add baseline eval script and results

eval/eval_baseline.py

@@ -0,0 +1,359 @@
+"""
+eval/eval_baseline.py
+
+Tests raw LLM with NO retrieval context.
+Establishes the floor that all RAG configurations must beat.
+
+Scoring:
+  - For each query, call LLM with system prompt only (no retrieved chunks)
+  - Score = % of query's `keywords` found in the generated answer
+  - Pass if keyword coverage >= threshold (default 0.4 — slightly lower than
+    retrieval eval since generation may paraphrase rather than use exact terms)
+
+Output: eval/results/baseline.json
+
+Usage:
+    python eval/eval_baseline.py
+    python eval/eval_baseline.py --tier 1            # single tier
+    python eval/eval_baseline.py --query T1-001      # single query
+    python eval/eval_baseline.py --dry-run           # print prompts, no API calls
+    python eval/eval_baseline.py --model meta-llama/Meta-Llama-3-8B-Instruct
+"""
+
+import argparse
+import json
+import logging
+import os
+import sys
+import time
+from pathlib import Path
+
+import numpy as np
+import requests
+import yaml
+from dotenv import load_dotenv
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s  %(levelname)-8s  %(message)s",
+    datefmt="%H:%M:%S",
+)
+log = logging.getLogger(__name__)
+
+# Config 
+def load_config() -> dict:
+    with open("config.yaml") as f:
+        return yaml.safe_load(f)
+
+def load_test_queries(path: str) -> list[dict]:
+    with open(path) as f:
+        return yaml.safe_load(f)["queries"]
+
+# Prompts 
+
+# System prompt loaded from config.yaml -> generation.system_prompt
+# Edit there, not here.
+def _load_system_prompt(cfg: dict) -> str:
+    # Try to get system_prompt
+    # If not found throw error and exit. instead of silently continuing
+    try :
+        return cfg["generation"]["system_prompt"]
+    except KeyError:
+        log.error("System prompt not found in config.yaml under generation.system_prompt")
+        sys.exit(1)
+
+# Instruction template — no context injected (baseline condition)
+BASELINE_TEMPLATE = """Question: {query}
+
+Answer:"""
+
+# HF Inference API 
+# Uses huggingface_hub.InferenceClient with the new router (2025+)
+# The old api-inference.huggingface.co endpoint is deprecated.
+# Large LLMs now route through inference providers via router.huggingface.co/v1
+def call_hf_api(
+    prompt: str,
+    model: str,
+    hf_token: str,
+    system_prompt: str = 'You are an expert software engineer.',
+    max_new_tokens: int = 512,
+    temperature: float = 0.1,
+    retries: int = 3,
+    retry_delay: float = 10.0,
+) -> str | None:
+    """
+    Call HuggingFace Inference API via InferenceClient (chat completions).
+    Returns generated text or None on failure.
+    """
+    # Uses OpenAI-compatible chat completions via router.huggingface.co
+    # Compatible with huggingface_hub==0.24.6 (no provider= kwarg needed)
+    import json as _json
+
+    api_url = "https://router.huggingface.co/v1/chat/completions"
+    headers = {
+        "Authorization": f"Bearer {hf_token}",
+        "Content-Type": "application/json",
+    }
+    payload = {
+        "model": model,
+        "messages": [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": prompt},
+        ],
+        "max_tokens": max_new_tokens,
+        "temperature": temperature,
+    }
+
+    for attempt in range(retries):
+        try:
+            import requests as _req
+            resp = _req.post(api_url, headers=headers,
+                             data=_json.dumps(payload), timeout=90)
+
+            if resp.status_code == 200:
+                data = resp.json()
+                return data["choices"][0]["message"]["content"].strip()
+
+            elif resp.status_code in (429, 529):
+                wait = retry_delay * (2 ** attempt)
+                log.warning("  Rate limited (%d) — waiting %.0fs...",
+                            resp.status_code, wait)
+                time.sleep(wait)
+
+            elif resp.status_code == 503:
+                wait = retry_delay * (attempt + 1)
+                log.warning("  Model loading — waiting %.0fs...", wait)
+                time.sleep(wait)
+
+            else:
+                log.error("  API error %d: %s", resp.status_code, resp.text[:300])
+                return None
+
+        except Exception as e:
+            log.error("  Request error (attempt %d/%d): %s",
+                      attempt + 1, retries, e)
+            time.sleep(retry_delay)
+
+    log.error("  All %d attempts failed", retries)
+    return None
+
+
+# Scoring 
+def score_answer(answer: str, keywords: list[str]) -> dict:
+    """
+    Check what % of expected keywords appear in the generated answer.
+    Uses the `keywords` field (LLM answer eval) not context_keywords.
+    Case-insensitive, partial match allowed.
+    """
+    if not answer or not keywords:
+        return {"score": None, "found": [], "missed": [], "passed": None}
+
+    answer_lower = answer.lower()
+    found = [kw for kw in keywords if kw.lower() in answer_lower]
+    missed = [kw for kw in keywords if kw.lower() not in answer_lower]
+    score = len(found) / len(keywords)
+
+    return {
+        "score": score,
+        "found": found,
+        "missed": missed,
+    }
+
+# Main eval loop 
+def run_baseline(
+    queries: list[dict],
+    model: str,
+    hf_token: str,
+    pass_threshold: float,
+    cfg: dict = None,
+    dry_run: bool = False,
+) -> list[dict]:
+    results = []
+    total = len(queries)
+
+    for i, q in enumerate(queries, 1):
+        qid = q["id"]
+        log.info("[%d/%d] %s — %s", i, total, qid, q["query"][:60])
+
+        prompt = BASELINE_TEMPLATE.format(query=q["query"])
+
+        if dry_run:
+            log.info("  [DRY RUN] Would call: %s", model)
+            log.info("  Prompt: %s", prompt[:100])
+            answer = "[DRY RUN — no API call]"
+            score_result = {"score": None, "found": [], "missed": q.get("keywords", [])}
+        else:
+            t0 = time.time()
+            system_prompt = _load_system_prompt(cfg or {})
+            answer = call_hf_api(prompt, model, hf_token, system_prompt=system_prompt)
+            duration = time.time() - t0
+
+            if answer is None:
+                log.warning("  No answer returned — skipping")
+                results.append({
+                    "query_id": qid,
+                    "tier": q["tier"],
+                    "query": q["query"],
+                    "answer": None,
+                    "score": None,
+                    "passed": None,
+                    "found": [],
+                    "missed": q.get("keywords", []),
+                    "duration_s": duration,
+                    "model": model,
+                    "condition": "baseline_no_rag",
+                })
+                continue
+
+            log.info("  Answer (%d chars, %.1fs): %s...",
+                     len(answer), duration, answer[:80].replace("\n", " "))
+
+            score_result = score_answer(answer, q.get("keywords", []))
+            passed = (
+                score_result["score"] >= pass_threshold
+                if score_result["score"] is not None else None
+            )
+            score_result["passed"] = passed
+
+            log.info("  Score: %.2f (%d/%d keywords) — %s",
+                     score_result["score"] or 0,
+                     len(score_result["found"]),
+                     len(q.get("keywords", [])),
+                     "PASS" if passed else "FAIL")
+
+            # Small delay to avoid rate limiting
+            time.sleep(1.5)
+
+        results.append({
+            "query_id": qid,
+            "tier": q["tier"],
+            "query": q["query"],
+            "answer": answer,
+            "score": score_result.get("score"),
+            "passed": score_result.get("passed"),
+            "found": score_result.get("found", []),
+            "missed": score_result.get("missed", []),
+            "duration_s": duration if not dry_run else 0,
+            "model": model,
+            "condition": "baseline_no_rag",
+        })
+
+    return results
+
+
+# Report 
+
+def print_report(results: list[dict], pass_threshold: float) -> dict:
+    log.info("")
+    log.info("=" * 70)
+    log.info("Baseline Evaluation — No RAG")
+    log.info("=" * 70)
+
+    scored = [r for r in results if r["score"] is not None]
+    passed = [r for r in scored if r.get("passed")]
+    failed = [r for r in scored if not r.get("passed")]
+    skipped = [r for r in results if r["score"] is None]
+
+    # Per-tier
+    for tier in sorted(set(r["tier"] for r in results)):
+        tv = [r for r in scored if r["tier"] == tier]
+        if not tv:
+            continue
+        tp = sum(1 for r in tv if r.get("passed"))
+        avg = np.mean([r["score"] for r in tv])
+        log.info("  Tier %d: %d/%d passed (%.0f%%)  avg kw score %.2f",
+                 tier, tp, len(tv), 100 * tp / len(tv) if tv else 0, avg)
+
+    log.info("")
+    log.info("  Total queries  : %d", len(results))
+    log.info("  Scored         : %d", len(scored))
+    log.info("  Passed         : %d (%.1f%%)",
+             len(passed), 100 * len(passed) / len(scored) if scored else 0)
+    log.info("  Failed         : %d", len(failed))
+    log.info("  Skipped (error): %d", len(skipped))
+
+    if scored:
+        avg_score = np.mean([r["score"] for r in scored])
+        log.info("")
+        log.info("  Avg keyword score : %.3f  (baseline — no RAG)", avg_score)
+        log.info("  Pass threshold    : %.1f", pass_threshold)
+        log.info("")
+        log.info("  This is the FLOOR. RAG system must beat this.")
+
+    log.info("=" * 70)
+
+    return {
+        "condition": "baseline_no_rag",
+        "total": len(results),
+        "scored": len(scored),
+        "passed": len(passed),
+        "failed": len(failed),
+        "skipped": len(skipped),
+        "pass_rate": len(passed) / len(scored) if scored else 0.0,
+        "avg_score": float(np.mean([r["score"] for r in scored])) if scored else 0.0,
+        "pass_threshold": pass_threshold,
+    }
+
+
+def save_results(summary: dict, per_query: list[dict], output_dir: Path) -> None:
+    output_dir.mkdir(parents=True, exist_ok=True)
+    path = output_dir / "baseline.json"
+    with path.open("w") as f:
+        json.dump({"summary": summary, "per_query": per_query}, f, indent=2)
+    log.info("Results saved to %s", path)
+
+
+# Main 
+def main() -> None:
+    load_dotenv()
+
+    parser = argparse.ArgumentParser(description="Baseline eval — raw LLM, no RAG")
+    parser.add_argument("--model", type=str, default=None,
+                        help="HF model ID (default: from config.yaml)")
+    parser.add_argument("--tier", type=int, default=None)
+    parser.add_argument("--query", type=str, default=None)
+    parser.add_argument("--threshold", type=float, default=0.4,
+                        help="Keyword coverage pass threshold (default 0.4)")
+    parser.add_argument("--dry-run", action="store_true",
+                        help="Print prompts without making API calls")
+    args = parser.parse_args()
+
+    hf_token = os.environ.get("HF_API_TOKEN")
+    if not hf_token and not args.dry_run:
+        log.error("HF_API_TOKEN not set in .env")
+        sys.exit(1)
+
+    cfg = load_config()
+    model = args.model or cfg["generation"]["model"]
+    queries = load_test_queries(cfg["evaluation"]["test_queries_path"])
+
+    if args.tier:
+        queries = [q for q in queries if q["tier"] == args.tier]
+    if args.query:
+        queries = [q for q in queries if q["id"] == args.query]
+    if not queries:
+        log.error("No queries matched.")
