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2 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Praxis CI
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b77536aa5e | ||
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Praxis CI
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415c8ac8a6 |
@@ -1,8 +1,10 @@
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{
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"phase": 0,
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"stage": "plan",
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"stage": "complete",
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"milestone": "v0.1",
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"phase_role": "pre_execution",
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"attempts": 0,
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"updated_at": "2026-08-01T00:03:00Z"
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"updated_at": "2026-08-01T00:04:00Z",
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"release_status": "pending",
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"release_reason": "Gitea repo coreci/praxis does not exist (HTTP 404). Tag+merge succeeded locally. Release will retry at milestone completion once remote repo is created."
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}
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+5
-4
@@ -1,7 +1,7 @@
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# Praxis — Roadmap
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**Milestone:** v0.1 (foundation)
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**Status:** specify
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**Status:** execute
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## Milestone Philosophy
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@@ -9,10 +9,11 @@ v0.1 is the **foundation milestone** — it establishes the minimal viable voice
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## v0.1 Phases (2 phases)
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### Phase 0 — Pre-Execution (current)
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### Phase 0 — Pre-Execution (complete)
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**Branch:** `phase/00-pre-execution`
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**Ship target:** patch release (first patch on the v0.0.x line)
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**Branch:** `phase/00-pre-execution` → merged to `milestone/v0.1-praxis`
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**Ship target:** `v0.0.0` (patch release, NFR milestone type — docs-only)
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**Status:** ✓ complete (tagged v0.0.0; release pending — Gitea repo not yet created)
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Pipeline stages: SPECIFY → CLARIFY → RESEARCH → PLAN → GRILL
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@@ -0,0 +1,286 @@
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# Praxis — Phase 1 Verification Report (VERIFY stage)
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> **Phase:** 1 — Minimal Viable Voice Loop
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> **Milestone:** v0.1
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> **Branch:** `phase/01-minimal-voice-loop`
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> **Reviewer:** CIAgent (mechanical, autonomy `full`, single-project mode)
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> **Date:** 2026-08-01
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> **Codebase state at review:** 22 commits since `milestone/v0.1-praxis`, working tree clean before VERIFY fixes
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> **Inputs:** PLAN.md (5 slices, 26 tasks, 10 exit criteria, 15 P1 REQs), REQUIREMENTS.md, ARCHITECTURE.md, GRILL.md (G-001..G-008)
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---
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## Overall Verdict
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| | |
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|---|---|
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| **Verdict** | **PASSED (with documented gaps)** |
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| **Confidence** | 0.82 |
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| **REQ coverage** | 15 / 15 P1 REQ-IDs covered by code |
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| **Exit criteria** | 8 / 10 fully verified; 2 pending live API keys (documented gap, not a failure) |
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| **Tests** | 73 passed, 9 skipped (pending-keys), 0 failed |
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| **P0 fixes applied** | 2 (cosmetic-typo + dead-code cleanup; no logic/behavior change) |
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| **P1+ flagged** | 6 (post-hoc review) |
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| **Escalations** | 0 |
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**One-line summary:** Phase 1 is structurally complete, behaviorally verified (all offline-testable paths green), and secure for a single-learner tech-validation harness. The two unverifiable exit criteria (live audio session + live latency measurement) are blocked on voice-service key provisioning, not on code defects — auto-generated tests in `tests/test_pending_keys.py` will exercise them when keys are present. Two risk-free cosmetic P0 fixes were applied (a misspelled constant `_DEBRIFF_` → `_DEBRIEF_` and a dead-code line in `debrief.py`); neither changed runtime behavior (verified by re-running the full suite).
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---
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## Layer 1 — Structural ✅ PASS
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### 1.1 Files referenced in PLAN.md exist on disk
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All 26 task deliverables verified present:
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| Slice | Expected artifact | Present? |
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|---|---|---|
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| SLICE-01 | `scripts/probe_deepgram.py`, `probe_cartesia.py`, `probe_ollama.py`, `probe_e2e.py`, `docs/latency-report.md` | ✅ all 5 |
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| SLICE-02 | `server/services/{base,registry,__init__}.py`, `server/tts/{cartesia_tts,piper_tts}.py`, `server/llm/ollama_cloud.py`, `server/pipeline.py`, `server/__main__.py`, `server/latency.py`, `server/guardrails/noop.py`, `client/src/{App.tsx,useVoiceSession.ts,main.tsx}` | ✅ all |
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| SLICE-03 | `server/scenarios/{schema,loader,runtime,classifier}.py`, `server/guardrails/customer_service.py`, `server/interruptibility.py`, `scenarios/customer_service_refund_ca_v01.yaml` | ✅ all |
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| SLICE-04 | `db/{schema.sql,store.py,migrate.py}`, `db/migrations/0001_init.sql`, `server/cost.py`, `server/session_recorder.py`, `scenarios/cost_rates.yaml` | ✅ all |
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| SLICE-05 | `server/debrief.py`, `db/migrations/0002_debrief.sql`, `docs/debrief/default.yaml`, `scripts/e2e_smoke.py`, `tests/test_e2e.py` | ✅ all |
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No referenced file is missing. `server/asr/__init__.py` exists but is empty (an organizational placeholder — ASR uses Pipecat's Deepgram service directly in `pipeline.py`; no adapter needed for v0.1 since Deepgram is the only ASR). Acceptable.
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### 1.2 Imports resolve (no dangling references)
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Ran `python3 -c "import ..."` for every server/db module + the public API:
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```
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ALL SERVER/DB IMPORTS OK
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PUBLIC EXPORTS OK
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PIPELINE+MAIN IMPORT OK
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pipecat 1.6.0 DEPS OK (pydantic, yaml, aiosqlite, httpx, websockets, loguru, fastapi)
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```
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Public exports verified present in their declared `__all__`:
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- `server.services` → `TTSProvider, LLMProvider, Guardrail, get_tts, get_llm, get_guardrail` ✅
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- `server.scenarios` → `Scenario, load, load_all, ...` ✅
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- `db` → `PraxisStore, apply_migrations, HARDCODED_LEARNER_ID, ...` ✅
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### 1.3 No stub implementations or TODO placeholders left behind
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Grep for `TODO|FIXME|XXX|HACK|NotImplemented|NotImplementedError` → **0 matches** in `.py` files (no `NotImplementedError` stubs; no TODO/FIXME markers).
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`pass` statements found: 9 — all legitimate (bare `except: pass` / `except ImportError: pass` in probe graceful-degradation paths and one no-op branch in `session_recorder.py:70` which is an intentional placeholder for future real audio-minute metering, documented in a comment). No empty-function-body stubs.
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### 1.4 Declared exports exist
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Verified each `__all__` entry resolves to a real symbol in its module. No dangling exports.
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### 1.5 Client typecheck + build
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```
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npm run typecheck → tsc -b --noEmit → clean (exit 0, no output)
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npm run build → vite build → ✓ built in 636ms (152 modules, dist/ produced)
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```
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**PASS.** (One vite chunk-size warning >500kB — a cosmetic bundling advisory, not an error; acceptable for a v0.1 single-page client.)
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### 1.6 Python syntax check
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`python3 -m py_compile` on all 20 key server/db/script modules → **PY_COMPILE OK** (no syntax errors).
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> **Note on Pipecat LSP static-type noise:** `pipeline.py` / `__main__.py` / `e2e_smoke.py` show Pyright/LSP errors (dataclass-settings API: `No parameter named "api_key"`/`"allow_interruptions"`; `LLMContextAggregator` "abstract"; `_FakeLLM` not assignable to `LLMProvider`). These are **static-type-only** — they stem from Pipecat's dataclass-`Settings` pattern (fields valid at runtime, not visible to the static analyzer) and test fakes that structurally satisfy the ABC but aren't registered as subclasses. **Runtime imports, the e2e smoke test, and all 73 tests pass despite the static warnings.** This matches the documented EXECUTE state. Flagged as P2 (maintainability) — see Quality findings.
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**Layer 1 verdict: PASS.**
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---
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## Layer 2 — Behavioral ✅ PASS (with 2 documented key-pending gaps)
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### 2.1 Test suite
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```
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python3 -m pytest → 73 passed, 9 skipped (pending-keys), 0 failed, 1 warning in 9.81s
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```
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The 1 warning is a benign `DeprecationWarning: 'audioop' is deprecated` from Pipecat's `audio/utils.py` (third-party, Python 3.13 advisory — not actionable in v0.1).
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Test file inventory (12 files, 73 offline tests + 9 pending-key tests):
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| File | Tests | Covers |
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|---|---|---|
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| `test_scenario_schema.py` | 5 | TASK-03-01/02 — Pydantic schema + YAML loader |
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| `test_scenario_runtime.py` | 7 | TASK-03-03/07 — runtime, flows spec, branch set |
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| `test_classifier.py` | 11 | TASK-03-05/06 — interruptibility + branch classifier (heuristic + LLM + parser) |
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| `test_guardrail.py` | 9 | TASK-03-04 — Customer Service ruleset + debrief filter + NoOp swap |
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| `test_llm_adapter.py` | 6 | TASK-02-03 — Ollama adapter (models, missing-key, mocked stream, chat_full) |
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| `test_tts_adapters.py` | 7 | TASK-02-02 — Cartesia/Piper (env selection, missing-key, synthesize_all, ABC) |
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| `test_store.py` | 6 | TASK-04-01/02 — migrations, hardcoded learner, CRUD, progress |
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| `test_cost_and_recorder.py` | 7 | TASK-04-03/04 — cost derivation + SessionRecorder lifecycle |
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| `test_debrief.py` | 5 | TASK-05-01/02/03 — debrief gen, no-think, guardrail filter, TTS voice |
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| `test_debrief_persistence.py` | 2 | TASK-05-05 — migration 0002 + debrief_text persisted |
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| `test_latency_observer.py` | 5 | TASK-02-06 — LatencyRecord math + observer state |
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| `test_e2e.py` | 3 | TASK-05-06 — full-loop smoke (DB assertions) |
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| `test_pending_keys.py` (NEW) | 9 (skipped) | Exit criteria #1/#2 — live-key verifications |
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### 2.2 E2E smoke test
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```
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python3 scripts/e2e_smoke.py
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→ E2E SMOKE TEST — PASSED
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session_id: sess-..., branch_id: accept_resolution, outcome: success,
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turns_logged: 4, cost_cents: 1, debrief_chars: 194,
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max_latency_ms: 510.0, within_budget: True, budget_ms: 600.0
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```
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The full offline loop works: scenario load → session start → 4 turns logged → heuristic branch classification → debrief generation (stub LLM) → guardrail filter → cost derivation → session/turns/progress/debrief persisted to SQLite. **PASS.**
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### 2.3 Phase 1 Exit Criteria (10 items — PLAN.md §4)
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| # | Criterion | Status | Evidence |
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|---|---|---|---|
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| 1 | Full session end-to-end (client → disclaimer → speak → AI responds → branch → debrief → SQLite) | **GAP (pending keys)** | Code-complete: `__main__.py` accepts WebRTC, loads scenario, logs disclaimer; `pipeline.py` wires VAD→STT→LLM→TTS; `debrief.py` + `session_recorder.py` close the loop. Cannot exercise live without DEEPGRAM/CARTESIA/OLLAMA keys. Auto-test: `tests/test_pending_keys.py::test_ollama_gemma4_cloud_returns_first_token` + `test_cartesia_tts_streams_audio` + `test_deepgram_stt_service_constructs_with_live_key`. |
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| 2 | Latency measured (R1-R4 real numbers) + TTS decision | **GAP (pending keys)** | `docs/latency-report.md` exists with budget, decision matrix, G-003 no-go actions, Piper pre-staging. Probes built and degrade gracefully (`KEY_MISSING` → exit 0). Live numbers pending keys. Auto-tests: `test_r1_deepgram_first_partial_latency`, `test_r2_...`, `test_r3_...`, `test_r4_...`, `test_live_latency_report_has_real_numbers`. |
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| 3 | TTS behind interface, swappable via `PRAXIS_TTS` | ✅ **PASS** | `server/services/base.py:TTSProvider` (ABC); `cartesia_tts.py` + `piper_tts.py` adapters; `registry.get_tts()` selects via env. Tests: `test_cartesia_selectable_via_env`, `test_piper_selectable_via_env`, `test_both_adapters_are_ttsprovider`. |
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| 4 | LLM behind interface, both models callable | ✅ **PASS** | `LLMProvider` ABC; `OllamaCloudLLM` with `roleplay_model`/`debrief_model` properties + `no_think` flag. Tests: `test_ollama_models_from_env_defaults`, `test_ollama_is_llmprovider`. Live call pending keys (auto-test: `test_ollama_deepseek_debrief_no_think_returns_text`). |
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| 5 | Guardrail pluggable + CustomerService ruleset + disclaimer + unit-tested | ✅ **PASS** | `Guardrail` ABC + `CustomerServiceGuardrail` + `NoOpGuardrail`; disclaimer text defined; 9 unit tests covering legal/financial/medical/impersonation blocks + debrief filter + NoOp swap. |
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| 6 | Scenario YAML → Pydantic → Flows, `failure_mode` present | ✅ **PASS** | `schema.py` (Pydantic) + `loader.py` (`yaml.safe_load`) + `runtime.py` (`as_flow_spec`); `customer_service_refund_ca_v01.yaml` has `failure_mode: escalates_unresolved`. Tests: 5 schema tests + 7 runtime tests. |
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| 7 | Interruptibility (learner cuts AI TTS, AI yields) | ✅ **PASS (structural)** | `pipeline.py` sets `allow_interruptions=True` (D-008); `interruptibility.py::pipeline_allows_interruptions` verified by 3 tests. Live manual test documented as pending in latency-report; Pipecat's built-in interrupt handling provides the runtime behavior. |
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| 8 | Learner state persists (session + turns + progress + cost; single learner, no auth) | ✅ **PASS** | `db/` schema + migrations + async store; hardcoded `learner-1` "Alex" row; `SessionRecorder` wires store into pipeline. Tests: `test_store_start_log_end_session`, `test_hardcoded_learner_row_exists`, `test_session_recorder_full_lifecycle`. |
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| 9 | Cost logged per session (`cost_estimated_cents` non-null + breakdown) | ✅ **PASS** | `server/cost.py::derive_cost` + `cost_rates.yaml`; `sessions.cost_estimated_cents` + `cost_breakdown_json` populated. Tests: `test_derive_cost_basic`, `test_session_recorder_full_lifecycle` (asserts `cost_estimated_cents > 0`). |
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| 10 | E2E smoke test passes (full loop + DB assertions) | ✅ **PASS** | `scripts/e2e_smoke.py` + `tests/test_e2e.py` (3 tests) — passes; asserts session/turns/cost/debrief/branch persisted. |
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**Exit criteria: 8/10 PASS, 2/10 GAP (pending keys, not code defects).**
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### 2.4 REQ Coverage Traceability (15 P1 REQ-IDs)
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| REQ-ID | Covered? | Files (trace) | Test status |
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|---|---|---|---|
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| REQ-VOICE-01 | ✅ | `server/pipeline.py:_build_stt` (Deepgram Nova-3) | structural test + pending live test |
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| REQ-VOICE-02 | ✅ | `server/services/base.py:TTSProvider`, `server/tts/cartesia_tts.py`, `server/tts/piper_tts.py` | 7 tests + pending live test |
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| REQ-VOICE-03 | ✅ | `server/latency.py`, `docs/latency-report.md` | 5 tests; live number pending keys |
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| REQ-VOICE-04 | ✅ | `server/pipeline.py` (`allow_interruptions=True`), `server/interruptibility.py` | 3 tests |
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| REQ-SCEN-01 | ✅ | `scenarios/customer_service_refund_ca_v01.yaml`, `server/scenarios/runtime.py` | 7 runtime + 5 schema tests |
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| REQ-STATE-01 | ✅ | `db/schema.sql`, `db/store.py`, `db/migrations/0001_init.sql`, `server/session_recorder.py` | 6 store + 7 recorder tests |
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| REQ-LLM-01 | ✅ | `server/llm/ollama_cloud.py` (gemma4:cloud) | 6 tests + pending live test |
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| REQ-LLM-02 | ✅ | `server/llm/ollama_cloud.py` (`no_think`), `server/debrief.py`, `server/scenarios/classifier.py` | 5 debrief tests + pending live test |
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| REQ-DEBRIEF-01 | ✅ | `server/debrief.py`, `docs/debrief/default.yaml`, `server/session_recorder.py` | 5 debrief + 2 persistence tests |
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| REQ-ORCH-01 | ✅ | `server/pipeline.py` (Pipecat + Silero VAD + interrupt) | imports + e2e smoke |
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| REQ-ORCH-02 | ✅ | `server/services/base.py:Guardrail`, `server/guardrails/customer_service.py`, `server/services/registry.py` | 9 guardrail tests |
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| REQ-SCEN-FMT-01 | ✅ | `server/scenarios/schema.py`, `server/scenarios/loader.py`, `server/scenarios/runtime.py` | 5 schema + 7 runtime tests |
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| REQ-NFR-LAT-01 | ✅ | `server/latency.py`, `docs/latency-report.md`, `scripts/probe_*.py` | 5 tests; live measurement pending keys |
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| REQ-NFR-SAFE-01 | ✅ | `server/guardrails/customer_service.py` (disclaimer + 4 block categories + debrief filter) | 9 guardrail tests |
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| REQ-NFR-COST-01 | ✅ | `server/cost.py`, `scenarios/cost_rates.yaml`, `server/session_recorder.py` | 7 cost/recorder tests |
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|
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**Coverage: 15/15 P1 REQ-IDs covered by code.** All have at least one offline test except where the requirement is inherently live-key-dependent (REQ-VOICE-03 live number, REQ-LLM-01/02 live call) — those are covered by auto-generated pending-key tests that activate when keys are provisioned.
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|
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### 2.5 Auto-generated tests for unverifiable items
|
||||
|
||||
`tests/test_pending_keys.py` (NEW — 9 tests, all skip cleanly without keys):
|
||||
|
||||
| Test | Verifies | Activates when |
|
||||
|---|---|---|
|
||||
| `test_r1_deepgram_first_partial_latency` | R1 probe runs live | DEEPGRAM_API_KEY |
|
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| `test_r2_cartesia_first_audio_latency` | R2 probe runs live | CARTESIA_API_KEY |
|
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| `test_r3_ollama_ttft_both_models` | R3 probe (R6 resolution) | OLLAMA_API_KEY |
|
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| `test_r4_integrated_e2e_latency_within_or_documented` | R4 integrated e2e | OLLAMA + CARTESIA |
|
||||
| `test_ollama_gemma4_cloud_returns_first_token` | REQ-LLM-01 live | OLLAMA_API_KEY |
|
||||
| `test_ollama_deepseek_debrief_no_think_returns_text` | REQ-LLM-02 live no-think | OLLAMA_API_KEY |
|
||||
| `test_cartesia_tts_streams_audio` | REQ-VOICE-02 live | CARTESIA_API_KEY |
|
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| `test_deepgram_stt_service_constructs_with_live_key` | REQ-VOICE-01 live | DEEPGRAM_API_KEY |
|
||||
| `test_live_latency_report_has_real_numbers` | Exit criterion #2 | OLLAMA + CARTESIA |
|
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|
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All 9 skip with a clear reason when keys are absent; the default fast suite stays green (73 passed, 9 skipped).
|
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|
||||
**Layer 2 verdict: PASS (8/10 exit criteria verified; 2/10 documented key-pending gaps with auto-tests ready).**
|
||||
|
||||
---
|
||||
|
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## Layer 3 — Security (STRIDE) ✅ ACCEPT (all dispositions low/medium for v0.1 pilot)
|
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|
||||
Threat model context: v0.1 is a **single-learner tech-validation harness** (G-008), local SQLite, no auth (D-007), no PII beyond a hardcoded display name, no network exposure beyond the pilot host. STRIDE findings are dispositioned per the auto-policy (low=accept, medium=mitigate, high=escalate).
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|
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| Category | Finding | Severity | Disposition | Evidence |
|
||||
|---|---|---|---|---|
|
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| **Spoofing** | No auth in v0.1 (D-007 — single hardcoded learner "Alex"). Anyone who can reach the Pipecat server's `/pipecat/webrtc` endpoint could start a session. | Low (pilot) | **Accept** | D-007 explicitly defers auth. Single-learner harness; the server binds `0.0.0.0:8789` but is intended for a single pilot host. CORS is `allow_origins=["*"]` (dev) — acceptable for v0.1, **flag for tightening before any multi-learner milestone** (P1). |
|
||||
| **Tampering** | SQLite local file (`praxis.db`) — no integrity protection. A local user can `sqlite3 praxis.db` and edit session/outcome/cost rows. | Low (pilot) | **Accept** | D-007: local pilot, single-learner. Trust model assumes the pilot host is trusted. No tamper-evidence needed for tech-validation. Documented in `db/schema.sql` header. |
|
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| **Repudiation** | Sessions are logged with auto-generated ids (`sess-<uuid>`) and timestamps; no signed audit trail. A learner could dispute "I never did that session." | N/A (pilot) | **Accept** | Single hardcoded learner, no auth → no multi-party repudiation surface. Sessions are for learner self-review, not compliance. |
|
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| **Info Disclosure** | (a) `.ciagent/.env.secrets` is `0600` perms + gitignored — ✅ verified. (b) `.env`, `.env.secrets`, `.env.*` all in `.gitignore` — ✅ verified. (c) `git ls-files` confirms **no secret/key/db files tracked**. (d) Grep for hardcoded API keys (`sk-...`, `*_API_KEY="..."` assignments) → **0 matches** in non-example files. (e) `db/*.db` gitignored — no learner data leaked. | Low | **Accept** | Secrets handling is correct. The local `.ciagent/.env.secrets` contains a `DEEPGRAM_API_KEY` value (40 chars) but it is **not committed** (gitignored, 0600) — this is the intended dev-secret pattern. No info-disclosure vulnerability found. |
|
||||
| **Denial of Service** | No rate limiting on the FastAPI/Pipecat server; no connection cap; a client can open many WebRTC sessions. `asyncio.create_task(runner.run(task))` fires-and-forgets per request. | Low-Medium (pilot) | **Accept (v0.1) / Flag (P1)** | D-007/D-012: single-learner pilot, no adversarial threat model. Acceptable for v0.1. **Flag for P1 post-hoc review**: before any multi-learner exposure, add connection limits + task lifecycle management (the current `create_task` without tracking could leak tasks on disconnect). |
|
||||
| **Elevation of Privilege** | No auth → no privilege ladder → no escalation surface. | N/A | **Accept** | N/A for v0.1. |
|
||||
|
||||
### Injection-vector review (security persona)
|
||||
|
||||
| Vector | Status | Evidence |
|
||||
|---|---|---|
|
||||
| **YAML scenario loading** | ✅ Safe | `server/scenarios/loader.py` uses `yaml.safe_load` (not `yaml.load`) — no arbitrary Python object construction. Scenario files are repo-authored (D-007: no user-uploaded scenarios in v0.1). |
|
||||
| **LLM prompt construction** | ✅ Contained | `classifier.py::_build_user_prompt` and `debrief.py::_render` interpolate learner text into the prompt via string replacement. A malicious learner ASR transcript could inject prompt text, but: (a) the LLM is role-playing a customer (no tool calls / no DB writes from LLM output), (b) the guardrail output filter runs on the response, (c) the branch classifier output is JSON-parsed leniently with fallback. Prompt injection impact is bounded to a misclassified branch or a weird debrief — not a security boundary for v0.1. **Accept.** |
|
||||
| **SQL injection** | ✅ Safe | `db/store.py` uses parameterized queries exclusively (`?` placeholders) — no string-interpolated SQL. |
|
||||
| **Path traversal (scenario id)** | Low | `loader.load(scenario_id)` builds `base / f"{scenario_id}.yaml"` — a `scenario_id` containing `../` could escape `scenarios/`. In v0.1 the id comes from the env var `PRAXIS_SCENARIO` (operator-controlled), not user input. **Accept for v0.1; flag for P1** if scenario ids ever become user-selectable. |
|
||||
|
||||
**Layer 3 verdict: ACCEPT.** No high-severity STRIDE findings. 3 P1 flags for future hardening (CORS tightening, DoS/connection limits, path-traversal guard) — all appropriate for a post-pilot milestone, not v0.1 blockers.
|
||||
|
||||
---
|
||||
|
||||
## Layer 4 — Quality (multi-persona review)
|
||||
|
||||
### P0 fixes applied (2)
|
||||
|
||||
Both are risk-free cosmetic cleanups with no logic/behavior change. Verified by re-running the full suite (73 passed, 9 skipped, 0 failed) + e2e smoke after each fix.
|
||||
|
||||
| # | File:line | Issue | Fix | Verification |
|
||||
|---|---|---|---|---|
|
||||
| P0-1 | `server/guardrails/customer_service.py:119,123` | Misspelled constant `_DEBRIFF_LEGAL_REDIRECT` (two F's; should be `_DEBRIEF_`). Worked at runtime only because the method references the constant by the same misspelled name and Python resolves globals at call time — but the typo is a latent trap: any future refactor that renames one occurrence would silently break the debrief filter, causing legal-action recommendations to pass unfiltered (a safety regression). | Renamed both occurrences to `_DEBRIEF_LEGAL_REDIRECT`. | `test_debrief_guardrail_blocks_legal_action` passes; manual end-to-end check confirms legal-action text still replaced by the redirect. |
|
||||
| P0-2 | `server/debrief.py:31` | Dead code: `rel = template_id.replace("/", ".") ...` computed but never used (the actual path resolution uses `template_id.split('/')[-1]`). Confusing for maintainers and flagged by linters. | Removed the dead line. | `test_debrief_*` (5 tests) pass; template loading verified. |
|
||||
|
||||
### P1+ findings flagged for post-hoc review (6)
|
||||
|
||||
| # | Severity | Persona | File:line | Finding | Recommendation |
|
||||
|---|---|---|---|---|---|
|
||||
| Q-1 | P1 | Maintainability | `server/pipeline.py`, `server/__main__.py`, `scripts/e2e_smoke.py` | Pipecat LSP static-type noise (~12 Pyright errors: dataclass-`Settings` fields, `LLMContextAggregator` abstractness, `_FakeLLM` not subclassing `LLMProvider`). Runtime is fine; static analysis is noisy. | Add `# type: ignore[...]` annotations with reasons, or wrap Pipecat service construction in typed helper functions. Register test fakes via `LLMProvider.register` or duck-type with `Protocol`. Non-blocking. |
|
||||
| Q-2 | P1 | Correctness | `server/latency.py:106-112` | `TextFrame` is treated as an LLM-first-token proxy, but `TextFrame` is generic — it can carry non-LLM text (e.g. the opening-line TTS input), which could misattribute the first-token timestamp. The `LLMFullResponseEndFrame` branch (L99) is a better proxy but also imperfect. | For v0.1 accept (latency is logged, not enforced); for Phase 2 use Pipecat's `LLMTokenUsageFrame` / metrics service for accurate TTFT. |
|
||||
| Q-3 | P1 | Adversarial/Security | `server/scenarios/loader.py:34` | `load(scenario_id)` builds `base / f"{scenario_id}.yaml"` without sanitizing `../` — path traversal possible if `scenario_id` is ever user-controlled. Currently env-var-controlled (operator), so low risk. | Add a guard: reject `scenario_id` containing path separators or `..`, or resolve + verify the result stays within `base`. |
|
||||
| Q-4 | P1 | Security/DoS | `server/__main__.py:96-98` | `asyncio.create_task(runner.run(task))` is fire-and-forget — no tracking of running tasks, no cap on concurrent sessions, no cancellation on client disconnect. Acceptable for single-learner pilot but would leak resources at scale. | Track tasks in a set; cancel on disconnect; cap concurrency. Defer to multi-learner milestone. |
|
||||
| Q-5 | P1 | Security | `server/__main__.py:55` | CORS `allow_origins=["*"]` — dev setting. Acceptable for v0.1 single-origin pilot but must be tightened before any non-local exposure. | Make CORS origin env-configurable (`PRAXIS_CORS_ORIGINS`); default to the client dev origin. |
|
||||
| Q-6 | P2 | Testing | `tests/test_e2e.py:16-37` | The 3 e2e test functions each call `asyncio.run(run_e2e(...))` independently — the full loop runs 3× per test session (wasteful, ~3× the DB writes). Also `test_e2e_debrief_non_empty` re-runs the whole loop just to assert `debrief_chars > 50`. | Refactor to a session-scoped fixture that runs `run_e2e` once and shares the result dict across the 3 assertions. Non-blocking. |
|
||||
|
||||
### Per-persona summary
|
||||
|
||||
**Correctness:** Logic is sound across the hot path. `classify_branch_sync_heuristic` correctly scores branches by signal-keyword overlap and tie-breaks to the first branch (deterministic). `derive_cost` arithmetic verified (`test_derive_cost_piper_zero_tts` confirms Piper $0 path). `LatencyRecord.e2e_asr_to_tts_ms` math correct (550ms in test). Branch classifier parser is lenient (handles code fences, malformed JSON, empty input) with safe fallbacks. **No correctness P0s.**
|
||||
|
||||
**Testing:** 73 tests are meaningful — they cover schema validation, adapter graceful degradation, guardrail block categories, cost math, store CRUD, recorder lifecycle, debrief generation/filter, latency math, and the full e2e loop with DB assertions. Coverage is broad; gaps are the live-key paths (now covered by `test_pending_keys.py` skips) and client-side (no React component tests — v0.1 relies on e2e smoke per `package.json` "test" script). The `_FakeLLM`/`_StubDebriefLLM` fakes structurally satisfy the `LLMProvider` contract. **No testing P0s.** One P2 (test redundancy, Q-6).
|
||||
|
||||
**Security:** See Layer 3. No hardcoded keys, safe YAML loading, parameterized SQL, bounded prompt-injection impact. 3 future-hardening P1s (Q-3/4/5). **No security P0s.**
|
||||
|
||||
**Performance:** No O(n²) in the voice-loop hot path. `LatencyObserver.process_frame` is O(1) per frame (passes through + records a timestamp). `lru_cache` on registry getters avoids repeated adapter construction. `SessionRecorder.log_turn` is O(1) per turn. The classifier runs once at session end (D-P1-05 — offline from the latency path). **No performance P0s.** One observation: `LLMContextAggregator` + Pipecat's context object grow with conversation length (unbounded turn history) — acceptable for v0.1 short sessions; flag for Phase 2 if sessions exceed ~50 turns.
|
||||
|
||||
**Maintainability:** Interfaces (`TTSProvider`/`LLMProvider`/`Guardrail`) are clean ABCs with typed dataclasses (`TTSResult`, `LLMStreamChunk`, `GuardrailVerdict`, `GuardrailContext`). The registry centralizes env-based selection. Adapters are thin and consistently degrade gracefully on missing keys/models. Naming is clear. The one maintainability defect was the `_DEBRIFF` typo (fixed as P0-1). Pipecat static-type noise (Q-1) is the remaining friction. **No maintainability P0s after fixes.**
|
||||
|
||||
**Adversarial:** What if the LLM returns malicious content? → Guardrail output filter (`_DEBRIEF_LEGAL_ACTION_RE` + 4 category regexes) blocks legal/financial/medical/impersonation; the debrief path replaces blocked content with a coaching redirect. What if the YAML scenario is malformed? → Pydantic `ValidationError` raised at load (typed, tested). What if the classifier returns garbage? → `_parse_branch` falls back to scanning for a known branch id, then to the first branch — never crashes. What if a probe key is missing? → `KEY_MISSING` banner, exit 0. **No adversarial P0s.** The guardrail regexes are heuristic (not LLM-based) and could be evaded by paraphrase — acceptable for v0.1 Customer Service (low-risk domain per D-019); the pluggable interface allows a stronger ruleset for high-risk domains later.
|
||||
|
||||
**Layer 4 verdict: PASS.** 2 P0 fixes applied (cosmetic, verified). 6 P1+ flags for post-hoc review (none blocking).
|
||||
|
||||
---
|
||||
|
||||
## GRILL binding decisions — status check
|
||||
|
||||
| ID | Decision | Honored? | Evidence |
|
||||
|---|---|---|---|
|
||||
| G-001 | v0.1 = tech-validation, not thesis validation | ✅ | `README.md` L3: "tech-validation harness (per G-008)"; `docs/latency-report.md` frames numbers as pilot-config. |
|
||||
| G-002 | Branch is post-hoc classification, not runtime fork | ✅ | `server/scenarios/runtime.py:as_flow_spec` → `transitions: []` with comment "v0.1: no in-flight transitions (G-002)"; classifier runs at session end. |
|
||||
| G-003 | Go/no-go gate has explicit no-go actions | ✅ | `docs/latency-report.md` §"SLICE-01 go/no-go gate" lists actions (a)/(b)/(c). |
|
||||
| G-004 | Per-slice estimates at EXECUTE | ⚠️ Partial | Commit messages carry slice/task ids; no explicit effort estimates in PLAN.md, but the wave structure + 26 tasks provide sizing. Acceptable for autonomous project. |
|
||||
| G-005 | v0.1 logged costs not representative of at-scale | ✅ | `server/cost.py` header + `scenarios/cost_rates.yaml` header both cite G-005. |
|
||||
| G-006 | No real-learner recruitment; tech harness | ✅ | Hardcoded `learner-1` "Alex"; no recruitment code/artifacts. |
|
||||
| G-007 | Stop-trigger defined (ties to G-003) | ✅ | latency-report §go/no-go gate documents the stop trigger. |
|
||||
| G-008 | "Pilot" = tech pilot, not learner pilot | ✅ | README + docs consistent. |
|
||||
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
| Layer | Verdict | Detail |
|
||||
|---|---|---|
|
||||
| 1 — Structural | ✅ PASS | All files present; imports resolve; no stubs/TODOs; exports valid; client typecheck+build clean; py_compile clean. |
|
||||
| 2 — Behavioral | ✅ PASS (2 documented gaps) | 73 tests pass; e2e smoke passes; 8/10 exit criteria verified; 15/15 REQs covered; 9 auto-tests ready for pending keys. |
|
||||
| 3 — Security (STRIDE) | ✅ ACCEPT | No high-severity findings; secrets handled correctly (0600 + gitignored, no hardcoded keys, safe YAML, parameterized SQL); 3 P1 future-hardening flags. |
|
||||
| 4 — Quality | ✅ PASS | 2 P0 cosmetic fixes applied + verified; 6 P1+ flagged; no logic/security/performance P0s. |
|
||||
|
||||
**Overall: PASSED (with documented gaps).** The two key-pending exit criteria are environment gaps (no voice-service keys provisioned), not code defects — `tests/test_pending_keys.py` will verify them automatically when keys are present. The codebase is ready for SHIP subject to the orchestrator's decision on the key-pending items.
|
||||
|
||||
---
|
||||
|
||||
*End of Phase 1 verification report. VERIFY only — SHIP is the orchestrator's next step.*
|
||||
+37
-1
@@ -1,3 +1,39 @@
|
||||
# Python
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
*.egg-info/
|
||||
.eggs/
|
||||
build/
|
||||
dist/
|
||||
.venv/
|
||||
venv/
|
||||
.env
|
||||
.env.secrets
|
||||
.env.*
|
||||
.env.*
|
||||
|
||||
# SQLite
|
||||
*.db
|
||||
*.db-journal
|
||||
*.db-wal
|
||||
*.db-shm
|
||||
|
||||
# Node / client
|
||||
client/node_modules/
|
||||
client/dist/
|
||||
client/.vite/
|
||||
|
||||
# Pytest / coverage
|
||||
.pytest_cache/
|
||||
.coverage
|
||||
htmlcov/
|
||||
|
||||
# OS
|
||||
.DS_Store
|
||||
Thumbs.db
|
||||
|
||||
# Piper voice models (pre-staged locally, not committed)
|
||||
*.onnx
|
||||
*.pt
|
||||
*.bin
|
||||
piper_models/
|
||||
@@ -0,0 +1,39 @@
|
||||
# Praxis — v0.1 Foundation
|
||||
|
||||
Voice-first AI apprenticeship platform. v0.1 is a **tech-validation harness** (per G-008) for the minimal viable voice loop: a single learner speaks to an AI tutor playing a Customer Service role-play scenario, hears a <600ms-latency response, receives an end-of-session coaching debrief, and has the session logged to SQLite.
|
||||
|
||||
## Status
|
||||
|
||||
Phase 1 (minimal viable voice loop) — code-complete, pending live API keys for runtime verification.
|
||||
|
||||
## Stack
|
||||
|
||||
- **Orchestration:** Pipecat (D-017) with Silero VAD + interruptibility
|
||||
- **ASR:** Deepgram Nova-3 streaming (D-013)
|
||||
- **LLM:** Ollama Cloud direct API (D-020) — `gemma4:cloud` (role-play) + `deepseek-v4-flash:cloud` no-think (debrief)
|
||||
- **TTS:** Cartesia Sonic (primary, D-014) / Piper (self-hosted, R4 mitigation) — behind an interface
|
||||
- **Client:** React + Vite + WebRTC (Pipecat client SDK, D-015)
|
||||
- **State:** SQLite `praxis.db` (D-007, single hardcoded learner, no auth)
|
||||
|
||||
## Layout
|
||||
|
||||
```
|
||||
server/ Pipecat pipeline, services (TTS/LLM/Guardrail interfaces), scenario runtime, adapters
|
||||
client/ React + Vite + WebRTC learner surface
|
||||
scenarios/ YAML scenario definitions (D-018)
|
||||
db/ SQLite schema, migrations, async store
|
||||
scripts/ Latency probes (R1-R4), e2e smoke
|
||||
tests/ Unit + e2e
|
||||
docs/ Latency report, debrief templates
|
||||
```
|
||||
|
||||
## Quickstart
|
||||
|
||||
1. Copy `.env.example` → `.env`, fill in `DEEPGRAM_API_KEY`, `CARTESIA_API_KEY`, `OLLAMA_API_KEY`.
|
||||
2. Install server deps: `pip install -e ".[dev]"`
|
||||
3. Install client deps: `cd client && npm install`
|
||||
4. Run probes: `python scripts/probe_deepgram.py` (etc.)
|
||||
5. Run server: `python -m server`
|
||||
6. Run client: `cd client && npm run dev`
|
||||
|
||||
See `docs/latency-report.md` for the R1-R4 spike status and TTS decision.
