mirror of
https://github.com/open-jarvis/OpenJarvis.git
synced 2026-07-30 10:52:15 +00:00
Merge origin/main into gabebo-ui
Integrate speech-to-text (Phase 24) and eval trackers features. Resolve conflicts: add speech Tauri commands to lib.rs, merge MicButton + useSpeech into InputArea, consolidate speech API functions into lib/api.ts (removed old api/client.ts). Made-with: Cursor
This commit is contained in:
Gabriel Bo
@@ -4,13 +4,13 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
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## Project Status
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|
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OpenJarvis is a research framework for studying on-device AI systems. Phase 23 complete. Five composable pillars: Intelligence, Engine, Agents, Tools (with storage + MCP), and Learning — with trace-driven learning as a cross-cutting concern. ~3240 tests pass (~44 skipped for optional deps). Python SDK (`Jarvis` class), composition layer (`SystemBuilder`/`JarvisSystem`), eval framework (15 real benchmarks), composable recipes, agent templates, bundled skills, operator recipes, trace-driven learning pipeline, Docker deployment, Tauri desktop app, 40+ tools, 20+ CLI commands, 40+ API endpoints all ready.
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OpenJarvis is a research framework for studying on-device AI systems. Phase 24 complete. Five composable pillars: Intelligence, Engine, Agents, Tools (with storage + MCP), and Learning — with trace-driven learning as a cross-cutting concern. Speech subsystem (STT) with pluggable backends. ~3295 tests pass (~44 skipped for optional deps). Python SDK (`Jarvis` class), composition layer (`SystemBuilder`/`JarvisSystem`), eval framework (15 real benchmarks), composable recipes, agent templates, bundled skills, operator recipes, trace-driven learning pipeline, Docker deployment, Tauri desktop app, 40+ tools, 20+ CLI commands, 40+ API endpoints all ready.
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## Build & Development Commands
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```bash
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uv sync --extra dev # Install deps + dev tools
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uv run pytest tests/ -v # Run ~3241 tests (~44 skipped if optional deps missing)
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uv run pytest tests/ -v # Run ~3295 tests (~44 skipped if optional deps missing)
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uv run ruff check src/ tests/ # Lint
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uv run jarvis --version # 1.0.0
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uv run jarvis ask "Hello" # Query via discovered engine (direct mode)
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@@ -103,7 +103,7 @@ j.close() # Release resources
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```
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- **Package manager:** `uv` with `hatchling` build backend
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- **Config:** `pyproject.toml` with extras for optional backends (e.g., `openjarvis[inference-vllm]`, `openjarvis[inference-mlx]`, `openjarvis[memory-colbert]`, `openjarvis[server]`, `openjarvis[openclaw]`, `openjarvis[energy-amd]`, `openjarvis[energy-apple]`, `openjarvis[energy-all]`, `openjarvis[security-signing]`, `openjarvis[sandbox-wasm]`, `openjarvis[dashboard]`, `openjarvis[browser]`, `openjarvis[media]`, `openjarvis[pdf]`, `openjarvis[channel-line]`, `openjarvis[channel-viber]`, `openjarvis[channel-reddit]`, `openjarvis[channel-mastodon]`, `openjarvis[channel-xmpp]`, `openjarvis[channel-rocketchat]`, `openjarvis[channel-zulip]`, `openjarvis[channel-twitch]`, `openjarvis[channel-nostr]`)
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- **Config:** `pyproject.toml` with extras for optional backends (e.g., `openjarvis[inference-vllm]`, `openjarvis[inference-mlx]`, `openjarvis[memory-colbert]`, `openjarvis[server]`, `openjarvis[openclaw]`, `openjarvis[energy-amd]`, `openjarvis[energy-apple]`, `openjarvis[energy-all]`, `openjarvis[security-signing]`, `openjarvis[sandbox-wasm]`, `openjarvis[dashboard]`, `openjarvis[browser]`, `openjarvis[media]`, `openjarvis[pdf]`, `openjarvis[channel-line]`, `openjarvis[channel-viber]`, `openjarvis[channel-reddit]`, `openjarvis[channel-mastodon]`, `openjarvis[channel-xmpp]`, `openjarvis[channel-rocketchat]`, `openjarvis[channel-zulip]`, `openjarvis[channel-twitch]`, `openjarvis[channel-nostr]`, `openjarvis[speech]`, `openjarvis[speech-deepgram]`)
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- **CLI entry point:** `jarvis` (Click-based) — subcommands: `init`, `ask`, `serve`, `start`, `stop`, `restart`, `status`, `chat`, `model`, `memory`, `telemetry`, `bench`, `eval`, `channel`, `scheduler`, `doctor`, `agent`, `workflow`, `skill`, `vault`, `add`
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- **Python:** 3.10+ required
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- **Node.js:** 22+ required only for OpenClaw agent
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@@ -133,6 +133,10 @@ OpenJarvis is a research framework for on-device AI organized around **five comp
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- All registered via `@ToolRegistry.register("name")` decorator
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5. **Learning** (`src/openjarvis/learning/`) — Structured learning with nested per-pillar sub-policies. `LearningConfig` sections: `routing` (heuristic/learned/grpo/bandit), `intelligence` (none/sft), `agent` (none/agent_advisor/icl_updater), `metrics` (accuracy/latency/cost/efficiency weights). Policies: `SFTRouterPolicy` (query→model from traces), `AgentAdvisorPolicy` (LM-guided), `ICLUpdaterPolicy` (in-context with example DB, versioning, rollback, quality gates), `GRPORouterPolicy` (softmax sampling, group relative advantage, per-query-class weights), `BanditRouterPolicy` (Thompson Sampling / UCB1, per-arm stats). `SkillDiscovery` mines tool subsequences from traces to auto-generate skill manifests. Router policies: `HeuristicRouter`, `TraceDrivenPolicy`. Orchestrator training subpackage provides SFT and GRPO pipelines. **Trace-driven learning pipeline**: `TrainingDataMiner` (extracts SFT pairs from traces with quality filters), `LoRATrainer` (LoRA fine-tuning with configurable rank/alpha, requires torch), `AgentConfigEvolver` (LM-guided agent config recommendations from trace patterns), `LearningOrchestrator` (wired into `SystemBuilder`, orchestrates mine→train→evolve cycle on schedule).
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### Speech Subsystem
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- **Speech** (`src/openjarvis/speech/`) — Speech-to-text with pluggable backends. `SpeechBackend` ABC (`transcribe()`, `health()`, `supported_formats()`). `TranscriptionResult` + `Segment` dataclasses. Backends: `FasterWhisperBackend` (local, CTranslate2, key `"faster-whisper"`), `OpenAIWhisperBackend` (cloud, `whisper-1`, key `"openai"`), `DeepgramSpeechBackend` (cloud, `nova-2`, key `"deepgram"`). Auto-discovery with local-first priority. `SpeechConfig` in `JarvisConfig`. `SpeechRegistry` for backend registration. Wired into `SystemBuilder.speech()`, `create_app()`, `jarvis serve`. API: `POST /v1/speech/transcribe` (multipart), `GET /v1/speech/health`. Frontend: `useSpeech` hook + `MicButton` component. Tauri: `transcribe_audio` + `speech_health` commands. Optional deps: `openjarvis[speech]` (faster-whisper), `openjarvis[speech-deepgram]` (deepgram-sdk).
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### Cross-cutting Systems
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- **Traces** (`src/openjarvis/traces/`) — Full interaction recording. `Trace` captures `TraceStep`s (route, retrieve, generate, tool_call, respond) with timing. `TraceStore` (SQLite), `TraceCollector` (auto-wraps agents), `TraceAnalyzer` (stats for learning).
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@@ -194,6 +198,7 @@ OpenAI-compatible server via `jarvis serve`:
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- **Telemetry**: `GET /v1/telemetry/stats`, `GET /v1/telemetry/energy`
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- **Learning**: `GET /v1/learning/stats`, `GET /v1/learning/policy`
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- **Skills**: `GET /v1/skills`, `POST /v1/skills`, `DELETE /v1/skills/{name}`
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- **Speech**: `POST /v1/speech/transcribe`, `GET /v1/speech/health`
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- **Sessions**: `GET /v1/sessions`, `GET /v1/sessions/{id}`
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- **Budget**: `GET /v1/budget`, `PUT /v1/budget/limits`
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- **Metrics**: `GET /metrics` (Prometheus-compatible)
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@@ -238,3 +243,4 @@ OpenAI-compatible server via `jarvis serve`:
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| v2.6 | 21 | 10 new channels: LINE, Viber, Messenger, Reddit, Mastodon, XMPP, Rocket.Chat, Zulip, Twitch, Nostr |
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| v2.7 | 22 | Operators: persistent, scheduled autonomous agents with recipe + schedule + channel output |
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| v2.8 | 23 | Differentiated functionalities: trace-driven learning pipeline (TrainingDataMiner, LoRATrainer, AgentConfigEvolver, LearningOrchestrator), 15 real IPW benchmarks, composable recipes, 15 agent templates, 20 bundled skills, 3 operator recipes |
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| v2.9 | 24 | Speech subsystem: SpeechBackend ABC, SpeechRegistry, 3 backends (FasterWhisper local, OpenAI cloud, Deepgram cloud), auto-discovery, API endpoints, frontend MicButton + useSpeech hook, Tauri commands, SystemBuilder wiring |
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Generated
+18
@@ -2161,6 +2161,16 @@ version = "0.3.17"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "6877bb514081ee2a7ff5ef9de3281f14a4dd4bceac4c09388074a6b5df8a139a"
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[[package]]
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name = "mime_guess"
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version = "2.0.5"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "f7c44f8e672c00fe5308fa235f821cb4198414e1c77935c1ab6948d3fd78550e"
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dependencies = [
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"mime",
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"unicase",
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]
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[[package]]
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name = "minisign-verify"
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version = "0.2.4"
|
||||
@@ -3289,6 +3299,7 @@ dependencies = [
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"bytes",
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"encoding_rs",
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"futures-core",
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"futures-util",
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"h2",
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"http",
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"http-body",
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@@ -3300,6 +3311,7 @@ dependencies = [
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"js-sys",
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"log",
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"mime",
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"mime_guess",
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"native-tls",
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"percent-encoding",
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"pin-project-lite",
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@@ -4921,6 +4933,12 @@ dependencies = [
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"unic-common",
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]
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|
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[[package]]
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name = "unicase"
|
||||
version = "2.9.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "dbc4bc3a9f746d862c45cb89d705aa10f187bb96c76001afab07a0d35ce60142"
|
||||
|
||||
[[package]]
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name = "unicode-ident"
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version = "1.0.24"
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|
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@@ -19,7 +19,7 @@ tauri-plugin-single-instance = "2"
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tauri-plugin-process = "2"
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serde = { version = "1", features = ["derive"] }
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serde_json = "1"
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reqwest = { version = "0.12", features = ["json"] }
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reqwest = { version = "0.12", features = ["json", "multipart"] }
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tokio = { version = "1", features = ["full"] }
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[features]
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@@ -405,6 +405,50 @@ async fn run_jarvis_command(args: Vec<String>) -> Result<String, String> {
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}
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}
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/// Transcribe audio via the speech API endpoint.
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#[tauri::command]
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async fn transcribe_audio(
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api_url: String,
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audio_data: Vec<u8>,
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filename: String,
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) -> Result<serde_json::Value, String> {
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let url = format!("{}/v1/speech/transcribe", api_url);
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let client = reqwest::Client::new();
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let part = reqwest::multipart::Part::bytes(audio_data)
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.file_name(filename)
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.mime_str("audio/webm")
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.map_err(|e| format!("Failed to create multipart: {}", e))?;
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let form = reqwest::multipart::Form::new().part("file", part);
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let resp = client
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.post(&url)
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.multipart(form)
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.send()
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.await
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.map_err(|e| format!("Connection failed: {}", e))?;
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let body: serde_json::Value = resp
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.json()
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.await
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.map_err(|e| format!("Invalid response: {}", e))?;
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Ok(body)
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}
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/// Check speech backend health.
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#[tauri::command]
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async fn speech_health(api_url: String) -> Result<serde_json::Value, String> {
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let url = format!("{}/v1/speech/health", api_url);
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let resp = reqwest::get(&url)
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.await
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.map_err(|e| format!("Connection failed: {}", e))?;
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let body: serde_json::Value = resp
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.json()
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.await
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.map_err(|e| format!("Invalid response: {}", e))?;
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Ok(body)
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}
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// ---------------------------------------------------------------------------
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// App entry point
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// ---------------------------------------------------------------------------
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@@ -497,6 +541,8 @@ pub fn run() {
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fetch_agents,
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fetch_models,
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run_jarvis_command,
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transcribe_audio,
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speech_health,
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])
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.build(tauri::generate_context!())
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.expect("error while building OpenJarvis Desktop")
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@@ -0,0 +1,280 @@
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# Speech-to-Text Design
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**Date:** 2026-03-03
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**Status:** Approved
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**Scope:** Add voice input (speech-to-text) to OpenJarvis — desktop app and browser
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## Overview
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Add a new Speech subsystem to OpenJarvis that lets users speak commands instead of typing them. The system transcribes audio to text using local open-source models (Faster-Whisper) by default, with cloud backends (OpenAI, Deepgram) as alternatives. Transcribed text is inserted into the input box for user review before sending.
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## Design Decisions
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| Decision | Choice | Rationale |
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|----------|--------|-----------|
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| Scope | STT only (no TTS) | Ship voice input first; TTS follows later |
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| Runtime | Separate process | Avoid VRAM conflicts with the LLM engine |
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| Surfaces | Desktop app + browser | Both use the same React frontend |
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| UX mode | Record-then-transcribe | Simpler to implement, works with all backends |
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| Architecture | New Speech subsystem | Fits OpenJarvis patterns (ABC + registry + decorator) |
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## Architecture
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### New Module: `src/openjarvis/speech/`
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```
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src/openjarvis/speech/
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├── __init__.py # Imports, ensure_registered()
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├── _stubs.py # SpeechBackend ABC, TranscriptionResult dataclass
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├── faster_whisper.py # FasterWhisperBackend (local, default)
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├── whisper_cpp.py # WhisperCppBackend (local, llama.cpp ecosystem)
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├── openai_whisper.py # OpenAIWhisperBackend (cloud)
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├── deepgram.py # DeepgramBackend (cloud)
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└── _discovery.py # Auto-discover available backend (local preferred)
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```
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### Core Types (`_stubs.py`)
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```python
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@dataclass
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class Segment:
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text: str
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start: float # Start time in seconds
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end: float # End time in seconds
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confidence: float | None
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|
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@dataclass
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class TranscriptionResult:
|
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text: str # The transcribed text
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language: str | None # Detected language code (e.g., "en")
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confidence: float | None # Overall confidence [0, 1]
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duration_seconds: float # Audio duration
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segments: list[Segment] # Word/phrase-level timing (optional)
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|
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class SpeechBackend(ABC):
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backend_id: str
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|
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@abstractmethod
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def transcribe(self, audio: bytes, *, format: str = "wav",
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language: str | None = None) -> TranscriptionResult: ...
|
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|
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@abstractmethod
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def health(self) -> bool: ...
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|
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@abstractmethod
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||||
def supported_formats(self) -> list[str]: ...
|
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```
|
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|
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### Registry
|
||||
|
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New `SpeechRegistry` added to `core/registry.py` using `RegistryBase[T]`. Backends register via `@SpeechRegistry.register("faster-whisper")`.
|
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|
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### Discovery (`_discovery.py`)
|
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|
||||
Priority order (local-first):
|
||||
1. Faster-Whisper (if `faster-whisper` package installed)
|
||||
2. WhisperCpp (if `whisper-cpp-python` package installed)
|
||||
3. OpenAI Whisper API (if `OPENAI_API_KEY` set)
|
||||
4. Deepgram (if `DEEPGRAM_API_KEY` set)
|
||||
|
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Function: `get_speech_backend(config) -> SpeechBackend | None`
|
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|
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### Config
|
||||
|
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New `[speech]` section in `JarvisConfig`:
|
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|
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```toml
|
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[speech]
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backend = "auto" # "auto", "faster-whisper", "whisper-cpp", "openai", "deepgram"
|
||||
model = "base" # Whisper model size: tiny, base, small, medium, large-v3
|
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language = "" # Empty = auto-detect
|
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device = "auto" # "auto", "cpu", "cuda"
|
||||
compute_type = "float16" # "float16", "int8", "float32"
|
||||
```
|
||||
|
||||
New `SpeechConfig` dataclass in `core/config.py`.
|
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|
||||
## API Layer
|
||||
|
||||
### New Endpoints
|
||||
|
||||
```
|
||||
POST /v1/speech/transcribe
|
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Content-Type: multipart/form-data
|
||||
Body: audio file (field name: "file")
|
||||
Optional form fields: language, model
|
||||
|
||||
Response 200: {
|
||||
"text": "Hello, what's the weather like?",
|
||||
"language": "en",
|
||||
"confidence": 0.94,
|
||||
"duration_seconds": 2.3
|
||||
}
|
||||
|
||||
GET /v1/speech/backends
|
||||
Response 200: {
|
||||
"backends": ["faster-whisper"],
|
||||
"active": "faster-whisper",
|
||||
"model": "base"
|
||||
}
|
||||
|
||||
GET /v1/speech/health
|
||||
Response 200: {"available": true, "backend": "faster-whisper", "model": "base"}
|
||||
Response 200: {"available": false, "reason": "No speech backend installed"}
|
||||
```
|
||||
|
||||
### Server Wiring
|
||||
|
||||
- `SystemBuilder.with_speech(backend=..., model=...)` — configure speech
|
||||
- `JarvisSystem.speech` — active `SpeechBackend` instance or `None`
|
||||
- Speech backend initializes lazily on first `transcribe()` call
|
||||
- Routes added to `server/api_routes.py`
|
||||
|
||||
## Frontend Integration
|
||||
|
||||
### Web Frontend (`frontend/src/`)
|
||||
|
||||
**New component: `MicButton.tsx`**
|
||||
- Sits next to the Send button in `InputArea.tsx`
|
||||
- States: idle (mic icon), recording (pulsing red), transcribing (spinner)
|
||||
- Uses `MediaRecorder` API to capture audio
|
||||
- Sends audio blob as multipart to `/v1/speech/transcribe`
|
||||
- Appends transcribed text to input textarea
|
||||
- Hidden if `/v1/speech/health` returns `available: false`
|
||||
|
||||
**New hook: `useSpeech.ts`**
|
||||
- Manages microphone permissions, `MediaRecorder` lifecycle
|
||||
- `startRecording()`, `stopRecording()`, `isRecording`, `isTranscribing`, `error`
|
||||
- Checks `navigator.mediaDevices` support
|
||||
|
||||
**Changes to existing components:**
|
||||
- `InputArea.tsx` — add `MicButton` next to send button
|
||||
- `App.tsx` — check speech availability on load
|
||||
|
||||
### Desktop App (`desktop/`)
|
||||
|
||||
Same React UI works in the Tauri WebView.
|
||||
|
||||
**New Tauri command: `transcribe_audio`** in `lib.rs` — proxies multipart POST to backend for cases where WebView fetch has CORS issues.
|
||||
|
||||
### Audio Format
|
||||
|
||||
Record as WebM/Opus (native browser format). Faster-Whisper handles WebM directly. Server-side conversion to WAV as fallback if a backend requires it.
