i}",
|
|
"title": s.get("title") or f"Slide {i}",
|
|
"subtitle": s.get("subtitle") or "",
|
|
"bullets": list(s.get("bullets") or []),
|
|
"image": s.get("image") or "",
|
|
"kind": s.get("kind") or "narrative",
|
|
**({"animation": s["animation"]} if s.get("animation") else {}),
|
|
}
|
|
for i, s in enumerate(src["slides"], start=1)
|
|
]
|
|
if not title or title == "Untitled deck":
|
|
title = f"{src.get('title', 'Deck')} (copy)"
|
|
deck = create_deck(title, slides)
|
|
return {"ok": True, "deck": deck}
|
|
|
|
|
|
@app.put("/api/presentation/decks/{deck_id}")
|
|
async def update_presentation_deck(deck_id: str, body: dict[str, Any] = Body(...)):
|
|
if deck_id == "live":
|
|
save_live_override(body)
|
|
_presentation_cache["ts"] = 0
|
|
_presentation_cache["data"] = None
|
|
payload = await get_presentation_data(use_cache=False)
|
|
return {"ok": True, "deck": payload}
|
|
deck = save_deck(deck_id, body)
|
|
if not deck:
|
|
return {"error": "deck not found or not editable"}
|
|
return {"ok": True, "deck": deck}
|
|
|
|
|
|
@app.post("/api/presentation/live/reset")
|
|
async def reset_live_presentation():
|
|
clear_live_override()
|
|
_presentation_cache["ts"] = 0
|
|
_presentation_cache["data"] = None
|
|
payload = await get_presentation_data(use_cache=False)
|
|
return {"ok": True, "deck": payload}
|
|
|
|
|
|
@app.delete("/api/presentation/decks/{deck_id}")
|
|
async def remove_presentation_deck(deck_id: str):
|
|
return {"ok": delete_deck(deck_id)}
|
|
|
|
|
|
@app.post("/api/presentation/decks/{deck_id}/image")
|
|
async def upload_presentation_image(deck_id: str, file: UploadFile = File(...)):
|
|
content = await file.read()
|
|
if len(content) > 12 * 1024 * 1024:
|
|
return {"error": "image too large (max 12MB)"}
|
|
result = save_image(deck_id, file.filename or "image.png", content)
|
|
if not result:
|
|
return {"error": "deck not found"}
|
|
return {"ok": True, **result}
|
|
|
|
|
|
@app.get("/api/presentation/decks/{deck_id}/assets/{name}")
|
|
async def get_presentation_asset(deck_id: str, name: str):
|
|
from fastapi.responses import FileResponse, Response
|
|
path = get_asset_path(deck_id, name)
|
|
if not path:
|
|
return Response(status_code=404)
|
|
return FileResponse(str(path))
|
|
|
|
@app.get("/api/workload")
|
|
async def get_workload(fast: bool = True):
|
|
return await collect_workload(fast=fast, use_cache=True)
|
|
|
|
|
|
@app.get("/api/status")
|
|
async def get_status():
|
|
return await collect_status()
|
|
|
|
|
|
@app.get("/api/gpu")
|
|
async def get_gpu():
|
|
return await collect_gpu()
|
|
|
|
|
|
def agent_stats() -> dict[str, dict[str, Any]]:
|
|
stats: dict[str, dict[str, Any]] = {a["id"]: {"tasks": 0, "last_active": None, "alerts": 0} for a in AGENTS}
|
|
with SessionLocal() as db:
|
|
rows = db.execute(select(FeedEntry).order_by(FeedEntry.ts.desc()).limit(200)).scalars().all()
|
|
for r in rows:
|
|
aid = r.agent_id
|
|
if aid not in stats:
|
|
continue
|
|
stats[aid]["tasks"] += 1
|
|
if r.level == "warn":
|
|
stats[aid]["alerts"] += 1
|
|
if stats[aid]["last_active"] is None and r.ts:
|
|
stats[aid]["last_active"] = r.ts.isoformat()
|
|
return stats
|
|
|
|
|
|
@app.get("/api/agents")
|
|
async def get_agents():
|
|
stats = agent_stats()
|
|
enriched = [{**a, "stats": stats.get(a["id"], {})} for a in AGENTS]
|
|
return {"agents": enriched, "zones": ZONES}
|
|
|
|
|
|
@app.get("/api/terminals")
|
|
async def get_terminals(limit: int = 200):