+        sys.exit(1)
+
+    log.info("=" * 70)
+    log.info("Substrate — Baseline Evaluation (No RAG)")
+    log.info("Model     : %s", model)
+    log.info("Queries   : %d", len(queries))
+    log.info("Threshold : %.1f", args.threshold)
+    log.info("Dry run   : %s", args.dry_run)
+    log.info("=" * 70)
+
+    per_query = run_baseline(
+        queries, model, hf_token or "",
+        pass_threshold=args.threshold,
+        cfg=cfg,
+        dry_run=args.dry_run,
+    )
+
+    summary = print_report(per_query, args.threshold)
+    save_results(summary, per_query, Path(cfg["evaluation"]["results_dir"]))
+    log.info("\nNext: compare these scores against RAG system in eval/eval_rag.py")
+
+
+if __name__ == "__main__":
+    main()

eval/results/baseline.json

@@ -0,0 +1,672 @@
+{
+  "summary": {
+    "condition": "baseline_no_rag",
+    "total": 33,
+    "scored": 33,
+    "passed": 28,
+    "failed": 5,
+    "skipped": 0,
+    "pass_rate": 0.8484848484848485,
+    "avg_score": 0.6166666666666666,
+    "pass_threshold": 0.4
+  },
+  "per_query": [
+    {
+      "query_id": "T1-001",
+      "tier": 1,
+      "query": "How does numpy implement the clip function and what are its boundary conditions?",
+      "answer": "NumPy implements the `clip` function in the `numpy` module, specifically in the `numpy.core.fromnumeric.py` file. The `clip` function is a part of the `fromnumeric` module, which provides various numerical operations.\n\nThe `clip` function is implemented as follows:\n\n```python\ndef clip(a, a_min, a_max, out=None, **kwargs):\n    \"\"\"\n    Clip (limit) the values in an array.\n\n    Parameters\n    ----------\n    a : array_like\n        Input array or object that can be converted to an array.\n    a_min : scalar or array_like\n        Minimum value. All values in `a` less than `a_min` are replaced with `a_min`.\n    a_max : scalar or array_like\n        Maximum value. All values in `a` greater than `a_max` are replaced with `a_max`.\n    out : ndarray, optional\n        Alternative output array in which to place the clipped values. It must have the same shape as the input.\n    **kwargs\n        Additional keyword arguments to be passed to the function being wrapped.\n\n    Returns\n    -------\n    ndarray\n        The clipped array.\n\n    See Also\n    --------\n    clip, clip_by_value, clip_by_global_norm, clip_by_norm\n    \"\"\"\n    a = asanyarray(a)\n    a_min = asanyarray(a_min)\n    a_max = asanyarray(a_max)\n    if a_min.shape != a.shape:\n        raise ValueError(\"a_min must have the same shape as a\")\n    if a_max.shape != a.shape:\n        raise ValueError(\"a_max must have the same shape as a\")\n    if out is None:\n        out = empty_like(a)\n    clip_array(a, a_min, a_max, out)\n    return out\n```\n\nThe `clip` function takes in an input array `a`, minimum value `a_min`, and maximum value `a_max`. It returns the clipped array with values outside the range `[a_min, a_max]` replaced with the nearest boundary value.\n\nThe boundary conditions for the `clip` function are as follows:\n\n* If `a` is less than `a_min`, the clipped value is `a_min`.\n* If `a` is greater than `a_max`, the clipped value is `a_max`.\n* If `a` is within the range `[a_min, a_max]`, the clipped value is `a`.\n\nHere's an example of how to use the `clip` function:\n\n```python",
+      "score": 0.75,
+      "passed": true,
+      "found": [
+        "min",
+        "max",
+        "ndarray"
+      ],
+      "missed": [
+        "ufunc"
+      ],
+      "duration_s": 2.62834095954895,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T1-002",
+      "tier": 1,
+      "query": "What is the purpose of numpy's _wrapreduction function and when is it called?",
+      "answer": "The `_wrapreduction` function in NumPy is a utility function used to wrap the reduction operation of an array. \n\nA reduction operation is a process where an array is reduced to a single value by applying a function to each element of the array. Examples of reduction operations include sum, mean, and product.\n\nThe `_wrapreduction` function is called when a reduction operation is performed on an array using a function that is not a built-in NumPy reduction function. \n\nWhen a reduction operation is performed using a custom function, NumPy calls `_wrapreduction` to handle the operation. This function takes care of the following tasks:\n\n1.  It checks if the input array is a scalar or a single-element array. If it is, it simply returns the input value.\n2.  It checks if the input array is a scalar or a single-element array with a single element of the same type as the input array. If it is, it returns the result of the reduction operation directly.\n3.  It checks if the input array is a scalar or a single-element array with a single element of a different type than the input array. If it is, it returns the result of the reduction operation after casting the input array to the type of the single element.\n4.  If none of the above conditions are met, it calls the reduction function on the input array and returns the result.\n\nIn summary, the `_wrapreduction` function is a helper function that wraps the reduction operation of an array and handles the edge cases where the input array is a scalar or a single-element array.\n\nHere is a simplified example of how `_wrapreduction` is used:\n\n```python\nimport numpy as np\n\ndef custom_sum(arr):\n    return np.sum(arr)\n\narr = np.array([1, 2, 3, 4, 5])\nresult = custom_sum(arr)\nprint(result)  # Output: 15\n```\n\nIn this example, the `custom_sum` function is a custom reduction function that uses NumPy's `sum` function to calculate the sum of the array elements. When `custom_sum` is called, NumPy's `_wrapreduction` function is called internally to handle the reduction operation.",
+      "score": 0.25,
+      "passed": false,
+      "found": [
+        "reduction"
+      ],
+      "missed": [
+        "ufunc",
+        "axis",
+        "dtype"
+      ],
+      "duration_s": 4.027457237243652,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T1-003",
+      "tier": 1,
+      "query": "How does numpy handle broadcasting when array shapes are incompatible?",
+      "answer": "NumPy handles broadcasting by applying the following rules when array shapes are incompatible:\n\n1.  **Matching Dimensions**: If the arrays have the same number of dimensions, but their shapes are not compatible, NumPy will broadcast the arrays by matching the dimensions. This means that if the shapes are not compatible, NumPy will add a new dimension of size 1 to the array with fewer dimensions.\n\n2.  **Size 1 Dimensions**: If an array has a size 1 dimension, NumPy will broadcast it by repeating the value along that dimension.\n\n3.  **Broadcasting Rules**:\n\n    *   If the arrays have the same shape, they can be broadcasted element-wise.\n    *   If the arrays have different shapes, but one of them has a size 1 dimension, the other array can be broadcasted by repeating its values along that dimension.\n    *   If the arrays have different shapes and no size 1 dimensions, NumPy will broadcast the arrays by matching the dimensions. This means that if the shapes are not compatible, NumPy will add a new dimension of size 1 to the array with fewer dimensions.\n\nHere's an example of broadcasting in NumPy:\n\n```python\nimport numpy as np\n\n# Create two arrays with incompatible shapes\narray1 = np.array([1, 2, 3])\narray2 = np.array([[4, 5], [6, 7]])\n\n# Broadcast array1 to match the shape of array2\nbroadcasted_array1 = np.broadcast_to(array1, array2.shape)\nprint(broadcasted_array1)\n```\n\nIn this example, `np.broadcast_to` is used to broadcast `array1` to match the shape of `array2`. The output will be:\n\n```\n[[1 1]\n [2 2]\n [3 3]]\n```\n\nThis shows that `array1` has been broadcasted to match the shape of `array2` by repeating its values along the first dimension.\n\nYou can also use the `np.newaxis` syntax to add a new dimension to an array, which can be useful for broadcasting:\n\n```python\narray1 = np.array([1, 2, 3])\narray2 = np.array([[4, 5], [6, 7]])\n\n# Add a new dimension to array1\narray1_with_newaxis = array1[:, np.newaxis]\nprint(array1_with_newaxis)\n```\n\nThe output will be:\n\n```\n[[1]\n [2]\n [3]]\n```\n\nThis shows that",
+      "score": 0.75,
+      "passed": true,
+      "found": [
+        "broadcast",
+        "shape",
+        "dimensions"
+      ],
+      "missed": [
+        "ValueError"
+      ],
+      "duration_s": 2.292353630065918,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T1-004",
+      "tier": 1,
+      "query": "How does scipy.optimize.minimize handle convergence criteria internally?",
+      "answer": "Scipy's `optimize.minimize` function uses various methods to determine convergence, depending on the optimization algorithm used. Here's a general overview of how it handles convergence criteria internally:\n\n1. **Gradient-based methods**: For gradient-based methods like `BFGS`, `L-BFGS-B`, and `Newton-CG`, the convergence is determined by the relative change in the function value (`ftol`) and the relative change in the gradient (`gtol`). The algorithm checks if the relative change in the function value is less than `ftol` or if the relative change in the gradient is less than `gtol`. If both conditions are met, the algorithm considers the optimization converged.\n\n2. **Line search methods**: For line search methods like `CG` and `Powell`, the convergence is determined by the relative change in the function value (`ftol`) and the maximum number of function evaluations (`maxiter`). The algorithm checks if the relative change in the function value is less than `ftol` or if the maximum number of function evaluations is reached. If both conditions are met, the algorithm considers the optimization converged.\n\n3. **Trust region methods**: For trust region methods like `trust-ncg` and `trust-krylov`, the convergence is determined by the relative change in the function value (`ftol`) and the trust region radius (`xtol`). The algorithm checks if the relative change in the function value is less than `ftol` or if the trust region radius is less than `xtol`. If both conditions are met, the algorithm considers the optimization converged.\n\n4. **Non-gradient methods**: For non-gradient methods like `Nelder-Mead` and `Powell`, the convergence is determined by the relative change in the function value (`ftol`) and the maximum number of function evaluations (`maxiter`). The algorithm checks if the relative change in the function value is less than `ftol` or if the maximum number of function evaluations is reached. If both conditions are met, the algorithm considers the optimization converged.\n\nIn all cases, the `maxiter` parameter is used to limit the number of function evaluations, which can prevent the algorithm from running indefinitely.\n\nHere's an example of how to use the `minimize` function with convergence criteria:\n```python\nfrom scipy.optimize import minimize\n\ndef objective(x):\n    return x**2\n\nres = minimize(objective, x0=1, method='BFGS', tol=1e-6, options={'maxiter': 100})",
+      "score": 1.0,
+      "passed": true,
+      "found": [
+        "tol",
+        "maxiter",
+        "ftol",
+        "gtol",
+        "convergence"
+      ],
+      "missed": [],
+      "duration_s": 2.1928160190582275,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T1-005",
+      "tier": 1,
+      "query": "What is the implementation of scipy's fft and how does it differ from numpy's fft?",
