Robert Helewka
ff7ea8623a
The call-flow learner finally gets fed: exploration mode records its IVR discoveries on the call (ActiveCall.exploration_steps) instead of throwing them away, persistence stores them in the call record's metadata, and the rebuilt learn_call_flow MCP tool turns a completed exploration call into a stored flow via CallFlowLearner — correct constructor (llm_client from get_llm, heuristic labels when the LLM is unavailable), build for a new number, merge/refine when a flow already exists. save_learned_flow/update_flow_from_model keep the CallFlow↔row mapping in call_persistence. Test gaps closed: tests/test_learner.py (discoveries→linked steps, exploration persistence, learn-then-refine through the in-memory MCP client, no-data and unknown-call answers) and tests/test_websocket.py (4401 without token, trunk-status-then-replay on connect, per-call stream filtering). Docs aligned to code: README (15 tools incl. learn_call_flow, HTTP not SSE, Python 3.12+, PostgreSQL+Alembic — no SQLite fallback, media pipeline marked stub-mode until pjsua2 installed, Alembic and honest /health checked off); docs/mcp-server.md rewritten against the actual tool surface (hangup not end_call, real params, 3 real resources, /mcp/ streamable HTTP + bearer auth); architecture/development/ configuration drift fixed. pyproject: pruned never-imported deps (websockets, librosa, soundfile, python-multipart). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Hold Slayer Documentation
Comprehensive documentation for the Hold Slayer AI telephony gateway.
Contents
| Document | Description |
|---|---|
| Architecture | System architecture, component diagram, data flow |
| Core Engine | SIP engine, media pipeline, call manager, event bus |
| Hold Slayer Service | IVR navigation, hold detection, human detection, transfer |
| Audio Classifier | Waveform analysis, feature extraction, classification logic |
| Services | LLM client, transcription, recording, analytics, notifications |
| Call Flows | Call flow model, step types, learner, CRUD API |
| API Reference | REST endpoints, WebSocket protocol, request/response schemas |
| MCP Server | MCP tools and resources for AI assistant integration |
| Configuration | Environment variables, settings, deployment options |
| Development | Setup, testing, contributing, project conventions |