|
|
return {"terminals": get_all_terminals(limit)}
|
|
|
|
|
|
@app.get("/api/terminals/{subject_id}")
|
|
async def get_subject_terminal(subject_id: str, limit: int = 200):
|
|
valid_agents = {a["id"] for a in AGENTS}
|
|
if subject_id not in valid_agents and not is_node_id(subject_id):
|
|
return {"error": "unknown subject"}
|
|
return {"agent_id": subject_id, "lines": get_terminal_lines(subject_id, limit)}
|
|
|
|
|
|
@app.get("/api/nodes")
|
|
async def list_nodes():
|
|
workload = await collect_workload()
|
|
nodes = workload.get("topology", {}).get("nodes", [])
|
|
return {"nodes": [{"id": n["id"], "label": n["label"], "ip": n["ip"], "level": n["level"]} for n in nodes]}
|
|
|
|
|
|
@app.get("/api/nodes/{node_id}")
|
|
async def get_node(node_id: str):
|
|
if not is_node_id(node_id):
|
|
return {"error": "unknown node"}
|
|
workload = await collect_workload()
|
|
wn = next((n for n in workload.get("topology", {}).get("nodes", []) if n["id"] == node_id), None)
|
|
gpu = await collect_gpu()
|
|
snap = await collect_full_lab_context(gpu_data=gpu)
|
|
return build_node_detail(node_id, snap, wn)
|
|
|
|
|
|
@app.post("/api/nodes/{node_id}/probe")
|
|
async def post_node_probe(node_id: str):
|
|
if not is_node_id(node_id):
|
|
return {"error": "unknown node"}
|
|
asyncio.create_task(run_node_probe_task(node_id))
|
|
return {"ok": True, "node_id": node_id, "status": "probing"}
|
|
|
|
|
|
async def run_node_ask_task(node_id: str, message: str) -> None:
|
|
agent_id = NODE_AGENT.get(node_id, "infra-sentinel")
|
|
meta = NODE_REGISTRY[node_id]
|
|
await terminal_log(node_id, f"▶ Query: {message}", level="info", phase="ask")
|
|
await terminal_log(node_id, f"→ Routing to agent {agent_id}", level="info", phase="ask")
|
|
log = make_logger(node_id)
|
|
status = await collect_status()
|
|
context = await gather_agent_context(agent_id, status, log=log)
|
|
node_ctx = f"\n\n=== FOCUSED NODE: {meta['label']} ({meta['ip']}) ===\n{meta.get('description', '')}\n"
|
|
answer = await ask_llm(agent_id, message, context + node_ctx, log=log)
|
|
if not answer:
|
|
answer = fallback_answer(agent_id, context)
|
|
await terminal_log(node_id, f"◆ {answer}", level="llm", phase="answer")
|
|
await publish_event({"type": "node_ask_result", "node_id": node_id, "agent_id": agent_id, "answer": answer})
|
|
|
|
|
|
@app.post("/api/nodes/{node_id}/ask")
|
|
async def post_node_ask(node_id: str, body: NodeAskRequest):
|
|
if not is_node_id(node_id):
|
|
return {"error": "unknown node"}
|
|
asyncio.create_task(run_node_ask_task(node_id, body.message))
|
|
return {"ok": True, "node_id": node_id, "agent_id": NODE_AGENT.get(node_id), "status": "processing"}
|
|
|
|
|
|
@app.get("/api/feed")
|
|
async def get_feed(limit: int = 50):
|
|
with SessionLocal() as db:
|
|
rows = db.execute(select(FeedEntry).order_by(FeedEntry.ts.desc()).limit(limit)).scalars().all()
|
|
return {
|
|
"entries": [
|
|
{
|
|
"id": r.id,
|
|
"ts": r.ts.isoformat() if r.ts else None,
|
|
"agent_id": r.agent_id,
|
|
"message": r.message,
|
|
"level": r.level,
|
|
}
|
|
for r in rows
|
|
]
|
|
}
|
|
|
|
|
|
@app.get("/api/approvals")
|
|
async def get_approvals(status: str = "pending", limit: int = 100):
|
|
with SessionLocal() as db:
|
|
items = list_approvals(db, Approval, status=status, limit=limit)
|
|
stats = approval_stats(db, Approval)
|
|
return {"approvals": items, "stats": stats, "action_types": APPROVAL_ACTION_TYPES}
