277 lines
11 KiB
Python
277 lines
11 KiB
Python
import os, json, argparse, math, time, csv, sys
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from typing import List, Dict, Tuple
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import numpy as np
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import faiss
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# ---------- IO ----------
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def load_chunks(p):
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with open(p, "r", encoding="utf-8") as f:
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chunks = json.load(f)
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for c in chunks:
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if "text" not in c or "file" not in c or "chunk_index" not in c:
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raise ValueError("Each chunk must have 'file', 'chunk_index', 'text'")
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c.setdefault("text", "")
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c["chunk_index"] = int(c["chunk_index"])
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return chunks
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def load_qrels(p):
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qrels, queries = {}, {}
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with open(p, "r", encoding="utf-8") as f:
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for line in f:
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r = json.loads(line)
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qid = r["qid"]
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queries[qid] = r.get("query", "")
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rel_list = r.get("relevant") or r.get("rels") or r.get("labels") or r.get("items")
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if rel_list is None:
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raise ValueError(f"{p}: missing 'relevant' field")
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gains = {(x.get("file") or x.get("pdf_path") or x.get("path"),
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int(x.get("chunk_index") or x.get("page_index") or (int(x["slide_number"])-1)))
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: int(x.get("rel", 1)) for x in rel_list}
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qrels[qid] = gains
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return queries, qrels
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def ensure_dir(p): os.makedirs(p, exist_ok=True)
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# ---------- Metrics ----------
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def precision_at_k(ranked, relset, k):
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k = min(k, len(ranked));
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return (sum(1 for d in ranked[:k] if d in relset)/k) if k>0 else 0.0
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def recall_at_k(ranked, relset, k):
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return (sum(1 for d in ranked[:k] if d in relset)/max(1,len(relset)))
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def average_precision(ranked, relset):
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if not relset: return 0.0
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ap, hits = 0.0, 0
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for i,d in enumerate(ranked,1):
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if d in relset: hits += 1; ap += hits/i
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return ap/max(1,len(relset))
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def reciprocal_rank(ranked, relset):
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for i,d in enumerate(ranked,1):
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if d in relset: return 1.0/i
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return 0.0
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def dcg_at_k(ranked, gains, k):
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dcg=0.0
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for i,d in enumerate(ranked[:k],1):
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g=gains.get(d,0)
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if g>0: dcg+=(2**g-1)/math.log2(i+1)
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return dcg
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def ndcg_at_k(ranked, gains, k):
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dcg=dcg_at_k(ranked,gains,k)
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ideal=sorted(gains.values(), reverse=True)
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idcg=0.0
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for i,g in enumerate(ideal[:k],1):
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idcg+=(2**g-1)/math.log2(i+1)
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return dcg/idcg if idcg>0 else 0.0
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def evaluate_run(run: Dict[str, List[Tuple[str,int]]], qrels: Dict[str, Dict[Tuple[str,int], int]], k_vals=(1,3,5,10)):
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agg = {f"P@{k}":0.0 for k in k_vals} | {f"R@{k}":0.0 for k in k_vals} | {f"nDCG@{k}":0.0 for k in k_vals}
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agg["MAP"] = 0.0; agg["MRR"] = 0.0
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N = 0
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for qid, gains in qrels.items():
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ranked = run.get(qid, [])
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relset = {d for d,g in gains.items() if g > 0}
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for k in k_vals:
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agg[f"P@{k}"] += precision_at_k(ranked, relset, k)
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agg[f"R@{k}"] += recall_at_k(ranked, relset, k)
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agg[f"nDCG@{k}"] += ndcg_at_k(ranked, gains, k)
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agg["MAP"] += average_precision(ranked, relset)
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agg["MRR"] += reciprocal_rank(ranked, relset)
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N += 1
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for m in agg: agg[m] /= max(N,1)
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return agg
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# ---------- Formatting ----------
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def model_family(model_id: str) -> str:
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mid = model_id.lower()
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if "e5" in mid and "multilingual" not in mid: return "e5" # English E5
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if "bge" in mid: return "bge" # BGE English