|
||||
@@ -0,0 +1,24 @@
|
||||
# Logs
|
||||
logs
|
||||
*.log
|
||||
npm-debug.log*
|
||||
yarn-debug.log*
|
||||
yarn-error.log*
|
||||
pnpm-debug.log*
|
||||
lerna-debug.log*
|
||||
|
||||
node_modules
|
||||
dist
|
||||
dist-ssr
|
||||
*.local
|
||||
|
||||
# Editor directories and files
|
||||
.vscode/*
|
||||
!.vscode/extensions.json
|
||||
.idea
|
||||
.DS_Store
|
||||
*.suo
|
||||
*.ntvs*
|
||||
*.njsproj
|
||||
*.sln
|
||||
*.sw?
|
||||
@@ -0,0 +1,8 @@
|
||||
{
|
||||
"$schema": "./node_modules/oxlint/configuration_schema.json",
|
||||
"plugins": ["react", "typescript", "oxc"],
|
||||
"rules": {
|
||||
"react/rules-of-hooks": "error",
|
||||
"react/only-export-components": ["warn", { "allowConstantExport": true }]
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,32 @@
|
||||
# React + TypeScript + Vite
|
||||
|
||||
This template provides a minimal setup to get React working in Vite with HMR and some Oxlint rules.
|
||||
|
||||
Currently, two official plugins are available:
|
||||
|
||||
- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Oxc](https://oxc.rs)
|
||||
- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/)
|
||||
|
||||
## React Compiler
|
||||
|
||||
The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation).
|
||||
|
||||
## Expanding the Oxlint configuration
|
||||
|
||||
If you are developing a production application, we recommend enabling type-aware lint rules by installing `oxlint-tsgolint` and editing `.oxlintrc.json`:
|
||||
|
||||
```json
|
||||
{
|
||||
"$schema": "./node_modules/oxlint/configuration_schema.json",
|
||||
"plugins": ["react", "typescript", "oxc"],
|
||||
"options": {
|
||||
"typeAware": true
|
||||
},
|
||||
"rules": {
|
||||
"react/rules-of-hooks": "error",
|
||||
"react/only-export-components": ["warn", { "allowConstantExport": true }]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
See the [Oxlint rules documentation](https://oxc.rs/docs/guide/usage/linter/rules) for the full list of rules and categories.
|
||||
@@ -0,0 +1,13 @@
|
||||
<!doctype html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8" />
|
||||
<link rel="icon" type="image/svg+xml" href="/favicon.svg" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>client</title>
|
||||
</head>
|
||||
<body>
|
||||
<div id="root"></div>
|
||||
<script type="module" src="/src/main.tsx"></script>
|
||||
</body>
|
||||
</html>
|
||||
Generated
+1672
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,29 @@
|
||||
{
|
||||
"name": "client",
|
||||
"private": true,
|
||||
"version": "0.0.0",
|
||||
"type": "module",
|
||||
"scripts": {
|
||||
"dev": "vite",
|
||||
"build": "tsc -b && vite build",
|
||||
"typecheck": "tsc -b --noEmit",
|
||||
"lint": "oxlint",
|
||||
"preview": "vite preview",
|
||||
"test": "echo 'client: no unit tests yet (v0.1 uses e2e smoke via server tests)' && exit 0"
|
||||
},
|
||||
"dependencies": {
|
||||
"@pipecat-ai/client-js": "^1.13.0",
|
||||
"@pipecat-ai/small-webrtc-transport": "^1.10.6",
|
||||
"react": "^19.2.8",
|
||||
"react-dom": "^19.2.8"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@types/node": "^24.13.3",
|
||||
"@types/react": "^19.2.17",
|
||||
"@types/react-dom": "^19.2.3",
|
||||
"@vitejs/plugin-react": "^6.0.4",
|
||||
"oxlint": "^1.75.0",
|
||||
"typescript": "~6.0.2",
|
||||
"vite": "^8.2.0"
|
||||
}
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
|
After Width: | Height: | Size: 9.3 KiB |
@@ -0,0 +1,24 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg">
|
||||
<symbol id="bluesky-icon" viewBox="0 0 16 17">
|
||||
<g clip-path="url(#bluesky-clip)"><path fill="#08060d" d="M7.75 7.735c-.693-1.348-2.58-3.86-4.334-5.097-1.68-1.187-2.32-.981-2.74-.79C.188 2.065.1 2.812.1 3.251s.241 3.602.398 4.13c.52 1.744 2.367 2.333 4.07 2.145-2.495.37-4.71 1.278-1.805 4.512 3.196 3.309 4.38-.71 4.987-2.746.608 2.036 1.307 5.91 4.93 2.746 2.72-2.746.747-4.143-1.747-4.512 1.702.189 3.55-.4 4.07-2.145.156-.528.397-3.691.397-4.13s-.088-1.186-.575-1.406c-.42-.19-1.06-.395-2.741.79-1.755 1.24-3.64 3.752-4.334 5.099"/></g>
|
||||
<defs><clipPath id="bluesky-clip"><path fill="#fff" d="M.1.85h15.3v15.3H.1z"/></clipPath></defs>
|
||||
</symbol>
|
||||
<symbol id="discord-icon" viewBox="0 0 20 19">
|
||||
<path fill="#08060d" d="M16.224 3.768a14.5 14.5 0 0 0-3.67-1.153c-.158.286-.343.67-.47.976a13.5 13.5 0 0 0-4.067 0c-.128-.306-.317-.69-.476-.976A14.4 14.4 0 0 0 3.868 3.77C1.546 7.28.916 10.703 1.231 14.077a14.7 14.7 0 0 0 4.5 2.306q.545-.748.965-1.587a9.5 9.5 0 0 1-1.518-.74q.191-.14.372-.293c2.927 1.369 6.107 1.369 8.999 0q.183.152.372.294-.723.437-1.52.74.418.838.963 1.588a14.6 14.6 0 0 0 4.504-2.308c.37-3.911-.63-7.302-2.644-10.309m-9.13 8.234c-.878 0-1.599-.82-1.599-1.82 0-.998.705-1.82 1.6-1.82.894 0 1.614.82 1.599 1.82.001 1-.705 1.82-1.6 1.82m5.91 0c-.878 0-1.599-.82-1.599-1.82 0-.998.705-1.82 1.6-1.82.893 0 1.614.82 1.599 1.82 0 1-.706 1.82-1.6 1.82"/>
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</symbol>
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<symbol id="documentation-icon" viewBox="0 0 21 20">
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<path fill="none" stroke="#aa3bff" stroke-linecap="round" stroke-linejoin="round" stroke-width="1.35" d="m15.5 13.333 1.533 1.322c.645.555.967.833.967 1.178s-.322.623-.967 1.179L15.5 18.333m-3.333-5-1.534 1.322c-.644.555-.966.833-.966 1.178s.322.623.966 1.179l1.534 1.321"/>
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<path fill="none" stroke="#aa3bff" stroke-linecap="round" stroke-linejoin="round" stroke-width="1.35" d="M17.167 10.836v-4.32c0-1.41 0-2.117-.224-2.68-.359-.906-1.118-1.621-2.08-1.96-.599-.21-1.349-.21-2.848-.21-2.623 0-3.935 0-4.983.369-1.684.591-3.013 1.842-3.641 3.428C3 6.449 3 7.684 3 10.154v2.122c0 2.558 0 3.838.706 4.726q.306.383.713.671c.76.536 1.79.64 3.581.66"/>
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<path fill="none" stroke="#aa3bff" stroke-linecap="round" stroke-linejoin="round" stroke-width="1.35" d="M3 10a2.78 2.78 0 0 1 2.778-2.778c.555 0 1.209.097 1.748-.047.48-.129.854-.503.982-.982.145-.54.048-1.194.048-1.749a2.78 2.78 0 0 1 2.777-2.777"/>
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</symbol>
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<symbol id="github-icon" viewBox="0 0 19 19">
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<path fill="#08060d" fill-rule="evenodd" d="M9.356 1.85C5.05 1.85 1.57 5.356 1.57 9.694a7.84 7.84 0 0 0 5.324 7.44c.387.079.528-.168.528-.376 0-.182-.013-.805-.013-1.454-2.165.467-2.616-.935-2.616-.935-.349-.91-.864-1.143-.864-1.143-.71-.48.051-.48.051-.48.787.051 1.2.805 1.2.805.695 1.194 1.817.857 2.268.649.064-.507.27-.857.49-1.052-1.728-.182-3.545-.857-3.545-3.87 0-.857.31-1.558.8-2.104-.078-.195-.349-1 .077-2.078 0 0 .657-.208 2.14.805a7.5 7.5 0 0 1 1.946-.26c.657 0 1.328.092 1.946.26 1.483-1.013 2.14-.805 2.14-.805.426 1.078.155 1.883.078 2.078.502.546.799 1.247.799 2.104 0 3.013-1.818 3.675-3.558 3.87.284.247.528.714.528 1.454 0 1.052-.012 1.896-.012 2.156 0 .208.142.455.528.377a7.84 7.84 0 0 0 5.324-7.441c.013-4.338-3.48-7.844-7.773-7.844" clip-rule="evenodd"/>
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</symbol>
|
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<symbol id="social-icon" viewBox="0 0 20 20">
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<path fill="none" stroke="#aa3bff" stroke-linecap="round" stroke-linejoin="round" stroke-width="1.35" d="M12.5 6.667a4.167 4.167 0 1 0-8.334 0 4.167 4.167 0 0 0 8.334 0"/>
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<path fill="none" stroke="#aa3bff" stroke-linecap="round" stroke-linejoin="round" stroke-width="1.35" d="M2.5 16.667a5.833 5.833 0 0 1 8.75-5.053m3.837.474.513 1.035c.07.144.257.282.414.309l.93.155c.596.1.736.536.307.965l-.723.73a.64.64 0 0 0-.152.531l.207.903c.164.715-.213.991-.84.618l-.872-.52a.63.63 0 0 0-.577 0l-.872.52c-.624.373-1.003.094-.84-.618l.207-.903a.64.64 0 0 0-.152-.532l-.723-.729c-.426-.43-.289-.864.306-.964l.93-.156a.64.64 0 0 0 .412-.31l.513-1.034c.28-.562.735-.562 1.012 0"/>
|
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</symbol>
|
||||
<symbol id="x-icon" viewBox="0 0 19 19">
|
||||
<path fill="#08060d" fill-rule="evenodd" d="M1.893 1.98c.052.072 1.245 1.769 2.653 3.77l2.892 4.114c.183.261.333.48.333.486s-.068.089-.152.183l-.522.593-.765.867-3.597 4.087c-.375.426-.734.834-.798.905a1 1 0 0 0-.118.148c0 .01.236.017.664.017h.663l.729-.83c.4-.457.796-.906.879-.999a692 692 0 0 0 1.794-2.038c.034-.037.301-.34.594-.675l.551-.624.345-.392a7 7 0 0 1 .34-.374c.006 0 .93 1.306 2.052 2.903l2.084 2.965.045.063h2.275c1.87 0 2.273-.003 2.266-.021-.008-.02-1.098-1.572-3.894-5.547-2.013-2.862-2.28-3.246-2.273-3.266.008-.019.282-.332 2.085-2.38l2-2.274 1.567-1.782c.022-.028-.016-.03-.65-.03h-.674l-.3.342a871 871 0 0 1-1.782 2.025c-.067.075-.405.458-.75.852a100 100 0 0 1-.803.91c-.148.172-.299.344-.99 1.127-.304.343-.32.358-.345.327-.015-.019-.904-1.282-1.976-2.808L6.365 1.85H1.8zm1.782.91 8.078 11.294c.772 1.08 1.413 1.973 1.425 1.984.016.017.241.02 1.05.017l1.03-.004-2.694-3.766L7.796 5.75 5.722 2.852l-1.039-.004-1.039-.004z" clip-rule="evenodd"/>
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</symbol>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 4.9 KiB |
@@ -0,0 +1,190 @@
|
||||
/* Praxis v0.1 session page — voice-first, minimal. */
|
||||
|
||||
#root {
|
||||
max-width: 720px;
|
||||
margin: 0 auto;
|
||||
padding: 2rem;
|
||||
font-family: system-ui, -apple-system, sans-serif;
|
||||
color: #1a1a1a;
|
||||
}
|
||||
|
||||
#praxis-session header h1 {
|
||||
margin: 0;
|
||||
font-size: 2rem;
|
||||
}
|
||||
|
||||
.subtitle {
|
||||
margin: 0.25rem 0 1.5rem;
|
||||
color: #666;
|
||||
font-size: 0.95rem;
|
||||
}
|
||||
|
||||
.disclaimer {
|
||||
background: #fff8e1;
|
||||
border-left: 3px solid #ffb300;
|
||||
padding: 0.75rem 1rem;
|
||||
margin-bottom: 1.5rem;
|
||||
font-size: 0.9rem;
|
||||
color: #5d4037;
|
||||
border-radius: 4px;
|
||||
}
|
||||
|
||||
.disclaimer-check {
|
||||
display: flex;
|
||||
align-items: flex-start;
|
||||
gap: 0.5rem;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
.disclaimer-check input {
|
||||
margin-top: 0.2rem;
|
||||
}
|
||||
|
||||
.controls {
|
||||
display: flex;
|
||||
gap: 0.75rem;
|
||||
margin-bottom: 1rem;
|
||||
}
|
||||
|
||||
.controls button {
|
||||
padding: 0.6rem 1.2rem;
|
||||
font-size: 1rem;
|
||||
border: none;
|
||||
border-radius: 6px;
|
||||
cursor: pointer;
|
||||
transition: opacity 0.15s;
|
||||
}
|
||||
|
||||
.controls button:disabled {
|
||||
opacity: 0.5;
|
||||
cursor: not-allowed;
|
||||
}
|
||||
|
||||
.controls .start {
|
||||
background: #2563eb;
|
||||
color: white;
|
||||
}
|
||||
|
||||
.controls .stop {
|
||||
background: #ef4444;
|
||||
color: white;
|
||||
}
|
||||
|
||||
.status {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 0.75rem;
|
||||
margin-bottom: 1rem;
|
||||
}
|
||||
|
||||
.badge {
|
||||
padding: 0.2rem 0.6rem;
|
||||
border-radius: 12px;
|
||||
font-size: 0.8rem;
|
||||
font-weight: 600;
|
||||
text-transform: uppercase;
|
||||
}
|
||||
|
||||
.badge--idle { background: #e5e7eb; color: #374151; }
|
||||
.badge--connecting { background: #dbeafe; color: #1d4ed8; }
|
||||
.badge--connected { background: #d1fae5; color: #047857; }
|
||||
.badge--error { background: #fee2e2; color: #b91c1c; }
|
||||
|
||||
.error {
|
||||
color: #b91c1c;
|
||||
font-size: 0.85rem;
|
||||
}
|
||||
|
||||
.latency {
|
||||
font-size: 0.95rem;
|
||||
}
|
||||
|
||||
.latency .ok { color: #047857; font-weight: 600; }
|
||||
.latency .over { color: #b91c1c; font-weight: 600; }
|
||||
.latency .budget { color: #666; font-size: 0.85rem; }
|
||||
|
||||
.transcript h2,
|
||||
.transcript h3 {
|
||||
font-size: 1.1rem;
|
||||
margin-bottom: 0.5rem;
|
||||
}
|
||||
|
||||
.transcript ul {
|
||||
list-style: none;
|
||||
padding: 0;
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
.transcript .turn {
|
||||
padding: 0.5rem 0.75rem;
|
||||
margin-bottom: 0.4rem;
|
||||
border-radius: 6px;
|
||||
display: flex;
|
||||
gap: 0.5rem;
|
||||
}
|
||||
|
||||
.turn--user {
|
||||
background: #e5e7eb;
|
||||
}
|
||||
|
||||
.turn--assistant {
|
||||
background: #dbeafe;
|
||||
}
|
||||
|
||||
.turn .role {
|
||||
font-weight: 600;
|
||||
min-width: 2.5rem;
|
||||
}
|
||||
|
||||
.turn .text {
|
||||
flex: 1;
|
||||
}
|
||||
|
||||
.muted {
|
||||
color: #666;
|
||||
font-size: 0.9rem;
|
||||
}
|
||||
|
||||
/* Full session UX (SLICE-05) */
|
||||
|
||||
.view {
|
||||
margin-bottom: 1rem;
|
||||
}
|
||||
|
||||
.scenario-card {
|
||||
background: #f0f9ff;
|
||||
border: 1px solid #bae6fd;
|
||||
border-radius: 8px;
|
||||
padding: 1rem 1.25rem;
|
||||
margin-bottom: 1.5rem;
|
||||
}
|
||||
|
||||
.scenario-card h2 {
|
||||
margin: 0 0 0.5rem;
|
||||
font-size: 1.15rem;
|
||||
}
|
||||
|
||||
.scenario-desc {
|
||||
margin: 0;
|
||||
color: #475569;
|
||||
font-size: 0.9rem;
|
||||
line-height: 1.4;
|
||||
}
|
||||
|
||||
.summary {
|
||||
background: #f0fdf4;
|
||||
border: 1px solid #bbf7d0;
|
||||
border-radius: 6px;
|
||||
padding: 0.75rem 1rem;
|
||||
margin-bottom: 1rem;
|
||||
}
|
||||
|
||||
.summary h3 {
|
||||
margin: 0 0 0.25rem;
|
||||
font-size: 0.95rem;
|
||||
}
|
||||
|
||||
.summary p {
|
||||
margin: 0;
|
||||
font-size: 0.9rem;
|
||||
}
|
||||
@@ -0,0 +1,181 @@
|
||||
/**
|
||||
* Praxis v0.1 — full session UX (SLICE-05 TASK-05-04).
|
||||
*
|
||||
* Three views: start → live → debrief. Replaces the SLICE-02 minimal page.
|
||||
* - Start: scenario title + disclaimer acknowledgement + Start button
|
||||
* - Live: turn indicators (learner/AI), interrupt feedback, latency readout
|
||||
* - Debrief: debrief text + audio replay control + latency/cost summary
|
||||
*/
|
||||
import { useVoiceSession } from './useVoiceSession'
|
||||
import { useEffect, useState } from 'react'
|
||||
import './App.css'
|
||||
|
||||
type View = 'start' | 'live' | 'debrief'
|
||||
|
||||
function App() {
|
||||
const { state, error, transcripts, latency, start, stop } = useVoiceSession()
|
||||
const [view, setView] = useState<View>('start')
|
||||
const [acknowledged, setAcknowledged] = useState(false)
|
||||
|
||||
useEffect(() => {
|
||||
if (state === 'connected' && view === 'start') {
|
||||
setView('live')
|
||||
}
|
||||
if (state === 'idle' && view === 'live') {
|
||||
setView('debrief')
|
||||
}
|
||||
}, [state, view])
|
||||
|
||||
const handleStart = async () => {
|
||||
await start()
|
||||
}
|
||||
|
||||
const handleEnd = async () => {
|
||||
await stop()
|
||||
setView('debrief')
|
||||
}
|
||||
|
||||
const handleRestart = () => {
|
||||
setView('start')
|
||||
setAcknowledged(false)
|
||||
}
|
||||
|
||||
return (
|
||||
<section id="praxis-session">
|
||||
<header>
|
||||
<h1>Praxis</h1>
|
||||
<p className="subtitle">Customer Service role-play — v0.1</p>
|
||||
</header>
|
||||
|
||||
{view === 'start' && (
|
||||
<div className="view view--start">
|
||||
<div className="scenario-card">
|
||||
<h2>Angry customer requesting refund on a damaged product</h2>
|
||||
<p className="scenario-desc">
|
||||
You are a customer service agent. An angry customer (Jordan) is
|
||||
demanding a refund for a cracked product. Handle the
|
||||
conversation. You'll receive a coaching debrief at the end.
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<div className="disclaimer">
|
||||
<label className="disclaimer-check">
|
||||
<input
|
||||
type="checkbox"
|
||||
checked={acknowledged}
|
||||
onChange={(e) => setAcknowledged(e.target.checked)}
|
||||
/>
|
||||
<span>
|
||||
This is an AI practice session for training purposes. It is
|
||||
not a real conversation and no real company is involved.
|
||||
</span>
|
||||
</label>
|
||||
</div>
|
||||
|
||||
<div className="controls">
|
||||
<button
|
||||
type="button"
|
||||
className="start"
|
||||
disabled={!acknowledged || state === 'connecting'}
|
||||
onClick={() => void handleStart()}
|
||||
>
|
||||
{state === 'connecting' ? 'Connecting…' : 'Start session'}
|
||||
</button>
|
||||
</div>
|
||||
|
||||
{error && <div className="error">{error}</div>}
|
||||
</div>
|
||||
)}
|
||||
|
||||
{view === 'live' && (
|
||||
<div className="view view--live">
|
||||
<div className="status">
|
||||
<span className={`badge badge--${state}`}>{state}</span>
|
||||
{latency && (
|
||||
<span className="latency">
|
||||
<span className="latency-label">{latency.label}:</span>{' '}
|
||||
<span className={latency.e2eMs !== null && latency.e2eMs <= 600 ? 'ok' : 'over'}>
|
||||
{latency.e2eMs !== null ? `${latency.e2eMs.toFixed(0)} ms` : '—'}
|
||||
</span>
|
||||
</span>
|
||||
)}
|
||||
</div>
|
||||
|
||||
<div className="controls">
|
||||
<button type="button" className="stop" onClick={() => void handleEnd()}>
|
||||
End session
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<div className="transcript">
|
||||
<h2>Live transcript</h2>
|
||||
{transcripts.length === 0 ? (
|
||||
<p className="muted">Speak to the AI customer…</p>
|
||||
) : (
|
||||
<ul>
|
||||
{transcripts.map((t, i) => (
|
||||
<li key={i} className={`turn turn--${t.role}`}>
|
||||
<span className="role">{t.role === 'user' ? 'You' : 'AI'}</span>
|
||||
<span className="text">{t.text}</span>
|
||||
</li>
|
||||
))}
|
||||
</ul>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{error && <div className="error">{error}</div>}
|
||||
</div>
|
||||
)}
|
||||
|
||||
{view === 'debrief' && (
|
||||
<div className="view view--debrief">
|
||||
<h2>Session debrief</h2>
|
||||
<p className="muted">
|
||||
Your coaching debrief would appear here, generated from your turns
|
||||
+ the branch outcome. In a live run (with API keys), the debrief
|
||||
is spoken in the same voice as the role-play.
|
||||
</p>
|
||||
|
||||
{latency && (
|
||||
<div className="summary">
|
||||
<h3>Latency summary</h3>
|
||||
<p>
|
||||
{latency.label}:{' '}
|
||||
<span className={latency.e2eMs !== null && latency.e2eMs <= 600 ? 'ok' : 'over'}>
|
||||
{latency.e2eMs !== null ? `${latency.e2eMs.toFixed(0)} ms` : '—'}
|
||||
</span>
|
||||
{latency.e2eMs !== null && (
|
||||
<span className="budget">
|
||||
{' '}(budget 600ms — {latency.e2eMs <= 600 ? 'within' : 'over'})
|
||||
</span>
|
||||
)}
|
||||
</p>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{transcripts.length > 0 && (
|
||||
<div className="transcript">
|
||||
<h3>Turns this session</h3>
|
||||
<ul>
|
||||
{transcripts.map((t, i) => (
|
||||
<li key={i} className={`turn turn--${t.role}`}>
|
||||
<span className="role">{t.role === 'user' ? 'You' : 'AI'}</span>
|
||||
<span className="text">{t.text}</span>
|
||||
</li>
|
||||
))}
|
||||
</ul>
|
||||
</div>
|
||||
)}
|
||||
|
||||
<div className="controls">
|
||||
<button type="button" className="start" onClick={handleRestart}>
|
||||
Start a new session
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
</section>
|
||||
)
|
||||
}
|
||||
|
||||
export default App
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 13 KiB |
@@ -0,0 +1 @@
|
||||
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|
||||
|
After Width: | Height: | Size: 4.0 KiB |
File diff suppressed because one or more lines are too long
|
After Width: | Height: | Size: 8.5 KiB |
@@ -0,0 +1,16 @@
|
||||
/* Praxis v0.1 — minimal global reset (voice-first, no marketing chrome). */
|
||||
|
||||
:root {
|
||||
font-family: system-ui, -apple-system, sans-serif;
|
||||
color: #1a1a1a;
|
||||
background: #fff;
|
||||
}
|
||||
|
||||
* {
|
||||
box-sizing: border-box;
|
||||
}
|
||||
|
||||
body {
|
||||
margin: 0;
|
||||
min-height: 100vh;
|
||||
}
|
||||
@@ -0,0 +1,10 @@
|
||||
import { StrictMode } from 'react'
|
||||
import { createRoot } from 'react-dom/client'
|
||||
import './index.css'
|
||||
import App from './App.tsx'
|
||||
|
||||
createRoot(document.getElementById('root')!).render(
|
||||
<StrictMode>
|
||||
<App />
|
||||
</StrictMode>,
|
||||
)
|
||||
@@ -0,0 +1,129 @@
|
||||
/**
|
||||
* Praxis voice session hook — wraps the Pipecat client + SmallWebRTCTransport.
|
||||
*
|
||||
* Connects to the server's POST /pipecat/webrtc endpoint, manages mic permission,
|
||||
* audio playback, live transcript, and a latency readout (ASR→TTS-first-audio).
|
||||
*
|
||||
* v0.1 SLICE-02: minimal start/speak/reply loop. SLICE-05 expands to the full
|
||||
* start → live → debrief session flow.
|
||||
*/
|
||||
import { useCallback, useEffect, useRef, useState } from 'react'
|
||||
import { PipecatClient, type PipecatClientOptions } from '@pipecat-ai/client-js'
|
||||
import { SmallWebRTCTransport } from '@pipecat-ai/small-webrtc-transport'
|
||||
|
||||
export type SessionState = 'idle' | 'connecting' | 'connected' | 'error'
|
||||
|
||||
export interface TranscriptEntry {
|
||||
role: 'user' | 'assistant'
|
||||
text: string
|
||||
ts: number
|
||||
}
|
||||
|
||||
export interface LatencyReading {
|
||||
/** ms from bot-ready to first assistant audio (approx ASR→TTS first audio). */
|
||||
e2eMs: number | null
|
||||
label: string
|
||||
}
|
||||
|
||||
export interface UseVoiceSessionResult {
|
||||
state: SessionState
|
||||
error: string | null
|
||||
transcripts: TranscriptEntry[]
|
||||
latency: LatencyReading | null
|
||||
start: () => Promise<void>
|
||||
stop: () => Promise<void>
|
||||
}
|
||||
|
||||
const SERVER_OFFER_URL = '/pipecat/webrtc'
|
||||
|
||||
export function useVoiceSession(): UseVoiceSessionResult {
|
||||
const [state, setState] = useState<SessionState>('idle')
|
||||
const [error, setError] = useState<string | null>(null)
|
||||
const [transcripts, setTranscripts] = useState<TranscriptEntry[]>([])
|
||||
const [latency, setLatency] = useState<LatencyReading | null>(null)
|
||||
const clientRef = useRef<PipecatClient | null>(null)
|
||||
const readyAtRef = useRef<number | null>(null)
|
||||
|
||||
const stop = useCallback(async () => {
|
||||
const c = clientRef.current
|
||||
if (c) {
|
||||
try {
|
||||
await c.disconnect()
|
||||
} catch {
|
||||
/* ignore */
|
||||
}
|
||||
clientRef.current = null
|
||||
}
|
||||
setState('idle')
|
||||
readyAtRef.current = null
|
||||
}, [])
|
||||
|
||||
const start = useCallback(async () => {
|
||||
setError(null)
|
||||
setState('connecting')
|
||||
try {
|
||||
const transport = new SmallWebRTCTransport({
|
||||
iceServers: [{ urls: 'stun:stun.l.google.com:19302' }],
|
||||
offerUrlTemplate: SERVER_OFFER_URL,
|
||||
})
|
||||
const options: PipecatClientOptions = {
|
||||
transport,
|
||||
enableMic: true,
|
||||
callbacks: {
|
||||
'bot-transport-ready': () => {
|
||||
readyAtRef.current = performance.now()
|
||||
},
|
||||
'bot-ready': () => {
|
||||
setState('connected')
|
||||
readyAtRef.current = performance.now()
|
||||
},
|
||||
'user-connected': () => {
|
||||
readyAtRef.current = performance.now()
|
||||
},
|
||||
// Latency: capture the metrics frame the server emits (TASK-02-06).
|
||||
metric: (m: { name?: string; value?: number }) => {
|
||||
if (m?.name === 'e2e_latency_ms' && typeof m.value === 'number') {
|
||||
setLatency({ e2eMs: m.value, label: 'ASR→TTS first audio' })
|
||||
}
|
||||
},
|
||||
// Transcript (optional display).
|
||||
'bot-transcription': (data: { text?: string }) => {
|
||||
const text = data?.text
|
||||
if (text) {
|
||||
setTranscripts((prev) => [
|
||||
...prev,
|
||||
{ role: 'assistant', text, ts: Date.now() },
|
||||
])
|
||||
}
|
||||
},
|
||||
'user-transcription': (data: { text?: string }) => {
|
||||
const text = data?.text
|
||||
if (text) {
|
||||
setTranscripts((prev) => [
|
||||
...prev,
|
||||
{ role: 'user', text, ts: Date.now() },
|
||||
])
|
||||
}
|
||||
},
|
||||
} as any,
|
||||
}
|
||||
|
||||
const client = new PipecatClient(options)
|
||||
clientRef.current = client
|
||||
// initDevices triggers mic permission; connect() opens the WebRTC session.
|
||||
await client.initDevices()
|
||||
await client.connect()
|
||||
} catch (e: any) {
|
||||
setError(e?.message ?? String(e))
|
||||
setState('error')
|
||||
}
|
||||
}, [])
|
||||
|
||||
useEffect(() => {
|
||||
return () => {
|
||||
void stop()
|
||||
}
|
||||
}, [stop])
|
||||
|
||||
return { state, error, transcripts, latency, start, stop }
|
||||
}
|
||||
@@ -0,0 +1,26 @@
|
||||
{
|
||||
"compilerOptions": {
|
||||
"tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo",
|
||||
"target": "es2023",
|
||||
"lib": ["ES2023", "DOM"],
|
||||
"module": "esnext",
|
||||
"types": ["vite/client"],
|
||||
"allowArbitraryExtensions": true,
|
||||
"skipLibCheck": true,
|
||||
|
||||
/* Bundler mode */
|
||||
"moduleResolution": "bundler",
|
||||
"allowImportingTsExtensions": true,
|
||||
"verbatimModuleSyntax": true,
|
||||
"moduleDetection": "force",
|
||||
"noEmit": true,
|
||||
"jsx": "react-jsx",
|
||||
|
||||
/* Linting */
|
||||
"noUnusedLocals": true,
|
||||
"noUnusedParameters": true,
|
||||
"erasableSyntaxOnly": true,
|
||||
"noFallthroughCasesInSwitch": true
|
||||
},
|
||||
"include": ["src"]
|
||||
}
|
||||
@@ -0,0 +1,7 @@
|
||||
{
|
||||
"files": [],
|
||||
"references": [
|
||||
{ "path": "./tsconfig.app.json" },
|
||||
{ "path": "./tsconfig.node.json" }
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"compilerOptions": {
|
||||
"tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo",
|
||||
"target": "es2023",
|
||||
"lib": ["ES2023"],
|
||||
"types": ["node"],
|
||||
"skipLibCheck": true,
|
||||
|
||||
/* Bundler mode */
|
||||
"module": "nodenext",
|
||||
"allowImportingTsExtensions": true,
|
||||
"verbatimModuleSyntax": true,
|
||||
"moduleDetection": "force",
|
||||
"noEmit": true,
|
||||
|
||||
/* Linting */
|
||||
"noUnusedLocals": true,
|
||||
"noUnusedParameters": true,
|
||||
"erasableSyntaxOnly": true,
|
||||
"noFallthroughCasesInSwitch": true
|
||||
},
|
||||
"include": ["vite.config.ts"]
|
||||
}
|
||||
@@ -0,0 +1,20 @@
|
||||
import { defineConfig } from 'vite'
|
||||
import react from '@vitejs/plugin-react'
|
||||
|
||||
// Praxis v0.1 client config — proxies /pipecat to the Python server in dev.
|
||||
export default defineConfig({
|
||||
plugins: [react()],
|
||||
server: {
|
||||
port: 5173,
|
||||
proxy: {
|
||||
'/pipecat': {
|
||||
target: 'http://localhost:8789',
|
||||
changeOrigin: true,
|
||||
},
|
||||
'/health': {
|
||||
target: 'http://localhost:8789',
|
||||
changeOrigin: true,
|
||||
},
|
||||
},
|
||||
},
|
||||
})
|
||||
@@ -0,0 +1,17 @@
|
||||
"""Praxis SQLite store package — async access layer (D-007)."""
|
||||
|
||||
from db.store import (
|
||||
PraxisStore,
|
||||
SessionRow,
|
||||
TurnRow,
|
||||
HARDCODED_LEARNER_ID,
|
||||
)
|
||||
from db.migrate import apply_migrations
|
||||
|
||||
__all__ = [
|
||||
"PraxisStore",
|
||||
"SessionRow",
|
||||
"TurnRow",
|
||||
"HARDCODED_LEARNER_ID",
|
||||
"apply_migrations",
|
||||
]
|
||||
@@ -0,0 +1,46 @@
|
||||
"""SQLite migration runner — applies db/migrations/*.sql in order."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import sqlite3
|
||||
from pathlib import Path
|
||||
|
||||
_DEFAULT_DB_PATH = Path("praxis.db")
|
||||
_DEFAULT_MIGRATIONS_DIR = Path(__file__).resolve().parent / "migrations"
|
||||
|
||||
|
||||
def apply_migrations(
|
||||
db_path: Path | str | None = None,
|
||||
migrations_dir: Path | None = None,
|
||||
) -> list[str]:
|
||||
"""Apply all pending migrations in order. Returns the list of applied names.
|
||||
|
||||
Uses a `_migrations` tracking table so re-running is idempotent.
|
||||
"""
|
||||
db = Path(db_path) if db_path else _DEFAULT_DB_PATH
|
||||
mdir = migrations_dir or _DEFAULT_MIGRATIONS_DIR
|
||||
|
||||
conn = sqlite3.connect(str(db))
|
||||
try:
|
||||
conn.execute(
|
||||
"CREATE TABLE IF NOT EXISTS _migrations (id TEXT PRIMARY KEY, applied_at TEXT NOT NULL DEFAULT (datetime('now')))"
|
||||
)
|
||||
applied: list[str] = []
|
||||
for sql_path in sorted(mdir.glob("*.sql")):
|
||||
mid = sql_path.stem
|
||||
already = conn.execute(
|
||||
"SELECT 1 FROM _migrations WHERE id = ?", (mid,)
|
||||
).fetchone()
|
||||
if already:
|
||||
continue
|
||||
sql = sql_path.read_text(encoding="utf-8")
|
||||
conn.executescript(sql)
|
||||
conn.execute("INSERT INTO _migrations (id) VALUES (?)", (mid,))
|
||||
conn.commit()
|
||||
applied.append(mid)
|
||||
return applied
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
|
||||
__all__ = ["apply_migrations"]
|
||||
@@ -0,0 +1,46 @@
|
||||
-- Migration 0001 — initial schema for v0.1 learner state (D-007).
|
||||
-- Creates learner, sessions, turns, progress tables + the hardcoded learner-1 row.
|
||||
|
||||
-- Schema (also in db/schema.sql for reference; this is the migration source).
|
||||
CREATE TABLE IF NOT EXISTS learner (
|
||||
id TEXT PRIMARY KEY,
|
||||
display_name TEXT NOT NULL,
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now'))
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS sessions (
|
||||
id TEXT PRIMARY KEY,
|
||||
learner_id TEXT NOT NULL REFERENCES learner(id),
|
||||
scenario_id TEXT NOT NULL,
|
||||
started_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
ended_at TEXT,
|
||||
branch_path_json TEXT,
|
||||
outcome TEXT,
|
||||
cost_estimated_cents INTEGER,
|
||||
debrief_text TEXT,
|
||||
cost_breakdown_json TEXT
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS turns (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
session_id TEXT NOT NULL REFERENCES sessions(id),
|
||||
seq INTEGER NOT NULL,
|
||||
role TEXT NOT NULL,
|
||||
asr_text TEXT,
|
||||
tts_text TEXT,
|
||||
latency_ms REAL,
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
UNIQUE(session_id, seq)
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS progress (
|
||||
learner_id TEXT NOT NULL REFERENCES learner(id),
|
||||
scenario_id TEXT NOT NULL,
|
||||
attempts INTEGER NOT NULL DEFAULT 0,
|
||||
last_outcome TEXT,
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
PRIMARY KEY (learner_id, scenario_id)
|
||||
);
|
||||
|
||||
-- The single hardcoded learner row (D-007 — no auth in v0.1).
|
||||
INSERT OR IGNORE INTO learner (id, display_name) VALUES ('learner-1', 'Alex');
|
||||
@@ -0,0 +1,16 @@
|
||||
-- Migration 0002 — add debrief_text column to sessions (TASK-05-05).
|
||||
-- The debrief_text column was already included in 0001_init.sql (forward-
|
||||
-- compatible schema), but this migration documents the explicit SLICE-05
|
||||
-- addition for any database created before SLICE-05. It is a no-op if the
|
||||
-- column already exists (SQLite ALTER TABLE ADD COLUMN is idempotent-safe
|
||||
-- via the IF NOT EXISTS guard below).
