"
+ "Left: ML prediction for Stress Gauge Factor (original scale, MPa-1). "
+ "Right: Literature Q&A via Hybrid RAG (BM25 + TF-IDF + optional dense) with MMR sentence selection. "
+ "Answers cite short document codes (e.g., S71, S92)."
+ "
papers/ — **{pdf_count} PDF(s)** indexed. "
+ "Upload more PDFs and reload the Space to expand coverage. "
+ "Answers cite short document codes such as S71, S92."
+ )
+ with gr.Row():
+ top_k = gr.Slider(5, 12, value=8, step=1, label="Top-K chunks")
+ n_sentences = gr.Slider(2, 6, value=4, step=1, label="Answer length (sentences)")
+ include_passages = gr.Checkbox(value=False, label="Include supporting passages", interactive=True)
+
+ with gr.Accordion("Retriever weights (advanced)", open=False):
+ w_tfidf = gr.Slider(0.0, 1.0, value=W_TFIDF_DEFAULT, step=0.05, label="TF-IDF weight")
+ w_bm25 = gr.Slider(0.0, 1.0, value=W_BM25_DEFAULT, step=0.05, label="BM25 weight")
+ w_emb = gr.Slider(0.0, 1.0, value=(0.0 if not USE_DENSE else 0.40), step=0.05, label="Dense weight (set 0 if disabled)")
+
+ # Hidden states (unchanged)
+ state_use_llm = gr.State(LLM_AVAILABLE)
+ state_model_name = gr.State(os.getenv("OPENAI_MODEL", OPENAI_MODEL))
+ state_temperature = gr.State(0.2)
+ state_strict = gr.State(False)
+
+ gr.ChatInterface(
+ fn=rag_chat_fn,
+ additional_inputs=[
+ top_k, n_sentences, include_passages,
+ state_use_llm, state_model_name, state_temperature, state_strict,
+ w_tfidf, w_bm25, w_emb
+ ],
+ title="Literature Q&A",
+ description="Hybrid retrieval with diversity. Answers carry inline short-code citations (e.g., (S92), (S71))."
+ )
+
+ # ====== Evaluate (Gold vs Logs) ======
+ with gr.Tab("📏 Evaluate (Gold vs Logs)", elem_id="eval-tab"):
+ gr.Markdown("Upload your **gold.csv** and compute metrics against the app logs.")
+ with gr.Row():
+ gold_file = gr.File(label="gold.csv", file_types=[".csv"], interactive=True)
+ k_slider = gr.Slider(3, 12, value=8, step=1, label="k for Hit/Recall/nDCG", elem_id="k-slider")
+ with gr.Row():
+ btn_eval = gr.Button("Compute Metrics", variant="primary")
+ with gr.Row():
+ out_perq = gr.File(label="Per-question metrics (CSV)", elem_id="perq-file")
+ out_agg = gr.File(label="Aggregate metrics (JSON)", elem_id="agg-file")
+ out_json = gr.JSON(label="Aggregate summary", elem_id="agg-json")
+ out_log = gr.Markdown(label="Run log", elem_id="eval-log")
+
+ def _run_eval_inproc(gold_path: str, k: int = 8):
+ import json as _json
+ out_dir = str(ARTIFACT_DIR)
+ logs = str(LOG_PATH)
+ cmd = [
+ sys.executable, "rag_eval_metrics.py",
+ "--gold_csv", gold_path,
+ "--logs_jsonl", logs,
+ "--k", str(k),
+ "--out_dir", out_dir
+ ]
+ try:
+ p = subprocess.run(cmd, capture_output=True, text=True, check=False)
+ stdout = p.stdout or ""
+ stderr = p.stderr or ""
+ perq = ARTIFACT_DIR / "metrics_per_question.csv"
+ agg = ARTIFACT_DIR / "metrics_aggregate.json"
+ agg_json = {}
+ if agg.exists():
+ agg_json = _json.loads(agg.read_text(encoding="utf-8"))
+ report = "```\n" + (stdout.strip() or "(no stdout)") + ("\n" + stderr.strip() if stderr else "") + "\n```"
+ return (str(perq) if perq.exists() else None,
+ str(agg) if agg.exists() else None,
+ agg_json,
+ report)
+ except Exception as e:
+ return (None, None, {}, f"**Eval error:** {e}")
+
+ def _eval_wrapper(gf, k):
+ from pathlib import Path as _Path
+ if gf is None:
+ default_gold = _Path("gold.csv")
+ if not default_gold.exists():
+ return None, None, {}, "**No gold.csv provided or found in repo root.**"
+ gold_path = str(default_gold)
+ else:
+ gold_path = gf.name
+ return _run_eval_inproc(gold_path, int(k))
