toki/iop
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions
iop/configs/edge.yaml
toki 445716cc3e feat(edge): runtime config refresh mechanism implementation 
- Add configrefresh package for config classification, request handling
- Add bootstrap refresh_admin.go for admin-level config refresh
- Add edgevalidate package for config validation
- Update edge bootstrap runtime with refresh capabilities
- Update edge service layer with config refresh integration
- Update openai layer routes/chat_handler/server for config refresh
- Update edgecmd config handling with refresh support
- Update input manager, transport server, status provider
- Add edge.yaml config updates and go/config package changes
- Add hostsetup templates and test updates
- Update roadmap and phase documentation for runtime-reconnect-config-refresh
2026-06-21 21:35:50 +09:00

307 lines
10 KiB
YAML
Raw Blame History

edge:
id: "edge-example"
name: "Example Edge"
server:
listen: "0.0.0.0:9090"
# advertise_host is the host other components (Node bootstrap, OpenAI client)
# use to reach this edge. Empty means resolve automatically from interfaces.
advertise_host: ""
# bootstrap is the artifact/bootstrap channel iop-edge can render in
# bootstrap commands and `env` output. Leave artifact_base_url empty until a
# field channel is provisioned.
bootstrap:
artifact_base_url: ""
tls:
enabled: false
logging:
level: "info"
pretty: true
# path is the edge log file. Empty falls back to <binary-dir>/logs/edge.log
# for the bundled dev binary; explicit values win.
path: ""
metrics:
port: 19092
# control_plane connects this Edge to a Control Plane instance.
# Set enabled: true and wire_addr to the Control Plane TCP endpoint to
# activate the outbound connector. Leave enabled: false (default) for
# standalone / local-only Edge deployments.
control_plane:
enabled: false
wire_addr: ""
reconnect_interval_sec: 5
# refresh enables the Edge-local admin API used by `iop-edge config refresh`.
# Keep it loopback-only unless an operator-specific access control layer is added.
refresh:
enabled: false
listen: "127.0.0.1:19093"
a2a:
enabled: false
listen: "0.0.0.0:8081"
path: "/a2a"
node: ""
adapter: "cli"
session_id: "a2a"
timeout_sec: 120
bearer_token: ""
openai:
enabled: false
listen: "0.0.0.0:18081"
node: ""
adapter: "ollama"
target: ""
models: []
