CRoM-EfficientLLM_Full_Report.md

CRoM-EfficientLLM 전체 프로젝트 보고서

1. 프로젝트 전체 구조 (Directory Tree)

CRoM-EfficientLLM/
├── .github/
│   └── workflows/
│       ├── ci.yml
│       └── release.yml
├── benchmarks/
│   ├── efficiency_eval.py
│   ├── longbench_eval.py
│   └── sample_results.json
├── dashboard/
│   ├── grafana_dashboard.json
│   └── prometheus_config.yml
├── docs/
│   ├── architecture.md
│   └── versioning.md
├── examples/
│   └── corpus/
│       ├── sample_docs.jsonl
│       └── sample_queries.jsonl
├── scripts/
│   ├── gen_release_notes.py
│   └── release.sh
├── src/
│   └── crom_efficientllm/
│       ├── budget_packer/
│       │   ├── __init__.py
│       │   └── packer.py
│       ├── drift_estimator/
│       │   ├── __init__.py
│       │   └── estimator.py
│       ├── plugins/
│       │   ├── evidently_drift.py
│       │   ├── flashrank_reranker.py
│       │   └── llmlingua_compressor.py
│       ├── rerank_engine/
│       │   ├── __init__.py
│       │   └── rerank.py
│       ├── __init__.py
│       ├── budget_packer.py
│       ├── capsule_logger.py
│       ├── cli.py
│       ├── cross_encoder.py
│       ├── demo.py
│       └── server.py
├── tests/
│   ├── test_drift.py
│   ├── test_packer.py
│   └── test_rerank.py
├── .gitignore
├── CHANGELOG.md
├── crom 1.0.1수정 업데이트 상세보고서.md
├── LICENSE
├── pyproject.toml
├── README.md
├── release_notes.md
└── requirements.txt

2. 파일별 상세 내용

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\.github\\workflows\\ci.yml

name: ci
on:
  push:
    branches: [ main ]
  pull_request:

jobs:
  test:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        python-version: ["3.9", "3.10", "3.11", "3.12"]
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: ${{ matrix.python-version }}
      - run: pip install -e .[dev]
      - run: pre-commit run --all-files || true
      - run: ruff --version && black --version
      - run: pytest -q

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\.github\\workflows\\release.yml

name: release
on:
  push:
    tags:
      - 'v*'
jobs:
  release:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - run: pip install -e .[dev]
      - run: pytest -q
      - name: Build distribution
        run: |
          python -m pip install build
          python -m build
      - name: Generate release notes from CHANGELOG
        run: |
          python scripts/gen_release_notes.py "$GITHUB_REF_NAME"
      - name: Publish GitHub Release
        uses: softprops/action-gh-release@v2
        with:
          name: ${{ github.ref_name }}
          body_path: release_notes.md
          files: |
            dist/*.whl
            dist/*.tar.gz

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\.gitignore

# Python
__pycache__/
*.py[cod]
*.egg-info/
.env
.venv/
virtualenv/
.idea/
.vscode/
.ipynb_checkpoints/
.dist/
.build/
.coverage
.pytest_cache/

# OS
.DS_Store
Thumbs.db

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\CHANGELOG.md

# Changelog

## [1.0.1] - 2025-09-06
### Added
- Implemented core modules from scratch based on design documents.
- Implemented FastAPI server with `/process` endpoint (`src/crom_efficientllm/server.py`).
- Added `enhanced_greedy_pack` with detailed statistics for budget packing (`src/crom_efficientllm/budget_packer.py`).
- Implemented `SafeCrossEncoderManager` for robust and observable Cross-Encoder handling (`src/crom_efficientllm/cross_encoder.py`).
- Added `ExplainCapsuleLogger` for structured JSONL logging of all processing events (`src/crom_efficientllm/capsule_logger.py`).

### Changed
- Major version bump to reflect the first functional implementation of core logic.


## [0.2.1] - 2025-09-02
### Added
- CLI `--save-plots` option for `sweep` and `dp-curve`; saves PNG charts to `benchmarks/out/` (or `--out-dir`).
- README Quick Examples mention of plotting flag.
- This CHANGELOG.

### Changed
- Dev tooling: recommend `matplotlib` via dev extra for plotting.

## [0.2.0] - 2025-09-02
### Added
- GitHub Actions CI (3.9–3.12), pre-commit(ruff/black).
- `crom-bench` CLI: `e2e`, `sweep`, `scale`, `dp-curve`, `haystack-compare`.
- Plugins: FlashRank/LLMLingua/Evidently (optional extras).
- Example corpus & queries (JSONL).

## [0.1.0] - 2025-09-02
- Initial packaging; budget packer, hybrid rerank, drift estimator, demo & metrics.

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\LICENSE

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File: D:\\Sanctum\\CRoM-EfficientLLM\\README.md

---
language: en
license: apache-2.0
library_name: crom-efficientllm
tags:
- rag
- llm
- retrieval
- rerank
- reranker
- context-management
- prompt-engineering
- observability
- python
---
# CRoM-Context-Rot-Mitigation--EfficientLLM: Context Reranking and Management for Efficient LLMs

<p align="left">
  <a href="https://github.com/Flamehaven/CRoM-Context-Rot-Mitigation--EfficientLLM/actions">
    <img alt="CI" src="https://img.shields.io/github/actions/workflow/status/Flamehaven/CRoM-Context-Rot-Mitigation--EfficientLLM/ci.yml?branch=main" />
  </a>
  <a href="#-benchmarks">
    <img alt="Bench" src="https://img.shields.io/badge/benchmarks-ready-success" />
  </a>
  <a href="LICENSE">
    <img alt="License" src="https://img.shields.io/badge/license-Apache%202.0-blue" />
  </a>
  <a href="https://github.com/Flamehaven/CRoM-Context-Rot-Mitigation--EfficientLLM/releases">
    <img alt="Release" src="https://img.shields.io/github/v/release/Flamehaven/CRoM-Context-Rot-Mitigation--EfficientLLM?display_name=tag" />
  </a>
  <a href="CHANGELOG.md">
    <img alt="Versioning" src="https://img.shields.io/badge/semver-0.2.x-lightgrey" />
  </a>
  <a href="https://github.com/Flamehaven/CRoM-Context-Rot-Mitigation--EfficientLLM/releases/latest">
    <img alt="Wheel" src="https://img.shields.io/badge/wheel-available-success" />
  </a>
</p>

**CRoM (Context Rot Mitigation)-EfficientLLM** is a Python toolkit designed to optimize the context provided to Large Language Models (LLMs). It provides a suite of tools to intelligently select, re-rank, and manage text chunks to fit within a model\'s context budget while maximizing relevance and minimizing performance drift.

This project is ideal for developers building RAG (Retrieval-Augmented Generation) pipelines who need to make the most of limited context windows.

## Key Features

*   **Budget Packer:** Greedily packs the highest-scoring text chunks into a defined token budget using a stable sorting algorithm.
*   **Hybrid Reranker:** Combines sparse (TF-IDF) and dense (Sentence-Transformers) retrieval scores for robust and high-quality reranking of documents.
*   **Drift Estimator:** Monitors the semantic drift between sequential model responses using L2 or cosine distance with EWMA smoothing.
*   **Observability:** Exposes Prometheus metrics for monitoring token savings and drift alerts in production.
*   **Extensible Plugins:** Supports optional plugins for advanced reranking (`FlashRank`), compression (`LLMLingua`), and drift analysis (`Evidently`).
*   **Comprehensive Benchmarking:** Includes a CLI for end-to-end pipeline evaluation, budget sweeps, and quality-vs-optimal analysis.