|
||||
|
||||
-- SQLite doesn't support ADD COLUMN IF NOT EXISTS directly; use a pragma check.
|
||||
-- This migration is intentionally a no-op for databases created with 0001_init
|
||||
-- (which already has debrief_text). It exists for migration-history completeness
|
||||
-- and for any pre-SLICE-05 database.
|
||||
|
||||
-- No SQL needed — 0001_init.sql already includes:
|
||||
-- debrief_text TEXT
|
||||
-- in the sessions table. This migration is a marker only.
|
||||
SELECT 1;
|
||||
@@ -0,0 +1,46 @@
|
||||
-- Praxis v0.1 SQLite schema — learner state (D-007).
|
||||
-- Single hardcoded learner, no auth, no multi-tenant.
|
||||
|
||||
-- The single learner row (D-007). v0.1 has one hardcoded profile.
|
||||
CREATE TABLE IF NOT EXISTS learner (
|
||||
id TEXT PRIMARY KEY,
|
||||
display_name TEXT NOT NULL,
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now'))
|
||||
);
|
||||
|
||||
-- Session log: one row per voice session.
|
||||
CREATE TABLE IF NOT EXISTS sessions (
|
||||
id TEXT PRIMARY KEY,
|
||||
learner_id TEXT NOT NULL REFERENCES learner(id),
|
||||
scenario_id TEXT NOT NULL,
|
||||
started_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
ended_at TEXT,
|
||||
branch_path_json TEXT, -- JSON array of branch ids taken
|
||||
outcome TEXT, -- 'success' | 'failure' | NULL
|
||||
cost_estimated_cents INTEGER, -- derived per-session cost (D-012)
|
||||
debrief_text TEXT, -- TASK-05-05: the generated debrief
|
||||
cost_breakdown_json TEXT -- TASK-04-04: token/minute/char breakdown
|
||||
);
|
||||
|
||||
-- Turn log: one row per ASR/TTS turn within a session.
|
||||
CREATE TABLE IF NOT EXISTS turns (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
session_id TEXT NOT NULL REFERENCES sessions(id),
|
||||
seq INTEGER NOT NULL,
|
||||
role TEXT NOT NULL, -- 'user' | 'assistant'
|
||||
asr_text TEXT,
|
||||
tts_text TEXT,
|
||||
latency_ms REAL,
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
UNIQUE(session_id, seq)
|
||||
);
|
||||
|
||||
-- Progress: per-learner per-scenario progression (v0.1: attempts + last outcome).
|
||||
CREATE TABLE IF NOT EXISTS progress (
|
||||
learner_id TEXT NOT NULL REFERENCES learner(id),
|
||||
scenario_id TEXT NOT NULL,
|
||||
attempts INTEGER NOT NULL DEFAULT 0,
|
||||
last_outcome TEXT,
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
PRIMARY KEY (learner_id, scenario_id)
|
||||
);
|
||||
+187
@@ -0,0 +1,187 @@
|
||||
"""Async SQLite store — learner state access layer (D-007, TASK-04-02).
|
||||
|
||||
Type-annotated async access via aiosqlite. Functions:
|
||||
- start_session(learner_id, scenario_id) → session_id
|
||||
- log_turn(session_id, seq, role, asr_text, tts_text, latency_ms)
|
||||
- end_session(session_id, branch_path, outcome, cost_cents, cost_breakdown, debrief_text)
|
||||
- update_progress(learner_id, scenario_id, outcome)
|
||||
- get_session(session_id) + get_turns(session_id)
|
||||
|
||||
No auth — learner_id is the hardcoded 'learner-1' (D-007).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import uuid
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import aiosqlite
|
||||
|
||||
from db.migrate import apply_migrations
|
||||
|
||||
_DEFAULT_DB_PATH = "praxis.db"
|
||||
HARDCODED_LEARNER_ID = "learner-1"
|
||||
|
||||
|
||||
@dataclass
|
||||
class SessionRow:
|
||||
id: str
|
||||
learner_id: str
|
||||
scenario_id: str
|
||||
started_at: str
|
||||
ended_at: str | None
|
||||
branch_path_json: str | None
|
||||
outcome: str | None
|
||||
cost_estimated_cents: int | None
|
||||
debrief_text: str | None
|
||||
cost_breakdown_json: str | None
|
||||
|
||||
@property
|
||||
def branch_path(self) -> list[str]:
|
||||
if self.branch_path_json:
|
||||
return json.loads(self.branch_path_json)
|
||||
return []
|
||||
|
||||
@property
|
||||
def cost_breakdown(self) -> dict[str, Any]:
|
||||
if self.cost_breakdown_json:
|
||||
return json.loads(self.cost_breakdown_json)
|
||||
return {}
|
||||
|
||||
|
||||
@dataclass
|
||||
class TurnRow:
|
||||
id: int
|
||||
session_id: str
|
||||
seq: int
|
||||
role: str
|
||||
asr_text: str | None
|
||||
tts_text: str | None
|
||||
latency_ms: float | None
|
||||
created_at: str
|
||||
|
||||
|
||||
class PraxisStore:
|
||||
"""Async SQLite store for v0.1 learner state."""
|
||||
|
||||
def __init__(self, db_path: str | Path = _DEFAULT_DB_PATH) -> None:
|
||||
self.db_path = str(db_path)
|
||||
|
||||
async def init(self) -> None:
|
||||
"""Apply migrations (idempotent). Call once at startup."""
|
||||
apply_migrations(self.db_path)
|
||||
|
||||
def _connect(self) -> aiosqlite.Connection:
|
||||
return aiosqlite.connect(self.db_path)
|
||||
|
||||
async def start_session(self, learner_id: str, scenario_id: str) -> str:
|
||||
"""Create a session row, return the new session id."""
|
||||
session_id = f"sess-{uuid.uuid4().hex[:12]}"
|
||||
async with self._connect() as db:
|
||||
await db.execute(
|
||||
"INSERT INTO sessions (id, learner_id, scenario_id) VALUES (?, ?, ?)",
|
||||
(session_id, learner_id, scenario_id),
|
||||
)
|
||||
await db.commit()
|
||||
return session_id
|
||||
|
||||
async def log_turn(
|
||||
self,
|
||||
session_id: str,
|
||||
seq: int,
|
||||
role: str,
|
||||
asr_text: str | None = None,
|
||||
tts_text: str | None = None,
|
||||
latency_ms: float | None = None,
|
||||
) -> None:
|
||||
async with self._connect() as db:
|
||||
await db.execute(
|
||||
"INSERT INTO turns (session_id, seq, role, asr_text, tts_text, latency_ms) "
|
||||
"VALUES (?, ?, ?, ?, ?, ?)",
|
||||
(session_id, seq, role, asr_text, tts_text, latency_ms),
|
||||
)
|
||||
await db.commit()
|
||||
|
||||
async def end_session(
|
||||
self,
|
||||
session_id: str,
|
||||
branch_path: list[str],
|
||||
outcome: str,
|
||||
cost_cents: int | None = None,
|
||||
cost_breakdown: dict[str, Any] | None = None,
|
||||
debrief_text: str | None = None,
|
||||
) -> None:
|
||||
async with self._connect() as db:
|
||||
await db.execute(
|
||||
"UPDATE sessions SET ended_at = datetime('now'), "
|
||||
"branch_path_json = ?, outcome = ?, cost_estimated_cents = ?, "
|
||||
"cost_breakdown_json = ?, debrief_text = ? WHERE id = ?",
|
||||
(
|
||||
json.dumps(branch_path),
|
||||
outcome,
|
||||
cost_cents,
|
||||
json.dumps(cost_breakdown) if cost_breakdown else None,
|
||||
debrief_text,
|
||||
session_id,
|
||||
),
|
||||
)
|
||||
await db.commit()
|
||||
|
||||
async def update_progress(
|
||||
self, learner_id: str, scenario_id: str, outcome: str
|
||||
) -> None:
|
||||
async with self._connect() as db:
|
||||
cur = await db.execute(
|
||||
"SELECT attempts FROM progress WHERE learner_id = ? AND scenario_id = ?",
|
||||
(learner_id, scenario_id),
|
||||
)
|
||||
row = await cur.fetchone()
|
||||
if row:
|
||||
await db.execute(
|
||||
"UPDATE progress SET attempts = attempts + 1, last_outcome = ?, "
|
||||
"updated_at = datetime('now') WHERE learner_id = ? AND scenario_id = ?",
|
||||
(outcome, learner_id, scenario_id),
|
||||
)
|
||||
else:
|
||||
await db.execute(
|
||||
"INSERT INTO progress (learner_id, scenario_id, attempts, last_outcome) "
|
||||
"VALUES (?, ?, 1, ?)",
|
||||
(learner_id, scenario_id, outcome),
|
||||
)
|
||||
await db.commit()
|
||||
|
||||
async def get_session(self, session_id: str) -> SessionRow | None:
|
||||
async with self._connect() as db:
|
||||
db.row_factory = aiosqlite.Row
|
||||
cur = await db.execute("SELECT * FROM sessions WHERE id = ?", (session_id,))
|
||||
row = await cur.fetchone()
|
||||
if row is None:
|
||||
return None
|
||||
return SessionRow(**dict(row))
|
||||
|
||||
async def get_turns(self, session_id: str) -> list[TurnRow]:
|
||||
async with self._connect() as db:
|
||||
db.row_factory = aiosqlite.Row
|
||||
cur = await db.execute(
|
||||
"SELECT * FROM turns WHERE session_id = ? ORDER BY seq", (session_id,)
|
||||
)
|
||||
rows = await cur.fetchall()
|
||||
return [TurnRow(**dict(r)) for r in rows]
|
||||
|
||||
async def get_learner(self, learner_id: str = HARDCODED_LEARNER_ID) -> dict | None:
|
||||
async with self._connect() as db:
|
||||
db.row_factory = aiosqlite.Row
|
||||
cur = await db.execute("SELECT * FROM learner WHERE id = ?", (learner_id,))
|
||||
row = await cur.fetchone()
|
||||
return dict(row) if row else None
|
||||
|
||||
|
||||
__all__ = [
|
||||
"PraxisStore",
|
||||
"SessionRow",
|
||||
"TurnRow",
|
||||
"HARDCODED_LEARNER_ID",
|
||||
]
|
||||
@@ -0,0 +1,26 @@
|
||||
# Default debrief prompt template (TASK-05-01).
|
||||
# Renders the learner's turns + branch outcome + debrief_focus into a coaching prompt.
|
||||
# Uses deepseek-v4-flash:cloud no_think mode (D-020) for latency.
|
||||
|
||||
system: |
|
||||
You are a coaching mentor for a customer-service role-play training session.
|
||||
Produce a concise (3-bullet) debrief about the learner's performance.
|
||||
Structure:
|
||||
- What you did well
|
||||
- What to improve
|
||||
- One next step
|
||||
Base your feedback on the learner's ACTUAL turns (quoted below) and the
|
||||
branch outcome. Do NOT reason step-by-step; respond directly (no_think).
|
||||
Keep it about the learner's communication performance, not about the
|
||||
customer's legal rights. Do not recommend that the learner advise a real
|
||||
customer to take legal action.
|
||||
|
||||
user: |
|
||||
Scenario: {{ scenario_title }}
|
||||
Branch outcome: {{ outcome }} ({{ branch_id }})
|
||||
Debrief focus: {{ debrief_focus }}
|
||||
|
||||
Learner turns:
|
||||
{{ learner_turns }}
|
||||
|
||||
Produce the 3-bullet debrief now.
|
||||
@@ -0,0 +1,183 @@
|
||||
# Praxis — Latency Report (R1–R4 Spike)
|
||||
|
||||
> **Phase:** 1 — SLICE-01
|
||||
> **Date:** 2026-08-01
|
||||
> **Status:** probe infrastructure built and ready; **live measurements pending API key provisioning**
|
||||
> **Branch:** `phase/01-minimal-voice-loop`
|
||||
|
||||
---
|
||||
|
||||
## Executive summary
|
||||
|
||||
The four latency probes (`probe_deepgram.py`, `probe_cartesia.py`, `probe_ollama.py`,
|
||||
`probe_e2e.py`) are implemented, executable, and degrade gracefully when API keys are
|
||||
absent (they print a `KEY_MISSING` banner and exit 0). At the time of this v0.1 EXECUTE
|
||||
run, only `GITEA_TOKEN` is provisioned (in `.ciagent/.env.secrets`); the three
|
||||
voice-service keys (`DEEPGRAM_API_KEY`, `CARTESIA_API_KEY`, `OLLAMA_API_KEY`) are **not
|
||||
present**, so live numbers cannot be collected in this run.
|
||||
|
||||
**This is an acceptable v0.1 outcome at full autonomy.** The probe infrastructure is
|
||||
the SLICE-01 deliverable; live measurements come when keys are provisioned. Per the
|
||||
execute directive: "Do NOT block execution on missing keys. Build the code, document
|
||||
the missing-key state, proceed."
|
||||
|
||||
The TTS decision is recorded below as **pending live measurement**, with Piper
|
||||
pre-staged as the R4 mitigation per ARCHITECTURE.md.
|
||||
|
||||
---
|
||||
|
||||
## Probe inventory
|
||||
|
||||
| Probe | File | Risk | Measures | Status |
|
||||
|-------|------|------|----------|--------|
|
||||
| R1 | `scripts/probe_deepgram.py` | R1 | Deepgram Nova-3 first-partial-transcript latency (20 iters, min/median/p95) | built; pending `DEEPGRAM_API_KEY` |
|
||||
| R2 | `scripts/probe_cartesia.py` | R2 | Cartesia Sonic first-audio-byte latency (20 iters, min/median/p95) | built; pending `CARTESIA_API_KEY` |
|
||||
| R3 | `scripts/probe_ollama.py` | R3 | Ollama Cloud direct-API TTFT for `gemma4:cloud` + `deepseek-v4-flash:cloud` no-think (20 iters); logs throttle/auth events (R5) | built; pending `OLLAMA_API_KEY`; also resolves R6 |
|
||||
| R4 | `scripts/probe_e2e.py` | R4 | Integrated three-hop e2e (transcript → Ollama → Cartesia/Piper); 10 iters; budget comparison vs 600ms | built; pending keys; Piper leg pre-staged |
|
||||
|
||||
All four probes:
|
||||
- read keys from `.env` / `.env.secrets` / environment,
|
||||
- accept `--iterations`, `--out` (JSON results path) flags,
|
||||
- print a clear `KEY_MISSING — cannot run live probe` message and **exit 0** when a key is absent,
|
||||
- print a latency table (min / median / p95 / mean in ms) when the key is present.
|
||||
|
||||
### How to run (once keys are provisioned)
|
||||
|
||||
```bash
|
||||
cp .env.example .env # fill in DEEPGRAM_API_KEY, CARTESIA_API_KEY, OLLAMA_API_KEY
|
||||
python scripts/probe_deepgram.py --iterations 20 --out reports/r1_deepgram.json
|
||||
python scripts/probe_cartesia.py --iterations 20 --out reports/r2_cartesia.json
|
||||
python scripts/probe_ollama.py --iterations 20 --out reports/r3_ollama.json
|
||||
python scripts/probe_e2e.py --iterations 10 --out reports/r4_e2e.json
|
||||
# with Piper (after downloading a voice model — see "Piper pre-staging" below):
|
||||
python scripts/probe_e2e.py --iterations 10 --piper --out reports/r4_e2e_piper.json
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Latency budget (research-revised, from ARCHITECTURE.md)
|
||||
|
||||
| Segment | Budget | Source / note |
|
||||
|---------|--------|---------------|
|
||||
| Client capture + WebRTC uplink | ~50ms | WebRTC UDP, Canada region |
|
||||
| ASR (Deepgram Nova-3 first partial) | ~250ms | Vendor claim; **R1: measure** |
|
||||
| LLM first token (gemma4:cloud direct API) | ~200ms | **R3: measure** |
|
||||
| TTS first audio (Cartesia Sonic) | ~120ms | Vendor/leaderboard; **R2: measure** |
|
||||
| WebRTC downlink + playback | ~50ms | |
|
||||
| **Total (all-cloud target)** | **~670ms** | ⚠️ Marginally over 600ms |
|
||||
| **Total (Piper TTS mitigation)** | **~550ms** | R4: pre-stage Piper self-hosted on pilot server |
|
||||
|
||||
**R4 — single biggest v0.1 technical risk:** the all-cloud three-hop path likely lands
|
||||
~670ms, marginally over the 600ms target. The TTS service sits behind an interface
|
||||
(D-014) from SLICE-02 and Piper-on-pilot-server is pre-staged as the likely production
|
||||
v0.1 TTS.
|
||||
|
||||
---
|
||||
|
||||
## TTS decision (D-014)
|
||||
|
||||
**Status: pending live measurement — Piper pre-staged as R4 mitigation.**
|
||||
|
||||
Per the execute directive, the TTS decision is recorded as:
|
||||
|
||||
> "pending live measurement — Piper pre-staged as R4 mitigation per ARCHITECTURE.md"
|
||||
|
||||
### Decision matrix (to be finalized with live R4 numbers)
|
||||
|
||||
| Outcome of R4 integrated measurement | Decision | Rationale |
|
||||
|---|---|---|
|
||||
| Cartesia e2e ≤ 600ms | Cartesia cloud is production v0.1 TTS | Best prosody (Speech Arena #1), simplest ops; Piper remains the post-pilot cost-reduction path. |
|
||||
| Cartesia e2e > 600ms **and** Piper e2e ≤ 600ms | **Piper self-hosted is production v0.1 TTS** (G-003 go/no-go action (a)) | Latency target met; prosody trade-off acceptable for a tech-validation harness. |
|
||||
| Both > 600ms | **Escalate (G-003 action (b))**: evaluate self-hosted `gemma4:e4b` for the LLM hop to recover ~150ms. | TTS swap alone insufficient; move the LLM hop self-hosted. |
|
||||
| Both > 600ms with LLM mitigation also insufficient | **Escalate (G-003 action (c))**: reduce the v0.1 latency target or rethink architecture. | Documented no-go action — not a silent failure. |
|
||||
|
||||
### Piper pre-staging (R4 mitigation)
|
||||
|
||||
Piper is installed (`piper-tts` 1.6.0 via `pipecat-ai[piper]`). A Piper voice model
|
||||
must be downloaded separately to run the Piper leg of `probe_e2e.py` and to use
|
||||
`PRAXIS_TTS=piper` in the pipeline:
|
||||
|
||||
```bash
|
||||
# Download a Piper voice model (en_CA, medium quality) — not committed to the repo.
|
||||
mkdir -p piper_models
|
||||
curl -L -o piper_models/en_CA-medium.onnx \
|
||||
https://huggingface.co/rhasspy/piper-voices/resolve/main/en/CA/medium/en_CA-medium.onnx
|
||||
curl -L -o piper_models/en_CA-medium.onnx.json \
|
||||
https://huggingface.co/rhasspy/piper-voices/resolve/main/en/CA/medium/en_CA-medium.onnx.json
|
||||
export PIPER_VOICE_MODEL=./piper_models/en_CA-medium.onnx
|
||||
python scripts/probe_e2e.py --piper
|
||||
```
|
||||
|
||||
The Pipecat `PiperTTSService` adapter is wired in SLICE-02 (TASK-02-02) behind the
|
||||
`TTSProvider` interface so the swap requires no pipeline change.
|
||||
|
||||
---
|
||||
|
||||
## SLICE-01 go/no-go gate (per G-003)
|
||||
|
||||
The SLICE-01 gate is the de facto stop-the-project trigger (G-007). Its no-go actions
|
||||
are now defined (G-003):
|
||||
|
||||
- **(a)** If e2e > 600ms with Cartesia but ≤ 600ms with Piper → swap TTS to Piper
|
||||
(SLICE-02 pre-stage). ✅ Piper adapter built in SLICE-02.
|
||||
- **(b)** If e2e > 600ms even with Piper → evaluate self-hosted `gemma4:e4b` for the
|
||||
LLM hop. (Architecture keeps the LLM swappable per D-020.)
|
||||
- **(c)** If e2e > 600ms with both mitigations → escalate: reduce the v0.1 latency
|
||||
target or rethink architecture. (Documented no-go action, not a silent failure.)
|
||||
|
||||
**Current state:** the gate cannot be exercised without live keys. This is documented,
|
||||
not silently skipped. When keys are provisioned, run the four probes and record the
|
||||
decision above.
|
||||
|
||||
---
|
||||
|
||||
## R6 resolution (Pipecat + Ollama direct API)
|
||||
|
||||
Pipecat's `OLLamaLLMService` (in `pipecat.services.ollama.llm`) extends
|
||||
`OpenAILLMService` and accepts a custom `base_url` (default
|
||||
`http://localhost:11434/v1`). It uses the OpenAI-compatible client with
|
||||
`api_key="ollama"` by default. To point it at Ollama Cloud direct API:
|
||||
|
||||
```python
|
||||
OLLamaLLMService(
|
||||
base_url="https://ollama.com/v1",
|
||||
settings=OLLamaLLMService.Settings(model="gemma4:cloud", api_key="OLLAMA_API_KEY"),
|
||||
)
|
||||
```
|
||||
|
||||
The `OpenAILLMService` passes `api_key` through to the OpenAI client as a bearer
|
||||
token. **R6 is resolved at the code level**: Pipecat's Ollama service accepts a custom
|
||||
host + bearer. A thin `OllamaCloudLLM` adapter (SLICE-02 TASK-02-03) wraps this to
|
||||
set the bearer from `OLLAMA_API_KEY` and centralize the model selection, so the
|
||||
pipeline never touches Pipecat's settings object directly. The live confirmation
|
||||
(that a real `gemma4:cloud` call returns a first token) is pending the R3 probe run
|
||||
with a real key.
|
||||
|
||||
---
|
||||
|
||||
## What's pending vs delivered
|
||||
|
||||
### Delivered (this run)
|
||||
- ✅ All four probe scripts run and produce structured output.
|
||||
- ✅ Graceful `KEY_MISSING` handling (exit 0, no crash).
|
||||
- ✅ Latency report file exists with the budget, decision matrix, go/no-go actions,
|
||||
Piper pre-staging instructions, and R6 resolution.
|
||||
- ✅ `pipecat-ai[deepgram,cartesia,piper,webrtc]` installed and importable.
|
||||
- ✅ `piper-tts` installed (Piper pre-staged at the package level).
|
||||
|
||||
### Pending API key provisioning
|
||||
- ⏳ R1 measured Deepgram first-partial latency (min/median/p95).
|
||||
- ⏳ R2 measured Cartesia first-audio latency (min/median/p95).
|
||||
- ⏳ R3 measured Ollama TTFT for both models + throttle events (R5).
|
||||
- ⏳ R4 measured integrated e2e (Cartesia + Piper legs) + budget comparison.
|
||||
- ⏳ Final TTS decision (Cartesia vs Piper) justified by R4 data.
|
||||
- ⏳ Live R6 confirmation (real `gemma4:cloud` first token).
|
||||
|
||||
When keys are provisioned, re-running the four probes populates this report with
|
||||
real numbers and finalizes the TTS decision per the matrix above. No code change is
|
||||
required — the probes are ready.
|
||||
|
||||
---
|
||||
|
||||
*End of latency report. SLICE-01 probe infrastructure is delivered; live numbers are
|
||||
pending API key provisioning per the documented v0.1 EXECUTE directive.*
|
||||
@@ -0,0 +1,56 @@
|
||||
[build-system]
|
||||
requires = ["setuptools>=68", "wheel"]
|
||||
build-backend = "setuptools.build_meta"
|
||||
|
||||
[project]
|
||||
name = "praxis-server"
|
||||
version = "0.1.0"
|
||||
description = "Praxis — voice-first AI apprenticeship platform (v0.1 foundation: minimal viable voice loop)"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.11"
|
||||
license = { text = "Proprietary" }
|
||||
authors = [{ name = "Praxis v0.1 (CIAgent)" }]
|
||||
|
||||
dependencies = [
|
||||
# Orchestration — Pipecat (D-017) with the three native service extras + WebRTC transport
|
||||
"pipecat-ai[deepgram,cartesia,piper,webrtc]>=1.6.0",
|
||||
# LLM access — Ollama Cloud direct API (D-020). Pipecat's OLLamaLLMService uses the
|
||||
# OpenAI-compatible client; we point base_url at https://ollama.com/v1 + bearer key.
|
||||
"openai>=1.40",
|
||||
# Scenario format — YAML DSL → Pydantic (D-018)
|
||||
"pydantic>=2.7",
|
||||
"pyyaml>=6.0",
|
||||
# Learner state — SQLite (D-007), async access
|
||||
"aiosqlite>=0.20",
|
||||
# Config
|
||||
"python-dotenv>=1.0",
|
||||
# Latency probes — HTTP client for the integrated e2e probe
|
||||
"httpx>=0.27",
|
||||
"websockets>=12.0",
|
||||
# Audio probe fixture generation (synthesized PCM) for the ASR probe
|
||||
"numpy>=1.26",
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
dev = [
|
||||
"pytest>=8.0",
|
||||
"pytest-asyncio>=0.23",
|
||||
"pytest-cov>=5.0",
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
praxis-server = "server.__main__:main"
|
||||
|
||||
[tool.setuptools.packages.find]
|
||||
where = ["."]
|
||||
include = ["server*", "db*", "scenarios*"]
|
||||
exclude = ["client*", "tests*", "scripts*"]
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
asyncio_mode = "auto"
|
||||
testpaths = ["tests"]
|
||||
python_files = ["test_*.py"]
|
||||
addopts = "-ra -q"
|
||||
|
||||
[tool.coverage.run]
|
||||
source = ["server", "db"]
|
||||
@@ -0,0 +1,20 @@
|
||||
# Praxis v0.1 cost rates — per-unit pricing for the cost logging (D-012, REQ-NFR-COST-01).
|
||||
# v0.1 logs actual per-session cost; no enforced ceiling (pilot).
|
||||
# Per G-005: these are pilot-config rates (Ollama tier + cloud), NOT at-scale
|
||||
# per-learner unit economics — the $3/learner target requires self-hosted
|
||||
# gemma4:e4b + Piper (post-pilot).
|
||||
|
||||
# LLM role-play (gemma4:cloud) — Ollama tier (Pro plan amortized, pilot estimate).
|
||||
gemma4_cloud_per_1k_tokens_cents: 0.5
|
||||
|
||||
# Debrief + classifier (deepseek-v4-flash:cloud) — Ollama tier.
|
||||
deepseek_v4_flash_per_1k_tokens_cents: 1.0
|
||||
|
||||
# ASR (Deepgram Nova-3 streaming) — $0.0043/min → 0.43 cents/min.
|
||||
deepgram_per_audio_minute_cents: 0.43
|
||||
|
||||
# TTS (Cartesia Sonic cloud) — per-char pricing (pilot estimate).
|
||||
cartesia_per_1k_chars_cents: 3.0
|
||||
|
||||
# TTS (Piper self-hosted) — open-weights, $0 marginal cost.
|
||||
piper_per_1k_chars_cents: 0.0
|
||||
@@ -0,0 +1,55 @@
|
||||
# Praxis v0.1 scenario — Customer Service refund role-play (D-010, D-018).
|
||||
# One branch point: accept_resolution vs escalate (D-010).
|
||||
# failure_mode present (D-009 — not provoked in v0.1).
|
||||
# Debrief via deepseek-v4-flash:cloud no_think (D-020).
|
||||
|
||||
id: cs_refund_ca_v01
|
||||
path: customer_service
|
||||
market: CA
|
||||
language: en-CA
|
||||
title: "Angry customer requesting refund on a damaged product"
|
||||
difficulty: 1
|
||||
failure_mode: escalates_unresolved # D-009: present, not provoked in v0.1
|
||||
|
||||
persona:
|
||||
voice_id: "cartesia:a3536a36-1d18-4efb-a95a-7c44b7b5e384" # D-006: same voice as mentor
|
||||
character: "Customer (Jordan)"
|
||||
|
||||
setup:
|
||||
system_prompt: |
|
||||
You are Jordan, a customer who received a damaged product.
|
||||
You are frustrated but not abusive. You want a refund.
|
||||
Stay in character. Do not break role.
|
||||
Keep responses concise for voice (1-3 sentences).
|
||||
Do not give legal, financial, or medical advice.
|
||||
Do not impersonate a real employee of any actual company.
|
||||
opening_line: "Hi, I received my order yesterday and the item is cracked. I want my money back."
|
||||
|
||||
success_criteria:
|
||||
- "Acknowledged the customer's frustration empathetically"
|
||||
- "Offered a concrete resolution (refund or replacement)"
|
||||
- "Confirmed next steps"
|
||||
|
||||
common_mistakes:
|
||||
- "Jumping to policy before acknowledging emotion"
|
||||
- "Using jargon ('RMA', 'SLA')"
|
||||
- "Getting defensive about the company"
|
||||
|
||||
branches:
|
||||
- id: accept_resolution
|
||||
trigger:
|
||||
learner_signals: ["empathy", "concrete_resolution", "next_steps"]
|
||||
outcome: success
|
||||
debrief_focus: "What you did well — you acknowledged the customer's frustration and offered a concrete resolution."
|
||||
|
||||
- id: escalate
|
||||
trigger:
|
||||
learner_signals: ["defensive", "policy_first", "no_acknowledgement"]
|
||||
outcome: failure
|
||||
failure_mode: escalates_unresolved
|
||||
debrief_focus: "The customer escalated because they felt unheard. You led with policy before acknowledging their frustration."
|
||||
|
||||
debrief:
|
||||
model: deepseek-v4-flash:cloud
|
||||
mode: no_think # D-020: latency
|
||||
prompt_template: debrief/default
|
||||
Executable
+165
@@ -0,0 +1,165 @@
|
||||
#!/usr/bin/env python3
|
||||
"""End-to-end smoke test (TASK-05-06) — also runnable as a pytest test.
|
||||
|
||||
Verifies the full v0.1 loop without live API keys (uses the heuristic
|
||||
classifier + a fake LLM for the debrief):
|
||||
start session → simulate 2-3 turns → trigger a branch (classifier) →
|
||||
end session → generate debrief → assert:
|
||||
- debrief non-empty
|
||||
- session + turns + cost logged in SQLite
|
||||
- latency < budget (or logged if exceeded — we log a synthetic value)
|
||||
|
||||
Run:
|
||||
python scripts/e2e_smoke.py
|
||||
# or
|
||||
pytest tests/test_e2e.py
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
import tempfile
|
||||
from pathlib import Path
|
||||
|
||||
# Make the project importable when run from the repo root.
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
|
||||
|
||||
from db.store import PraxisStore, HARDCODED_LEARNER_ID
|
||||
from server.scenarios.loader import load
|
||||
from server.scenarios.classifier import classify_branch_sync_heuristic
|
||||
from server.scenarios.runtime import build_runtime
|
||||
from server.session_recorder import SessionRecorder
|
||||
from server.debrief import generate_debrief
|
||||
from server.guardrails.customer_service import CustomerServiceGuardrail
|
||||
from server.services.base import LLMProvider, LLMStreamChunk
|
||||
|
||||
|
||||
class _StubDebriefLLM(LLMProvider):
|
||||
"""A stub LLMProvider that returns a canned debrief (no API key needed)."""
|
||||
|
||||
name = "stub-debrief"
|
||||
roleplay_model = "gemma4:cloud"
|
||||
debrief_model = "deepseek-v4-flash:cloud"
|
||||
|
||||
async def chat(self, messages, *, stream=True, model=None, no_think=False):
|
||||
yield LLMStreamChunk(content="You did well acknowledging the customer.", is_first=True)
|
||||
|
||||
async def chat_full(self, messages, *, model=None, no_think=False):
|
||||
return (
|
||||
"- What you did well: you acknowledged the customer's frustration and "
|
||||
"offered a concrete refund.\n"
|
||||
"- What to improve: confirm next steps explicitly.\n"
|
||||
"- Next step: practice the empathy-first opening.",
|
||||
{"output_tokens": 60, "model": model or self.debrief_model},
|
||||
)
|
||||
|
||||
|
||||
async def run_e2e(db_path: Path | str | None = None) -> dict:
|
||||
"""Run the full e2e smoke sequence; return a result dict for assertions."""
|
||||
if db_path is None:
|
||||
tmp = tempfile.NamedTemporaryFile(suffix=".db", delete=False)
|
||||
tmp.close()
|
||||
db_path = tmp.name
|
||||
|
||||
store = PraxisStore(db_path)
|
||||
await store.init()
|
||||
|
||||
# 1. Load the scenario.
|
||||
scenario = load("customer_service_refund_ca_v01")
|
||||
runtime = build_runtime(scenario)
|
||||
assert scenario.failure_mode == "escalates_unresolved", "failure_mode field present (D-009)"
|
||||
|
||||
# 2. Start a session.
|
||||
recorder = SessionRecorder(store, scenario_id=scenario.id)
|
||||
session_id = await recorder.start()
|
||||
|
||||
# 3. Simulate 3 turns (accept-resolution path).
|
||||
turns = [
|
||||
{"role": "assistant", "tts_text": scenario.setup.opening_line, "latency_ms": None},
|
||||
{"role": "user", "asr_text": "I'm really sorry you're frustrated. I can offer a full refund right now.", "latency_ms": 420.0},
|
||||
{"role": "assistant", "tts_text": "A refund? Okay, that's something.", "latency_ms": 510.0},
|
||||
{"role": "user", "asr_text": "Let me confirm the next steps for you.", "latency_ms": 380.0},
|
||||
]
|
||||
for t in turns:
|
||||
await recorder.log_turn(
|
||||
role=t["role"],
|
||||
asr_text=t.get("asr_text"),
|
||||
tts_text=t.get("tts_text"),
|
||||
latency_ms=t.get("latency_ms"),
|
||||
)
|
||||
recorder.add_audio_minutes(1.2)
|
||||
|
||||
# 4. Classify the branch (R7, offline — heuristic fallback, no API key).
|
||||
learner_turn_texts = [t["asr_text"] for t in turns if t["role"] == "user"]
|
||||
branch_id = classify_branch_sync_heuristic(scenario, learner_turn_texts)
|
||||
runtime.set_branch(branch_id)
|
||||
recorder.set_branch_path([branch_id])
|
||||
|
||||
# 5. Generate the debrief (stub LLM — no API key needed).
|
||||
llm = _StubDebriefLLM()
|
||||
guardrail = CustomerServiceGuardrail()
|
||||
debrief_text, _usage = await generate_debrief(
|
||||
llm, scenario,
|
||||
branch_id=branch_id,
|
||||
outcome=runtime.outcome,
|
||||
debrief_focus=runtime.debrief_focus(),
|
||||
learner_turns=[
|
||||
{"role": t["role"], "asr_text": t.get("asr_text"), "tts_text": t.get("tts_text")}
|
||||
for t in turns
|
||||
],
|
||||
guardrail=guardrail,
|
||||
)
|
||||
recorder.add_debrief_tokens(input_tokens=150, output_tokens=60)
|
||||
|
||||
# 6. End the session (derives cost + writes outcome + debrief + progress).
|
||||
breakdown = await recorder.end(
|
||||
outcome=runtime.outcome,
|
||||
tts_provider=os.environ.get("PRAXIS_TTS", "cartesia"),
|
||||
debrief_text=debrief_text,
|
||||
)
|
||||
|
||||
# 7. Assert DB state.
|
||||
sess = await store.get_session(session_id)
|
||||
db_turns = await store.get_turns(session_id)
|
||||
assert sess is not None, "session row exists"
|
||||
assert sess.outcome == runtime.outcome, f"outcome matches branch: {sess.outcome}"
|
||||
assert sess.branch_path == [branch_id], "branch path logged"
|
||||
assert sess.cost_estimated_cents is not None and sess.cost_estimated_cents >= 0, "cost non-null"
|
||||
assert sess.debrief_text == debrief_text, "debrief text persisted"
|
||||
assert len(db_turns) == len(turns), f"all {len(turns)} turns logged"
|
||||
assert breakdown.derived_cents >= 0, "cost breakdown derived"
|
||||
|
||||
# Synthetic latency (real latency comes from the live pipeline; here we
|
||||
# log the max turn latency as a proxy and check against the budget).
|
||||
max_latency = max((t.get("latency_ms") or 0) for t in turns)
|
||||
budget = 600.0
|
||||
within_budget = max_latency <= budget
|
||||
|
||||
return {
|
||||
"session_id": session_id,
|
||||
"branch_id": branch_id,
|
||||
"outcome": sess.outcome,
|
||||
"turns_logged": len(db_turns),
|
||||
"cost_cents": sess.cost_estimated_cents,
|
||||
"debrief_chars": len(sess.debrief_text or ""),
|
||||
"max_latency_ms": max_latency,
|
||||
"within_budget": within_budget,
|
||||
"budget_ms": budget,
|
||||
}
|
||||
|
||||
|
||||
def main() -> int:
|
||||
result = asyncio.run(run_e2e())
|
||||
print("\n" + "=" * 60)
|
||||
print("E2E SMOKE TEST — PASSED")
|
||||
print("=" * 60)
|
||||
for k, v in result.items():
|
||||
print(f" {k}: {v}")
|
||||
print()
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
Executable
+166
@@ -0,0 +1,166 @@
|
||||
#!/usr/bin/env python3
|
||||
"""R2 probe — Cartesia Sonic TTS first-audio-byte latency.
|
||||
|
||||
Per PLAN.md SLICE-01 TASK-01-03: WebSocket to Cartesia Sonic, send a sample text
|
||||
chunk, measure first-audio-byte latency over 20 iterations; log min/median/p95.
|
||||
|
||||
Exit code 0 in all cases:
|
||||
- If CARTESIA_API_KEY is missing, print KEY_MISSING and exit 0.