|
||||
|
||||
### User Flow
|
||||
|
||||
```
|
||||
User clicks mic button
|
||||
→ Browser requests microphone permission (first time)
|
||||
→ Recording starts (button pulses red)
|
||||
User clicks mic button again (or releases)
|
||||
→ Recording stops
|
||||
→ Button shows spinner
|
||||
→ Audio blob sent to POST /v1/speech/transcribe
|
||||
→ Response text inserted into input textarea
|
||||
→ User reviews/edits text
|
||||
→ User clicks Send (normal flow)
|
||||
```
|
||||
|
||||
## Backend Implementations
|
||||
|
||||
### Faster-Whisper (Default Local)
|
||||
|
||||
```python
|
||||
@SpeechRegistry.register("faster-whisper")
|
||||
class FasterWhisperBackend(SpeechBackend):
|
||||
backend_id = "faster-whisper"
|
||||
# Uses CTranslate2-based Faster-Whisper (4x faster than original Whisper)
|
||||
# Lazy model loading on first transcribe() call
|
||||
# Model sizes: tiny (39M), base (74M), small (244M), medium (769M), large-v3 (1.5B)
|
||||
# GPU: device="cuda", compute_type="float16" or "int8"
|
||||
# CPU: device="cpu", compute_type="int8"
|
||||
```
|
||||
|
||||
Optional dep: `openjarvis[speech]` → `faster-whisper>=1.0`
|
||||
|
||||
### OpenAI Whisper API (Cloud)
|
||||
|
||||
```python
|
||||
@SpeechRegistry.register("openai")
|
||||
class OpenAIWhisperBackend(SpeechBackend):
|
||||
backend_id = "openai"
|
||||
# Uses openai.audio.transcriptions.create()
|
||||
# Requires OPENAI_API_KEY
|
||||
# Uses existing openai dependency
|
||||
```
|
||||
|
||||
### Deepgram (Cloud)
|
||||
|
||||
```python
|
||||
@SpeechRegistry.register("deepgram")
|
||||
class DeepgramBackend(SpeechBackend):
|
||||
backend_id = "deepgram"
|
||||
# Uses deepgram-sdk
|
||||
# Requires DEEPGRAM_API_KEY
|
||||
```
|
||||
|
||||
Optional dep: `openjarvis[speech-deepgram]` → `deepgram-sdk>=3.0`
|
||||
|
||||
## Error Handling
|
||||
|
||||
| Scenario | Behavior |
|
||||
|----------|----------|
|
||||
| Mic permission denied | Toast error in frontend, no crash |
|
||||
| No speech backend available | Mic button hidden, health endpoint returns `available: false` |
|
||||
| Audio too short / silence | Return empty text with low confidence |
|
||||
| Model loading failure | Health returns false, error logged |
|
||||
| Network failure (cloud) | Error response, frontend shows retry option |
|
||||
| Unsupported audio format | Server converts to WAV, or returns 400 with message |
|
||||
|
||||
## Testing
|
||||
|
||||
### Backend Tests
|
||||
- `tests/speech/test_faster_whisper.py` — mock `faster_whisper` import, test transcribe with synthetic WAV
|
||||
- `tests/speech/test_openai_whisper.py` — mock `openai` client, test transcribe
|
||||
- `tests/speech/test_deepgram.py` — mock `deepgram` client, test transcribe
|
||||
- `tests/speech/test_discovery.py` — test auto-discovery priority order
|
||||
|
||||
### API Tests
|
||||
- `tests/server/test_speech_routes.py` — test endpoints with mocked backend
|
||||
|
||||
### Config Tests
|
||||
- Test `SpeechConfig` defaults, TOML parsing, `[speech]` section
|
||||
|
||||
All optional-dep backends behind `pytest.importorskip()`.
|
||||
|
||||
## Optional Dependencies
|
||||
|
||||
```toml
|
||||
[project.optional-dependencies]
|
||||
speech = ["faster-whisper>=1.0"]
|
||||
speech-deepgram = ["deepgram-sdk>=3.0"]
|
||||
```
|
||||
|
||||
## Files Changed (Estimated)
|
||||
|
||||
### New Files (~12)
|
||||
- `src/openjarvis/speech/__init__.py`
|
||||
- `src/openjarvis/speech/_stubs.py`
|
||||
- `src/openjarvis/speech/faster_whisper.py`
|
||||
- `src/openjarvis/speech/openai_whisper.py`
|
||||
- `src/openjarvis/speech/deepgram.py`
|
||||
- `src/openjarvis/speech/_discovery.py`
|
||||
- `frontend/src/components/MicButton.tsx`
|
||||
- `frontend/src/hooks/useSpeech.ts`
|
||||
- `tests/speech/test_faster_whisper.py`
|
||||
- `tests/speech/test_openai_whisper.py`
|
||||
- `tests/speech/test_deepgram.py`
|
||||
- `tests/speech/test_discovery.py`
|
||||
- `tests/server/test_speech_routes.py`
|
||||
|
||||
### Modified Files (~8)
|
||||
- `src/openjarvis/core/registry.py` — add `SpeechRegistry`
|
||||
- `src/openjarvis/core/config.py` — add `SpeechConfig`, `[speech]` section
|
||||
- `src/openjarvis/system.py` — add `with_speech()`, wire speech backend
|
||||
- `src/openjarvis/server/api_routes.py` — add speech endpoints
|
||||
- `frontend/src/components/InputArea.tsx` — add MicButton
|
||||
- `frontend/src/App.tsx` — check speech availability
|
||||
- `desktop/src-tauri/src/lib.rs` — add `transcribe_audio` command
|
||||
- `pyproject.toml` — add `[speech]` and `[speech-deepgram]` extras
|
||||
- `tests/conftest.py` — add SpeechRegistry to `_clean_registries`
|
||||
File diff suppressed because it is too large
Load Diff
@@ -3,6 +3,8 @@ import { Send, Square, Paperclip } from 'lucide-react';
|
||||
import { useAppStore, generateId } from '../../lib/store';
|
||||
import { streamChat } from '../../lib/sse';
|
||||
import { fetchSavings } from '../../lib/api';
|
||||
import { MicButton } from './MicButton';
|
||||
import { useSpeech } from '../../hooks/useSpeech';
|
||||
import type { ChatMessage, ToolCallInfo, TokenUsage } from '../../types';
|
||||
|
||||
export function InputArea() {
|
||||
@@ -21,7 +23,23 @@ export function InputArea() {
|
||||
const setStreamState = useAppStore((s) => s.setStreamState);
|
||||
const resetStream = useAppStore((s) => s.resetStream);
|
||||
|
||||
// Auto-resize textarea
|
||||
const { state: speechState, available: speechAvailable, startRecording, stopRecording } = useSpeech();
|
||||
|
||||
const handleMicClick = useCallback(async () => {
|
||||
if (speechState === 'recording') {
|
||||
try {
|
||||
const text = await stopRecording();
|
||||
if (text) {
|
||||
setInput((prev) => (prev ? prev + ' ' + text : text));
|
||||
}
|
||||
} catch {
|
||||
// Error is captured in useSpeech
|
||||
}
|
||||
} else {
|
||||
await startRecording();
|
||||
}
|
||||
}, [speechState, startRecording, stopRecording]);
|
||||
|
||||
useEffect(() => {
|
||||
const el = textareaRef.current;
|
||||
if (!el) return;
|
||||
@@ -238,18 +256,27 @@ export function InputArea() {
|
||||
<Square size={16} />
|
||||
</button>
|
||||
) : (
|
||||
<button
|
||||
onClick={sendMessage}
|
||||
disabled={!input.trim()}
|
||||
className="p-2 rounded-xl transition-colors shrink-0 cursor-pointer disabled:opacity-30 disabled:cursor-default"
|
||||
style={{
|
||||
background: input.trim() ? 'var(--color-accent)' : 'var(--color-bg-tertiary)',
|
||||
color: input.trim() ? 'white' : 'var(--color-text-tertiary)',
|
||||
}}
|
||||
title="Send message"
|
||||
>
|
||||
<Send size={16} />
|
||||
</button>
|
||||
<div className="flex items-center gap-1">
|
||||
{speechAvailable && (
|
||||
<MicButton
|
||||
state={speechState}
|
||||
onClick={handleMicClick}
|
||||
disabled={streamState.isStreaming}
|
||||
/>
|
||||
)}
|
||||
<button
|
||||
onClick={sendMessage}
|
||||
disabled={!input.trim()}
|
||||
className="p-2 rounded-xl transition-colors shrink-0 cursor-pointer disabled:opacity-30 disabled:cursor-default"
|
||||
style={{
|
||||
background: input.trim() ? 'var(--color-accent)' : 'var(--color-bg-tertiary)',
|
||||
color: input.trim() ? 'white' : 'var(--color-text-tertiary)',
|
||||
}}
|
||||
title="Send message"
|
||||
>
|
||||
<Send size={16} />
|
||||
</button>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
<div className="flex items-center justify-center mt-2 text-[11px]" style={{ color: 'var(--color-text-tertiary)' }}>
|
||||
|
||||
@@ -0,0 +1,53 @@
|
||||
import type { SpeechState } from '../../hooks/useSpeech';
|
||||
|
||||
interface MicButtonProps {
|
||||
state: SpeechState;
|
||||
onClick: () => void;
|
||||
disabled?: boolean;
|
||||
}
|
||||
|
||||
export function MicButton({ state, onClick, disabled }: MicButtonProps) {
|
||||
const title =
|
||||
state === 'recording'
|
||||
? 'Stop recording'
|
||||
: state === 'transcribing'
|
||||
? 'Transcribing...'
|
||||
: 'Voice input';
|
||||
|
||||
return (
|
||||
<button
|
||||
className={`mic-btn ${state !== 'idle' ? `mic-${state}` : ''}`}
|
||||
onClick={onClick}
|
||||
disabled={disabled || state === 'transcribing'}
|
||||
title={title}
|
||||
style={{
|
||||
background: state === 'recording' ? '#e74c3c' : 'transparent',
|
||||
border: '1px solid var(--border, #555)',
|
||||
borderRadius: '8px',
|
||||
padding: '8px',
|
||||
cursor: disabled || state === 'transcribing' ? 'default' : 'pointer',
|
||||
display: 'flex',
|
||||
alignItems: 'center',
|
||||
justifyContent: 'center',
|
||||
minWidth: '36px',
|
||||
height: '36px',
|
||||
color: state === 'recording' ? '#fff' : 'var(--text, #cdd6f4)',
|
||||
opacity: disabled || state === 'transcribing' ? 0.5 : 1,
|
||||
animation: state === 'recording' ? 'pulse 1.5s ease-in-out infinite' : 'none',
|
||||
}}
|
||||
>
|
||||
{state === 'transcribing' ? (
|
||||
<svg width="16" height="16" viewBox="0 0 16 16" fill="currentColor">
|
||||
<circle cx="8" cy="8" r="6" fill="none" stroke="currentColor" strokeWidth="2" strokeDasharray="28" strokeDashoffset="10">
|
||||
<animateTransform attributeName="transform" type="rotate" from="0 8 8" to="360 8 8" dur="1s" repeatCount="indefinite" />
|
||||
</circle>
|
||||
</svg>
|
||||
) : (
|
||||
<svg width="16" height="16" viewBox="0 0 16 16" fill="currentColor">
|
||||
<path d="M5 3a3 3 0 0 1 6 0v5a3 3 0 0 1-6 0V3z" />
|
||||
<path d="M3.5 6.5A.5.5 0 0 1 4 7v1a4 4 0 0 0 8 0V7a.5.5 0 0 1 1 0v1a5 5 0 0 1-4.5 4.975V15h3a.5.5 0 0 1 0 1h-7a.5.5 0 0 1 0-1h3v-2.025A5 5 0 0 1 3 8V7a.5.5 0 0 1 .5-.5z" />
|
||||
</svg>
|
||||
)}
|
||||
</button>
|
||||
);
|
||||
}
|
||||
@@ -0,0 +1,92 @@
|
||||
import { useState, useCallback, useRef, useEffect } from 'react';
|
||||
import { transcribeAudio, fetchSpeechHealth } from '../lib/api';
|
||||
|
||||
export type SpeechState = 'idle' | 'recording' | 'transcribing';
|
||||
|
||||
export function useSpeech() {
|
||||
const [state, setState] = useState<SpeechState>('idle');
|
||||
const [error, setError] = useState<string | null>(null);
|
||||
const [available, setAvailable] = useState(false);
|
||||
const mediaRecorderRef = useRef<MediaRecorder | null>(null);
|
||||
const chunksRef = useRef<Blob[]>([]);
|
||||
const streamRef = useRef<MediaStream | null>(null);
|
||||
|
||||
// Check if speech backend is available on mount
|
||||
useEffect(() => {
|
||||
fetchSpeechHealth()
|
||||
.then((health) => setAvailable(health.available))
|
||||
.catch(() => setAvailable(false));
|
||||
}, []);
|
||||
|
||||
const startRecording = useCallback(async (): Promise<void> => {
|
||||
setError(null);
|
||||
|
||||
if (!navigator.mediaDevices?.getUserMedia) {
|
||||
setError('Microphone not supported in this browser');
|
||||
return;
|
||||
}
|
||||
|
||||
try {
|
||||
const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
|
||||
streamRef.current = stream;
|
||||
|
||||
const recorder = new MediaRecorder(stream);
|
||||
chunksRef.current = [];
|
||||
|
||||
recorder.ondataavailable = (e) => {
|
||||
if (e.data.size > 0) chunksRef.current.push(e.data);
|
||||
};
|
||||
|
||||
recorder.start();
|
||||
mediaRecorderRef.current = recorder;
|
||||
setState('recording');
|
||||
} catch (err) {
|
||||
setError('Microphone access denied');
|
||||
setState('idle');
|
||||
}
|
||||
}, []);
|
||||
|
||||
const stopRecording = useCallback(async (): Promise<string> => {
|
||||
return new Promise((resolve, reject) => {
|
||||
const recorder = mediaRecorderRef.current;
|
||||
if (!recorder || recorder.state !== 'recording') {
|
||||
reject(new Error('Not recording'));
|
||||
return;
|
||||
}
|
||||
|
||||
recorder.onstop = async () => {
|
||||
setState('transcribing');
|
||||
|
||||
// Stop all audio tracks
|
||||
streamRef.current?.getTracks().forEach((track) => track.stop());
|
||||
streamRef.current = null;
|
||||
|
||||
const blob = new Blob(chunksRef.current, { type: recorder.mimeType || 'audio/webm' });
|
||||
chunksRef.current = [];
|
||||
|
||||
try {
|
||||
const result = await transcribeAudio(blob);
|
||||
setState('idle');
|
||||
resolve(result.text);
|
||||
} catch (err) {
|
||||
setState('idle');
|
||||
const msg = err instanceof Error ? err.message : 'Transcription failed';
|
||||
setError(msg);
|
||||
reject(err);
|
||||
}
|
||||
};
|
||||
|
||||
recorder.stop();
|
||||
});
|
||||
}, []);
|
||||
|
||||
return {
|
||||
state,
|
||||
error,
|
||||
available,
|
||||
startRecording,
|
||||
stopRecording,
|
||||
isRecording: state === 'recording',
|
||||
isTranscribing: state === 'transcribing',
|
||||
};
|
||||
}
|
||||
@@ -125,3 +125,55 @@ export async function fetchTraces(limit: number = 50): Promise<unknown> {
|
||||
if (!res.ok) throw new Error(`Failed: ${res.status}`);
|
||||
return res.json();
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Speech
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
export interface TranscriptionResult {
|
||||
text: string;
|
||||
language: string | null;
|
||||
confidence: number | null;
|
||||
duration_seconds: number;
|
||||
}
|
||||
|
||||
export interface SpeechHealth {
|
||||
available: boolean;
|
||||
backend?: string;
|
||||
reason?: string;
|
||||
}
|
||||
|
||||
export async function transcribeAudio(audioBlob: Blob, filename = 'recording.webm'): Promise<TranscriptionResult> {
|
||||
if (isTauri()) {
|
||||
try {
|
||||
const buffer = await audioBlob.arrayBuffer();
|
||||
return await tauriInvoke<TranscriptionResult>('transcribe_audio', {
|
||||
audioData: Array.from(new Uint8Array(buffer)),
|
||||
filename,
|
||||
});
|
||||
} catch {
|
||||
// Fall through to fetch
|
||||
}
|
||||
}
|
||||
const formData = new FormData();
|
||||
formData.append('file', audioBlob, filename);
|
||||
const res = await fetch(`${getBase()}/v1/speech/transcribe`, {
|
||||
method: 'POST',
|
||||
body: formData,
|
||||
});
|
||||
if (!res.ok) throw new Error(`Transcription failed: ${res.status}`);
|
||||
return res.json();
|
||||
}
|
||||
|
||||
export async function fetchSpeechHealth(): Promise<SpeechHealth> {
|
||||
if (isTauri()) {
|
||||
try {
|
||||
return await tauriInvoke<SpeechHealth>('speech_health');
|
||||
} catch {
|
||||
return { available: false };
|
||||
}
|
||||
}
|
||||
const res = await fetch(`${getBase()}/v1/speech/health`);
|
||||
if (!res.ok) return { available: false };
|
||||
return res.json();
|
||||
}
|
||||
|
||||
+4
-19
@@ -24,9 +24,6 @@ dev = [
|
||||
"respx>=0.22",
|
||||
"ruff>=0.4",
|
||||
]
|
||||
inference-ollama = []
|
||||
inference-vllm = []
|
||||
inference-llamacpp = []
|
||||
inference-mlx = ["mlx-lm>=0.19; sys_platform == 'darwin'"]
|
||||
inference-cloud = [
|
||||
"openai>=1.30",
|
||||
@@ -55,31 +52,15 @@ server = [
|
||||
"uvicorn>=0.30",
|
||||
"pydantic>=2.0",
|
||||
]
|
||||
agents = []
|
||||
openhands = ["openhands-sdk>=1.0; python_version >= '3.12'"]
|
||||
claude-code = []
|
||||
gpu-metrics = ["pynvml>=12.0"]
|
||||
energy-amd = ["amdsmi>=6.1"]
|
||||
energy-apple = ["zeus-ml[apple]"]
|
||||
energy-all = ["pynvml>=12.0", "amdsmi>=6.1", "zeus-ml[apple]"]
|
||||
learning = []
|
||||
orchestrator-training = ["torch>=2.0", "transformers>=4.40"]
|
||||
channel-telegram = ["python-telegram-bot>=21.0"]
|
||||
channel-discord = ["discord.py>=2.3"]
|
||||
channel-slack = ["slack-sdk>=3.27"]
|
||||
channel-webhook = []
|
||||
channel-email = []
|
||||
channel-whatsapp = []
|
||||
channel-signal = []
|
||||
channel-google-chat = []
|
||||
channel-irc = []
|
||||
channel-webchat = []
|
||||
channel-teams = []
|
||||
channel-matrix = []
|
||||
channel-mattermost = []
|
||||
channel-feishu = []
|
||||
channel-bluebubbles = []
|
||||
channel-whatsapp-baileys = []
|
||||
channel-line = ["line-bot-sdk>=3.0"]
|
||||
channel-viber = ["viberbot>=1.0"]
|
||||
channel-messenger = ["pymessenger>=0.0.7"]
|
||||
@@ -97,6 +78,10 @@ scheduler = ["croniter>=2.0"]
|
||||
security-signing = ["cryptography>=43"]
|
||||
sandbox-wasm = ["wasmtime>=25"]
|
||||
dashboard = ["textual>=0.80"]
|
||||
speech = ["faster-whisper>=1.0"]
|
||||
speech-deepgram = ["deepgram-sdk>=3.0"]
|
||||
eval-wandb = ["wandb>=0.17"]
|
||||
eval-sheets = ["gspread>=6.0", "google-auth>=2.0"]
|
||||
docs = [
|
||||
"mkdocs>=1.6",
|
||||
"mkdocs-material>=9.5",
|
||||
|
||||
@@ -14,6 +14,7 @@ from openjarvis.cli.chat_cmd import chat
|
||||
from openjarvis.cli.daemon_cmd import restart, start, status, stop
|
||||
from openjarvis.cli.doctor_cmd import doctor
|
||||
from openjarvis.cli.eval_cmd import eval_group
|
||||
from openjarvis.cli.host_cmd import host
|
||||
from openjarvis.cli.init_cmd import init
|
||||
from openjarvis.cli.memory_cmd import memory
|
||||
from openjarvis.cli.model import model
|
||||
@@ -64,6 +65,7 @@ cli.add_command(vault, "vault")
|
||||
cli.add_command(add, "add")
|
||||
cli.add_command(operators, "operators")
|
||||
cli.add_command(eval_group, "eval")
|
||||
cli.add_command(host, "host")
|
||||
cli.add_command(quickstart, "quickstart")
|
||||
|
||||
|
||||
|
||||
+137
-2
@@ -4,13 +4,15 @@ from __future__ import annotations
|
||||
|
||||
import json as json_mod
|
||||
import sys
|
||||
import time
|
||||
|
||||
import click
|
||||
from rich.console import Console
|
||||
from rich.table import Table
|
||||
|
||||
from openjarvis.cli.hints import hint_no_engine
|
||||
from openjarvis.core.config import load_config
|
||||
from openjarvis.core.events import EventBus
|
||||
from openjarvis.core.events import EventBus, EventType
|
||||
from openjarvis.core.types import Message, Role
|
||||
from openjarvis.engine import (
|
||||
EngineConnectionError,
|
||||
@@ -147,6 +149,119 @@ def _run_agent(
|
||||
return agent.run(query_text, context=ctx)
|
||||
|
||||
|
||||
def _print_profile(
|
||||
bus: EventBus,
|
||||
wall_seconds: float,
|
||||
engine_name: str,
|
||||
model_name: str,
|
||||
console: Console,
|
||||
) -> None:
|
||||
"""Print an inference telemetry profile table from EventBus history."""