## Installation

Install the package directly from source using pip. For development, it\'s recommended to install in editable mode with the `[dev]` extras.

```bash
# Clone the repository
git clone https://github.com/Flamehaven/CRoM-Context-Rot-Mitigation--EfficientLLM.git
cd CRoM-Context-Rot-Mitigation--EfficientLLM

# Install in editable mode with development and plugin dependencies
pip install -e .[dev,plugins]

Quickstart

Demo

Run a simple, self-contained demonstration of the core components:

# Run the demo script
crom-demo demo

CLI Benchmarking Examples

The package includes a powerful crom-bench CLI for evaluation.

# Default E2E (Search→Rerank→Pack→Mock LLM)
crom-bench e2e --budget 0.3

# Optional: High-precision configuration with plugins
crom-bench e2e --budget 0.3 \
  --use-flashrank --flashrank-model ms-marco-TinyBERT-L-2-v2 \
  --use-llmlingua --compress-ratio=0.6 \
  --use-evidently

Plotting

If matplotlib is installed (pip install -e .[dev]), you can save benchmark plots directly:

# Save budget sweep result plots
crom-bench sweep --save-plots

# Save DP-curve plots
crom-bench dp-curve --save-plots

Release & Changelog

This project follows semantic versioning. For detailed changes, see the CHANGELOG.md.

Releases are automated via GitHub Actions when a v* tag is pushed.

License

This project is licensed under the Apache 2.0 License. See the LICENSE file for details. ```

File: D:\\Sanctum\\CRoM-EfficientLLM\\benchmarks\\efficiency_eval.py

"""
Efficiency Evaluation for CRoM-EfficientLLM
- Synthetic workload to measure token savings, selection quality, and runtime.
- No third-party deps beyond numpy/matplotlib (pandas optional for CSVs).

Usage:
  python benchmarks/efficiency_eval.py --budget 0.3 --n 5000 --seed 123 --plot --save
"""
from __future__ import annotations

import argparse
import math
import time
from dataclasses import dataclass
from typing import List, Sequence, Tuple, Union

import numpy as np

try:
    import pandas as pd  # optional
except Exception:  # pragma: no cover
    pd = None

try:
    import matplotlib.pyplot as plt  # optional
except Exception:  # pragma: no cover
    plt = None

# --- Local packers (self-contained to avoid imports during quick eval) ---
@dataclass(frozen=True)
class Chunk:
    text: str
    score: float
    tokens: int

def _estimate_tokens(text: str) -> int:
    return max(1, len(text) // 4)

def _coerce_chunk(obj: Union[Chunk, dict], idx: int) -> Chunk:
    if isinstance(obj, Chunk):
        return obj
    if not isinstance(obj, dict):
        raise TypeError(f"Chunk #{idx} must be Chunk or dict, got {type(obj)}")
    text = str(obj.get("text", ""))
    if not text:
        raise ValueError(f"Chunk #{idx} has empty text")
    score = float(obj.get("score", 0.0))
    tokens = int(obj["tokens"]) if "tokens" in obj else _estimate_tokens(text)
    if tokens <= 0:
        raise ValueError(f"Chunk #{idx} has non-positive tokens: {tokens}")
    return Chunk(text=text, score=score, tokens=tokens)

def budget_pack(text_chunks: Sequence[Union[Chunk, dict]], budget: int = 1000) -> List[Chunk]:
    if budget <= 0:
        raise ValueError("budget must be > 0")
    coerced: List[Chunk] = [_coerce_chunk(c, i) for i, c in enumerate(text_chunks)]
    indexed = list(enumerate(coerced))
    indexed.sort(key=lambda it: (-it[1].score, it[1].tokens, it[0]))
    selected: List[Chunk] = []
    total = 0
    for _, ch in indexed:
        if total + ch.tokens <= budget:
            selected.append(ch)
            total += ch.tokens
    return selected

def pack_fcfs(text_chunks: Sequence[Union[Chunk, dict]], budget: int) -> List[Chunk]:
    sel, total = [], 0
    for i, obj in enumerate(text_chunks):
        ch = _coerce_chunk(obj, i)
        if total + ch.tokens <= budget:
            sel.append(ch)
            total += ch.tokens
    return sel

def pack_random(text_chunks: Sequence[Union[Chunk, dict]], budget: int, seed: int = 0) -> List[Chunk]:
    rng = np.random.default_rng(seed)
    indices = np.arange(len(text_chunks))
    rng.shuffle(indices)
    sel, total = [], 0
    for i in indices:
        ch = _coerce_chunk(text_chunks[i], i)
        if total + ch.tokens <= budget:
            sel.append(ch)
            total += ch.tokens
    return sel

# --- Data generation and metrics ---

def make_synthetic_chunks(n=2000, seed=42, corr=0.6):
    rng = np.random.default_rng(seed)
    true_rel = rng.normal(0, 1, size=n)
    noise = rng.normal(0, 1, size=n) * math.sqrt(1 - corr**2)
    score = corr * true_rel + noise
    tokens = np.clip(rng.lognormal(mean=4.0, sigma=0.6, size=n).astype(int), 5, 2000)
    chunks = [Chunk(text=("x"*int(t*4)), score=float(s), tokens=int(t)) for s, t in zip(score, tokens)]
    return chunks, true_rel

def eval_once(n=5000, budget_ratio=0.3, seed=123, corr=0.6):
    chunks, true_rel = make_synthetic_chunks(n=n, seed=seed, corr=corr)
    total_tokens = sum(c.tokens for c in chunks)
    budget = int(total_tokens * budget_ratio)

    def run(name, fn):
        t0 = time.perf_counter()
        sel = fn(chunks, budget)
        dt = time.perf_counter() - t0
        idx_map = {id(c): i for i, c in enumerate(chunks)}
        picked_idx = [idx_map[id(c)] for c in sel]
        rel_sum = float(np.sum(true_rel[picked_idx])) if picked_idx else 0.0
        sel_tokens = sum(c.tokens for c in sel)
        return {
            "name": name,
            "time_ms": dt*1000,
            "selected_chunks": len(sel),
            "selected_tokens": sel_tokens,
            "tokens_budget": budget,
            "tokens_total_unpacked": total_tokens,
            "tokens_saved": total_tokens - sel_tokens,
            "save_ratio": (total_tokens - sel_tokens)/total_tokens,
            "relevance_sum": rel_sum,
        }

    rows = [
        run("budget_pack", budget_pack),
        run("fcfs", pack_fcfs),
        run("random", lambda ch, b: pack_random(ch, b, seed=seed)),
    ]
    return rows

def quality_vs_optimal(n=200, budget_ratio=0.3, seed=123, corr=0.6):
    chunks, true_rel = make_synthetic_chunks(n=n, seed=seed, corr=corr)
    budget = int(sum(c.tokens for c in chunks) * budget_ratio)
    values = np.maximum(true_rel, 0.0)

    def optimal(chunks_sub, values, budget):
        items = chunks_sub
        vals = list(values)
        B = budget
        dp = [0.0]*(B+1)
        keep = [[False]*(B+1) for _ in range(len(items))]
        for i, it in enumerate(items):
            wt = it.tokens
            val = vals[i]
            for b in range(B, wt-1, -1):
                alt = dp[b - wt] + val
                if alt > dp[b]:
                    dp[b] = alt
                    keep[i][b] = True
        b = B
        picked_idx = []
        for i in range(len(items)-1, -1, -1):
            if keep[i][b]:
                picked_idx.append(i)
                b -= items[i].tokens
        picked_idx.reverse()
        rel_sum = float(np.sum([values[i] for i in picked_idx])) if picked_idx else 0.0
        total_tokens = sum(items[i].tokens for i in picked_idx)
        return picked_idx, rel_sum, total_tokens

    opt_idx, opt_rel, opt_tokens = optimal(chunks, values, budget)

    # selections
    idx_map = {id(c): i for i, c in enumerate(chunks)}
    def rel_of(selection):
        pid = [idx_map[id(c)] for c in selection]
        return float(np.sum(values[pid])) if pid else 0.0

    sel_bp = budget_pack(chunks, budget)
    sel_fc = pack_fcfs(chunks, budget)
    sel_rd = pack_random(chunks, budget, seed=seed)

    rows = [
        {"name":"optimal_true_rel", "relevance_sum": opt_rel, "selected_tokens": opt_tokens, "selected_chunks": len(opt_idx)},
        {"name":"budget_pack_small", "relevance_sum": rel_of(sel_bp), "selected_tokens": sum(c.tokens for c in sel_bp), "selected_chunks": len(sel_bp)},
        {"name":"fcfs_small", "relevance_sum": rel_of(sel_fc), "selected_tokens": sum(c.tokens for c in sel_fc), "selected_chunks": len(sel_fc)},
        {"name":"random_small", "relevance_sum": rel_of(sel_rd), "selected_tokens": sum(c.tokens for c in sel_rd), "selected_chunks": len(sel_rd)},
    ]
    return rows

def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--n", type=int, default=5000)
    ap.add_argument("--budget", type=float, default=0.3)
    ap.add_argument("--seed", type=int, default=123)
    ap.add_argument("--corr", type=float, default=0.6)
    ap.add_argument("--plot", action="store_true")
    ap.add_argument("--save", action="store_true")
    args = ap.parse_args()

    rows = eval_once(n=args.n, budget_ratio=args.budget, seed=args.seed, corr=args.corr)
    rows_q = quality_vs_optimal(n=min(200, args.n), budget_ratio=args.budget, seed=args.seed, corr=args.corr)

    print("\n=== Efficiency (n={}, budget={{:.0%}}) ===".format(args.n, args.budget))
    for r in rows:
        print("{name:12s} time={{time_ms:7.2f}}ms  save_ratio={{save_ratio:6.3f}}  tokens_saved={{tokens_saved:8d}}  rel_sum={{relevance_sum:8.3f}}".format(**r))

    print("\n=== Quality vs Optimal (subset) ===")
    for r in rows_q:
        print("{name:18s} rel_sum={{relevance_sum:8.3f}}  tokens={{selected_tokens:5d}} chunks={{selected_chunks:4d}}".format(**r))

    if pd is not None and args.save:
        pd.DataFrame(rows).to_csv("benchmarks/results_efficiency.csv", index=False)
        pd.DataFrame(rows_q).to_csv("benchmarks/results_quality.csv", index=False)
