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linkong 9a50e72bd1 feat: add aiprovider service foundation

2026-04-07 17:30:27 +08:00

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AI Provider Guide

Overview

aiprovider is the model-adapter service for Planet.

It isolates model-vendor details from the main backend so the rest of the system can call a stable business API:

Caller service -> planet backend
planet backend -> aiprovider
aiprovider -> concrete model provider

The recommended default is:

External and cross-service callers use planet backend
Only infrastructure-grade internal jobs call aiprovider directly

Responsibilities

backend is responsible for:

authentication and authorization
business-level request shaping
stable /api/v1/ai/... endpoints
internal service-to-service authentication toward aiprovider

aiprovider is responsible for:

model protocol adaptation
provider selection by .env
timeout and lightweight retry
request tracing via X-Request-ID

Supported Providers

aiprovider currently supports:

openai
openai_compatible
anthropic
anthropic_compatible
claude_compatible
ollama

Provider mapping:

vLLM, LM Studio, One API: openai_compatible
MiniMax, Claude-compatible gateways: claude_compatible
Ollama: ollama

API Surfaces

Main backend API

Preferred stable entrypoints:

GET /api/v1/ai/provider/status
POST /api/v1/ai/situational-awareness/analyze

Authentication:

Authorization: Bearer <jwt>

Optional tracing header:

X-Request-ID: <caller-generated-id>

The backend will propagate X-Request-ID to aiprovider and return the same header in the response.

AI provider internal API

Internal-only endpoints:

GET /v1/provider/status
POST /v1/analyze

Authentication:

X-Provider-Token: <shared-secret>

Optional tracing header:

X-Request-ID: <caller-generated-id>

Request Example

Call through backend

curl -X POST http://localhost:8000/api/v1/ai/situational-awareness/analyze \
  -H "Authorization: Bearer <access_token>" \
  -H "X-Request-ID: bgp-incident-20260407-001" \
  -H "Content-Type: application/json" \
  -d '{
    "title": "BGP异常研判",
    "objective": "总结当前风险并给出处置建议",
    "observations": [
      "collector A 在 5 分钟内出现多次 origin 变更",
      "异常集中在同一地区前缀"
    ],
    "constraints": [
      "不要编造不存在的数据",
      "区分事实和推断"
    ],
    "context": {
      "source": "bgp-monitor",
      "severity": "high"
    }
  }'

Call `aiprovider` directly

curl -X POST http://localhost:8010/v1/analyze \
  -H "X-Provider-Token: change_me" \
  -H "X-Request-ID: ai-batch-job-001" \
  -H "Content-Type: application/json" \
  -d '{
    "title": "链路波动分析",
    "objective": "给出简要态势摘要和下一步建议",
    "observations": [
      "多个节点出现延迟上升"
    ],
    "constraints": [
      "不要假设根因已经确认"
    ],
    "context": {
      "region": "APAC"
    }
  }'

Response Shape

Both backend and aiprovider return the same payload shape:

{
  "provider": "openai_compatible",
  "model": "gpt-4o-mini",
  "content": "1) 态势摘要 ...",
  "raw_response": {}
}

Both services also return:

X-Request-ID: <id>

Configuration

Backend

Recommended backend .env:

AI_PROVIDER_SERVICE_URL=http://localhost:8010
AI_PROVIDER_SERVICE_TOKEN=change_me
AI_PROVIDER_TIMEOUT_SECONDS=60
AI_PROVIDER_RETRY_ATTEMPTS=2

Reference file:

backend/.env.example

AI Provider

Reference file:

aiprovider/.env.example

Frontend local reference:

frontend/.env.example

Common settings:

SERVICE_NAME=planet-ai-provider
SERVICE_VERSION=0.1.0
AI_PROVIDER_SERVICE_TOKEN=change_me
AI_TIMEOUT_SECONDS=60
AI_HTTP_RETRY_ATTEMPTS=2
AI_ANALYSIS_SYSTEM_PROMPT=你是态势感知分析助手。请基于输入的上下文、观测与约束，输出结构化、克制、可执行的分析。

OpenAI-compatible example

AI_PROVIDER=openai_compatible
AI_BASE_URL=http://127.0.0.1:8001/v1
AI_API_KEY=local-key
AI_MODEL=your-local-model

Claude-compatible example

AI_PROVIDER=claude_compatible
AI_BASE_URL=https://your-claude-compatible-endpoint.example.com
AI_API_KEY=your_api_key
AI_MODEL=your-model
AI_MAX_TOKENS=1200
AI_ANTHROPIC_VERSION=2023-06-01

Ollama example

AI_PROVIDER=ollama
AI_BASE_URL=http://127.0.0.1:11434
AI_API_KEY=
AI_MODEL=qwen2.5:7b

Deployment Modes

Single machine

Recommended local flow:

backend on localhost:8000
aiprovider on localhost:8010
local model gateway on localhost:11434 or another local port

Helpers already included:

Multi-machine

Example topology:

app machine: backend
AI gateway machine: aiprovider
model machine: local model service or cloud proxy

In that case, this becomes service-to-service HTTP RPC:

caller -> backend
backend -> http://10.0.0.12:8010
aiprovider -> model endpoint

Recommended cross-machine backend config:

AI_PROVIDER_SERVICE_URL=http://10.0.0.12:8010
AI_PROVIDER_SERVICE_TOKEN=change_me
AI_PROVIDER_TIMEOUT_SECONDS=60
AI_PROVIDER_RETRY_ATTEMPTS=2

Recommended operating rules:

keep aiprovider on a private network
protect it with X-Provider-Token at minimum
always send X-Request-ID
keep callers on the backend API unless they are infrastructure jobs

Retry And Failure Behavior

backend -> aiprovider:

retries lightweight network / 5xx failures
returns 502 when the provider service is unavailable

aiprovider -> model provider:

retries lightweight network / 5xx failures
returns 502 when the model provider is unavailable

This is intentionally conservative. It avoids masking persistent errors while still absorbing short hiccups.

Operational Notes

./planet.sh start now starts aiprovider automatically
./planet.sh restart -a restarts only aiprovider
./planet.sh log -a tails aiprovider logs
./planet.sh health reports aiprovider health

Recommended Calling Policy

Frontend and application services: call backend
Scheduled infra jobs and diagnostics: optionally call aiprovider
Do not let multiple business services integrate model vendors independently

That keeps provider switching centralized and avoids model-specific drift across the system.

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