|
||||
- If present, run the live probe and print a latency table.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import asyncio
|
||||
import json
|
||||
import os
|
||||
import statistics
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
|
||||
|
||||
try:
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
except ImportError: # pragma: no cover
|
||||
pass
|
||||
|
||||
|
||||
def _banner(msg: str) -> None:
|
||||
print("\n" + "=" * 72)
|
||||
print(msg)
|
||||
print("=" * 72 + "\n")
|
||||
|
||||
|
||||
def _require_key() -> str | None:
|
||||
key = os.environ.get("CARTESIA_API_KEY", "").strip()
|
||||
if not key:
|
||||
_banner(
|
||||
"KEY_MISSING — CARTESIA_API_KEY not set.\n"
|
||||
" Cannot run live Cartesia probe. Probe infrastructure is built\n"
|
||||
" and ready; live measurements are pending API key provisioning.\n"
|
||||
" Set CARTESIA_API_KEY in .env (see .env.example) and re-run."
|
||||
)
|
||||
return None
|
||||
return key
|
||||
|
||||
|
||||
SAMPLE_TEXT = (
|
||||
"Hi, I received my order yesterday and the item is cracked. "
|
||||
"I want my money back."
|
||||
)
|
||||
|
||||
CARTESIA_WS_URL = "wss://api.cartesia.ai/tts/websocket"
|
||||
DEFAULT_VOICE_ID = "a3536a36-1d18-4efb-a95a-7c44b7b5e384"
|
||||
|
||||
|
||||
async def _probe_once(api_key: str, voice_id: str, model_id: str) -> float | None:
|
||||
"""Open Cartesia WS, request TTS, return ms-to-first-audio-byte."""
|
||||
import websockets
|
||||
|
||||
headers = [("x-api-key", api_key), ("cartesia-version", "2024-06-10")]
|
||||
t0 = time.perf_counter()
|
||||
first_audio_ms: float | None = None
|
||||
|
||||
try:
|
||||
async with websockets.connect(
|
||||
CARTESIA_WS_URL, additional_headers=headers, open_timeout=10
|
||||
) as ws:
|
||||
req = {
|
||||
"model_id": model_id,
|
||||
"transcript": SAMPLE_TEXT,
|
||||
"voice": {"id": voice_id},
|
||||
"output_format": {
|
||||
"container": "raw",
|
||||
"encoding": "pcm_s16le",
|
||||
"sample_rate": 24000,
|
||||
},
|
||||
"stream": True,
|
||||
}
|
||||
await ws.send(json.dumps(req))
|
||||
# Read frames until we get the first audio chunk.
|
||||
while True:
|
||||
msg = await asyncio.wait_for(ws.recv(), timeout=10)
|
||||
if isinstance(msg, (bytes, bytearray)):
|
||||
first_audio_ms = (time.perf_counter() - t0) * 1000.0
|
||||
break
|
||||
# JSON control messages (e.g. done) — ignore until audio.
|
||||
if isinstance(msg, str):
|
||||
data = json.loads(msg)
|
||||
if data.get("type") == "done":
|
||||
break
|
||||
except Exception as exc: # pragma: no cover - network/auth errors
|
||||
print(f" [probe] Cartesia connection failed: {exc}")
|
||||
return None
|
||||
|
||||
return first_audio_ms
|
||||
|
||||
|
||||
async def run_live(api_key: str, iterations: int, voice_id: str, model_id: str) -> list[float]:
|
||||
samples: list[float] = []
|
||||
print(f" Running {iterations} Cartesia Sonic iterations (voice={voice_id})...")
|
||||
for i in range(iterations):
|
||||
ms = await _probe_once(api_key, voice_id, model_id)
|
||||
if ms is not None:
|
||||
samples.append(ms)
|
||||
print(f" [{i + 1:2d}/{iterations}] first-audio: {ms:6.1f} ms")
|
||||
else:
|
||||
print(f" [{i + 1:2d}/{iterations}] no audio received (skipped)")
|
||||
await asyncio.sleep(0.3)
|
||||
return samples
|
||||
|
||||
|
||||
def _summarize(samples: list[float], label: str) -> dict:
|
||||
if not samples:
|
||||
print(f"\n {label}: no samples collected.\n")
|
||||
return {"label": label, "n": 0}
|
||||
s = sorted(samples)
|
||||
p95 = s[int(0.95 * (len(s) - 1))]
|
||||
row = {
|
||||
"label": label,
|
||||
"n": len(s),
|
||||
"min_ms": round(min(s), 1),
|
||||
"median_ms": round(statistics.median(s), 1),
|
||||
"p95_ms": round(p95, 1),
|
||||
"mean_ms": round(statistics.mean(s), 1),
|
||||
}
|
||||
print(
|
||||
f" {label}: n={row['n']} min={row['min_ms']:.1f} "
|
||||
f"median={row['median_ms']:.1f} p95={row['p95_ms']:.1f} "
|
||||
f"mean={row['mean_ms']:.1f} (ms)"
|
||||
)
|
||||
return row
|
||||
|
||||
|
||||
async def amain() -> int:
|
||||
parser = argparse.ArgumentParser(description="R2 Cartesia Sonic latency probe")
|
||||
parser.add_argument("--iterations", type=int, default=20)
|
||||
parser.add_argument("--voice-id", default=os.environ.get("CARTESIA_VOICE_ID", DEFAULT_VOICE_ID))
|
||||
parser.add_argument("--model-id", default="sonic-2")
|
||||
parser.add_argument("--out", default=None)
|
||||
args = parser.parse_args()
|
||||
|
||||
_banner("R2 PROBE — Cartesia Sonic first-audio-byte latency")
|
||||
api_key = _require_key()
|
||||
if api_key is None:
|
||||
return 0
|
||||
|
||||
samples = await run_live(api_key, args.iterations, args.voice_id, args.model_id)
|
||||
summary = _summarize(samples, "cartesia_sonic_first_audio")
|
||||
print()
|
||||
if args.out:
|
||||
Path(args.out).write_text(json.dumps(summary, indent=2))
|
||||
print(f" Wrote {args.out}")
|
||||
return 0
|
||||
|
||||
|
||||
def main() -> int:
|
||||
return asyncio.run(amain())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
Executable
+199
@@ -0,0 +1,199 @@
|
||||
#!/usr/bin/env python3
|
||||
"""R1 probe — Deepgram Nova-3 streaming ASR first-partial-transcript latency.
|
||||
|
||||
Per PLAN.md SLICE-01 TASK-01-02: measure first-partial-transcript latency from a
|
||||
sample audio file (synthesized PCM) over 20 iterations; log min/median/p95.
|
||||
|
||||
Exit code 0 in all cases:
|
||||
- If DEEPGRAM_API_KEY is missing, print a clear KEY_MISSING banner and exit 0
|
||||
(the probe infrastructure is the deliverable; live numbers come when keys
|
||||
are provisioned).
|
||||
- If the key is present, run the live probe and print a latency table.
|
||||
|
||||
Usage:
|
||||
python scripts/probe_deepgram.py [--iterations N] [--model nova-3]
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import asyncio
|
||||
import json
|
||||
import os
|
||||
import statistics
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
# Make the project importable when run from the repo root.
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
|
||||
|
||||
try:
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
except ImportError: # pragma: no cover - dotenv is a declared dep
|
||||
pass
|
||||
|
||||
|
||||
def _banner(msg: str) -> None:
|
||||
print("\n" + "=" * 72)
|
||||
print(msg)
|
||||
print("=" * 72 + "\n")
|
||||
|
||||
|
||||
def _require_key() -> str | None:
|
||||
"""Return the Deepgram API key or None (with a printed banner if missing)."""
|
||||
key = os.environ.get("DEEPGRAM_API_KEY", "").strip()
|
||||
if not key:
|
||||
_banner(
|
||||
"KEY_MISSING — DEEPGRAM_API_KEY not set.\n"
|
||||
" Cannot run live Deepgram probe. Probe infrastructure is built\n"
|
||||
" and ready; live measurements are pending API key provisioning.\n"
|
||||
" Set DEEPGRAM_API_KEY in .env (see .env.example) and re-run."
|
||||
)
|
||||
return None
|
||||
return key
|
||||
|
||||
|
||||
def _synth_pcm(duration_s: float = 2.0, sample_rate: int = 16000) -> bytes:
|
||||
"""Synthesize a short mono 16-bit PCM buffer (silence + a low tone).
|
||||
|
||||
Deepgram needs real audio frames; we generate a recognizable signal so the
|
||||
streaming endpoint returns a partial. The exact transcript content is not
|
||||
the point — the *latency to first partial* is.
|
||||
"""
|
||||
import math
|
||||
import struct
|
||||
|
||||
n = int(duration_s * sample_rate)
|
||||
frames = bytearray()
|
||||
for i in range(n):
|
||||
# 220 Hz tone for the first 1.5s, then silence — a clearly voiced segment.
|
||||
if i < int(1.5 * sample_rate):
|
||||
sample = int(16000 * math.sin(2 * math.pi * 220 * i / sample_rate))
|
||||
else:
|
||||
sample = 0
|
||||
frames += struct.pack("<h", sample)
|
||||
return bytes(frames)
|
||||
|
||||
|
||||
DEEPGRAM_WS_URL = "wss://api.deepgram.com/v1/listen"
|
||||
|
||||
|
||||
async def _probe_once(api_key: str, model: str, pcm: bytes, sample_rate: int) -> float | None:
|
||||
"""Open a Deepgram streaming WebSocket, send PCM, return ms-to-first-partial.
|
||||
|
||||
Uses the raw Deepgram streaming WebSocket API (not the SDK) so the probe is
|
||||
independent of SDK version churn and measures the actual network path.
|
||||
"""
|
||||
import websockets
|
||||
|
||||
params = (
|
||||
f"?model={model}&language=en&encoding=linear16&channels=1"
|
||||
f"&sample_rate={sample_rate}&interim_results=true&endpointing=300"
|
||||
)
|
||||
headers = [("Authorization", f"Token {api_key}")]
|
||||
t0 = time.perf_counter()
|
||||
first_partial_ms: float | None = None
|
||||
|
||||
try:
|
||||
async with websockets.connect(
|
||||
DEEPGRAM_WS_URL + params, additional_headers=headers, open_timeout=10
|
||||
) as ws:
|
||||
# Send in small chunks to mimic real streaming.
|
||||
chunk = 3200 # 100ms of 16kHz mono 16-bit
|
||||
for i in range(0, len(pcm), chunk):
|
||||
await ws.send(pcm[i : i + chunk])
|
||||
await asyncio.sleep(0.02)
|
||||
# Wait for the first transcript message.
|
||||
try:
|
||||
while True:
|
||||
msg = await asyncio.wait_for(ws.recv(), timeout=5)
|
||||
if isinstance(msg, str):
|
||||
data = json.loads(msg)
|
||||
if data.get("type") == "Results":
|
||||
channel = data.get("channel", {})
|
||||
alts = channel.get("alternatives", [])
|
||||
if alts and alts[0].get("transcript", "").strip():
|
||||
first_partial_ms = (time.perf_counter() - t0) * 1000.0
|
||||
break
|
||||
except asyncio.TimeoutError:
|
||||
pass
|
||||
# Signal close.
|
||||
try:
|
||||
await ws.send(json.dumps({"type": "CloseStream"}))
|
||||
except Exception:
|
||||
pass
|
||||
except Exception as exc: # pragma: no cover - network/auth errors
|
||||
print(f" [probe] Deepgram connection failed: {exc}")
|
||||
return None
|
||||
|
||||
return first_partial_ms
|
||||
|
||||
|
||||
async def run_live(api_key: str, iterations: int, model: str) -> list[float]:
|
||||
sample_rate = 16000
|
||||
pcm = _synth_pcm(duration_s=2.0, sample_rate=sample_rate)
|
||||
samples: list[float] = []
|
||||
print(f" Running {iterations} Deepgram Nova-3 iterations (model={model})...")
|
||||
for i in range(iterations):
|
||||
ms = await _probe_once(api_key, model, pcm, sample_rate)
|
||||
if ms is not None:
|
||||
samples.append(ms)
|
||||
print(f" [{i + 1:2d}/{iterations}] first-partial: {ms:6.1f} ms")
|
||||
else:
|
||||
print(f" [{i + 1:2d}/{iterations}] no partial received (skipped)")
|
||||
await asyncio.sleep(0.3)
|
||||
return samples
|
||||
|
||||
|
||||
def _summarize(samples: list[float], label: str) -> dict:
|
||||
if not samples:
|
||||
print(f"\n {label}: no samples collected.\n")
|
||||
return {"label": label, "n": 0}
|
||||
s = sorted(samples)
|
||||
p95 = s[int(0.95 * (len(s) - 1))]
|
||||
row = {
|
||||
"label": label,
|
||||
"n": len(s),
|
||||
"min_ms": round(min(s), 1),
|
||||
"median_ms": round(statistics.median(s), 1),
|
||||
"p95_ms": round(p95, 1),
|
||||
"mean_ms": round(statistics.mean(s), 1),
|
||||
}
|
||||
print(
|
||||
f" {label}: n={row['n']} min={row['min_ms']:.1f} "
|
||||
f"median={row['median_ms']:.1f} p95={row['p95_ms']:.1f} "
|
||||
f"mean={row['mean_ms']:.1f} (ms)"
|
||||
)
|
||||
return row
|
||||
|
||||
|
||||
async def amain() -> int:
|
||||
parser = argparse.ArgumentParser(description="R1 Deepgram Nova-3 latency probe")
|
||||
parser.add_argument("--iterations", type=int, default=20)
|
||||
parser.add_argument("--model", default=os.environ.get("DEEPGRAM_MODEL", "nova-3"))
|
||||
parser.add_argument("--out", default=None, help="optional JSON results path")
|
||||
args = parser.parse_args()
|
||||
|
||||
_banner("R1 PROBE — Deepgram Nova-3 first-partial-transcript latency")
|
||||
api_key = _require_key()
|
||||
if api_key is None:
|
||||
return 0
|
||||
|
||||
samples = await run_live(api_key, args.iterations, args.model)
|
||||
summary = _summarize(samples, "deepgram_nova3_first_partial")
|
||||
print()
|
||||
if args.out:
|
||||
Path(args.out).write_text(json.dumps(summary, indent=2))
|
||||
print(f" Wrote {args.out}")
|
||||
return 0
|
||||
|
||||
|
||||
def main() -> int:
|
||||
return asyncio.run(amain())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
Executable
+348
@@ -0,0 +1,348 @@
|
||||
#!/usr/bin/env python3
|
||||
"""R4 probe — integrated three-hop end-to-end latency.
|
||||
|
||||
Per PLAN.md SLICE-01 TASK-01-05: feed a sample ASR transcript → Ollama
|
||||
gemma4:cloud streaming → Cartesia TTS streaming; measure end-to-end
|
||||
(transcript-in → first-audio-out). Run 10 iterations. Also measure the same
|
||||
path with Piper self-hosted (if Piper can be stood up locally; otherwise note
|
||||
as pending and pre-stage in SLICE-02).
|
||||
|
||||
Exit code 0 in all cases:
|
||||
- If OLLAMA_API_KEY or CARTESIA_API_KEY is missing, print KEY_MISSING and
|
||||
exit 0 (the probe infrastructure is the deliverable).
|
||||
- If present, run the live integrated probe and print e2e latency.
|
||||
|
||||
The Piper leg is invoked only if PRAXIS_TTS=piper is set AND a Piper voice
|
||||
model is available; otherwise it is documented as pre-staged (R4 mitigation).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import asyncio
|
||||
import json
|
||||
import os
|
||||
import statistics
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
|
||||
|
||||
try:
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
except ImportError: # pragma: no cover
|
||||
pass
|
||||
|
||||
|
||||
def _banner(msg: str) -> None:
|
||||
print("\n" + "=" * 72)
|
||||
print(msg)
|
||||
print("=" * 72 + "\n")
|
||||
|
||||
|
||||
def _missing(keys: list[str]) -> None:
|
||||
_banner(
|
||||
"KEY_MISSING — " + ", ".join(keys) + " not set.\n"
|
||||
" Cannot run live integrated e2e probe. Probe infrastructure is built\n"
|
||||
" and ready; live measurements are pending API key provisioning.\n"
|
||||
" Set the missing key(s) in .env (see .env.example) and re-run."
|
||||
)
|
||||
|
||||
|
||||
CHAT_URL = os.environ.get("OLLAMA_CHAT_URL", "https://ollama.com/api/chat")
|
||||
CARTESIA_WS_URL = "wss://api.cartesia.ai/tts/websocket"
|
||||
ROLEPLAY_MODEL = os.environ.get("OLLAMA_ROLEPLAY_MODEL", "gemma4:cloud")
|
||||
DEFAULT_VOICE_ID = "a3536a36-1d18-4efb-a95a-7c44b7b5e384"
|
||||
|
||||
# The "transcript-in" — a realistic ASR final transcript from the learner.
|
||||
SAMPLE_TRANSCRIPT = "Hi, I want to help you with your order. What happened?"
|
||||
SYSTEM_PROMPT = (
|
||||
"You are Jordan, a customer who received a damaged product. "
|
||||
"You are frustrated but not abusive. Stay in character. Keep responses "
|
||||
"to 1-2 sentences."
|
||||
)
|
||||
|
||||
|
||||
async def _ollama_first_token(api_key: str) -> tuple[str | None, float | None, str | None]:
|
||||
"""Stream Ollama gemma4:cloud, return (full_text, ttft_ms, error)."""
|
||||
import httpx
|
||||
|
||||
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
|
||||
body = {
|
||||
"model": ROLEPLAY_MODEL,
|
||||
"messages": [
|
||||
{"role": "system", "content": SYSTEM_PROMPT},
|
||||
{"role": "user", "content": SAMPLE_TRANSCRIPT},
|
||||
],
|
||||
"stream": True,
|
||||
}
|
||||
t0 = time.perf_counter()
|
||||
ttft_ms: float | None = None
|
||||
chunks: list[str] = []
|
||||
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=30.0) as client:
|
||||
async with client.stream("POST", CHAT_URL, headers=headers, json=body) as resp:
|
||||
if resp.status_code != 200:
|
||||
text = await resp.aread()
|
||||
return None, None, f"HTTP {resp.status_code}: {text[:200]!r}"
|
||||
async for line in resp.aiter_lines():
|
||||
if not line:
|
||||
continue
|
||||
try:
|
||||
chunk = json.loads(line)
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
content = chunk.get("message", {}).get("content", "")
|
||||
if content:
|
||||
if ttft_ms is None:
|
||||
ttft_ms = (time.perf_counter() - t0) * 1000.0
|
||||
chunks.append(content)
|
||||
except Exception as exc: # pragma: no cover
|
||||
return None, None, f"connection error: {exc}"
|
||||
|
||||
return "".join(chunks), ttft_ms, None
|
||||
|
||||
|
||||
async def _cartesia_first_audio(
|
||||
api_key: str, text: str, voice_id: str, model_id: str
|
||||
) -> tuple[float | None, str | None]:
|
||||
"""Send text to Cartesia WS, return (first_audio_ms_from_t0, error)."""
|
||||
import websockets
|
||||
|
||||
headers = [("x-api-key", api_key), ("cartesia-version", "2024-06-10")]
|
||||
t0 = time.perf_counter()
|
||||
first_audio_ms: float | None = None
|
||||
|
||||
try:
|
||||
async with websockets.connect(
|
||||
CARTESIA_WS_URL, additional_headers=headers, open_timeout=10
|
||||
) as ws:
|
||||
req = {
|
||||
"model_id": model_id,
|
||||
"transcript": text,
|
||||
"voice": {"id": voice_id},
|
||||
"output_format": {
|
||||
"container": "raw",
|
||||
"encoding": "pcm_s16le",
|
||||
"sample_rate": 24000,
|
||||
},
|
||||
"stream": True,
|
||||
}
|
||||
await ws.send(json.dumps(req))
|
||||
while True:
|
||||
msg = await asyncio.wait_for(ws.recv(), timeout=10)
|
||||
if isinstance(msg, (bytes, bytearray)):
|
||||
first_audio_ms = (time.perf_counter() - t0) * 1000.0
|
||||
break
|
||||
if isinstance(msg, str):
|
||||
data = json.loads(msg)
|
||||
if data.get("type") == "done":
|
||||
break
|
||||
except Exception as exc: # pragma: no cover
|
||||
return None, f"cartesia error: {exc}"
|
||||
|
||||
return first_audio_ms, None
|
||||
|
||||
|
||||
async def _piper_first_audio(text: str) -> tuple[float | None, str | None]:
|
||||
"""Synthesize via Piper self-hosted, return (first_audio_ms, error).
|
||||
|
||||
Piper pre-staging note (R4 mitigation): Piper is pre-staged as the
|
||||
production v0.1 TTS fallback per ARCHITECTURE.md. The voice model must be
|
||||
downloaded separately (see docs/latency-report.md). If not available,
|
||||
returns an error string that the caller documents as pending.
|
||||
"""
|
||||
try:
|
||||
from piper import PiperVoice # type: ignore
|
||||
except ImportError:
|
||||
return None, "piper-tts not installed (pre-staged for SLICE-02)"
|
||||
|
||||
model_path = os.environ.get("PIPER_VOICE_MODEL", "")
|
||||
if not model_path or not Path(model_path).exists():
|
||||
return None, "PIPER_VOICE_MODEL not set or file missing (pre-staged for SLICE-02)"
|
||||
|
||||
import io
|
||||
|
||||
t0 = time.perf_counter()
|
||||
try:
|
||||
voice = PiperVoice.load(model_path)
|
||||
wav_bytes = io.BytesIO()
|
||||
for chunk in voice.synthesize(text):
|
||||
wav_bytes.write(chunk.audio_int16_bytes)
|
||||
first_audio_ms = (time.perf_counter() - t0) * 1000.0
|
||||
return first_audio_ms, None
|
||||
except Exception as exc: # pragma: no cover
|
||||
return None, f"piper error: {exc}"
|
||||
|
||||
|
||||
async def _e2e_once_cartesia(ollama_key: str, cartesia_key: str, voice_id: str, model_id: str) -> dict:
|
||||
"""Run the integrated ASR-transcript → Ollama → Cartesia path once."""
|
||||
t_start = time.perf_counter()
|
||||
text, ttft_ms, llm_err = await _ollama_first_token(ollama_key)
|
||||
if llm_err or not text:
|
||||
return {"ok": False, "error": llm_err or "empty LLM output", "ttft_ms": None}
|
||||
tts_ms, tts_err = await _cartesia_first_audio(cartesia_key, text, voice_id, model_id)
|
||||
if tts_err or tts_ms is None:
|
||||
return {"ok": False, "error": tts_err or "no TTS audio", "ttft_ms": ttft_ms}
|
||||
e2e_ms = (time.perf_counter() - t_start) * 1000.0
|
||||
return {
|
||||
"ok": True,
|
||||
"ttft_ms": ttft_ms,
|
||||
"tts_first_audio_ms": tts_ms,
|
||||
"e2e_ms": e2e_ms,
|
||||
"llm_text": text[:80],
|
||||
}
|
||||
|
||||
|
||||
async def _e2e_once_piper(ollama_key: str) -> dict:
|
||||
"""Run the integrated ASR-transcript → Ollama → Piper path once."""
|
||||
t_start = time.perf_counter()
|
||||
text, ttft_ms, llm_err = await _ollama_first_token(ollama_key)
|
||||
if llm_err or not text:
|
||||
return {"ok": False, "error": llm_err or "empty LLM output", "ttft_ms": None}
|
||||
tts_ms, tts_err = await _piper_first_audio(text)
|
||||
if tts_err or tts_ms is None:
|
||||
return {"ok": False, "error": tts_err or "no TTS audio", "ttft_ms": ttft_ms, "piper_pending": True}
|
||||
e2e_ms = (time.perf_counter() - t_start) * 1000.0
|
||||
return {
|
||||
"ok": True,
|
||||
"ttft_ms": ttft_ms,
|
||||
"tts_first_audio_ms": tts_ms,
|
||||
"e2e_ms": e2e_ms,
|
||||
"llm_text": text[:80],
|
||||
}
|
||||
|
||||
|
||||
def _summarize(samples: list[float], label: str) -> dict:
|
||||
if not samples:
|
||||
print(f" {label}: no samples collected.")
|
||||
return {"label": label, "n": 0}
|
||||
s = sorted(samples)
|
||||
p95 = s[int(0.95 * (len(s) - 1))]
|
||||
row = {
|
||||
"label": label,
|
||||
"n": len(s),
|
||||
"min_ms": round(min(s), 1),
|
||||
"median_ms": round(statistics.median(s), 1),
|
||||
"p95_ms": round(p95, 1),
|
||||
"mean_ms": round(statistics.mean(s), 1),
|
||||
}
|
||||
print(
|
||||
f" {label}: n={row['n']} min={row['min_ms']:.1f} "
|
||||
f"median={row['median_ms']:.1f} p95={row['p95_ms']:.1f} "
|
||||
f"mean={row['mean_ms']:.1f} (ms)"
|
||||
)
|
||||
return row
|
||||
|
||||
|
||||
async def amain() -> int:
|
||||
parser = argparse.ArgumentParser(description="R4 integrated e2e latency probe")
|
||||
parser.add_argument("--iterations", type=int, default=10)
|
||||
parser.add_argument("--voice-id", default=os.environ.get("CARTESIA_VOICE_ID", DEFAULT_VOICE_ID))
|
||||
parser.add_argument("--cartesia-model", default="sonic-2")
|
||||
parser.add_argument("--out", default=None)
|
||||
parser.add_argument("--piper", action="store_true", help="also run the Piper leg")
|
||||
args = parser.parse_args()
|
||||
|
||||
_banner("R4 PROBE — integrated three-hop e2e (transcript → Ollama → TTS)")
|
||||
ollama_key = os.environ.get("OLLAMA_API_KEY", "").strip()
|
||||
cartesia_key = os.environ.get("CARTESIA_API_KEY", "").strip()
|
||||
|
||||
missing = []
|
||||
if not ollama_key:
|
||||
missing.append("OLLAMA_API_KEY")
|
||||
if not cartesia_key:
|
||||
missing.append("CARTESIA_API_KEY")
|
||||
if missing:
|
||||
_missing(missing)
|
||||
return 0
|
||||
|
||||
# ── Cartesia leg ────────────────────────────────────────────────────────
|
||||
print(f"\n Cartesia leg — {args.iterations} iterations:")
|
||||
e2e_samples: list[float] = []
|
||||
ttft_samples: list[float] = []
|
||||
tts_samples: list[float] = []
|
||||
for i in range(args.iterations):
|
||||
r = await _e2e_once_cartesia(ollama_key, cartesia_key, args.voice_id, args.cartesia_model)
|
||||
if r.get("ok"):
|
||||
e2e_samples.append(r["e2e_ms"])
|
||||
ttft_samples.append(r["ttft_ms"])
|
||||
tts_samples.append(r["tts_first_audio_ms"])
|
||||
print(f" [{i + 1:2d}/{args.iterations}] e2e={r['e2e_ms']:.1f}ms "
|
||||
f"(llm_ttft={r['ttft_ms']:.1f}, tts={r['tts_first_audio_ms']:.1f})")
|
||||
else:
|
||||
print(f" [{i + 1:2d}/{args.iterations}] error: {r.get('error')}")
|
||||
await asyncio.sleep(0.5)
|
||||
|
||||
print()
|
||||
e2e_summary = _summarize(e2e_samples, "e2e_cartesia")
|
||||
ttft_summary = _summarize(ttft_samples, "e2e_cartesia_llm_ttft")
|
||||
tts_summary = _summarize(tts_samples, "e2e_cartesia_tts_first_audio")
|
||||
|
||||
# ── Piper leg (optional / pre-staged) ───────────────────────────────────
|
||||
piper_summary: dict = {}
|
||||
if args.piper:
|
||||
print(f"\n Piper leg — {args.iterations} iterations:")
|
||||
p_e2e: list[float] = []
|
||||
p_ttft: list[float] = []
|
||||
p_tts: list[float] = []
|
||||
for i in range(args.iterations):
|
||||
r = await _e2e_once_piper(ollama_key)
|
||||
if r.get("ok"):
|
||||
p_e2e.append(r["e2e_ms"])
|
||||
p_ttft.append(r["ttft_ms"])
|
||||
p_tts.append(r["tts_first_audio_ms"])
|
||||
print(f" [{i + 1:2d}/{args.iterations}] e2e={r['e2e_ms']:.1f}ms")
|
||||
elif r.get("piper_pending"):
|
||||
print(f" [{i + 1:2d}/{args.iterations}] Piper pre-staged (pending voice model) — skipping")
|
||||
break
|
||||
else:
|
||||
print(f" [{i + 1:2d}/{args.iterations}] error: {r.get('error')}")
|
||||
await asyncio.sleep(0.5)
|
||||
print()
|
||||
piper_summary = _summarize(p_e2e, "e2e_piper")
|
||||
else:
|
||||
print("\n Piper leg not requested (--piper). Piper is pre-staged as the R4 "
|
||||
"mitigation per ARCHITECTURE.md; live Piper measurement pending "
|
||||
"voice-model provisioning (see docs/latency-report.md).")
|
||||
|
||||
# ── Budget comparison ───────────────────────────────────────────────────
|
||||
budget = 600.0
|
||||
print(f"\n Latency budget: {budget:.0f}ms")
|
||||
if e2e_samples:
|
||||
med = statistics.median(e2e_samples)
|
||||
over = med > budget
|
||||
print(f" Cartesia median e2e: {med:.1f}ms — {'OVER' if over else 'WITHIN'} budget "
|
||||
f"(delta {med - budget:+.1f}ms)")
|
||||
if piper_summary.get("n"):
|
||||
# type: ignore
|
||||
med = piper_summary.get("median_ms")
|
||||
if med:
|
||||
over = med > budget
|
||||
print(f" Piper median e2e: {med:.1f}ms — {'OVER' if over else 'WITHIN'} budget "
|
||||
f"(delta {med - budget:+.1f}ms)")
|
||||
|
||||
print()
|
||||
if args.out:
|
||||
result = {
|
||||
"e2e_cartesia": e2e_summary,
|
||||
"e2e_cartesia_llm_ttft": ttft_summary,
|
||||
"e2e_cartesia_tts_first_audio": tts_summary,
|
||||
"e2e_piper": piper_summary,
|
||||
"budget_ms": budget,
|
||||
}
|
||||
Path(args.out).write_text(json.dumps(result, indent=2))
|
||||
print(f" Wrote {args.out}")
|
||||
return 0
|
||||
|
||||
|
||||
def main() -> int:
|
||||
return asyncio.run(amain())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
Executable
+226
@@ -0,0 +1,226 @@
|
||||
#!/usr/bin/env python3
|
||||
"""R3 probe — Ollama Cloud direct-API time-to-first-token (TTFT).
|
||||
|
||||
Per PLAN.md SLICE-01 TASK-01-04: direct API call to https://ollama.com/api/chat
|
||||
with OLLAMA_API_KEY bearer, model gemma4:cloud, stream=True, measure TTFT over
|
||||
20 iterations; also probe deepseek-v4-flash:cloud no-think mode TTFT. Log
|
||||
min/median/p95 + any throttle events (R5).
|
||||
|
||||
Exit code 0 in all cases:
|
||||
- If OLLAMA_API_KEY is missing, print KEY_MISSING and exit 0.
|
||||
- If present, run the live probe for both models and print TTFT tables.
|
||||
|
||||
R6 note: this probe also confirms the Ollama Cloud direct API is callable with a
|
||||
bearer token (R6). If it returns 401/403, that is recorded as a throttle/auth
|
||||
event, not a crash.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import asyncio
|
||||
import json
|
||||
import os
|
||||
import statistics
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
|
||||
|
||||
try:
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
except ImportError: # pragma: no cover
|
||||
pass
|
||||
|
||||
|
||||
def _banner(msg: str) -> None:
|
||||
print("\n" + "=" * 72)
|
||||
print(msg)
|
||||
print("=" * 72 + "\n")
|
||||
|
||||
|
||||
def _require_key() -> str | None:
|
||||
key = os.environ.get("OLLAMA_API_KEY", "").strip()
|
||||
if not key:
|
||||
_banner(
|
||||
"KEY_MISSING — OLLAMA_API_KEY not set.\n"
|
||||
" Cannot run live Ollama Cloud probe. Probe infrastructure is built\n"
|
||||
" and ready; live measurements are pending API key provisioning.\n"
|
||||
" Set OLLAMA_API_KEY in .env (see .env.example) and re-run."
|
||||
)
|
||||
return None
|
||||
return key
|
||||
|
||||
|
||||
CHAT_URL = os.environ.get("OLLAMA_CHAT_URL", "https://ollama.com/api/chat")
|
||||
|
||||
ROLEPLAY_MODEL = os.environ.get("OLLAMA_ROLEPLAY_MODEL", "gemma4:cloud")
|
||||
DEBRIEF_MODEL = os.environ.get("OLLAMA_DEBRIEF_MODEL", "deepseek-v4-flash:cloud")
|
||||
|
||||
# A short role-play prompt that should produce a fast first token.
|
||||
ROLEPLAY_MESSAGES = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": (
|
||||
"You are Jordan, a customer who received a damaged product. "
|
||||
"You are frustrated but not abusive. Stay in character. Keep "
|
||||
"responses to 1-2 sentences."
|
||||
),
|
||||
},
|
||||
{"role": "user", "content": "Hi, I want to help you with your order. What happened?"},
|
||||
]
|
||||
|
||||
# Debrief prompt — no_think mode for latency (D-020).
|
||||
DEBRIEF_MESSAGES = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": (
|
||||
"You are a coaching mentor. Produce a concise (3-bullet) debrief "
|
||||
"about the learner's customer-service performance. "
|
||||
"Do not reason step-by-step; respond directly."
|
||||
),
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "The learner said: 'I'm sorry you're upset. I can offer a refund.'",
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
async def _probe_once(
|
||||
api_key: str, model: str, messages: list[dict], no_think: bool
|
||||
) -> tuple[float | None, str | None]:
|
||||
"""Call Ollama Cloud /api/chat streaming, return (ttft_ms, error_or_none)."""
|
||||
import httpx
|
||||
|
||||
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
|
||||
body: dict = {"model": model, "messages": messages, "stream": True}
|
||||
if no_think:
|
||||
# Ollama no-think mode for deepseek-v4-flash:cloud (D-020).
|
||||
body["think"] = False
|
||||
|
||||
t0 = time.perf_counter()
|
||||
ttft_ms: float | None = None
|
||||
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=30.0) as client:
|
||||
async with client.stream(
|
||||
"POST", CHAT_URL, headers=headers, json=body
|
||||
) as resp:
|
||||
if resp.status_code != 200:
|
||||
text = await resp.aread()
|
||||
return None, f"HTTP {resp.status_code}: {text[:200]!r}"
|
||||
async for line in resp.aiter_lines():
|
||||
if not line:
|
||||
continue
|
||||
try:
|
||||
chunk = json.loads(line)
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
msg = chunk.get("message", {})
|
||||
content = msg.get("content", "")
|
||||
if content and ttft_ms is None:
|
||||
ttft_ms = (time.perf_counter() - t0) * 1000.0
|
||||
break
|
||||
except Exception as exc: # pragma: no cover - network errors
|
||||
return None, f"connection error: {exc}"
|
||||
|
||||
return ttft_ms, None
|
||||
|
||||
|
||||
async def run_model(
|
||||
api_key: str, model: str, messages: list[dict], iterations: int, label: str, no_think: bool
|
||||
) -> tuple[list[float], list[str]]:
|
||||
samples: list[float] = []
|
||||
errors: list[str] = []
|
||||
print(f" Running {iterations} iterations for {label} (model={model}, no_think={no_think})...")
|
||||
for i in range(iterations):
|
||||
ms, err = await _probe_once(api_key, model, messages, no_think)
|
||||
if ms is not None:
|
||||
samples.append(ms)
|
||||
print(f" [{i + 1:2d}/{iterations}] TTFT: {ms:6.1f} ms")
|
||||
else:
|
||||
errors.append(err or "unknown")
|
||||
print(f" [{i + 1:2d}/{iterations}] error: {err}")
|
||||
await asyncio.sleep(0.5)
|
||||
return samples, errors
|
||||
|
||||
|
||||
def _summarize(samples: list[float], label: str) -> dict:
|
||||
if not samples:
|
||||
print(f"\n {label}: no samples collected.\n")
|
||||
return {"label": label, "n": 0}
|
||||
s = sorted(samples)
|
||||
p95 = s[int(0.95 * (len(s) - 1))]
|
||||
row = {
|
||||
"label": label,
|
||||
"n": len(s),
|
||||
"min_ms": round(min(s), 1),
|
||||
"median_ms": round(statistics.median(s), 1),
|
||||
"p95_ms": round(p95, 1),
|
||||
"mean_ms": round(statistics.mean(s), 1),
|
||||
}
|
||||
print(
|
||||
f" {label}: n={row['n']} min={row['min_ms']:.1f} "
|
||||
f"median={row['median_ms']:.1f} p95={row['p95_ms']:.1f} "
|
||||
f"mean={row['mean_ms']:.1f} (ms)"
|
||||
)
|
||||
return row
|
||||
|
||||
|
||||
async def amain() -> int:
|
||||
parser = argparse.ArgumentParser(description="R3 Ollama Cloud TTFT probe")
|
||||
parser.add_argument("--iterations", type=int, default=20)
|
||||
parser.add_argument("--out", default=None)
|
||||
args = parser.parse_args()
|
||||
|
||||
_banner("R3 PROBE — Ollama Cloud direct-API time-to-first-token")
|
||||
api_key = _require_key()
|
||||
if api_key is None:
|
||||
return 0
|
||||
|
||||
# R6: confirms the direct API + bearer works for the role-play model.
|
||||
rp_samples, rp_errors = await run_model(
|
||||
api_key, ROLEPLAY_MODEL, ROLEPLAY_MESSAGES, args.iterations,
|
||||
"ollama_gemma4_cloud_ttft", no_think=False,
|
||||
)
|
||||
rp_summary = _summarize(rp_samples, "ollama_gemma4_cloud_ttft")
|
||||
|
||||
print()
|
||||
# Debrief model with no_think (D-020).
|
||||
db_samples, db_errors = await run_model(
|
||||
api_key, DEBRIEF_MODEL, DEBRIEF_MESSAGES, args.iterations,
|
||||
"ollama_deepseek_v4_flash_nothink_ttft", no_think=True,
|
||||
)
|
||||
db_summary = _summarize(db_samples, "ollama_deepseek_v4_flash_nothink_ttft")
|
||||
|
||||
# R5: log throttle events (any error could indicate throttling/auth).
|
||||
all_errors = rp_errors + db_errors
|
||||
if all_errors:
|
||||
print(f"\n R5 — {len(all_errors)} error/throttle event(s) recorded:")
|
||||
for e in all_errors[:10]:
|
||||
print(f" - {e}")
|
||||
else:
|
||||
print("\n R5 — no throttle/auth events recorded.")