        print("Saved CSVs to benchmarks حضرتك.")

    if plt is not None and args.plot:
        # single-figure plots, no explicit colors
        x = [r["name"] for r in rows]
        y = [r["time_ms"] for r in rows]
        import matplotlib.pyplot as plt
        plt.figure()
        plt.bar(x, y)
        plt.title("Packer Runtime (ms)")
        plt.xlabel("method")
        plt.ylabel("ms")
        plt.show()

if __name__ == "__main__":
    main()

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\benchmarks\\longbench_eval.py

"""
Benchmark script: LongBench-like evaluation.
Simulates context packing efficiency.
"""
from crom_efficientllm.budget_packer.packer import budget_pack

def evaluate():
    chunks = [{"text": f"chunk {i}", "score": i % 5, "tokens": 100} for i in range(20)]
    packed = budget_pack(chunks, budget=500)
    print("Selected:", len(packed), "chunks")

if __name__ == "__main__":
    evaluate()

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\benchmarks\\sample_results.json

{}

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\crom 1.0.1수정 업데이트 상세보고서.md

# CRoM-EfficientLLM v1.0.1 업데이트 상세 보고서

**문서 목적:** 소셜 미디어 (LinkedIn, Twitter, Medium) 포스팅을 위한 마케팅 AI의 정보 소스 제공
**작성일:** 2025-09-06
**작성자:** CLI ↯C01∞ | Σψ∴

---

## 1. 개요 (Overview)

- **프로젝트명:** CRoM-EfficientLLM (Context Rot Mitigation for Efficient LLMs)
- **이전 버전:** 0.2.1
- **신규 버전:** 1.0.1

**핵심 요약:**
이번 v1.0.1 업데이트는 CRoM-EfficientLLM 프로젝트의 **첫 번째 기능 구현(First Functional Implementation)**을 의미합니다. 기존의 아이디어와 뼈대만 있던 상태에서, 실제 동작하는 핵심 로직을 모두 구현하여 **작동 가능한 프로토타입(Working Prototype)**으로 전환했습니다. 이제 사용자들은 RAG 파이프라인의 컨텍스트를 효율적으로 관리하고 최적화하는 핵심 기능들을 직접 테스트하고 활용할 수 있습니다.

---

## 2. 배경 (Background)

기존 v0.2.1은 `pyproject.toml`, `README.md` 등 프로젝트의 방향성과 구조만 정의된 **설계 단계의 스캐폴드(Scaffold)**였습니다. 실제 핵심 로직을 담고 있는 Python 소스 코드가 부재하여 아이디어를 실제로 검증할 수 없었습니다.

이번 업데이트의 목표는 이 설계도에 따라, **처음부터(from scratch) 핵심 기능들을 모두 구현**하여 프로젝트에 생명을 불어넣고, 실제 사용 가능한 상태로 만드는 것이었습니다.

---

## 3. 상세 변경 내역 (Detailed Changes)

이번 업데이트를 통해 4개의 핵심 모듈이 `src/crom_efficientllm/` 디렉토리 내에 새롭게 구현되었습니다.

### 가. `budget_packer.py` - 지능형 컨텍스트 패킹 엔진
- **기능:** LLM에 전달할 컨텍스트(청크)를 주어진 토큰 예산 내에서 가장 효율적으로 구성합니다.
- **세부 사항:**
    - 단순히 텍스트를 자르는 것이 아니라, **점수/토큰 비율**을 기준으로 가장 중요한 정보를 우선적으로 선택합니다.
    - 패킹 후 **압축률, 절약된 토큰 수, 예산 효율성** 등 상세한 통계를 제공하여, 컨텍스트 관리 전략의 효과를 정량적으로 분석할 수 있는 기반을 마련했습니다.

### 나. `cross_encoder.py` - 안정성 강화 Cross-Encoder 관리자
- **기능:** RAG 파이프라인의 핵심인 Cross-Encoder 모델을 안정적으로 관리하고 오류 발생 시 시스템 전체의 다운을 방지합니다.
- **세부 사항:**
    - `sentence-transformers` 라이브러리가 없거나 모델 로딩에 실패하는 등 다양한 **오류 상황을 자동으로 감지하고 우아하게 처리(Graceful Fallback)**합니다.
    - 시스템이 멈추는 대신, "비활성화", "오류" 등의 명확한 상태를 API 응답에 포함시켜 **시스템의 안정성과 예측 가능성**을 크게 높였습니다.

### 다. `capsule_logger.py` - 투명성 확보를 위한 캡슐 로거
- **기능:** 시스템의 모든 처리 과정을 **구조화된 로그(Structured Log)**로 기록하여 투명성과 감사 가능성을 제공합니다.
- **세부 사항:**
    - 모든 API 요청, 처리 통계, 시스템 상태를 **"설명 캡슐(Explain Capsule)"**이라는 JSONL 형식으로 영구 저장합니다.
    - 이는 추후 시스템의 동작을 디버깅하거나, 성능 저하의 원인을 분석하고, AI의 판단 근거를 추적하는 데 필수적인 데이터가 됩니다.

### 라. `server.py` - 핵심 기능 통합 API 서버
- **기능:** 위에서 설명한 모든 모듈(패킹, 리랭킹, 로깅)을 하나로 묶어, 사용자가 쉽게 접근할 수 있는 **FastAPI 기반의 API 서버**를 제공합니다.
- **세부 사항:**
    - `/process` 엔드포인트를 통해 쿼리와 컨텍스트 데이터를 받아, 리랭킹부터 패킹, 로깅까지의 전 과정을 **하나의 트랜잭션으로 처리(Orchestration)**합니다.
    - `/healthz` 엔드포인트를 통해 외부 모니터링 시스템이 서버의 상태를 쉽게 확인할 수 있도록 구현했습니다.

---

## 4. 버전 관리 및 문서화 (Versioning & Documentation)

- **버전 업데이트:** 핵심 기능이 구현됨에 따라, 프로젝트의 버전을 `0.2.1`에서 **`1.0.1`**로 상향 조정하여 중요한 진전을 명시했습니다.
- **변경 이력 관리:** `CHANGELOG.md` 파일에 상기된 모든 구현 내역을 상세히 기록하여, 사용자와 기여자가 프로젝트의 발전 과정을 쉽게 추적할 수 있도록 투명성을 확보했습니다.

---

## 5. 기대 효과 및 다음 단계 (Expected Impact & Next Steps)

- **기대 효과:**
    - CRoM-EfficientLLM은 더 이상 아이디어가 아닌, **실제 RAG 시스템에 적용하여 컨텍스트 관리 효율성을 테스트할 수 있는 실용적인 도구**로 발전했습니다.
    - 개발자들은 LLM의 제한된 컨텍스트 창을 어떻게 하면 가장 효율적으로 사용할 수 있는지에 대한 **정량적인 데이터**를 얻을 수 있게 되었습니다.

- **다음 단계:**
    - `README.md`에 명시된 `crom-demo` 및 `crom-bench` CLI 기능 구현
    - 사용자가 원하는 토크나이저(Tokenizer)를 선택할 수 있는 기능 추가
    - 다양한 컨텍스트 관리 전략의 성능을 비교할 수 있는 벤치마크 시스템 고도화

---

**보고서 종료.**

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\dashboard\\grafana_dashboard.json

{}

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\dashboard\\prometheus_config.yml

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\docs\\architecture.md

# Architecture

This document outlines the architecture of the CRoM-EfficientLLM project.

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\docs\\versioning.md

# Versioning & PyPI Guidance

This document defines package naming, SemVer rules, and a future path to publish to PyPI.

## 1) Package name
- Distribution name (PyPI): `crom-efficientllm` (lowercase, hyphen-separated)
- Import name (module): `crom_efficientllm` (PEP 8 underscore)

> **Tip**: Keep both names consistent to avoid confusion in docs.

### Check name availability on PyPI
- Visit: https://pypi.org/project/crom-efficientllm/ (404 → available)
- If taken, consider: `crom-efficient-llm`, `crom-llm-efficient`, `crom-ctx-pack`
- Reserve on TestPyPI first: use `test.pypi.org` to validate metadata & upload

## 2) Semantic Versioning (SemVer)
We follow **MAJOR.MINOR.PATCH**.

- **MAJOR**: Backward-incompatible API changes
  - e.g., rename function signatures (`budget_pack`), move/rename modules, change return schemas
- **MINOR**: Backward-compatible features
  - new functions/flags (e.g., `pack_summary`, CLI subcommands), performance improvements
- **PATCH**: Backward-compatible bug fixes
  - logic corrections, docs/CI fixes, dependency pin updates without API changes

### Pre-releases
Use suffixes: `-a.1`, `-b.1`, `-rc.1` (alpha/beta/release-candidate)
- Example: `0.3.0-rc.1`

### Deprecation Policy
- Mark deprecated APIs in `CHANGELOG.md` and docstrings
- Provide at least **one MINOR release** with warnings before removal

### Public API Surface
We commit compatibility for:
- `crom_efficientllm.budget_packer.packer`: `Chunk`, `budget_pack`, `pack_summary`
- `crom_efficientllm.rerank_engine.rerank`: `hybrid_rerank`
- `crom_efficientllm.drift_estimator.estimator`: `DriftEstimator`, `DriftMode`
- CLI entrypoints: `crom-demo`, `crom-bench` and their documented flags

## 3) Release Flow (GitHub → PyPI later)
- Tag: `vX.Y.Z` → GitHub Actions builds & creates a Release (artifacts attached)
- Keep `CHANGELOG.md` updated per release
- After API stabilizes, enable **PyPI publish** using a separate workflow with `PYPI_API_TOKEN` secret

### (Future) PyPI publishing steps
1. Create a PyPI account & project
2. Add `PYPI_API_TOKEN` to repo `Settings → Secrets and variables → Actions`
3. Add `release-pypi.yml` workflow to upload on tag
4. Verify install: `pip install crom-efficientllm` and import `crom_efficientllm`

---
_Last updated: 2025-09-02_

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\examples\\corpus\\sample_docs.jsonl

{"id": 1, "text": "AI ethics and governance frameworks for responsible AI."}
{"id": 2, "text": "Techniques for detecting model drift in production systems."}
{"id": 3, "text": "A recipe for sourdough bread and fermentation tips."}
{"id": 4, "text": "Hybrid search: combining sparse and dense retrieval methods."}
{"id": 5, "text": "Token budgets and prompt compression strategies for LLMs."}
{"id": 6, "text": "Monitoring with Prometheus and building Grafana dashboards."}

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\examples\\corpus\\sample_queries.jsonl

{"query": "how to detect drift in ai models"}
{"query": "ways to reduce llm token usage"}
{"query": "observability stack prometheus grafana"}

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\pyproject.toml

[build-system]
requires = ["setuptools>=68", "wheel"]
build-backend = "setuptools.build_meta"

[project]
name = "crom-efficientllm"
version = "1.0.1"
description = "CRoM (Context Rot Mitigation)-EfficientLLM: Budget packing, hybrid rerank, and drift estimation with observability"
readme = "README.md"
requires-python = ">=3.9"
license = { text = "Apache-2.0" }
authors = [ { name = "Your Name" } ]
dependencies = [
  "numpy>=1.24,<3",
  "scikit-learn>=1.3,<2",
  "transformers>=4.41,<5",
  "sentence-transformers>=2.2,<3",
  "flask>=3,<4",
  "prometheus-client>=0.20,<1"
]

[project.optional-dependencies]
dev = [
  "pytest>=7",
  "ruff>=0.4",
  "black>=24.4",
  "pre-commit>=3.6",
  "matplotlib>=3.8,<4"
]
plugins = [
  "flashrank>=0.2; python_version>='3.9'",
  "llmlingua>=0.2; python_version>='3.9'",
  "evidently>=0.4; python_version>='3.9'"
]
haystack = [
  "farm-haystack[faiss,inference]>=1.26; python_version>='3.9'"
]

[project.urls]
Homepage = "https://github.com/Flamehaven/CRoM-Context-Rot-Mitigation--EfficientLLM"

[project.scripts]
"crom-demo" = "crom_efficientllm.demo:main"
"crom-bench" = "crom_efficientllm.cli:main"

[tool.setuptools]
package-dir = {"" = "src"}
packages = { find = { where = ["src"] } }

[tool.pytest.ini_options]
addopts = "-q"

[tool.black]
line-length = 100

[tool.ruff]
target-version = "py39"

[tool.ruff.lint]
select = ["E","F","I","UP","B","C4","SIM","PL","PERF","RUF","ANN"]
ignore = ["ANN101","ANN102"]

[tool.ruff.lint.per-file-ignores]
"tests/*" = ["S101","ANN","PLR2004"]

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\release_notes.md

# Release v0.2.1

## [0.2.1] - 2025-09-02
### Added
- CLI `--save-plots` option for `sweep` and `dp-curve`; saves PNG charts to `benchmarks/out/` (or `--out-dir`).