+      "answer": "**Implementation of SciPy's FFT**\n\nSciPy's FFT (Fast Fourier Transform) implementation is based on the FFTW library, which is a widely used and highly optimized FFT library. The FFTW library provides a C API that SciPy wraps to provide a Python interface.\n\nWhen you call `scipy.fftpack.fft()` or `scipy.fftpack.fft2()`, SciPy uses the FFTW library to perform the FFT. The FFTW library uses a combination of algorithms, including the Cooley-Tukey algorithm, the Bluestein's algorithm, and the Winograd's algorithm, to efficiently compute the FFT.\n\n**Differences from NumPy's FFT**\n\nNumPy's FFT implementation is based on the FFTW library as well, but it uses a different interface and provides a more limited set of features compared to SciPy's FFT implementation.\n\nHere are some key differences between SciPy's FFT and NumPy's FFT:\n\n1. **Algorithm selection**: SciPy's FFT implementation allows you to select the algorithm used to compute the FFT, whereas NumPy's FFT implementation uses a fixed algorithm.\n2. **Multi-threading**: SciPy's FFT implementation can take advantage of multi-threading to speed up the computation, whereas NumPy's FFT implementation does not.\n3. **Support for non-standard FFT sizes**: SciPy's FFT implementation supports non-standard FFT sizes, whereas NumPy's FFT implementation only supports standard FFT sizes.\n4. **Support for complex-valued inputs**: SciPy's FFT implementation supports complex-valued inputs, whereas NumPy's FFT implementation only supports real-valued inputs.\n5. **Additional features**: SciPy's FFT implementation provides additional features, such as support for FFTs of real-valued inputs, support for FFTs of complex-valued inputs, and support for multi-dimensional FFTs.\n\n**Example Code**\n\nHere is an example code snippet that demonstrates the difference between SciPy's FFT and NumPy's FFT:\n```python\nimport numpy as np\nfrom scipy.fftpack import fft\nimport time\n\n# Create a random array of size 1024\nx = np.random.rand(1024)\n\n# Measure the time taken by NumPy's FFT\nstart_time = time.time()\nnp.fft.fft(x)\nend_time = time.time()\nprint(\"NumPy's FFT took {:.2f} seconds\".format(end_time - start_time))\n\n# Measure the time taken by SciPy's FFT\nstart_time = time.time()\nfft(x)\nend_time = time.time()\nprint(\"SciPy",
+      "score": 0.25,
+      "passed": false,
+      "found": [
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+      ],
+      "missed": [
+        "pocketfft",
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+      ],
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+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T1-006",
+      "tier": 1,
+      "query": "How does pandas implement groupby aggregation internally?",
+      "answer": "Pandas implements groupby aggregation internally using a combination of data structures and algorithms. Here's a high-level overview of the process:\n\n1. **Grouping**: When you call `df.groupby()`, pandas creates a `GroupBy` object, which is an instance of the `GroupBy` class. This object contains information about the grouping keys, such as the column names and the data types.\n2. **Categorical Encoding**: Pandas converts the grouping keys into categorical variables using the `Categorical` data type. This is done to improve performance and reduce memory usage.\n3. **Grouping Keys Hashing**: Pandas hashes the categorical values to create a unique integer representation for each group. This is done using the `hash` function.\n4. **Grouping Keys Sorting**: Pandas sorts the hashed grouping keys to ensure that groups are processed in a consistent order.\n5. **Group Iteration**: Pandas iterates over the sorted grouping keys and creates a new `BlockManager` object for each group. A `BlockManager` is a data structure that represents a block of data in memory.\n6. **Aggregation**: When you call an aggregation function, such as `mean` or `sum`, pandas applies the function to each group using the `BlockManager` object.\n7. **Result Construction**: Pandas constructs the resulting DataFrame by concatenating the aggregated values from each group.\n\nInternally, pandas uses a combination of data structures, including:\n\n* `BlockManager`: A data structure that represents a block of data in memory.\n* `GroupBy` object: An instance of the `GroupBy` class that contains information about the grouping keys.\n* `Categorical` data type: A data type that represents categorical variables.\n* `hash` function: A function that creates a unique integer representation for each group.\n\nPandas also uses various algorithms, including:\n\n* Hashing: A technique used to create a unique integer representation for each group.\n* Sorting: A technique used to ensure that groups are processed in a consistent order.\n* Iteration: A technique used to iterate over the sorted grouping keys.\n\nOverall, pandas' groupby aggregation implementation is highly optimized and efficient, making it suitable for large datasets.\n\nHere's some sample code to illustrate the process:\n```python\nimport pandas as pd\n\n# Create a sample DataFrame\ndf = pd.DataFrame({\n    'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],\n    'B': ['",
+      "score": 0.25,
+      "passed": false,
+      "found": [
+        "GroupBy"
+      ],
+      "missed": [
+        "apply",
+        "transform",
+        "cython"
+      ],
+      "duration_s": 2.290992259979248,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T1-007",
+      "tier": 1,
+      "query": "What happens to NaN values in the output of pandas merge() \u2014 are they propagated, dropped, or filled by default?",
+      "answer": "By default, NaN values in the output of pandas merge() are propagated. \n\nWhen you merge two DataFrames using pandas merge(), NaN values are not dropped or filled by default. Instead, NaN values are propagated to the resulting DataFrame if the corresponding values in the merged columns are NaN in either of the input DataFrames.\n\nHowever, you can specify the behavior of NaN values in the merge operation by using the `how` parameter and the `indicator` parameter. \n\nFor example, you can use the `how='outer'` parameter to propagate NaN values, or you can use the `how='inner'` parameter to drop NaN values. \n\nYou can also use the `indicator=True` parameter to include an 'indicator' column in the result, which indicates whether each row originated from the left DataFrame, the right DataFrame, or both.\n\nHere's an example:\n\n```python\nimport pandas as pd\n\n# Create two DataFrames\ndf1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})\ndf2 = pd.DataFrame({'A': [1, 2, 4], 'C': [7, 8, 9]})\n\n# Merge the DataFrames\nmerged_df = pd.merge(df1, df2, how='outer')\n\nprint(merged_df)\n```\n\nIn this example, NaN values are propagated to the resulting DataFrame because we used the `how='outer'` parameter.",
+      "score": 0.6,
+      "passed": true,
+      "found": [
+        "NaN",
+        "na",
+        "how"
+      ],
+      "missed": [
+        "join",
+        "missing"
+      ],
+      "duration_s": 2.650789737701416,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T1-008",
+      "tier": 1,
+      "query": "How does scikit-learn's StandardScaler compute mean and variance during fit()?",
+      "answer": "Scikit-learn's `StandardScaler` computes the mean and variance of the data during the `fit()` method by using the following formulas:\n\n1.  Mean computation:\n\n    *   It iterates over each feature (column) in the data.\n    *   For each feature, it calculates the mean by summing up all the values and dividing by the number of samples (rows).\n    *   The mean is stored in the `mean_` attribute of the `StandardScaler` object.\n\n    ```python\nmean_ = np.mean(X, axis=0)\n```\n\n2.  Variance computation:\n\n    *   It iterates over each feature (column) in the data.\n    *   For each feature, it calculates the variance by subtracting the mean from each value, squaring the result, summing up these squared differences, and dividing by the number of samples (rows) minus one (Bessel's correction).\n    *   The variance is stored in the `var_` attribute of the `StandardScaler` object.\n\n    ```python\nvar_ = np.var(X, axis=0, ddof=1)\n```\n\nHere's a simplified example to illustrate this:\n\n```python\nimport numpy as np\nfrom sklearn.preprocessing import StandardScaler\n\n# Sample data\nX = np.array([[1, 2], [3, 4], [5, 6]])\n\n# Create a StandardScaler object\nscaler = StandardScaler()\n\n# Fit the scaler to the data\nscaler.fit(X)\n\n# Print the computed mean and variance\nprint(\"Mean:\", scaler.mean_)\nprint(\"Variance:\", scaler.var_)\n```\n\nIn this example, the `fit()` method computes the mean and variance of the data and stores them in the `mean_` and `var_` attributes of the `StandardScaler` object, respectively.",
+      "score": 0.6,
+      "passed": true,
+      "found": [
+        "mean_",
+        "var_",
+        "numpy"
+      ],
+      "missed": [
+        "scale_",
+        "partial_fit"
+      ],
+      "duration_s": 7.409135580062866,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T1-009",
+      "tier": 1,
+      "query": "What is the implementation of train_test_split in scikit-learn?",
+      "answer": "The `train_test_split` function in scikit-learn is used to split arrays or matrices into random train and test subsets. \n\nHere's a simplified implementation of `train_test_split`:\n\n```python\nimport numpy as np\n\ndef train_test_split(X, y, test_size=0.2, random_state=None):\n    \"\"\"\n    Split arrays or matrices into random train and test subsets.\n\n    Parameters\n    ----------\n    X : array-like, shape (n_samples, n_features)\n        Training set.\n    y : array-like, shape (n_samples,)\n        Target values.\n    test_size : float, optional (default=0.2)\n        Proportion of samples to include in the test split.\n    random_state : int or RandomState instance, optional (default=None)\n        If int, random_state is the seed used by the random number generator;\n        If RandomState instance, random_state is the random number generator;\n        If None, the random number generator is the RandomState instance used\n        by `np.random`.\n\n    Returns\n    -------\n    X_train : array-like, shape (n_samples_train, n_features)\n        Training set.\n    X_test : array-like, shape (n_samples_test, n_features)\n        Test set.\n    y_train : array-like, shape (n_samples_train,)\n        Target values for the training set.\n    y_test : array-like, shape (n_samples_test,)\n        Target values for the test set.\n    \"\"\"\n    if random_state is not None:\n        np.random.seed(random_state)\n\n    indices = np.arange(X.shape[0])\n    np.random.shuffle(indices)\n\n    test_size = int(X.shape[0] * test_size)\n    train_indices = indices[test_size:]\n    test_indices = indices[:test_size]\n\n    X_train = X[train_indices]\n    X_test = X[test_indices]\n    y_train = y[train_indices]\n    y_test = y[test_indices]\n\n    return X_train, X_test, y_train, y_test\n```\n\nThis implementation uses NumPy to shuffle the indices of the input data and then splits the data into training and test sets based on the specified `test_size`. The `random_state` parameter is used to ensure reproducibility of the split.",
+      "score": 0.75,
+      "passed": true,
+      "found": [
+        "shuffle",
+        "random_state",
+        "arrays"
+      ],
+      "missed": [
+        "stratify"
+      ],
+      "duration_s": 2.3865489959716797,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T1-010",
+      "tier": 1,
+      "query": "How does PyTorch implement the Adam optimizer update step?",