|
|
|
|
|
|
@app.get("/api/approvals/stats")
|
|
async def get_approval_stats():
|
|
with SessionLocal() as db:
|
|
return approval_stats(db, Approval)
|
|
|
|
|
|
@app.post("/api/approvals")
|
|
async def post_approval(body: ApprovalCreateRequest):
|
|
valid_ids = {a["id"] for a in AGENTS}
|
|
if body.agent_id not in valid_ids:
|
|
return {"error": "unknown agent_id"}
|
|
if body.action_type not in APPROVAL_ACTION_TYPES:
|
|
body.action_type = "generic.mutate"
|
|
with SessionLocal() as db:
|
|
item = await create_approval_request(
|
|
db=db,
|
|
ApprovalModel=Approval,
|
|
agent_id=body.agent_id,
|
|
action=body.action,
|
|
reason=body.reason,
|
|
action_type=body.action_type,
|
|
target=body.target,
|
|
payload=body.payload,
|
|
priority=body.priority,
|
|
terminal_log=terminal_log,
|
|
mirror_supervisors=mirror_to_supervisors,
|
|
publish=publish_event,
|
|
add_feed=add_feed,
|
|
)
|
|
return {"ok": True, "approval": item}
|
|
|
|
|
|
@app.post("/api/approvals/{approval_id}/decide")
|
|
async def decide_approval(approval_id: str, body: ApprovalDecision):
|
|
valid_supervisors = {"mo-commander", "bart-commander"}
|
|
decided_by = body.decided_by if body.decided_by in valid_supervisors else "mo-commander"
|
|
with SessionLocal() as db:
|
|
item = await decide_approval_request(
|
|
db=db,
|
|
ApprovalModel=Approval,
|
|
approval_id=approval_id,
|
|
approved=body.approved,
|
|
decided_by=decided_by,
|
|
note=body.note,
|
|
terminal_log=terminal_log,
|
|
publish=publish_event,
|
|
add_feed=add_feed,
|
|
)
|
|
if not item:
|
|
return {"error": "not found"}
|
|
|
|
# Approval-gated executor: run the underlying action once a supervisor approves it.
|
|
if item.get("status") == "approved":
|
|
payload = item.get("payload") or {}
|
|
if isinstance(payload, dict) and payload.get("executor") == "movement":
|
|
mid = payload.get("movement_id")
|
|
if mid in MOVEMENT_BY_ID:
|
|
add_feed("etl-guardian", f"Approval {approval_id} granted — executing movement '{mid}'", "info")
|
|
asyncio.create_task(trigger_and_watch(mid, payload.get("conf")))
|
|
|
|
return {"ok": True, "approval": item}
|
|
|
|
|
|
@app.post("/api/prompt")
|
|
async def post_prompt(body: PromptRequest):
|
|
prompt_id = str(uuid.uuid4())[:8]
|
|
valid_ids = {a["id"] for a in AGENTS}
|
|
agent_id = body.agent_id if body.agent_id in valid_ids else route_agent(body.message)
|
|
add_feed(agent_id, f"Prompt received: {body.message}", "info")
|
|
asyncio.create_task(_run_agent_task_safe(agent_id, body.message, prompt_id))
|
|
return {"prompt_id": prompt_id, "agent_id": agent_id, "status": "dispatched"}
|
|
|
|
|
|
@app.websocket("/api/ws/ssh")
|
|
async def ws_ssh(websocket: WebSocket):
|
|
await ssh_session(websocket)
|
|
|
|
|
|
@app.websocket("/api/ws/ops")
|
|
async def ws_ops(websocket: WebSocket):
|
|
await websocket.accept()
|
|
ws_clients.add(websocket)
|
|
try:
|
|
status = await collect_status()
|
|
workload = await collect_workload()
|
|
await websocket.send_text(json.dumps({"type": "status", "data": status}, default=str))
|
|
await websocket.send_text(json.dumps({"type": "workload", "data": workload}, default=str))
|
|
await websocket.send_text(json.dumps({
|
|
"type": "terminal_history",
|
|
"terminals": get_all_terminals(150),
|
|
}, default=str))
|
|
while True:
|
|
await websocket.receive_text()
|
|
except WebSocketDisconnect:
|
|
pass
|
|
finally:
|
|
ws_clients.discard(websocket)
|