+
+ btn_eval.click(_eval_wrapper, inputs=[gold_file, k_slider],
+ outputs=[out_perq, out_agg, out_json, out_log])
+
+# ------------- Launch -------------
+if __name__ == "__main__":
+ # 1. Start the Chatbot (This is what gives you the link)
+ # If using Gradio:
+ demo.launch()
+
+ # Or if using Flask:
+ # app.run(debug=True)
+
+ # 2. Everything below here only runs AFTER the server stops
+ # (or might not run at all depending on how the server handles the exit)
+ import os as _os
+ import pandas as _pd
+ folder = "papers"
+ files = sorted(_os.listdir(folder)) if _os.path.exists(folder) else []
+ _pd.DataFrame({"doc": files}).to_csv("paper_list.csv", index=False)
+ print("✅ Saved paper_list.csv with", len(files), "papers")
diff --git a/download_logic.py b/download_logic.py
new file mode 100644
index 0000000000000000000000000000000000000000..98cab1d715b83fa8b1f8415ac89e990fe00c2b2f
--- /dev/null
+++ b/download_logic.py
@@ -0,0 +1,13 @@
+from huggingface_hub import snapshot_download
+
+# This pulls the 'brain' (logic) but skips the 'heavy' stuff
+# so your Galaxy Book stays fast.
+snapshot_download(
+ repo_id="Inframat-x/ML-Chatbot",
+ repo_type="space",
+ local_dir=".",
+ ignore_patterns=["*.bin", "*.onnx", "*.pkl", "*.joblib", "*.pth"]
+)
+
+print("\n--- Handshake Complete ---")
+print("The vibe-code is now local. Check your sidebar!")
\ No newline at end of file
diff --git a/paper_list.csv b/paper_list.csv
new file mode 100644
index 0000000000000000000000000000000000000000..7a3e53b70ec5381991152d75af27229dcdbc5d71
--- /dev/null
+++ b/paper_list.csv
@@ -0,0 +1,131 @@
+doc
+1-s2.0-S095006181732278X-main.pdf
+1-s2.0-S0950061820330786-main.pdf
+1-s2.0-S1359836816316882-main.pdf
+1-s2.0-S2090447920301593-main.pdf
+2011-EffectofSpecimenSizeonStaticStrengthandDIFofHSCfromSHPBTest.pdf
+Capacitance-based stress self-sensing in cement paste without requiring any admixture.pdf
+Capacitive compressive stress self-sensing behavior of cement mortar and its dependence on the thickness.pdf
+Carbon fiber reinforced cement improved by using silane-treated carbon fibers.pdf
+Development of self-sensing ultra-high-performance concrete using hybrid carbon black and carbon nanofibers.pdf
+"Development of sensing concrete Principles, properties and its applications.pdf"
+EVALUA~1.PDF
+Effect of silane treatment on microstructure of sisal fibers.pdf
+"Graphene family (GFMs), carbon nanotubes (CNTs) and carbon black (CB) on smart materials for civil construction.pdf"
+Influence of the structures of polycarboxylate superplasticizer on its performance in cement-based materials-A review.pdf
+Investigating the synergistic effects of carbon fiber and silica fume on concrete strength and eco-efficiency.pdf
+Investigation of 3D Printed Self-Sensing UHPC Composites Using Graphite and Hybrid Carbon Microfibers.pdf
+Ozone treatment of carbon fiber for reinforcing cement.pdf
+PIEZOE~1.PDF
+Performance of silica fume slurry treated recycled aggregate concrete reinforced with carbon fibers.pdf
+Piezopermittivity for capacitance-based strain stress sensing.pdf
+Review Improving cement-based materials by using silica fume.pdf
+Revolutionizing infrastructure The evolving landscape of electricity-based multifunctional concrete from concept to practice.pdf
+S1-An-experimental-study-of-self-sensing-concrete-enhanced_2020_Construction-an.pdf
+S10-Enhancing-self-stress-sensing-ability-of-smart-ultra-high_2021_Journal-of-Bu.pdf
+S100-C~1.PDF
+"S11-Environment-Friendly, Self-Sensing Concrete Blended with Byproduct Wastes.pdf"