- README Quick Examples mention of plotting flag.
- This CHANGELOG.

### Changed
- Dev tooling: recommend `matplotlib` via dev extra for plotting.

— generated from [CHANGELOG.md](CHANGELOG.md)

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\requirements.txt

numpy>=1.24,<3
scikit-learn>=1.3,<2
transformers>=4.41,<5
sentence-transformers>=2.2,<3
flask>=3,<4
prometheus-client>=0.20,<1

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\scripts\\gen_release_notes.py

#!/usr/bin/env python3
from __future__ import annotations
import os
import re
import sys
from pathlib import Path

ROOT = Path(__file__).resolve().parents[1]
CHANGELOG = ROOT / "CHANGELOG.md"
OUT = ROOT / "release_notes.md"

def main(tag: str) -> None:
    version = tag.lstrip("v").strip()
    if not CHANGELOG.exists():
        OUT.write_text(f"# Release {tag}\n\n(CHANGELOG.md not found)
", encoding="utf-8")
        return
    text = CHANGELOG.read_text(encoding="utf-8")
    pat = re.compile(rf"^##\s*[[^{re.escape(version)}]]?[^\n]*$", re.MULTILINE)
    m = pat.search(text)
    if not m:
        OUT.write_text(
            f"# Release {tag}\n\nSection for {version} not found in CHANGELOG.\n\n" + text,
            encoding="utf-8",
        )
        return
    start = m.end()
    m2 = re.search(r"^##\s+", text[start:], re.MULTILINE)
    end = start + (m2.start() if m2 else len(text) - start)
    section = text[m.start():end].strip()
    body = f"# Release {tag}\n\n{section}\n\n— generated from [CHANGELOG.md](CHANGELOG.md)"
    OUT.write_text(body, encoding="utf-8")

if __name__ == "__main__":
    tag = sys.argv[1] if len(sys.argv) > 1 else os.environ.get("GITHUB_REF_NAME", "")
    if not tag:
        print("Usage: gen_release_notes.py vX.Y.Z", file=sys.stderr)
        sys.exit(2)
    main(tag)

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\scripts\\release.sh

#!/usr/bin/env bash
set -euo pipefail

TAG=${1:-}
if [[ -z "$TAG" ]]; then
  echo "Usage: scripts/release.sh vX.Y.Z"; exit 1
fi

# sanity checks
if [[ -n $(git status --porcelain) ]]; then
  echo "❌ Working tree not clean"; exit 1
fi

# ensure deps
python -m pip install -e .[dev]
pre-commit run --all-files
pytest -q

# generate release notes preview from CHANGELOG
python scripts/gen_release_notes.py "$TAG"
if [[ -f release_notes.md ]]; then
  echo "--- release_notes.md (preview top 60 lines) ---"
  head -n 60 release_notes.md || true
  echo "--- end preview ---"
else
  echo "⚠️ release_notes.md not generated; will fall back to default notes in GH release"
fi

# tag & push


git tag -a "$TAG" -m "Release $TAG"
git push origin "$TAG"

echo "✅ Pushed tag $TAG. GitHub Actions will create the Release automatically."
echo "➡️  Watch: https://github.com/Flamehaven/CRoM-EfficientLLM/actions"

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\src\\crom_efficientllm\\__init__.py

"""Public API for CRoM-EfficientLLM."""
from .budget_packer.packer import Chunk, budget_pack, pack_summary
from .rerank_engine.rerank import hybrid_rerank
from .drift_estimator.estimator import DriftEstimator, DriftMode

__all__ = [
    "Chunk",
    "budget_pack",
    "pack_summary",
    "hybrid_rerank",
    "DriftEstimator",
    "DriftMode",
]

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\src\\crom_efficientllm\\budget_packer.py

from typing import List, Dict
import logging

def enhanced_greedy_pack(chunks: List[Dict], budget: int, 
                        score_key: str = "score") -> tuple[List[Dict], Dict]:
    """
    기존 greedy_pack 함수를 확장하여 상세 통계 반환
    
    Returns:
        tuple: (packed_chunks, stats_dict)
    """
    if not chunks:
        return [], {
            "selected_count": 0,
            "packed_count": 0,
            "selected_tokens": 0,
            "packed_tokens": 0,
            "compression_ratio": 0.0,
            "token_savings": 0,
            "efficiency": 0.0
        }
    
    # 토큰 수 미리 계산
    for chunk in chunks:
        if "token_count" not in chunk:
            chunk["token_count"] = max(1, len(chunk.get("text", "")) // 4)
    
    # 효율성 기준 정렬 (score/token 비율)
    sorted_chunks = sorted(
        chunks, 
        key=lambda x: x.get(score_key, 0) / x["token_count"], 
        reverse=True
    )
    
    # 그리디 패킹
    packed_chunks = []
    used_tokens = 0
    
    for chunk in sorted_chunks:
        if used_tokens + chunk["token_count"] <= budget:
            packed_chunks.append(chunk)
            used_tokens += chunk["token_count"]
    
    # 상세 통계 계산
    total_selected_tokens = sum(chunk["token_count"] for chunk in chunks)
    
    stats = {
        "selected_count": len(chunks),
        "packed_count": len(packed_chunks),
        "selected_tokens": total_selected_tokens,
        "packed_tokens": used_tokens,
        "compression_ratio": len(packed_chunks) / len(chunks) if chunks else 0.0,
        "token_savings": total_selected_tokens - used_tokens,
        "efficiency": used_tokens / budget if budget > 0 else 0.0
    }
    
    # 📊 로깅 추가 (기존 코드에 없던 통계 가시성)
    logging.info(f"Packing completed: {stats['packed_count']}/{stats['selected_count']} chunks, "
                f"tokens: {stats['packed_tokens']}/{stats['selected_tokens']} "
                f"(efficiency: {stats['efficiency']:.1%})")
    
    return packed_chunks, stats

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\src\\crom_efficientllm\\capsule_logger.py

import json
from pathlib import Path
from datetime import datetime
from typing import Union, Dict
import logging

class ExplainCapsuleLogger:
    """스키마 기반 설명 캡슐 저장 시스템"""
    
    def __init__(self, log_directory: str = "artifacts/logs"):
        self.log_dir = Path(log_directory)
        self.log_dir.mkdir(parents=True, exist_ok=True)
        
        # 로그 파일 경로들
        self.capsules_file = self.log_dir / "explain_capsules.jsonl"
        self.metrics_file = self.log_dir / "processing_metrics.jsonl"
        self.errors_file = self.log_dir / "error_log.jsonl"
        
        logging.info(f"ExplainCapsule Logger initialized: {self.log_dir}")
    
    def create_explain_capsule(self, query: str, response_data: Dict, 
                              processing_stats: Dict, 
                              cross_encoder_status: str) -> Dict:
        """스키마 준수 설명 캡슐 생성"""
        
        capsule = {
            # 🔖 메타데이터 (필수)
            "timestamp": datetime.now().isoformat(),
            "version": "1.0",
            "processor": "CRoM-Enhanced",
            
            # 📝 쿼리 정보
            "query": {
                "text": query,
                "length": len(query),
                "token_estimate": len(query) // 4
            },
            
            # 📊 처리 통계 (패치 1에서 확장된 정보)
            "processing_stats": {
                **processing_stats,
                "cross_encoder_status": cross_encoder_status
            },
            
            # 🔧 시스템 상태
            "system_state": {
                "cross_encoder_available": cross_encoder_status not in ["disabled", "unavailable"]
            },

            # 📦 원본 및 결과 청크
            "chunks": {
                "packed": response_data.get("chunks", [])
            }
        }
        return capsule

    def log_capsule(self, capsule: Dict):
        """설명 캡슐을 .jsonl 파일에 기록"""
        try:
            with open(self.capsules_file, "a", encoding="utf-8") as f:
                f.write(json.dumps(capsule, ensure_ascii=False) + "\n")
        except Exception as e:
            logging.error(f"Failed to log explain capsule: {e}")

    def log_error(self, error_details: Dict):
        """오류 정보를 .jsonl 파일에 기록"""
        try:
            error_details["timestamp"] = datetime.now().isoformat()
            with open(self.errors_file, "a", encoding="utf-8") as f:
                f.write(json.dumps(error_details, ensure_ascii=False) + "\n")
        except Exception as e:
            logging.error(f"Failed to log error: {e}")

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\src\\crom_efficientllm\\cli.py

from __future__ import annotations

import argparse
import json
import os
import time
from dataclasses import dataclass
from typing import List, Dict, Sequence

import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

from crom_efficientllm.budget_packer.packer import budget_pack, Chunk
from crom_efficientllm.rerank_engine.rerank import hybrid_rerank

try:
    from sentence_transformers import SentenceTransformer
except Exception:  # pragma: no cover
    SentenceTransformer = None  # type: ignore

# Optional plugins are imported lazily when flags are set

@dataclass
class Doc:
    id: str
    text: str

def load_jsonl(path: str) -> List[Dict]:
    with open(path, "r", encoding="utf-8") as f:
        return [json.loads(line) for line in f]

def build_corpus(path: str) -> List[Doc]:
    rows = load_jsonl(path)
    return [Doc(id=str(r.get("id", i)), text=str(r["text"])) for i, r in enumerate(rows)]

def sparse_retrieval(query: str, corpus: Sequence[Doc], k: int = 100) -> List[Dict]:
    texts = [d.text for d in corpus]
    vect = TfidfVectorizer(ngram_range=(1, 2)).fit(texts)
    D = vect.transform(texts)
    Q = vect.transform([query])
    sims = cosine_similarity(Q, D).ravel()
    order = np.argsort(-sims)[:k]
    return [{"id": corpus[i].id, "text": corpus[i].text, "score_sparse": float(sims[i])} for i in order]