+      "answer": "The Adam optimizer update step in PyTorch is implemented as follows:\n\nGiven the model parameters `params`, the learning rate `lr`, the first moment `m` and the second moment `v` of the gradient, the bias correction terms `m_hat` and `v_hat`, and the gradient `g`, the Adam update step is computed as:\n\n1. Compute the bias correction terms:\n   ```python\nm_hat = m + beta_2 * (1 - beta_2 ** t) * (m - m_prev)\nv_hat = v + beta_2 * (1 - beta_2 ** t) * (g ** 2 - v_prev)\n```\n   where `t` is the current iteration, `m_prev` and `v_prev` are the previous values of `m` and `v`, and `beta_2` is the second moment decay rate.\n\n2. Compute the Adam update step:\n   ```python\nupdate = -lr * m_hat / (sqrt(v_hat) + eps)\n```\n   where `eps` is a small value to prevent division by zero.\n\n3. Update the model parameters:\n   ```python\nparams -= update\n```\n\nIn PyTorch, the Adam optimizer is implemented in the `torch.optim.Adam` class. The `step` method of this class performs the Adam update step.\n\nHere is a simplified version of the `step` method:\n```python\ndef step(self, closure=None):\n    loss = None\n    if closure is not None:\n        loss = closure()\n\n    for group in self.param_groups:\n        for p in group['params']:\n            if p.grad is None:\n                continue\n            grad = p.grad.data\n            if grad.is_sparse:\n                raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')\n            amsgrad = group['amsgrad']\n\n            state = self.state[p]\n\n            # State initialization\n            if len(state) == 0:\n                state['step'] = 0\n                # Exponential moving average of gradient values, and of squared gradient values\n                state['exp_avg'] = torch.zeros_like(p.data)\n                state['exp_avg_sq'] = torch.zeros_like(p.data)\n                if amsgrad:\n                    # Maintains a list of the exponential moving averages of past squared gradient values\n                    state['max_exp_avg_sq'] = torch.zeros_like(p.data)\n\n            exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']",
+      "score": 0.6666666666666666,
+      "passed": true,
+      "found": [
+        "lr",
+        "eps",
+        "grad",
+        "exp_avg"
+      ],
+      "missed": [
+        "betas",
+        "bias_correction"
+      ],
+      "duration_s": 1.9527583122253418,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T1-011",
+      "tier": 1,
+      "query": "How does torch.autograd track gradients through operations?",
+      "answer": "`torch.autograd` is a module in PyTorch that enables automatic differentiation, which is a fundamental concept in backpropagation. It tracks gradients through operations by maintaining a graph of computational dependencies between tensors.\n\nHere's a step-by-step explanation of how `torch.autograd` tracks gradients:\n\n1. **Tensor Creation**: When you create a tensor using `torch.tensor()`, `torch.randn()`, or other tensor creation functions, PyTorch assigns a `requires_grad` attribute to the tensor. This attribute determines whether the tensor's gradients should be tracked.\n\n2. **Operation Execution**: When you perform operations on tensors, such as addition, multiplication, or activation functions, PyTorch creates a new tensor to store the result. The `requires_grad` attribute is propagated to the new tensor.\n\n3. **Graph Construction**: PyTorch constructs a directed acyclic graph (DAG) to represent the computational dependencies between tensors. Each node in the graph represents a tensor, and each edge represents the dependency between two tensors.\n\n4. **Gradient Accumulation**: When you call `backward()` on a tensor, PyTorch starts accumulating gradients from the leaf nodes (tensors that don't depend on any other tensors) and propagates them backwards through the graph.\n\n5. **Gradient Computation**: At each node, PyTorch computes the gradient of the loss function with respect to the tensor stored at that node. This is done using the chain rule of calculus.\n\n6. **Gradient Accumulation**: The gradients computed at each node are accumulated in the `grad` attribute of the tensor.\n\n7. **Final Gradient**: The final gradients are stored in the `grad` attribute of the tensor that triggered the `backward()` call.\n\nHere's a simple example to illustrate this process:\n```python\nimport torch\n\n# Create two tensors with requires_grad=True\nx = torch.tensor(2.0, requires_grad=True)\ny = torch.tensor(3.0, requires_grad=True)\n\n# Perform an operation\nz = x + y\n\n# Compute the loss\nloss = z * z\n\n# Call backward() to accumulate gradients\nloss.backward()\n\n# Print the gradients\nprint(x.grad)  # Output: 6.0\nprint(y.grad)  # Output: 6.0\n```\nIn this example, PyTorch constructs a graph with three nodes: `x`, `y`, and `z`. The `backward()` call accumulates gradients from the leaf node `z` and propagates them backwards through the",
+      "score": 0.8,
+      "passed": true,
+      "found": [
+        "requires_grad",
+        "backward",
+        "autograd",
+        "leaf"
+      ],
+      "missed": [
+        "grad_fn"
+      ],
+      "duration_s": 2.5202138423919678,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T1-012",
+      "tier": 1,
+      "query": "How does the HuggingFace tokenizer handle out-of-vocabulary tokens?",
+      "answer": "The HuggingFace tokenizer, specifically the `AutoTokenizer` and `PreTrainedTokenizer` classes, handle out-of-vocabulary (OOV) tokens in the following ways:\n\n1.  **Special Tokens**: OOV tokens are replaced with special tokens, such as `[UNK]` (unknown) or `[MASK]` (mask), which are predefined in the tokenizer. These special tokens are used as placeholders for tokens that are not in the tokenizer's vocabulary.\n\n2.  **WordPiece Tokenization**: When using WordPiece tokenization (default for BERT and RoBERTa models), the tokenizer splits the input text into subwords (smaller units of words). If an OOV token is encountered, the tokenizer will split it into subwords and replace the original token with these subwords.\n\n3.  **Add Special Tokens to Vocabulary**: If you want to handle OOV tokens more robustly, you can add the special tokens to the tokenizer's vocabulary. This way, when an OOV token is encountered, the tokenizer will use the special token from the vocabulary instead of replacing it with `[UNK]`.\n\n4.  **Custom Handling**: You can also implement custom handling for OOV tokens by using the `add_tokens` method to add the OOV token to the tokenizer's vocabulary. This way, you can define how the tokenizer should handle the OOV token.\n\nHere's an example of how to add a custom token to the tokenizer's vocabulary:\n\n```python\nfrom transformers import AutoTokenizer\n\n# Load the tokenizer\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\n\n# Add a custom token to the tokenizer's vocabulary\ntokenizer.add_tokens(['<custom_token>'])\n\n# Now, when you tokenize a text with the custom token, it will be handled correctly\ninput_text = 'This is a <custom_token> example.'\nencoding = tokenizer.encode_plus(input_text, \n                                  add_special_tokens=True, \n                                  max_length=512, \n                                  return_attention_mask=True, \n                                  return_tensors='pt')\n```\n\nIn summary, the HuggingFace tokenizer handles OOV tokens by replacing them with special tokens, using WordPiece tokenization, adding special tokens to the vocabulary, or implementing custom handling.",
+      "score": 0.6,
+      "passed": true,
+      "found": [
+        "subword",
+        "wordpiece",
+        "vocab"
+      ],
+      "missed": [
+        "unk_token",
+        "BPE"
+      ],
+      "duration_s": 1.9576311111450195,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T1-013",
+      "tier": 1,
+      "query": "What happens inside the forward pass of BertModel?",
+      "answer": "The forward pass of `BertModel` is a crucial component of the BERT (Bidirectional Encoder Representations from Transformers) architecture. Here's a step-by-step explanation of what happens inside the forward pass of `BertModel`:\n\n1. **Tokenization**: The input text is tokenized into subwords or wordpieces using the `WordPieceTokenizer`. This is done to handle out-of-vocabulary words and to reduce the dimensionality of the input.\n\n2. **Embedding**: The tokenized input is then embedded into a vector space using the `BertEmbeddings` layer. This layer consists of a `WordEmbeddings` layer, which maps each token to a dense vector, and a `PositionalEmbeddings` layer, which adds positional information to the input.\n\n3. **Layer Normalization**: The embedded input is then passed through a layer normalization layer, which normalizes the input along the last dimension (i.e., the embedding dimension).\n\n4. **Self-Attention Mechanism**: The normalized input is then passed through a multi-head self-attention mechanism, which allows the model to attend to different parts of the input simultaneously and weigh their importance. This is done using the `BertSelfAttention` layer.\n\n5. **Feed Forward Network (FFN)**: The output of the self-attention mechanism is then passed through a feed-forward network (FFN), which consists of two linear layers with a ReLU activation function in between. This helps to transform the input into a higher-dimensional space.\n\n6. **Layer Normalization (again)**: The output of the FFN is then passed through another layer normalization layer.\n\n7. **Residual Connection**: The output of the layer normalization layer is then added to the input of the layer (i.e., the output of the previous layer), which helps to preserve the information from the previous layer.\n\n8. **Repeat**: Steps 4-7 are repeated for a specified number of layers (i.e., the number of BERT layers).\n\n9. **Pooler**: After the last layer, the output is passed through a pooler layer, which reduces the dimensionality of the output to a fixed size (i.e., the hidden size of the model).\n\n10. **Return**: The final output of the `BertModel` is the output of the pooler layer.\n\nHere's a simplified representation of the forward pass of `BertModel` in PyTorch:\n```python\nclass BertModel(nn.Module):\n    def __init__(self,",
+      "score": 0.8,
+      "passed": true,
+      "found": [
+        "attention",
+        "encoder",
+        "pooler",
+        "embedding"
+      ],
+      "missed": [
+        "hidden_states"
+      ],
+      "duration_s": 4.017573595046997,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T2-001",
+      "tier": 2,
+      "query": "How does pandas use numpy arrays internally to store DataFrame column data?",
+      "answer": "Pandas uses NumPy arrays internally to store DataFrame column data. \n\nWhen you create a pandas DataFrame, it stores the data in a block of memory, which is a NumPy array. This array is divided into blocks, known as \"blocks\" or \"chunks,\" which are stored in a data structure called a \"BlockManager.\" \n\nEach block in the BlockManager corresponds to a column in the DataFrame. The BlockManager is essentially a dictionary that maps column names to their corresponding blocks. \n\nHere's a simplified representation of how pandas stores DataFrame column data internally:\n\n```python\nimport pandas as pd\nimport numpy as np\n\n# Create a sample DataFrame\ndf = pd.DataFrame({\n    'A': [1, 2, 3],\n    'B': [4, 5, 6]\n})\n\n# Get the BlockManager\nblock_manager = df._data.blocks\n\n# Print the block sizes\nfor column, block in block_manager.items():\n    print(f\"Column: {column}, Block size: {block.size}\")\n```\n\nIn this example, the `df._data.blocks` attribute returns a dictionary where the keys are the column names and the values are the corresponding NumPy arrays. \n\nThis internal representation allows pandas to efficiently store and manipulate large datasets, leveraging the performance benefits of NumPy arrays.",
+      "score": 0.6,
+      "passed": true,
+      "found": [
+        "Block",
+        "numpy",
+        "values"
+      ],
+      "missed": [
+        "ndarray",
+        "dtype"
+      ],
+      "duration_s": 3.5244126319885254,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T2-002",
+      "tier": 2,
+      "query": "How does scikit-learn validate that input arrays are numpy-compatible before fitting?",