|
||||
# Collect all INFERENCE_END events (agents may fire multiple)
|
||||
inf_events = [
|
||||
e for e in bus.history if e.event_type == EventType.INFERENCE_END
|
||||
]
|
||||
if not inf_events:
|
||||
console.print("[dim]No inference telemetry recorded.[/dim]")
|
||||
return
|
||||
|
||||
total_calls = len(inf_events)
|
||||
|
||||
# Aggregate across all inference calls
|
||||
total_latency = sum(e.data.get("latency", 0.0) for e in inf_events)
|
||||
total_tokens = sum(
|
||||
e.data.get("usage", {}).get("completion_tokens", 0)
|
||||
or e.data.get("completion_tokens", 0)
|
||||
for e in inf_events
|
||||
)
|
||||
total_prompt = sum(
|
||||
e.data.get("usage", {}).get("prompt_tokens", 0)
|
||||
for e in inf_events
|
||||
)
|
||||
total_energy = sum(e.data.get("energy_joules", 0.0) for e in inf_events)
|
||||
avg_power = 0.0
|
||||
power_vals = [e.data.get("power_watts", 0.0) for e in inf_events
|
||||
if e.data.get("power_watts", 0.0) > 0]
|
||||
if power_vals:
|
||||
avg_power = sum(power_vals) / len(power_vals)
|
||||
|
||||
throughput = total_tokens / total_latency if total_latency > 0 else 0.0
|
||||
energy_per_tok = total_energy / total_tokens if total_tokens > 0 else 0.0
|
||||
tpw = throughput / avg_power if avg_power > 0 else 0.0
|
||||
tok_per_j = total_tokens / total_energy if total_energy > 0 else 0.0
|
||||
|
||||
last = inf_events[-1].data
|
||||
ttft = last.get("ttft", 0.0)
|
||||
prefill_lat = last.get("prefill_latency_seconds", 0.0)
|
||||
decode_lat = last.get("decode_latency_seconds", 0.0)
|
||||
prefill_e = sum(e.data.get("prefill_energy_joules", 0.0) for e in inf_events)
|
||||
decode_e = sum(e.data.get("decode_energy_joules", 0.0) for e in inf_events)
|
||||
gpu_util = last.get("gpu_utilization_pct", 0.0)
|
||||
gpu_mem = last.get("gpu_memory_used_gb", 0.0)
|
||||
gpu_temp = last.get("gpu_temperature_c", 0.0)
|
||||
mean_itl = last.get("mean_itl_ms", 0.0)
|
||||
e_method = last.get("energy_method", "")
|
||||
e_vendor = last.get("energy_vendor", "")
|
||||
|
||||
# Build the profile table
|
||||
table = Table(
|
||||
title=f"Inference Profile ({engine_name} / {model_name})",
|
||||
show_header=True,
|
||||
header_style="bold bright_white",
|
||||
border_style="bright_blue",
|
||||
title_style="bold cyan",
|
||||
)
|
||||
table.add_column("Metric", style="cyan", no_wrap=True)
|
||||
table.add_column("Value", justify="right", style="green")
|
||||
|
||||
def _row(label: str, val: str) -> None:
|
||||
table.add_row(label, val)
|
||||
|
||||
_row("Wall time", f"{wall_seconds:.3f} s")
|
||||
_row("Inference calls", str(total_calls))
|
||||
_row("Total latency", f"{total_latency:.3f} s")
|
||||
if ttft > 0:
|
||||
_row("TTFT", f"{ttft * 1000:.1f} ms")
|
||||
if prefill_lat > 0:
|
||||
_row("Prefill latency", f"{prefill_lat * 1000:.1f} ms")
|
||||
if decode_lat > 0:
|
||||
_row("Decode latency", f"{decode_lat:.3f} s")
|
||||
if mean_itl > 0:
|
||||
_row("Mean ITL", f"{mean_itl:.2f} ms")
|
||||
_row("Prompt tokens", str(total_prompt))
|
||||
_row("Completion tokens", str(total_tokens))
|
||||
_row("Throughput", f"{throughput:.1f} tok/s")
|
||||
|
||||
if total_energy > 0:
|
||||
_row("", "") # separator
|
||||
_row("Energy", f"{total_energy:.4f} J")
|
||||
if prefill_e > 0:
|
||||
_row(" Prefill energy", f"{prefill_e:.4f} J")
|
||||
if decode_e > 0:
|
||||
_row(" Decode energy", f"{decode_e:.4f} J")
|
||||
_row("Energy / output token", f"{energy_per_tok:.6f} J")
|
||||
_row("Tokens / joule", f"{tok_per_j:.1f}")
|
||||
_row("Throughput / watt (IPW)", f"{tpw:.2f} tok/s/W")
|
||||
if avg_power > 0:
|
||||
_row("Avg power draw", f"{avg_power:.1f} W")
|
||||
if e_vendor:
|
||||
_row("Energy vendor", e_vendor)
|
||||
if e_method:
|
||||
_row("Energy method", e_method)
|
||||
|
||||
if gpu_util > 0 or gpu_mem > 0 or gpu_temp > 0:
|
||||
_row("", "") # separator
|
||||
if gpu_util > 0:
|
||||
_row("GPU utilization", f"{gpu_util:.1f} %")
|
||||
if gpu_mem > 0:
|
||||
_row("GPU memory used", f"{gpu_mem:.2f} GB")
|
||||
if gpu_temp > 0:
|
||||
_row("GPU temperature", f"{gpu_temp:.0f} °C")
|
||||
|
||||
console.print()
|
||||
console.print(table)
|
||||
|
||||
|
||||
@click.command()
|
||||
@click.argument("query", nargs=-1, required=True)
|
||||
@click.option("-m", "--model", "model_name", default=None, help="Model to use.")
|
||||
@@ -173,6 +288,10 @@ def _run_agent(
|
||||
"--tools", "tool_names", default=None,
|
||||
help="Comma-separated tool names to enable (e.g. calculator,think).",
|
||||
)
|
||||
@click.option(
|
||||
"--profile", "enable_profile", is_flag=True,
|
||||
help="Print inference telemetry profile (latency, tokens, energy, IPW).",
|
||||
)
|
||||
def ask(
|
||||
query: tuple[str, ...],
|
||||
model_name: str | None,
|
||||
@@ -184,11 +303,14 @@ def ask(
|
||||
no_context: bool,
|
||||
agent_name: str | None,
|
||||
tool_names: str | None,
|
||||
enable_profile: bool,
|
||||
) -> None:
|
||||
"""Ask Jarvis a question."""
|
||||
console = Console(stderr=True)
|
||||
query_text = " ".join(query)
|
||||
|
||||
wall_start = time.monotonic() if enable_profile else None
|
||||
|
||||
# Load config
|
||||
config = load_config()
|
||||
|
||||
@@ -228,7 +350,8 @@ def ask(
|
||||
|
||||
# Wrap engine with InstrumentedEngine for telemetry (energy + GPU metrics)
|
||||
energy_monitor = None
|
||||
if config.telemetry.gpu_metrics:
|
||||
want_energy = config.telemetry.gpu_metrics or enable_profile
|
||||
if want_energy:
|
||||
try:
|
||||
from openjarvis.telemetry.energy_monitor import create_energy_monitor
|
||||
|
||||
@@ -288,6 +411,12 @@ def ask(
|
||||
else:
|
||||
click.echo(result.content)
|
||||
|
||||
if enable_profile:
|
||||
_print_profile(
|
||||
bus, time.monotonic() - wall_start,
|
||||
engine_name, model_name, console,
|
||||
)
|
||||
|
||||
if telem_store is not None:
|
||||
try:
|
||||
telem_store.close()
|
||||
@@ -341,6 +470,12 @@ def ask(
|
||||
else:
|
||||
click.echo(result.get("content", ""))
|
||||
|
||||
if enable_profile:
|
||||
_print_profile(
|
||||
bus, time.monotonic() - wall_start,
|
||||
engine_name, model_name, console,
|
||||
)
|
||||
|
||||
# Cleanup
|
||||
if energy_monitor is not None:
|
||||
try:
|
||||
|
||||
@@ -113,6 +113,34 @@ def eval_list() -> None:
|
||||
"-o", "--output", "output_path", default=None, type=click.Path(),
|
||||
help="Output JSONL path.",
|
||||
)
|
||||
@click.option(
|
||||
"--wandb-project", "wandb_project", default="",
|
||||
help="W&B project name (enables W&B tracking).",
|
||||
)
|
||||
@click.option(
|
||||
"--wandb-entity", "wandb_entity", default="",
|
||||
help="W&B entity (team or user).",
|
||||
)
|
||||
@click.option(
|
||||
"--wandb-tags", "wandb_tags", default="",
|
||||
help="Comma-separated W&B tags.",
|
||||
)
|
||||
@click.option(
|
||||
"--wandb-group", "wandb_group", default="",
|
||||
help="W&B run group.",
|
||||
)
|
||||
@click.option(
|
||||
"--sheets-id", "sheets_spreadsheet_id", default="",
|
||||
help="Google Sheets spreadsheet ID.",
|
||||
)
|
||||
@click.option(
|
||||
"--sheets-worksheet", "sheets_worksheet", default="Results",
|
||||
help="Google Sheets worksheet name.",
|
||||
)
|
||||
@click.option(
|
||||
"--sheets-creds", "sheets_credentials_path", default="",
|
||||
help="Path to Google service account JSON.",
|
||||
)
|
||||
@click.option(
|
||||
"-v", "--verbose", "verbose", is_flag=True, default=False,
|
||||
help="Verbose logging.",
|
||||
@@ -127,6 +155,13 @@ def eval_run(
|
||||
tools: str,
|
||||
telemetry: bool,
|
||||
output_path: Optional[str],
|
||||
wandb_project: str,
|
||||
wandb_entity: str,
|
||||
wandb_tags: str,
|
||||
wandb_group: str,
|
||||
sheets_spreadsheet_id: str,
|
||||
sheets_worksheet: str,
|
||||
sheets_credentials_path: str,
|
||||
verbose: bool,
|
||||
) -> None:
|
||||
"""Run evaluation benchmarks."""
|
||||
@@ -216,6 +251,13 @@ def eval_run(
|
||||
tools=tool_list,
|
||||
output_path=output_path,
|
||||
telemetry=telemetry,
|
||||
wandb_project=wandb_project,
|
||||
wandb_entity=wandb_entity,
|
||||
wandb_tags=wandb_tags,
|
||||
wandb_group=wandb_group,
|
||||
sheets_spreadsheet_id=sheets_spreadsheet_id,
|
||||
sheets_worksheet=sheets_worksheet,
|
||||
sheets_credentials_path=sheets_credentials_path,
|
||||
)
|
||||
|
||||
try:
|
||||
|
||||
@@ -0,0 +1,366 @@
|
||||
"""``jarvis host`` — download and serve a model locally with auto backend setup."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
import shutil
|
||||
import signal
|
||||
import subprocess
|
||||
import sys
|
||||
from typing import Optional
|
||||
|
||||
import click
|
||||
from rich.console import Console
|
||||
from rich.panel import Panel
|
||||
from rich.table import Table
|
||||
|
||||
_BACKENDS = {
|
||||
"mlx": {
|
||||
"display": "MLX (Apple Silicon)",
|
||||
"package": "mlx-lm",
|
||||
"import_check": "mlx_lm",
|
||||
"pip_spec": "mlx-lm>=0.19",
|
||||
"uv_extra": "inference-mlx",
|
||||
"platform": "darwin",
|
||||
"default_port": 8080,
|
||||
},
|
||||
"vllm": {
|
||||
"display": "vLLM (NVIDIA GPU)",
|
||||
"package": "vllm",
|
||||
"import_check": "vllm",
|
||||
"pip_spec": "vllm",
|
||||
"uv_extra": None,
|
||||
"platform": "linux",
|
||||
"default_port": 8000,
|
||||
},
|
||||
"sglang": {
|
||||
"display": "SGLang (NVIDIA GPU)",
|
||||
"package": "sglang",
|
||||
"import_check": "sglang",
|
||||
"pip_spec": "sglang[all]",
|
||||
"uv_extra": None,
|
||||
"platform": None,
|
||||
"default_port": 30000,
|
||||
},
|
||||
"ollama": {
|
||||
"display": "Ollama",
|
||||
"package": "ollama",
|
||||
"import_check": None,
|
||||
"pip_spec": None,
|
||||
"uv_extra": None,
|
||||
"platform": None,
|
||||
"default_port": 11434,
|
||||
"binary": "ollama",
|
||||
},
|
||||
"llamacpp": {
|
||||
"display": "llama.cpp",
|
||||
"package": "llama.cpp",
|
||||
"import_check": None,
|
||||
"pip_spec": None,
|
||||
"uv_extra": None,
|
||||
"platform": None,
|
||||
"default_port": 8080,
|
||||
"binary": "llama-server",
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def _is_package_available(backend: str) -> bool:
|
||||
"""Check whether the backend's Python package or binary is importable/available."""
|
||||
info = _BACKENDS[backend]
|
||||
|
||||
if info.get("import_check"):
|
||||
try:
|
||||
__import__(info["import_check"])
|
||||
return True
|
||||
except ImportError:
|
||||
return False
|
||||
|
||||
binary = info.get("binary")
|
||||
if binary:
|
||||
return shutil.which(binary) is not None
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def _detect_backend() -> str | None:
|
||||
"""Auto-detect the best backend for the current platform."""
|
||||
import platform
|
||||
|
||||
system = platform.system().lower()
|
||||
|
||||
if system == "darwin":
|
||||
try:
|
||||
result = subprocess.run(
|
||||
["sysctl", "-n", "machdep.cpu.brand_string"],
|
||||
capture_output=True,
|
||||
text=True,
|
||||
)
|
||||
if "Apple" in result.stdout:
|
||||
return "mlx"
|
||||
except FileNotFoundError:
|
||||
pass
|
||||
return "ollama"
|
||||
|
||||
if system == "linux":
|
||||
if shutil.which("nvidia-smi"):
|
||||
return "vllm"
|
||||
return "ollama"
|
||||
|
||||
return "ollama"
|
||||
|
||||
|
||||
def _install_backend(backend: str, console: Console) -> bool:
|
||||
"""Prompt the user and install the backend package. Returns True on success."""
|
||||
info = _BACKENDS[backend]
|
||||
display = info["display"]
|
||||
|
||||
console.print()
|
||||
console.print(
|
||||
f"[yellow]Backend [bold]{display}[/bold] is not installed.[/yellow]"
|
||||
)
|
||||
|
||||
uv_available = shutil.which("uv") is not None
|
||||
in_uv_project = os.path.exists("pyproject.toml") and os.path.exists("uv.lock")
|
||||
|
||||
if info.get("binary") and not info.get("pip_spec"):
|
||||
return _install_binary_backend(backend, console)
|
||||
|
||||
pip_spec = info["pip_spec"]
|
||||
uv_extra = info.get("uv_extra")
|
||||
|
||||
if uv_available and in_uv_project and uv_extra:
|
||||
install_cmd = ["uv", "pip", "install", pip_spec]
|
||||
install_label = f"uv pip install {pip_spec}"
|
||||
elif uv_available:
|
||||
install_cmd = ["uv", "pip", "install", pip_spec]
|
||||
install_label = f"uv pip install {pip_spec}"
|
||||
else:
|
||||
install_cmd = [sys.executable, "-m", "pip", "install", pip_spec]
|
||||
install_label = f"pip install {pip_spec}"
|
||||
|
||||
console.print(f"\n Install command: [cyan]{install_label}[/cyan]\n")
|
||||
if not click.confirm("Install now?", default=True):
|
||||
console.print("[dim]Skipped. Install manually and retry.[/dim]")
|
||||
return False
|
||||
|
||||
console.print(f"[bold]Running:[/bold] {install_label}")
|
||||
result = subprocess.run(install_cmd)
|
||||
if result.returncode != 0:
|
||||
console.print(
|
||||
f"[red]Installation failed (exit {result.returncode}).[/red]"
|
||||
)
|
||||
return False
|
||||
|
||||
console.print(f"[green]{display} installed successfully.[/green]\n")
|
||||
return True
|
||||
|
||||
|
||||
def _install_binary_backend(backend: str, console: Console) -> bool:
|
||||
"""Guide the user through installing a binary backend (Ollama, llama.cpp)."""
|
||||
info = _BACKENDS[backend]
|
||||
binary = info["binary"]
|
||||
|
||||
instructions = {
|
||||
"ollama": (
|
||||
"Install Ollama:\n"
|
||||
"\n"
|
||||
" macOS / Linux:\n"
|
||||
" curl -fsSL https://ollama.com/install.sh | sh\n"
|
||||
"\n"
|
||||
" Or download from: https://ollama.com/download"
|
||||
),
|
||||
"llamacpp": (
|
||||
"Install llama.cpp:\n"
|
||||
"\n"
|
||||
" macOS:\n"
|
||||
" brew install llama.cpp\n"
|
||||
"\n"
|
||||
" From source:\n"
|
||||
" https://github.com/ggerganov/llama.cpp"
|
||||
),
|
||||
}
|
||||
|
||||
console.print()
|
||||
console.print(
|
||||
Panel(
|
||||
instructions.get(
|
||||
backend,
|
||||
f"Install {binary} and ensure it's on PATH.",
|
||||
),
|
||||
title=f"{info['display']} Installation",
|
||||
border_style="yellow",
|
||||
)
|
||||
)
|
||||
|
||||
if backend == "ollama":
|
||||
import platform
|
||||
|
||||
system = platform.system().lower()
|
||||
if system in ("linux", "darwin"):
|
||||
if click.confirm("Run the Ollama install script now?", default=True):
|
||||
console.print("[bold]Running Ollama installer...[/bold]")
|
||||
result = subprocess.run(
|
||||
["sh", "-c", "curl -fsSL https://ollama.com/install.sh | sh"],
|
||||
)
|
||||
if result.returncode == 0 and shutil.which("ollama"):
|
||||
console.print("[green]Ollama installed successfully.[/green]\n")
|
||||
return True
|
||||
console.print(
|
||||
"[red]Installation may have failed. "
|
||||
"Check above for errors.[/red]"
|
||||
)
|
||||
return False
|
||||
|
||||
console.print("[dim]Install manually and retry.[/dim]")
|
||||
return False
|
||||
|
||||
|
||||
def _build_serve_command(backend: str, model: str, port: int) -> list[str]:
|
||||
"""Build the subprocess command list to start the inference server."""