def dense_embed_model(name: str):
    if SentenceTransformer is None:
        raise RuntimeError("sentence-transformers not installed. Install with `pip install -e .`.")
    return SentenceTransformer(name)

def _apply_flashrank(query: str, docs: List[Dict], model_name: str) -> List[Dict]:
    try:
        from crom_efficientllm.plugins.flashrank_reranker import flashrank_rerank
    except Exception as e:  # pragma: no cover
        raise RuntimeError("FlashRank plugin not available. Install extras: pip install .[plugins]") from e
    ranked = flashrank_rerank(query, docs, model_name=model_name)
    # Normalize plugin score to 0..1 and put into score_final
    scores = np.array([d.get("score_flashrank", 0.0) for d in ranked], dtype=np.float32)
    if scores.size and float(scores.max() - scores.min()) > 1e-12:
        s = (scores - scores.min()) / (scores.max() - scores.min())
    else:
        s = np.zeros_like(scores)
    for i, d in enumerate(ranked):
        d["score_final"] = float(s[i])
    return ranked

def _apply_llmlingua(text: str, ratio: float) -> str:
    try:
        from crom_efficientllm.plugins.llmlingua_compressor import compress_prompt
    except Exception as e:  # pragma: no cover
        raise RuntimeError("LLMLingua plugin not available. Install extras: pip install .[plugins]") from e
    return compress_prompt(text, target_ratio=ratio)

def _save_evidently_report(all_embs: List[List[float]], out_html: str) -> None:
    try:
        from crom_efficientllm.plugins.evidently_drift import drift_report
    except Exception as e:  # pragma: no cover
        raise RuntimeError("Evidently plugin not available. Install extras: pip install .[plugins]") from e
    n = len(all_embs)
    if n < 4:
        return
    ref = all_embs[: n // 2]
    cur = all_embs[n // 2 :]
    rep = drift_report(ref, cur)
    rep.save_html(out_html)

def mock_llm_generate(prompt: str) -> str:
    time.sleep(0.005)  # simulate small latency
    return "[MOCK] " + prompt[:160]

def e2e(args: argparse.Namespace) -> None:
    corpus = build_corpus(args.corpus)
    queries = [r["query"] for r in load_jsonl(args.queries)]
    embed = dense_embed_model(args.model)
    all_embs: List[List[float]] = []

    t0 = time.perf_counter()
    all_rows = []
    for q in queries:
        t_s = time.perf_counter()
        cands = sparse_retrieval(q, corpus, k=args.k)
        t_sparse = (time.perf_counter() - t_s) * 1000

        t_r = time.perf_counter()
        if args.use_flashrank:
            reranked = _apply_flashrank(q, cands, args.flashrank_model)
        else:
            reranked = hybrid_rerank(q, cands, embed, alpha=args.alpha)
        t_rerank = (time.perf_counter() - t_r) * 1000

        # token heuristic + budget pack
        chunks = [
            Chunk(text=d["text"], score=d.get("score_final", d.get("score_sparse", 0.0)), tokens=max(1, len(d["text"]) // 4))
            for d in reranked
        ]
        budget_tokens = int(sum(c.tokens for c in chunks) * args.budget)
        t_p = time.perf_counter()
        packed = budget_pack(chunks, budget=budget_tokens)
        t_pack = (time.perf_counter() - t_p) * 1000

        prompt = "\n\n".join(c.text for c in packed) + f"\n\nQ: {q}\nA:"
        if args.use_llmlingua:
            prompt = _apply_llmlingua(prompt, ratio=args.compress_ratio)

        # collect embeddings for drift snapshot (mean-pooled)
        with np.errstate(all="ignore"):
            if len(packed) > 0:
                doc_embs = embed.encode([c.text for c in packed], convert_to_numpy=True)
                vec = np.mean(doc_embs, axis=0).tolist()
                all_embs.append(vec)

        t_l = time.perf_counter()
        _ = mock_llm_generate(prompt)
        t_llm = (time.perf_counter() - t_l) * 1000

        total = (time.perf_counter() - t_s) * 1000
        all_rows.append({
            "query": q,
            "sparse_ms": t_sparse,
            "rerank_ms": t_rerank,
            "pack_ms": t_pack,
            "llm_ms": t_llm,
            "total_ms": total,
            "packed_tokens": sum(c.tokens for c in packed),
            "orig_tokens": sum(c.tokens for c in chunks),
            "save_ratio": 1 - (sum(c.tokens for c in packed) / max(1, sum(c.tokens for c in chunks))),
            "used_flashrank": bool(args.use_flashrank),
            "used_llmlingua": bool(args.use_llmlingua),
        })

    elapsed = (time.perf_counter() - t0) * 1000
    os.makedirs(args.out_dir, exist_ok=True)
    out_path = os.path.join(args.out_dir, "e2e_results.jsonl")
    with open(out_path, "w", encoding="utf-8") as f:
        for r in all_rows:
            f.write(json.dumps(r, ensure_ascii=False) + "\n")
    print(f"saved results -> {out_path} ({len(all_rows)} queries) ; elapsed={elapsed:.2f}ms")

    if args.use_evidently and all_embs:
        html_path = os.path.join(args.out_dir, "evidently_report.html")
        _save_evidently_report(all_embs, html_path)
        print(f"evidently report -> {html_path}")

def budget_sweep(args: argparse.Namespace) -> None:
    import itertools
    corpus = build_corpus(args.corpus)
    queries = [r["query"] for r in load_jsonl(args.queries)][: args.max_q]
    embed = dense_embed_model(args.model)

    budgets = [b / 100.0 for b in range(args.b_min, args.b_max + 1, args.b_step)]
    rows = []
    for q, b in itertools.product(queries, budgets):
        cands = sparse_retrieval(q, corpus, k=args.k)
        reranked = hybrid_rerank(q, cands, embed, alpha=args.alpha)
        chunks = [Chunk(text=d["text"], score=d["score_final"], tokens=max(1, len(d["text"]) // 4)) for d in reranked]
        budget_tokens = int(sum(c.tokens for c in chunks) * b)
        packed = budget_pack(chunks, budget=budget_tokens)
        rows.append({
            "query": q,
            "budget": b,
            "packed_tokens": sum(c.tokens for c in packed),
            "orig_tokens": sum(c.tokens for c in chunks),
            "save_ratio": 1 - (sum(c.tokens for c in packed) / max(1, sum(c.tokens for c in chunks))),
            "avg_score": float(np.mean([c.score for c in packed])) if packed else 0.0,
        })

    os.makedirs(args.out_dir, exist_ok=True)
    out_path = os.path.join(args.out_dir, "budget_sweep.jsonl")
    with open(out_path, "w", encoding="utf-8") as f:
        for r in rows:
            f.write(json.dumps(r, ensure_ascii=False) + "\n")
    print(f"saved results -> {out_path} ; points={len(rows)}")

    if args.save_plots:
        try:
            import matplotlib.pyplot as plt  # noqa: F401
            import matplotlib.pyplot as _plt
        except Exception:
            print("[warn] matplotlib not installed; install dev extras: pip install -e .[dev]")
        else:
            # Aggregate by budget
            import collections
            agg = collections.defaultdict(list)
            for r in rows:
                agg[r["budget"]].append(r)
            budgets_sorted = sorted(agg.keys())
            avg_save = [float(np.mean([x["save_ratio"] for x in agg[b]])) for b in budgets_sorted]
            avg_score = [float(np.mean([x["avg_score"] for x in agg[b]])) for b in budgets_sorted]

            _plt.figure()
            _plt.plot([b * 100 for b in budgets_sorted], [s * 100 for s in avg_save], marker="o")
            _plt.xlabel("Budget (%)")
            _plt.ylabel("Avg Save Ratio (%)")
            _plt.title("Budget Sweep: Save Ratio vs Budget")
            _plt.grid(True)
            _plt.tight_layout()
            _plt.savefig(os.path.join(args.out_dir, "budget_sweep.png")),

            _plt.figure()
            _plt.plot([s * 100 for s in avg_save], avg_score, marker="o")
            _plt.xlabel("Save Ratio (%)")
            _plt.ylabel("Avg Score (packed)")
            _plt.title("Pareto: Quality vs Savings")
            _plt.grid(True)
            _plt.tight_layout()
            _plt.savefig(os.path.join(args.out_dir, "budget_pareto.png")),
            print("plots ->", os.path.join(args.out_dir, "budget_sweep.png"), ",", os.path.join(args.out_dir, "budget_pareto.png"))

def scaling(args: argparse.Namespace) -> None:
    def make_synth(n: int, seed: int = 42):
        rng = np.random.default_rng(seed)
        tokens = np.clip(rng.lognormal(4.0, 0.6, n).astype(int), 5, 2000)
        score = rng.normal(0, 1, n)
        return [Chunk(text="x" * int(t * 4), score=float(s), tokens=int(t)) for s, t in zip(score, tokens)]

    for n in [1000, 5000, 10000, 20000, 50000, 100000]:
        if n > args.n_max:
            break
        chunks = make_synth(n)
        budget = int(sum(c.tokens for c in chunks) * args.budget)
        t0 = time.perf_counter()
        _ = budget_pack(chunks, budget)
        ms = (time.perf_counter() - t0) * 1000
        print(f"n={n:6d}  budget={args.budget:.0%}  time={ms:8.2f} ms")

def dp_curve(args: argparse.Namespace) -> None:
    def make_synth(n: int, seed: int = 123, corr: float = 0.6):
        rng = np.random.default_rng(seed)
        true_rel = rng.normal(0, 1, n)
        noise = rng.normal(0, 1, n) * np.sqrt(1 - corr**2)
        score = corr * true_rel + noise
        tokens = np.clip(rng.lognormal(4.0, 0.6, n).astype(int), 5, 2000)
        chunks = [Chunk(text="x" * int(t * 4), score=float(s), tokens=int(t)) for s, t in zip(score, tokens)]
        return chunks, true_rel

    def optimal(chunks: Sequence[Chunk], values: np.ndarray, budget: int) -> float:
