# AI Service

Technical AI job service for Portal workloads.

AI Service owns technical AI job lifecycle, provider execution and metrics.
Business data stays in domain services such as `telephony`, `monitoring-tg` and
`monitoring-pf`.

## Generic job contract

The service is intentionally domain-agnostic:

- `owner_service` names the caller, for example `telephony`, `monitoring-tg`,
  `monitoring-pf` or a future Portal module.
- `owner_ref` is the caller's stable object reference, for example
  `beeline/{call_id}` or `channel/{message_id}`.
- `task_type` describes the technical task class, for example
  `transcription`, `transcript_summary`, `call_analysis`,
  `telegram_classification`, `tg_analysis`, `pf_competitor_analysis`.
- `model_profile` selects a runtime profile, for example `whisper-large-v3`,
  `qwen2.5-14b`, `vision`, or a future provider profile.
- `input` and `result` are JSON payloads owned by the caller and worker.

This keeps AI service as shared infrastructure rather than a telephony-specific
service.

## Built-in workers

The LLM worker processes `llm_chat`, `chat_completion`, `call_analysis`,
`transcript_summary` and `telegram_classification` jobs whose `model_profile`
equals `LLM_MODEL`.

Input can be either explicit messages:

```json
{
  "messages": [
    {"role": "system", "content": "Answer as JSON."},
    {"role": "user", "content": "Classify this text"}
  ],
  "max_tokens": 256
}
```

or compact `system` / `user` fields. The completed job result contains
`content`, `model`, `usage` and `duration_ms`.

`call_analysis` and `transcript_summary` use the same input contract as
`llm_chat`; callers may include domain metadata fields in `input`, but the
worker only reads chat fields such as `system`, `user`, `messages`,
`max_tokens` and `response_format`.

`transcription` jobs are processed only by Whisper Large v3
(`openai/whisper-large-v3`) through an OpenAI-compatible
`/v1/audio/transcriptions` endpoint. The returned `segments` field stays
compatible with telephony. If the provider returns one long segment, AI Service
splits it into smaller transcript segments without inventing speaker labels.

AI-server compose snippet for Whisper Large v3 lives in
`deploy/ai-server/docker-compose.audio.yml`:

- Whisper endpoint: `http://10.2.3.5:8004`
- Start Whisper:
  `docker compose -f docker-compose.yml -f docker-compose.audio.yml --profile whisper-large-v3 up -d whisper-large-v3`

In Kubernetes the dedicated transcription worker may claim more than one
`whisper-large-v3` job at a time. This keeps download/upload/wait overhead from
serializing the queue while the Whisper provider still controls the actual GPU
scheduling.

## API

- `POST /api/v1/jobs` creates one job.
- `GET /api/v1/jobs` lists jobs with query filters.
- `POST /api/v1/jobs/batch` creates many jobs with shared defaults.
- `POST /api/v1/jobs/retry` retries failed/running jobs by filter.
- `POST /api/v1/jobs/cancel` cancels pending/running jobs by filter.
- `POST /api/v1/jobs/claim` atomically claims pending jobs for a worker.
- `GET /api/v1/jobs/{id}` returns technical job state and result.
- `POST /api/v1/jobs/{id}/complete` stores a successful job result.
- `POST /api/v1/jobs/{id}/fail` stores a failed job category and message.
- `POST /api/v1/jobs/{id}/retry` resets failed/running jobs to `pending`.
- `GET /api/v1/stats` returns queue and error counters.
- `GET /api/v1/providers/status` checks configured AI providers without
  returning secrets.
- `GET /api/v1/infra/status` returns AI-server sidecar telemetry
  (GPU, containers and vLLM live metrics) when configured.
- `GET /health/detail` returns PostgreSQL, provider, queue, error, throughput
  and infra components for Portal `admin/health`.
- `GET /healthz` returns process health.
- `GET /readyz` checks PostgreSQL readiness.
- Built-in workers expose open Kubernetes endpoints on `WORKER_HTTP_PORT`:
  `GET /healthz`, `GET /readyz` and `GET /worker/status`.

All `/api/v1/*` endpoints require `Authorization: Bearer <AI_SERVICE_TOKEN>`
when `AI_SERVICE_TOKEN` is configured. Health and readiness endpoints stay open
for Kubernetes probes.

## Retry policy

Workers store a normalized `error_code` on failed jobs. AI Service requeues only
explicitly retryable categories while attempts remain.

| Category | Retry | Delay |
| --- | --- | --- |
| `provider_unavailable`, `model_unavailable`, `provider_error`, `dependency_error`, `timeout`, `storage_error`, `stale_worker` | yes | 30s |
| `bad_response`, `transcript_hallucination`, `transcript_incomplete`, `internal_error`, `unknown` | yes | 2m |
| `bad_audio`, `bad_input`, `context_length`, `unsupported_task`, `cancelled` | no | - |

Domain services may still expose manual retry for terminal errors after the
underlying data or prompt is corrected.

## Configuration

- `HTTP_HOST`, default `0.0.0.0`
- `HTTP_PORT`, default `8080`
- `DATABASE_URL`, required
- `MIGRATE_ON_START`, default `true`
- `AI_SERVICE_TOKEN`, optional bearer token for service-to-service API calls
- `LLM_BASE_URL`, primary OpenAI-compatible LLM endpoint
- `LLM_API_KEY`, primary LLM API key
- `LLM_MODEL`, default `qwen2.5-14b`
- `LLM_TIMEOUT`, default `5m`
- `AUDIO_TRANSCRIPTION_BASE_URL`, OpenAI-compatible transcription endpoint
- `AUDIO_TRANSCRIPTION_MODEL`, default `openai/whisper-large-v3`
- `AUDIO_TRANSCRIPTION_API_KEY`, optional bearer token; falls back to
  `AUDIO_LLM_API_KEY`, then `LLM_API_KEY`
- `AUDIO_TRANSCRIPTION_PROMPT`, transcription instruction
- `WORKER_ID`, default hostname
- `WORKER_HTTP_HOST`, default `0.0.0.0`
- `WORKER_HTTP_PORT`, default `8081`
- `WORKER_POLL_INTERVAL`, default `2s`
- `WORKER_CLAIM_LIMIT`, default `4`
- `WORKER_LEASE_TIMEOUT`, default `15m`

## Current telephony pipeline

`telephony` now uses AI Service as the only AI execution path:

1. `transcription` turns call audio into segments.
2. `transcript_summary` creates a detailed Russian call summary.
3. `call_analysis` runs tags and negotiation rules against the summary.