Spaces:
Paused
Paused
File size: 3,628 Bytes
8c1b9fe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | #!/usr/bin/env python3
"""Reproducible end-to-end Auralynq demo (`make demo`).
Runs the full pipeline locally at $0 with only HUGGINGFACE_TOKEN (optional):
1. ensure sample data exists (download_data)
2. build the vector index + knowledge graph
3. answer a typed question → grounded, cited answer (fast path)
4. answer a relational question → routed to PathRAG with evidence paths
5. run a voice turn on the synthetic audio → speaker/timestamp citations
"""
from __future__ import annotations
from auralynq.config import get_settings
from auralynq.telemetry import configure_logging
from rich.console import Console
from rich.panel import Panel
from rich.table import Table
console = Console()
def _print_answer(title: str, res) -> None:
console.print(Panel(res.answer or "(no answer)", title=title, border_style="cyan"))
meta = Table.grid(padding=(0, 2))
meta.add_row("route", f"[bold]{res.route}[/] ({res.route_confidence})")
meta.add_row("rationale", res.route_rationale)
meta.add_row("iterations", str(res.iterations))
meta.add_row("elapsed_ms", str(res.elapsed_ms))
console.print(meta)
if res.citations:
t = Table(title="Citations", show_lines=False)
t.add_column("#")
t.add_column("source")
t.add_column("locator / timestamp")
for c in res.citations:
t.add_row(
str(c.get("marker", "?")), str(c.get("source", "?")), str(c.get("locator", ""))
)
console.print(t)
if res.path_evidence:
console.print("[bold]PathRAG evidence paths:[/]")
for ev in res.path_evidence[:4]:
console.print(f" • {' → '.join(ev['nodes'])} (reliability {ev['reliability']})")
def main() -> None:
s = get_settings()
configure_logging(level="WARNING")
s.ensure_dirs()
console.rule("[bold cyan]Auralynq — Talk to Your Data[/]")
# 1-2. data + index
from auralynq.pipeline import build_index
from scripts.download_data import download
corpus = s.data_dir / "corpus"
if not corpus.exists() or not any(corpus.iterdir()):
console.print("→ downloading sample data …")
download(sample=True)
console.print("→ building index (vector + knowledge graph) …")
stats = build_index(corpus)
st = Table(title="Index")
st.add_column("metric")
st.add_column("value", justify="right")
for k, v in stats.items():
st.add_row(k, str(v))
console.print(st)
from auralynq.agent import runner
from auralynq.agent.runner import answer_question
runner._CACHE.clear()
# 3. simple typed question → fast path
_print_answer("Q1 — typed (fast path)", answer_question("What is the capital of France?"))
# 4. relational question → PathRAG
_print_answer(
"Q2 — relational (PathRAG)", answer_question("How are Paris, France and Europe related?")
)
# 5. voice turn on the synthetic lecture audio
audio = corpus / "lecture_pathrag.wav"
if audio.exists():
from auralynq.voice.loop import run_voice_turn
console.rule("[bold magenta]Voice turn[/]")
v = run_voice_turn(audio_path=audio, speak=True)
console.print(
Panel(v.transcript or "(no speech)", title="Heard (ASR)", border_style="magenta")
)
console.print(Panel(v.answer, title="Spoken answer", border_style="cyan"))
if v.audio_out_path:
console.print(f"[green]✓[/] TTS audio → {v.audio_out_path} ({v.tts_provider})")
console.rule("[bold green]Demo complete[/]")
if __name__ == "__main__":
main()
|