|
||||
|
||||
print()
|
||||
if args.out:
|
||||
result = {
|
||||
"roleplay": rp_summary,
|
||||
"debrief": db_summary,
|
||||
"errors": all_errors,
|
||||
}
|
||||
Path(args.out).write_text(json.dumps(result, indent=2))
|
||||
print(f" Wrote {args.out}")
|
||||
return 0
|
||||
|
||||
|
||||
def main() -> int:
|
||||
return asyncio.run(amain())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
@@ -0,0 +1,129 @@
|
||||
"""Praxis server entrypoint — starts the Pipecat WebRTC bot server.
|
||||
|
||||
Run: `python -m server`
|
||||
|
||||
Exposes a FastAPI app with:
|
||||
GET /health — liveness
|
||||
POST /pipecat/webrtc — accept a WebRTC offer SDP, start a pipeline task
|
||||
|
||||
The server starts and accepts connections even if upstream voice-service keys
|
||||
are absent (SLICE-02 deliverable = code structure). Missing keys degrade to
|
||||
no audio/no tokens at runtime, not a crash.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
from typing import Any
|
||||
|
||||
from loguru import logger
|
||||
from pydantic import BaseModel
|
||||
|
||||
# Load .env if present (dev). In production, env is injected directly.
|
||||
try:
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
except ImportError: # pragma: no cover
|
||||
pass
|
||||
|
||||
from fastapi import FastAPI, HTTPException
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from pipecat.transports.smallwebrtc.connection import SmallWebRTCConnection
|
||||
|
||||
from server.pipeline import build_pipeline
|
||||
|
||||
|
||||
def _env(key: str, default: str = "") -> str:
|
||||
return os.environ.get(key, default).strip()
|
||||
|
||||
|
||||
HOST = _env("PRAXIS_HOST", "0.0.0.0")
|
||||
PORT = int(_env("PRAXIS_PORT", "8789"))
|
||||
|
||||
|
||||
class WebRTCOffer(BaseModel):
|
||||
"""Client→server WebRTC offer (SDP + type)."""
|
||||
|
||||
sdp: str
|
||||
type: str = "offer"
|
||||
|
||||
|
||||
app = FastAPI(title="Praxis v0.1 voice server", version="0.1.0")
|
||||
app.add_middleware(
|
||||
CORSMiddleware,
|
||||
allow_origins=["*"], # dev — the client is a separate Vite origin
|
||||
allow_methods=["*"],
|
||||
allow_headers=["*"],
|
||||
)
|
||||
|
||||
|
||||
@app.get("/health")
|
||||
async def health() -> dict[str, Any]:
|
||||
"""Liveness probe. Reports key-provisioning status for the client."""
|
||||
return {
|
||||
"status": "ok",
|
||||
"version": "0.1.0",
|
||||
"keys": {
|
||||
"deepgram": bool(_env("DEEPGRAM_API_KEY")),
|
||||
"cartesia": bool(_env("CARTESIA_API_KEY")),
|
||||
"ollama": bool(_env("OLLAMA_API_KEY")),
|
||||
},
|
||||
"tts": _env("PRAXIS_TTS", "cartesia"),
|
||||
}
|
||||
|
||||
|
||||
@app.post("/pipecat/webrtc")
|
||||
async def webrtc_offer(offer: WebRTCOffer) -> dict[str, str]:
|
||||
"""Accept a WebRTC offer, start a Pipecat pipeline task, return the answer.
|
||||
|
||||
Loads the v0.1 scenario (customer_service_refund_ca_v01) so the pipeline
|
||||
uses the scenario-driven system prompt + opening line (TASK-03-07).
|
||||
"""
|
||||
scenario_id = _env("PRAXIS_SCENARIO", "customer_service_refund_ca_v01")
|
||||
try:
|
||||
connection = SmallWebRTCConnection(
|
||||
ice_servers=[{"urls": "stun:stun.l.google.com:19302"}],
|
||||
)
|
||||
await connection.receive_offer({"sdp": offer.sdp, "type": offer.type})
|
||||
await connection.accept()
|
||||
answer = connection.get_answer()
|
||||
# Build + run the pipeline for this connection.
|
||||
pipeline, task, runner, transport, scenario_runtime = build_pipeline(
|
||||
connection, scenario_id=scenario_id
|
||||
)
|
||||
# Run the pipeline task in the background; the runner manages its lifecycle.
|
||||
import asyncio
|
||||
|
||||
asyncio.create_task(runner.run(task))
|
||||
# Play the session-start disclaimer as the first AI utterance (D-019,
|
||||
# RESEARCH.md safety baseline), then the scenario opening line.
|
||||
from server.services.registry import get_guardrail
|
||||
|
||||
guardrail = get_guardrail()
|
||||
disclaimer = guardrail.session_start_disclaimer
|
||||
if scenario_runtime is not None:
|
||||
logger.info(
|
||||
f"Session starting with scenario {scenario_id!r}; "
|
||||
f"disclaimer: {disclaimer[:50]!r}; "
|
||||
f"opening line: {scenario_runtime.opening_line[:60]!r}"
|
||||
)
|
||||
else:
|
||||
logger.info(f"Session starting (no scenario); disclaimer: {disclaimer[:50]!r}")
|
||||
return {"sdp": answer["sdp"], "type": answer["type"]}
|
||||
except Exception as exc:
|
||||
logger.error(f"WebRTC offer failed: {exc}")
|
||||
raise HTTPException(status_code=500, detail=str(exc))
|
||||
|
||||
|
||||
def main() -> int:
|
||||
"""Run the server with uvicorn."""
|
||||
import uvicorn
|
||||
|
||||
logger.info(f"Praxis v0.1 voice server starting on {HOST}:{PORT}")
|
||||
uvicorn.run(app, host=HOST, port=PORT, log_level="info")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
+111
@@ -0,0 +1,111 @@
|
||||
"""Cost logging — per-session cost derivation (REQ-NFR-COST-01, D-012, TASK-04-04).
|
||||
|
||||
Counts LLM input/output tokens (gemma4 + deepseek-v4-flash), Deepgram audio
|
||||
minutes, Cartesia/Piper characters; derives an estimated cost in cents using
|
||||
cost_rates.yaml. No enforced ceiling (D-012 — pilot). The derived cost +
|
||||
breakdown are stored in sessions.cost_estimated_cents / cost_breakdown_json.
|
||||
|
||||
v0.1 logged costs are NOT representative of at-scale per-learner cost (G-005):
|
||||
Ollama tier-based pricing + Canada cloud + low volume = the most expensive
|
||||
configuration. The $3/learner target requires self-hosted gemma4:e4b + Piper
|
||||
(post-pilot). The logging infrastructure is the v0.1 contribution.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import yaml
|
||||
|
||||
_DEFAULT_RATES_PATH = Path(__file__).resolve().parent.parent / "scenarios" / "cost_rates.yaml"
|
||||
|
||||
|
||||
@dataclass
|
||||
class CostBreakdown:
|
||||
"""Per-session cost inputs + derived cents."""
|
||||
|
||||
llm_input_tokens: int = 0
|
||||
llm_output_tokens: int = 0
|
||||
deepgram_audio_minutes: float = 0.0
|
||||
tts_characters: int = 0
|
||||
debrief_input_tokens: int = 0
|
||||
debrief_output_tokens: int = 0
|
||||
rates: dict[str, float] = field(default_factory=dict)
|
||||
derived_cents: int = 0
|
||||
|
||||
def as_dict(self) -> dict[str, Any]:
|
||||
return {
|
||||
"llm_input_tokens": self.llm_input_tokens,
|
||||
"llm_output_tokens": self.llm_output_tokens,
|
||||
"deepgram_audio_minutes": round(self.deepgram_audio_minutes, 3),
|
||||
"tts_characters": self.tts_characters,
|
||||
"debrief_input_tokens": self.debrief_input_tokens,
|
||||
"debrief_output_tokens": self.debrief_output_tokens,
|
||||
"rates": self.rates,
|
||||
"derived_cents": self.derived_cents,
|
||||
}
|
||||
|
||||
|
||||
def load_rates(path: Path | None = None) -> dict[str, float]:
|
||||
"""Load cost rates from cost_rates.yaml (or defaults if absent)."""
|
||||
p = path or _DEFAULT_RATES_PATH
|
||||
if p.exists():
|
||||
with p.open("r", encoding="utf-8") as f:
|
||||
return yaml.safe_load(f) or {}
|
||||
# Defaults — vendor-list prices, per-unit (pilot estimates, G-005).
|
||||
return {
|
||||
"gemma4_cloud_per_1k_tokens_cents": 0.5, # Ollama tier (pro plan amortized)
|
||||
"deepseek_v4_flash_per_1k_tokens_cents": 1.0, # Ollama tier
|
||||
"deepgram_per_audio_minute_cents": 0.43, # $0.0043/min
|
||||
"cartesia_per_1k_chars_cents": 3.0, # per-char pricing
|
||||
"piper_per_1k_chars_cents": 0.0, # self-hosted, $0
|
||||
}
|
||||
|
||||
|
||||
def derive_cost(
|
||||
llm_input_tokens: int = 0,
|
||||
llm_output_tokens: int = 0,
|
||||
deepgram_audio_minutes: float = 0.0,
|
||||
tts_characters: int = 0,
|
||||
debrief_input_tokens: int = 0,
|
||||
debrief_output_tokens: int = 0,
|
||||
tts_provider: str = "cartesia",
|
||||
rates: dict[str, float] | None = None,
|
||||
) -> CostBreakdown:
|
||||
"""Derive the per-session cost in cents from the usage inputs + rates."""
|
||||
r = rates or load_rates()
|
||||
|
||||
# LLM role-play (gemma4:cloud).
|
||||
rp_tokens = llm_input_tokens + llm_output_tokens
|
||||
rp_cents = (rp_tokens / 1000.0) * r.get("gemma4_cloud_per_1k_tokens_cents", 0.5)
|
||||
|
||||
# Debrief (deepseek-v4-flash:cloud).
|
||||
db_tokens = debrief_input_tokens + debrief_output_tokens
|
||||
db_cents = (db_tokens / 1000.0) * r.get("deepseek_v4_flash_per_1k_tokens_cents", 1.0)
|
||||
|
||||
# ASR (Deepgram).
|
||||
asr_cents = deepgram_audio_minutes * r.get("deepgram_per_audio_minute_cents", 0.43)
|
||||
|
||||
# TTS (Cartesia or Piper).
|
||||
tts_rate_key = (
|
||||
"piper_per_1k_chars_cents" if tts_provider == "piper"
|
||||
else "cartesia_per_1k_chars_cents"
|
||||
)
|
||||
tts_cents = (tts_characters / 1000.0) * r.get(tts_rate_key, 3.0)
|
||||
|
||||
total = int(round(rp_cents + db_cents + asr_cents + tts_cents))
|
||||
return CostBreakdown(
|
||||
llm_input_tokens=llm_input_tokens,
|
||||
llm_output_tokens=llm_output_tokens,
|
||||
deepgram_audio_minutes=deepgram_audio_minutes,
|
||||
tts_characters=tts_characters,
|
||||
debrief_input_tokens=debrief_input_tokens,
|
||||
debrief_output_tokens=debrief_output_tokens,
|
||||
rates=r,
|
||||
derived_cents=total,
|
||||
)
|
||||
|
||||
|
||||
__all__ = ["CostBreakdown", "derive_cost", "load_rates"]
|
||||
@@ -0,0 +1,114 @@
|
||||
"""Coaching debrief generation (TASK-05-01, TASK-05-02, TASK-05-03).
|
||||
|
||||
On session end, loads the session turns + branch outcome + scenario
|
||||
debrief.debrief_focus, calls deepseek-v4-flash:cloud in no_think mode (D-020)
|
||||
with the debrief prompt template, produces a concise 3-bullet text summary
|
||||
(what you did well / what to improve / one next step).
|
||||
|
||||
TASK-05-02: routes the debrief text through the CustomerServiceGuardrail
|
||||
output filter (blocks legal-action recommendations).
|
||||
|
||||
TASK-05-03: synthesizes the debrief as voice via the TTSProvider (same voice
|
||||
as the role-play per D-006) — handled by the caller via synthesize().
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import yaml
|
||||
|
||||
from server.scenarios.schema import Scenario
|
||||
from server.services.base import Guardrail, GuardrailContext, LLMProvider
|
||||
|
||||
_DEFAULT_TEMPLATE_DIR = Path(__file__).resolve().parent.parent / "docs" / "debrief"
|
||||
|
||||
|
||||
def _load_template(template_id: str) -> dict[str, str]:
|
||||
"""Load a debrief prompt template by id (e.g. 'debrief/default')."""
|
||||
# template_id is 'debrief/default' → docs/debrief/default.yaml
|
||||
path = _DEFAULT_TEMPLATE_DIR / f"{template_id.split('/')[-1]}.yaml"
|
||||
if not path.exists():
|
||||
# Fallback to the default template.
|
||||
path = _DEFAULT_TEMPLATE_DIR / "default.yaml"
|
||||
with path.open("r", encoding="utf-8") as f:
|
||||
return yaml.safe_load(f)
|
||||
|
||||
|
||||
def _render(template_str: str, **kwargs: Any) -> str:
|
||||
"""Simple {{ var }} rendering (no Jinja dependency for v0.1)."""
|
||||
out = template_str
|
||||
for k, v in kwargs.items():
|
||||
out = out.replace("{{ " + k + " }}", str(v))
|
||||
out = out.replace("{{" + k + "}}", str(v))
|
||||
return out
|
||||
|
||||
|
||||
def _format_learner_turns(turns: list[dict[str, str]]) -> str:
|
||||
lines = []
|
||||
for t in turns:
|
||||
role = t.get("role", "?")
|
||||
text = t.get("asr_text") or t.get("tts_text") or ""
|
||||
if text:
|
||||
lines.append(f" {'Learner' if role == 'user' else 'AI'}: {text}")
|
||||
return "\n".join(lines) if lines else " (no turns recorded)"
|
||||
|
||||
|
||||
async def generate_debrief(
|
||||
llm: LLMProvider,
|
||||
scenario: Scenario,
|
||||
branch_id: str,
|
||||
outcome: str,
|
||||
debrief_focus: str,
|
||||
learner_turns: list[dict[str, str]],
|
||||
guardrail: Guardrail | None = None,
|
||||
) -> tuple[str, dict[str, Any]]:
|
||||
"""Generate the coaching debrief text (TASK-05-01, TASK-05-02).
|
||||
|
||||
Args:
|
||||
llm: the LLMProvider (uses debrief_model = deepseek-v4-flash:cloud no_think).
|
||||
scenario: the loaded Scenario.
|
||||
branch_id: the classified branch id.
|
||||
outcome: the branch outcome ('success' | 'failure').
|
||||
debrief_focus: the per-branch debrief focus from the scenario.
|
||||
learner_turns: list of {role, asr_text, tts_text} dicts (the session turns).
|
||||
guardrail: if provided, the debrief text is routed through the guardrail
|
||||
output filter (TASK-05-02). Blocked text is replaced with a redirect.
|
||||
|
||||
Returns:
|
||||
(debrief_text, usage_metadata).
|
||||
"""
|
||||
template = _load_template(scenario.debrief.prompt_template)
|
||||
turns_str = _format_learner_turns(learner_turns)
|
||||
system_prompt = _render(
|
||||
template["system"],
|
||||
scenario_title=scenario.title,
|
||||
)
|
||||
user_prompt = _render(
|
||||
template["user"],
|
||||
scenario_title=scenario.title,
|
||||
outcome=outcome,
|
||||
branch_id=branch_id,
|
||||
debrief_focus=debrief_focus,
|
||||
learner_turns=turns_str,
|
||||
)
|
||||
|
||||
messages = [
|
||||
{"role": "system", "content": system_prompt},
|
||||
{"role": "user", "content": user_prompt},
|
||||
]
|
||||
text, usage = await llm.chat_full(
|
||||
messages, model=llm.debrief_model, no_think=True
|
||||
)
|
||||
|
||||
# TASK-05-02: route through the guardrail output filter.
|
||||
if guardrail is not None:
|
||||
verdict = await guardrail.check(text, GuardrailContext(role="debrief"))
|
||||
if not verdict.allowed and verdict.filtered_text:
|
||||
text = verdict.filtered_text
|
||||
|
||||
return text, usage
|
||||
|
||||
|
||||
__all__ = ["generate_debrief"]
|
||||
@@ -0,0 +1,5 @@
|
||||
"""Guardrail package — pluggable rulesets behind the Guardrail interface (D-019)."""
|
||||
|
||||
from server.services.base import Guardrail, GuardrailContext, GuardrailVerdict
|
||||
|
||||
__all__ = ["Guardrail", "GuardrailContext", "GuardrailVerdict"]
|
||||
@@ -0,0 +1,129 @@
|
||||
"""CustomerServiceGuardrail — v0.1 Customer Service ruleset (D-019, TASK-03-04).
|
||||
|
||||
Pluggable implementation of the Guardrail interface. Enforces the RESEARCH.md
|
||||
safety baseline for the Customer Service path:
|
||||
- system-prompt constraints: no legal/financial/medical advice, no real-company
|
||||
impersonation, stay-in-role, concise-for-voice
|
||||
- debrief output filter: block recommendations that the learner advise legal action
|
||||
- session-start disclaimer audio (defined text)
|
||||
- no PII collection beyond the hardcoded profile
|
||||
|
||||
Selected via PRAXIS_GUARDRAIL=customer_service (default). Replaces the
|
||||
SLICE-02 NoOpGuardrail with no pipeline change (D-019).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
|
||||
from server.services.base import Guardrail, GuardrailContext, GuardrailVerdict
|
||||
|
||||
# The session-start disclaimer (RESEARCH.md §Safety). Played as the first AI
|
||||
# utterance of every session.
|
||||
DISCLAIMER_TEXT = (
|
||||
"This is an AI practice session for training purposes. "
|
||||
"It is not a real conversation and no real company is involved."
|
||||
)
|
||||
|
||||
# Patterns that indicate the model is giving advice it shouldn't (per D-019).
|
||||
_LEGAL_ADVICE_RE = re.compile(
|
||||
r"\b(sue|lawsuit|take legal action|small claims|hire a lawyer|attorney|"
|
||||
r"file a complaint with .* tribun|legal rights)\b",
|
||||
re.IGNORECASE,
|
||||
)
|
||||
_FINANCIAL_ADVICE_RE = re.compile(
|
||||
r"\b(invest|stock|bond|crypto|retirement fund|tax write-?off|bankruptcy)\b",
|
||||
re.IGNORECASE,
|
||||
)
|
||||
_MEDICAL_ADVICE_RE = re.compile(
|
||||
r"\b(diagnosis|prescribe|medication|therapy|see a doctor|medical condition|"
|
||||
r"mental health condition)\b",
|
||||
re.IGNORECASE,
|
||||
)
|
||||
_IMPERSONATION_RE = re.compile(
|
||||
# Claiming to work for a real named company — heuristic.
|
||||
r"\b(I (?:work|am employed) (?:at|for|with))\b.*\b(Inc\.|Corp\.|LLC|Ltd\.|"
|
||||
r"Amazon|Apple|Google|Microsoft|Walmart|Costco|Telus|Rogers|Bell|Shopify)\b",
|
||||
re.IGNORECASE,
|
||||
)
|
||||
# Debrief-specific: block recommendations that the learner tell a real customer
|
||||
# to take legal action. Catches "sue them", "take legal action", "file a lawsuit",
|
||||
# "small claims", etc. when phrased as advice to the customer.
|
||||
_DEBRIEF_LEGAL_ACTION_RE = re.compile(
|
||||
r"\b(tell (?:the |a )?customer to (?:sue|take legal action|file a lawsuit)|"
|
||||
r"advise.*(?:sue|legal action|lawsuit|small claims)|"
|
||||
r"recommend.*(?:sue|legal action|lawsuit|small claims)|"
|
||||
r"(?:suggest|tell|recommend).*sue them|"
|
||||
r"customer should (?:sue|take legal action|file a lawsuit))\b",
|
||||
re.IGNORECASE,
|
||||
)
|
||||
|
||||
|
||||
class CustomerServiceGuardrail(Guardrail):
|
||||
"""Customer Service ruleset (D-019). Low-risk domain, baseline guardrails."""
|
||||
|
||||
name = "customer_service"
|
||||
|
||||
async def check(
|
||||
self, text: str, context: GuardrailContext | None = None
|
||||
) -> GuardrailVerdict:
|
||||
ctx = context or GuardrailContext()
|
||||
role = ctx.role
|
||||
|
||||
# Debrief output filter — block legal-action recommendations.
|
||||
if role == "debrief":
|
||||
if _DEBRIEF_LEGAL_ACTION_RE.search(text):
|
||||
return GuardrailVerdict(
|
||||
allowed=False,
|
||||
reason="blocked: debrief recommends legal action (D-019 debrief filter)",
|
||||
category="blocked_legal",
|
||||
filtered_text=self._filter_legal(text),
|
||||
)
|
||||
return GuardrailVerdict(allowed=True, reason="debrief ok", category="ok")
|
||||
|
||||
# System / assistant / user content checks.
|
||||
if _LEGAL_ADVICE_RE.search(text):
|
||||
return GuardrailVerdict(
|
||||
allowed=False,
|
||||
reason="blocked: legal advice (D-019 no-legal-advice)",
|
||||
category="blocked_legal",
|
||||
)
|
||||
if _FINANCIAL_ADVICE_RE.search(text):
|
||||
return GuardrailVerdict(
|
||||
allowed=False,
|
||||
reason="blocked: financial advice (D-019 no-financial-advice)",
|
||||
category="blocked_financial",
|
||||
)
|
||||
if _MEDICAL_ADVICE_RE.search(text):
|
||||
return GuardrailVerdict(
|
||||
allowed=False,
|
||||
reason="blocked: medical advice (D-019 no-medical-advice)",
|
||||
category="blocked_medical",
|
||||
)
|
||||
if _IMPERSONATION_RE.search(text):
|
||||
return GuardrailVerdict(
|
||||
allowed=False,
|
||||
reason="blocked: real-company impersonation (D-019)",
|
||||
category="blocked_impersonation",
|
||||
)
|
||||
|
||||
return GuardrailVerdict(allowed=True, reason="ok", category="ok")
|
||||
|
||||
@property
|
||||
def session_start_disclaimer(self) -> str:
|
||||
return DISCLAIMER_TEXT
|
||||
|
||||
@staticmethod
|
||||
def _filter_legal(text: str) -> str:
|
||||
"""Replace legal-action recommendations with a coaching redirect."""
|
||||
return _DEBRIEF_LEGAL_REDIRECT if _DEBRIEF_LEGAL_REDIRECT else text
|
||||
|
||||
|
||||
# Coaching redirect used when a debrief recommends legal action (D-019).
|
||||
_DEBRIEF_LEGAL_REDIRECT = (
|
||||
"Focus your coaching on the learner's communication performance, "
|
||||
"not on advising the customer to take legal action."
|
||||
)
|
||||
|
||||
|
||||
__all__ = ["CustomerServiceGuardrail", "DISCLAIMER_TEXT"]
|
||||
@@ -0,0 +1,37 @@
|
||||
"""NoOpGuardrail — always-allow stub implementing the Guardrail interface (TASK-02-07).
|
||||
|
||||
SLICE-02 ships this stub so the Pipecat pipeline has the pluggable guardrail hook
|
||||
in place from the first slice. SLICE-03 TASK-03-04 swaps in CustomerServiceGuardrail
|
||||
with no pipeline change (D-019). The disclaimer text is defined here (matches the
|
||||
RESEARCH.md safety baseline) so the pipeline can play it as the first AI utterance
|
||||
even before the real ruleset lands.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from server.services.base import Guardrail, GuardrailContext, GuardrailVerdict
|
||||
|
||||
# The session-start disclaimer (RESEARCH.md §Safety). Played as the first AI
|
||||
# utterance of every session. Defined here so it exists from SLICE-02.
|
||||
DISCLAIMER_TEXT = (
|
||||
"This is an AI practice session for training purposes. "
|
||||
"It is not a real conversation and no real company is involved."
|
||||
)
|
||||
|
||||
|
||||
class NoOpGuardrail(Guardrail):
|
||||
"""Always-allow stub (SLICE-02 placeholder for the Guardrail slot)."""
|
||||
|
||||
name = "noop"
|
||||
|
||||
async def check(
|
||||
self, text: str, context: GuardrailContext | None = None
|
||||
) -> GuardrailVerdict:
|
||||
return GuardrailVerdict(allowed=True, reason="noop guardrail — all allowed", category="ok")
|
||||
|
||||
@property
|
||||
def session_start_disclaimer(self) -> str:
|
||||
return DISCLAIMER_TEXT
|
||||
|
||||
|
||||
__all__ = ["NoOpGuardrail", "DISCLAIMER_TEXT"]
|
||||
@@ -0,0 +1,29 @@
|
||||
"""Interruptibility verification harness (TASK-03-05).
|
||||
|
||||
Verifies D-008 (abort-and-yield): learner VAD during AI TTS aborts TTS and
|
||||
yields the floor. The Pipecat pipeline has allow_interruptions=True (set in
|
||||
build_pipeline), so the abort is handled by Pipecat's built-in interrupt
|
||||
handling. This module provides:
|
||||
|
||||
- a programmatic check that the pipeline is configured for interruptions
|
||||
- a test that confirms a TTS-abort event fires on VAD during TTS
|
||||
|
||||
The manual test (speaking during AI speech cuts it off) is documented in
|
||||
docs/latency-report.md; the automated test is in tests/test_interruptibility.py.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
|
||||
def pipeline_allows_interruptions(pipeline_task: Any) -> bool:
|
||||
"""Confirm the pipeline task is configured with allow_interruptions=True (D-008)."""
|
||||
# PipelineParams stores the flag; the task's params attribute carries it.
|
||||
params = getattr(pipeline_task, "params", None)
|
||||
if params is None:
|
||||
return False
|
||||
return bool(getattr(params, "allow_interruptions", False))
|
||||
|
||||
|
||||
__all__ = ["pipeline_allows_interruptions"]
|
||||
@@ -0,0 +1,131 @@
|
||||
"""Latency observer — measures ASR→TTS-first-audio per turn (TASK-02-06).
|
||||
|
||||
Hooks into the Pipecat pipeline frame flow to timestamp:
|
||||
- final-transcript-ready (ASR done)
|
||||
- LLM-first-token
|
||||
- TTS-first-audio
|
||||
- client-playback-start (approx via output frame)
|
||||
|
||||
Surfaces the ASR→TTS-first-audio number to the client as a metric frame so the
|
||||
React client can display it (TASK-02-05 latency readout). Also logs to console
|
||||
for the server-side record.
|
||||
|
||||
This is a thin Pipecat FrameProcessor; it does not alter the frame stream, only
|
||||
observes. Per-segment latencies are stored in a per-session LatencyRecord and
|
||||
emitted via the task's metrics channel.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import time
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Any
|
||||
|
||||
from pipecat.frames.frames import (
|
||||
Frame,
|
||||
TranscriptionFrame,
|
||||
LLMFullResponseEndFrame,
|
||||
TextFrame,
|
||||
TTSStartedFrame,
|
||||
TTSAudioRawFrame,
|
||||
BotStartedSpeakingFrame,
|
||||
)
|
||||
from pipecat.processors.frame_processor import FrameProcessor
|
||||
|
||||
|
||||
@dataclass
|
||||
class LatencyRecord:
|
||||
"""Per-turn latency segments (ms)."""
|
||||
|
||||
transcript_ready_ms: float | None = None
|
||||
llm_first_token_ms: float | None = None
|
||||
tts_first_audio_ms: float | None = None
|
||||
playback_start_ms: float | None = None
|
||||
|
||||
@property
|
||||
def e2e_asr_to_tts_ms(self) -> float | None:
|
||||
"""ASR transcript-ready → TTS first-audio (the v0.1 latency target)."""
|
||||
if self.transcript_ready_ms and self.tts_first_audio_ms:
|
||||
return self.tts_first_audio_ms - self.transcript_ready_ms
|
||||
return None
|
||||
|
||||
def as_metric(self) -> dict[str, Any]:
|
||||
return {
|
||||
"e2e_latency_ms": self.e2e_asr_to_tts_ms,
|
||||
"transcript_ready_ms": self.transcript_ready_ms,
|
||||
"llm_first_token_ms": self.llm_first_token_ms,
|
||||
"tts_first_audio_ms": self.tts_first_audio_ms,
|
||||
"playback_start_ms": self.playback_start_ms,
|
||||
}
|
||||
|
||||
|
||||
@dataclass
|
||||
class LatencyObserverState:
|
||||
"""Accumulates per-turn records and the current in-flight turn."""
|
||||
|
||||
current: LatencyRecord = field(default_factory=LatencyRecord)
|
||||
records: list[LatencyRecord] = field(default_factory=list)
|
||||
|
||||
def reset_turn(self) -> LatencyRecord:
|
||||
if self.current.transcript_ready_ms is not None:
|
||||
self.records.append(self.current)
|
||||
self.current = LatencyRecord()
|
||||
return self.current
|
||||
|
||||
|
||||
class LatencyObserver(FrameProcessor):
|
||||
"""Observes frames, timestamps the latency-critical segments, emits metrics.
|
||||
|
||||
This processor is inserted into the pipeline (it passes frames through
|
||||
unchanged). On each TTS-first-audio it logs the turn's e2e latency and
|
||||
pushes a metric frame downstream for the client to read.
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs) -> None:
|
||||
super().__init__(**kwargs)
|
||||
self.state = LatencyObserverState()
|
||||
|
||||
async def process_frame(self, frame: Frame, direction) -> None:
|
||||
# Always pass the frame through first (observation only).
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
now_ms = time.perf_counter() * 1000.0
|
||||
|
||||
if isinstance(frame, TranscriptionFrame):
|
||||
# ASR final transcript — start of a new turn's latency measurement.
|
||||
rec = self.state.reset_turn()
|
||||
rec.transcript_ready_ms = now_ms
|
||||
|
||||
elif isinstance(frame, LLMFullResponseEndFrame):
|
||||
# LLM emitted a full response; first token timestamp is approximated
|
||||
# by this frame's arrival (Pipecat doesn't emit a dedicated
|
||||
# first-token frame; the metrics service handles TTFT separately).
|
||||
if self.state.current.llm_first_token_ms is None:
|
||||
self.state.current.llm_first_token_ms = now_ms
|
||||
|
||||
elif isinstance(frame, TextFrame):
|
||||
# Intermediate LLM text frame — closest proxy to first-token time.
|
||||
if (
|
||||
self.state.current.transcript_ready_ms is not None
|
||||
and self.state.current.llm_first_token_ms is None
|
||||
):
|
||||
self.state.current.llm_first_token_ms = now_ms
|
||||
|
||||
elif isinstance(frame, (TTSStartedFrame, TTSAudioRawFrame)):
|
||||
if self.state.current.tts_first_audio_ms is None:
|
||||
self.state.current.tts_first_audio_ms = now_ms
|
||||
e2e = self.state.current.e2e_asr_to_tts_ms
|
||||
if e2e is not None:
|
||||
from loguru import logger
|
||||
|
||||
logger.info(
|
||||
f"[latency] ASR→TTS first-audio: {e2e:.1f}ms "
|
||||
f"(budget 600ms — {'within' if e2e <= 600 else 'OVER'})"
|
||||
)
|
||||
|
||||
elif isinstance(frame, BotStartedSpeakingFrame):
|
||||
if self.state.current.playback_start_ms is None:
|
||||
self.state.current.playback_start_ms = now_ms
|
||||
|
||||
|
||||
__all__ = ["LatencyObserver", "LatencyRecord", "LatencyObserverState"]
|
||||
@@ -0,0 +1,5 @@
|
||||
"""LLM adapter package — Ollama Cloud direct API behind LLMProvider."""
|
||||
|
||||
from server.services.base import LLMProvider, LLMStreamChunk
|
||||
|
||||
__all__ = ["LLMProvider", "LLMStreamChunk"]
|
||||
@@ -0,0 +1,147 @@
|
||||
"""Ollama Cloud LLM adapter behind the LLMProvider interface (D-020).
|
||||
|
||||
Direct API to https://ollama.com/api/chat with OLLAMA_API_KEY bearer,
|
||||
stream=True. Two models:
|
||||
- gemma4:cloud (role-play fast path, 256K ctx)
|
||||
- deepseek-v4-flash:cloud (debrief + branch classifier, no-think mode)
|
||||
|
||||
R6 resolution: Pipecat's OLLamaLLMService accepts a custom base_url + bearer
|
||||
(see docs/latency-report.md). This adapter is a thin wrapper over the raw
|
||||
/api/chat streaming endpoint so the pipeline has a stable, testable contract
|
||||
independent of Pipecat's OpenAI-compat shim. The Pipecat pipeline wires the
|
||||
LLM via this adapter (TASK-02-04) so a swap (e.g. self-hosted gemma4:e4b
|
||||
post-pilot) requires no pipeline change.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
from typing import Any, AsyncIterator
|
||||
|
||||
from server.services.base import LLMProvider, LLMStreamChunk
|
||||
|
||||
CHAT_URL_DEFAULT = "https://ollama.com/api/chat"
|
||||
|
||||
|
||||
class OllamaCloudLLM(LLMProvider):
|
||||
"""Ollama Cloud direct-API LLM adapter (D-020)."""
|
||||
|
||||
name = "ollama-cloud"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
api_key: str | None = None,
|
||||
chat_url: str | None = None,
|
||||
roleplay_model: str | None = None,
|
||||
debrief_model: str | None = None,
|
||||
) -> None:
|
||||
self._api_key = (api_key or os.environ.get("OLLAMA_API_KEY", "")).strip()
|
||||
self._chat_url = (chat_url or os.environ.get("OLLAMA_CHAT_URL", CHAT_URL_DEFAULT)).strip()
|
||||
self._roleplay_model = (
|
||||
roleplay_model or os.environ.get("OLLAMA_ROLEPLAY_MODEL", "gemma4:cloud")
|
||||
).strip()
|
||||
self._debrief_model = (
|
||||
debrief_model
|
||||
or os.environ.get("OLLAMA_DEBRIEF_MODEL", "deepseek-v4-flash:cloud")
|
||||
).strip()
|
||||
|
||||
@property
|
||||
def roleplay_model(self) -> str:
|
||||
return self._roleplay_model
|
||||
|
||||
@property
|
||||
def debrief_model(self) -> str:
|
||||
return self._debrief_model
|
||||
|
||||
def _missing(self) -> bool:
|
||||
return not self._api_key
|
||||
|
||||
def _headers(self) -> dict[str, str]:
|
||||
return {"Authorization": f"Bearer {self._api_key}", "Content-Type": "application/json"}
|
||||
|
||||
def _body(
|
||||
self,
|
||||
messages: list[dict[str, str]],
|
||||
model: str,
|
||||
stream: bool,
|
||||
no_think: bool,
|
||||
) -> dict[str, Any]:
|
||||
body: dict[str, Any] = {
|
||||
"model": model,
|
||||
"messages": messages,
|
||||
"stream": stream,
|
||||
}
|
||||
if no_think:
|
||||
# deepseek-v4-flash:cloud no-think mode (D-020) — skips reasoning
|
||||
# tokens for latency on the debrief / branch-classifier path.
|
||||
body["think"] = False
|
||||
return body
|
||||
|
||||
async def chat(
|
||||
self,
|
||||
messages: list[dict[str, str]],
|
||||
*,
|
||||
stream: bool = True,
|
||||
model: str | None = None,
|
||||
no_think: bool = False,
|
||||
) -> AsyncIterator[LLMStreamChunk]:
|
||||
"""Stream chat-completion chunks from Ollama Cloud /api/chat."""
|
||||
mdl = model or self._roleplay_model
|
||||
if self._missing():
|
||||
# Graceful: yield a single empty chunk so callers don't crash.
|
||||
return
|
||||
import httpx
|
||||
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=60.0) as client:
|
||||
async with client.stream(
|
||||
"POST", self._chat_url, headers=self._headers(),
|
||||
json=self._body(messages, mdl, stream, no_think),
|
||||
) as resp:
|
||||
if resp.status_code != 200:
|
||||
# Auth/error — degrade to no chunks (pipeline stays up).
|
||||
return
|
||||
is_first = True
|
||||
async for line in resp.aiter_lines():
|
||||
if not line:
|
||||
continue
|
||||
try:
|
||||
chunk = json.loads(line)
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
content = chunk.get("message", {}).get("content", "")
|
||||
if content:
|
||||
yield LLMStreamChunk(
|
||||
content=content,
|
||||
is_first=is_first,
|
||||
finish_reason=chunk.get("done") and "stop" or None,
|
||||
extra={"eval_count": chunk.get("eval_count")},
|
||||
)
|
||||
is_first = False
|
||||
except Exception:
|
||||
# Network/auth errors degrade to no chunks; the pipeline stays up.
|
||||
return
|
||||
|
||||
async def chat_full(
|
||||
self,
|
||||
messages: list[dict[str, str]],
|
||||
*,
|
||||
model: str | None = None,
|
||||
no_think: bool = False,
|
||||
) -> tuple[str, dict[str, Any]]:
|
||||
"""Return (full_text, usage) for non-streaming (debrief / classifier)."""
|
||||
mdl = model or self._debrief_model
|
||||
parts: list[str] = []
|
||||
usage: dict[str, Any] = {"input_tokens": 0, "output_tokens": 0, "model": mdl}
|
||||
async for chunk in self.chat(
|
||||
messages, stream=True, model=mdl, no_think=no_think
|
||||
):
|
||||
parts.append(chunk.content)
|
||||
if chunk.extra.get("eval_count"):
|
||||
usage["output_tokens"] = chunk.extra["eval_count"]
|
||||
return "".join(parts), usage
|
||||
|
||||
|
||||
__all__ = ["OllamaCloudLLM"]
|
||||
@@ -0,0 +1,231 @@
|
||||
"""Praxis Pipecat server pipeline — minimal viable voice loop (SLICE-02 TASK-02-04).
|
||||
|
||||
Pipeline (D-017):
|
||||
WebRTC audio in → Silero VAD → Deepgram Nova-3 STT → LLMContextAggregator(user)
|
||||
→ OllamaCloudLLM (gemma4:cloud) → LLMContextAggregator(assistant) → Cartesia/Piper TTS
|
||||
→ WebRTC audio out
|
||||
|
||||
Interruptibility (D-008): Pipecat's built-in interrupt handling aborts TTS + yields
|
||||
the floor when learner VAD fires during AI speech.
|
||||
|
||||
The pipeline starts and accepts connections even if upstream services return auth
|
||||
errors at runtime — the code structure is the SLICE-02 deliverable. All keys come
|
||||
from env; missing keys degrade to no audio / no tokens, not crashes.
|
||||
|
||||
Hardcoded single-turn system prompt (no YAML scenario yet — SLICE-03 replaces it).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
from typing import Any
|
||||
|
||||
from loguru import logger
|
||||
|
||||
|
||||
def _env(key: str, default: str = "") -> str:
|
||||
return os.environ.get(key, default).strip()
|
||||
|
||||
|
||||
# Hardcoded single-turn system prompt (SLICE-02 walking skeleton).
|
||||
# SLICE-03 TASK-03-07 replaces this with the scenario-driven prompt from YAML.
|
||||
WALKING_SKELETON_SYSTEM_PROMPT = (
|
||||
"You are Jordan, a customer who received a damaged product. "
|
||||
"You are frustrated but not abusive. You want a refund. "
|
||||
"Stay in character. Do not break role. "
|
||||
"Keep responses concise for voice (1-3 sentences)."
|
||||
)
|
||||
|
||||
WALKING_SKELETON_OPENING_LINE = (
|
||||
"Hi, I received my order yesterday and the item is cracked. I want my money back."
|
||||
)
|
||||
|
||||
|
||||
def _build_llm_context(scenario_runtime=None):
|
||||
"""Build the LLMContext with the scenario-driven system prompt (TASK-03-07).
|
||||
|
||||
If a scenario_runtime is provided, uses scenario.setup.system_prompt.
|
||||
Otherwise falls back to the SLICE-02 walking-skeleton prompt.
|
||||
"""
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
|
||||
if scenario_runtime is not None:
|
||||
system_prompt = scenario_runtime.system_prompt
|
||||
else:
|
||||
system_prompt = WALKING_SKELETON_SYSTEM_PROMPT
|
||||
|
||||
messages = [
|
||||
{"role": "system", "content": system_prompt},
|
||||
]
|
||||
return LLMContext(messages=messages)
|
||||
|
||||
|
||||
def _build_transport(webrtc_connection) -> Any:
|
||||
"""Build the SmallWebRTCTransport with audio in/out enabled."""
|
||||
from pipecat.transports.base_transport import TransportParams
|
||||
from pipecat.transports.smallwebrtc.transport import SmallWebRTCTransport
|
||||
|
||||
params = TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
audio_out_sample_rate=24000,
|
||||
)
|
||||
return SmallWebRTCTransport(webrtc_connection, params)
|
||||
|
||||
|
||||
def _build_stt() -> Any:
|
||||
"""Build the Deepgram Nova-3 STT service (D-013)."""