|
||||
if backend == "mlx":
|
||||
return [
|
||||
sys.executable,
|
||||
"-m",
|
||||
"mlx_lm.server",
|
||||
"--model",
|
||||
model,
|
||||
"--port",
|
||||
str(port),
|
||||
]
|
||||
|
||||
if backend == "vllm":
|
||||
return ["vllm", "serve", model, "--port", str(port)]
|
||||
|
||||
if backend == "sglang":
|
||||
return [
|
||||
sys.executable,
|
||||
"-m",
|
||||
"sglang.launch_server",
|
||||
"--model-path",
|
||||
model,
|
||||
"--port",
|
||||
str(port),
|
||||
]
|
||||
|
||||
if backend == "ollama":
|
||||
return ["ollama", "run", model]
|
||||
|
||||
if backend == "llamacpp":
|
||||
return ["llama-server", "-m", model, "--port", str(port)]
|
||||
|
||||
raise ValueError(f"Unknown backend: {backend}")
|
||||
|
||||
|
||||
@click.command()
|
||||
@click.argument("model")
|
||||
@click.option(
|
||||
"-b",
|
||||
"--backend",
|
||||
type=click.Choice(list(_BACKENDS.keys()), case_sensitive=False),
|
||||
default=None,
|
||||
help="Inference backend to use. Auto-detected if omitted.",
|
||||
)
|
||||
@click.option(
|
||||
"-p",
|
||||
"--port",
|
||||
type=int,
|
||||
default=None,
|
||||
help="Port to serve on (default depends on backend).",
|
||||
)
|
||||
@click.option(
|
||||
"--trust-remote-code",
|
||||
is_flag=True,
|
||||
default=False,
|
||||
help="Pass --trust-remote-code to the backend.",
|
||||
)
|
||||
def host(
|
||||
model: str,
|
||||
backend: Optional[str],
|
||||
port: Optional[int],
|
||||
trust_remote_code: bool,
|
||||
) -> None:
|
||||
"""Download (if needed) and serve a model locally.
|
||||
|
||||
Examples:
|
||||
|
||||
\b
|
||||
jarvis host mlx-community/Qwen2.5-7B-4bit --backend mlx
|
||||
jarvis host Qwen/Qwen3-8B --backend vllm
|
||||
jarvis host qwen3:8b --backend ollama
|
||||
jarvis host meta-llama/Llama-3-8B -b sglang
|
||||
"""
|
||||
console = Console()
|
||||
|
||||
if backend is None:
|
||||
detected = _detect_backend()
|
||||
if detected is None:
|
||||
console.print("[red]Could not auto-detect a suitable backend.[/red]")
|
||||
console.print("Specify one with [cyan]--backend[/cyan].")
|
||||
raise SystemExit(1)
|
||||
backend = detected
|
||||
name = _BACKENDS[backend]["display"]
|
||||
console.print(
|
||||
f"Auto-detected backend: [bold cyan]{name}[/bold cyan]"
|
||||
)
|
||||
|
||||
info = _BACKENDS[backend]
|
||||
|
||||
if not _is_package_available(backend):
|
||||
if not _install_backend(backend, console):
|
||||
raise SystemExit(1)
|
||||
if not _is_package_available(backend):
|
||||
console.print(
|
||||
f"[red]{info['display']} still not available after install.[/red]"
|
||||
)
|
||||
console.print(
|
||||
"You may need to restart your shell or "
|
||||
"activate the correct environment."
|
||||
)
|
||||
raise SystemExit(1)
|
||||
|
||||
serve_port = port or info["default_port"]
|
||||
cmd = _build_serve_command(backend, model, serve_port)
|
||||
|
||||
if trust_remote_code:
|
||||
if backend in ("vllm", "sglang"):
|
||||
cmd.append("--trust-remote-code")
|
||||
elif backend == "mlx":
|
||||
cmd.extend(["--trust-remote-code", "True"])
|
||||
|
||||
host_url = f"http://localhost:{serve_port}"
|
||||
|
||||
table = Table.grid(padding=(0, 2))
|
||||
table.add_row("[bold]Backend:[/bold]", info["display"])
|
||||
table.add_row("[bold]Model:[/bold]", model)
|
||||
table.add_row("[bold]Endpoint:[/bold]", host_url)
|
||||
table.add_row("[bold]Command:[/bold]", " ".join(cmd))
|
||||
|
||||
console.print()
|
||||
console.print(Panel(table, title="Hosting Model", border_style="green"))
|
||||
console.print()
|
||||
|
||||
if backend != "ollama":
|
||||
console.print(
|
||||
f"[dim]The model server will be available at {host_url}[/dim]"
|
||||
)
|
||||
console.print(
|
||||
"[dim]OpenJarvis will auto-discover it. "
|
||||
"Press Ctrl+C to stop.[/dim]\n"
|
||||
)
|
||||
|
||||
try:
|
||||
proc = subprocess.Popen(cmd)
|
||||
proc.wait()
|
||||
except KeyboardInterrupt:
|
||||
console.print("\n[yellow]Shutting down model server...[/yellow]")
|
||||
proc.send_signal(signal.SIGTERM)
|
||||
try:
|
||||
proc.wait(timeout=10)
|
||||
except subprocess.TimeoutExpired:
|
||||
proc.kill()
|
||||
console.print("[green]Server stopped.[/green]")
|
||||
except FileNotFoundError:
|
||||
console.print(f"[red]Command not found:[/red] {cmd[0]}")
|
||||
console.print(f"Make sure {info['display']} is installed and on your PATH.")
|
||||
raise SystemExit(1)
|
||||
@@ -5,6 +5,8 @@ from __future__ import annotations
|
||||
import click
|
||||
from rich.console import Console
|
||||
from rich.panel import Panel
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
from openjarvis.core.config import (
|
||||
DEFAULT_CONFIG_DIR,
|
||||
@@ -113,7 +115,12 @@ def _next_steps_text(engine: str) -> str:
|
||||
@click.option(
|
||||
"--force", is_flag=True, help="Overwrite existing config without prompting."
|
||||
)
|
||||
def init(force: bool) -> None:
|
||||
@click.option(
|
||||
"--config",
|
||||
type=click.Path(exists=True),
|
||||
help="Path to config file to use.",
|
||||
)
|
||||
def init(force: bool, config: Optional[Path]) -> None:
|
||||
"""Detect hardware and generate ~/.openjarvis/config.toml."""
|
||||
console = Console()
|
||||
|
||||
@@ -137,10 +144,16 @@ def init(force: bool) -> None:
|
||||
else:
|
||||
console.print(" GPU : none detected")
|
||||
|
||||
toml_content = generate_default_toml(hw)
|
||||
if config:
|
||||
toml_content = config.read_text()
|
||||
else:
|
||||
toml_content = generate_default_toml(hw)
|
||||
|
||||
DEFAULT_CONFIG_DIR.mkdir(parents=True, exist_ok=True)
|
||||
DEFAULT_CONFIG_PATH.write_text(toml_content)
|
||||
if config:
|
||||
config.write_text(toml_content)
|
||||
else:
|
||||
DEFAULT_CONFIG_PATH.write_text(toml_content)
|
||||
|
||||
console.print()
|
||||
console.print(
|
||||
@@ -157,3 +170,4 @@ def init(force: bool) -> None:
|
||||
border_style="cyan",
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
@@ -185,6 +185,16 @@ def serve(
|
||||
console.print(f"[yellow]Channel failed to start: {exc}[/yellow]")
|
||||
channel_bridge = None
|
||||
|
||||
# Set up speech backend
|
||||
speech_backend = None
|
||||
try:
|
||||
from openjarvis.speech._discovery import get_speech_backend
|
||||
speech_backend = get_speech_backend(config)
|
||||
if speech_backend:
|
||||
console.print(f" Speech: [cyan]{speech_backend.backend_id}[/cyan]")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# Create app
|
||||
from openjarvis.server.app import create_app
|
||||
|
||||
@@ -192,6 +202,7 @@ def serve(
|
||||
engine, model_name, agent=agent, bus=bus,
|
||||
engine_name=engine_name, agent_name=agent_key or "",
|
||||
channel_bridge=channel_bridge, config=config,
|
||||
speech_backend=speech_backend,
|
||||
)
|
||||
|
||||
console.print(
|
||||
|
||||
@@ -830,6 +830,17 @@ class OperatorsConfig:
|
||||
auto_activate: str = "" # Comma-separated operator IDs
|
||||
|
||||
|
||||
@dataclass(slots=True)
|
||||
class SpeechConfig:
|
||||
"""Speech-to-text settings."""
|
||||
|
||||
backend: str = "auto" # "auto", "faster-whisper", "openai", "deepgram"
|
||||
model: str = "base" # Whisper model size: tiny, base, small, medium, large-v3
|
||||
language: str = "" # Empty = auto-detect
|
||||
device: str = "auto" # "auto", "cpu", "cuda"
|
||||
compute_type: str = "float16" # "float16", "int8", "float32"
|
||||
|
||||
|
||||
@dataclass
|
||||
class JarvisConfig:
|
||||
"""Top-level configuration for OpenJarvis."""
|
||||
@@ -851,6 +862,7 @@ class JarvisConfig:
|
||||
sessions: SessionConfig = field(default_factory=SessionConfig)
|
||||
a2a: A2AConfig = field(default_factory=A2AConfig)
|
||||
operators: OperatorsConfig = field(default_factory=OperatorsConfig)
|
||||
speech: SpeechConfig = field(default_factory=SpeechConfig)
|
||||
|
||||
@property
|
||||
def memory(self) -> StorageConfig:
|
||||
@@ -945,6 +957,7 @@ def load_config(path: Optional[Path] = None) -> JarvisConfig:
|
||||
"server", "telemetry", "traces", "security",
|
||||
"channel", "tools", "sandbox", "scheduler",
|
||||
"workflow", "sessions", "a2a", "operators",
|
||||
"speech",
|
||||
)
|
||||
for section_name in top_sections:
|
||||
if section_name in data:
|
||||
@@ -1196,6 +1209,7 @@ __all__ = [
|
||||
"SessionConfig",
|
||||
"SignalChannelConfig",
|
||||
"SlackChannelConfig",
|
||||
"SpeechConfig",
|
||||
"StorageConfig",
|
||||
"TeamsChannelConfig",
|
||||
"TelegramChannelConfig",
|
||||
|
||||
@@ -137,6 +137,10 @@ class SkillRegistry(RegistryBase[Any]):
|
||||
"""Registry for skill manifests."""
|
||||
|
||||
|
||||
class SpeechRegistry(RegistryBase[Any]):
|
||||
"""Registry for speech backend implementations."""
|
||||
|
||||
|
||||
__all__ = [
|
||||
"AgentRegistry",
|
||||
"BenchmarkRegistry",
|
||||
@@ -148,5 +152,6 @@ __all__ = [
|
||||
"RegistryBase",
|
||||
"RouterPolicyRegistry",
|
||||
"SkillRegistry",
|
||||
"SpeechRegistry",
|
||||
"ToolRegistry",
|
||||
]
|
||||
|
||||
@@ -217,6 +217,39 @@ def _print_summary(
|
||||
print_completion(console, summary, output_path, traces_dir)
|
||||
|
||||
|
||||
def _build_trackers(config) -> list:
|
||||
"""Build tracker instances from RunConfig fields."""
|
||||
trackers = []
|
||||
if getattr(config, "wandb_project", ""):
|
||||
try:
|
||||
from openjarvis.evals.trackers.wandb_tracker import WandbTracker
|
||||
trackers.append(WandbTracker(
|
||||
project=config.wandb_project,
|
||||
entity=getattr(config, "wandb_entity", ""),
|
||||
tags=getattr(config, "wandb_tags", ""),
|
||||
group=getattr(config, "wandb_group", ""),
|
||||
))
|
||||
except ImportError as exc:
|
||||
raise click.ClickException(
|
||||
f"wandb not installed: {exc}\n"
|
||||
"Install with: pip install 'openjarvis[eval-wandb]'"
|
||||
) from exc
|
||||
if getattr(config, "sheets_spreadsheet_id", ""):
|
||||
try:
|
||||
from openjarvis.evals.trackers.sheets_tracker import SheetsTracker
|
||||
trackers.append(SheetsTracker(
|
||||
spreadsheet_id=config.sheets_spreadsheet_id,
|
||||
worksheet=getattr(config, "sheets_worksheet", "Results"),
|
||||
credentials_path=getattr(config, "sheets_credentials_path", ""),
|
||||
))
|
||||
except ImportError as exc:
|
||||
raise click.ClickException(
|
||||
f"gspread not installed: {exc}\n"
|
||||
"Install with: pip install 'openjarvis[eval-sheets]'"
|
||||
) from exc
|
||||
return trackers
|
||||
|
||||
|
||||
def _run_single(config, console: Optional[Console] = None) -> object:
|
||||
"""Run a single eval from a RunConfig and return the summary."""
|
||||
from openjarvis.evals.core.runner import EvalRunner
|
||||
@@ -236,7 +269,8 @@ def _run_single(config, console: Optional[Console] = None) -> object:
|
||||
judge_backend = _build_judge_backend(config.judge_model)
|
||||
scorer = _build_scorer(config.benchmark, judge_backend, config.judge_model)
|
||||
|
||||
runner = EvalRunner(config, dataset, eval_backend, scorer)
|
||||
trackers = _build_trackers(config)
|
||||
runner = EvalRunner(config, dataset, eval_backend, scorer, trackers=trackers)
|
||||
try:
|
||||
num_samples = config.max_samples or 0
|
||||
# Use progress bar if we know the sample count
|
||||
@@ -292,6 +326,11 @@ def _run_from_config(config_path: str, verbose: bool) -> None:
|
||||
output_dir = Path(suite.run.output_dir)
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Auto-set wandb_group to suite name if W&B enabled and no explicit group
|
||||
for rc in run_configs:
|
||||
if rc.wandb_project and not rc.wandb_group:
|
||||
rc.wandb_group = suite_name
|
||||
|
||||
summaries = []
|
||||
for i, rc in enumerate(run_configs, 1):
|
||||
print_section(
|
||||
@@ -355,12 +394,30 @@ def main():
|
||||
help="Enable GPU metrics collection")
|
||||
@click.option("--compact", is_flag=True, default=False, help="Dense single-table output")
|
||||
@click.option("--trace-detail", is_flag=True, default=False, help="Full per-step trace listing")
|
||||
@click.option("--wandb-project", default="",
|
||||
help="W&B project name (enables tracking)")
|
||||
@click.option("--wandb-entity", default="",
|
||||
help="W&B entity (team or user)")
|
||||
@click.option("--wandb-tags", default="",
|
||||
help="Comma-separated W&B tags")
|
||||
@click.option("--wandb-group", default="",
|
||||
help="W&B run group")
|
||||
@click.option("--sheets-id", "sheets_spreadsheet_id", default="",
|
||||
help="Google Sheets spreadsheet ID")
|
||||
@click.option("--sheets-worksheet", default="Results",
|
||||
help="Google Sheets worksheet name")
|
||||
@click.option("--sheets-creds", "sheets_credentials_path",
|
||||
default="",
|
||||
help="Service account JSON path")
|
||||
@click.option("-v", "--verbose", is_flag=True, help="Verbose logging")
|
||||
@click.pass_context
|
||||
def run(ctx, config_path, benchmark, backend, model, engine_key, agent_name,
|
||||
tools, max_samples, max_workers, judge_model, output_path, seed,
|
||||
dataset_split, temperature, max_tokens, telemetry, gpu_metrics,
|
||||
compact, trace_detail, verbose):
|
||||
compact, trace_detail,
|
||||
wandb_project, wandb_entity, wandb_tags, wandb_group,
|
||||
sheets_spreadsheet_id, sheets_worksheet, sheets_credentials_path,
|
||||
verbose):
|
||||
"""Run a single benchmark evaluation, or a full suite from a TOML config."""
|
||||
_setup_logging(verbose)
|
||||
|
||||
@@ -404,6 +461,13 @@ def run(ctx, config_path, benchmark, backend, model, engine_key, agent_name,
|
||||
dataset_split=dataset_split,
|
||||
telemetry=telemetry,
|
||||
gpu_metrics=gpu_metrics,
|
||||
wandb_project=wandb_project,
|
||||
wandb_entity=wandb_entity,
|
||||
wandb_tags=wandb_tags,
|
||||
wandb_group=wandb_group,
|
||||
sheets_spreadsheet_id=sheets_spreadsheet_id,
|
||||
sheets_worksheet=sheets_worksheet,
|
||||
sheets_credentials_path=sheets_credentials_path,
|
||||
)
|
||||
|
||||
# Banner + config
|
||||
@@ -493,7 +557,8 @@ def run_all(model, engine_key, max_samples, max_workers, judge_model,
|
||||
judge_backend = _build_judge_backend(judge_model)
|
||||
scorer = _build_scorer(bench_name, judge_backend, judge_model)
|
||||
|
||||
runner = EvalRunner(config, dataset, eval_backend, scorer)
|
||||
trackers = _build_trackers(config)
|
||||
runner = EvalRunner(config, dataset, eval_backend, scorer, trackers=trackers)
|
||||
try:
|
||||
if max_samples and max_samples > 0:
|
||||
with Progress(
|
||||
|
||||
@@ -99,6 +99,13 @@ def load_eval_config(path: str | Path) -> EvalSuiteConfig:
|
||||
gpu_metrics=bool(run_raw.get("gpu_metrics", False)),
|
||||
warmup_samples=int(run_raw.get("warmup_samples", 0)),
|
||||
energy_vendor=run_raw.get("energy_vendor", ""),
|
||||
wandb_project=run_raw.get("wandb_project", ""),
|
||||
wandb_entity=run_raw.get("wandb_entity", ""),
|
||||
wandb_tags=run_raw.get("wandb_tags", ""),
|
||||
wandb_group=run_raw.get("wandb_group", ""),
|
||||
sheets_spreadsheet_id=run_raw.get("sheets_spreadsheet_id", ""),
|
||||
sheets_worksheet=run_raw.get("sheets_worksheet", "Results"),
|
||||
sheets_credentials_path=run_raw.get("sheets_credentials_path", ""),
|
||||
)
|
||||
|
||||
# Parse [[models]]
|
||||
@@ -243,6 +250,13 @@ def expand_suite(suite: EvalSuiteConfig) -> List[RunConfig]:
|
||||
gpu_metrics=suite.run.gpu_metrics,
|
||||
metadata=model_meta,
|
||||
warmup_samples=suite.run.warmup_samples,
|
||||
wandb_project=suite.run.wandb_project,
|
||||
wandb_entity=suite.run.wandb_entity,
|
||||
wandb_tags=suite.run.wandb_tags,
|
||||
wandb_group=suite.run.wandb_group,
|
||||
sheets_spreadsheet_id=suite.run.sheets_spreadsheet_id,
|
||||
sheets_worksheet=suite.run.sheets_worksheet,
|
||||
sheets_credentials_path=suite.run.sheets_credentials_path,
|
||||
))
|
||||
|
||||
return configs
|
||||
|
||||
@@ -14,7 +14,14 @@ from typing import Any, Callable, Dict, List, Optional
|
||||
from openjarvis.evals.core.backend import InferenceBackend
|
||||
from openjarvis.evals.core.dataset import DatasetProvider
|
||||
from openjarvis.evals.core.scorer import Scorer
|
||||
from openjarvis.evals.core.types import EvalRecord, EvalResult, MetricStats, RunConfig, RunSummary
|
||||
from openjarvis.evals.core.tracker import ResultTracker
|
||||
from openjarvis.evals.core.types import (
|
||||
EvalRecord,
|
||||
EvalResult,
|
||||
MetricStats,
|
||||
RunConfig,
|
||||
RunSummary,
|
||||
)
|
||||
|
||||
try:
|
||||
from openjarvis.telemetry.efficiency import compute_efficiency
|
||||
@@ -33,11 +40,13 @@ class EvalRunner:
|
||||
dataset: DatasetProvider,
|
||||
backend: InferenceBackend,
|
||||
scorer: Scorer,
|
||||
trackers: Optional[List[ResultTracker]] = None,
|
||||
) -> None:
|
||||
self._config = config
|
||||
self._dataset = dataset
|
||||
self._backend = backend
|
||||
self._scorer = scorer
|
||||
self._trackers: List[ResultTracker] = trackers or []
|
||||
self._results: List[EvalResult] = []
|
||||
self._output_file: Optional[Any] = None
|
||||
|
||||
@@ -79,6 +88,16 @@ class EvalRunner:
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
self._output_file = open(output_path, "w")
|
||||
|
||||
# Notify trackers of run start
|
||||
for tracker in self._trackers:
|
||||
try:
|
||||
tracker.on_run_start(cfg)
|
||||
except Exception as exc:
|
||||
LOGGER.warning(
|
||||
"Tracker %s.on_run_start failed: %s",
|
||||
type(tracker).__name__, exc,
|
||||
)
|
||||
|
||||
total = len(records)
|
||||
try:
|
||||
with ThreadPoolExecutor(max_workers=cfg.max_workers) as pool:
|
||||
@@ -99,6 +118,23 @@ class EvalRunner:
|
||||
ended_at = time.time()
|
||||
summary = self._compute_summary(records, started_at, ended_at)
|
||||
|
||||
# Notify trackers of summary and run end
|
||||
for tracker in self._trackers:
|
||||
try:
|
||||
tracker.on_summary(summary)
|
||||
except Exception as exc:
|
||||
LOGGER.warning(
|
||||
"Tracker %s.on_summary failed: %s",
|
||||
type(tracker).__name__, exc,
|
||||
)
|
||||
try:
|
||||
tracker.on_run_end()
|
||||
except Exception as exc:
|
||||
LOGGER.warning(
|
||||
"Tracker %s.on_run_end failed: %s",
|
||||
type(tracker).__name__, exc,
|
||||
)
|
||||
|
||||
# Write summary JSON alongside JSONL
|
||||
traces_dir: Optional[Path] = None
|
||||
if output_path:
|
||||
@@ -255,6 +291,16 @@ class EvalRunner:
|
||||
self._output_file.write(json.dumps(record_dict) + "\n")
|
||||
self._output_file.flush()
|
||||
|
||||
# Notify trackers of each result
|
||||
for tracker in self._trackers:
|
||||
try:
|
||||
tracker.on_result(result, self._config)
|
||||
except Exception as exc:
|
||||
LOGGER.warning(
|
||||
"Tracker %s.on_result failed: %s",
|
||||
type(tracker).__name__, exc,
|
||||
)
|
||||
|
||||
def _resolve_output_path(self) -> Optional[Path]:
|
||||
"""Determine the output file path."""
|
||||
if self._config.output_path:
|
||||
|
||||
@@ -0,0 +1,34 @@
|
||||
"""ResultTracker ABC for external experiment tracking."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
|
||||
from openjarvis.evals.core.types import EvalResult, RunConfig, RunSummary
|
||||
|
||||
|
||||
class ResultTracker(ABC):
|
||||
"""Abstract base class for experiment result trackers.
|
||||
|
||||
Lifecycle: on_run_start -> on_result (per sample)
|
||||
-> on_summary -> on_run_end.
