Business-Management/features/ai-service/STATUS.md

AI Service — Status

What it is

Shared AI intermediary container. All feature containers (doc-service, future services) POST prompts here. It routes requests to the configured model (LM Studio / Ollama / Anthropic) and returns a normalised response. It is stateless — no database, no conversation history. History and context are the caller's responsibility.

Port: 8010 (internal only, not exposed to host).

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Current functionality

Endpoints

Method	Path	Description
POST	/chat	Synchronous chat: submits at NORMAL priority, blocks until done
GET	/health	{"status": "ok"}
GET	/health/provider	Active provider name, model, configured flag
POST	/queue/jobs	Async enqueue — returns job_id immediately
GET	/queue/jobs/{id}	Poll job: status, position, result, error
DELETE	/queue/jobs/{id}	Cancel a pending job
GET	/queue/status	Worker state: running, paused, queue_size, current_job_id
POST	/queue/pause	Finish current job, stop picking new ones
POST	/queue/resume	Unpause
POST	/queue/start	Start (or restart) the worker task
POST	/queue/stop	Stop worker (pending jobs stay queued)

Priority queue

• Three levels: high (1) > normal (3) > low (5)
• FIFO within same priority level (monotonic sequence counter)
• Single async worker — one LLM call at a time
• Pause / resume / start / stop without restarting the container
• POST /chat is a synchronous wrapper: enqueues at NORMAL, awaits the future

Providers

Provider	Protocol	SDK
LM Studio	OpenAI-compatible HTTP	openai
Ollama	OpenAI-compatible HTTP	openai
Anthropic	Anthropic API (HTTPS)	anthropic

Active provider is selected by "provider" key in /config/ai_service_config.json (shared Docker volume), with env var overrides for dev.

Configuration (env var overrides)

AI_PROVIDER          lmstudio | ollama | anthropic
LMSTUDIO_BASE_URL    http://host.docker.internal:1234/v1
LMSTUDIO_API_KEY     sk-lm-…
LMSTUDIO_MODEL       gemma-4-e4b-it          ← current
OLLAMA_BASE_URL / OLLAMA_MODEL / OLLAMA_API_KEY
ANTHROPIC_API_KEY / ANTHROPIC_MODEL

Credentials live in features/ai-service/.env (gitignored).

Error codes

Code	Meaning
422	Bad request (empty messages, unknown priority)
502	Provider connection / API error
503	Provider not configured / unknown provider
504	Provider timeout

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Architecture

Callers (doc-service, future services)
    │
    └─▶ POST /chat (sync)         ─┐
    └─▶ POST /queue/jobs (async)  ─┤
                                   ▼
                        asyncio.PriorityQueue
                        (HIGH=1, NORMAL=3, LOW=5)
                                   │
                        QueueWorker (single task)
                                   │
                        execute_chat(request)
                                   │
                        Provider SDK (openai / anthropic)
                                   │
                        LM Studio / Ollama / Anthropic API

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Known limitations / not implemented

• TLS to LM Studio — communication is plain HTTP (http://host.docker.internal:1234). Deferred until LM Studio HTTPS configuration is confirmed. When ready: set LMSTUDIO_BASE_URL=https://... and optionally add ssl_verify + ca_bundle config keys to the OpenAI-compat provider.
• True preemption — a HIGH job arriving while a LOW job is processing will be next in queue but will not interrupt the running inference.
• Queue persistence — the in-memory queue is lost on container restart. Pending jobs are not persisted to disk.
• Authentication on queue endpoints — /queue/* management endpoints have no auth guard. Should be protected before any public/multi-tenant deployment (internal network is the only current protection).
• Streaming responses — /chat returns the full response after generation. Streaming (Server-Sent Events) not implemented.
• Metrics / observability — no Prometheus metrics, no structured request logging per job.

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System prompts

Each feature service (doc-service, future services) owns its own system prompt, stored in that service's config JSON on the shared volume. The backend settings API (GET/PATCH /api/settings/system-prompts) aggregates and edits them. The AI Service Settings UI exposes a System Prompts tab for editing all registered service prompts at runtime.

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Future work

• TLS support for LM Studio / Ollama (ssl_verify, ca_bundle config)
• Auth guard on queue management endpoints (admin token or internal-only route)
• Streaming responses via SSE (POST /chat/stream)
• Queue persistence (SQLite or Redis-backed) so jobs survive restarts
• Job result TTL / cleanup (currently jobs accumulate in _jobs dict indefinitely)
• Per-caller priority override (e.g. doc-service background jobs = LOW, user-triggered = NORMAL)
• Metrics endpoint (/metrics) for queue depth, job latency, provider error rate