+      "answer": "Scikit-learn validates that input arrays are numpy-compatible before fitting by using the `numpy` library's `asarray` function. \n\nWhen you pass an array to a scikit-learn estimator, it is first converted to a numpy array using `numpy.asarray`. This function attempts to convert the input to a numpy array, raising a `ValueError` if the conversion fails.\n\nHere's an example:\n\n```python\nfrom sklearn.linear_model import LinearRegression\nimport numpy as np\n\n# Create a LinearRegression estimator\nestimator = LinearRegression()\n\n# Try to fit the estimator with a non-numpy array\ntry:\n    estimator.fit([[1, 2], [3, 4]], [5, 6])\nexcept ValueError as e:\n    print(e)\n\n# Output: could not convert string to float: '5'\n\n# Now, convert the input to a numpy array\ninput_array = np.array([[1, 2], [3, 4]])\nestimator.fit(input_array, np.array([5, 6]))\n```\n\nIn this example, the `fit` method raises a `ValueError` when trying to fit the estimator with a non-numpy array. However, when the input array is converted to a numpy array using `np.array`, the `fit` method succeeds.\n\nAdditionally, scikit-learn also checks the type of the input arrays. It expects them to be of type `numpy.ndarray`. If the input array is not of this type, it raises a `TypeError`.\n\n```python\n# Create a LinearRegression estimator\nestimator = LinearRegression()\n\n# Try to fit the estimator with a list\ntry:\n    estimator.fit([[1, 2], [3, 4]], [5, 6])\nexcept TypeError as e:\n    print(e)\n\n# Output: fit() missing 1 required positional argument: 'y'\n```\n\nIn this example, the `fit` method raises a `TypeError` when trying to fit the estimator with a list.",
+      "score": 0.4,
+      "passed": true,
+      "found": [
+        "ndarray",
+        "numpy"
+      ],
+      "missed": [
+        "check_array",
+        "dtype",
+        "ensure_2d"
+      ],
+      "duration_s": 2.3224408626556396,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T2-003",
+      "tier": 2,
+      "query": "When scipy computes a matrix inverse, how does it use numpy's linear algebra routines?",
+      "answer": "When SciPy computes a matrix inverse using the `linalg.inv()` function, it uses NumPy's linear algebra routines under the hood. Specifically, it calls the `numpy.linalg.inv()` function, which is implemented in C and provides an efficient and robust way to compute the inverse of a matrix.\n\nThe `numpy.linalg.inv()` function uses a combination of algorithms to compute the matrix inverse, depending on the input matrix. For example:\n\n* For small matrices (up to 3x3), it uses a direct method based on the Gauss-Jordan elimination algorithm.\n* For larger matrices, it uses a more efficient method based on the LU decomposition algorithm, which is a factorization of the matrix into a lower triangular matrix (L) and an upper triangular matrix (U).\n* For matrices with a special structure, such as symmetric or Hermitian matrices, it uses a more efficient method based on the Cholesky decomposition algorithm.\n\nIn all cases, the `numpy.linalg.inv()` function returns the inverse of the input matrix, which is a matrix that, when multiplied by the original matrix, produces the identity matrix.\n\nSciPy's `linalg.inv()` function simply wraps the `numpy.linalg.inv()` function and provides a more convenient interface for computing matrix inverses. It also includes some additional features, such as support for complex matrices and matrices with a non-standard data type.\n\nIn summary, when SciPy computes a matrix inverse using `linalg.inv()`, it relies on NumPy's efficient and robust linear algebra routines to perform the computation.",
+      "score": 0.2,
+      "passed": false,
+      "found": [
+        "numpy.linalg"
+      ],
+      "missed": [
+        "lapack",
+        "BLAS",
+        "gesv",
+        "getrf"
+      ],
+      "duration_s": 2.135221004486084,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T2-004",
+      "tier": 2,
+      "query": "How does PyTorch's tensor.numpy() method convert a tensor to a numpy array and what are the constraints?",
+      "answer": "PyTorch's `tensor.numpy()` method converts a PyTorch tensor to a NumPy array. This method creates a new NumPy array from the tensor's data, without copying the data. \n\nHere's a step-by-step explanation of the conversion process:\n\n1.  **Data Copying**: The `numpy()` method does not copy the tensor's data. Instead, it creates a new NumPy array that shares the same memory location as the tensor's data. This is done to avoid unnecessary data copying and improve performance.\n2.  **Data Type Conversion**: If the tensor's data type is not a NumPy-supported type, it is converted to a compatible type. For example, PyTorch's `torch.float16` is converted to NumPy's `float32`.\n3.  **Shape and Strides**: The resulting NumPy array has the same shape and strides as the original tensor.\n\nConstraints:\n\n*   **Tensor must be on the CPU**: The `numpy()` method only works on tensors that are currently on the CPU. If the tensor is on a GPU, you need to move it to the CPU using `tensor.cpu()` before calling `numpy()`.\n*   **Tensor must be a leaf tensor**: The `numpy()` method only works on leaf tensors, which are tensors that are not part of a larger computation graph. If you try to call `numpy()` on a tensor that is part of a computation graph, you will get an error.\n*   **Tensor must not have a gradient**: The `numpy()` method does not work on tensors that have a gradient. If you try to call `numpy()` on a tensor that has a gradient, you will get an error.\n\nHere's an example code snippet that demonstrates the conversion of a PyTorch tensor to a NumPy array using the `numpy()` method:\n\n```python\nimport torch\nimport numpy as np\n\n# Create a PyTorch tensor\ntensor = torch.tensor([1, 2, 3, 4, 5], dtype=torch.float32)\n\n# Convert the tensor to a NumPy array\nnumpy_array = tensor.numpy()\n\n# Print the NumPy array\nprint(numpy_array)\n\n# Verify that the NumPy array has the same shape and data type as the tensor\nprint(numpy_array.shape)  # Output: (5,)\nprint(numpy_array.dtype)  # Output: float32\n```\n\nIn summary, the `tensor.numpy()` method provides a convenient way to convert PyTorch tensors to NumPy arrays, while",
+      "score": 0.4,
+      "passed": true,
+      "found": [
+        "cpu",
+        "grad"
+      ],
+      "missed": [
+        "detach",
+        "contiguous",
+        "shared memory"
+      ],
+      "duration_s": 2.299438714981079,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T2-005",
+      "tier": 2,
+      "query": "How does HuggingFace's Trainer class use PyTorch DataLoader for batching?",
+      "answer": "HuggingFace's `Trainer` class uses PyTorch's `DataLoader` to batch data for training. \n\nHere's a high-level overview of how it works:\n\n1.  **Data Preparation**: The `Trainer` class expects a dataset to be prepared in a specific format, typically using the `Dataset` class from PyTorch. This dataset should contain the input data and corresponding labels.\n2.  **DataLoader Creation**: When creating a `Trainer` instance, you can pass a `DataLoader` object to it. If no `DataLoader` is provided, the `Trainer` will create one automatically using the `DataLoader` class from PyTorch.\n3.  **Batching**: The `DataLoader` is responsible for batching the data. It does this by iterating over the dataset and grouping the data into batches of a specified size (defined by the `batch_size` parameter). The `DataLoader` will also shuffle the data if specified.\n4.  **Data Loading**: When the `Trainer` is training, it will load a batch of data from the `DataLoader` at each iteration. This batch is then passed to the model for training.\n\nHere's a simplified example of how you might use the `Trainer` class with a custom dataset and `DataLoader`:\n\n```python\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nfrom transformers import Trainer, TrainingArguments\n\n# Define a custom dataset class\nclass MyDataset(Dataset):\n    def __init__(self, data, labels):\n        self.data = data\n        self.labels = labels\n\n    def __getitem__(self, idx):\n        return {\n            'input_ids': self.data[idx],\n            'labels': self.labels[idx]\n        }\n\n    def __len__(self):\n        return len(self.data)\n\n# Create a custom dataset instance\ndata = ...  # Load your data here\nlabels = ...  # Load your labels here\ndataset = MyDataset(data, labels)\n\n# Create a DataLoader instance\nbatch_size = 32\ndata_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n\n# Define the model and training arguments\nmodel = ...  # Load your model here\ntraining_args = TrainingArguments(\n    output_dir='./results',\n    num_train_epochs=3,\n    per_device_train_batch_size=batch_size,\n    per_device_eval_batch_size=batch_size,\n    evaluation_strategy='epoch',\n    learning_rate=5e-5,\n    save_steps=500,",
+      "score": 0.6,
+      "passed": true,
+      "found": [
+        "DataLoader",
+        "batch_size",
+        "torch"
+      ],
+      "missed": [
+        "collate_fn",
+        "sampler"
+      ],
+      "duration_s": 2.4715218544006348,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T2-006",
+      "tier": 2,
+      "query": "How does pandas rolling() use numpy operations under the hood?",
+      "answer": "Pandas' rolling() function leverages NumPy operations under the hood to perform windowed operations on a Series or DataFrame. Here's a high-level overview of how it works:\n\n1.  **Window Generation**: When you call rolling() on a pandas Series or DataFrame, pandas generates a window object that defines the size and type of the rolling window. This window object is used to slice the underlying data.\n2.  **Window Slicing**: The window object is then used to slice the underlying data into chunks of the specified size. This is done using NumPy's advanced indexing capabilities.\n3.  **NumPy Operations**: Once the data is sliced, pandas uses NumPy operations to perform the desired calculations on each chunk of data. This can include operations like mean, sum, min, max, etc.\n4.  **Result Concatenation**: The results of the NumPy operations on each chunk are then concatenated together to form the final output.\n\nHere's a simplified example to illustrate this process:\n\n```python\nimport pandas as pd\nimport numpy as np\n\n# Create a sample DataFrame\ndata = np.random.rand(10)\ndf = pd.DataFrame(data, columns=['Values'])\n\n# Apply rolling mean with a window size of 3\nrolling_mean = df['Values'].rolling(3).mean()\n\n# The rolling() function uses NumPy operations under the hood\n# to calculate the mean of each window\nprint(rolling_mean)\n```\n\nIn this example, the rolling() function uses NumPy's advanced indexing capabilities to slice the data into chunks of size 3. It then uses NumPy's mean() function to calculate the mean of each chunk. The results are then concatenated together to form the final output.\n\nTo confirm this, you can use the `get_window_values` method of the rolling object to inspect the underlying NumPy arrays used for the calculation:\n\n```python\nrolling = df['Values'].rolling(3)\nwindow_values = rolling._get_window_values()\n\nprint(window_values)\n```\n\nThis will print the underlying NumPy arrays used for the calculation, demonstrating how pandas leverages NumPy operations under the hood.",
+      "score": 0.6,
+      "passed": true,
+      "found": [
+        "rolling",
+        "window",
+        "numpy"
+      ],
+      "missed": [
+        "ndarray",
+        "stride"
+      ],
+      "duration_s": 5.954064607620239,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T2-007",
+      "tier": 2,
+      "query": "How does scikit-learn's PCA implementation use numpy's SVD?",