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def on_run_start(self, config: RunConfig) -> None:
|
||||
"""Called once before evaluation begins."""
|
||||
|
||||
@abstractmethod
|
||||
def on_result(self, result: EvalResult, config: RunConfig) -> None:
|
||||
"""Called after each sample is evaluated."""
|
||||
|
||||
@abstractmethod
|
||||
def on_summary(self, summary: RunSummary) -> None:
|
||||
"""Called after all samples are evaluated with aggregate stats."""
|
||||
|
||||
@abstractmethod
|
||||
def on_run_end(self) -> None:
|
||||
"""Called at the very end of a run for cleanup."""
|
||||
|
||||
|
||||
__all__ = ["ResultTracker"]
|
||||
@@ -71,6 +71,13 @@ class RunConfig:
|
||||
gpu_metrics: bool = False
|
||||
metadata: Dict[str, Any] = field(default_factory=dict)
|
||||
warmup_samples: int = 0
|
||||
wandb_project: str = ""
|
||||
wandb_entity: str = ""
|
||||
wandb_tags: str = ""
|
||||
wandb_group: str = ""
|
||||
sheets_spreadsheet_id: str = ""
|
||||
sheets_worksheet: str = "Results"
|
||||
sheets_credentials_path: str = ""
|
||||
|
||||
|
||||
@dataclass(slots=True)
|
||||
@@ -176,6 +183,13 @@ class ExecutionConfig:
|
||||
gpu_metrics: bool = False
|
||||
warmup_samples: int = 0
|
||||
energy_vendor: str = ""
|
||||
wandb_project: str = ""
|
||||
wandb_entity: str = ""
|
||||
wandb_tags: str = ""
|
||||
wandb_group: str = ""
|
||||
sheets_spreadsheet_id: str = ""
|
||||
sheets_worksheet: str = "Results"
|
||||
sheets_credentials_path: str = ""
|
||||
|
||||
|
||||
@dataclass(slots=True)
|
||||
|
||||
@@ -0,0 +1,23 @@
|
||||
"""External experiment trackers for the eval framework.
|
||||
|
||||
Trackers are lazily imported to avoid mandatory dependencies on wandb/gspread.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
|
||||
def WandbTracker(*args, **kwargs): # noqa: N802
|
||||
"""Lazy constructor — imports the real class on first use."""
|
||||
from openjarvis.evals.trackers.wandb_tracker import WandbTracker as _Cls
|
||||
|
||||
return _Cls(*args, **kwargs)
|
||||
|
||||
|
||||
def SheetsTracker(*args, **kwargs): # noqa: N802
|
||||
"""Lazy constructor — imports the real class on first use."""
|
||||
from openjarvis.evals.trackers.sheets_tracker import SheetsTracker as _Cls
|
||||
|
||||
return _Cls(*args, **kwargs)
|
||||
|
||||
|
||||
__all__ = ["WandbTracker", "SheetsTracker"]
|
||||
@@ -0,0 +1,162 @@
|
||||
"""Google Sheets experiment tracker for the eval framework."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import time
|
||||
from typing import Any, List, Optional
|
||||
|
||||
from openjarvis.evals.core.tracker import ResultTracker
|
||||
from openjarvis.evals.core.types import EvalResult, MetricStats, RunConfig, RunSummary
|
||||
|
||||
try:
|
||||
import gspread
|
||||
from google.oauth2.service_account import Credentials
|
||||
except ImportError:
|
||||
gspread = None # type: ignore[assignment]
|
||||
Credentials = None # type: ignore[assignment,misc]
|
||||
|
||||
LOGGER = logging.getLogger(__name__)
|
||||
|
||||
# Canonical column order for the summary row.
|
||||
SHEET_COLUMNS: List[str] = [
|
||||
"timestamp",
|
||||
"benchmark",
|
||||
"model",
|
||||
"backend",
|
||||
"total_samples",
|
||||
"scored_samples",
|
||||
"correct",
|
||||
"accuracy",
|
||||
"errors",
|
||||
"mean_latency_seconds",
|
||||
"total_cost_usd",
|
||||
"total_energy_joules",
|
||||
"avg_power_watts",
|
||||
"total_input_tokens",
|
||||
"total_output_tokens",
|
||||
"latency_mean",
|
||||
"latency_p90",
|
||||
"latency_p95",
|
||||
"energy_mean",
|
||||
"energy_p90",
|
||||
"throughput_mean",
|
||||
"throughput_p90",
|
||||
"ipw_mean",
|
||||
"ipj_mean",
|
||||
"mfu_mean",
|
||||
"mbu_mean",
|
||||
"ttft_mean",
|
||||
"ttft_p90",
|
||||
"gpu_utilization_mean",
|
||||
]
|
||||
|
||||
|
||||
def _stat_val(ms: Optional[MetricStats], attr: str) -> Any:
|
||||
"""Safely extract a stat value from a MetricStats, returning '' if None."""
|
||||
if ms is None:
|
||||
return ""
|
||||
return getattr(ms, attr, "")
|
||||
|
||||
|
||||
class SheetsTracker(ResultTracker):
|
||||
"""Appends a summary row to a Google Sheet after each eval run."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
spreadsheet_id: str,
|
||||
worksheet: str = "Results",
|
||||
credentials_path: str = "",
|
||||
) -> None:
|
||||
if gspread is None:
|
||||
raise ImportError(
|
||||
"gspread is not installed. "
|
||||
"Install it with: pip install 'openjarvis[eval-sheets]'"
|
||||
)
|
||||
self._spreadsheet_id = spreadsheet_id
|
||||
self._worksheet_name = worksheet
|
||||
self._credentials_path = credentials_path
|
||||
|
||||
def on_run_start(self, config: RunConfig) -> None:
|
||||
pass
|
||||
|
||||
def on_result(self, result: EvalResult, config: RunConfig) -> None:
|
||||
# No-op: summary-only to avoid excessive API calls.
|
||||
pass
|
||||
|
||||
def on_summary(self, summary: RunSummary) -> None:
|
||||
row = self._build_row(summary)
|
||||
try:
|
||||
gc = self._authorize()
|
||||
spreadsheet = gc.open_by_key(self._spreadsheet_id)
|
||||
try:
|
||||
ws = spreadsheet.worksheet(self._worksheet_name)
|
||||
except gspread.exceptions.WorksheetNotFound:
|
||||
ws = spreadsheet.add_worksheet(
|
||||
title=self._worksheet_name, rows=1000, cols=len(SHEET_COLUMNS),
|
||||
)
|
||||
# Ensure header row exists (idempotent)
|
||||
existing = ws.row_values(1)
|
||||
if not existing or existing[0] != SHEET_COLUMNS[0]:
|
||||
ws.update(range_name="A1", values=[SHEET_COLUMNS])
|
||||
ws.append_row(row, value_input_option="RAW")
|
||||
LOGGER.info("Appended summary row to Google Sheet")
|
||||
except Exception as exc:
|
||||
LOGGER.warning("SheetsTracker.on_summary failed: %s", exc)
|
||||
|
||||
def on_run_end(self) -> None:
|
||||
pass
|
||||
|
||||
def _authorize(self):
|
||||
"""Authenticate with Google Sheets API."""
|
||||
scopes = [
|
||||
"https://www.googleapis.com/auth/spreadsheets",
|
||||
"https://www.googleapis.com/auth/drive",
|
||||
]
|
||||
if self._credentials_path:
|
||||
creds = Credentials.from_service_account_file(
|
||||
self._credentials_path, scopes=scopes,
|
||||
)
|
||||
else:
|
||||
# Fall back to Application Default Credentials
|
||||
import google.auth
|
||||
|
||||
creds, _ = google.auth.default(scopes=scopes)
|
||||
return gspread.authorize(creds)
|
||||
|
||||
def _build_row(self, s: RunSummary) -> List[Any]:
|
||||
"""Build a flat row matching SHEET_COLUMNS order."""
|
||||
return [
|
||||
time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
|
||||
s.benchmark,
|
||||
s.model,
|
||||
s.backend,
|
||||
s.total_samples,
|
||||
s.scored_samples,
|
||||
s.correct,
|
||||
s.accuracy,
|
||||
s.errors,
|
||||
s.mean_latency_seconds,
|
||||
s.total_cost_usd,
|
||||
s.total_energy_joules,
|
||||
s.avg_power_watts,
|
||||
s.total_input_tokens,
|
||||
s.total_output_tokens,
|
||||
_stat_val(s.latency_stats, "mean"),
|
||||
_stat_val(s.latency_stats, "p90"),
|
||||
_stat_val(s.latency_stats, "p95"),
|
||||
_stat_val(s.energy_stats, "mean"),
|
||||
_stat_val(s.energy_stats, "p90"),
|
||||
_stat_val(s.throughput_stats, "mean"),
|
||||
_stat_val(s.throughput_stats, "p90"),
|
||||
_stat_val(s.ipw_stats, "mean"),
|
||||
_stat_val(s.ipj_stats, "mean"),
|
||||
_stat_val(s.mfu_stats, "mean"),
|
||||
_stat_val(s.mbu_stats, "mean"),
|
||||
_stat_val(s.ttft_stats, "mean"),
|
||||
_stat_val(s.ttft_stats, "p90"),
|
||||
_stat_val(s.gpu_utilization_stats, "mean"),
|
||||
]
|
||||
|
||||
|
||||
__all__ = ["SheetsTracker", "SHEET_COLUMNS"]
|
||||
@@ -0,0 +1,154 @@
|
||||
"""W&B experiment tracker for the eval framework."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from openjarvis.evals.core.tracker import ResultTracker
|
||||
from openjarvis.evals.core.types import EvalResult, MetricStats, RunConfig, RunSummary
|
||||
|
||||
try:
|
||||
import wandb
|
||||
except ImportError:
|
||||
wandb = None # type: ignore[assignment]
|
||||
|
||||
LOGGER = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _flatten_metric_stats(prefix: str, ms: Optional[MetricStats]) -> Dict[str, float]:
|
||||
"""Flatten a MetricStats into a dict with prefixed keys."""
|
||||
if ms is None:
|
||||
return {}
|
||||
return {
|
||||
f"{prefix}_mean": ms.mean,
|
||||
f"{prefix}_median": ms.median,
|
||||
f"{prefix}_min": ms.min,
|
||||
f"{prefix}_max": ms.max,
|
||||
f"{prefix}_std": ms.std,
|
||||
f"{prefix}_p90": ms.p90,
|
||||
f"{prefix}_p95": ms.p95,
|
||||
f"{prefix}_p99": ms.p99,
|
||||
}
|
||||
|
||||
|
||||
class WandbTracker(ResultTracker):
|
||||
"""Streams per-sample metrics to Weights & Biases."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
project: str,
|
||||
entity: str = "",
|
||||
tags: str = "",
|
||||
group: str = "",
|
||||
) -> None:
|
||||
if wandb is None:
|
||||
raise ImportError(
|
||||
"wandb is not installed. "
|
||||
"Install it with: pip install 'openjarvis[eval-wandb]'"
|
||||
)
|
||||
self._project = project
|
||||
self._entity = entity or None
|
||||
self._tags: List[str] = [
|
||||
t.strip() for t in tags.split(",") if t.strip()
|
||||
] if tags else []
|
||||
self._group = group or None
|
||||
self._run: Any = None
|
||||
self._step = 0
|
||||
|
||||
def on_run_start(self, config: RunConfig) -> None:
|
||||
run_config = {
|
||||
"benchmark": config.benchmark,
|
||||
"model": config.model,
|
||||
"backend": config.backend,
|
||||
"max_samples": config.max_samples,
|
||||
"max_workers": config.max_workers,
|
||||
"temperature": config.temperature,
|
||||
"max_tokens": config.max_tokens,
|
||||
"seed": config.seed,
|
||||
}
|
||||
if config.agent_name:
|
||||
run_config["agent_name"] = config.agent_name
|
||||
if config.tools:
|
||||
run_config["tools"] = ",".join(config.tools)
|
||||
if config.engine_key:
|
||||
run_config["engine_key"] = config.engine_key
|
||||
|
||||
self._run = wandb.init(
|
||||
project=self._project,
|
||||
entity=self._entity,
|
||||
tags=self._tags or None,
|
||||
group=self._group,
|
||||
config=run_config,
|
||||
reinit=True,
|
||||
)
|
||||
self._step = 0
|
||||
|
||||
def on_result(self, result: EvalResult, config: RunConfig) -> None:
|
||||
if self._run is None:
|
||||
return
|
||||
self._step += 1
|
||||
log_data: Dict[str, Any] = {
|
||||
"sample/is_correct": 1.0 if result.is_correct else 0.0,
|
||||
"sample/latency_seconds": result.latency_seconds,
|
||||
"sample/prompt_tokens": result.prompt_tokens,
|
||||
"sample/completion_tokens": result.completion_tokens,
|
||||
"sample/cost_usd": result.cost_usd,
|
||||
"sample/ttft": result.ttft,
|
||||
"sample/energy_joules": result.energy_joules,
|
||||
"sample/power_watts": result.power_watts,
|
||||
"sample/throughput_tok_per_sec": result.throughput_tok_per_sec,
|
||||
"sample/ipw": result.ipw,
|
||||
"sample/ipj": result.ipj,
|
||||
}
|
||||
if result.error:
|
||||
log_data["sample/has_error"] = 1.0
|
||||
wandb.log(log_data, step=self._step)
|
||||
|
||||
def on_summary(self, summary: RunSummary) -> None:
|
||||
if self._run is None:
|
||||
return
|
||||
flat: Dict[str, Any] = {
|
||||
"accuracy": summary.accuracy,
|
||||
"total_samples": summary.total_samples,
|
||||
"scored_samples": summary.scored_samples,
|
||||
"correct": summary.correct,
|
||||
"errors": summary.errors,
|
||||
"mean_latency_seconds": summary.mean_latency_seconds,
|
||||
"total_cost_usd": summary.total_cost_usd,
|
||||
"total_energy_joules": summary.total_energy_joules,
|
||||
"avg_power_watts": summary.avg_power_watts,
|
||||
"total_input_tokens": summary.total_input_tokens,
|
||||
"total_output_tokens": summary.total_output_tokens,
|
||||
}
|
||||
flat.update(_flatten_metric_stats("accuracy", summary.accuracy_stats))
|
||||
flat.update(_flatten_metric_stats("latency", summary.latency_stats))
|
||||
flat.update(_flatten_metric_stats("ttft", summary.ttft_stats))
|
||||
flat.update(_flatten_metric_stats("energy", summary.energy_stats))
|
||||
flat.update(_flatten_metric_stats("power", summary.power_stats))
|
||||
flat.update(
|
||||
_flatten_metric_stats("gpu_utilization", summary.gpu_utilization_stats)
|
||||
)
|
||||
flat.update(_flatten_metric_stats("throughput", summary.throughput_stats))
|
||||
flat.update(_flatten_metric_stats("mfu", summary.mfu_stats))
|
||||
flat.update(_flatten_metric_stats("mbu", summary.mbu_stats))
|
||||
flat.update(_flatten_metric_stats("ipw", summary.ipw_stats))
|
||||
flat.update(_flatten_metric_stats("ipj", summary.ipj_stats))
|
||||
flat.update(_flatten_metric_stats(
|
||||
"energy_per_output_token",
|
||||
summary.energy_per_output_token_stats,
|
||||
))
|
||||
flat.update(_flatten_metric_stats(
|
||||
"throughput_per_watt",
|
||||
summary.throughput_per_watt_stats,
|
||||
))
|
||||
flat.update(_flatten_metric_stats("itl", summary.itl_stats))
|
||||
wandb.run.summary.update(flat)
|
||||
|
||||
def on_run_end(self) -> None:
|
||||
if self._run is not None:
|
||||
self._run.finish()
|
||||
self._run = None
|
||||
|
||||
|
||||
__all__ = ["WandbTracker"]
|
||||
+18
-2
@@ -108,6 +108,12 @@ class MemoryHandle:
|
||||
self._backend.close()
|
||||
self._backend = None
|
||||
|
||||
def __enter__(self) -> MemoryHandle:
|
||||
return self
|
||||
|
||||
def __exit__(self, *exc: Any) -> None:
|
||||
self.close()
|
||||
|
||||
|
||||
class Jarvis:
|
||||
"""High-level OpenJarvis SDK.
|
||||
@@ -116,9 +122,13 @@ class Jarvis:
|
||||
|
||||
from openjarvis import Jarvis
|
||||
|
||||
with Jarvis() as j:
|
||||
response = j.ask("Hello, what can you do?")
|
||||
print(response)
|
||||
|
||||
# Or without context manager:
|
||||
j = Jarvis()
|
||||
response = j.ask("Hello, what can you do?")
|
||||
print(response)
|
||||
response = j.ask("Hello")
|
||||
j.close()
|
||||
"""
|
||||
|
||||
@@ -479,5 +489,11 @@ class Jarvis:
|
||||
self._audit_logger = None
|
||||
self._engine = None
|
||||
|
||||
def __enter__(self) -> Jarvis:
|
||||
return self
|
||||
|
||||
def __exit__(self, *exc: Any) -> None:
|
||||
self.close()
|
||||
|
||||
|
||||
__all__ = ["Jarvis", "JarvisSystem", "MemoryHandle", "SystemBuilder"]
|
||||
|
||||
@@ -618,6 +618,51 @@ async def learning_policy(request: Request):
|
||||
return result
|
||||
|
||||
|
||||
# ---- Speech routes ----
|
||||
|
||||
speech_router = APIRouter(prefix="/v1/speech", tags=["speech"])
|
||||
|
||||
|
||||
@speech_router.post("/transcribe")
|
||||
async def transcribe_speech(request: Request):
|
||||
"""Transcribe uploaded audio to text."""
