Ely-Eye — local multimodal context operating system on Qwen3.5-9B
Real-models implementation: multimodal ingestion, Context Cartridge memory, sparse + Qwen embedding retrieval, Cache Fabric, Qwen runtime adapters, verifier contracts, Visual HashHop proofs, BF16 LoRA fine-tuning, and a local dashboard. Verified by a 15-check PRD proof suite on the live model.
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from __future__ import annotations
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from pathlib import Path
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import typer
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import uvicorn
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from .api import create_app
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from .benchmarks import (
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benchmark_prediction_report,
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benchmark_registry_report,
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benchmark_sample_report,
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benchmark_source_report,
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run_prediction_benchmark,
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)
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from .cartridge import CartridgeService
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from .cartridge_assets import CartridgeAssetService
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from .config import get_settings
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from .db import Database
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from .embeddings import EmbeddingService
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from .evals import HashHopEvaluator, PRDProofSuite
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from .ingestion import IngestionService
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from .profiles import runtime_profile_report
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from .research_modules import ResearchModuleService
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from .runtime import QwenRuntime
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from .schemas import TrainingAdapterKind
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from .training import TrainingService
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app = typer.Typer(no_args_is_help=True)
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def json_dump(value: object) -> str:
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import json
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return json.dumps(value, ensure_ascii=False, indent=2)
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@app.command()
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def serve(host: str = "127.0.0.1", port: int = 8765) -> None:
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uvicorn.run(create_app(), host=host, port=port)
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@app.command()
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def status() -> None:
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settings = get_settings()
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runtime = QwenRuntime(settings).status()
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db = Database(settings)
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typer.echo(
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{
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"runtime": runtime.model_dump(mode="json"),
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"atoms": db.atom_count(),
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"token_equivalent": db.token_equivalent_sum(),
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"library_token_equivalent": max(
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db.token_equivalent_sum(),
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db.cartridge_token_equivalent_sum(),
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),
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"cartridges": db.cartridge_count(),
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"data_home": str(settings.home),
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}
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)
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@app.command()
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def ingest(path: Path, cartridge_name: str | None = None) -> None:
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result = IngestionService(get_settings()).ingest_path(path, cartridge_name)
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typer.echo(result.model_dump_json(indent=2))
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@app.command("build-dense-index")
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def build_dense_index() -> None:
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index = EmbeddingService(get_settings()).build_index()
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dimensions = int(index.vectors.shape[1]) if index.vectors.size else 0
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typer.echo({"atoms": len(index.atom_ids), "dimensions": dimensions})
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@app.command("list-cartridges")
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def list_cartridges() -> None:
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for manifest in CartridgeService(get_settings()).list_manifests():
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typer.echo(manifest.model_dump_json(indent=2))
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@app.command("hashhop")
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def hashhop(kind: str = "visual", hops: int = 2, token_equivalent: int = 262_144) -> None:
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evaluator = HashHopEvaluator(get_settings())
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if kind == "text":
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proof = evaluator.generate_text_proof(hops, token_equivalent)
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elif kind == "visual":
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proof = evaluator.generate_visual_proof(hops, token_equivalent)
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else:
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raise typer.BadParameter("kind must be text or visual")
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typer.echo(proof.model_dump_json(indent=2))
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@app.command("proof-suite")
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def proof_suite(cartridge_id: str | None = None) -> None:
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report = PRDProofSuite(get_settings()).run(cartridge_id)
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typer.echo(report.model_dump_json(indent=2))
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@app.command("latest-proof-suite")
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def latest_proof_suite() -> None:
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report = PRDProofSuite(get_settings()).latest()
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if report is None:
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raise typer.Exit(code=1)
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typer.echo(report.model_dump_json(indent=2))
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@app.command("runtime-generation-proof")
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def runtime_generation_proof() -> None:
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check = PRDProofSuite(get_settings()).runtime_generation_proof()
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typer.echo(check.model_dump_json(indent=2))
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@app.command("visual-contradiction-proof")
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def visual_contradiction_proof() -> None:
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check = PRDProofSuite(get_settings()).visual_contradiction_proof()
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typer.echo(check.model_dump_json(indent=2))
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@app.command("finalize-cartridge")
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def finalize_cartridge(cartridge_id: str) -> None:
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report = CartridgeAssetService(get_settings()).finalize(cartridge_id)
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typer.echo(report.model_dump_json(indent=2))
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@app.command("refresh-cartridge")
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def refresh_cartridge(cartridge_id: str) -> None:
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settings = get_settings()
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db = Database(settings)
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manifest = CartridgeService(settings, db).rebuild(
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cartridge_id=cartridge_id,
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atoms=db.list_atoms(),
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sources=db.list_sources(),
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)
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typer.echo(manifest.model_dump_json(indent=2))
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@app.command("benchmark-registry")
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def benchmark_registry() -> None:
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typer.echo(benchmark_registry_report().model_dump_json(indent=2))
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@app.command("benchmark-sources")
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def benchmark_sources() -> None:
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typer.echo(benchmark_source_report().model_dump_json(indent=2))
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@app.command("benchmark-samples")
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def benchmark_samples() -> None:
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settings = get_settings()
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output_dir = settings.data_dir / "benchmark_samples"
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typer.echo(benchmark_sample_report(output_dir).model_dump_json(indent=2))
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@app.command("benchmark-predictions")
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def benchmark_predictions() -> None:
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settings = get_settings()
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output_dir = settings.data_dir / "benchmark_predictions"
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typer.echo(benchmark_prediction_report(settings, output_dir).model_dump_json(indent=2))
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@app.command("run-benchmark")
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def run_benchmark(
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benchmark: str,
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dataset_path: Path,
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prediction_field: str = "prediction",
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answer_field: str = "answer",
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limit: int | None = None,
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) -> None:
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report = run_prediction_benchmark(
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benchmark=benchmark,