+      "answer": "Scikit-learn's PCA implementation uses numpy's SVD (Singular Value Decomposition) to compute the principal components. \n\nWhen you call `PCA(n_components=None)` in scikit-learn, it defaults to using SVD to compute the principal components. This is because SVD is a more numerically stable method for computing the principal components, especially when dealing with large datasets.\n\nHere's a simplified overview of how scikit-learn's PCA uses SVD:\n\n1.  **Data Matrix**: The input data is represented as a matrix `X` where each row is a data point and each column is a feature.\n2.  **SVD**: The `numpy.linalg.svd` function is used to compute the SVD of the data matrix `X`. This decomposes `X` into three matrices: `U`, `s`, and `Vh`.\n    *   `U` is an orthogonal matrix containing the left singular vectors of `X`.\n    *   `s` is a vector containing the singular values of `X`.\n    *   `Vh` is an orthogonal matrix containing the right singular vectors of `X`.\n3.  **Principal Components**: The principal components are computed by taking the top `n_components` singular values from `s` and the corresponding singular vectors from `U` and `Vh`.\n4.  **Transformation**: The transformed data is computed by projecting the original data onto the principal components.\n\nBy using SVD, scikit-learn's PCA implementation can efficiently compute the principal components and transform the data, even for large datasets.\n\nHere's a simple example to illustrate this:\n\n```python\nimport numpy as np\nfrom sklearn.decomposition import PCA\nfrom sklearn.datasets import load_iris\n\n# Load the iris dataset\niris = load_iris()\nX = iris.data\n\n# Create a PCA instance with 2 components\npca = PCA(n_components=2)\n\n# Fit and transform the data\nX_pca = pca.fit_transform(X)\n\n# Print the shape of the transformed data\nprint(X_pca.shape)\n```\n\nIn this example, the `PCA` instance is created with 2 components, and the `fit_transform` method is used to compute the principal components and transform the data. The transformed data is then printed to the console.",
+      "score": 0.6,
+      "passed": true,
+      "found": [
+        "svd",
+        "numpy.linalg",
+        "singular"
+      ],
+      "missed": [
+        "components_",
+        "explained_variance_"
+      ],
+      "duration_s": 2.0521628856658936,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T2-008",
+      "tier": 2,
+      "query": "Where does the transformers library convert between PyTorch tensors and numpy arrays for metric computation?",
+      "answer": "The Hugging Face Transformers library uses the `torch` tensor type for model inputs and outputs, but it often requires numpy arrays for metric computation. \n\nThe conversion between PyTorch tensors and numpy arrays typically occurs in the `compute_metrics` function of the `Trainer` class in the Transformers library. \n\nHere's a simplified example of where this conversion might happen:\n\n```python\nfrom transformers import Trainer\n\nclass CustomTrainer(Trainer):\n    def compute_metrics(self, pred):\n        # Convert the prediction tensors to numpy arrays\n        pred_logits = pred.predictions.detach().cpu().numpy()\n        pred_labels = pred.label_ids.detach().cpu().numpy()\n\n        # Compute the metrics using numpy arrays\n        accuracy = np.mean(pred_logits.argmax(-1) == pred_labels)\n        return {\"accuracy\": accuracy}\n```\n\nIn this example, the `compute_metrics` function converts the prediction tensors to numpy arrays using the `detach().cpu().numpy()` method, and then computes the accuracy metric using these numpy arrays.\n\nHowever, the exact location of this conversion might vary depending on the specific use case and the customizations made to the `Trainer` class. \n\nIt's worth noting that the Transformers library also provides a `compute_metrics` function in the `Trainer` class that can be used to compute custom metrics. This function takes the prediction tensors as input and returns a dictionary with the computed metrics. \n\nIf you're looking for a specific implementation, I would recommend checking the source code of the Transformers library or looking at the documentation for the `compute_metrics` function in the `Trainer` class.",
+      "score": 1.0,
+      "passed": true,
+      "found": [
+        "numpy",
+        "detach",
+        "cpu",
+        "predictions",
+        "label_ids"
+      ],
+      "missed": [],
+      "duration_s": 1.9863712787628174,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T3-001",
+      "tier": 3,
+      "query": "What sampling strategies does HuggingFace model.generate() use and how does it call into PyTorch operations?",
+      "answer": "HuggingFace's `model.generate()` function uses various sampling strategies to generate text. The default strategy is `sampling`, which uses the `top_k` and `top_p` parameters to filter the output distribution. However, you can also use other strategies like `beam_search`, `beam_search_with_diversity`, `top_k`, `top_p`, `random`, `random_sample`, `random_sample_with_replacement`, `sequence`, `sequence_with_diversity`, `sequence_with_diversity_and_repetition_protection`, `sequence_with_repetition`, `sequence_with_repetition_and_replacement`, `sequence_with_repetition_and_replacement_with_repetition_protection`, `greedy`, `greedy_sample`, `greedy_sample_with_replacement`, `greedy_sample_with_replacement_with_repetition_protection`, `greedy_sample_with_repetition`, `greedy_sample_with_repetition_and_replacement`, `greedy_sample_with_repetition_and_replacement_with_repetition_protection`, `top_k_sample`, `top_k_sample_with_replacement`, `top_k_sample_with_replacement_with_repetition_protection`, `top_k_sample_with_repetition`, `top_k_sample_with_replacement_and_repetition`, `top_k_sample_with_replacement_and_repetition_with_repetition_protection`, `top_p_sample`, `top_p_sample_with_replacement`, `top_p_sample_with_replacement_with_repetition_protection`, `top_p_sample_with_replacement_and_repetition`, `top_p_sample_with_replacement_and_repetition_with_repetition_protection`, `random_sample_with_replacement_with_repetition_protection`, `random_sample_with_repetition_and_replacement_with_repetition_protection`, `random_sample_with_repetition_and_replacement`, `random_sample_with_replacement`, `random_sample_with_replacement_with_repetition_protection`, `random_sample_with_repetition`, `random_sample_with_replacement_and_repetition_with_repetition_protection`, `random_sample_with_replacement_and_repetition`, `random_sample_with_replacement_with_repetition_protection`, `random_sample_with_repetition_and_replacement_with_repetition_protection`, `random_sample_with_repetition_and_replacement`, `random_sample_with_replacement_with_repetition_protection`, `random_sample_with_repetition`, `random_sample_with_replacement_and_repetition_with_repetition_protection`, `random_sample_with_replacement_and_repetition`, `random_sample_with_replacement_with_repetition_protection`, `random_sample_with_repetition_and_repetition_protection`, `random_sample",
+      "score": 0.5,
+      "passed": true,
+      "found": [
+        "generate",
+        "sample",
+        "beam_search"
+      ],
+      "missed": [
+        "logits",
+        "torch",
+        "tensor"
+      ],
+      "duration_s": 2.486739158630371,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T3-002",
+      "tier": 3,
+      "query": "How does pandas.DataFrame.corr() ultimately compute correlation \u2014 trace through to the underlying math?",
+      "answer": "The `pandas.DataFrame.corr()` function ultimately computes correlation using the Pearson correlation coefficient, which is a measure of linear correlation between two variables. Here's a step-by-step breakdown of how it works:\n\n1. **Data Preparation**: The function first checks if the input DataFrame is a 2D array-like object. If it is, it converts it to a DataFrame. It also checks if the DataFrame has at least two columns, as correlation is only defined between two variables.\n\n2. **Centering**: The function then centers the data by subtracting the mean of each column from the corresponding values. This is done to ensure that the correlation calculation is not biased by the scale of the variables.\n\n   Mathematically, this can be represented as:\n   ```\n   X_centered = X - X.mean(axis=0)\n   ```\n\n3. **Normalization**: The function then normalizes the centered data by dividing it by its standard deviation. This is done to ensure that the correlation calculation is not biased by the scale of the variables.\n\n   Mathematically, this can be represented as:\n   ```\n   X_normalized = X_centered / X_centered.std(axis=0)\n   ```\n\n4. **Correlation Calculation**: The function then calculates the correlation between each pair of columns using the following formula:\n\n   For two variables `X` and `Y`, the Pearson correlation coefficient `\u03c1` is defined as:\n   ```\n   \u03c1 = cov(X, Y) / (\u03c3_X * \u03c3_Y)\n   ```\n   where `cov(X, Y)` is the covariance between `X` and `Y`, and `\u03c3_X` and `\u03c3_Y` are the standard deviations of `X` and `Y`, respectively.\n\n   The covariance between `X` and `Y` can be calculated as:\n   ```\n   cov(X, Y) = (X - X.mean()) * (Y - Y.mean()).mean()\n   ```\n\n   The standard deviations of `X` and `Y` can be calculated as:\n   ```\n   \u03c3_X = (X - X.mean())^2.mean() ** 0.5\n   \u03c3_Y = (Y - Y.mean())^2.mean() ** 0.5\n   ```\n\n5. **Result**: The function returns a correlation matrix, which is a square matrix where the entry at row `i` and column `j` represents the correlation between the `i`-th and `j`-th columns of the input DataFrame.\n\nIn summary, the `",
+      "score": 0.5,
+      "passed": true,
+      "found": [
+        "pearson",
+        "cov",
+        "std"
+      ],
+      "missed": [
+        "spearman",
+        "numpy",
+        "scipy.stats"
+      ],
+      "duration_s": 3.118736982345581,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T3-003",
+      "tier": 3,
+      "query": "How does scikit-learn's SVM implementation use scipy's sparse matrices and what numpy operations are at the core?",
+      "answer": "Scikit-learn's Support Vector Machine (SVM) implementation uses scipy's sparse matrices to efficiently handle large datasets. Here's a high-level overview of how it works:\n\n1. **Sparse Matrix Representation**: When dealing with large datasets, many features may be zero or have a small value. Scipy's sparse matrices (e.g., `scipy.sparse.csr_matrix`) are designed to store only non-zero elements, reducing memory usage and improving computational efficiency.\n2. **Kernel Trick**: SVMs can be trained using a kernel function, which maps the original feature space to a higher-dimensional feature space. Scikit-learn's SVM implementation uses the kernel trick to avoid explicit computation of the high-dimensional feature space. This is where numpy operations come in.\n3. **Numpy Operations**: The core of scikit-learn's SVM implementation involves numpy operations on the sparse matrices. Specifically:\n\t* **Matrix Multiplication**: Numpy's `matmul` function is used to compute the dot product of the sparse matrices, which is essential for the kernel trick.\n\t* **Element-wise Operations**: Numpy's `add`, `subtract`, and `multiply` functions are used for element-wise operations on the sparse matrices.\n\t* **Linear Algebra Operations**: Numpy's `linalg` functions (e.g., `linalg.solve`) are used for linear algebra operations, such as solving linear systems and computing eigenvalues.\n4. **Scipy Integration**: Scikit-learn's SVM implementation integrates with scipy's sparse matrices to leverage their efficiency and flexibility. For example, the `scipy.sparse.linalg` module is used for sparse linear algebra operations.\n\nSome specific numpy operations that are at the core of scikit-learn's SVM implementation include:\n\n* `np.matmul`: Matrix multiplication\n* `np.add`, `np.subtract`, `np.multiply`: Element-wise operations\n* `np.linalg.solve`: Solving linear systems\n* `np.linalg.eig`: Computing eigenvalues\n\nKeep in mind that the specific numpy operations used may vary depending on the kernel type and the specific implementation details of scikit-learn's SVM.",
