Configuration

AI Teaching Assistant uses three configuration layers:

1. Launcher environment variables from .env

2. Service YAML config files (audio-analyzer, text-to-speech, rag-service)

3. Per-session runtime parameters from UI requests

This guide documents where to change each type.

1) .env (Launcher-Level)

File:

• .env

Loaded by:

• start_ata.ps1

Typical variables:

Variable	Purpose
KIOSK_CORE_ANALYZER_URL	kiosk-core -> audio-analyzer endpoint
KIOSK_CORE_RAG_URL	kiosk-core -> rag-service endpoint
KIOSK_CORE_TTS_URL	kiosk-core -> text-to-speech endpoint
KIOSK_CORE_METRICS_URL	kiosk-core -> metrics-collector endpoint
KIOSK_CORE_SAMPLE_RATE	Default sample rate
KIOSK_CORE_CHUNK_SECONDS	Chunk duration
KIOSK_CORE_SILENCE_TIMEOUT_SECONDS	Silence timeout
KIOSK_CORE_MAX_SESSION_SECONDS	Max session length
KIOSK_CORE_SILENCE_THRESHOLD	Speech detection threshold
SMART_KIOSK_RAG__MODELS__LLM__BACKEND	RAG LLM backend (openvino, local-only)
KIOSK_CORE_ORDERING_ENABLED	Ordering agent (disabled for teaching)
KIOSK_CORE_IDENTITY_ENABLED	Face/voice identity (disabled for teaching)
KIOSK_CORE_QUEUE_SERVICE_ENABLED	Queue service (disabled for teaching)
KIOSK_CORE_DIARIZATION_ENABLED	Speaker diarization (disabled for teaching)

After .env changes, restart services.

2) Service YAML Files

audio-analyzer

Active runtime file:

• edge-ai-libraries/microservices/audio-analyzer/config.yaml

text-to-speech

Active runtime file:

• edge-ai-libraries/microservices/text-to-speech/config.yaml

rag-service

Active runtime file:

• voice-enabled-interactions/smart-kiosk-assistant/rag-service/config.yaml

Key model fields in rag-service/config.yaml:

• models.llm.hf_id

• models.embedding.hf_id

• retrieval.reranker.hf_id

• per-model device and weight_format

3) UI Runtime Parameters

The React UI sends stream session options through POST /api/v1/sessions/start-stream:

• sample_rate

• chunk_seconds

• silence_timeout_seconds

• max_session_seconds

• silence_threshold

Defaults are defined in:

• assistant-react-ui/src/config.ts

UI Proxy Configuration

The ai-teaching-assistant ui reverse-proxy routes are configured in:

• ata_ui_server.py

Proxy env vars:

• KIOSK_UI_KIOSK_CORE_URL

• KIOSK_UI_RAG_URL

• KIOSK_UI_TTS_URL

• KIOSK_UI_ANALYZER_URL

Recommended Change Workflow

1. Edit one config surface at a time (.env, YAML, or UI defaults).

2. Restart only impacted services.

3. Verify health endpoints.

4. Run one upload + voice query validation.

Validation Commands

curl http://127.0.0.1:8010/health
curl http://127.0.0.1:8011/health
curl http://127.0.0.1:8020/health
curl http://127.0.0.1:8012/health
curl http://127.0.0.1:7860/healthz