+S12-Hybrid-effects-of-steel-fiber-and-carbon-nanotube-on-s_2018_Construction-and.pdf
+S13-Increasing-self-sensing-capability-of-carbon-nanotubes-c_2020_Construction-a.pdf
+S14-Influence-of-carbon-nanofiber-content-and-sodium-chloride-_2019_Case-Studies.pdf
+S15-Influence-of-water-ingress-on-the-electrical-properties-_2021_Journal-of-Bui.pdf
+S16-Investigations-on-scalable-fabrication-procedures-for-sel_2016_Cement-and-Co.pdf
+S17-Cross tension and compression loading and large-scale testing of strain and damage sensing smart concrete.pdf
+S18-Nano graphite platelets-enabled piezoresistive cementitious composites for structural health monitoring.pdf
+S19-Self-sensing-piezoresistive-cement-composite-loaded_2017_Cement-and-Concrete.pdf
+S2-Characterization-of-smart-brass-fiber-reinforced-co_2020_Construction-and-Bu.pdf
+S20-IN~1.PDF
+"S21-Mechanical, electrical and self-sensing properties of cementitious mortars containing short carbon fibers.pdf"
+S22-Improved strain sensing properties of cement-based sensors through enhanced carbon nanotube dispersion.pdf
+S23-Increasing self-sensing capability of carbon nanotubes cement-based materials by simultaneous addition of Ni nanofibers.pdf
+S24-Multifunctional-self-sensing-and-ductile-cementit_2019_Cement-and-Concrete-R.pdf
+S25-Self-sensing-capability-of-ultra-high-performance-concr_2018_Sensors-and-Act.pdf
+S26-TE~1.PDF
+S27-Effect of aspect ratio on strain sensing capacity of carbon fiber reinforced cement composites.pdf
+S28-Smart Graphite–Cement Composites with Low Percolation Threshold.pdf
+S29-Hybrid Carbon Microfibers-Graphite Fillers for Piezoresistive Cementitious Composites.pdf
+S3-Effect of characteristics of assembly unit of CNTNCB composite fillers on properties of smart cement-based materials.pdf
+S30-Smart Graphite–Cement Composite for Roadway-Integrated Weigh-In-Motion Sensing.pdf
+S31-Electrical and piezoresistive properties of carbon nanofiber cement mortar under different temperatures and water contents.pdf
+S32-Self-stress-sensing-smart-concrete-containing-fine-stee_2019_Construction-an.pdf
+S33-IN~1.PDF
+S34-Self-sensing-ultra-high-performance-concrete-fo_2021_Sensors-and-Actuators-A.pdf
+S35-EL~1.PDF
+S36-Piezoresistivity enhancement of functional carbon black filled cement-based sensor using polypropylene fibre.pdf
+S37-Test and Study on Electrical Property of Conductive Concrete.pdf
+S38 - Electrical-resistance-based Sensing of Impact Damage in Carbon Fiber Reinforced Cement-based Materials.pdf
+S39 - Electrical conductivity of self-monitoring CFRC.pdf
+S4-Effect-of-steel-fiber-and-carbon-black-on-the-self-s_2019_Construction-and-B.pdf
+S40 - Resistance Changes during Compression of Carbon Fiber Cement COmposites.pdf
+S41 - Electrical-resistance-based damage self-sensing in carbon fiber reinforced cement.pdf
+S42-SE~1.PDF
+S43 - the 100th anniversary of the four-point probe technique the role of probe geometries in isotropic andanisotropic systems.pdf
+S44-Sensing performance of engineered cementitious composites in different application forms.pdf
+S45-Insitu synthesizing carbon nanotubes on cement to develop self-sensing cementitious composites.pdf
+S46-SE~1.PDF
+S47-The applicability of shungite as an electrically conductive additive in cement composites.pdf
+S48-Self-sensing properties and piezoresistive effect of high ductility cementitious composite.pdf
+S49-ME~1.PDF
+S5-Effects-of-carbon-nanomaterial-type-and-amount-on-self-sensing-_2019_Measure.pdf
+S50-IM~1.PDF
+S51-Electrical and piezoresistive properties of cement composites with carbon nanomaterials.pdf