+      "score": 0.6666666666666666,
+      "passed": true,
+      "found": [
+        "sparse",
+        "scipy.sparse",
+        "kernel",
+        "numpy"
+      ],
+      "missed": [
+        "libsvm",
+        "support_vectors_"
+      ],
+      "duration_s": 2.5554585456848145,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T3-004",
+      "tier": 3,
+      "query": "How does HuggingFace's attention mechanism implement scaled dot-product attention at the PyTorch level?",
+      "answer": "HuggingFace's attention mechanism, specifically in the `transformers` library, implements scaled dot-product attention using PyTorch's tensor operations. Here's a simplified breakdown of how it's implemented:\n\n1. **Query (Q), Key (K), and Value (V) matrices**: The scaled dot-product attention mechanism takes three input matrices:\n   - `Q` (Query): The query matrix, which represents the input to be processed.\n   - `K` (Key): The key matrix, which represents the context or the input to be matched against.\n   - `V` (Value): The value matrix, which represents the output of the attention mechanism.\n\n2. **Scaled Dot-Product Attention**: The scaled dot-product attention is computed as follows:\n\n   ```python\nattention_scores = torch.matmul(Q, K.T) / math.sqrt(d_model)\n```\n\n   Here, `d_model` is the embedding dimension, and `math.sqrt(d_model)` is used as the scaling factor.\n\n3. **Softmax**: The attention scores are then passed through a softmax function to obtain the weights:\n\n   ```python\nattention_weights = torch.softmax(attention_scores, dim=-1)\n```\n\n   The `dim=-1` argument specifies that the softmax function should be applied along the last dimension of the tensor.\n\n4. **Attention Output**: The final output of the attention mechanism is computed by taking the dot product of the attention weights and the value matrix:\n\n   ```python\nattention_output = torch.matmul(attention_weights, V)\n```\n\nIn the HuggingFace `transformers` library, this is implemented in the `Attention` class, specifically in the `compute_attention` method. Here's a simplified example of how it's implemented:\n\n```python\nclass Attention(nn.Module):\n    def __init__(self, config):\n        super(Attention, self).__init__()\n        self.config = config\n        self.query = nn.Linear(config.hidden_size, config.hidden_size)\n        self.key = nn.Linear(config.hidden_size, config.hidden_size)\n        self.value = nn.Linear(config.hidden_size, config.hidden_size)\n        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)\n\n    def compute_attention(self, query, key, value):\n        query = self.query(query)\n        key = self.key(key)\n        value = self.value(value)\n        attention_scores = torch.matmul(query, key.T) / math.sqrt(self.config.hidden_size)\n        attention_weights = self.dropout(torch.softmax(attention_scores, dim=-1))\n        attention_output = torch",
+      "score": 1.0,
+      "passed": true,
+      "found": [
+        "query",
+        "key",
+        "value",
+        "softmax",
+        "matmul",
+        "scale",
+        "torch"
+      ],
+      "missed": [],
+      "duration_s": 1.9051036834716797,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T3-005",
+      "tier": 3,
+      "query": "Trace how scikit-learn's KMeans uses numpy and scipy for distance computation.",
+      "answer": "Scikit-learn's KMeans implementation leverages NumPy and SciPy for efficient distance computation. Here's a step-by-step breakdown of how it works:\n\n1. **Initialization**: The KMeans algorithm starts by initializing the centroids randomly or using the KMeans++ method. The centroids are stored in a 2D array, where each row represents a centroid.\n\n2. **Distance Computation**: When computing the distances between data points and centroids, scikit-learn uses NumPy's vectorized operations to perform the calculations efficiently. Specifically, it uses the `numpy.linalg.norm` function to compute the Euclidean distance between each data point and each centroid.\n\n   ```python\nimport numpy as np\n\n# Assume X is the data matrix and centroids is the array of centroids\ndistances = np.sqrt(np.sum((X[:, np.newaxis] - centroids) ** 2, axis=2))\n```\n\n   In this code snippet, `X[:, np.newaxis]` adds a new axis to the data matrix `X`, allowing NumPy to broadcast the subtraction operation. The resulting array has shape `(n_samples, n_centroids)`, where each element represents the squared Euclidean distance between a data point and a centroid.\n\n3. **Assignment**: After computing the distances, scikit-learn assigns each data point to the closest centroid using NumPy's `argmin` function.\n\n   ```python\nlabels = np.argmin(distances, axis=1)\n```\n\n   This line of code returns the indices of the minimum distances, which correspond to the labels of the closest centroids for each data point.\n\n4. **Update Centroids**: To update the centroids, scikit-learn uses SciPy's `scipy.spatial.KDTree` class to efficiently compute the mean of the data points assigned to each centroid. However, in the latest versions of scikit-learn, the `KDTree` is not used directly. Instead, the centroids are updated using the following formula:\n\n   ```python\ncentroids = np.array([X[labels == i].mean(axis=0) for i in range(n_clusters)])\n```\n\n   This code snippet uses NumPy's vectorized operations to compute the mean of the data points assigned to each centroid.\n\nIn summary, scikit-learn's KMeans implementation uses NumPy for efficient vectorized operations and SciPy's `KDTree` class (although not directly in the latest versions) for computing the mean of the data points assigned to each centroid.",
+      "score": 0.6666666666666666,
+      "passed": true,
+      "found": [
+        "euclidean",
+        "scipy",
+        "numpy",
+        "centroid"
+      ],
+      "missed": [
+        "cdist",
+        "pairwise"
+      ],
+      "duration_s": 2.2999536991119385,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T4-001",
+      "tier": 4,
+      "query": "What functions in transformers would break if numpy changed the default dtype of np.float_ from float64 to float32?",
+      "answer": "If NumPy changed the default dtype of `np.float_` from `float64` to `float32`, several functions in the Hugging Face Transformers library might break or behave unexpectedly. Here are some potential issues:\n\n1. **Model weights and activations**: Many transformer models are trained using floating-point arithmetic, which relies on the precision of the floating-point numbers. If NumPy switches to `float32` by default, models might experience numerical instability or precision issues, leading to incorrect predictions or NaN values.\n\n2. **Attention mechanisms**: Attention mechanisms, such as those used in BERT and other transformer models, rely on matrix multiplications and dot products, which can be sensitive to the precision of the floating-point numbers. A switch to `float32` might lead to reduced precision and accuracy in these calculations.\n\n3. **Layer normalization**: Layer normalization, used in many transformer models, relies on the computation of mean and variance, which can be sensitive to the precision of the floating-point numbers. A switch to `float32` might lead to reduced precision and accuracy in these calculations.\n\n4. **AdamW optimizer**: The AdamW optimizer, used in many transformer models, relies on the computation of gradients, which can be sensitive to the precision of the floating-point numbers. A switch to `float32` might lead to reduced precision and accuracy in these calculations.\n\n5. **Tensor operations**: Many tensor operations in the Transformers library, such as `torch.matmul()` and `torch.mm()`, rely on the precision of the floating-point numbers. A switch to `float32` might lead to reduced precision and accuracy in these calculations.\n\nSome specific functions that might break or behave unexpectedly include:\n\n* `transformers.models.bert.modeling_bert.BertModel.forward()`\n* `transformers.models.roberta.modeling_roberta.RobertaModel.forward()`\n* `transformers.models.distilbert.modeling_distilbert.DistilBertModel.forward()`\n* `transformers.optimization.AdamW`\n* `transformers.models.auto.modeling_auto.AutoModel.forward()`\n\nTo mitigate these issues, you can:\n\n* Set the `dtype` of NumPy arrays to `float64` explicitly using `np.float64`.\n* Use the `torch.float64` dtype in PyTorch tensor operations.\n* Use the `torch.set_default_dtype(torch.float64)` function to set the default dtype for PyTorch tensors.\n* Update the Transformers library to use the `torch.float64` dtype by default.\n\nHowever, please note",
+      "score": 1.0,
+      "passed": true,
+      "found": [
+        "float64",
+        "float32",
+        "dtype",
+        "precision",
+        "numpy",
+        "tensor"
+      ],
+      "missed": [],
+      "duration_s": 2.541300058364868,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T4-002",
+      "tier": 4,
+      "query": "If numpy deprecated np.bool (alias for Python bool), which pandas and scikit-learn functions would be affected?",
+      "answer": "If `np.bool` is deprecated and removed, several functions in pandas and scikit-learn that rely on it might be affected. Here are some examples:\n\n**Pandas:**\n\n1. `pandas.DataFrame.astype(bool)`: This method converts the DataFrame to a boolean type, which relies on `np.bool`. It might need to be updated to use `np.bool_` (the underlying boolean type) instead.\n2. `pandas.Series.astype(bool)`: Similar to the DataFrame method, this might need to be updated.\n3. `pandas.to_numeric()`: This function has a `downcast` parameter that can be set to `'bool'` to convert to a boolean type. It might need to be updated to use `np.bool_` instead.\n\n**Scikit-learn:**\n\n1. `sklearn.utils._testing.assert_array_equal()`: This function checks if two arrays are equal, and it uses `np.bool` to convert the result to a boolean type. It might need to be updated to use `np.bool_` instead.\n2. `sklearn.utils._testing.assert_array_almost_equal()`: Similar to the previous function, it uses `np.bool` to convert the result to a boolean type. It might need to be updated.\n3. `sklearn.utils._testing.assert_array_less()`: This function checks if one array is less than another, and it uses `np.bool` to convert the result to a boolean type. It might need to be updated.\n\n**Other functions:**\n\n1. `numpy.isclose()`: This function checks if two arrays are close to each other, and it returns a boolean array. If `np.bool` is removed, this function might need to be updated to use `np.bool_` instead.\n2. `numpy.isin()`: This function checks if elements of one array are in another array, and it returns a boolean array. If `np.bool` is removed, this function might need to be updated to use `np.bool_` instead.\n\nNote that these are just potential examples, and the actual functions that would be affected might be different. It's always a good idea to check the documentation and source code of the libraries you're using to see if they rely on `np.bool`.",
+      "score": 0.75,
+      "passed": true,
+      "found": [
+        "np.bool",
+        "bool_",
+        "deprecated"
+      ],
+      "missed": [
+        "dtype"
+      ],
+      "duration_s": 2.266112804412842,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T4-003",
+      "tier": 4,
+      "query": "If PyTorch changed the default behavior of torch.no_grad() to not propagate to nested functions, what would break in HuggingFace transformers?",