|
||||
backend = getattr(request.app.state, "speech_backend", None)
|
||||
if backend is None:
|
||||
raise HTTPException(status_code=501, detail="Speech backend not configured")
|
||||
|
||||
form = await request.form()
|
||||
audio_file = form.get("file")
|
||||
if audio_file is None:
|
||||
raise HTTPException(status_code=400, detail="Missing 'file' field")
|
||||
|
||||
audio_bytes = await audio_file.read()
|
||||
language = form.get("language")
|
||||
|
||||
# Detect format from filename
|
||||
filename = getattr(audio_file, "filename", "audio.wav")
|
||||
ext = filename.rsplit(".", 1)[-1] if "." in filename else "wav"
|
||||
|
||||
result = backend.transcribe(audio_bytes, format=ext, language=language or None)
|
||||
return {
|
||||
"text": result.text,
|
||||
"language": result.language,
|
||||
"confidence": result.confidence,
|
||||
"duration_seconds": result.duration_seconds,
|
||||
}
|
||||
|
||||
|
||||
@speech_router.get("/health")
|
||||
async def speech_health(request: Request):
|
||||
"""Check if a speech backend is available."""
|
||||
backend = getattr(request.app.state, "speech_backend", None)
|
||||
if backend is None:
|
||||
return {"available": False, "reason": "No speech backend configured"}
|
||||
return {
|
||||
"available": backend.health(),
|
||||
"backend": backend.backend_id,
|
||||
}
|
||||
|
||||
|
||||
def include_all_routes(app) -> None:
|
||||
"""Include all extended API routers in a FastAPI app."""
|
||||
app.include_router(agents_router)
|
||||
@@ -630,6 +675,7 @@ def include_all_routes(app) -> None:
|
||||
app.include_router(metrics_router)
|
||||
app.include_router(websocket_router)
|
||||
app.include_router(learning_router)
|
||||
app.include_router(speech_router)
|
||||
|
||||
|
||||
__all__ = [
|
||||
@@ -644,4 +690,5 @@ __all__ = [
|
||||
"metrics_router",
|
||||
"websocket_router",
|
||||
"learning_router",
|
||||
"speech_router",
|
||||
]
|
||||
|
||||
@@ -52,6 +52,7 @@ def create_app(
|
||||
agent_name: str = "",
|
||||
channel_bridge=None,
|
||||
config=None,
|
||||
speech_backend=None,
|
||||
) -> FastAPI:
|
||||
"""Create and configure the FastAPI application.
|
||||
|
||||
@@ -116,6 +117,7 @@ def create_app(
|
||||
getattr(agent, "agent_id", None) if agent else None
|
||||
)
|
||||
app.state.channel_bridge = channel_bridge
|
||||
app.state.speech_backend = speech_backend
|
||||
app.state.session_start = time.time()
|
||||
|
||||
app.include_router(router)
|
||||
|
||||
@@ -0,0 +1,10 @@
|
||||
"""Speech subsystem — speech-to-text backends."""
|
||||
|
||||
import importlib
|
||||
|
||||
# Optional backends — each registers itself via @SpeechRegistry.register()
|
||||
for _mod in ("faster_whisper", "openai_whisper", "deepgram"):
|
||||
try:
|
||||
importlib.import_module(f".{_mod}", __name__)
|
||||
except ImportError:
|
||||
pass
|
||||
@@ -0,0 +1,75 @@
|
||||
"""Auto-discover available speech-to-text backends."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
from typing import TYPE_CHECKING, Optional
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from openjarvis.core.config import JarvisConfig
|
||||
from openjarvis.speech._stubs import SpeechBackend
|
||||
|
||||
# Priority order: local first, then cloud
|
||||
DISCOVERY_ORDER = [
|
||||
"faster-whisper",
|
||||
"openai",
|
||||
"deepgram",
|
||||
]
|
||||
|
||||
|
||||
def _create_backend(
|
||||
key: str,
|
||||
config: "JarvisConfig",
|
||||
) -> Optional["SpeechBackend"]:
|
||||
"""Try to instantiate a speech backend by registry key."""
|
||||
from openjarvis.core.registry import SpeechRegistry
|
||||
|
||||
if not SpeechRegistry.contains(key):
|
||||
return None
|
||||
|
||||
try:
|
||||
backend_cls = SpeechRegistry.get(key)
|
||||
|
||||
if key == "faster-whisper":
|
||||
return backend_cls(
|
||||
model_size=config.speech.model,
|
||||
device=config.speech.device,
|
||||
compute_type=config.speech.compute_type,
|
||||
)
|
||||
elif key == "openai":
|
||||
api_key = os.environ.get("OPENAI_API_KEY", "")
|
||||
if not api_key:
|
||||
return None
|
||||
return backend_cls(api_key=api_key)
|
||||
elif key == "deepgram":
|
||||
api_key = os.environ.get("DEEPGRAM_API_KEY", "")
|
||||
if not api_key:
|
||||
return None
|
||||
return backend_cls(api_key=api_key)
|
||||
else:
|
||||
return backend_cls()
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
def get_speech_backend(config: "JarvisConfig") -> Optional["SpeechBackend"]:
|
||||
"""Resolve the speech backend from config.
|
||||
|
||||
If ``config.speech.backend`` is ``"auto"``, tries backends in
|
||||
priority order and returns the first healthy one.
|
||||
"""
|
||||
# Trigger registration of built-in backends
|
||||
import openjarvis.speech # noqa: F401
|
||||
|
||||
backend_key = config.speech.backend
|
||||
|
||||
if backend_key != "auto":
|
||||
return _create_backend(backend_key, config)
|
||||
|
||||
# Auto-discovery: try each in priority order
|
||||
for key in DISCOVERY_ORDER:
|
||||
backend = _create_backend(key, config)
|
||||
if backend is not None:
|
||||
return backend
|
||||
|
||||
return None
|
||||
@@ -0,0 +1,55 @@
|
||||
"""Abstract base classes and data types for the speech subsystem."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from dataclasses import dataclass, field
|
||||
from typing import List, Optional
|
||||
|
||||
|
||||
@dataclass
|
||||
class Segment:
|
||||
"""A timed segment of transcribed text."""
|
||||
|
||||
text: str
|
||||
start: float # Start time in seconds
|
||||
end: float # End time in seconds
|
||||
confidence: Optional[float] = None
|
||||
|
||||
|
||||
@dataclass
|
||||
class TranscriptionResult:
|
||||
"""Result of a speech-to-text transcription."""
|
||||
|
||||
text: str
|
||||
language: Optional[str] = None
|
||||
confidence: Optional[float] = None
|
||||
duration_seconds: float = 0.0
|
||||
segments: List[Segment] = field(default_factory=list)
|
||||
|
||||
|
||||
class SpeechBackend(ABC):
|
||||
"""Abstract base class for speech-to-text backends."""
|
||||
|
||||
backend_id: str = ""
|
||||
|
||||
@abstractmethod
|
||||
def transcribe(
|
||||
self,
|
||||
audio: bytes,
|
||||
*,
|
||||
format: str = "wav",
|
||||
language: Optional[str] = None,
|
||||
) -> TranscriptionResult:
|
||||
"""Transcribe audio bytes to text."""
|
||||
|
||||
@abstractmethod
|
||||
def health(self) -> bool:
|
||||
"""Check if the backend is ready."""
|
||||
|
||||
@abstractmethod
|
||||
def supported_formats(self) -> List[str]:
|
||||
"""Return list of supported audio formats."""
|
||||
|
||||
|
||||
__all__ = ["Segment", "SpeechBackend", "TranscriptionResult"]
|
||||
@@ -0,0 +1,96 @@
|
||||
"""Deepgram speech-to-text backend (cloud)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
from typing import List, Optional
|
||||
|
||||
from openjarvis.core.registry import SpeechRegistry
|
||||
from openjarvis.speech._stubs import SpeechBackend, TranscriptionResult
|
||||
|
||||
try:
|
||||
from deepgram import DeepgramClient, PrerecordedOptions
|
||||
except ImportError:
|
||||
DeepgramClient = None # type: ignore[assignment, misc]
|
||||
PrerecordedOptions = None # type: ignore[assignment, misc]
|
||||
|
||||
|
||||
@SpeechRegistry.register("deepgram")
|
||||
class DeepgramSpeechBackend(SpeechBackend):
|
||||
"""Cloud speech-to-text using Deepgram API."""
|
||||
|
||||
backend_id = "deepgram"
|
||||
|
||||
def __init__(self, api_key: Optional[str] = None) -> None:
|
||||
self._api_key = api_key or os.environ.get("DEEPGRAM_API_KEY", "")
|
||||
self._client = None
|
||||
if self._api_key and DeepgramClient is not None:
|
||||
self._client = DeepgramClient(self._api_key)
|
||||
|
||||
def transcribe(
|
||||
self,
|
||||
audio: bytes,
|
||||
*,
|
||||
format: str = "wav",
|
||||
language: Optional[str] = None,
|
||||
) -> TranscriptionResult:
|
||||
"""Transcribe audio using Deepgram's API."""
|
||||
if self._client is None:
|
||||
raise RuntimeError("Deepgram client not initialized (missing API key?)")
|
||||
|
||||
mime_map = {
|
||||
"wav": "audio/wav",
|
||||
"mp3": "audio/mpeg",
|
||||
"ogg": "audio/ogg",
|
||||
"flac": "audio/flac",
|
||||
"webm": "audio/webm",
|
||||
"m4a": "audio/mp4",
|
||||
}
|
||||
mime_type = mime_map.get(format, "audio/wav")
|
||||
|
||||
options_kwargs: dict = {"model": "nova-2", "smart_format": True}
|
||||
if language:
|
||||
options_kwargs["language"] = language
|
||||
else:
|
||||
options_kwargs["detect_language"] = True
|
||||
|
||||
payload = {"buffer": audio, "mimetype": mime_type}
|
||||
|
||||
if PrerecordedOptions is not None:
|
||||
options = PrerecordedOptions(**options_kwargs)
|
||||
else:
|
||||
options = options_kwargs
|
||||
|
||||
response = self._client.listen.rest.v("1").transcribe_file(
|
||||
payload, options,
|
||||
)
|
||||
|
||||
# Extract transcript from response
|
||||
channels = response.results.channels
|
||||
if channels and channels[0].alternatives:
|
||||
alt = channels[0].alternatives[0]
|
||||
text = alt.transcript
|
||||
confidence = getattr(alt, "confidence", None)
|
||||
else:
|
||||
text = ""
|
||||
confidence = None
|
||||
|
||||
detected_lang = None
|
||||
if channels:
|
||||
detected_lang = getattr(channels[0], "detected_language", None)
|
||||
|
||||
duration = getattr(response.metadata, "duration", 0.0)
|
||||
|
||||
return TranscriptionResult(
|
||||
text=text,
|
||||
language=detected_lang,
|
||||
confidence=confidence,
|
||||
duration_seconds=duration,
|
||||
segments=[],
|
||||
)
|
||||
|
||||
def health(self) -> bool:
|
||||
return self._client is not None and bool(self._api_key)
|
||||
|
||||
def supported_formats(self) -> List[str]:
|
||||
return ["wav", "mp3", "ogg", "flac", "webm", "m4a"]
|
||||
@@ -0,0 +1,100 @@
|
||||
"""Faster-Whisper speech-to-text backend (local, CTranslate2-based)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import tempfile
|
||||
from typing import List, Optional
|
||||
|
||||
from openjarvis.core.registry import SpeechRegistry
|
||||
from openjarvis.speech._stubs import Segment, SpeechBackend, TranscriptionResult
|
||||
|
||||
try:
|
||||
from faster_whisper import WhisperModel
|
||||
except ImportError:
|
||||
WhisperModel = None # type: ignore[assignment, misc]
|
||||
|
||||
|
||||
@SpeechRegistry.register("faster-whisper")
|
||||
class FasterWhisperBackend(SpeechBackend):
|
||||
"""Local speech-to-text using Faster-Whisper (CTranslate2)."""
|
||||
|
||||
backend_id = "faster-whisper"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_size: str = "base",
|
||||
device: str = "auto",
|
||||
compute_type: str = "float16",
|
||||
) -> None:
|
||||
self._model_size = model_size
|
||||
self._device = device
|
||||
self._compute_type = compute_type
|
||||
self._model: Optional[WhisperModel] = None
|
||||
|
||||
def _ensure_model(self) -> WhisperModel:
|
||||
"""Lazy-load the Whisper model on first use."""
|
||||
if self._model is None:
|
||||
if WhisperModel is None:
|
||||
raise ImportError(
|
||||
"faster-whisper is not installed. "
|
||||
"Install with: pip install 'openjarvis[speech]'"
|
||||
)
|
||||
self._model = WhisperModel(
|
||||
self._model_size,
|
||||
device=self._device,
|
||||
compute_type=self._compute_type,
|
||||
)
|
||||
return self._model
|
||||
|
||||
def transcribe(
|
||||
self,
|
||||
audio: bytes,
|
||||
*,
|
||||
format: str = "wav",
|
||||
language: Optional[str] = None,
|
||||
) -> TranscriptionResult:
|
||||
"""Transcribe audio bytes using Faster-Whisper."""
|
||||
model = self._ensure_model()
|
||||
|
||||
# Write audio to a temp file (faster-whisper needs a file path)
|
||||
suffix = f".{format}" if not format.startswith(".") else format
|
||||
with tempfile.NamedTemporaryFile(suffix=suffix, delete=True) as tmp:
|
||||
tmp.write(audio)
|
||||
tmp.flush()
|
||||
|
||||
kwargs = {}
|
||||
if language:
|
||||
kwargs["language"] = language
|
||||
|
||||
segments_iter, info = model.transcribe(tmp.name, **kwargs)
|
||||
segments_list = list(segments_iter)
|
||||
|
||||
# Build result
|
||||
text = "".join(seg.text for seg in segments_list).strip()
|
||||
segments = [
|
||||
Segment(
|
||||
text=seg.text.strip(),
|
||||
start=seg.start,
|
||||
end=seg.end,
|
||||
confidence=None,
|
||||
)
|
||||
for seg in segments_list
|
||||
]
|
||||
|
||||
return TranscriptionResult(
|
||||
text=text,
|
||||
language=getattr(info, "language", None),
|
||||
confidence=getattr(info, "language_probability", None),
|
||||
duration_seconds=getattr(info, "duration", 0.0),
|
||||
segments=segments,
|
||||
)
|
||||
|
||||
def health(self) -> bool:
|
||||
"""Check if model is loaded or loadable."""
|
||||
if self._model is not None:
|
||||
return True
|
||||
return WhisperModel is not None
|
||||
|
||||
def supported_formats(self) -> List[str]:
|
||||
"""Supported audio formats (same as ffmpeg/Whisper)."""
|
||||
return ["wav", "mp3", "m4a", "ogg", "flac", "webm"]
|
||||
@@ -0,0 +1,64 @@
|
||||
"""OpenAI Whisper API speech-to-text backend (cloud)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import io
|
||||
import os
|
||||
from typing import List, Optional
|
||||
|
||||
from openjarvis.core.registry import SpeechRegistry
|
||||
from openjarvis.speech._stubs import SpeechBackend, TranscriptionResult
|
||||
|
||||
try:
|
||||
from openai import OpenAI
|
||||
except ImportError:
|
||||
OpenAI = None # type: ignore[assignment, misc]
|
||||
|
||||
|
||||
@SpeechRegistry.register("openai")
|
||||
class OpenAIWhisperBackend(SpeechBackend):
|
||||
"""Cloud speech-to-text using OpenAI Whisper API."""
|
||||
|
||||
backend_id = "openai"
|
||||
|
||||
def __init__(self, api_key: Optional[str] = None) -> None:
|
||||
self._api_key = api_key or os.environ.get("OPENAI_API_KEY", "")
|
||||
self._client: Optional[OpenAI] = None
|
||||
if self._api_key and OpenAI is not None:
|
||||
self._client = OpenAI(api_key=self._api_key)
|
||||
|
||||
def transcribe(
|
||||
self,
|
||||
audio: bytes,
|
||||
*,
|
||||
format: str = "wav",
|
||||
language: Optional[str] = None,
|
||||
) -> TranscriptionResult:
|
||||
"""Transcribe audio using OpenAI's Whisper API."""
|
||||
if self._client is None:
|
||||
raise RuntimeError("OpenAI client not initialized (missing API key?)")
|
||||
|
||||
ext = format if not format.startswith(".") else format[1:]
|
||||
audio_file = io.BytesIO(audio)
|
||||
audio_file.name = f"audio.{ext}"
|
||||
|
||||
kwargs: dict = {"model": "whisper-1", "file": audio_file}
|
||||
if language:
|
||||
kwargs["language"] = language
|
||||
kwargs["response_format"] = "verbose_json"
|
||||
|
||||
response = self._client.audio.transcriptions.create(**kwargs)
|
||||
|
||||
return TranscriptionResult(
|
||||
text=getattr(response, "text", str(response)),
|
||||
language=getattr(response, "language", None),
|
||||
confidence=None,
|
||||
duration_seconds=getattr(response, "duration", 0.0),
|
||||
segments=[],
|
||||
)
|
||||
|
||||
def health(self) -> bool:
|
||||
return self._client is not None and bool(self._api_key)
|
||||
|
||||
def supported_formats(self) -> List[str]:
|
||||
return ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"]
|
||||
@@ -40,6 +40,7 @@ class JarvisSystem:
|
||||
session_store: Optional[Any] = None # SessionStore
|
||||
capability_policy: Optional[Any] = None # CapabilityPolicy
|
||||
operator_manager: Optional[Any] = None # OperatorManager
|
||||
speech_backend: Optional[Any] = None # SpeechBackend
|
||||
_learning_orchestrator: Optional[Any] = None # LearningOrchestrator
|
||||
|
||||
def ask(
|
||||
@@ -274,6 +275,12 @@ class JarvisSystem:
|
||||
if self.trace_store and hasattr(self.trace_store, "close"):
|
||||
self.trace_store.close()
|
||||
|
||||
def __enter__(self) -> JarvisSystem:
|
||||
return self
|
||||
|
||||
def __exit__(self, *exc: Any) -> None:
|
||||
self.close()
|
||||
|
||||
|
||||
class SystemBuilder:
|
||||
"""Config-driven fluent builder for JarvisSystem."""
|
||||
@@ -304,6 +311,7 @@ class SystemBuilder:
|
||||
self._scheduler: Optional[bool] = None
|
||||
self._workflow: Optional[bool] = None
|
||||
self._sessions: Optional[bool] = None
|
||||
self._speech: Optional[bool] = None
|
||||
|
||||
def engine(self, key: str) -> SystemBuilder:
|
||||
self._engine_key = key
|
||||
@@ -345,6 +353,10 @@ class SystemBuilder:
|
||||
self._sessions = enabled
|
||||
return self
|
||||
|
||||
def speech(self, enabled: bool) -> SystemBuilder:
|
||||
self._speech = enabled
|
||||
return self
|
||||
|
||||
def event_bus(self, bus: EventBus) -> SystemBuilder:
|
||||
self._bus = bus
|
||||
return self
|
||||
@@ -447,6 +459,16 @@ class SystemBuilder:
|
||||
# Set up learning orchestrator (when training is enabled)
|
||||
learning_orchestrator = self._setup_learning_orchestrator(config)
|
||||
|
||||
# Set up speech backend
|
||||
speech_backend = None
|
||||
speech_enabled = self._speech if self._speech is not None else True
|
||||
if speech_enabled:
|
||||
try:
|
||||
from openjarvis.speech._discovery import get_speech_backend
|
||||
speech_backend = get_speech_backend(config)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
system = JarvisSystem(
|
||||
config=config,
|
||||
bus=bus,
|
||||
@@ -466,6 +488,7 @@ class SystemBuilder:
|
||||
workflow_engine=workflow_engine,
|
||||
session_store=session_store,
|
||||
capability_policy=capability_policy,
|
||||
speech_backend=speech_backend,
|
||||
)
|
||||
system._learning_orchestrator = learning_orchestrator
|
||||
return system
|
||||
|
||||
@@ -17,6 +17,7 @@ from openjarvis.core.registry import (
|
||||
MemoryRegistry,
|
||||
ModelRegistry,
|
||||
RouterPolicyRegistry,
|
||||
SpeechRegistry,
|
||||
ToolRegistry,
|
||||
)
|
||||
|
||||
@@ -32,6 +33,7 @@ def _clean_registries() -> None:
|
||||
RouterPolicyRegistry.clear()
|
||||
BenchmarkRegistry.clear()
|
||||
ChannelRegistry.clear()
|
||||
SpeechRegistry.clear()
|
||||
reset_event_bus()
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,333 @@
|
||||
"""Tests for eval result trackers (W&B + Google Sheets)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
from typing import List
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
import pytest
|
||||
|
||||
from openjarvis.evals.core.tracker import ResultTracker
|
||||
from openjarvis.evals.core.types import EvalResult, RunConfig, RunSummary
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Test double
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class RecordingTracker(ResultTracker):
|
||||
"""Records all lifecycle calls for testing."""
