// Build and deploy the realtime-api Docker container with full verification that deployed code matches local source.
| name | deploy |
| description | Build and deploy the realtime-api Docker container with full verification that deployed code matches local source. |
| triggers | ["deploy","redeploy","rebuild","restart container","ship it"] |
Build, deploy, and verify the realtime-api Docker container.
src/realtime_api/ filesRun the deploy script:
cd /home/spark/git/autonomous-intelligence/realtime-api && bash scripts/deploy.sh
The script will:
--no-cache (guarantees fresh copy)realtime-api container (preserves TTS/STT/LLM)After a successful deploy, speak a brief confirmation:
/home/spark/git/autonomous-intelligence/realtime-api/scripts/speak.sh "Deploy complete. All checks passed."
--no-cachedocker compose logs realtime-apidocker compose up -d to start all servicesSpeak text aloud using the Magpie TTS container in the realtime-api Docker stack. Zero external dependencies — just curl + aplay.
Run TTS round-trip tests — pure-logic unit tests and optional integration tests against live TTS/STT services.
Unified memory management for notes, knowledge graph, RAG search, and file analysis. Use when working with: (1) Core memory — protected identity, projects, relationships, and system facts that should never be forgotten, (2) Working notes — per-session ephemeral notes organized by section, (3) MongoDB RAG — vector-search-enabled notes with importance scoring, decay, deduplication, and archival, (4) Neo4j knowledge graph — entities, relationships, merge duplicates, reinforce mentions, Cypher queries, (5) File analysis — deep file reading that extracts knowledge into all memory layers, (6) Service initialization — health-check, start/stop MongoDB, Neo4j, TEI embeddings via docker-compose with partial setup support.
Query MongoDB notes store for memory analysis and statistics.
Query Neo4j knowledge graph for entities, relationships, and graph analysis.
Calls qq agent from cli.