Hold Slayer's logs are shipped to Loki by the host's Alloy agent, which
reads container stdout. Text lines arrive there as an opaque blob:
filtering on a status code meant regex over a formatted string. This adds
LOG_FORMAT=json (default "text", so local dev stays readable) rendering
one JSON object per line.
Two parts were less obvious than a format= argument would suggest, and
both are why this is a module rather than a basicConfig tweak:
Uvicorn attaches its own handlers to `uvicorn` and `uvicorn.access` with
propagate=False, so configuring only the root logger would have left the
access log — the highest-volume, most useful stream — as colourised text
next to our JSON. configure_logging clears those handlers and re-enables
propagation, and is called both at import (for startup config checks) and
in lifespan (uvicorn configures itself after importing the app). The
__main__ path passes log_config=None so uvicorn never applies its own.
The access record's payload lives in record.args as a 5-tuple, not in the
message. Formatting it would throw the structure away and force Loki to
parse it back out, so the tuple is unpacked into real fields and
status_code is emitted as a number for range filtering.
Also drops uvicorn's `color_message` extra, an ANSI-coloured duplicate of
the message that generic extra-promotion would otherwise copy into every
startup line — the same unreadable-in-Grafana problem recently fixed for
the lab's Asterisk logs.
Verified against a real uvicorn server: 39/39 lines valid JSON, zero ANSI
escapes, no duplicates, access lines structured with correct status codes;
text mode unchanged. Thread name is included off the main thread, since
"which execution context logged this" is the first question when debugging
across the asyncio/Sippy/PJSUA2 boundary. SecretStr extras stay masked.
README Phase 4 item ticked; LOG_FORMAT and the previously-undocumented
LOG_LEVEL added to the config table and .env.example.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Add comprehensive deployment and validation plan documenting a staged
bring-up approach that gates each layer on its predecessor and defers
PSTN testing until everything else is proven.
Update .env.example to reflect current configuration:
- Replace static API_TOKEN with Casdoor SSO + owner-minted PAT auth
- Add Rhema TTS settings with port-collision warning
- Add AI Receptionist settings for inbound calls
- Reconcile GATEWAY_SIP_PORT to 5060 and default SIP domain
Engine mode is now explicit: USE_MOCK_SIP=true is the only way to get
the mock engine; an unconfigured trunk fails startup with guidance
instead of silently degrading. Root-caused why the engine always ran
mock: nested pydantic-settings never read .env (no env_file on the
sub-settings classes) — all 8 now declare it.
/health stops lying: reports engine mode (sippy|mock), a live DB
SELECT 1, trunk registration state with reason, and TTS/STT
availability from their last real request; "healthy" now requires
ready + db + sippy + registered trunk.
Error policy: leaf services (tts/transcription/llm_client) raise and
track availability; call-loop callers catch, publish EventType.ERROR
naming the failed service, and apply an explicit fallback. Persistence
writes get one bounded 3x exponential retry, then an ERROR log — no
more silent data loss.
Event bus: a full subscriber queue drops its oldest event (counted)
instead of silently evicting the subscription; subscribe(replay_last=N)
delivers the advertised history replay, used by /ws/events (25).
Receptionist correctness: a matched TAKE_MESSAGE rule beats the LLM;
voicemail polls for early hangup and stops/transcribes/hangs up in
finally; RecordingSession finally keeps its leg_ids so taps detach.
Dead code removed: models/contact.py + Contact table, dtmf_buffer,
transcribe_stream stub, SMS stub in notification.py.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The MCP server was created but never mounted — no client could reach
it. Mount it at /mcp/ over streamable HTTP with a combined lifespan,
resolving the gateway lazily so mounting happens at app construction.
Security and safety for the agent surface:
- One static API_TOKEN (SecretStr) enforced across REST (dependency),
WebSocket (query param/header before accept), and MCP
(StaticTokenVerifier). Startup refuses tokenless non-loopback binds.
- Emergency numbers (911/9911/112) always refused on make_call, plus a
MAX_CONCURRENT_CALLS cap; ValueError surfaces as 400/ToolError.
- Safe defaults: debug off, no credential in default DATABASE_URL,
SIP/LLM/TTS secrets as SecretStr.
Cleanups:
- Delete broken learn_call_flow tool (wrong ctor args, nonexistent
method) and the never-fed CallAnalytics service; keep
call_flow_learner for proper wiring later.
- Trim dial_plan to what is actually used (emergency guard, extension
allocation); delete the unreferenced matcher/normaliser.
- Register call_history before calls so /api/calls/history is no
longer shadowed by /api/calls/{call_id}.
- fastmcp pinned >=3.0 (http_app + StaticTokenVerifier).
New tests: MCP in-memory client (tool surface, lazy gateway, emergency
refusal, call cap) and API security (401 paths, route order, mount).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- Simplified .env.example to use localhost SPEACHES_URL
- Removed unused prod_url from SpeachesSettings config
- Added dashboard node_modules and build dirs to .gitignore
- Streamlines local development setup
Complete project scaffolding and core implementation of an AI-powered
telephony system that calls companies, navigates IVR menus, waits on
hold, and transfers to the user when a human answers.
Key components:
- FastAPI server with REST API, WebSocket, and MCP (SSE) interfaces
- SIP/VoIP call management via PJSUA2 with RTP audio streaming
- LLM-powered IVR navigation using OpenAI/Anthropic with tool calling
- Hold detection service combining audio analysis and silence detection
- Real-time STT (Whisper/Deepgram) and TTS (OpenAI/Piper) pipelines
- Call recording with per-channel and mixed audio capture
- Event bus (asyncio pub/sub) for real-time client updates
- Web dashboard with live call monitoring
- SQLite persistence via SQLAlchemy with call history and analytics
- Notification support (email, SMS, webhook, desktop)
- Docker Compose deployment with Opal VoIP and Opal Media containers
- Comprehensive test suite with unit, integration, and E2E tests
- Simplified .gitignore and full project documentation in README