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return "plain" # MPNet, GTE, MiniLM, multi-qa-*
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def format_passages(texts: List[str], family: str) -> List[str]:
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if family == "e5":
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return [f"passage: {t.strip()}" for t in texts]
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if family == "bge":
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return [f"Represent this document for retrieval: {t.strip()}" for t in texts]
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return [t if isinstance(t, str) else str(t) for t in texts]
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def format_queries(texts: List[str], family: str) -> List[str]:
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if family == "e5":
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return [f"query: {t.strip()}" for t in texts]
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if family == "bge":
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return [f"Represent this query for retrieval: {t.strip()}" for t in texts]
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return [t.strip() for t in texts]
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# ---------- FAISS ----------
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def build_index(embs: np.ndarray) -> faiss.IndexFlatIP:
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X = embs.astype("float32")
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faiss.normalize_L2(X)
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idx = faiss.IndexFlatIP(X.shape[1])
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idx.add(X)
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return idx
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# ---------- Helpers ----------
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def parse_model_spec(spec: str):
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"""
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Allow 'model_id' or 'model_id::/path/to/index.faiss'
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Returns (model_id, faiss_override_path or None)
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"""
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if "::" in spec:
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m, p = spec.split("::", 1)
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return m.strip(), p.strip()
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return spec.strip(), None
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def load_st_model(model_id: str):
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from sentence_transformers import SentenceTransformer
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try:
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return SentenceTransformer(model_id)
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except Exception as e:
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print(f"[WARN] Could not load {model_id}: {e}", file=sys.stderr)
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return None
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# ---------- Main ----------
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--chunks", default='./assets/lecture_chunks.json', help="lecture_chunks.json")
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ap.add_argument("--qrels", default='./assets/qrels_recording.jsonl', help="qrels_recording.jsonl")
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ap.add_argument("--out_dir", default='./')
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ap.add_argument("--models", nargs="+", required=True,
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help="List like: 'sentence-transformers/multi-qa-mpnet-base-dot-v1::/path/embeddings_enhanced.faiss' 'BAAI/bge-base-en-v1.5' ...")
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ap.add_argument("--k_list", nargs="+", type=int, default=[1,3,5,10,20])
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ap.add_argument("--topk", type=int, default=20)
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ap.add_argument("--batch", type=int, default=128)
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args = ap.parse_args()
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ensure_dir(args.out_dir)
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chunks = load_chunks(args.chunks)
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queries, qrels = load_qrels(args.qrels)
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qids = list(qrels.keys())
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# Prepare raw query texts once
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q_texts_raw = [queries.get(qid, "") for qid in qids]
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results = []
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per_model_runs = {}
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for spec in args.models:
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model_id, faiss_override = parse_model_spec(spec)
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fam = model_family(model_id)
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print(f"\n===== {model_id} (family={fam}) =====")
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mslug = model_id.replace("/", "_").replace(":", "_")
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mdir = os.path.join(args.out_dir, mslug)
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ensure_dir(mdir)
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emb_path = os.path.join(mdir, "doc_embs.npy")
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idx_path = os.path.join(mdir, "faiss.index")
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# Encoder (needed for queries; for docs only if we rebuild)
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enc = load_st_model(model_id)
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if enc is None:
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continue
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# ----- Build or load index -----
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index = None
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if faiss_override:
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if not os.path.exists(faiss_override):
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print(f"[WARN] FAISS override not found: {faiss_override}")
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else:
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index = faiss.read_index(faiss_override)
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if index.ntotal != len(chunks):
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print(f"[WARN] Override index size {index.ntotal} != chunks {len(chunks)}; ignoring override.")