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
|
||||
api_key = _env("DEEPGRAM_API_KEY")
|
||||
if not api_key:
|
||||
logger.warning("DEEPGRAM_API_KEY not set — STT will not transcribe (pipeline still starts).")
|
||||
return DeepgramSTTService(
|
||||
api_key=api_key or "missing",
|
||||
live_options=None, # Deepgram defaults are fine for nova-3 + en.
|
||||
)
|
||||
|
||||
|
||||
def _build_llm() -> Any:
|
||||
"""Build the Pipecat Ollama LLM service pointed at Ollama Cloud (D-020, R6).
|
||||
|
||||
Pipecat's OLLamaLLMService extends OpenAILLMService and accepts a custom
|
||||
base_url + the OpenAI client api_key (bearer). We point it at
|
||||
https://ollama.com/v1 with OLLAMA_API_KEY as the bearer.
|
||||
"""
|
||||
from pipecat.services.ollama.llm import OLLamaLLMService
|
||||
|
||||
api_key = _env("OLLAMA_API_KEY")
|
||||
base_url = _env("OLLAMA_BASE_URL", "https://ollama.com/v1")
|
||||
model = _env("OLLAMA_ROLEPLAY_MODEL", "gemma4:cloud")
|
||||
if not api_key:
|
||||
logger.warning("OLLAMA_API_KEY not set — LLM will not respond (pipeline still starts).")
|
||||
return OLLamaLLMService(
|
||||
base_url=base_url,
|
||||
settings=OLLamaLLMService.Settings(model=model, api_key=api_key or "missing"),
|
||||
)
|
||||
|
||||
|
||||
def _build_tts() -> Any:
|
||||
"""Build the Pipecat TTS service for the selected provider (D-014)."""
|
||||
choice = _env("PRAXIS_TTS", "cartesia").lower()
|
||||
|
||||
if choice == "piper":
|
||||
from pipecat.services.piper.tts import PiperTTSService
|
||||
|
||||
voice_model = _env("PIPER_VOICE_MODEL")
|
||||
if not voice_model:
|
||||
logger.warning("PIPER_VOICE_MODEL not set — Piper TTS will not speak (pipeline still starts).")
|
||||
return PiperTTSService(
|
||||
voice_id=voice_model or "missing",
|
||||
)
|
||||
|
||||
# Default: Cartesia
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
|
||||
api_key = _env("CARTESIA_API_KEY")
|
||||
voice_id = _env("CARTESIA_VOICE_ID", "a3536a36-1d18-4efb-a95a-7c44b7b5e384")
|
||||
if not api_key:
|
||||
logger.warning("CARTESIA_API_KEY not set — TTS will not speak (pipeline still starts).")
|
||||
return CartesiaTTSService(
|
||||
api_key=api_key or "missing",
|
||||
voice_id=voice_id,
|
||||
)
|
||||
|
||||
|
||||
def _build_vad_analyzer() -> Any:
|
||||
"""Build the Silero VAD analyzer (D-008 interruptibility)."""
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
|
||||
return SileroVADAnalyzer()
|
||||
|
||||
|
||||
def build_pipeline(webrtc_connection, *, scenario_id: str | None = None):
|
||||
"""Assemble the full Pipecat pipeline + task + runner for one WebRTC session.
|
||||
|
||||
Args:
|
||||
webrtc_connection: a SmallWebRTCConnection with an accepted offer.
|
||||
scenario_id: if set, load the scenario and use its system prompt + opening
|
||||
line (TASK-03-07). If None, falls back to the walking-skeleton prompt.
|
||||
|
||||
Returns (pipeline, task, runner, transport, scenario_runtime) so the caller
|
||||
can start the task on connection, play the opening line, and run the branch
|
||||
classifier + debrief at session end.
|
||||
"""
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregator,
|
||||
)
|
||||
|
||||
# Load the scenario runtime (TASK-03-03, TASK-03-07).
|
||||
scenario_runtime = None
|
||||
if scenario_id:
|
||||
try:
|
||||
from server.scenarios.runtime import build_runtime_from_id
|
||||
|
||||
scenario_runtime = build_runtime_from_id(scenario_id)
|
||||
logger.info(
|
||||
f"Loaded scenario {scenario_id!r}: branches={scenario_runtime.scenario.branch_ids()}"
|
||||
)
|
||||
except Exception as exc:
|
||||
logger.warning(
|
||||
f"Could not load scenario {scenario_id!r}: {exc}. "
|
||||
f"Falling back to walking-skeleton prompt."
|
||||
)
|
||||
|
||||
transport = _build_transport(webrtc_connection)
|
||||
stt = _build_stt()
|
||||
llm = _build_llm()
|
||||
tts = _build_tts()
|
||||
|
||||
from server.latency import LatencyObserver
|
||||
|
||||
latency_observer = LatencyObserver()
|
||||
|
||||
context = _build_llm_context(scenario_runtime)
|
||||
user_aggregator = LLMContextAggregator(context=context, role="user")
|
||||
assistant_aggregator = LLMContextAggregator(context=context, role="assistant")
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # WebRTC audio in
|
||||
stt, # Deepgram Nova-3
|
||||
latency_observer, # timestamp ASR-ready (TASK-02-06)
|
||||
user_aggregator, # collect user transcript into context
|
||||
llm, # Ollama gemma4:cloud
|
||||
latency_observer, # timestamp LLM-first-token (passes through)
|
||||
tts, # Cartesia/Piper
|
||||
latency_observer, # timestamp TTS-first-audio + emit metric
|
||||
transport.output(), # WebRTC audio out
|
||||
assistant_aggregator, # collect assistant text into context
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True, # D-008 abort-and-yield
|
||||
enable_metrics=True, # latency measurement (TASK-02-06)
|
||||
metrics_request_timeout=10.0,
|
||||
),
|
||||
)
|
||||
|
||||
runner = PipelineRunner(handle_sigint=False)
|
||||
return pipeline, task, runner, transport, scenario_runtime
|
||||
|
||||
|
||||
def build_runtime_from_id(scenario_id: str):
|
||||
"""Re-export of the scenario runtime builder (TASK-03-07)."""
|
||||
from server.scenarios.runtime import build_runtime_from_id as _br
|
||||
|
||||
return _br(scenario_id)
|
||||
|
||||
|
||||
__all__ = [
|
||||
"build_pipeline",
|
||||
"build_runtime_from_id",
|
||||
"WALKING_SKELETON_SYSTEM_PROMPT",
|
||||
"WALKING_SKELETON_OPENING_LINE",
|
||||
]
|
||||
@@ -0,0 +1,24 @@
|
||||
"""Scenario runtime package — YAML → Pydantic → Pipecat Flows (D-018)."""
|
||||
|
||||
from server.scenarios.schema import (
|
||||
Branch,
|
||||
BranchTrigger,
|
||||
Scenario,
|
||||
ScenarioDebrief,
|
||||
ScenarioPersona,
|
||||
ScenarioSetup,
|
||||
ValidationError,
|
||||
)
|
||||
from server.scenarios.loader import load, load_all
|
||||
|
||||
__all__ = [
|
||||
"Scenario",
|
||||
"ScenarioPersona",
|
||||
"ScenarioSetup",
|
||||
"Branch",
|
||||
"BranchTrigger",
|
||||
"ScenarioDebrief",
|
||||
"ValidationError",
|
||||
"load",
|
||||
"load_all",
|
||||
]
|
||||
@@ -0,0 +1,137 @@
|
||||
"""Branch classifier — LLM-as-judge for learner-signal classification (R7, TASK-03-06).
|
||||
|
||||
At session end (or turn boundary), classifies the learner's turn transcripts
|
||||
into a scenario branch (accept_resolution or escalate) based on the scenario's
|
||||
learner_signals definitions. Runs OFFLINE from the voice loop (not on the
|
||||
latency-critical path) per D-P1-05.
|
||||
|
||||
Uses deepseek-v4-flash:cloud in no-think mode (D-020) via the LLMProvider —
|
||||
cheap + fast enough for a one-shot end-of-session classification.
|
||||
|
||||
Per G-002: the v0.1 branch is a post-hoc outcome classification, not a runtime
|
||||
conversation fork. This classifier produces the label that the debrief + DB
|
||||
log consume.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from typing import Any
|
||||
|
||||
from server.scenarios.schema import Scenario
|
||||
from server.services.base import GuardrailContext, LLMProvider
|
||||
|
||||
|
||||
CLASSIFIER_SYSTEM_PROMPT = """\
|
||||
You are a conversation-branch classifier for a customer-service role-play
|
||||
training session. Given the learner's turns and the scenario's branch
|
||||
definitions (each with learner_signals), classify which branch the learner's
|
||||
behavior matches.
|
||||
|
||||
Respond with ONLY a JSON object: {"branch_id": "<id>", "reason": "<short>"}
|
||||
No other text. If the signals are mixed, pick the closest match and explain in
|
||||
the reason field.
|
||||
"""
|
||||
|
||||
|
||||
def _build_user_prompt(scenario: Scenario, learner_turns: list[str]) -> str:
|
||||
branches_desc = "\n".join(
|
||||
f" - {b.id}: signals={b.trigger.learner_signals}, outcome={b.outcome}"
|
||||
for b in scenario.branches
|
||||
)
|
||||
turns_desc = "\n".join(f" Learner: {t}" for t in learner_turns)
|
||||
return (
|
||||
f"Scenario: {scenario.title}\n"
|
||||
f"Branches:\n{branches_desc}\n\n"
|
||||
f"Learner turns:\n{turns_desc}\n\n"
|
||||
f"Which branch does the learner's behavior match? "
|
||||
f"Respond with JSON {{\"branch_id\": ..., \"reason\": ...}}."
|
||||
)
|
||||
|
||||
|
||||
async def classify_branch(
|
||||
llm: LLMProvider,
|
||||
scenario: Scenario,
|
||||
learner_turns: list[str],
|
||||
) -> tuple[str, str]:
|
||||
"""Classify the learner's turns into a branch id.
|
||||
|
||||
Args:
|
||||
llm: the LLMProvider (uses debrief_model = deepseek-v4-flash:cloud no_think).
|
||||
scenario: the loaded Scenario.
|
||||
learner_turns: the learner's ASR transcripts for the session.
|
||||
|
||||
Returns:
|
||||
(branch_id, reason) — branch_id is one of scenario.branch_ids().
|
||||
"""
|
||||
messages = [
|
||||
{"role": "system", "content": CLASSIFIER_SYSTEM_PROMPT},
|
||||
{"role": "user", "content": _build_user_prompt(scenario, learner_turns)},
|
||||
]
|
||||
text, _usage = await llm.chat_full(
|
||||
messages, model=llm.debrief_model, no_think=True
|
||||
)
|
||||
return _parse_branch(text, scenario)
|
||||
|
||||
|
||||
def _parse_branch(text: str, scenario: Scenario) -> tuple[str, str]:
|
||||
"""Parse the LLM's JSON response into (branch_id, reason)."""
|
||||
# Be lenient — strip code fences, find the JSON object.
|
||||
cleaned = text.strip()
|
||||
if cleaned.startswith("```"):
|
||||
cleaned = cleaned.strip("`")
|
||||
if cleaned.lower().startswith("json"):
|
||||
cleaned = cleaned[4:]
|
||||
try:
|
||||
obj = json.loads(cleaned)
|
||||
branch_id = obj.get("branch_id", "")
|
||||
reason = obj.get("reason", "")
|
||||
except json.JSONDecodeError:
|
||||
# Fall back to a heuristic scan for a known branch id.
|
||||
reason = "fallback: could not parse LLM JSON"
|
||||
for b in scenario.branches:
|
||||
if b.id in text:
|
||||
return b.id, reason
|
||||
return scenario.branches[0].id, reason
|
||||
|
||||
# Validate the branch id is known.
|
||||
if branch_id not in scenario.branch_ids():
|
||||
reason = f"fallback: unknown branch_id {branch_id!r}; {reason}"
|
||||
branch_id = scenario.branches[0].id
|
||||
return branch_id, reason
|
||||
|
||||
|
||||
def classify_branch_sync_heuristic(
|
||||
scenario: Scenario, learner_turns: list[str]
|
||||
) -> str:
|
||||
"""A rule-based fallback classifier for tests (no LLM call).
|
||||
|
||||
Used by the e2e smoke test when no API key is present. Scans for keywords
|
||||
matching each branch's learner_signals. Signal tokens are matched as
|
||||
substrings (e.g. 'policy' matches 'policy_first'; 'empathy' matches
|
||||
'empathy'; 'concrete resolution' matches 'concrete_resolution').
|
||||
"""
|
||||
text = " ".join(learner_turns).lower()
|
||||
best = scenario.branches[0]
|
||||
best_score = -1
|
||||
for b in scenario.branches:
|
||||
score = 0
|
||||
for sig in b.trigger.learner_signals:
|
||||
# Match the signal as a space- or underscore-separated phrase.
|
||||
token = sig.replace("_", " ").lower()
|
||||
# Use the first significant word as a loose keyword (e.g. 'policy'
|
||||
# for 'policy_first', 'defensive' for 'defensive').
|
||||
keyword = token.split()[0] if " " in token else token
|
||||
if keyword in text or token in text:
|
||||
score += 1
|
||||
if score > best_score:
|
||||
best_score = score
|
||||
best = b
|
||||
return best.id
|
||||
|
||||
|
||||
__all__ = [
|
||||
"classify_branch",
|
||||
"classify_branch_sync_heuristic",
|
||||
"CLASSIFIER_SYSTEM_PROMPT",
|
||||
]
|
||||
@@ -0,0 +1,58 @@
|
||||
"""Scenario loader — YAML → Pydantic Scenario (D-018).
|
||||
|
||||
Loads a scenario by id from the scenarios/ directory, validates it against the
|
||||
Pydantic schema, and returns a typed Scenario object. Used by the pipeline
|
||||
(TASK-03-07) and the e2e smoke test.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import yaml
|
||||
|
||||
from server.scenarios.schema import Scenario, ValidationError
|
||||
|
||||
_DEFAULT_SCENARIOS_DIR = Path(__file__).resolve().parent.parent.parent / "scenarios"
|
||||
|
||||
|
||||
def load(scenario_id: str, scenarios_dir: Path | None = None) -> Scenario:
|
||||
"""Load and validate a scenario by id.
|
||||
|
||||
Args:
|
||||
scenario_id: e.g. 'customer_service_refund_ca_v01' (the YAML filename stem).
|
||||
scenarios_dir: override the scenarios directory (default: repo /scenarios).
|
||||
|
||||
Returns:
|
||||
A validated Scenario object.
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: if the YAML file doesn't exist.
|
||||
ValidationError: if the YAML fails schema validation (typed Pydantic error).
|
||||
"""
|
||||
base = scenarios_dir or _DEFAULT_SCENARIOS_DIR
|
||||
path = base / f"{scenario_id}.yaml"
|
||||
if not path.exists():
|
||||
# Try the id-with-cs-prefix alias (RESEARCH example used 'cs_refund_ca_v01').
|
||||
path = base / f"{scenario_id.replace('cs_', 'customer_service_')}.yaml"
|
||||
if not path.exists():
|
||||
raise FileNotFoundError(f"Scenario YAML not found: {scenario_id} in {base}")
|
||||
|
||||
with path.open("r", encoding="utf-8") as f:
|
||||
raw = yaml.safe_load(f)
|
||||
|
||||
return Scenario.model_validate(raw)
|
||||
|
||||
|
||||
def load_all(scenarios_dir: Path | None = None) -> list[Scenario]:
|
||||
"""Load all scenarios in the directory (for the future scenario library)."""
|
||||
base = scenarios_dir or _DEFAULT_SCENARIOS_DIR
|
||||
out: list[Scenario] = []
|
||||
for p in sorted(base.glob("*.yaml")):
|
||||
with p.open("r", encoding="utf-8") as f:
|
||||
raw = yaml.safe_load(f)
|
||||
out.append(Scenario.model_validate(raw))
|
||||
return out
|
||||
|
||||
|
||||
__all__ = ["load", "load_all", "ValidationError"]
|
||||
@@ -0,0 +1,103 @@
|
||||
"""Scenario runtime — maps a Scenario to a Pipecat Flows state machine (TASK-03-03).
|
||||
|
||||
The v0.1 branch point is a post-hoc outcome classification (G-002): the
|
||||
conversation is linear, and at session end an LLM-as-judge (TASK-03-06)
|
||||
classifies the learner's signals into accept_resolution or escalate. Pipecat
|
||||
Flows is wired so the branch field is part of the data model; Phase 2+ can
|
||||
activate true in-flight branching without a schema change.
|
||||
|
||||
This module:
|
||||
- builds the system prompt from scenario.setup.system_prompt
|
||||
- provides the opening line (scenario.setup.opening_line) as the first TTS utterance
|
||||
- exposes the branch transition logic (driven by the classifier in TASK-03-06)
|
||||
|
||||
TASK-03-07: the pipeline uses scenario-driven prompts instead of the
|
||||
SLICE-02 hardcoded walking-skeleton prompt.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import Any
|
||||
|
||||
from server.scenarios.schema import Branch, Scenario
|
||||
|
||||
|
||||
@dataclass
|
||||
class ScenarioRuntime:
|
||||
"""Runtime state for one scenario session."""
|
||||
|
||||
scenario: Scenario
|
||||
branch: Branch | None = None
|
||||
turn_count: int = 0
|
||||
|
||||
@property
|
||||
def system_prompt(self) -> str:
|
||||
return self.scenario.setup.system_prompt
|
||||
|
||||
@property
|
||||
def opening_line(self) -> str:
|
||||
return self.scenario.setup.opening_line
|
||||
|
||||
@property
|
||||
def branch_id(self) -> str | None:
|
||||
return self.branch.id if self.branch else None
|
||||
|
||||
@property
|
||||
def outcome(self) -> str | None:
|
||||
return self.branch.outcome if self.branch else None
|
||||
|
||||
def set_branch(self, branch_id: str) -> Branch:
|
||||
"""Set the session's branch outcome (from the classifier, TASK-03-06)."""
|
||||
b = self.scenario.branch_by_id(branch_id)
|
||||
if b is None:
|
||||
raise ValueError(
|
||||
f"Unknown branch id {branch_id!r} for scenario {self.scenario.id!r}; "
|
||||
f"known: {self.scenario.branch_ids()}"
|
||||
)
|
||||
self.branch = b
|
||||
return b
|
||||
|
||||
def debrief_focus(self) -> str:
|
||||
"""The debrief focus for the resolved branch (or a default)."""
|
||||
if self.branch:
|
||||
return self.branch.debrief_focus
|
||||
return "General coaching feedback for this session."
|
||||
|
||||
def as_flow_spec(self) -> dict[str, Any]:
|
||||
"""Render the scenario as a Pipecat Flows state-machine spec.
|
||||
|
||||
v0.1: a single 'conversation' state with the system prompt; branch
|
||||
transitions are post-hoc (G-002). The spec carries the branch metadata
|
||||
so Phase 2+ can fork in-flight.
|
||||
"""
|
||||
return {
|
||||
"initial_state": "conversation",
|
||||
"states": {
|
||||
"conversation": {
|
||||
"system_prompt": self.system_prompt,
|
||||
"opening_line": self.opening_line,
|
||||
"branches": [
|
||||
{"id": b.id, "outcome": b.outcome,
|
||||
"learner_signals": b.trigger.learner_signals}
|
||||
for b in self.scenario.branches
|
||||
],
|
||||
},
|
||||
},
|
||||
"transitions": [], # v0.1: no in-flight transitions (G-002)
|
||||
}
|
||||
|
||||
|
||||
def build_runtime(scenario: Scenario) -> ScenarioRuntime:
|
||||
"""Construct a ScenarioRuntime for the given scenario."""
|
||||
return ScenarioRuntime(scenario=scenario)
|
||||
|
||||
|
||||
def build_runtime_from_id(scenario_id: str) -> ScenarioRuntime:
|
||||
"""Load + build a runtime by scenario id (convenience for the pipeline)."""
|
||||
from server.scenarios.loader import load
|
||||
|
||||
return build_runtime(load(scenario_id))
|
||||
|
||||
|
||||
__all__ = ["ScenarioRuntime", "build_runtime", "build_runtime_from_id"]
|
||||
@@ -0,0 +1,100 @@
|
||||
"""Praxis scenario schema — YAML DSL → Pydantic (D-018, D-009, D-010).
|
||||
|
||||
Defines the typed model for a branching role-play scenario. Loaded from YAML
|
||||
by server/scenarios/loader.py. Drives Pipecat Flows (TASK-03-03).
|
||||
|
||||
Per RESEARCH.md example + PROJECT.md D-010: one branch point (escalate vs
|
||||
accept), failure_mode field present (D-009 — not provoked in v0.1).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Literal
|
||||
|
||||
from pydantic import BaseModel, Field, ValidationError
|
||||
|
||||
|
||||
class ScenarioPersona(BaseModel):
|
||||
"""The AI character's voice + identity (D-006 — one voice for role-play + mentor)."""
|
||||
|
||||
voice_id: str = Field(..., description="TTS voice id (Cartesia/Piper) — same as mentor per D-006")
|
||||
character: str = Field(..., description="Character name + role, e.g. 'Customer (Jordan)'")
|
||||
|
||||
|
||||
class ScenarioSetup(BaseModel):
|
||||
"""The system prompt + opening line that start the role-play."""
|
||||
|
||||
system_prompt: str = Field(..., description="LLM system prompt (stays in character)")
|
||||
opening_line: str = Field(..., description="First TTS utterance the AI speaks")
|
||||
|
||||
|
||||
class BranchTrigger(BaseModel):
|
||||
"""Learner signals that trigger a branch transition (R7 classification)."""
|
||||
|
||||
learner_signals: list[str] = Field(
|
||||
..., description="Signals the branch classifier looks for (e.g. 'empathy', 'defensive')"
|
||||
)
|
||||
|
||||
|
||||
class Branch(BaseModel):
|
||||
"""One branch outcome (D-010 — v0.1 has two: accept_resolution + escalate)."""
|
||||
|
||||
id: str = Field(..., description="Branch id, e.g. 'accept_resolution' / 'escalate'")
|
||||
trigger: BranchTrigger
|
||||
outcome: Literal["success", "failure"] = Field(..., description="Branch outcome label")
|
||||
failure_mode: str | None = Field(
|
||||
None, description="D-009 failure_mode (present, not provoked in v0.1)"
|
||||
)
|
||||
debrief_focus: str = Field(..., description="What the debrief emphasizes for this branch")
|
||||
|
||||
|
||||
class ScenarioDebrief(BaseModel):
|
||||
"""Debrief generation config (D-020 — deepseek-v4-flash:cloud, no-think)."""
|
||||
|
||||
model: str = Field("deepseek-v4-flash:cloud", description="Ollama model for the debrief")
|
||||
mode: Literal["no_think", "think", "max_think"] = Field(
|
||||
"no_think", description="Reasoning mode (no_think for latency, D-020)"
|
||||
)
|
||||
prompt_template: str = Field(
|
||||
"debrief/default", description="Prompt template id (resolved by server/debrief.py)"
|
||||
)
|
||||
|
||||
|
||||
class Scenario(BaseModel):
|
||||
"""A Praxis role-play scenario (D-018 — YAML → Pydantic → Pipecat Flows)."""
|
||||
|
||||
id: str = Field(..., description="Scenario id, e.g. 'cs_refund_ca_v01'")
|
||||
path: str = Field(..., description="Skill path, e.g. 'customer_service'")
|
||||
market: str = Field(..., description="Market code, e.g. 'CA'")
|
||||
language: str = Field("en-CA", description="Language code")
|
||||
title: str = Field(..., description="Human-readable scenario title")
|
||||
difficulty: int = Field(1, ge=1, le=5, description="Difficulty 1-5")
|
||||
failure_mode: str = Field(
|
||||
..., description="D-009 failure_mode — present (not provoked in v0.1)"
|
||||
)
|
||||
persona: ScenarioPersona
|
||||
setup: ScenarioSetup
|
||||
success_criteria: list[str] = Field(..., min_length=1)
|
||||
common_mistakes: list[str] = Field(..., min_length=1)
|
||||
branches: list[Branch] = Field(..., min_length=1, description="Branch points (v0.1: 2)")
|
||||
debrief: ScenarioDebrief
|
||||
|
||||
def branch_ids(self) -> list[str]:
|
||||
return [b.id for b in self.branches]
|
||||
|
||||
def branch_by_id(self, branch_id: str) -> Branch | None:
|
||||
for b in self.branches:
|
||||
if b.id == branch_id:
|
||||
return b
|
||||
return None
|
||||
|
||||
|
||||
__all__ = [
|
||||
"Scenario",
|
||||
"ScenarioPersona",
|
||||
"ScenarioSetup",
|
||||
"Branch",
|
||||
"BranchTrigger",
|
||||
"ScenarioDebrief",
|
||||
"ValidationError",
|
||||
]
|
||||
@@ -0,0 +1,36 @@
|
||||
"""Praxis service interfaces and adapter registry.
|
||||
|
||||
Public API:
|
||||
from server.services import TTSProvider, LLMProvider, Guardrail
|
||||
from server.services import get_tts, get_llm, get_guardrail
|
||||
|
||||
Adapters are resolved from env vars:
|
||||
PRAXIS_TTS=cartesia|piper
|
||||
OLLAMA_ROLEPLAY_MODEL / OLLAMA_DEBRIEF_MODEL
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from server.services.base import (
|
||||
Guardrail,
|
||||
GuardrailContext,
|
||||
GuardrailVerdict,
|
||||
LLMProvider,
|
||||
LLMStreamChunk,
|
||||
TTSProvider,
|
||||
TTSResult,
|
||||
)
|
||||
from server.services.registry import get_guardrail, get_llm, get_tts
|
||||
|
||||
__all__ = [
|
||||
"TTSProvider",
|
||||
"TTSResult",
|
||||
"LLMProvider",
|
||||
"LLMStreamChunk",
|
||||
"Guardrail",
|
||||
"GuardrailVerdict",
|
||||
"GuardrailContext",
|
||||
"get_tts",
|
||||
"get_llm",
|
||||
"get_guardrail",
|
||||
]
|
||||
@@ -0,0 +1,201 @@
|
||||
"""Praxis service interfaces — abstract base classes for the swappable voice-loop services.
|
||||
|
||||
Per PLAN.md SLICE-02 TASK-02-01 and the D-014/D-019/D-020 swap requirements:
|
||||
- TTSProvider (D-014): Cartesia (cloud) | Piper (self-hosted)
|
||||
- LLMProvider (D-020): Ollama Cloud direct API (gemma4:cloud / deepseek-v4-flash:cloud)
|
||||
- Guardrail (D-019): pluggable; v0.1 = Customer Service ruleset
|
||||
|
||||
These ABCs are the contract the Pipecat pipeline depends on. Adapters wrap the
|
||||
underlying Pipecat services (or raw APIs) so a swap requires no pipeline change.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Any, AsyncIterator, Literal
|
||||
|
||||
|
||||
# ─── TTS ─────────────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
@dataclass
|
||||
class TTSResult:
|
||||
"""Result metadata from a TTS synthesis call."""
|
||||
|
||||
first_audio_ms: float | None = None
|
||||
chars: int = 0
|
||||
voice_id: str | None = None
|
||||
audio_format: str = "pcm_s16le"
|
||||
sample_rate: int = 24000
|
||||
extra: dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
|
||||
class TTSProvider(ABC):
|
||||
"""Abstract TTS provider (D-014).
|
||||
|
||||
One voice persona (D-006) for both role-play and mentor/debrief.
|
||||
Selection via env var `PRAXIS_TTS=cartesia|piper`.
|
||||
"""
|
||||
|
||||
name: str = "abstract"
|
||||
|
||||
@abstractmethod
|
||||
async def synthesize(self, text: str) -> AsyncIterator[bytes]:
|
||||
"""Stream audio chunks (PCM s16le) for the given text.
|
||||
|
||||
Yields bytes as they arrive from the upstream TTS (streaming-first).
|
||||
The first yielded chunk is the first-audio byte — measure latency there.
|
||||
"""
|
||||
...
|
||||
# pragma: no cover — abstract
|
||||
yield b"" # type: ignore[unreachable]
|
||||
|
||||
@abstractmethod
|
||||
async def synthesize_all(self, text: str) -> tuple[bytes, TTSResult]:
|
||||
"""Fully synthesize `text`, returning (audio_bytes, result_metadata).
|
||||
|
||||
Convenience wrapper for the debrief path where streaming isn't required
|
||||
on the critical latency path (the debrief is spoken after session end).
|
||||
"""
|
||||
...
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def voice_id(self) -> str:
|
||||
"""The configured voice persona id (D-006 — one voice)."""
|
||||
...
|
||||
|
||||
|
||||
# ─── LLM ─────────────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
@dataclass
|
||||
class LLMStreamChunk:
|
||||
"""A single chunk from a streaming LLM response."""
|
||||
|
||||
content: str
|
||||
is_first: bool = False
|
||||
finish_reason: str | None = None
|
||||
input_tokens: int | None = None
|
||||
output_tokens: int | None = None
|
||||
extra: dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
|
||||
class LLMProvider(ABC):
|
||||
"""Abstract LLM provider (D-020).
|
||||
|
||||
Wraps Ollama Cloud direct API (https://ollama.com/v1 + bearer). Two models:
|
||||
- gemma4:cloud (role-play fast path)
|
||||
- deepseek-v4-flash:cloud (debrief / branch classifier, no-think mode)
|
||||
"""
|
||||
|
||||
name: str = "abstract"
|
||||
|
||||
@abstractmethod
|
||||
async def chat(
|
||||
self,
|
||||
messages: list[dict[str, str]],
|
||||
*,
|
||||
stream: bool = True,
|
||||
model: str | None = None,
|
||||
no_think: bool = False,
|
||||
) -> AsyncIterator[LLMStreamChunk]:
|
||||
"""Stream chat-completion chunks for the given messages.
|
||||
|
||||
`model` overrides the provider default (e.g. deepseek-v4-flash:cloud for
|
||||
the debrief). `no_think=True` requests no-think mode (deepseek-v4-flash).
|
||||
"""
|
||||
...
|
||||
# pragma: no cover — abstract
|
||||
yield LLMStreamChunk(content="") # type: ignore[unreachable]
|
||||
|
||||
@abstractmethod
|
||||
async def chat_full(
|
||||
self,
|
||||
messages: list[dict[str, str]],
|
||||
*,
|
||||
model: str | None = None,
|
||||
no_think: bool = False,
|
||||
) -> tuple[str, dict[str, Any]]:
|
||||
"""Return (full_text, usage_metadata) for non-streaming calls.
|
||||
|
||||
Used by the debrief + branch classifier (offline from the voice loop).
|
||||
"""
|
||||
...
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def roleplay_model(self) -> str:
|
||||
"""The role-play fast-path model id (gemma4:cloud)."""
|
||||
...
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def debrief_model(self) -> str:
|
||||
"""The debrief/branch-classifier model id (deepseek-v4-flash:cloud)."""
|
||||
...
|
||||
|
||||
|
||||
# ─── Guardrail ───────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
@dataclass
|
||||
class GuardrailVerdict:
|
||||
"""Verdict from a guardrail check (D-019)."""
|
||||
|
||||
allowed: bool
|
||||
reason: str = ""
|
||||
filtered_text: str | None = None
|
||||
category: str = "ok" # ok | blocked_legal | blocked_financial | blocked_medical |
|
||||
# blocked_impersonation | blocked_off_role | blocked_pii
|
||||
extra: dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
|
||||
@dataclass
|
||||
class GuardrailContext:
|
||||
"""Context passed to a guardrail check."""
|
||||
|
||||
role: Literal["system", "user", "assistant", "debrief"] = "user"
|
||||
scenario_id: str | None = None
|
||||
session_id: str | None = None
|
||||
turn_seq: int | None = None
|
||||
extra: dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
|
||||
class Guardrail(ABC):
|
||||
"""Abstract guardrail layer (D-019).
|
||||
|
||||
Pluggable so health/electrical domains (later milestones) can inject
|
||||
domain-specific rules without touching the pipeline. v0.1 ships one
|
||||
implementation: CustomerServiceGuardrail (SLICE-03 TASK-03-04).
|
||||
"""
|
||||
|
||||
name: str = "abstract"
|
||||
|
||||
@abstractmethod
|
||||
async def check(
|
||||
self, text: str, context: GuardrailContext | None = None
|
||||
) -> GuardrailVerdict:
|
||||
"""Check `text` against the ruleset; return a verdict."""
|
||||
...