# model_routes is the current compatibility route catalog. It maps external
# model ids to internal adapter/target routing.
# When set, /v1/models exposes these ids and /v1/chat/completions + /v1/responses
# resolve adapter/target per-entry. Entries not matched fall back to target/adapter above.
# workspace_required: true marks CLI agent routes that require a workspace path from the caller.
# max_queue and queue_timeout_ms configure the Edge-owned queue policy for the model alias.
#
# Provider-pool migration direction:
# - Keep the external model id as the future models[].id canonical routing key.
# - Represent a 1:1 model_route as one models[] entry with one provider id.
# - Use the route target as models[].providers[provider_id], the concrete served model.
# - Continue sending adapter + target to Node after Edge rewrites the selected provider target.
# model_routes (legacy/adapter/type reference):
# - model: "codex"
# adapter: "cli"
# target: "codex"
# workspace_required: true
# - model: "llama3"
# adapter: "ollama"
# target: "llama3:8b"
# max_queue: 10
# queue_timeout_ms: 30000
#
# # adapter: "vllm" — legacy path: node runs the built-in vllm adapter
# # (direct HTTP-to-vLLM-engine). Deprecated/discouraged for new deploys.
# # New deploys should use openai_compat adapter with provider="vllm"
# # instead, so that auth/headers/options can be configured per-route.
# - model: "qwen3"
# adapter: "vllm" # legacy — prefer openai_compat+provider:vllm
# target: "qwen3-72b"
# node: "node-gpu-01"
#
# - model: "lemonade"
# adapter: "openai_compat"
# target: "lemonade-served-model"
# node: "node-lemonade-01"
#
# vLLM OpenAI-compatible provider example (recommended for new deploys):
# 1. Define the route alias in model_routes with adapter="openai_compat"
# and target set to the served model id (no provider field here).
# 2. Define the vLLM instance in nodes[].adapters.openai_compat_instances
# with provider="vllm", endpoint, and queue policy.
# - alias: "qwen3.6:35b", served model: "nvidia/Qwen3.6-35B-A3B-NVFP4"
# model_routes:
# - model: "qwen3.6:35b"
# adapter: "openai_compat"
# target: "nvidia/Qwen3.6-35B-A3B-NVFP4"
# node: "node-vllm-01"
# max_queue: 10
# queue_timeout_ms: 30000
#
# === Provider-pool (models[] / nodes[].providers[]) example ===
# Top-level models[] defines canonical routing keys and their provider-pool mapping.
# Each entry id is the external model id; providers map maps provider id → served model.
# models:
# - id: "qwen3.6:35b"
# display_name: "Qwen 3.6 35B"
# providers:
# vllm-gpu: "nvidia/Qwen3.6-35B-A3B-NVFP4"
# ollama-local: "qwen3.6:35b"
#
# nodes[].providers[] defines each provider candidate with catalog fields.
# nodes[].providers[].id is the stable provider identity referenced by models[].providers keys.
# nodes[].providers[].type — runtime type (vllm, ollama, lemonade, sglang, openai_api, cli).
# nodes[].providers[].category — "api", "cli", or "local_inference".
# nodes[].providers[].adapter — concrete adapter instance key used by edge to dispatch.
# nodes[].providers[].models — served model names this provider can serve.
# nodes[].providers[].health — observed health state string.
# nodes[].providers[].capacity — max concurrent execution slots.
# nodes[].providers[].max_queue — max queue depth (per-provider).
# nodes[].providers[].queue_timeout_ms — queue timeout in milliseconds.
# nodes[].providers[].lifecycle_capabilities — coarse lifecycle capabilities list.
#
# Example node with provider pool:
# nodes:
# - id: "node-gpu-01"
# alias: "gpu-node"
# providers:
# - id: "vllm-gpu"
# type: "vllm"
# category: "api"
# adapter: "vllm-gpu"
# models:
# - "nvidia/Qwen3.6-35B-A3B-NVFP4"
# health: "healthy"
# capacity: 4
# max_queue: 16
# queue_timeout_ms: 30000
# lifecycle_capabilities: ["scale_up", "scale_down"]
# - id: "ollama-local"
# type: "ollama"
# category: "local_inference"
# adapter: "ollama-local"
# models:
# - "qwen3.6:35b"
# health: "healthy"
# capacity: 2
# max_queue: 8
# queue_timeout_ms: 30000
# - id: "node-vllm-01"
# alias: "vllm-gpu-node"
# token: "<node-token>"
# adapters:
# openai_compat_instances:
# - name: "vllm-gpu"
# enabled: true
# provider: "vllm"
# endpoint: "http://127.0.0.1:8000/v1"
# # headers: no default auth (use proxy/credential path)
# capacity: 4
# max_queue: 16
# queue_timeout_ms: 30000
# request_timeout_ms: 120000
session_id: "openai"
timeout_sec: 120
strict_output: true
strict_stream_buffer: false
console:
adapter: "cli"
target: "codex"
session_id: "default"
background: false
timeout_sec: 240
nodes:
# id is the stable node identity; omitting it falls back to an auto UUID (dev only).
# agent_kind selects the registration kind; omitting it defaults to "generic-node".
# Allowed values: "generic-node" (default).
#
# Single-adapter example (legacy style — still supported):
- id: "node-example-01"
alias: "example-node"
token: "<node-token>"
agent_kind: "generic-node"
adapters:
ollama:
enabled: false
base_url: ""
context_size: 0
# capacity is the per-node concurrent execution slot limit.
capacity: 0
# max_queue and queue_timeout_ms are fallbacks used only if not specified in route-level queue policy.
max_queue: 0
queue_timeout_ms: 0
request_timeout_ms: 0
vllm:
enabled: false
endpoint: ""
capacity: 0
max_queue: 0
queue_timeout_ms: 0
request_timeout_ms: 0
cli:
enabled: true
profiles:
claude-tui:
command: "claude"
args:
- "--dangerously-skip-permissions"
env:
- "TERM=xterm-256color"
persistent: true
terminal: true
response_idle_timeout_ms: 5000
startup_idle_timeout_ms: 5000
mode: "persistent-lazy"
codex:
command: "codex"
args:
- "app-server"
env: []
persistent: false
terminal: false
mode: "codex-app-server"
codex-exec:
command: "codex"
args:
- "exec"
- "--json"
resume_args:
- "exec"
- "resume"
- "--json"
env: []
persistent: false
terminal: false
output_format: "codex-json"
mode: "codex-exec"
runtime:
concurrency: 1
workspace_root: ""
# Multi-adapter example: two Ollama instances + CLI profiles on one node.
# Each ollama_instances entry requires a unique "name" within the node.
# - id: "node-multi-01"
# alias: "multi-engine-node"
# token: "<node-token>"
# adapters:
# ollama_instances:
# - name: "ollama-local"
# enabled: true
# base_url: "http://127.0.0.1:11434"
# context_size: 131072
# # capacity is the per-node concurrent execution slot limit.
# capacity: 4
# # max_queue and queue_timeout_ms are fallbacks used only if not specified in route-level queue policy.
# max_queue: 16
# queue_timeout_ms: 30000
# request_timeout_ms: 300000
# - name: "ollama-dgx"
# enabled: true
# base_url: "http://192.168.0.91:11434"
# context_size: 262144
# capacity: 8
# max_queue: 32
# queue_timeout_ms: 30000
# request_timeout_ms: 300000
# vllm_instances:
# - name: "vllm-a100"
# enabled: true
# endpoint: "http://10.0.0.5:8000"
# capacity: 8
# max_queue: 32
# queue_timeout_ms: 30000
# request_timeout_ms: 300000
# cli:
# enabled: true
# profiles:
# claude:
# command: "claude"
# mode: "antigravity-print"
# openai_compat_instances:
# - name: "lemonade"
# enabled: true
# provider: "lemonade"
# endpoint: "http://127.0.0.1:13305"
# headers:
# Authorization: "Bearer <lemonade-api-key-placeholder>"
# capacity: 4
# max_queue: 16
# queue_timeout_ms: 30000
# request_timeout_ms: 300000
# runtime:
# concurrency: 4
# workspace_root: "/workspace"
Reference in a new issue View git blame Copy permalink