+      "answer": "If PyTorch changed the default behavior of `torch.no_grad()` to not propagate to nested functions, several things could break in HuggingFace transformers:\n\n1. **Model evaluation**: In HuggingFace transformers, model evaluation is often performed using `model.eval()` which internally calls `torch.no_grad()`. However, if `torch.no_grad()` no longer propagates to nested functions, the gradients of the model's weights would still be computed during evaluation, leading to incorrect results.\n\n2. **Tracing and caching**: HuggingFace transformers use PyTorch's tracing and caching mechanisms to speed up model inference. If `torch.no_grad()` no longer propagates to nested functions, the traced functions would still compute gradients, which could lead to incorrect caching and tracing results.\n\n3. **Custom forward passes**: Some users may define custom forward passes in their models, which could involve calling `torch.no_grad()` to disable gradient computation. If `torch.no_grad()` no longer propagates to nested functions, these custom forward passes could still compute gradients, leading to incorrect results.\n\n4. **Gradient checkpointing**: HuggingFace transformers support gradient checkpointing, which involves saving and restoring the gradients of the model's weights at certain points during training. If `torch.no_grad()` no longer propagates to nested functions, gradient checkpointing could still compute gradients, leading to incorrect results.\n\nTo mitigate these issues, HuggingFace transformers could be modified to explicitly call `torch.no_grad()` on all nested functions, or to use a different mechanism to disable gradient computation. However, this would likely require significant changes to the library's codebase.\n\nIt's worth noting that this change would not break the HuggingFace transformers library itself, but rather the models and scripts that rely on it. Users would need to update their code to account for the changed behavior of `torch.no_grad()`.",
+      "score": 0.8,
+      "passed": true,
+      "found": [
+        "no_grad",
+        "inference",
+        "eval",
+        "grad"
+      ],
+      "missed": [
+        "context_manager"
+      ],
+      "duration_s": 3.6099400520324707,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T4-004",
+      "tier": 4,
+      "query": "If numpy removed the np.matrix class entirely, which scipy functions would need to be updated?",
+      "answer": "If numpy removed the `np.matrix` class entirely, several scipy functions would need to be updated. Here are some of the most affected functions:\n\n1. **scipy.linalg.inv**: This function currently accepts a matrix-like object (e.g., `np.matrix`) and returns its inverse. It would need to be updated to handle only numpy arrays.\n\n2. **scipy.linalg.solve**: Similar to `scipy.linalg.inv`, this function would need to be updated to handle only numpy arrays.\n\n3. **scipy.linalg.eig**: This function currently accepts a matrix-like object (e.g., `np.matrix`) and returns its eigenvalues and eigenvectors. It would need to be updated to handle only numpy arrays.\n\n4. **scipy.linalg.eigvals**: This function is similar to `scipy.linalg.eig` and would also need to be updated.\n\n5. **scipy.linalg.eigvalsh**: This function currently accepts a matrix-like object (e.g., `np.matrix`) and returns its symmetric eigenvalues. It would need to be updated to handle only numpy arrays.\n\n6. **scipy.linalg.eigh**: This function currently accepts a matrix-like object (e.g., `np.matrix`) and returns its eigenvalues and eigenvectors for a Hermitian matrix. It would need to be updated to handle only numpy arrays.\n\n7. **scipy.linalg.svd**: This function currently accepts a matrix-like object (e.g., `np.matrix`) and returns its singular value decomposition. It would need to be updated to handle only numpy arrays.\n\n8. **scipy.linalg.pinv**: This function currently accepts a matrix-like object (e.g., `np.matrix`) and returns its Moore-Penrose inverse. It would need to be updated to handle only numpy arrays.\n\n9. **scipy.linalg.pinv2**: This function is similar to `scipy.linalg.pinv` and would also need to be updated.\n\n10. **scipy.linalg.det**: This function currently accepts a matrix-like object (e.g., `np.matrix`) and returns its determinant. It would need to be updated to handle only numpy arrays.\n\nThese are just a few examples of scipy functions that would need to be updated if numpy removed the `np.matrix` class. The actual number of affected functions might be higher, depending on the specific use cases and requirements of scipy.",
+      "score": 0.8,
+      "passed": true,
+      "found": [
+        "np.matrix",
+        "matrix",
+        "scipy",
+        "linalg"
+      ],
+      "missed": [
+        "ndarray"
+      ],
+      "duration_s": 2.616403341293335,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T4-005",
+      "tier": 4,
+      "query": "What would happen to pandas DataFrame operations if numpy changed integer overflow behavior to raise exceptions instead of wrapping?",
+      "answer": "If NumPy changed its integer overflow behavior to raise exceptions instead of wrapping, it would significantly impact pandas DataFrame operations that rely heavily on NumPy's integer arithmetic.\n\nIn pandas, many operations such as indexing, slicing, and arithmetic operations on DataFrames involve NumPy arrays under the hood. When NumPy arrays overflow, pandas would propagate the NumPy exception, causing the DataFrame operation to fail.\n\nHere are some specific scenarios where pandas DataFrame operations would be affected:\n\n1.  **Indexing and Slicing**: When creating a slice or index for a DataFrame, NumPy's integer overflow behavior would raise an exception if the index exceeds the maximum integer value. This would prevent pandas from creating the slice or index, resulting in a `ValueError`.\n2.  **Arithmetic Operations**: When performing arithmetic operations on DataFrames, such as addition or multiplication, NumPy's integer overflow behavior would raise an exception if the result overflows. This would prevent pandas from performing the operation, resulting in a `ValueError`.\n3.  **Reshaping and Indexing**: When reshaping or indexing a DataFrame, NumPy's integer overflow behavior would raise an exception if the new shape or index exceeds the maximum integer value. This would prevent pandas from reshaping or indexing the DataFrame, resulting in a `ValueError`.\n\nTo mitigate these issues, pandas might need to implement additional checks and error handling mechanisms to detect and handle NumPy integer overflows. However, this would likely introduce additional complexity and performance overhead.\n\nIn summary, if NumPy changed its integer overflow behavior to raise exceptions instead of wrapping, pandas DataFrame operations would fail with `ValueError` exceptions in various scenarios, requiring additional error handling and checks to ensure robustness.",
+      "score": 0.6,
+      "passed": true,
+      "found": [
+        "overflow",
+        "integer",
+        "wrap"
+      ],
+      "missed": [
+        "int64",
+        "dtype"
+      ],
+      "duration_s": 1.9555792808532715,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T4-006",
+      "tier": 4,
+      "query": "If HuggingFace removed the return_dict parameter from model forward() calls, what downstream code would break?",
+      "answer": "If HuggingFace removed the `return_dict` parameter from model forward() calls, several downstream code would break. \n\n1. **Model-based Pipelines**: In HuggingFace's pipeline API, the `return_dict` parameter is used to control the output format of the model's predictions. If this parameter is removed, pipelines that rely on it would need to be updated to handle the new output format.\n\n2. **Custom Post-processing**: Some users may have implemented custom post-processing logic that relies on the `return_dict` parameter to access specific model outputs. Without this parameter, these custom post-processing steps would need to be revised.\n\n3. **Model-based Transformers**: In some cases, users may have implemented custom transformers that rely on the `return_dict` parameter to access specific model outputs. These transformers would need to be updated to handle the new output format.\n\n4. **Model-based Integrations**: If users have integrated HuggingFace models with other libraries or frameworks, these integrations may rely on the `return_dict` parameter to function correctly. These integrations would need to be updated to handle the new output format.\n\n5. **Model-based Unit Tests**: Unit tests that rely on the `return_dict` parameter to verify model outputs would need to be updated to handle the new output format.\n\nTo mitigate these issues, users can:\n\n* Update their code to handle the new output format.\n* Use the `return_dict` parameter when calling the model's forward() method, if it is still available.\n* Use the `return_tensors` parameter to control the output format of the model's predictions.\n\nHowever, if the `return_dict` parameter is removed, users would need to adapt their code to handle the new output format, which may involve changes to their model-based pipelines, custom post-processing logic, model-based transformers, model-based integrations, and model-based unit tests.",
+      "score": 0.4,
+      "passed": true,
+      "found": [
+        "return_dict",
+        "output"
+      ],
+      "missed": [
+        "BaseModelOutput",
+        "tuple",
+        "Trainer"
+      ],
+      "duration_s": 3.3030495643615723,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    },
+    {
+      "query_id": "T4-007",
+      "tier": 4,
+      "query": "If numpy's legacy np.random functions were removed, which scikit-learn estimators would break?",
+      "answer": "If numpy's legacy `np.random` functions were removed, several scikit-learn estimators would break or behave unexpectedly. Here are some of them:\n\n1. **Random Forests and Extra Trees**: These ensemble methods rely heavily on numpy's legacy random number generation functions. Specifically, they use `np.random.rand` and `np.random.randint` to generate random indices for bootstrapping and feature selection.\n\n2. **Gradient Boosting**: Similar to Random Forests and Extra Trees, Gradient Boosting also relies on numpy's legacy random number generation functions for generating random indices and splitting data.\n\n3. **K-Means and Hierarchical Clustering**: These clustering algorithms use numpy's legacy random number generation functions to initialize cluster centers and perform random permutations.\n\n4. **K-Nearest Neighbors (KNN)**: KNN uses numpy's legacy random number generation functions to generate random permutations for feature selection.\n\n5. **RandomizedSearchCV and GridSearchCV**: These hyperparameter tuning tools rely on numpy's legacy random number generation functions to generate random hyperparameter combinations.\n\n6. **BernoulliRBM and GaussianNB**: These naive Bayes classifiers use numpy's legacy random number generation functions to generate random weights and biases.\n\n7. **BernoulliRBM and GaussianMixture**: These mixture models use numpy's legacy random number generation functions to generate random weights and biases.\n\n8. **RandomizedLasso and RandomizedLogisticRegression**: These Lasso and logistic regression variants use numpy's legacy random number generation functions to generate random coefficients.\n\nThese estimators would likely raise errors or produce incorrect results if numpy's legacy `np.random` functions were removed. However, it's worth noting that scikit-learn has been actively maintaining and updating its codebase to use the new numpy random number generation functions, so this issue might be mitigated in future versions.",
+      "score": 0.2,
+      "passed": false,
+      "found": [
+        "np.random"
+      ],
+      "missed": [
+        "random_state",
+        "RandomState",
+        "Generator",
+        "seed"
+      ],
+      "duration_s": 2.7585573196411133,
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "condition": "baseline_no_rag"
+    }
+  ]
+}