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def session_start_disclaimer(self) -> str:
|
||||
"""The session-start disclaimer audio text (RESEARCH.md §Safety).
|
||||
|
||||
Played as the first AI utterance of every session.
|
||||
"""
|
||||
...
|
||||
|
||||
|
||||
__all__ = [
|
||||
"TTSProvider",
|
||||
"TTSResult",
|
||||
"LLMProvider",
|
||||
"LLMStreamChunk",
|
||||
"Guardrail",
|
||||
"GuardrailVerdict",
|
||||
"GuardrailContext",
|
||||
]
|
||||
@@ -0,0 +1,74 @@
|
||||
"""Adapter registry — resolves the active TTS / LLM / Guardrail from env.
|
||||
|
||||
Centralizes the D-014 (TTS swap), D-020 (LLM swap), D-019 (guardrail plug) wiring
|
||||
so the Pipecat pipeline never imports a concrete adapter directly.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
from functools import lru_cache
|
||||
|
||||
|
||||
def _require(key: str, *, default: str | None = None) -> str:
|
||||
val = os.environ.get(key, default or "").strip()
|
||||
if not val:
|
||||
raise RuntimeError(
|
||||
f"Required env var {key} is not set. See .env.example."
|
||||
)
|
||||
return val
|
||||
|
||||
|
||||
@lru_cache(maxsize=1)
|
||||
def get_tts() -> "TTSProvider": # type: ignore[name-defined]
|
||||
"""Return the active TTSProvider based on PRAXIS_TTS (D-014)."""
|
||||
# Imported lazily so importing the registry doesn't drag in Pipecat/TTS deps
|
||||
# for tools that only need the interfaces.
|
||||
choice = os.environ.get("PRAXIS_TTS", "cartesia").strip().lower()
|
||||
if choice == "piper":
|
||||
from server.tts.piper_tts import PiperTTS
|
||||
|
||||
return PiperTTS()
|
||||
if choice == "cartesia":
|
||||
from server.tts.cartesia_tts import CartesiaTTS
|
||||
|
||||
return CartesiaTTS()
|
||||
raise RuntimeError(
|
||||
f"Unknown PRAXIS_TTS={choice!r}; expected 'cartesia' or 'piper'."
|
||||
)
|
||||
|
||||
|
||||
@lru_cache(maxsize=1)
|
||||
def get_llm() -> "LLMProvider": # type: ignore[name-defined]
|
||||
"""Return the active LLMProvider (Ollama Cloud direct API, D-020)."""
|
||||
from server.llm.ollama_cloud import OllamaCloudLLM
|
||||
|
||||
return OllamaCloudLLM()
|
||||
|
||||
|
||||
@lru_cache(maxsize=1)
|
||||
def get_guardrail() -> "Guardrail": # type: ignore[name-defined]
|
||||
"""Return the active Guardrail (D-019).
|
||||
|
||||
v0.1 SLICE-02 returns NoOpGuardrail; SLICE-03 swaps in CustomerServiceGuardrail.
|
||||
Selection via PRAXIS_GUARDRAIL=none|customer_service (default: customer_service
|
||||
once implemented; falls back to none if the ruleset isn't importable yet).
|
||||
"""
|
||||
choice = os.environ.get("PRAXIS_GUARDRAIL", "customer_service").strip().lower()
|
||||
if choice == "none":
|
||||
from server.guardrails.noop import NoOpGuardrail
|
||||
|
||||
return NoOpGuardrail()
|
||||
if choice == "customer_service":
|
||||
try:
|
||||
from server.guardrails.customer_service import CustomerServiceGuardrail
|
||||
|
||||
return CustomerServiceGuardrail()
|
||||
except ImportError:
|
||||
# SLICE-02 fallback — real ruleset arrives in SLICE-03.
|
||||
from server.guardrails.noop import NoOpGuardrail
|
||||
|
||||
return NoOpGuardrail()
|
||||
raise RuntimeError(
|
||||
f"Unknown PRAXIS_GUARDRAIL={choice!r}; expected 'none' or 'customer_service'."
|
||||
)
|
||||
@@ -0,0 +1,114 @@
|
||||
"""Session recorder — wires the SQLite store into the pipeline lifecycle (TASK-04-03).
|
||||
|
||||
On session start: create a sessions row.
|
||||
Per turn: log a turns row with ASR/TTS text + latency.
|
||||
On branch decision: update branch_path.
|
||||
On session end: set outcome + update progress + store cost + debrief.
|
||||
|
||||
No auth — learner_id is the hardcoded 'learner-1' (D-007).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
from db.store import PraxisStore, HARDCODED_LEARNER_ID
|
||||
from server.cost import CostBreakdown, derive_cost
|
||||
|
||||
|
||||
class SessionRecorder:
|
||||
"""Records a voice session to SQLite (TASK-04-03)."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
store: PraxisStore,
|
||||
learner_id: str = HARDCODED_LEARNER_ID,
|
||||
scenario_id: str = "cs_refund_ca_v01",
|
||||
) -> None:
|
||||
self.store = store
|
||||
self.learner_id = learner_id
|
||||
self.scenario_id = scenario_id
|
||||
self.session_id: str | None = None
|
||||
self._turn_seq = 0
|
||||
# Cost inputs accumulated over the session.
|
||||
self._llm_input_tokens = 0
|
||||
self._llm_output_tokens = 0
|
||||
self._deepgram_minutes = 0.0
|
||||
self._tts_chars = 0
|
||||
self._debrief_input_tokens = 0
|
||||
self._debrief_output_tokens = 0
|
||||
self._branch_path: list[str] = []
|
||||
|
||||
async def start(self) -> str:
|
||||
"""Create the session row; return the session id."""
|
||||
self.session_id = await self.store.start_session(self.learner_id, self.scenario_id)
|
||||
return self.session_id
|
||||
|
||||
async def log_turn(
|
||||
self,
|
||||
role: str,
|
||||
asr_text: str | None = None,
|
||||
tts_text: str | None = None,
|
||||
latency_ms: float | None = None,
|
||||
) -> None:
|
||||
"""Log one turn to the turns table."""
|
||||
if self.session_id is None:
|
||||
return
|
||||
await self.store.log_turn(
|
||||
self.session_id, self._turn_seq, role, asr_text, tts_text, latency_ms
|
||||
)
|
||||
self._turn_seq += 1
|
||||
# Accumulate cost inputs.
|
||||
if asr_text:
|
||||
# Rough: 1 token ≈ 4 chars.
|
||||
self._llm_input_tokens += len(asr_text) // 4
|
||||
if tts_text:
|
||||
self._tts_chars += len(tts_text)
|
||||
self._llm_output_tokens += len(tts_text) // 4
|
||||
if latency_ms and role == "assistant":
|
||||
# Rough audio-minutes estimate from latency (placeholder for real metering).
|
||||
pass
|
||||
|
||||
def add_audio_minutes(self, minutes: float) -> None:
|
||||
self._deepgram_minutes += minutes
|
||||
|
||||
def add_debrief_tokens(self, input_tokens: int, output_tokens: int) -> None:
|
||||
self._debrief_input_tokens += input_tokens
|
||||
self._debrief_output_tokens += output_tokens
|
||||
|
||||
def set_branch_path(self, branch_path: list[str]) -> None:
|
||||
self._branch_path = branch_path
|
||||
|
||||
async def end(
|
||||
self,
|
||||
outcome: str,
|
||||
tts_provider: str = "cartesia",
|
||||
debrief_text: str | None = None,
|
||||
) -> CostBreakdown:
|
||||
"""End the session: derive cost, write the session row, update progress."""
|
||||
if self.session_id is None:
|
||||
raise RuntimeError("SessionRecorder.end() called before start()")
|
||||
|
||||
breakdown = derive_cost(
|
||||
llm_input_tokens=self._llm_input_tokens,
|
||||
llm_output_tokens=self._llm_output_tokens,
|
||||
deepgram_audio_minutes=self._deepgram_minutes,
|
||||
tts_characters=self._tts_chars,
|
||||
debrief_input_tokens=self._debrief_input_tokens,
|
||||
debrief_output_tokens=self._debrief_output_tokens,
|
||||
tts_provider=tts_provider,
|
||||
)
|
||||
|
||||
await self.store.end_session(
|
||||
self.session_id,
|
||||
branch_path=self._branch_path,
|
||||
outcome=outcome,
|
||||
cost_cents=breakdown.derived_cents,
|
||||
cost_breakdown=breakdown.as_dict(),
|
||||
debrief_text=debrief_text,
|
||||
)
|
||||
await self.store.update_progress(self.learner_id, self.scenario_id, outcome)
|
||||
return breakdown
|
||||
|
||||
|
||||
__all__ = ["SessionRecorder"]
|
||||
@@ -0,0 +1,5 @@
|
||||
"""TTS adapter package — Cartesia (cloud) + Piper (self-hosted) behind TTSProvider."""
|
||||
|
||||
from server.services.base import TTSProvider, TTSResult
|
||||
|
||||
__all__ = ["TTSProvider", "TTSResult"]
|
||||
@@ -0,0 +1,108 @@
|
||||
"""Cartesia Sonic TTS adapter behind the TTSProvider interface (D-014).
|
||||
|
||||
Wraps the raw Cartesia WebSocket API (wss://api.cartesia.ai/tts/websocket) for
|
||||
the probe-style streaming path, and exposes the TTSProvider contract so the
|
||||
Pipecat pipeline can swap to Piper with no code change (PRAXIS_TTS=piper).
|
||||
|
||||
One voice persona (D-006) — CARTESIA_VOICE_ID from env.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import os
|
||||
import time
|
||||
from typing import AsyncIterator
|
||||
|
||||
from server.services.base import TTSProvider, TTSResult
|
||||
|
||||
CARTESIA_WS_URL = "wss://api.cartesia.ai/tts/websocket"
|
||||
DEFAULT_VOICE_ID = "a3536a36-1d18-4efb-a95a-7c44b7b5e384"
|
||||
DEFAULT_MODEL = "sonic-2"
|
||||
|
||||
|
||||
class CartesiaTTS(TTSProvider):
|
||||
"""Cartesia Sonic cloud TTS adapter (D-014 primary)."""
|
||||
|
||||
name = "cartesia"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
api_key: str | None = None,
|
||||
voice_id: str | None = None,
|
||||
model: str | None = None,
|
||||
sample_rate: int = 24000,
|
||||
) -> None:
|
||||
self._api_key = (api_key or os.environ.get("CARTESIA_API_KEY", "")).strip()
|
||||
self._voice_id = (
|
||||
voice_id or os.environ.get("CARTESIA_VOICE_ID", DEFAULT_VOICE_ID)
|
||||
).strip()
|
||||
self._model = model or DEFAULT_MODEL
|
||||
self._sample_rate = sample_rate
|
||||
|
||||
@property
|
||||
def voice_id(self) -> str:
|
||||
return self._voice_id
|
||||
|
||||
def _missing(self) -> bool:
|
||||
return not self._api_key
|
||||
|
||||
async def synthesize(self, text: str) -> AsyncIterator[bytes]:
|
||||
"""Stream PCM s16le audio chunks from Cartesia Sonic."""
|
||||
if self._missing():
|
||||
# Graceful no-op: yield silence so the pipeline doesn't crash.
|
||||
# The code structure is the deliverable; live audio needs a key.
|
||||
return
|
||||
import websockets
|
||||
|
||||
headers = [
|
||||
("x-api-key", self._api_key),
|
||||
("cartesia-version", "2024-06-10"),
|
||||
]
|
||||
try:
|
||||
async with websockets.connect(
|
||||
CARTESIA_WS_URL, additional_headers=headers, open_timeout=10
|
||||
) as ws:
|
||||
req = {
|
||||
"model_id": self._model,
|
||||
"transcript": text,
|
||||
"voice": {"id": self._voice_id},
|
||||
"output_format": {
|
||||
"container": "raw",
|
||||
"encoding": "pcm_s16le",
|
||||
"sample_rate": self._sample_rate,
|
||||
},
|
||||
"stream": True,
|
||||
}
|
||||
await ws.send(json.dumps(req))
|
||||
while True:
|
||||
msg = await asyncio.wait_for(ws.recv(), timeout=15)
|
||||
if isinstance(msg, (bytes, bytearray)):
|
||||
yield bytes(msg)
|
||||
elif isinstance(msg, str):
|
||||
data = json.loads(msg)
|
||||
if data.get("type") == "done":
|
||||
break
|
||||
except Exception:
|
||||
# Live-key/auth errors degrade to no audio; the pipeline stays up.
|
||||
return
|
||||
|
||||
async def synthesize_all(self, text: str) -> tuple[bytes, TTSResult]:
|
||||
t0 = time.perf_counter()
|
||||
chunks = bytearray()
|
||||
first_audio_ms: float | None = None
|
||||
async for chunk in self.synthesize(text):
|
||||
if first_audio_ms is None:
|
||||
first_audio_ms = (time.perf_counter() - t0) * 1000.0
|
||||
chunks += chunk
|
||||
return bytes(chunks), TTSResult(
|
||||
first_audio_ms=first_audio_ms,
|
||||
chars=len(text),
|
||||
voice_id=self._voice_id,
|
||||
sample_rate=self._sample_rate,
|
||||
)
|
||||
|
||||
|
||||
__all__ = ["CartesiaTTS"]
|
||||
@@ -0,0 +1,85 @@
|
||||
"""Piper self-hosted TTS adapter behind the TTSProvider interface (D-014).
|
||||
|
||||
Piper is the R4 mitigation (ARCHITECTURE.md): self-hosted, ~80ms first-audio on
|
||||
CPU, open-weights, $0 marginal cost. Selected via PRAXIS_TTS=piper. A voice
|
||||
model must be downloaded separately (see docs/latency-report.md §Piper
|
||||
pre-staging). The adapter degrades gracefully if the voice model is absent.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import AsyncIterator
|
||||
|
||||
from server.services.base import TTSProvider, TTSResult
|
||||
|
||||
|
||||
class PiperTTS(TTSProvider):
|
||||
"""Piper self-hosted TTS adapter (D-014 fallback / R4 mitigation)."""
|
||||
|
||||
name = "piper"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
voice_model: str | None = None,
|
||||
voice_id: str | None = None,
|
||||
sample_rate: int = 22050,
|
||||
) -> None:
|
||||
self._voice_model = (
|
||||
voice_model or os.environ.get("PIPER_VOICE_MODEL", "")
|
||||
).strip()
|
||||
self._voice_id = (voice_id or "piper-en_CA-medium").strip()
|
||||
self._sample_rate = sample_rate
|
||||
self._voice = None # loaded lazily
|
||||
|
||||
@property
|
||||
def voice_id(self) -> str:
|
||||
return self._voice_id
|
||||
|
||||
def _model_available(self) -> bool:
|
||||
return bool(self._voice_model) and Path(self._voice_model).exists()
|
||||
|
||||
def _load_voice(self):
|
||||
if self._voice is not None:
|
||||
return self._voice
|
||||
if not self._model_available():
|
||||
return None
|
||||
try:
|
||||
from piper import PiperVoice # type: ignore
|
||||
except ImportError:
|
||||
return None
|
||||
self._voice = PiperVoice.load(self._voice_model)
|
||||
return self._voice
|
||||
|
||||
async def synthesize(self, text: str) -> AsyncIterator[bytes]:
|
||||
"""Stream PCM s16le audio chunks from Piper."""
|
||||
voice = self._load_voice()
|
||||
if voice is None:
|
||||
# Graceful no-op when the voice model isn't provisioned.
|
||||
return
|
||||
import io
|
||||
|
||||
for chunk in voice.synthesize(text):
|
||||
# Piper yields AudioChunk with .audio_int16_bytes (PCM s16le).
|
||||
yield chunk.audio_int16_bytes
|
||||
|
||||
async def synthesize_all(self, text: str) -> tuple[bytes, TTSResult]:
|
||||
t0 = time.perf_counter()
|
||||
chunks = bytearray()
|
||||
first_audio_ms: float | None = None
|
||||
async for chunk in self.synthesize(text):
|
||||
if first_audio_ms is None:
|
||||
first_audio_ms = (time.perf_counter() - t0) * 1000.0
|
||||
chunks += chunk
|
||||
return bytes(chunks), TTSResult(
|
||||
first_audio_ms=first_audio_ms,
|
||||
chars=len(text),
|
||||
voice_id=self._voice_id,
|
||||
sample_rate=self._sample_rate,
|
||||
)
|
||||
|
||||
|
||||
__all__ = ["PiperTTS"]
|
||||
@@ -0,0 +1,13 @@
|
||||
"""Shared pytest fixtures."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def tmp_db(tmp_path: Path) -> Path:
|
||||
"""A temporary SQLite database path."""
|
||||
return tmp_path / "test_praxis.db"
|
||||
@@ -0,0 +1,125 @@
|
||||
"""Tests for interruptibility (TASK-03-05) + branch classifier (TASK-03-06)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
from types import SimpleNamespace
|
||||
|
||||
import pytest
|
||||
|
||||
from server.interruptibility import pipeline_allows_interruptions
|
||||
from server.scenarios.classifier import (
|
||||
classify_branch,
|
||||
classify_branch_sync_heuristic,
|
||||
_parse_branch,
|
||||
)
|
||||
from server.scenarios.loader import load
|
||||
|
||||
|
||||
# ─── TASK-03-05: interruptibility ────────────────────────────────────────────
|
||||
|
||||
|
||||
def test_pipeline_allows_interruptions_flag():
|
||||
"""The pipeline task must be configured with allow_interruptions=True (D-008)."""
|
||||
task = SimpleNamespace(params=SimpleNamespace(allow_interruptions=True))
|
||||
assert pipeline_allows_interruptions(task) is True
|
||||
|
||||
|
||||
def test_pipeline_allows_interruptions_false_flag():
|
||||
task = SimpleNamespace(params=SimpleNamespace(allow_interruptions=False))
|
||||
assert pipeline_allows_interruptions(task) is False
|
||||
|
||||
|
||||
def test_pipeline_allows_interruptions_missing_params():
|
||||
task = SimpleNamespace()
|
||||
assert pipeline_allows_interruptions(task) is False
|
||||
|
||||
|
||||
# ─── TASK-03-06: branch classifier ───────────────────────────────────────────
|
||||
|
||||
|
||||
def test_heuristic_classifier_accept():
|
||||
"""A scripted empathetic transcript → accept_resolution."""
|
||||
scenario = load("customer_service_refund_ca_v01")
|
||||
turns = [
|
||||
"I'm really sorry you're frustrated. I can offer a full refund right now.",
|
||||
"Let me confirm the next steps for you.",
|
||||
]
|
||||
branch_id = classify_branch_sync_heuristic(scenario, turns)
|
||||
assert branch_id == "accept_resolution"
|
||||
|
||||
|
||||
def test_heuristic_classifier_escalate():
|
||||
"""A scripted defensive transcript → escalate."""
|
||||
scenario = load("customer_service_refund_ca_v01")
|
||||
turns = [
|
||||
"Well, our policy says we don't do refunds after 30 days.",
|
||||
"That's just how it works, I can't help you.",
|
||||
]
|
||||
branch_id = classify_branch_sync_heuristic(scenario, turns)
|
||||
assert branch_id == "escalate"
|
||||
|
||||
|
||||
def test_parse_branch_valid_json():
|
||||
scenario = load("customer_service_refund_ca_v01")
|
||||
text = '{"branch_id": "accept_resolution", "reason": "empathy shown"}'
|
||||
bid, reason = _parse_branch(text, scenario)
|
||||
assert bid == "accept_resolution"
|
||||
assert "empathy" in reason
|
||||
|
||||
|
||||
def test_parse_branch_code_fenced_json():
|
||||
scenario = load("customer_service_refund_ca_v01")
|
||||
text = '```json\n{"branch_id": "escalate", "reason": "defensive"}\n```'
|
||||
bid, reason = _parse_branch(text, scenario)
|
||||
assert bid == "escalate"
|
||||
|
||||
|
||||
def test_parse_branch_unknown_id_falls_back():
|
||||
scenario = load("customer_service_refund_ca_v01")
|
||||
text = '{"branch_id": "not_real", "reason": "x"}'
|
||||
bid, reason = _parse_branch(text, scenario)
|
||||
# Falls back to the first branch.
|
||||
assert bid in scenario.branch_ids()
|
||||
assert "fallback" in reason
|
||||
|
||||
|
||||
def test_parse_branch_malformed_json_scans_for_id():
|
||||
scenario = load("customer_service_refund_ca_v01")
|
||||
text = "The learner matches the escalate branch."
|
||||
bid, reason = _parse_branch(text, scenario)
|
||||
assert bid == "escalate"
|
||||
assert "fallback" in reason
|
||||
|
||||
|
||||
class _FakeLLM:
|
||||
"""A fake LLMProvider for the classifier test (no real API call)."""
|
||||
|
||||
debrief_model = "deepseek-v4-flash:cloud"
|
||||
roleplay_model = "gemma4:cloud"
|
||||
|
||||
async def chat_full(self, messages, *, model=None, no_think=False):
|
||||
return (
|
||||
'{"branch_id": "accept_resolution", "reason": "empathy + concrete_resolution"}',
|
||||
{"output_tokens": 10},
|
||||
)
|
||||
|
||||
|
||||
def test_classify_branch_with_fake_llm():
|
||||
"""The async classifier returns the LLM's branch verdict (R7, offline)."""
|
||||
scenario = load("customer_service_refund_ca_v01")
|
||||
turns = ["I'm sorry, I can offer a refund."]
|
||||
bid, reason = asyncio.run(classify_branch(_FakeLLM(), scenario, turns))
|
||||
assert bid == "accept_resolution"
|
||||
assert "empathy" in reason
|
||||
|
||||
|
||||
def test_classifier_runs_offline_from_voice_loop():
|
||||
"""D-P1-05: the classifier is a one-shot end-of-session call, not per-turn."""
|
||||
# This is a structural assertion: classify_branch takes the full turns list,
|
||||
# not a single turn — confirming it runs at session end, not on the
|
||||
# latency-critical voice path.
|
||||
scenario = load("customer_service_refund_ca_v01")
|
||||
turns = ["turn 1", "turn 2", "turn 3"]
|
||||
bid = classify_branch_sync_heuristic(scenario, turns)
|
||||
assert bid in scenario.branch_ids()
|
||||
@@ -0,0 +1,128 @@
|
||||
"""Tests for cost logging (TASK-04-04) + session recorder (TASK-04-03)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
from server.cost import CostBreakdown, derive_cost, load_rates
|
||||
from server.session_recorder import SessionRecorder
|
||||
from db.store import PraxisStore, HARDCODED_LEARNER_ID
|
||||
|
||||
|
||||
# ─── TASK-04-04: cost logging ────────────────────────────────────────────────
|
||||
|
||||
|
||||
def test_derive_cost_basic():
|
||||
"""derive_cost produces a non-null cents value from usage inputs."""
|
||||
b = derive_cost(
|
||||
llm_input_tokens=500,
|
||||
llm_output_tokens=200,
|
||||
deepgram_audio_minutes=2.0,
|
||||
tts_characters=800,
|
||||
debrief_input_tokens=300,
|
||||
debrief_output_tokens=150,
|
||||
tts_provider="cartesia",
|
||||
)
|
||||
assert b.derived_cents > 0
|
||||
assert b.llm_input_tokens == 500
|
||||
assert b.tts_characters == 800
|
||||
|
||||
|
||||
def test_derive_cost_piper_zero_tts():
|
||||
"""Piper self-hosted TTS is $0 marginal cost (R4 mitigation, post-pilot path)."""
|
||||
b = derive_cost(tts_characters=10000, tts_provider="piper")
|
||||
# Piper rate is 0.0 per 1k chars → TTS contributes 0.
|
||||
assert b.derived_cents == 0
|
||||
|
||||
|
||||
def test_derive_cost_breakdown_dict():
|
||||
b = derive_cost(llm_input_tokens=1000, llm_output_tokens=500)
|
||||
d = b.as_dict()
|
||||
assert d["llm_input_tokens"] == 1000
|
||||
assert d["derived_cents"] > 0
|
||||
assert "rates" in d
|
||||
|
||||
|
||||
def test_load_rates_from_yaml():
|
||||
"""cost_rates.yaml is present and loadable."""
|
||||
rates = load_rates()
|
||||
assert "gemma4_cloud_per_1k_tokens_cents" in rates
|
||||
assert rates["piper_per_1k_chars_cents"] == 0.0
|
||||
|
||||
|
||||
def test_cost_no_enforced_ceiling():
|
||||
"""D-012: v0.1 has no enforced cost ceiling (pilot). A high-cost session
|
||||
is still logged, not rejected."""
|
||||
b = derive_cost(
|
||||
llm_input_tokens=1_000_000,
|
||||
llm_output_tokens=500_000,
|
||||
deepgram_audio_minutes=600.0,
|
||||
tts_characters=2_000_000,
|
||||
)
|
||||
# No ceiling — just a (large) number.
|
||||
assert b.derived_cents > 0
|
||||
|
||||
|
||||
# ─── TASK-04-03: session recorder wiring ─────────────────────────────────────
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def tmp_db(tmp_path: Path) -> Path:
|
||||
return tmp_path / "test_recorder.db"
|
||||
|
||||
|
||||
def test_session_recorder_full_lifecycle(tmp_db: Path):
|
||||
"""TASK-04-03: start → log turns → set branch → end → DB has session + turns + progress."""
|
||||
store = PraxisStore(tmp_db)
|
||||
|
||||
async def _run():
|
||||
await store.init()
|
||||
rec = SessionRecorder(store, scenario_id="cs_refund_ca_v01")
|
||||
sid = await rec.start()
|
||||
await rec.log_turn("assistant", tts_text="Hi, I want a refund.", latency_ms=None)
|
||||
await rec.log_turn("user", asr_text="I'm sorry, I can offer a refund.", latency_ms=450.0)
|
||||
await rec.log_turn("assistant", tts_text="Okay, what's the issue?", latency_ms=520.0)
|
||||
rec.add_audio_minutes(1.5)
|
||||
rec.add_debrief_tokens(input_tokens=200, output_tokens=100)
|
||||
rec.set_branch_path(["accept_resolution"])
|
||||
breakdown = await rec.end(outcome="success", debrief_text="You did well.")
|
||||
sess = await store.get_session(sid)
|
||||
turns = await store.get_turns(sid)
|
||||
return sess, turns, breakdown
|
||||
|
||||
sess, turns, breakdown = asyncio.run(_run())
|
||||
assert sess is not None
|
||||
assert sess.outcome == "success"
|
||||
assert sess.branch_path == ["accept_resolution"]
|
||||
assert sess.cost_estimated_cents is not None and sess.cost_estimated_cents > 0
|
||||
assert sess.debrief_text == "You did well."
|
||||
assert len(turns) == 3
|
||||
assert breakdown.derived_cents > 0
|
||||
|
||||
|
||||
def test_session_recorder_progress_updated(tmp_db: Path):
|
||||
"""TASK-04-03: end() updates the progress table (attempts + last_outcome)."""
|
||||
store = PraxisStore(tmp_db)
|
||||
|
||||
async def _run():
|
||||
await store.init()
|
||||
rec = SessionRecorder(store, scenario_id="cs_refund_ca_v01")
|
||||
await rec.start()
|
||||
await rec.log_turn("user", asr_text="policy says no refunds")
|
||||
rec.set_branch_path(["escalate"])
|
||||
await rec.end(outcome="failure")
|
||||
|
||||
async with store._connect() as db:
|
||||
cur = await db.execute(
|
||||
"SELECT attempts, last_outcome FROM progress WHERE learner_id = ? AND scenario_id = ?",
|
||||
(HARDCODED_LEARNER_ID, "cs_refund_ca_v01"),
|
||||
)
|
||||
return await cur.fetchone()
|
||||
|
||||
row = asyncio.run(_run())
|
||||
assert row is not None
|
||||
assert row[0] == 1
|
||||
assert row[1] == "failure"
|
||||
@@ -0,0 +1,164 @@
|
||||
"""Tests for debrief generation + guardrail filter (TASK-05-01, TASK-05-02, TASK-05-03)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
from typing import Any
|
||||
|
||||
import pytest
|
||||
|
||||
from server.debrief import generate_debrief
|
||||
from server.guardrails.customer_service import CustomerServiceGuardrail
|
||||
from server.scenarios.loader import load
|
||||
from server.services.base import GuardrailContext
|
||||
|
||||
|
||||
class _FakeLLM:
|
||||
"""A fake LLMProvider that returns a canned debrief (no real API call)."""
|
||||
|
||||
debrief_model = "deepseek-v4-flash:cloud"
|
||||
roleplay_model = "gemma4:cloud"
|
||||
|
||||
def __init__(self, response: str) -> None:
|
||||
self._response = response
|
||||
|
||||
async def chat_full(self, messages, *, model=None, no_think=False):
|
||||
return self._response, {"output_tokens": 50, "model": model or self.debrief_model}
|
||||
|
||||
|
||||
def _load_scenario():
|
||||
return load("customer_service_refund_ca_v01")
|
||||
|
||||
|
||||
def test_debrief_references_learner_turns_and_branch():
|
||||
"""TASK-05-01: a scripted escalate session produces a debrief referencing the
|
||||
learner's turns + the escalates_unresolved focus."""
|
||||
scenario = _load_scenario()
|
||||
turns = [
|
||||
{"role": "user", "asr_text": "Our policy says no refunds after 30 days."},
|
||||
{"role": "assistant", "tts_text": "But I just want my money back!"},
|
||||
{"role": "user", "asr_text": "I can't help you, that's the policy."},
|
||||
]
|
||||
llm = _FakeLLM(
|
||||
"- What you did well: you stayed calm.\n"
|
||||
"- What to improve: you led with policy before acknowledging the customer's "
|
||||
"frustration — they escalated because they felt unheard.\n"
|
||||
"- Next step: acknowledge emotion first, then explain policy."
|
||||
)
|
||||
|
||||
async def _run():
|
||||
return await generate_debrief(
|
||||
llm, scenario,
|
||||
branch_id="escalate", outcome="failure",
|
||||
debrief_focus=scenario.branch_by_id("escalate").debrief_focus,
|
||||
learner_turns=turns,
|
||||
)
|
||||
|
||||
text, usage = asyncio.run(_run())
|
||||
assert "policy" in text.lower() or "frustration" in text.lower()
|
||||
assert usage["model"] == "deepseek-v4-flash:cloud"
|
||||
|
||||
|
||||
def test_debrief_uses_no_think_mode():
|
||||
"""REQ-LLM-02: the debrief uses deepseek-v4-flash:cloud no_think (D-020)."""
|
||||
scenario = _load_scenario()
|
||||
llm = _FakeLLM("debrief text")
|
||||
|
||||
captured: dict[str, Any] = {}
|
||||
|
||||
async def _chat_full(messages, *, model=None, no_think=False):
|
||||
captured["model"] = model
|
||||
captured["no_think"] = no_think
|
||||
return "debrief", {"output_tokens": 1}
|
||||
|
||||
llm.chat_full = _chat_full # type: ignore
|
||||
|
||||
async def _run():
|
||||
return await generate_debrief(
|
||||
llm, scenario, "accept_resolution", "success",
|
||||
"focus", [{"role": "user", "asr_text": "hi"}],
|
||||
)
|
||||
|
||||
asyncio.run(_run())
|
||||
assert captured["model"] == "deepseek-v4-flash:cloud"
|
||||
assert captured["no_think"] is True
|
||||
|
||||
|
||||
def test_debrief_guardrail_blocks_legal_action():
|
||||
"""TASK-05-02: a debrief containing 'tell the customer to sue' is filtered."""
|
||||
scenario = _load_scenario()
|
||||
guardrail = CustomerServiceGuardrail()
|
||||
llm = _FakeLLM(
|
||||
"- What you did well: nothing.\n"
|
||||
"- What to improve: you should tell the customer to sue them.\n"
|
||||
"- Next step: recommend legal action."
|
||||
)
|
||||
|
||||
async def _run():
|
||||
return await generate_debrief(
|
||||
llm, scenario, "escalate", "failure", "focus",
|
||||
[{"role": "user", "asr_text": "policy"}],
|
||||
guardrail=guardrail,
|
||||
)
|
||||
|
||||
text, _ = asyncio.run(_run())
|
||||
# The guardrail filtered_text replaces the legal-action recommendation.
|
||||
assert "Focus your coaching" in text or "legal action" not in text.lower() or "learner" in text.lower()
|
||||
|
||||
|
||||
def test_debrief_normal_coaching_passes_guardrail():
|
||||
"""TASK-05-02: a normal coaching debrief passes the guardrail filter."""
|
||||
scenario = _load_scenario()
|
||||
guardrail = CustomerServiceGuardrail()
|
||||
normal_debrief = (
|
||||
"- What you did well: you acknowledged the customer's frustration.\n"
|
||||
"- What to improve: offer a concrete resolution sooner.\n"
|
||||
"- Next step: practice the empathy-first opening."
|
||||
)
|
||||
llm = _FakeLLM(normal_debrief)
|
||||
|
||||
async def _run():
|
||||
return await generate_debrief(
|
||||
llm, scenario, "accept_resolution", "success", "focus",
|
||||
[{"role": "user", "asr_text": "I'm sorry, I can offer a refund."}],
|
||||
guardrail=guardrail,
|
||||
)
|
||||
|
||||
text, _ = asyncio.run(_run())
|
||||
assert text == normal_debrief # unchanged — guardrail allowed it
|
||||
|
||||
|
||||
def test_debrief_voice_via_ttsprovider():
|
||||
"""TASK-05-03: the debrief is synthesized via the TTSProvider interface (D-006).
|
||||
|
||||
This is a structural test: the debrief text is passed to TTSProvider.synthesize,
|
||||
reusing the same voice as the role-play (no separate TTS path).
|
||||
"""
|
||||
from server.services.base import TTSProvider, TTSResult
|
||||
|
||||
class _FakeTTS(TTSProvider):
|
||||
name = "fake"
|
||||
synthesized: list[str] = []
|
||||
|
||||
@property
|
||||
def voice_id(self) -> str:
|
||||
return "fake-voice"
|
||||
|
||||
async def synthesize(self, text):
|
||||
self.synthesized.append(text)
|
||||
yield b"\x00\x00"
|
||||
|
||||
async def synthesize_all(self, text):
|
||||
self.synthesized.append(text)
|
||||
return b"\x00\x00", TTSResult(chars=len(text), voice_id=self.voice_id)
|
||||
|
||||
tts = _FakeTTS()
|
||||
debrief_text = "You did well. Improve your empathy. Next: practice."
|
||||
|
||||
async def _run():
|
||||
return await tts.synthesize_all(debrief_text)
|
||||
|
||||
audio, result = asyncio.run(_run())
|
||||
assert tts.synthesized == [debrief_text] # same TTS path as role-play
|
||||
assert result.voice_id == "fake-voice" # D-006: one voice
|
||||
assert len(audio) > 0
|
||||
@@ -0,0 +1,50 @@
|
||||
"""Test for debrief persistence migration (TASK-05-05)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import sqlite3
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
from db.migrate import apply_migrations
|
||||
from db.store import PraxisStore, HARDCODED_LEARNER_ID
|
||||
|
||||
|
||||
def test_migration_0002_debrief_applies(tmp_db: Path):
|
||||
"""Both migrations apply cleanly; the debrief_text column exists."""
|
||||
applied = apply_migrations(tmp_db)
|
||||
assert "0001_init" in applied
|
||||
assert "0002_debrief" in applied
|
||||
|
||||
conn = sqlite3.connect(str(tmp_db))
|
||||
cols = {
|
||||
r[1]
|
||||
for r in conn.execute("PRAGMA table_info(sessions)").fetchall()
|
||||
}
|
||||
conn.close()
|
||||
assert "debrief_text" in cols
|
||||
|
||||
|
||||
def test_debrief_text_persisted(tmp_db: Path):
|
||||
"""TASK-05-05: after a session, SELECT debrief_text returns the debrief."""
|
||||
store = PraxisStore(tmp_db)
|
||||
|
||||
async def _run():
|
||||
await store.init()
|
||||
sid = await store.start_session(HARDCODED_LEARNER_ID, "cs_refund_ca_v01")
|
||||
await store.end_session(
|
||||
sid,
|
||||
branch_path=["accept_resolution"],
|
||||
outcome="success",
|
||||
cost_cents=5,
|
||||
debrief_text="You acknowledged the customer well. Improve your speed. Next: practice empathy-first.",
|
||||
)
|
||||
sess = await store.get_session(sid)
|
||||
return sess
|
||||
|
||||
sess = asyncio.run(_run())
|
||||
assert sess is not None
|
||||
assert "acknowledged" in sess.debrief_text
|
||||
assert "empathy" in sess.debrief_text
|
||||
@@ -0,0 +1,38 @@
|
||||
"""End-to-end smoke test (TASK-05-06) — pytest entry point.
|
||||
|
||||
Runs scripts/e2e_smoke.py::run_e2e and asserts the full loop:
|
||||
session → turns → branch → debrief → DB logged.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
|
||||
import pytest
|
||||
|
||||
from scripts.e2e_smoke import run_e2e
|
||||
|
||||
|
||||
def test_e2e_full_loop(tmp_db):
|
||||
"""The full v0.1 loop completes and DB assertions pass (no live keys needed)."""
|
||||
result = asyncio.run(run_e2e(db_path=str(tmp_db)))
|
||||
assert result["branch_id"] in ("accept_resolution", "escalate")
|
||||
assert result["turns_logged"] == 4
|
||||
assert result["cost_cents"] >= 0
|
||||
assert result["debrief_chars"] > 0
|
||||
# Latency is logged (within or over budget); the test asserts it's logged,
|
||||
# not that it's within budget (that requires live keys + real network).
|
||||
assert result["max_latency_ms"] > 0
|
||||
assert result["budget_ms"] == 600.0
|
||||
|
||||
|
||||
def test_e2e_debrief_non_empty(tmp_db):
|
||||
"""The debrief text is non-empty (TASK-05-06 must-have)."""
|
||||
result = asyncio.run(run_e2e(db_path=str(tmp_db)))
|
||||
assert result["debrief_chars"] > 50 # a real debrief is more than a stub
|
||||
|
||||
|
||||
def test_e2e_cost_non_null(tmp_db):
|
||||
"""cost_estimated_cents is non-null for a completed session (TASK-05-06 must-have)."""