        B = budget
        dp = np.zeros(B + 1, dtype=np.float32)
        for i, ch in enumerate(chunks):
            wt = ch.tokens
            val = max(0.0, float(values[i]))
            for b in range(B, wt - 1, -1):
                dp[b] = max(dp[b], dp[b - wt] + val)
        return float(dp[B])

    chunks, true_rel = make_synth(args.n)
    total = sum(c.tokens for c in chunks)
    budgets = [int(total * b / 100.0) for b in range(args.b_min, args.b_max + 1, args.b_step)]
    out_rows = []

    for B in budgets:
        sel = budget_pack(chunks, B)
        idx_map = {id(c): i for i, c in enumerate(chunks)}
        rel_bp = float(np.sum([max(0.0, true_rel[idx_map[id(c)]]) for c in sel]))
        rel_opt = optimal(chunks[: args.n_opt], true_rel[: args.n_opt], min(B, sum(c.tokens for c in chunks[: args.n_opt])))
        pct = rel_bp / max(rel_opt, 1e-9)
        out_rows.append({"budget": B, "pct": pct, "rel_bp": rel_bp, "rel_opt": rel_opt})
        print(f"budget={B:8d}  rel_bp={rel_bp:8.3f}  rel_opt≈{rel_opt:8.3f}  pct≈{pct*100:5.1f}% (subset n={args.n_opt})")

    if args.save_plots:
        try:
            import matplotlib.pyplot as plt  # noqa: F401
            import matplotlib.pyplot as _plt
        except Exception:
            print("[warn] matplotlib not installed; install dev extras: pip install -e .[dev]")
        else:
            _plt.figure()
            xs = [r["budget"] * 100.0 / total for r in out_rows]
            ys = [r["pct"] * 100 for r in out_rows]
            _plt.plot(xs, ys, marker="o")
            _plt.xlabel("Budget (%)")
            _plt.ylabel("% of optimal (subset)")
            _plt.title("DP Curve: Greedy vs Optimal")
            _plt.grid(True)
            _plt.tight_layout()
            os.makedirs(args.out_dir, exist_ok=True)
            _plt.savefig(os.path.join(args.out_dir, "dp_curve.png")),
            print("plot ->", os.path.join(args.out_dir, "dp_curve.png")),

def compare_haystack(args: argparse.Namespace) -> None:
    try:
        from haystack.nodes import BM25Retriever, SentenceTransformersRetriever
        from haystack.document_stores import InMemoryDocumentStore
    except Exception as e:  # pragma: no cover
        raise RuntimeError("Install extras: pip install .[haystack]") from e

    corpus = build_corpus(args.corpus)
    docs = [{"content": d.text, "meta": {"id": d.id}} for d in corpus]
    store = InMemoryDocumentStore(use_bm25=True)
    store.write_documents(docs)

    bm25 = BM25Retriever(document_store=store)
    dretr = SentenceTransformersRetriever(document_store=store, model_name_or_path=args.model)

    queries = [r["query"] for r in load_jsonl(args.queries)][: args.max_q]
    for q in queries:
        t0 = time.perf_counter()
        bm = bm25.retrieve(q, top_k=args.k)
        dn = dretr.retrieve(q, top_k=args.k)
        ms = (time.perf_counter() - t0) * 1000
        print(f"{q[:40]:40s}  bm25={len(bm):3d}  dense={len(dn):3d}  time={ms:7.2f} ms")

def main() -> None:
    ap = argparse.ArgumentParser(prog="crom-bench")
    sub = ap.add_subparsers(dest="cmd", required=True)

    p = sub.add_parser("e2e", help="end-to-end: retrieval → rerank → pack → mock LLM")
    p.add_argument("--corpus", default="examples/corpus/sample_docs.jsonl")
    p.add_argument("--queries", default="examples/corpus/sample_queries.jsonl")
    p.add_argument("--model", default="sentence-transformers/all-MiniLM-L6-v2")
    p.add_argument("--k", type=int, default=200)
    p.add_argument("--alpha", type=float, default=0.5)
    p.add_argument("--budget", type=float, default=0.3)
    # plugins
    p.add_argument("--use-flashrank", action="store_true")
    p.add_argument("--flashrank-model", default="ms-marco-TinyBERT-L-2-v2")
    p.add_argument("--use-llmlingua", action="store_true")
    p.add_argument("--compress-ratio", type=float, default=0.6)
    p.add_argument("--use-evidently", action="store_true")

    p.add_argument("--out-dir", default="benchmarks/out")
    p.set_defaults(func=e2e)

    p2 = sub.add_parser("sweep", help="budget sweep + Pareto csv")
    p2.add_argument("--corpus", default="examples/corpus/sample_docs.jsonl")
    p2.add_argument("--queries", default="examples/corpus/sample_queries.jsonl")
    p2.add_argument("--model", default="sentence-transformers/all-MiniLM-L6-v2")
    p2.add_argument("--k", type=int, default=200)
    p2.add_argument("--alpha", type=float, default=0.5)
    p2.add_argument("--b-min", type=int, default=10)
    p2.add_argument("--b-max", type=int, default=90)
    p2.add_argument("--b-step", type=int, default=10)
    p2.add_argument("--max-q", type=int, default=20)
    p2.add_argument("--out-dir", default="benchmarks/out")
    p2.add_argument("--save-plots", action="store_true")
    p2.set_defaults(func=budget_sweep)

    p3 = sub.add_parser("scale", help="scaling runtime with synthetic data")
    p3.add_argument("--n-max", type=int, default=100000)
    p3.add_argument("--budget", type=float, default=0.3)
    p3.set_defaults(func=scaling)

    p4 = sub.add_parser("dp-curve", help="% of optimal vs budget (synthetic)")
    p4.add_argument("--n", type=int, default=2000)
    p4.add_argument("--n-opt", type=int, default=200)
    p4.add_argument("--b-min", type=int, default=10)
    p4.add_argument("--b-max", type=int, default=90)
    p4.add_argument("--b-step", type=int, default=10)
    p4.add_argument("--out-dir", default="benchmarks/out")
    p4.add_argument("--save-plots", action="store_true")
    p4.set_defaults(func=dp_curve)

    p5 = sub.add_parser("haystack-compare", help="compare BM25 vs dense retrievers (Haystack)")
    p5.add_argument("--corpus", default="examples/corpus/sample_docs.jsonl")
    p5.add_argument("--queries", default="examples/corpus/sample_queries.jsonl")
    p5.add_argument("--model", default="sentence-transformers/all-MiniLM-L6-v2")
    p5.add_argument("--k", type=int, default=50)
    p5.add_argument("--max-q", type=int, default=10)
    p5.set_defaults(func=compare_haystack)

    args = ap.parse_args()
    args.func(args)

if __name__ == "__main__":
    main()

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\src\\crom_efficientllm\\cross_encoder.py

from typing import List, Optional
import logging

class SafeCrossEncoderManager: 
    """Cross-Encoder 상태를 명시적으로 관리하는 클래스"""
    
    def __init__(self, model_name: Optional[str] = None, device: str = "cpu"):
        self.model_name = model_name
        self.device = device
        self.model = None
        self.status = "unknown"
        self.last_error = None
        
        self._initialize()
    
    def _initialize(self):
        """Cross-Encoder 초기화 with 상세 상태 추적"""
        if not self.model_name:
            self.status = "disabled"
            logging.info("Cross-Encoder: DISABLED (no model specified)")
            return
        
        try:
            # sentence-transformers 임포트 체크
            from sentence_transformers import CrossEncoder
            
            # 모델 로딩 시도
            self.model = CrossEncoder(self.model_name, device=self.device)
            self.status = f"active ({self.model_name})"
            
            # 🆕 성공 시 상세 로깅
            logging.info(f"Cross-Encoder: ACTIVE")
            logging.info(f"  └─ Model: {self.model_name}")
            logging.info(f"  └─ Device: {self.device}")
            
        except ImportError as e:
            self.status = "unavailable (sentence-transformers not installed)"
            self.last_error = str(e)
            
            # 🆕 의존성 누락 시 명확한 안내
            logging.warning("Cross-Encoder: UNAVAILABLE")
            logging.warning("  └─ Reason: sentence-transformers not installed")
            logging.warning("  └─ Install: pip install sentence-transformers")
            
        except Exception as e:
            self.status = f"error ({type(e).__name__})"
            self.last_error = str(e)
            
            # 🆕 기타 오류 시 상세 로깅
            logging.error(f"Cross-Encoder: ERROR")
            logging.error(f"  └─ Model: {self.model_name}")
            logging.error(f"  └─ Error: {str(e)}")
    
    def get_status_for_response(self) -> str:
        """API 응답용 상태 문자열"""        return self.status
    
    def rerank(self, query: str, documents: List[str]) -> List[float]:
        """안전한 리랭킹 with 상태 로깅"""
        if self.model is None:
            # 🆕 비활성화 상태 명시적 로깅
            logging.debug(f"Cross-Encoder rerank skipped: {self.status}")
            return [0.5] * len(documents)  # 중립 점수
        
        try:
            pairs = [(query, doc) for doc in documents]
            scores = self.model.predict(pairs)
            
            # 🆕 성공적 리랭킹 로깅
            logging.debug(f"Cross-Encoder reranked {len(documents)} documents")
            
            return scores.tolist() if hasattr(scores, 'tolist') else list(scores)
            
        except Exception as e:
            # 🆕 런타임 오류 시 상세 로깅
            logging.error(f"Cross-Encoder rerank failed: {str(e)}")
            logging.error(f"  └─ Fallback: returning neutral scores")
            return [0.5] * len(documents)

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\src\\crom_efficientllm\\demo.py

"""
Demo & Metrics Server for CRoM-EfficientLLM
------------------------------------------
- `crom-demo demo`  : run sample pipeline
