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62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
"""Optional plotting: one cumulative-cost cross-over figure per llm_model.
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Matplotlib is imported lazily so a JSON-only run needs no extra dependency. The
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curves are rebuilt from the four numbers already in the report, so this stays a
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pure consumer of the report with no cost-model knowledge.
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"""
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from __future__ import annotations
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from pathlib import Path
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def write_plots(report: dict, out_dir: Path, name: str = "report") -> list[Path]:
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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out_dir = Path(out_dir)
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out_dir.mkdir(parents=True, exist_ok=True)
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return [
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_plot_crossover(name, llm_model, result, out_dir, plt)
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for llm_model, result in report.items()
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]
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def _plot_crossover(name: str, llm_model: str, result: dict, out_dir: Path, plt) -> Path:
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full_per_query = result["full_context"]["per_query_tokens"]
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ingestion = result["cognee"]["ingestion_tokens"]
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cognee_per_query = result["cognee"]["per_query_tokens"]
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parity = result["reduction_milestones"].get("1")
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max_queries = int((parity or 20) * 2)
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queries = list(range(max_queries + 1))
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full_context = [q * full_per_query for q in queries]
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cognee = [ingestion + q * cognee_per_query for q in queries]
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figure, axes = plt.subplots(figsize=(7, 4.5))
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axes.plot(queries, full_context, label="full-context")
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axes.plot(queries, cognee, label="cognee memory")
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if parity is not None:
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axes.axvline(parity, linestyle="--", color="gray", linewidth=1)
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axes.annotate(
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f"parity ≈ {parity:g} queries",
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xy=(parity, parity * full_per_query),
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xytext=(6, 6),
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textcoords="offset points",
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)
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axes.set_xlabel("queries")
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axes.set_ylabel("cumulative tokens")
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axes.set_title(f"Full-context vs. cognee — {llm_model}")
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axes.legend()
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path = out_dir / f"{name}__{_safe(llm_model)}.png"
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figure.savefig(path, dpi=120, bbox_inches="tight")
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plt.close(figure)
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return path
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def _safe(llm_model: str) -> str:
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return llm_model.replace("/", "_").replace(":", "_")
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