|
||||
result = asyncio.run(run_e2e(db_path=str(tmp_db)))
|
||||
assert result["cost_cents"] is not None
|
||||
@@ -0,0 +1,114 @@
|
||||
"""Unit tests for the CustomerServiceGuardrail (TASK-03-04)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
|
||||
import pytest
|
||||
|
||||
from server.guardrails.customer_service import (
|
||||
CustomerServiceGuardrail,
|
||||
DISCLAIMER_TEXT,
|
||||
)
|
||||
from server.guardrails.noop import NoOpGuardrail
|
||||
from server.services.base import Guardrail, GuardrailContext
|
||||
|
||||
|
||||
def test_is_guardrail():
|
||||
assert isinstance(CustomerServiceGuardrail(), Guardrail)
|
||||
|
||||
|
||||
def test_disclaimer_text_defined():
|
||||
g = CustomerServiceGuardrail()
|
||||
assert "AI practice session" in g.session_start_disclaimer
|
||||
assert "not a real conversation" in g.session_start_disclaimer
|
||||
|
||||
|
||||
def test_blocks_legal_advice():
|
||||
g = CustomerServiceGuardrail()
|
||||
|
||||
async def _run():
|
||||
return await g.check("You should sue the company in small claims court.")
|
||||
|
||||
v = asyncio.run(_run())
|
||||
assert not v.allowed
|
||||
assert v.category == "blocked_legal"
|
||||
|
||||
|
||||
def test_blocks_financial_advice():
|
||||
g = CustomerServiceGuardrail()
|
||||
|
||||
async def _run():
|
||||
return await g.check("You should invest in crypto for retirement.")
|
||||
|
||||
v = asyncio.run(_run())
|
||||
assert not v.allowed
|
||||
assert v.category == "blocked_financial"
|
||||
|
||||
|
||||
def test_blocks_medical_advice():
|
||||
g = CustomerServiceGuardrail()
|
||||
|
||||
async def _run():
|
||||
return await g.check("That sounds like a diagnosis; see a doctor.")
|
||||
|
||||
v = asyncio.run(_run())
|
||||
assert not v.allowed
|
||||
assert v.category == "blocked_medical"
|
||||
|
||||
|
||||
def test_allows_normal_coaching_line():
|
||||
g = CustomerServiceGuardrail()
|
||||
|
||||
async def _run():
|
||||
return await g.check("You acknowledged the customer's frustration well.")
|
||||
|
||||
v = asyncio.run(_run())
|
||||
assert v.allowed
|
||||
assert v.category == "ok"
|
||||
|
||||
|
||||
def test_debrief_filter_blocks_sue_them():
|
||||
"""PLAN.md must-have: guardrail flags a 'sue them' recommendation."""
|
||||
g = CustomerServiceGuardrail()
|
||||
ctx = GuardrailContext(role="debrief")
|
||||
|
||||
async def _run():
|
||||
return await g.check("You should tell the customer to sue them.", ctx)
|
||||
|
||||
v = asyncio.run(_run())
|
||||
assert not v.allowed
|
||||
assert v.category == "blocked_legal"
|
||||
assert v.filtered_text is not None
|
||||
|
||||
|
||||
def test_debrief_allows_normal_coaching():
|
||||
g = CustomerServiceGuardrail()
|
||||
ctx = GuardrailContext(role="debrief")
|
||||
|
||||
async def _run():
|
||||
return await g.check(
|
||||
"What you did well: you acknowledged the customer's frustration "
|
||||
"and offered a concrete resolution.",
|
||||
ctx,
|
||||
)
|
||||
|
||||
v = asyncio.run(_run())
|
||||
assert v.allowed
|
||||
assert v.category == "ok"
|
||||
|
||||
|
||||
def test_guardrail_swappable_with_noop():
|
||||
"""D-019: swapping CustomerServiceGuardrail ↔ NoOpGuardrail requires no
|
||||
pipeline change (both implement the same interface)."""
|
||||
cs = CustomerServiceGuardrail()
|
||||
noop = NoOpGuardrail()
|
||||
|
||||
async def _run(g):
|
||||
return await g.check("anything", GuardrailContext())
|
||||
|
||||
v_cs = asyncio.run(_run(cs))
|
||||
v_noop = asyncio.run(_run(noop))
|
||||
# Both return a GuardrailVerdict — interface-compatible.
|
||||
assert hasattr(v_cs, "allowed")
|
||||
assert hasattr(v_noop, "allowed")
|
||||
@@ -0,0 +1,48 @@
|
||||
"""Unit tests for the LatencyObserver (TASK-02-06)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import pytest
|
||||
|
||||
from server.latency import LatencyObserver, LatencyRecord
|
||||
|
||||
|
||||
def test_latency_record_e2e():
|
||||
r = LatencyRecord(transcript_ready_ms=100.0, tts_first_audio_ms=650.0)
|
||||
assert r.e2e_asr_to_tts_ms == 550.0
|
||||
|
||||
|
||||
def test_latency_record_missing_segments():
|
||||
r = LatencyRecord(transcript_ready_ms=100.0)
|
||||
assert r.e2e_asr_to_tts_ms is None
|
||||
r2 = LatencyRecord(tts_first_audio_ms=650.0)
|
||||
assert r2.e2e_asr_to_tts_ms is None
|
||||
|
||||
|
||||
def test_latency_record_metric_dict():
|
||||
r = LatencyRecord(
|
||||
transcript_ready_ms=100.0, llm_first_token_ms=300.0, tts_first_audio_ms=650.0
|
||||
)
|
||||
m = r.as_metric()
|
||||
assert m["e2e_latency_ms"] == 550.0
|
||||
assert m["llm_first_token_ms"] == 300.0
|
||||
|
||||
|
||||
def test_latency_observer_construction():
|
||||
"""The observer constructs cleanly and starts with an empty state."""
|
||||
obs = LatencyObserver()
|
||||
assert obs.state.records == []
|
||||
assert obs.state.current.transcript_ready_ms is None
|
||||
|
||||
|
||||
def test_latency_observer_state_reset_turn():
|
||||
"""reset_turn archives the current record and starts a fresh one."""
|
||||
from server.latency import LatencyObserverState
|
||||
|
||||
state = LatencyObserverState()
|
||||
state.current.transcript_ready_ms = 100.0
|
||||
state.current.tts_first_audio_ms = 650.0
|
||||
state.reset_turn()
|
||||
assert len(state.records) == 1
|
||||
assert state.records[0].e2e_asr_to_tts_ms == 550.0
|
||||
assert state.current.transcript_ready_ms is None
|
||||
@@ -0,0 +1,118 @@
|
||||
"""Unit tests for the OllamaCloudLLM adapter (TASK-02-03).
|
||||
|
||||
The adapter must work with a mocked HTTP streaming response and degrade
|
||||
gracefully when OLLAMA_API_KEY is absent. The role-play + debrief model ids
|
||||
must come from env / defaults.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
from typing import AsyncIterator
|
||||
|
||||
import pytest
|
||||
|
||||
from server.llm.ollama_cloud import OllamaCloudLLM
|
||||
from server.services.base import LLMProvider
|
||||
|
||||
|
||||
def test_ollama_is_llmprovider():
|
||||
assert isinstance(OllamaCloudLLM(api_key="k"), LLMProvider)
|
||||
|
||||
|
||||
def test_ollama_models_from_env_defaults(monkeypatch):
|
||||
monkeypatch.delenv("OLLAMA_ROLEPLAY_MODEL", raising=False)
|
||||
monkeypatch.delenv("OLLAMA_DEBRIEF_MODEL", raising=False)
|
||||
llm = OllamaCloudLLM(api_key="k")
|
||||
assert llm.roleplay_model == "gemma4:cloud"
|
||||
assert llm.debrief_model == "deepseek-v4-flash:cloud"
|
||||
|
||||
|
||||
def test_ollama_models_from_env(monkeypatch):
|
||||
monkeypatch.setenv("OLLAMA_ROLEPLAY_MODEL", "custom-roleplay")
|
||||
monkeypatch.setenv("OLLAMA_DEBRIEF_MODEL", "custom-debrief")
|
||||
llm = OllamaCloudLLM()
|
||||
assert llm.roleplay_model == "custom-roleplay"
|
||||
assert llm.debrief_model == "custom-debrief"
|
||||
|
||||
|
||||
def test_ollama_missing_key_no_chunks():
|
||||
"""No API key → no chunks, no crash (graceful)."""
|
||||
|
||||
llm = OllamaCloudLLM(api_key="")
|
||||
|
||||
async def _run():
|
||||
return [c async for c in llm.chat([{"role": "user", "content": "hi"}])]
|
||||
|
||||
chunks = asyncio.run(_run())
|
||||
assert chunks == []
|
||||
|
||||
|
||||
def test_ollama_chat_with_mocked_stream(monkeypatch):
|
||||
"""Adapter streams chunks from a mocked httpx streaming response."""
|
||||
llm = OllamaCloudLLM(api_key="test-key")
|
||||
|
||||
# Fake NDJSON lines as Ollama /api/chat would emit.
|
||||
lines = [
|
||||
json.dumps({"message": {"content": "Hi"}, "done": False}),
|
||||
json.dumps({"message": {"content": " there"}, "done": False}),
|
||||
json.dumps({"message": {"content": ""}, "done": True, "eval_count": 7}),
|
||||
]
|
||||
|
||||
class _FakeResp:
|
||||
status_code = 200
|
||||
|
||||
async def aiter_lines(self):
|
||||
for line in lines:
|
||||
yield line
|
||||
|
||||
async def __aenter__(self):
|
||||
return self
|
||||
|
||||
async def __aexit__(self, *a):
|
||||
return False
|
||||
|
||||
class _FakeClient:
|
||||
def __init__(self, *a, **kw):
|
||||
pass
|
||||
|
||||
async def __aenter__(self):
|
||||
return self
|
||||
|
||||
async def __aexit__(self, *a):
|
||||
return False
|
||||
|
||||
def stream(self, *a, **kw):
|
||||
return _FakeResp()
|
||||
|
||||
import httpx
|
||||
|
||||
monkeypatch.setattr(httpx, "AsyncClient", _FakeClient)
|
||||
|
||||
async def _run():
|
||||
out = []
|
||||
async for c in llm.chat([{"role": "user", "content": "hi"}]):
|
||||
out.append(c)
|
||||
return out
|
||||
|
||||
chunks = asyncio.run(_run())
|
||||
assert len(chunks) == 2
|
||||
assert chunks[0].content == "Hi"
|
||||
assert chunks[0].is_first is True
|
||||
assert chunks[1].content == " there"
|
||||
assert chunks[1].is_first is False
|
||||
|
||||
|
||||
def test_ollama_chat_full_accumulates(monkeypatch):
|
||||
"""chat_full joins all streamed content into a single string."""
|
||||
llm = OllamaCloudLLM(api_key="test-key")
|
||||
|
||||
async def _fake_chat(messages, *, stream, model, no_think):
|
||||
yield type("C", (), {"content": "Hello", "is_first": True, "finish_reason": None, "extra": {}})()
|
||||
yield type("C", (), {"content": " world", "is_first": False, "finish_reason": "stop", "extra": {"eval_count": 5}})()
|
||||
|
||||
monkeypatch.setattr(llm, "chat", _fake_chat)
|
||||
text, usage = asyncio.run(llm.chat_full([{"role": "user", "content": "hi"}]))
|
||||
assert text == "Hello world"
|
||||
assert usage["output_tokens"] == 5
|
||||
@@ -0,0 +1,213 @@
|
||||
"""Auto-generated tests for Phase 1 exit criteria that require live API keys.
|
||||
|
||||
Per the VERIFY stage directive ("For unverifiable items: auto-generate test
|
||||
scripts that WOULD verify them when keys are present"), these tests exercise the
|
||||
two Phase 1 exit criteria that are pending voice-service key provisioning:
|
||||
|
||||
- Exit criterion #1 (live audio session): a real WebRTC voice turn completes.
|
||||
- Exit criterion #2 (live latency measurement): R1-R4 probes produce real
|
||||
numbers and the TTS decision is finalized.
|
||||
|
||||
At v0.1 VERIFY time, only GITEA_TOKEN (operational) is guaranteed; the three
|
||||
voice-service keys (DEEPGRAM_API_KEY, CARTESIA_API_KEY, OLLAMA_API_KEY) are NOT
|
||||
provisioned in this environment. These tests are therefore SKIPPED when the keys
|
||||
are absent, and will run automatically once the keys are provided via `.env` /
|
||||
`.ciagent/.env.secrets` / the environment.
|
||||
|
||||
Run:
|
||||
pytest tests/test_pending_keys.py -rs # shows skip reasons
|
||||
DEEPGRAM_API_KEY=... pytest tests/test_pending_keys.py # runs the live tests
|
||||
|
||||
These tests are intentionally network-bound and are NOT part of the default
|
||||
fast suite. They are gated behind the key presence checks so CI without keys
|
||||
stays green.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import socket
|
||||
import time
|
||||
|
||||
import pytest
|
||||
|
||||
# Keys that must be present for the live verifications.
|
||||
REQUIRED_KEYS = ("DEEPGRAM_API_KEY", "CARTESIA_API_KEY", "OLLAMA_API_KEY")
|
||||
|
||||
|
||||
def _have_live_keys() -> bool:
|
||||
"""True iff all three voice-service keys are non-empty in the environment."""
|
||||
return all(os.environ.get(k, "").strip() for k in REQUIRED_KEYS)
|
||||
|
||||
|
||||
pytestmark = pytest.mark.skipif(
|
||||
not _have_live_keys(),
|
||||
reason=(
|
||||
"Live voice-service keys (DEEPGRAM_API_KEY, CARTESIA_API_KEY, "
|
||||
"OLLAMA_API_KEY) are not provisioned in this environment. "
|
||||
"Set them in .env / .ciagent/.env.secrets and re-run to exercise "
|
||||
"Phase 1 exit criteria #1 (live audio session) and #2 (live latency)."
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
# ─── Exit criterion #2: live latency measurement (R1-R4) ────────────────────
|
||||
|
||||
|
||||
def test_r1_deepgram_first_partial_latency():
|
||||
"""R1: Deepgram Nova-3 first-partial-transcript latency is measured (not
|
||||
vendor-claimed) and recorded. Runs scripts/probe_deepgram.py end-to-end."""
|
||||
import subprocess
|
||||
import sys
|
||||
|
||||
proc = subprocess.run(
|
||||
[sys.executable, "scripts/probe_deepgram.py", "--iterations", "5"],
|
||||
capture_output=True, text=True, timeout=120,
|
||||
)
|
||||
assert proc.returncode == 0, f"probe failed: {proc.stderr[:500]}"
|
||||
assert "KEY_MISSING" not in proc.stdout, "probe did not detect a key (unexpected)"
|
||||
|
||||
|
||||
def test_r2_cartesia_first_audio_latency():
|
||||
"""R2: Cartesia Sonic first-audio-byte latency is measured and recorded."""
|
||||
import subprocess
|
||||
import sys
|
||||
|
||||
proc = subprocess.run(
|
||||
[sys.executable, "scripts/probe_cartesia.py", "--iterations", "5"],
|
||||
capture_output=True, text=True, timeout=120,
|
||||
)
|
||||
assert proc.returncode == 0, f"probe failed: {proc.stderr[:500]}"
|
||||
assert "KEY_MISSING" not in proc.stdout
|
||||
|
||||
|
||||
def test_r3_ollama_ttft_both_models():
|
||||
"""R3: Ollama Cloud TTFT for gemma4:cloud + deepseek-v4-flash:cloud no-think
|
||||
is measured. Also confirms R6 (Pipecat Ollama direct-API integration)."""
|
||||
import subprocess
|
||||
import sys
|
||||
|
||||
proc = subprocess.run(
|
||||
[sys.executable, "scripts/probe_ollama.py", "--iterations", "5"],
|
||||
capture_output=True, text=True, timeout=120,
|
||||
)
|
||||
assert proc.returncode == 0, f"probe failed: {proc.stderr[:500]}"
|
||||
assert "KEY_MISSING" not in proc.stdout
|
||||
|
||||
|
||||
def test_r4_integrated_e2e_latency_within_or_documented():
|
||||
"""R4: the integrated three-hop e2e (transcript → Ollama → Cartesia) is
|
||||
measured against the 600ms budget. Per G-003, if OVER budget with Cartesia,
|
||||
the Piper leg must be measured and the TTS decision finalized. This test
|
||||
asserts the probe runs and produces a median; it does NOT hard-assert
|
||||
<600ms (the G-003 no-go actions handle an over-budget result)."""
|
||||
import subprocess
|
||||
import sys
|
||||
|
||||
proc = subprocess.run(
|
||||
[sys.executable, "scripts/probe_e2e.py", "--iterations", "5"],
|
||||
capture_output=True, text=True, timeout=180,
|
||||
)
|
||||
assert proc.returncode == 0, f"probe failed: {proc.stderr[:500]}"
|
||||
assert "KEY_MISSING" not in proc.stdout
|
||||
# The probe prints a median line when it collects samples.
|
||||
assert "median" in proc.stdout.lower(), "no median reported (probe did not collect samples)"
|
||||
|
||||
|
||||
# ─── Exit criterion #1: live audio session (R6 + full loop) ─────────────────
|
||||
|
||||
|
||||
def test_ollama_gemma4_cloud_returns_first_token():
|
||||
"""R6 / REQ-LLM-01: a real call to gemma4:cloud via Ollama Cloud direct API
|
||||
returns at least one token. Confirms the LLM adapter + bearer auth work
|
||||
against the live endpoint."""
|
||||
from server.llm.ollama_cloud import OllamaCloudLLM
|
||||
|
||||
llm = OllamaCloudLLM()
|
||||
|
||||
async def _run():
|
||||
out = []
|
||||
async for chunk in llm.chat(
|
||||
[
|
||||
{"role": "system", "content": "You are Jordan, a frustrated customer."},
|
||||
{"role": "user", "content": "Hi, I want a refund."},
|
||||
],
|
||||
stream=True,
|
||||
):
|
||||
out.append(chunk.content)
|
||||
if len(out) >= 1:
|
||||
break
|
||||
return out
|
||||
|
||||
chunks = asyncio.run(_run())
|
||||
assert len(chunks) > 0, "gemma4:cloud returned no tokens (auth or endpoint issue)"
|
||||
|
||||
|
||||
def test_ollama_deepseek_debrief_no_think_returns_text():
|
||||
"""REQ-LLM-02: deepseek-v4-flash:cloud in no-think mode returns a debrief-
|
||||
style response. Confirms the debrief model + no-think flag work live."""
|
||||
from server.llm.ollama_cloud import OllamaCloudLLM
|
||||
|
||||
llm = OllamaCloudLLM()
|
||||
|
||||
async def _run():
|
||||
text, _usage = await llm.chat_full(
|
||||
[
|
||||
{"role": "system", "content": "Reply with one word."},
|
||||
{"role": "user", "content": "Say hello."},
|
||||
],
|
||||
model=llm.debrief_model,
|
||||
no_think=True,
|
||||
)
|
||||
return text
|
||||
|
||||
text = asyncio.run(_run())
|
||||
assert len(text.strip()) > 0, "deepseek-v4-flash:cloud no_think returned empty"
|
||||
|
||||
|
||||
def test_cartesia_tts_streams_audio():
|
||||
"""REQ-VOICE-02: Cartesia Sonic TTS streams real PCM audio for a sample
|
||||
line. Confirms the TTS adapter + WebSocket auth work live."""
|
||||
from server.tts.cartesia_tts import CartesiaTTS
|
||||
|
||||
tts = CartesiaTTS()
|
||||
|
||||
async def _run():
|
||||
chunks = [c async for c in tts.synthesize("Hi, I want my money back.")]
|
||||
return chunks
|
||||
|
||||
chunks = asyncio.run(_run())
|
||||
assert len(chunks) > 0, "Cartesia returned no audio (auth or endpoint issue)"
|
||||
assert sum(len(c) for c in chunks) > 0
|
||||
|
||||
|
||||
def test_deepgram_stt_service_constructs_with_live_key():
|
||||
"""REQ-VOICE-01 / REQ-ORCH-01: the Deepgram Nova-3 STT service constructs
|
||||
with a live key (the pipeline wiring is verified separately; this confirms
|
||||
the key is accepted by the Deepgram client)."""
|
||||
from server.pipeline import _build_stt
|
||||
|
||||
stt = _build_stt()
|
||||
assert stt is not None
|
||||
|
||||
|
||||
def test_live_latency_report_has_real_numbers(tmp_path):
|
||||
"""Exit criterion #2: after running the probes, docs/latency-report.md (or a
|
||||
generated report) contains real measured numbers, not vendor claims. This
|
||||
test re-runs the e2e probe and checks the structured output has a non-zero
|
||||
median."""
|
||||
import json
|
||||
import subprocess
|
||||
import sys
|
||||
|
||||
out = tmp_path / "r4.json"
|
||||
proc = subprocess.run(
|
||||
[sys.executable, "scripts/probe_e2e.py", "--iterations", "3", "--out", str(out)],
|
||||
capture_output=True, text=True, timeout=180,
|
||||
)
|
||||
assert proc.returncode == 0, f"probe failed: {proc.stderr[:500]}"
|
||||
data = json.loads(out.read_text())
|
||||
e2e = data.get("e2e_cartesia", {})
|
||||
assert e2e.get("n", 0) > 0, "no e2e samples collected"
|
||||
assert e2e.get("median_ms", 0) > 0, "median latency is not a real positive number"
|
||||
@@ -0,0 +1,68 @@
|
||||
"""Unit tests for the scenario runtime + flows spec (TASK-03-03, TASK-03-07)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import pytest
|
||||
|
||||
from server.scenarios.runtime import (
|
||||
ScenarioRuntime,
|
||||
build_runtime,
|
||||
build_runtime_from_id,
|
||||
)
|
||||
from server.scenarios.loader import load
|
||||
|
||||
|
||||
def test_runtime_uses_scenario_system_prompt():
|
||||
s = load("customer_service_refund_ca_v01")
|
||||
rt = build_runtime(s)
|
||||
assert "Jordan" in rt.system_prompt
|
||||
assert "cracked" in rt.opening_line
|
||||
|
||||
|
||||
def test_runtime_set_branch_escalate():
|
||||
rt = build_runtime_from_id("customer_service_refund_ca_v01")
|
||||
b = rt.set_branch("escalate")
|
||||
assert b.outcome == "failure"
|
||||
assert b.failure_mode == "escalates_unresolved"
|
||||
assert rt.outcome == "failure"
|
||||
assert rt.branch_id == "escalate"
|
||||
|
||||
|
||||
def test_runtime_set_branch_accept():
|
||||
rt = build_runtime_from_id("customer_service_refund_ca_v01")
|
||||
b = rt.set_branch("accept_resolution")
|
||||
assert b.outcome == "success"
|
||||
assert rt.outcome == "success"
|
||||
|
||||
|
||||
def test_runtime_set_branch_unknown_raises():
|
||||
rt = build_runtime_from_id("customer_service_refund_ca_v01")
|
||||
with pytest.raises(ValueError, match="Unknown branch id"):
|
||||
rt.set_branch("nonexistent_branch")
|
||||
|
||||
|
||||
def test_runtime_debrief_focus_per_branch():
|
||||
rt = build_runtime_from_id("customer_service_refund_ca_v01")
|
||||
# No branch set → default focus.
|
||||
assert "General" in rt.debrief_focus()
|
||||
rt.set_branch("escalate")
|
||||
assert "escalated" in rt.debrief_focus().lower()
|
||||
rt.set_branch("accept_resolution")
|
||||
assert "did well" in rt.debrief_focus().lower()
|
||||
|
||||
|
||||
def test_runtime_as_flow_spec_has_branches():
|
||||
rt = build_runtime_from_id("customer_service_refund_ca_v01")
|
||||
spec = rt.as_flow_spec()
|
||||
assert spec["initial_state"] == "conversation"
|
||||
assert "system_prompt" in spec["states"]["conversation"]
|
||||
assert len(spec["states"]["conversation"]["branches"]) == 2
|
||||
# v0.1: no in-flight transitions (G-002 — post-hoc classification).
|
||||
assert spec["transitions"] == []
|
||||
|
||||
|
||||
def test_runtime_default_debrief_model():
|
||||
"""The scenario's debrief config uses deepseek-v4-flash:cloud no_think (D-020)."""
|
||||
rt = build_runtime_from_id("customer_service_refund_ca_v01")
|
||||
assert rt.scenario.debrief.model == "deepseek-v4-flash:cloud"
|
||||
assert rt.scenario.debrief.mode == "no_think"
|
||||
@@ -0,0 +1,102 @@
|
||||
"""Unit tests for the scenario schema + loader (TASK-03-01, TASK-03-02)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
import yaml
|
||||
|
||||
from server.scenarios.schema import Scenario, ValidationError
|
||||
from server.scenarios.loader import load
|
||||
|
||||
|
||||
VALID_SCENARIO_DICT = {
|
||||
"id": "cs_refund_ca_v01",
|
||||
"path": "customer_service",
|
||||
"market": "CA",
|
||||
"language": "en-CA",
|
||||
"title": "Angry customer requesting refund on a damaged product",
|
||||
"difficulty": 1,
|
||||
"failure_mode": "escalates_unresolved",
|
||||
"persona": {
|
||||
"voice_id": "cartesia:some-voice-id",
|
||||
"character": "Customer (Jordan)",
|
||||
},
|
||||
"setup": {
|
||||
"system_prompt": "You are Jordan, a customer who received a damaged product.",
|
||||
"opening_line": "Hi, I received my order yesterday and the item is cracked.",
|
||||
},
|
||||
"success_criteria": ["Acknowledged the customer's frustration empathetically"],
|
||||
"common_mistakes": ["Jumping to policy before acknowledging emotion"],
|
||||
"branches": [
|
||||
{
|
||||
"id": "accept_resolution",
|
||||
"trigger": {"learner_signals": ["empathy", "concrete_resolution"]},
|
||||
"outcome": "success",
|
||||
"debrief_focus": "What you did well",
|
||||
},
|
||||
{
|
||||
"id": "escalate",
|
||||
"trigger": {"learner_signals": ["defensive", "policy_first"]},
|
||||
"outcome": "failure",
|
||||
"failure_mode": "escalates_unresolved",
|
||||
"debrief_focus": "The customer escalated because they felt unheard",
|
||||
},
|
||||
],
|
||||
"debrief": {
|
||||
"model": "deepseek-v4-flash:cloud",
|
||||
"mode": "no_think",
|
||||
"prompt_template": "debrief/default",
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def test_valid_scenario_parses():
|
||||
s = Scenario.model_validate(VALID_SCENARIO_DICT)
|
||||
assert s.id == "cs_refund_ca_v01"
|
||||
assert s.failure_mode == "escalates_unresolved"
|
||||
assert len(s.branches) == 2
|
||||
assert s.branch_ids() == ["accept_resolution", "escalate"]
|
||||
|
||||
|
||||
def test_invalid_scenario_raises_typed_error():
|
||||
bad = dict(VALID_SCENARIO_DICT)
|
||||
bad["failure_mode"] = None # required field → ValidationError
|
||||
with pytest.raises(ValidationError):
|
||||
Scenario.model_validate(bad)
|
||||
|
||||
|
||||
def test_invalid_branch_outcome_raises():
|
||||
bad = dict(VALID_SCENARIO_DICT)
|
||||
bad["branches"] = [
|
||||
{
|
||||
"id": "x",
|
||||
"trigger": {"learner_signals": ["a"]},
|
||||
"outcome": "not_a_real_outcome", # Literal mismatch
|
||||
"debrief_focus": "f",
|
||||
}
|
||||
]
|
||||
with pytest.raises(ValidationError):
|
||||
Scenario.model_validate(bad)
|
||||
|
||||
|
||||
def test_scenario_branch_by_id():
|
||||
s = Scenario.model_validate(VALID_SCENARIO_DICT)
|
||||
b = s.branch_by_id("escalate")
|
||||
assert b is not None
|
||||
assert b.outcome == "failure"
|
||||
assert b.failure_mode == "escalates_unresolved"
|
||||
assert s.branch_by_id("nonexistent") is None
|
||||
|
||||
|
||||
def test_load_customer_service_refund_scenario():
|
||||
"""TASK-03-02 verification: the real YAML loads and validates."""
|
||||
s = load("customer_service_refund_ca_v01")
|
||||
assert s.id == "cs_refund_ca_v01"
|
||||
assert s.failure_mode == "escalates_unresolved"
|
||||
assert len(s.branches) == 2
|
||||
assert s.branch_by_id("accept_resolution") is not None
|
||||
assert s.branch_by_id("escalate") is not None
|
||||
assert s.debrief.model == "deepseek-v4-flash:cloud"
|
||||
assert s.debrief.mode == "no_think"
|
||||
@@ -0,0 +1,121 @@
|
||||
"""Unit tests for the SQLite schema + async store (TASK-04-01, TASK-04-02)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import sqlite3
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
from db.migrate import apply_migrations
|
||||
from db.store import PraxisStore, HARDCODED_LEARNER_ID
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def tmp_db(tmp_path: Path) -> Path:
|
||||
return tmp_path / "test_praxis.db"
|
||||
|
||||
|
||||
def test_migration_creates_all_tables(tmp_db: Path):
|
||||
"""TASK-04-01: migration creates learner, sessions, turns, progress."""
|
||||
applied = apply_migrations(tmp_db)
|
||||
assert "0001_init" in applied
|
||||
|
||||
conn = sqlite3.connect(str(tmp_db))
|
||||
tables = {
|
||||
r[0] for r in conn.execute("SELECT name FROM sqlite_master WHERE type='table'").fetchall()
|
||||
}
|
||||
conn.close()
|
||||
assert {"learner", "sessions", "turns", "progress"} <= tables
|
||||
|
||||
|
||||
def test_hardcoded_learner_row_exists(tmp_db: Path):
|
||||
"""TASK-04-01: the hardcoded learner-1 'Alex' row exists (D-007, no auth)."""
|
||||
apply_migrations(tmp_db)
|
||||
conn = sqlite3.connect(str(tmp_db))
|
||||
row = conn.execute(
|
||||
"SELECT id, display_name FROM learner WHERE id = ?", (HARDCODED_LEARNER_ID,)
|
||||
).fetchone()
|
||||
conn.close()
|
||||
assert row is not None
|
||||
assert row[0] == "learner-1"
|
||||
assert row[1] == "Alex"
|
||||
|
||||
|
||||
def test_migrations_are_idempotent(tmp_db: Path):
|
||||
"""Re-running migrations doesn't re-apply."""
|
||||
apply_migrations(tmp_db)
|
||||
applied = apply_migrations(tmp_db)
|
||||
assert applied == []
|
||||
|
||||
|
||||
def test_store_start_log_end_session(tmp_db: Path):
|
||||
"""TASK-04-02: start session → log 3 turns → end session → query returns full session."""
|
||||
store = PraxisStore(tmp_db)
|
||||
|
||||
async def _run():
|
||||
await store.init()
|
||||
sid = await store.start_session(HARDCODED_LEARNER_ID, "cs_refund_ca_v01")
|
||||
await store.log_turn(sid, 0, "assistant", tts_text="Hi, I want a refund.", latency_ms=None)
|
||||
await store.log_turn(sid, 1, "user", asr_text="I'm sorry, I can help.", latency_ms=450.0)
|
||||
await store.log_turn(sid, 2, "assistant", tts_text="Okay, what's the issue?", latency_ms=520.0)
|
||||
await store.end_session(
|
||||
sid,
|
||||
branch_path=["accept_resolution"],
|
||||
outcome="success",
|
||||
cost_cents=12,
|
||||
cost_breakdown={"tokens": 500, "minutes": 1.2, "chars": 320},
|
||||
debrief_text="You did well acknowledging the customer.",
|
||||
)
|
||||
sess = await store.get_session(sid)
|
||||
turns = await store.get_turns(sid)
|
||||
return sess, turns
|
||||
|
||||
sess, turns = asyncio.run(_run())
|
||||
assert sess is not None
|
||||
assert sess.learner_id == "learner-1"
|
||||
assert sess.scenario_id == "cs_refund_ca_v01"
|
||||
assert sess.outcome == "success"
|
||||
assert sess.branch_path == ["accept_resolution"]
|
||||
assert sess.cost_estimated_cents == 12
|
||||
assert sess.debrief_text == "You did well acknowledging the customer."
|
||||
assert sess.cost_breakdown["tokens"] == 500
|
||||
assert len(turns) == 3
|
||||
assert turns[0].role == "assistant"
|
||||
assert turns[1].asr_text == "I'm sorry, I can help."
|
||||
assert turns[2].latency_ms == 520.0
|
||||
|
||||
|
||||
def test_store_update_progress(tmp_db: Path):
|
||||
"""TASK-04-02: update_progress increments attempts + sets last_outcome."""
|
||||
store = PraxisStore(tmp_db)
|
||||
|
||||
async def _run():
|
||||
await store.init()
|
||||
await store.update_progress(HARDCODED_LEARNER_ID, "cs_refund_ca_v01", "success")
|
||||
await store.update_progress(HARDCODED_LEARNER_ID, "cs_refund_ca_v01", "failure")
|
||||
|
||||
async with store._connect() as db:
|
||||
cur = await db.execute(
|
||||
"SELECT attempts, last_outcome FROM progress WHERE learner_id = ? AND scenario_id = ?",
|
||||
(HARDCODED_LEARNER_ID, "cs_refund_ca_v01"),
|
||||
)
|
||||
return await cur.fetchone()
|
||||
|
||||
row = asyncio.run(_run())
|
||||
assert row is not None
|
||||
assert row[0] == 2 # two attempts
|
||||
assert row[1] == "failure" # last outcome
|
||||
|
||||
|
||||
def test_store_get_learner(tmp_db: Path):
|
||||
store = PraxisStore(tmp_db)
|
||||
|
||||
async def _run():
|
||||
await store.init()
|
||||
return await store.get_learner()
|
||||
|
||||
learner = asyncio.run(_run())
|
||||
assert learner["id"] == "learner-1"
|
||||
assert learner["display_name"] == "Alex"
|
||||
@@ -0,0 +1,101 @@
|
||||
"""Unit tests for the TTS adapters (TASK-02-02).
|
||||
|
||||
Both adapters must pass with a mock stream and degrade gracefully when keys /
|
||||
voice models are absent. PRAXIS_TTS selection must route to the right adapter.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
from unittest.mock import patch
|
||||
|
||||
import pytest
|
||||
|
||||
from server.services.base import TTSProvider, TTSResult
|
||||
from server.tts.cartesia_tts import CartesiaTTS
|
||||
from server.tts.piper_tts import PiperTTS
|
||||
|
||||
|
||||
def test_cartesia_selectable_via_env(monkeypatch):
|
||||
"""PRAXIS_TTS=cartesia selects CartesiaTTS."""
|
||||
from server.services.registry import get_tts
|
||||
|
||||
monkeypatch.setenv("PRAXIS_TTS", "cartesia")
|
||||
monkeypatch.setenv("CARTESIA_API_KEY", "test-key")
|
||||
get_tts.cache_clear()
|
||||
tts = get_tts()
|
||||
assert isinstance(tts, CartesiaTTS)
|
||||
assert tts.name == "cartesia"
|
||||
assert tts.voice_id # has a default voice id
|
||||
|
||||
|
||||
def test_piper_selectable_via_env(monkeypatch):
|
||||
"""PRAXIS_TTS=piper selects PiperTTS."""
|
||||
from server.services.registry import get_tts
|
||||
|
||||
monkeypatch.setenv("PRAXIS_TTS", "piper")
|
||||
get_tts.cache_clear()
|
||||
tts = get_tts()
|
||||
assert isinstance(tts, PiperTTS)
|
||||
assert tts.name == "piper"
|
||||
|
||||
|
||||
def test_cartesia_missing_key_no_audio():
|
||||
"""Cartesia with no API key yields no audio but doesn't crash (graceful)."""
|
||||
tts = CartesiaTTS(api_key="")
|
||||
|
||||
async def _run():
|
||||
chunks = [c async for c in tts.synthesize("hello")]
|
||||
return chunks
|
||||
|
||||
chunks = asyncio.run(_run())
|
||||
assert chunks == []
|
||||
|
||||
|
||||
def test_cartesia_synthesize_all_with_mock(monkeypatch):
|
||||
"""Cartesia.synthesize_all returns audio + TTSResult with a mocked stream."""
|
||||
tts = CartesiaTTS(api_key="test-key", voice_id="v1")
|
||||
|
||||
async def _fake_stream(text):
|
||||
yield b"\x00\x01"
|
||||
yield b"\x02\x03"
|
||||
|
||||
monkeypatch.setattr(tts, "synthesize", _fake_stream)
|
||||
audio, result = asyncio.run(tts.synthesize_all("hi"))
|
||||
assert audio == b"\x00\x01\x02\x03"
|
||||
assert result.chars == 2
|
||||
assert result.voice_id == "v1"
|
||||
assert result.first_audio_ms is not None
|
||||
|
||||
|
||||
def test_piper_missing_model_no_audio():
|
||||
"""Piper with no voice model yields no audio but doesn't crash (graceful)."""
|
||||
tts = PiperTTS(voice_model="")
|
||||
|
||||
async def _run():
|
||||
chunks = [c async for c in tts.synthesize("hello")]
|
||||
return chunks
|
||||
|
||||
chunks = asyncio.run(_run())
|
||||
assert chunks == []
|
||||
|
||||
|
||||
def test_piper_synthesize_all_with_mock(monkeypatch):
|
||||
"""Piper.synthesize_all returns audio + TTSResult with a mocked stream."""
|
||||
tts = PiperTTS(voice_model="/nonexistent", voice_id="p1")
|
||||
|
||||
async def _fake_stream(text):
|
||||
yield b"\x10\x20"
|
||||
yield b"\x30\x40"
|
||||
|
||||
monkeypatch.setattr(tts, "synthesize", _fake_stream)
|
||||
audio, result = asyncio.run(tts.synthesize_all("hi"))
|
||||
assert audio == b"\x10\x20\x30\x40"
|
||||
assert result.chars == 2
|
||||
assert result.voice_id == "p1"
|
||||
|
||||
|
||||
def test_both_adapters_are_ttsprovider():
|
||||
"""Both adapters satisfy the TTSProvider ABC."""
|
||||
assert isinstance(CartesiaTTS(api_key="k"), TTSProvider)
|
||||
assert isinstance(PiperTTS(), TTSProvider)
|
||||
Reference in New Issue
Block a user