- `crom-demo serve` : start Flask + Prometheus metrics on :8000
"""
from __future__ import annotations

import argparse
from typing import List

from flask import Flask, Response
from prometheus_client import Counter, Gauge, generate_latest, CONTENT_TYPE_LATEST

from crom_efficientllm.budget_packer.packer import budget_pack, pack_summary, Chunk
from crom_efficientllm.rerank_engine.rerank import hybrid_rerank
from crom_efficientllm.drift_estimator.estimator import DriftEstimator, DriftMode

# ---- Prometheus metrics ----
TOKENS_SAVED = Gauge("crom_tokens_saved", "Tokens saved by budget packer")
DRIFT_ALERTS = Counter("crom_drift_alerts_total", "Total drift alerts emitted")

class DummyEmbed:
    def encode(self, text_or_list, convert_to_numpy=False):
        if isinstance(text_or_list, list):
            return [self.encode(t) for t in text_or_list]
        vec = [ord(c) % 7 for c in str(text_or_list)[:16]]
        while len(vec) < 16:
            vec.append(0)
        return vec

def run_demo() -> None:
    chunks: List[Chunk] = [
        Chunk(text="AI ethics is crucial", score=0.9, tokens=50),
        Chunk(text="Unrelated text", score=0.2, tokens=40),
        Chunk(text="Drift detection research", score=0.8, tokens=60),
    ]
    packed = budget_pack(chunks, budget=80)
    summary = pack_summary(packed)
    print("Packed:", [c.text for c in packed], summary)

    docs = [{"text": "AI drift measurement"}, {"text": "Cooking recipes"}]
    reranked = hybrid_rerank("AI ethics", docs, DummyEmbed(), alpha=0.5)
    print("Reranked:", [d["text"] for d in reranked])

    de = DriftEstimator(threshold=0.5, mode=DriftMode.L2)
    print("Drift state:", de.state())
    print("Drift alert?", de.update([1, 2, 3]))
    print("Drift alert?", de.update([10, 10, 10]))
    print("Drift state:", de.state())

    # Update metrics
    TOKENS_SAVED.set(max(0, sum(c.tokens for c in chunks) - summary["tokens"]))
    alert1, *_ = de.update([1, 2, 3])
    alert2, *_ = de.update([10, 10, 10])
    if alert1:
        DRIFT_ALERTS.inc()
    if alert2:
        DRIFT_ALERTS.inc()

def create_app() -> Flask:
    app = Flask(__name__)

    @app.get("/healthz")
    def healthz():
        return {"status": "ok"}

    @app.get("/metrics")
    def metrics():
        return Response(generate_latest(), mimetype=CONTENT_TYPE_LATEST)

    return app

def main() -> None:
    parser = argparse.ArgumentParser(prog="crom-demo")
    sub = parser.add_subparsers(dest="cmd", required=True)
    sub.add_parser("demo", help="run sample pipeline")

    pserve = sub.add_parser("serve", help="start metrics server on :8000")
    pserve.add_argument("--host", default="0.0.0.0")
    pserve.add_argument("--port", type=int, default=8000)

    args = parser.parse_args()

    if args.cmd == "demo":
        run_demo()
        return

    if args.cmd == "serve":
        app = create_app()
        app.run(host=args.host, port=args.port)
        return

if __name__ == "__main__":
    main()

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\src\\crom_efficientllm\\server.py

from fastapi import FastAPI, HTTPException
import time
from typing import List, Dict
import logging

# 내부 모듈 임포트
from .budget_packer import enhanced_greedy_pack
from .cross_encoder import SafeCrossEncoderManager
from .capsule_logger import ExplainCapsuleLogger

# --- FastAPI 앱 및 주요 컴포넌트 초기화 ---

app = FastAPI(
    title="CRoM-EfficientLLM Server",
    description="Context Reranking and Management for Efficient LLMs",
    version="1.0.1"
)

logging.basicConfig(level=logging.INFO)

# 컴포넌트 인스턴스화
# TODO: 설정 파일(config.yaml)에서 모델 이름 등을 로드하도록 개선 필요
ce_manager = SafeCrossEncoderManager(model_name="ms-marco-TinyBERT-L-2-v2")
capsule_logger = ExplainCapsuleLogger(log_directory="artifacts/logs")


# --- 응답 스키마 및 헬퍼 함수 ---

class ProcessResponseV2:
    """확장된 /process 엔드포인트 응답 스키마 헬퍼"""
    
    @staticmethod
    def create_response(query: str, packed_chunks: List[Dict], 
                       processing_stats: Dict, cross_encoder_status: str, 
                       processing_time: float) -> Dict:
        """개선된 응답 생성"""
        
        response = {
            "success": True,
            "query": query,
            "chunks": packed_chunks,
            "stats": processing_stats, # packing 통계
            "meta": {
                "cross_encoder_status": cross_encoder_status,
                "processing_time_ms": processing_time * 1000,
                "timestamp": time.time()
            }
        }
        return response

# --- API 엔드포인트 정의 ---

@app.post("/process", summary="Rerank and pack text chunks")
def process_chunks(query: str, chunks: List[Dict], budget: int = 4096):
    """
    주어진 쿼리와 청크 목록을 리랭킹하고 예산에 맞게 패킹합니다.
    """
    start_time = time.time()

    try:
        # 1. Cross-Encoder로 리랭킹 (활성화 시)
        doc_texts = [chunk.get("text", "") for chunk in chunks]
        scores = ce_manager.rerank(query, doc_texts)
        for chunk, score in zip(chunks, scores):
            chunk["score"] = score

        # 2. 예산에 맞게 패킹
        packed_chunks, stats = enhanced_greedy_pack(chunks, budget=budget, score_key="score")

        # 3. 최종 응답 생성
        processing_time = time.time() - start_time
        response_data = ProcessResponseV2.create_response(
            query=query,
            packed_chunks=packed_chunks,
            processing_stats=stats,
            cross_encoder_status=ce_manager.get_status_for_response(),
            processing_time=processing_time
        )

        # 4. 설명 캡슐 로깅
        capsule = capsule_logger.create_explain_capsule(
            query=query,
            response_data=response_data,
            processing_stats=stats,
            cross_encoder_status=ce_manager.get_status_for_response()
        )
        capsule_logger.log_capsule(capsule)

        return response_data

    except Exception as e:
        logging.error(f"Error during /process: {e}", exc_info=True)
        # 오류 로깅
        capsule_logger.log_error({
            "endpoint": "/process",
            "error": str(e),
            "query": query,
        })
        raise HTTPException(status_code=500, detail=f"Internal Server Error: {e}")

@app.get("/healthz", summary="Health check")
def health_check():
    """서버의 상태를 확인합니다."""
    return {"status": "ok", "cross_encoder": ce_manager.get_status_for_response()}

@app.get("/metrics", summary="Get Prometheus metrics")
def get_metrics():
    """Prometheus 메트릭을 노출합니다."""
    # TODO: Prometheus-client를 사용하여 실제 메트릭을 구현해야 함
    return {"message": "Metrics endpoint is active. Implement with prometheus-client."}

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\tests\\test_drift.py

from crom_efficientllm.drift_estimator.estimator import DriftEstimator, DriftMode

def test_drift_triggers():
    de = DriftEstimator(threshold=0.1, mode=DriftMode.L2)
    alert, dist, ewma = de.update([0, 0, 0])
    assert alert is False
    alert, dist, ewma = de.update([1, 0, 0])
    assert isinstance(alert, bool)

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\tests\\test_packer.py

from crom_efficientllm.budget_packer.packer import budget_pack, Chunk

def test_budget_pack_respects_budget():
    chunks = [Chunk("a", 1.0, 60), Chunk("b", 0.9, 50), Chunk("c", 0.5, 20)]
    sel = budget_pack(chunks, budget=70)
    assert sum(c.tokens for c in sel) <= 70

def test_budget_pack_sorting_stable():
    chunks = [
        {"text": "x", "score": 0.9, "tokens": 30},
        {"text": "y", "score": 0.9, "tokens": 20},
        {"text": "z", "score": 0.8, "tokens": 10},
    ]
    sel = budget_pack(chunks, budget=60)
    assert [c.text for c in sel] == ["y", "x", "z"]

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\tests\\test_rerank.py

from crom_efficientllm.rerank_engine.rerank import hybrid_rerank

class Dummy:
    def encode(self, text_or_list, convert_to_numpy=False):
        if isinstance(text_or_list, list):
            return [self.encode(t) for t in text_or_list]
        vec = [ord(c) % 5 for c in str(text_or_list)[:8]]
        while len(vec) < 8:
            vec.append(0)
        return vec

def test_hybrid_rerank_returns_scores():
    docs = [{"text": "alpha"}, {"text": "beta"}]
    out = hybrid_rerank("alp", docs, Dummy(), alpha=0.5)
    assert len(out) == 2
    assert {"score_sparse", "score_dense", "score_final"} <= set(out[0].keys())

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\src\\crom_efficientllm\\budget_packer\\__init__.py

from .packer import Chunk, budget_pack, pack_summary
__all__ = ["Chunk", "budget_pack", "pack_summary"]

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\src\\crom_efficientllm\\budget_packer\\packer.py

"""
Budget Packer
-------------
Greedy packing of highest-scoring chunks under a token budget.
- Stable ordering (score desc, tokens asc, original index asc)
- Input validation and optional token estimation
"""
from __future__ import annotations

from dataclasses import dataclass
from typing import Any, Iterable, List, Sequence, Tuple, Union, Optional

@dataclass(frozen=True)
class Chunk:
    text: str
    score: float
    tokens: int

def _estimate_tokens(text: str) -> int:
    """Lightweight heuristic when `tokens` absent. Avoids heavy tokenizers.
    Why: keeps demo dependency-light and deterministic.
    """
    # approx: 4 chars ≈ 1 token; floor at 1
    return max(1, len(text) // 4)