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dataset_path=dataset_path,
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prediction_field=prediction_field,
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answer_field=answer_field,
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limit=limit,
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)
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typer.echo(report)
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@app.command("runtime-profiles")
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def runtime_profiles() -> None:
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typer.echo(runtime_profile_report().model_dump_json(indent=2))
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@app.command("research-modules")
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def research_modules(query: str = "Ely-Eye V-NSA DVKV x-modal KV speculative prefetch evidence graph") -> None:
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settings = get_settings()
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db = Database(settings)
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from .compiler import ContextCompiler
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from .schemas import RuntimeProfile
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context = ContextCompiler(settings, db).compile(query, RuntimeProfile.research_theater)
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report = ResearchModuleService(settings, db).build_report(query, context.hits)
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typer.echo(json_dump(report))
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@app.command("validate-training-data")
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def validate_training_data(path: Path) -> None:
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samples = TrainingService(get_settings()).validate_dataset(path)
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typer.echo({"samples": len(samples)})
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@app.command("build-prd-training-data")
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def build_prd_training_data(
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prd_path: Path = Path("PRD.md"),
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output_path: Path | None = None,
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cartridge_id: str | None = None,
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max_sections: int = 48,
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) -> None:
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path = TrainingService(get_settings()).build_prd_dataset(
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prd_path=prd_path,
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output_path=output_path,
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cartridge_id=cartridge_id,
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max_sections=max_sections,
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)
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samples = TrainingService(get_settings()).validate_dataset(path)
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typer.echo({"dataset": str(path), "samples": len(samples)})
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@app.command("build-visual-training-data")
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def build_visual_training_data(
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prd_path: Path = Path("PRD.md"),
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output_path: Path | None = None,
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cartridge_id: str | None = None,
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image_path: list[Path] | None = typer.Option(None, "--image-path"),
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) -> None:
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path = TrainingService(get_settings()).build_visual_dataset(
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prd_path=prd_path,
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output_path=output_path,
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image_paths=image_path,
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cartridge_id=cartridge_id,
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)
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samples = TrainingService(get_settings()).validate_dataset(path)
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typer.echo({"dataset": str(path), "samples": len(samples)})
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@app.command("build-router-training-data")
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def build_router_training_data(
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prd_path: Path = Path("PRD.md"),
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output_path: Path | None = None,
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cartridge_id: str | None = None,
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) -> None:
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path = TrainingService(get_settings()).build_router_dataset(
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prd_path=prd_path,
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output_path=output_path,
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cartridge_id=cartridge_id,
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)
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samples = TrainingService(get_settings()).validate_dataset(path)
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typer.echo({"dataset": str(path), "samples": len(samples)})
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@app.command("build-retrieval-training-data")
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def build_retrieval_training_data(
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prd_path: Path = Path("PRD.md"),
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output_path: Path | None = None,
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cartridge_id: str | None = None,
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max_sections: int = 32,
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) -> None:
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path = TrainingService(get_settings()).build_retrieval_dataset(
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prd_path=prd_path,
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output_path=output_path,
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cartridge_id=cartridge_id,
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max_sections=max_sections,
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)
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samples = TrainingService(get_settings()).validate_dataset(path)
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typer.echo({"dataset": str(path), "samples": len(samples)})
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@app.command("train-lora")
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def train_lora(
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dataset_path: Path,
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output_dir: Path,
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max_steps: int = 8,
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kind: TrainingAdapterKind = TrainingAdapterKind.hme_core_lora,
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model_id: str | None = None,
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rank: int = 16,
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alpha: int = 32,
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max_length: int | None = None,
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gradient_accumulation_steps: int | None = None,
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learning_rate: float | None = None,
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cartridge_id: str | None = None,
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) -> None:
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manifest = TrainingService(get_settings()).train_lora(
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dataset_path=dataset_path,
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output_dir=output_dir,
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max_steps=max_steps,
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kind=kind,
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model_id=model_id,
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rank=rank,
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alpha=alpha,
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max_length=max_length,
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gradient_accumulation_steps=gradient_accumulation_steps,
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learning_rate=learning_rate,
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cartridge_id=cartridge_id,
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)
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if cartridge_id:
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CartridgeService(get_settings()).attach_adapter(cartridge_id, output_dir)
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typer.echo(manifest.model_dump_json(indent=2))
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@app.command("train-retrieval-lora")
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def train_retrieval_lora(
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dataset_path: Path,
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output_dir: Path,
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max_steps: int = 8,
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rank: int = 8,
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alpha: int = 16,
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max_length: int | None = None,
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learning_rate: float | None = None,
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cartridge_id: str | None = None,
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) -> None:
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manifest = TrainingService(get_settings()).train_retrieval_lora(
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dataset_path=dataset_path,
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output_dir=output_dir,
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max_steps=max_steps,
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rank=rank,
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alpha=alpha,
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max_length=max_length,
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learning_rate=learning_rate,
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cartridge_id=cartridge_id,
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)
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if cartridge_id:
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CartridgeService(get_settings()).attach_adapter(cartridge_id, output_dir)
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typer.echo(manifest.model_dump_json(indent=2))
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@app.command("attach-adapter")
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def attach_adapter(cartridge_id: str, adapter_dir: Path) -> None:
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manifest = CartridgeService(get_settings()).attach_adapter(cartridge_id, adapter_dir)
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typer.echo(manifest.model_dump_json(indent=2))
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if __name__ == "__main__":
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app()
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