|
||||
|
||||
def __init__(self) -> None:
|
||||
self.calls: List[str] = []
|
||||
self.results: List[EvalResult] = []
|
||||
self.summary: RunSummary | None = None
|
||||
|
||||
def on_run_start(self, config: RunConfig) -> None:
|
||||
self.calls.append("on_run_start")
|
||||
|
||||
def on_result(self, result: EvalResult, config: RunConfig) -> None:
|
||||
self.calls.append("on_result")
|
||||
self.results.append(result)
|
||||
|
||||
def on_summary(self, summary: RunSummary) -> None:
|
||||
self.calls.append("on_summary")
|
||||
self.summary = summary
|
||||
|
||||
def on_run_end(self) -> None:
|
||||
self.calls.append("on_run_end")
|
||||
|
||||
|
||||
class CrashingTracker(ResultTracker):
|
||||
"""Raises on every lifecycle call."""
|
||||
|
||||
def on_run_start(self, config: RunConfig) -> None:
|
||||
raise RuntimeError("boom start")
|
||||
|
||||
def on_result(self, result: EvalResult, config: RunConfig) -> None:
|
||||
raise RuntimeError("boom result")
|
||||
|
||||
def on_summary(self, summary: RunSummary) -> None:
|
||||
raise RuntimeError("boom summary")
|
||||
|
||||
def on_run_end(self) -> None:
|
||||
raise RuntimeError("boom end")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _make_config(**overrides) -> RunConfig:
|
||||
defaults = dict(benchmark="test", backend="jarvis-direct", model="test-model")
|
||||
defaults.update(overrides)
|
||||
return RunConfig(**defaults)
|
||||
|
||||
|
||||
def _make_summary(**overrides) -> RunSummary:
|
||||
defaults = dict(
|
||||
benchmark="test",
|
||||
category="chat",
|
||||
backend="jarvis-direct",
|
||||
model="test-model",
|
||||
total_samples=10,
|
||||
scored_samples=10,
|
||||
correct=8,
|
||||
accuracy=0.8,
|
||||
errors=0,
|
||||
mean_latency_seconds=1.0,
|
||||
total_cost_usd=0.01,
|
||||
)
|
||||
defaults.update(overrides)
|
||||
return RunSummary(**defaults)
|
||||
|
||||
|
||||
def _make_result(**overrides) -> EvalResult:
|
||||
defaults = dict(record_id="r1", model_answer="answer", is_correct=True)
|
||||
defaults.update(overrides)
|
||||
return EvalResult(**defaults)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# RecordingTracker through EvalRunner lifecycle
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestRecordingTrackerIntegration:
|
||||
"""Test that trackers receive all lifecycle calls through EvalRunner."""
|
||||
|
||||
def test_tracker_lifecycle(self, tmp_path):
|
||||
"""RecordingTracker receives start, result, summary, end calls."""
|
||||
from openjarvis.evals.core.runner import EvalRunner
|
||||
|
||||
# Minimal stubs
|
||||
dataset = MagicMock()
|
||||
record = MagicMock()
|
||||
record.record_id = "r1"
|
||||
record.problem = "What is 1+1?"
|
||||
record.reference = "2"
|
||||
record.category = "chat"
|
||||
record.subject = ""
|
||||
dataset.load = MagicMock()
|
||||
dataset.iter_records = MagicMock(return_value=[record])
|
||||
|
||||
backend = MagicMock()
|
||||
backend.generate_full = MagicMock(return_value={
|
||||
"content": "2",
|
||||
"usage": {"prompt_tokens": 10, "completion_tokens": 5},
|
||||
"latency_seconds": 0.5,
|
||||
})
|
||||
|
||||
scorer = MagicMock()
|
||||
scorer.score = MagicMock(return_value=(True, {}))
|
||||
|
||||
tracker = RecordingTracker()
|
||||
config = _make_config(output_path=str(tmp_path / "out.jsonl"))
|
||||
|
||||
runner = EvalRunner(config, dataset, backend, scorer, trackers=[tracker])
|
||||
runner.run()
|
||||
|
||||
assert "on_run_start" in tracker.calls
|
||||
assert "on_result" in tracker.calls
|
||||
assert "on_summary" in tracker.calls
|
||||
assert "on_run_end" in tracker.calls
|
||||
# Order matters
|
||||
assert tracker.calls.index("on_run_start") < tracker.calls.index("on_result")
|
||||
assert tracker.calls.index("on_result") < tracker.calls.index("on_summary")
|
||||
assert tracker.calls.index("on_summary") < tracker.calls.index("on_run_end")
|
||||
assert len(tracker.results) == 1
|
||||
assert tracker.summary is not None
|
||||
|
||||
def test_crashing_tracker_does_not_abort(self, tmp_path):
|
||||
"""A tracker that raises exceptions must not prevent JSONL output."""
|
||||
from openjarvis.evals.core.runner import EvalRunner
|
||||
|
||||
dataset = MagicMock()
|
||||
record = MagicMock()
|
||||
record.record_id = "r1"
|
||||
record.problem = "What?"
|
||||
record.reference = "yes"
|
||||
record.category = "chat"
|
||||
record.subject = ""
|
||||
dataset.load = MagicMock()
|
||||
dataset.iter_records = MagicMock(return_value=[record])
|
||||
|
||||
backend = MagicMock()
|
||||
backend.generate_full = MagicMock(return_value={
|
||||
"content": "yes",
|
||||
"usage": {},
|
||||
"latency_seconds": 0.1,
|
||||
})
|
||||
|
||||
scorer = MagicMock()
|
||||
scorer.score = MagicMock(return_value=(True, {}))
|
||||
|
||||
output = tmp_path / "out.jsonl"
|
||||
config = _make_config(output_path=str(output))
|
||||
|
||||
crasher = CrashingTracker()
|
||||
runner = EvalRunner(config, dataset, backend, scorer, trackers=[crasher])
|
||||
summary = runner.run()
|
||||
|
||||
# Run completed, JSONL written despite crashing tracker
|
||||
assert summary.total_samples == 1
|
||||
assert output.exists()
|
||||
assert output.read_text().strip() != ""
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# WandbTracker unit tests
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestWandbTracker:
|
||||
"""Unit tests for WandbTracker (mocked wandb module)."""
|
||||
|
||||
def test_import_error_when_wandb_missing(self):
|
||||
"""WandbTracker raises ImportError when wandb is not installed."""
|
||||
with patch.dict(sys.modules, {"wandb": None}):
|
||||
import openjarvis.evals.trackers.wandb_tracker as wt_mod
|
||||
original = wt_mod.wandb
|
||||
wt_mod.wandb = None
|
||||
try:
|
||||
with pytest.raises(ImportError, match="wandb is not installed"):
|
||||
wt_mod.WandbTracker(project="test")
|
||||
finally:
|
||||
wt_mod.wandb = original
|
||||
|
||||
def test_on_result_calls_wandb_log(self):
|
||||
"""on_result calls wandb.log with sample/ prefixed keys."""
|
||||
import openjarvis.evals.trackers.wandb_tracker as wt_mod
|
||||
|
||||
mock_wandb = MagicMock()
|
||||
mock_run = MagicMock()
|
||||
mock_wandb.init = MagicMock(return_value=mock_run)
|
||||
original = wt_mod.wandb
|
||||
wt_mod.wandb = mock_wandb
|
||||
try:
|
||||
tracker = wt_mod.WandbTracker(project="test-proj")
|
||||
config = _make_config()
|
||||
tracker.on_run_start(config)
|
||||
|
||||
result = _make_result(latency_seconds=0.5, energy_joules=1.0)
|
||||
tracker.on_result(result, config)
|
||||
|
||||
mock_wandb.log.assert_called_once()
|
||||
call_args = mock_wandb.log.call_args
|
||||
log_data = call_args[0][0]
|
||||
assert "sample/is_correct" in log_data
|
||||
assert "sample/latency_seconds" in log_data
|
||||
assert log_data["sample/is_correct"] == 1.0
|
||||
assert call_args[1]["step"] == 1
|
||||
|
||||
tracker.on_run_end()
|
||||
finally:
|
||||
wt_mod.wandb = original
|
||||
|
||||
def test_on_summary_updates_run_summary(self):
|
||||
"""on_summary calls wandb.run.summary.update with flat dict."""
|
||||
import openjarvis.evals.trackers.wandb_tracker as wt_mod
|
||||
|
||||
mock_wandb = MagicMock()
|
||||
mock_run = MagicMock()
|
||||
mock_wandb.init = MagicMock(return_value=mock_run)
|
||||
mock_wandb.run = mock_run
|
||||
original = wt_mod.wandb
|
||||
wt_mod.wandb = mock_wandb
|
||||
try:
|
||||
tracker = wt_mod.WandbTracker(project="test-proj")
|
||||
config = _make_config()
|
||||
tracker.on_run_start(config)
|
||||
|
||||
summary = _make_summary()
|
||||
tracker.on_summary(summary)
|
||||
|
||||
mock_run.summary.update.assert_called_once()
|
||||
flat = mock_run.summary.update.call_args[0][0]
|
||||
assert flat["accuracy"] == 0.8
|
||||
assert flat["total_samples"] == 10
|
||||
|
||||
tracker.on_run_end()
|
||||
finally:
|
||||
wt_mod.wandb = original
|
||||
|
||||
def test_reinit_true_for_suite_mode(self):
|
||||
"""wandb.init is called with reinit=True."""
|
||||
import openjarvis.evals.trackers.wandb_tracker as wt_mod
|
||||
|
||||
mock_wandb = MagicMock()
|
||||
mock_run = MagicMock()
|
||||
mock_wandb.init = MagicMock(return_value=mock_run)
|
||||
original = wt_mod.wandb
|
||||
wt_mod.wandb = mock_wandb
|
||||
try:
|
||||
tracker = wt_mod.WandbTracker(project="test-proj", entity="team")
|
||||
config = _make_config()
|
||||
tracker.on_run_start(config)
|
||||
|
||||
call_kwargs = mock_wandb.init.call_args[1]
|
||||
assert call_kwargs["reinit"] is True
|
||||
assert call_kwargs["project"] == "test-proj"
|
||||
assert call_kwargs["entity"] == "team"
|
||||
|
||||
tracker.on_run_end()
|
||||
finally:
|
||||
wt_mod.wandb = original
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# SheetsTracker unit tests
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestSheetsTracker:
|
||||
"""Unit tests for SheetsTracker."""
|
||||
|
||||
def test_import_error_when_gspread_missing(self):
|
||||
"""SheetsTracker raises ImportError when gspread not installed."""
|
||||
import openjarvis.evals.trackers.sheets_tracker as st_mod
|
||||
original = st_mod.gspread
|
||||
st_mod.gspread = None
|
||||
try:
|
||||
with pytest.raises(ImportError, match="gspread is not installed"):
|
||||
st_mod.SheetsTracker(spreadsheet_id="abc123")
|
||||
finally:
|
||||
st_mod.gspread = original
|
||||
|
||||
def test_on_result_is_noop(self):
|
||||
"""on_result does nothing (no API calls for individual samples)."""
|
||||
import openjarvis.evals.trackers.sheets_tracker as st_mod
|
||||
|
||||
mock_gspread = MagicMock()
|
||||
original = st_mod.gspread
|
||||
st_mod.gspread = mock_gspread
|
||||
original_creds = st_mod.Credentials
|
||||
st_mod.Credentials = MagicMock()
|
||||
try:
|
||||
tracker = st_mod.SheetsTracker(spreadsheet_id="abc123")
|
||||
result = _make_result()
|
||||
config = _make_config()
|
||||
|
||||
# on_result should not call any external API
|
||||
tracker.on_result(result, config)
|
||||
mock_gspread.authorize.assert_not_called()
|
||||
finally:
|
||||
st_mod.gspread = original
|
||||
st_mod.Credentials = original_creds
|
||||
|
||||
def test_build_row_matches_columns(self):
|
||||
"""_build_row returns a list matching SHEET_COLUMNS length."""
|
||||
import openjarvis.evals.trackers.sheets_tracker as st_mod
|
||||
|
||||
mock_gspread = MagicMock()
|
||||
original = st_mod.gspread
|
||||
st_mod.gspread = mock_gspread
|
||||
original_creds = st_mod.Credentials
|
||||
st_mod.Credentials = MagicMock()
|
||||
try:
|
||||
tracker = st_mod.SheetsTracker(spreadsheet_id="abc123")
|
||||
summary = _make_summary()
|
||||
row = tracker._build_row(summary)
|
||||
assert len(row) == len(st_mod.SHEET_COLUMNS), (
|
||||
f"Row length {len(row)} != columns length {len(st_mod.SHEET_COLUMNS)}"
|
||||
)
|
||||
finally:
|
||||
st_mod.gspread = original
|
||||
st_mod.Credentials = original_creds
|
||||
@@ -0,0 +1,84 @@
|
||||
"""Tests for speech API endpoints."""
|
||||
|
||||
import pytest
|
||||
|
||||
fastapi = pytest.importorskip("fastapi")
|
||||
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
from fastapi.testclient import TestClient
|
||||
|
||||
from openjarvis.speech._stubs import TranscriptionResult
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_speech_backend():
|
||||
backend = MagicMock()
|
||||
backend.backend_id = "mock"
|
||||
backend.health.return_value = True
|
||||
backend.transcribe.return_value = TranscriptionResult(
|
||||
text="Hello world",
|
||||
language="en",
|
||||
confidence=0.95,
|
||||
duration_seconds=1.5,
|
||||
segments=[],
|
||||
)
|
||||
return backend
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def app_with_speech(mock_speech_backend):
|
||||
from fastapi import FastAPI
|
||||
from openjarvis.server.api_routes import speech_router
|
||||
|
||||
app = FastAPI()
|
||||
app.state.speech_backend = mock_speech_backend
|
||||
app.include_router(speech_router)
|
||||
return app
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def client(app_with_speech):
|
||||
return TestClient(app_with_speech)
|
||||
|
||||
|
||||
def test_transcribe_endpoint(client, mock_speech_backend):
|
||||
response = client.post(
|
||||
"/v1/speech/transcribe",
|
||||
files={"file": ("test.wav", b"fake audio data", "audio/wav")},
|
||||
)
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
assert data["text"] == "Hello world"
|
||||
assert data["language"] == "en"
|
||||
assert data["confidence"] == 0.95
|
||||
assert data["duration_seconds"] == 1.5
|
||||
|
||||
|
||||
def test_transcribe_no_file(client):
|
||||
response = client.post("/v1/speech/transcribe")
|
||||
assert response.status_code == 400 or response.status_code == 422
|
||||
|
||||
|
||||
def test_health_endpoint(client):
|
||||
response = client.get("/v1/speech/health")
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
assert data["available"] is True
|
||||
assert data["backend"] == "mock"
|
||||
|
||||
|
||||
def test_health_no_backend():
|
||||
from fastapi import FastAPI
|
||||
from fastapi.testclient import TestClient
|
||||
from openjarvis.server.api_routes import speech_router
|
||||
|
||||
app = FastAPI()
|
||||
app.state.speech_backend = None
|
||||
app.include_router(speech_router)
|
||||
client = TestClient(app)
|
||||
|
||||
response = client.get("/v1/speech/health")
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
assert data["available"] is False
|
||||
@@ -0,0 +1,19 @@
|
||||
"""Tests for speech configuration."""
|
||||
|
||||
from openjarvis.core.config import JarvisConfig, SpeechConfig
|
||||
|
||||
|
||||
def test_speech_config_defaults():
|
||||
cfg = SpeechConfig()
|
||||
assert cfg.backend == "auto"
|
||||
assert cfg.model == "base"
|
||||
assert cfg.language == ""
|
||||
assert cfg.device == "auto"
|
||||
assert cfg.compute_type == "float16"
|
||||
|
||||
|
||||
def test_jarvis_config_has_speech():
|
||||
cfg = JarvisConfig()
|
||||
assert hasattr(cfg, "speech")
|
||||
assert isinstance(cfg.speech, SpeechConfig)
|
||||
assert cfg.speech.backend == "auto"
|
||||
@@ -0,0 +1,61 @@
|
||||
"""Tests for Deepgram speech backend."""
|
||||
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
import pytest
|
||||
|
||||
from openjarvis.core.registry import SpeechRegistry
|
||||
from openjarvis.speech._stubs import TranscriptionResult
|
||||
from openjarvis.speech.deepgram import DeepgramSpeechBackend
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def _register_deepgram():
|
||||
"""Re-register after any registry clear."""
|
||||
if not SpeechRegistry.contains("deepgram"):
|
||||
SpeechRegistry.register_value("deepgram", DeepgramSpeechBackend)
|
||||
|
||||
|
||||
def test_deepgram_registers():
|
||||
assert SpeechRegistry.contains("deepgram")
|
||||
|
||||
|
||||
def test_deepgram_transcribe():
|
||||
mock_client = MagicMock()
|
||||
mock_result = MagicMock()
|
||||
mock_channel = MagicMock()
|
||||
mock_alternative = MagicMock()
|
||||
mock_alternative.transcript = "Hello from Deepgram"
|
||||
mock_alternative.confidence = 0.92
|
||||
mock_channel.alternatives = [mock_alternative]
|
||||
mock_channel.detected_language = "en"
|
||||
mock_result.results.channels = [mock_channel]
|
||||
mock_result.metadata.duration = 1.8
|
||||
mock_client.listen.rest.v.return_value.transcribe_file.return_value = mock_result
|
||||
|
||||
with patch("openjarvis.speech.deepgram.DeepgramClient", return_value=mock_client):
|
||||
from openjarvis.speech.deepgram import DeepgramSpeechBackend
|
||||
|
||||
backend = DeepgramSpeechBackend(api_key="test-key")
|
||||
result = backend.transcribe(b"fake audio", format="wav")
|
||||
|
||||
assert isinstance(result, TranscriptionResult)
|
||||
assert result.text == "Hello from Deepgram"
|
||||
|
||||
|
||||
def test_deepgram_health():
|
||||
with patch("openjarvis.speech.deepgram.DeepgramClient"):
|
||||
from openjarvis.speech.deepgram import DeepgramSpeechBackend
|
||||
|
||||
backend = DeepgramSpeechBackend(api_key="test-key")
|
||||
assert backend.health() is True
|
||||
|
||||
|
||||
def test_deepgram_health_no_key():
|
||||
with patch("openjarvis.speech.deepgram.DeepgramClient"):
|
||||
from openjarvis.speech.deepgram import DeepgramSpeechBackend
|
||||
|
||||
backend = DeepgramSpeechBackend.__new__(DeepgramSpeechBackend)
|
||||
backend._client = None
|
||||
backend._api_key = ""
|
||||
assert backend.health() is False
|
||||
@@ -0,0 +1,44 @@
|
||||
"""Tests for speech backend auto-discovery."""
|
||||
|
||||
from unittest.mock import patch
|
||||
|
||||
from openjarvis.core.config import JarvisConfig
|
||||
|
||||
|
||||
def test_get_speech_backend_explicit():
|
||||
"""Explicit backend selection works."""