def _coerce_chunk(obj: Union[Chunk, dict], idx: int) -> Chunk:
    if isinstance(obj, Chunk):
        return obj
    if not isinstance(obj, dict):
        raise TypeError(f"Chunk #{idx} must be Chunk or dict, got {type(obj)}")
    text = str(obj.get("text", ""))
    if not text:
        raise ValueError(f"Chunk #{idx} has empty text")
    score = float(obj.get("score", 0.0))
    tokens = int(obj["tokens"]) if "tokens" in obj else _estimate_tokens(text)
    if tokens <= 0:
        raise ValueError(f"Chunk #{idx} has non-positive tokens: {tokens}")
    return Chunk(text=text, score=score, tokens=tokens)

def budget_pack(
    text_chunks: Sequence[Union[Chunk, dict]],
    budget: int = 1000,
) -> List[Chunk]:
    """
    Args:
        text_chunks: iterable of Chunk or dict with keys {text, score, tokens}
        budget: max token budget (int > 0)
    Returns:
        list of selected chunks (order of selection)
    """
    if budget <= 0:
        raise ValueError("budget must be > 0")

    coerced: List[Chunk] = [_coerce_chunk(c, i) for i, c in enumerate(text_chunks)]

    # stable sort by (-score, tokens, original_index)
    indexed: List[Tuple[int, Chunk]] = list(enumerate(coerced))
    indexed.sort(key=lambda it: (-it[1].score, it[1].tokens, it[0]))

    selected: List[Chunk] = []
    total = 0
    for _, ch in indexed:
        if total + ch.tokens <= budget:
            selected.append(ch)
            total += ch.tokens
    return selected

def pack_summary(selected: Sequence[Chunk]) -> dict:
    tokens = sum(c.tokens for c in selected)
    return {
        "num_chunks": len(selected),
        "tokens": tokens,
        "avg_score": (sum(c.score for c in selected) / len(selected)) if selected else 0.0,
    }

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\src\\crom_efficientllm\\drift_estimator\\__init__.py

from .estimator import DriftEstimator, DriftMode
__all__ = ["DriftEstimator", "DriftMode"]

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\src\\crom_efficientllm\\drift_estimator\\estimator.py

"""
Drift Estimator
---------------
Monitors embedding shift using L2 or cosine distance.
Supports EWMA smoothing and exposes state for dashboards.
"""
from __future__ import annotations

from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Tuple
import numpy as np

class DriftMode(str, Enum):
    L2 = "l2"
    COSINE = "cosine"

@dataclass
class DriftEstimator:
    threshold: float = 0.2
    mode: DriftMode = DriftMode.L2
    ewma_alpha: float = 0.3  # smoothing for stability

    history: List[np.ndarray] = field(default_factory=list)
    distances: List[float] = field(default_factory=list)
    ewma: Optional[float] = None

    def _distance(self, a: np.ndarray, b: np.ndarray) -> float:
        a = np.asarray(a, dtype=np.float32).ravel()
        b = np.asarray(b, dtype=np.float32).ravel()
        if self.mode == DriftMode.L2:
            return float(np.linalg.norm(a - b))
        # cosine distance = 1 - cosine similarity
        denom = (np.linalg.norm(a) * np.linalg.norm(b)) + 1e-12
        return float(1.0 - float(np.dot(a, b)) / denom)

    def update(self, embedding) -> Tuple[bool, float, float]:
        """
        Args:
            embedding: vector representation of current response
        Returns:
            (drift_alert, distance, ewma)
        """
        emb = np.asarray(embedding, dtype=np.float32)
        if emb.ndim != 1:
            emb = emb.ravel()

        if not self.history:
            self.history.append(emb)
            self.ewma = 0.0
            self.distances.append(0.0)
            return (False, 0.0, 0.0)

        last = self.history[-1]
        dist = self._distance(emb, last)
        self.history.append(emb)
        self.distances.append(dist)

        # EWMA update
        if self.ewma is None:
            self.ewma = dist
        else:
            self.ewma = self.ewma_alpha * dist + (1 - self.ewma_alpha) * self.ewma

        return (bool(self.ewma > self.threshold), float(dist), float(self.ewma))

    def state(self) -> dict:
        return {
            "count": len(self.history),
            "last_distance": self.distances[-1] if self.distances else 0.0,
            "ewma": self.ewma or 0.0,
            "mode": self.mode.value,
            "threshold": self.threshold,
        }

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\src\\crom_efficientllm\\plugins\\evidently_drift.py

from __future__ import annotations
from typing import List

try:
    from evidently.metric_preset import DataDriftPreset
    from evidently.report import Report
    import pandas as pd
except Exception as e:  # pragma: no cover
    raise RuntimeError("evidently not installed. Install extras: pip install .[plugins]") from e

def drift_report(ref: List[List[float]], cur: List[List[float]]):
    ref_df = pd.DataFrame(ref)
    cur_df = pd.DataFrame(cur)
    rep = Report(metrics=[DataDriftPreset()])
    rep.run(reference_data=ref_df, current_data=cur_df)
    return rep

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\src\\crom_efficientllm\\plugins\\flashrank_reranker.py

from __future__ import annotations
from typing import List, Dict

try:
    from flashrank import Reranker
except Exception as e:  # pragma: no cover
    raise RuntimeError("flashrank not installed. Install extras: pip install .[plugins]") from e

def flashrank_rerank(query: str, docs: List[Dict[str, str]], model_name: str = "ms-marco-TinyBERT-L-2-v2") -> List[Dict]:
    rr = Reranker(model_name)
    pairs = [(query, d["text"]) for d in docs]
    scores = rr.rerank(pairs)
    order = sorted(range(len(docs)), key=lambda i: -scores[i])
    return [docs[i] | {"score_flashrank": float(scores[i])} for i in order]

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\src\\crom_efficientllm\\plugins\\llmlingua_compressor.py

from __future__ import annotations

try:
    from llmlingua import PromptCompressor
except Exception as e:  # pragma: no cover
    raise RuntimeError("llmlingua not installed. Install extras: pip install .[plugins]") from e

def compress_prompt(text: str, target_ratio: float = 0.5) -> str:
    pc = PromptCompressor()
    out = pc.compress(text, target_ratio=target_ratio)
    return out["compressed_prompt"] if isinstance(out, dict) and "compressed_prompt" in out else str(out)

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\src\\crom_efficientllm\\rerank_engine\\__init__.py

from .rerank import hybrid_rerank
__all__ = ["hybrid_rerank"]

---

File: D:\\Sanctum\\CRoM-EfficientLLM\\src\\crom_efficientllm\\rerank_engine\\rerank.py

"""
Hybrid Rerank Engine
--------------------
Combines sparse (TF-IDF cosine) and dense (embedding cosine) scores with
min-max normalization for robust fusion.
"""
from __future__ import annotations

from typing import Dict, List, Sequence
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

def _to_numpy(x):
    arr = np.asarray(x)
    return arr.astype(np.float32)

def _batch_encode(embed_model, texts: Sequence[str]) -> np.ndarray:
    # Try common API of sentence-transformers: encode(list, convert_to_numpy=True)
    if hasattr(embed_model, "encode"):
        try:
            return _to_numpy(embed_model.encode(list(texts), convert_to_numpy=True))
        except TypeError:
            # Fallback: per-text encode
            return _to_numpy([embed_model.encode(t) for t in texts])
    raise TypeError("embed_model must provide .encode()")

def _minmax(x: np.ndarray) -> np.ndarray:
    if x.size == 0:
        return x
    mn, mx = float(np.min(x)), float(np.max(x))
    if mx - mn <= 1e-12:
        return np.zeros_like(x)
    return (x - mn) / (mx - mn)

def hybrid_rerank(
    query: str,
    docs: List[Dict[str, str]],
    embed_model,
    alpha: float = 0.5,
) -> List[Dict[str, object]]:
    """
    Args:
        query: query string
        docs: list of {"text": str}
        embed_model: model with .encode() -> vector(s)
        alpha: weight between sparse/dense in [0,1]
    Returns:
        ranked list of enriched docs with scores {score_sparse, score_dense, score_final}
    """
    if not 0.0 <= alpha <= 1.0:
        raise ValueError("alpha must be in [0, 1]")
    if not docs:
        return []

    texts = [d.get("text", "") for d in docs]

    # Sparse: TF-IDF cosine
    tfidf = TfidfVectorizer(ngram_range=(1, 2), min_df=1).fit(texts)
    Q = tfidf.transform([query])
    D = tfidf.transform(texts)
    sparse_scores = cosine_similarity(Q, D).ravel()

    # Dense: cosine(sim) between L2-normalized embeddings
    q_emb = _to_numpy(embed_model.encode(query))
    d_embs = _batch_encode(embed_model, texts)
    # L2 normalize
    def _l2norm(a):
        n = np.linalg.norm(a, axis=-1, keepdims=True) + 1e-12
        return a / n

    qn = _l2norm(q_emb.reshape(1, -1))
    dn = _l2norm(d_embs)
    dense_scores = cosine_similarity(qn, dn).ravel()

    # Min-max to [0,1] before fusion to avoid scale issues
    s_sparse = _minmax(sparse_scores)
    s_dense = _minmax(dense_scores)

    final_scores = alpha * s_sparse + (1 - alpha) * s_dense
    order = np.argsort(-final_scores)

    ranked = []
    for i in order:
        item = dict(docs[i])
        item.update(
            score_sparse=float(s_sparse[i]),
            score_dense=float(s_dense[i]),
            score_final=float(final_scores[i]),
        )
        ranked.append(item)
    return ranked