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index = None
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if index is None:
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# use cached per-model artifacts if present
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if os.path.exists(emb_path) and os.path.exists(idx_path):
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try:
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doc_embs = np.load(emb_path)
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index = faiss.read_index(idx_path)
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if index.ntotal != len(chunks):
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index = None
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except Exception:
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index = None
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if index is None:
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corpus_texts = [c["text"] for c in chunks]
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corpus_fmt = format_passages(corpus_texts, fam)
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t0 = time.time()
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doc_embs = enc.encode(corpus_fmt, batch_size=args.batch, convert_to_numpy=True,
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normalize_embeddings=True).astype("float32")
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t1 = time.time() - t0
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print(f"[TIME] Encoded {len(chunks)} chunks in {t1:.1f}s")
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index = build_index(doc_embs.copy())
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faiss.write_index(index, idx_path)
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np.save(emb_path, doc_embs)
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# ----- Encode queries -----
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q_fmt = format_queries(q_texts_raw, fam)
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t0 = time.time()
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q_embs = enc.encode(q_fmt, batch_size=args.batch, convert_to_numpy=True,
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normalize_embeddings=True).astype("float32")
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t1 = time.time() - t0
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print(f"[TIME] Encoded {len(q_embs)} queries in {t1:.1f}s")
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# ----- Search -----
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sims, ids = index.search(q_embs, args.topk)
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# Build run keyed by (file, chunk_index)
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run = {}
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for i, qid in enumerate(qids):
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rows = ids[i].tolist()
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run[qid] = [(chunks[r]["file"], int(chunks[r]["chunk_index"])) for r in rows]
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per_model_runs[model_id] = run
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# ----- Evaluate -----
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k_vals = tuple(sorted(set(args.k_list)))
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metrics = evaluate_run(run, qrels, k_vals=k_vals)
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row = {"model": model_id} | {k: round(float(v), 6) for k, v in metrics.items()}
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results.append(row)
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print("[RESULT]", row)
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# ----- Leaderboard -----
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if results:
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results_sorted = sorted(results, key=lambda r: (r.get("nDCG@10",0.0), r.get("R@10",0.0)), reverse=True)
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csv_path = os.path.join(args.out_dir, "embedding_ablation_leaderboard.csv")
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with open(csv_path, "w", newline="", encoding="utf-8") as f:
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cols = ["model"] + [k for k in results_sorted[0].keys() if k != "model"]
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w = csv.DictWriter(f, fieldnames=cols); w.writeheader()
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for r in results_sorted: w.writerow(r)
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print(f"\n[OK] Wrote leaderboard: {csv_path}")
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# Plots (top-5 for clarity)
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try:
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import matplotlib.pyplot as plt
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k_vals = sorted(set(args.k_list))
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topN = results_sorted[:5]
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plt.figure(figsize=(7,5))
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for r in topN:
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m = r["model"]
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M = evaluate_run(per_model_runs[m], qrels, k_vals=tuple(k_vals))
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ys = [M[f"R@{k}"] for k in k_vals]
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plt.plot(k_vals, ys, marker="o", label=m)
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plt.xlabel("k"); plt.ylabel("Recall"); plt.title("RecordingRAG: Recall@k across models")
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plt.legend(); plt.tight_layout()
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plt.savefig(os.path.join(args.out_dir, "recall_at_k_models.png"), dpi=150, bbox_inches="tight"); plt.close()
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plt.figure(figsize=(7,5))
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for r in topN:
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m = r["model"]
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M = evaluate_run(per_model_runs[m], qrels, k_vals=tuple(k_vals))
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ys = [M[f"nDCG@{k}"] for k in k_vals]
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plt.plot(k_vals, ys, marker="o", label=m)
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plt.xlabel("k"); plt.ylabel("nDCG"); plt.title("RecordingRAG: nDCG@k across models")
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plt.legend(); plt.tight_layout()
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plt.savefig(os.path.join(args.out_dir, "ndcg_at_k_models.png"), dpi=150, bbox_inches="tight"); plt.close()
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print(f"[OK] Saved plots to {args.out_dir}")
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except Exception as e:
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print(f"[WARN] Plotting failed: {e}")
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else:
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print("[ERROR] No models ran successfully.")
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if __name__ == "__main__":
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main()
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