|
||||
from openjarvis.speech._discovery import get_speech_backend
|
||||
|
||||
config = JarvisConfig()
|
||||
config.speech.backend = "faster-whisper"
|
||||
|
||||
with patch("openjarvis.speech._discovery._create_backend") as mock_create:
|
||||
mock_backend = type("MockBackend", (), {
|
||||
"backend_id": "faster-whisper",
|
||||
"health": lambda self: True,
|
||||
})()
|
||||
mock_create.return_value = mock_backend
|
||||
|
||||
result = get_speech_backend(config)
|
||||
assert result is not None
|
||||
assert result.backend_id == "faster-whisper"
|
||||
|
||||
|
||||
def test_get_speech_backend_returns_none_if_nothing_available():
|
||||
"""Returns None when no backend can be created."""
|
||||
from openjarvis.speech._discovery import get_speech_backend
|
||||
|
||||
config = JarvisConfig()
|
||||
config.speech.backend = "nonexistent"
|
||||
|
||||
result = get_speech_backend(config)
|
||||
assert result is None
|
||||
|
||||
|
||||
def test_auto_discovery_priority():
|
||||
"""Auto mode tries backends in priority order."""
|
||||
from openjarvis.speech._discovery import DISCOVERY_ORDER
|
||||
|
||||
assert DISCOVERY_ORDER[0] == "faster-whisper"
|
||||
assert "openai" in DISCOVERY_ORDER
|
||||
assert "deepgram" in DISCOVERY_ORDER
|
||||
@@ -0,0 +1,78 @@
|
||||
"""Tests for Faster-Whisper speech backend."""
|
||||
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
import pytest
|
||||
|
||||
from openjarvis.core.registry import SpeechRegistry
|
||||
from openjarvis.speech.faster_whisper import FasterWhisperBackend
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def _register_faster_whisper():
|
||||
"""Re-register after any registry clear."""
|
||||
if not SpeechRegistry.contains("faster-whisper"):
|
||||
SpeechRegistry.register_value("faster-whisper", FasterWhisperBackend)
|
||||
|
||||
|
||||
def test_faster_whisper_backend_registers():
|
||||
"""Backend registers itself in SpeechRegistry."""
|
||||
assert SpeechRegistry.contains("faster-whisper")
|
||||
|
||||
|
||||
def test_faster_whisper_transcribe():
|
||||
"""Transcribe returns a TranscriptionResult."""
|
||||
from openjarvis.speech._stubs import TranscriptionResult
|
||||
|
||||
mock_model = MagicMock()
|
||||
mock_segment = MagicMock()
|
||||
mock_segment.text = " Hello world"
|
||||
mock_segment.start = 0.0
|
||||
mock_segment.end = 1.2
|
||||
mock_segment.avg_logprob = -0.3
|
||||
|
||||
mock_info = MagicMock()
|
||||
mock_info.language = "en"
|
||||
mock_info.language_probability = 0.95
|
||||
mock_info.duration = 1.5
|
||||
|
||||
mock_model.transcribe.return_value = ([mock_segment], mock_info)
|
||||
|
||||
with patch(
|
||||
"openjarvis.speech.faster_whisper.WhisperModel",
|
||||
return_value=mock_model,
|
||||
):
|
||||
from openjarvis.speech.faster_whisper import FasterWhisperBackend
|
||||
|
||||
backend = FasterWhisperBackend(model_size="base", device="cpu")
|
||||
result = backend.transcribe(b"fake audio bytes")
|
||||
|
||||
assert isinstance(result, TranscriptionResult)
|
||||
assert result.text == "Hello world"
|
||||
assert result.language == "en"
|
||||
assert result.duration_seconds == 1.5
|
||||
|
||||
|
||||
def test_faster_whisper_health_no_model():
|
||||
"""Health returns False before model is loaded."""
|
||||
with patch(
|
||||
"openjarvis.speech.faster_whisper.WhisperModel",
|
||||
new=None,
|
||||
):
|
||||
from openjarvis.speech.faster_whisper import FasterWhisperBackend
|
||||
|
||||
backend = FasterWhisperBackend.__new__(FasterWhisperBackend)
|
||||
backend._model = None
|
||||
assert backend.health() is False
|
||||
|
||||
|
||||
def test_faster_whisper_supported_formats():
|
||||
"""Backend supports standard audio formats."""
|
||||
with patch("openjarvis.speech.faster_whisper.WhisperModel"):
|
||||
from openjarvis.speech.faster_whisper import FasterWhisperBackend
|
||||
|
||||
backend = FasterWhisperBackend.__new__(FasterWhisperBackend)
|
||||
formats = backend.supported_formats()
|
||||
assert "wav" in formats
|
||||
assert "mp3" in formats
|
||||
assert "webm" in formats
|
||||
@@ -0,0 +1,57 @@
|
||||
"""Tests for OpenAI Whisper API speech backend."""
|
||||
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
import pytest
|
||||
|
||||
from openjarvis.core.registry import SpeechRegistry
|
||||
from openjarvis.speech._stubs import TranscriptionResult
|
||||
from openjarvis.speech.openai_whisper import OpenAIWhisperBackend
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def _register_openai_whisper():
|
||||
"""Re-register after any registry clear."""
|
||||
if not SpeechRegistry.contains("openai"):
|
||||
SpeechRegistry.register_value("openai", OpenAIWhisperBackend)
|
||||
|
||||
|
||||
def test_openai_whisper_registers():
|
||||
assert SpeechRegistry.contains("openai")
|
||||
|
||||
|
||||
def test_openai_whisper_transcribe():
|
||||
mock_client = MagicMock()
|
||||
mock_response = MagicMock()
|
||||
mock_response.text = "Hello from OpenAI"
|
||||
mock_response.language = "en"
|
||||
mock_response.duration = 2.0
|
||||
mock_client.audio.transcriptions.create.return_value = mock_response
|
||||
|
||||
with patch("openjarvis.speech.openai_whisper.OpenAI", return_value=mock_client):
|
||||
from openjarvis.speech.openai_whisper import OpenAIWhisperBackend
|
||||
|
||||
backend = OpenAIWhisperBackend(api_key="test-key")
|
||||
result = backend.transcribe(b"fake audio", format="wav")
|
||||
|
||||
assert isinstance(result, TranscriptionResult)
|
||||
assert result.text == "Hello from OpenAI"
|
||||
assert result.language == "en"
|
||||
|
||||
|
||||
def test_openai_whisper_health():
|
||||
with patch("openjarvis.speech.openai_whisper.OpenAI"):
|
||||
from openjarvis.speech.openai_whisper import OpenAIWhisperBackend
|
||||
|
||||
backend = OpenAIWhisperBackend(api_key="test-key")
|
||||
assert backend.health() is True
|
||||
|
||||
|
||||
def test_openai_whisper_health_no_key():
|
||||
with patch("openjarvis.speech.openai_whisper.OpenAI"):
|
||||
from openjarvis.speech.openai_whisper import OpenAIWhisperBackend
|
||||
|
||||
backend = OpenAIWhisperBackend.__new__(OpenAIWhisperBackend)
|
||||
backend._client = None
|
||||
backend._api_key = ""
|
||||
assert backend.health() is False
|
||||
@@ -0,0 +1,32 @@
|
||||
"""Tests for speech ABC and data types."""
|
||||
|
||||
from openjarvis.speech._stubs import Segment, SpeechBackend, TranscriptionResult
|
||||
|
||||
|
||||
def test_transcription_result():
|
||||
result = TranscriptionResult(
|
||||
text="Hello world",
|
||||
language="en",
|
||||
confidence=0.95,
|
||||
duration_seconds=1.5,
|
||||
segments=[],
|
||||
)
|
||||
assert result.text == "Hello world"
|
||||
assert result.language == "en"
|
||||
assert result.confidence == 0.95
|
||||
assert result.duration_seconds == 1.5
|
||||
assert result.segments == []
|
||||
|
||||
|
||||
def test_segment():
|
||||
seg = Segment(text="Hello", start=0.0, end=0.5, confidence=0.98)
|
||||
assert seg.text == "Hello"
|
||||
assert seg.start == 0.0
|
||||
assert seg.end == 0.5
|
||||
|
||||
|
||||
def test_speech_backend_is_abstract():
|
||||
import pytest
|
||||
|
||||
with pytest.raises(TypeError):
|
||||
SpeechBackend()
|
||||
@@ -0,0 +1,8 @@
|
||||
"""Tests for speech integration in SystemBuilder/JarvisSystem."""
|
||||
|
||||
from openjarvis.system import JarvisSystem
|
||||
|
||||
|
||||
def test_jarvis_system_has_speech_backend():
|
||||
"""JarvisSystem has a speech_backend attribute."""
|
||||
assert "speech_backend" in JarvisSystem.__dataclass_fields__
|
||||
@@ -4,17 +4,22 @@ requires-python = ">=3.10"
|
||||
resolution-markers = [
|
||||
"python_full_version >= '3.14' and sys_platform == 'win32'",
|
||||
"python_full_version >= '3.14' and sys_platform == 'emscripten'",
|
||||
"python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
|
||||
"python_full_version >= '3.14' and sys_platform == 'linux'",
|
||||
"python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
|
||||
"python_full_version == '3.13.*' and sys_platform == 'win32'",
|
||||
"python_full_version == '3.12.*' and sys_platform == 'win32'",
|
||||
"python_full_version == '3.13.*' and sys_platform == 'emscripten'",
|
||||
"python_full_version == '3.12.*' and sys_platform == 'emscripten'",
|
||||
"python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
|
||||
"python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
|
||||
"python_full_version == '3.13.*' and sys_platform == 'linux'",
|
||||
"python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
|
||||
"python_full_version == '3.12.*' and sys_platform == 'linux'",
|
||||
"python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
|
||||
"python_full_version == '3.11.*' and sys_platform == 'win32'",
|
||||
"python_full_version == '3.11.*' and sys_platform == 'emscripten'",
|
||||
"python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
|
||||
"python_full_version < '3.11'",
|
||||
"python_full_version == '3.11.*' and sys_platform == 'linux'",
|
||||
"python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
|
||||
"python_full_version < '3.11' and sys_platform == 'linux'",
|
||||
"python_full_version < '3.11' and sys_platform != 'linux'",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -338,6 +343,63 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/9b/73/f7084bf12755113cd535ae586782ff3a6e710bfbe6a0d13d1c2f81ffbbfa/authlib-1.6.8-py2.py3-none-any.whl", hash = "sha256:97286fd7a15e6cfefc32771c8ef9c54f0ed58028f1322de6a2a7c969c3817888", size = 244116, upload-time = "2026-02-14T04:02:15.579Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "av"
|
||||
version = "16.1.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/78/cd/3a83ffbc3cc25b39721d174487fb0d51a76582f4a1703f98e46170ce83d4/av-16.1.0.tar.gz", hash = "sha256:a094b4fd87a3721dacf02794d3d2c82b8d712c85b9534437e82a8a978c175ffd", size = 4285203, upload-time = "2026-01-11T07:31:33.772Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/97/51/2217a9249409d2e88e16e3f16f7c0def9fd3e7ffc4238b2ec211f9935bdb/av-16.1.0-cp310-cp310-macosx_11_0_x86_64.whl", hash = "sha256:2395748b0c34fe3a150a1721e4f3d4487b939520991b13e7b36f8926b3b12295", size = 26942590, upload-time = "2026-01-09T20:17:58.588Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/bf/cd/a7070f4febc76a327c38808e01e2ff6b94531fe0b321af54ea3915165338/av-16.1.0-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:72d7ac832710a158eeb7a93242370aa024a7646516291c562ee7f14a7ea881fd", size = 21507910, upload-time = "2026-01-09T20:18:02.309Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/ae/30/ec812418cd9b297f0238fe20eb0747d8a8b68d82c5f73c56fe519a274143/av-16.1.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:6cbac833092e66b6b0ac4d81ab077970b8ca874951e9c3974d41d922aaa653ed", size = 38738309, upload-time = "2026-01-09T20:18:04.701Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/3a/b8/6c5795bf1f05f45c5261f8bce6154e0e5e86b158a6676650ddd77c28805e/av-16.1.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:eb990672d97c18f99c02f31c8d5750236f770ffe354b5a52c5f4d16c5e65f619", size = 40293006, upload-time = "2026-01-09T20:18:07.238Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/a7/44/5e183bcb9333fc3372ee6e683be8b0c9b515a506894b2d32ff465430c074/av-16.1.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:05ad70933ac3b8ef896a820ea64b33b6cca91a5fac5259cb9ba7fa010435be15", size = 40123516, upload-time = "2026-01-09T20:18:09.955Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/12/1d/b5346d582a3c3d958b4d26a2cc63ce607233582d956121eb20d2bbe55c2e/av-16.1.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:d831a1062a3c47520bf99de6ec682bd1d64a40dfa958e5457bb613c5270e7ce3", size = 41463289, upload-time = "2026-01-09T20:18:12.459Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/fa/31/acc946c0545f72b8d0d74584cb2a0ade9b7dfe2190af3ef9aa52a2e3c0b1/av-16.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:358ab910fef3c5a806c55176f2b27e5663b33c4d0a692dafeb049c6ed71f8aff", size = 31754959, upload-time = "2026-01-09T20:18:14.718Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/48/d0/b71b65d1b36520dcb8291a2307d98b7fc12329a45614a303ff92ada4d723/av-16.1.0-cp311-cp311-macosx_11_0_x86_64.whl", hash = "sha256:e88ad64ee9d2b9c4c5d891f16c22ae78e725188b8926eb88187538d9dd0b232f", size = 26927747, upload-time = "2026-01-09T20:18:16.976Z" },
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||||
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{ url = "https://files.pythonhosted.org/packages/8e/4f/a1ba8d922f2f6d1a3d52419463ef26dd6c4d43ee364164a71b424b5ae204/av-16.1.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:720edd4d25aa73723c1532bb0597806d7b9af5ee34fc02358782c358cfe2f879", size = 39291737, upload-time = "2026-01-09T20:18:21.513Z" },
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{ url = "https://files.pythonhosted.org/packages/1a/31/fc62b9fe8738d2693e18d99f040b219e26e8df894c10d065f27c6b4f07e3/av-16.1.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:c7f2bc703d0df260a1fdf4de4253c7f5500ca9fc57772ea241b0cb241bcf972e", size = 40846822, upload-time = "2026-01-09T20:18:24.275Z" },
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{ url = "https://files.pythonhosted.org/packages/53/10/ab446583dbce730000e8e6beec6ec3c2753e628c7f78f334a35cad0317f4/av-16.1.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d69c393809babada7d54964d56099e4b30a3e1f8b5736ca5e27bd7be0e0f3c83", size = 40675604, upload-time = "2026-01-09T20:18:26.866Z" },
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sdist = { url = "https://files.pythonhosted.org/packages/c8/e9/2e3a46c304e7fa21eaa70612f60354e32699c7102eb961f67448e222ad7c/sentry_sdk-2.54.0.tar.gz", hash = "sha256:2620c2575128d009b11b20f7feb81e4e4e8ae08ec1d36cbc845705060b45cc1b", size = 413813, upload-time = "2026-03-02T15:12:41.355Z" }
|
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wheels = [
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]
|
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|
||||
[[package]]
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name = "setuptools"
|
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version = "82.0.0"
|
||||
@@ -5492,7 +5780,8 @@ name = "slixmpp"
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"python_full_version < '3.11' and sys_platform == 'linux'",
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"python_full_version < '3.11' and sys_platform != 'linux'",
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]
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dependencies = [
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{ name = "aiodns", marker = "python_full_version < '3.11'" },
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@@ -5530,16 +5819,20 @@ source = { registry = "https://pypi.org/simple" }
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resolution-markers = [
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"python_full_version >= '3.14' and sys_platform == 'emscripten'",
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"python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
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"python_full_version >= '3.14' and sys_platform == 'linux'",
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"python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
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"python_full_version == '3.13.*' and sys_platform == 'win32'",
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"python_full_version == '3.12.*' and sys_platform == 'win32'",
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"python_full_version == '3.13.*' and sys_platform == 'emscripten'",
|
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"python_full_version == '3.12.*' and sys_platform == 'emscripten'",
|
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"python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
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"python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
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"python_full_version == '3.13.*' and sys_platform == 'linux'",
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"python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
|
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"python_full_version == '3.12.*' and sys_platform == 'linux'",
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"python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
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"python_full_version == '3.11.*' and sys_platform == 'win32'",
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"python_full_version == '3.11.*' and sys_platform == 'emscripten'",
|
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"python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
|
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"python_full_version == '3.11.*' and sys_platform == 'linux'",
|
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"python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
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]
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dependencies = [
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{ name = "aiodns", marker = "python_full_version >= '3.11'" },
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@@ -5959,7 +6252,8 @@ name = "twitchio"
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version = "2.10.0"
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source = { registry = "https://pypi.org/simple" }
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resolution-markers = [
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"python_full_version < '3.11'",
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"python_full_version < '3.11' and sys_platform == 'linux'",
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"python_full_version < '3.11' and sys_platform != 'linux'",
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]
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dependencies = [
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{ name = "aiohttp", marker = "python_full_version < '3.11'" },
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@@ -5978,16 +6272,20 @@ source = { registry = "https://pypi.org/simple" }
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resolution-markers = [
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"python_full_version >= '3.14' and sys_platform == 'win32'",
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"python_full_version >= '3.14' and sys_platform == 'emscripten'",
|
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"python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
|
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"python_full_version >= '3.14' and sys_platform == 'linux'",
|
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"python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
|
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"python_full_version == '3.13.*' and sys_platform == 'win32'",
|
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"python_full_version == '3.12.*' and sys_platform == 'win32'",
|
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"python_full_version == '3.13.*' and sys_platform == 'emscripten'",
|
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"python_full_version == '3.12.*' and sys_platform == 'emscripten'",
|
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"python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
|
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"python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
|
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"python_full_version == '3.13.*' and sys_platform == 'linux'",
|
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"python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
|
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"python_full_version == '3.12.*' and sys_platform == 'linux'",
|
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"python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
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"python_full_version == '3.11.*' and sys_platform == 'win32'",
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"python_full_version == '3.11.*' and sys_platform == 'emscripten'",
|
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"python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
|
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"python_full_version == '3.11.*' and sys_platform == 'linux'",
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"python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
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]
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dependencies = [
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{ name = "aiohttp", marker = "python_full_version >= '3.11'" },
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@@ -6217,6 +6515,35 @@ wheels = [
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|
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[[package]]
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name = "wandb"
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version = "0.25.0"
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{ name = "click" },
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{ name = "gitpython" },
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{ name = "packaging" },
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{ name = "platformdirs" },
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{ name = "protobuf" },
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{ name = "pydantic" },
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{ name = "pyyaml" },
|
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{ name = "requests" },
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{ name = "sentry-sdk" },
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{ name = "typing-extensions" },
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{ url = "https://files.pythonhosted.org/packages/d9/a1/258cdedbf30cebc692198a774cf0ef945b7ed98ee64bdaf62621281c95d8/wandb-0.25.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:5e0127dbcef13eea48f4b84268da7004d34d3120ebc7b2fa9cefb72b49dbb825", size = 22799744, upload-time = "2026-02-13T00:17:26.437Z" },
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{ url = "https://files.pythonhosted.org/packages/cc/fb/9578eed2c01b2fc6c8b693da110aa9c73a33d7bb556480f5cfc42e48c94e/wandb-0.25.0-py3-none-win32.whl", hash = "sha256:020b42ca4d76e347709d65f59b30d4623a115edc28f462af1c92681cb17eae7c", size = 24604118, upload-time = "2026-02-13T00:17:37.641Z" },
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{ url = "https://files.pythonhosted.org/packages/27/6c/5847b4dda1dfd52630dac08711d4348c69ed657f0698fc2d949c7f7a6622/wandb-0.25.0-py3-none-win_arm64.whl", hash = "sha256:c6174401fd6fb726295e98d57b4231c100eca96bd17de51bfc64038a57230aaf", size = 21785298, upload-time = "2026-02-13T00:17:42.475Z" },
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[[package]]
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|
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Reference in New Issue
Block a user