+"S52-Influences of (MCNT) fraction, moisture, stressstrain level on the electrical properties of MCNT of cement-based composites.pdf"
+S53-CA~1.PDF
+S54-Carbon Nanofibers Grown in CaO for Self-Sensing in Mortar.pdf
+S55-Electro-mechanical self-sensing response of ultra-high-performance fiber-reinforced concrete in tension.pdf
+S55-Nanocarbon black-based ultra-high-performance concrete (UHPC) with self-strain sensing capability.pdf
+S56-Self-sensing cementitious composites incorporated with botryoid hybrid nano-carbon materials for smart infrastructures.pdf
+S57-IN~1.PDF
+S58-DE~1.PDF
+S59-Modifying self-sensing cement-based composites through multiscale composition.pdf
+S6-Electrically conductive behaviors and mechanisms of short-cut super-fine stainless wire reinforced reactive powder concrete.pdf
+S60-Study on self-sensing capabilities of smart cements filled with graphene oxide under dynamic cyclic loading.pdf
+"S61-Piezoresistivity, mechanisms and model of cement-based materials with CNT_NCB composite fillers.pdf"
+S62-MU~1.PDF
+S63-Piezoresistive properties of cement composites with expanded graphite.pdf
+"S64-Electrical Properties of Cement-Based Composites with Carbon Nanotubes, Graphene, and Graphite Nanofibers.pdf"
+S65-AN~1.PDF
+S66-Experimental Investigation of the Piezoresistive Properties of Cement Composites with Hybrid Carbon Fibers and Nanotubes.pdf
+S67-Strain and damage sensing properties on multifunctional cement composites with CNF admixture.pdf
+S68-EF~1.PDF
+S69-Cement-based sensors with carbon fibers and carbon nanotubes for piezoresistive sensing.pdf
+S7-Electrical characteristics and pressure-sensitive response measurements of carboxyl MWNT_cement composites.pdf
+S70-EV~1.PDF
+S71-Enhanced sensing performance of cement-based composites achieved via magnetically aligned nickel particle network.pdf
+S72-Anisotropic electrical and piezoresistive sensing properties of cement-based sensors with aligned carbon fibers.pdf
+S73-Development of self-sensing cement-based sensor using recycled fine waste glass aggregates coated with carbon nanotube.pdf
+S74-Strain sensitivity of steel-fiber-reinforced industrial smart concrete.pdf
+S75-SE~1.PDF
+S76-Strain-sensing characteristics of self-consolidating concrete with micro-carbon fibre.pdf
+S77-SE~1.PDF
+S78-Mechanical and self-sensing properties of concrete reinforced with carbon nanofibres.pdf
+S79-Carbon nanotube cement-based transducers for dynamic sensing of strain.pdf
+S8-Electrically-cured-ultra-high-performance-concrete--UHPC--embe_2020_Material.pdf
+S80-MA~1.PDF
+S81-Piezoresistive properties of ultra-high-performance fiber-reinforced concrete incorporating few-layer graphene.pdf
+S82-SY~1.PDF
+S83-Effect of compressive strain on electrical resistivity of carbon black-filled cement-based composites.pdf
+S84-TA~1.PDF
+S85-Performance of cement-based sensors with CNT for strain sensing.pdf
+S86-EL~1.PDF
+S87-EL~1.PDF
+S88-ST~1.PDF
+S89-Piezoresistivity of carbon fiber graphite cement-based composites with CCCW.pdf
+S9-Electro-mechanical-self-sensing-response-of-ultra-high-_2018_Composites-Part.pdf
+S90-EX~1.PDF
+S91-A comparative study on the influences of CNT and GNP on the piezoresistivity of cement composites.pdf
+S92-Research-on-the-self-sensing-and-mechanical-properties-of_2021_Cement-and-Co.pdf
+S93-Enhanced effects of carbon-based conductive materials on the piezoresistive characteristics of cementitious composites.pdf
+S94-The Utilization of Pearson’s Method to Analyze Piezoresistive Effect in Self-Sensing Cement Composite with Graphite.pdf
+S95-SE~1.PDF
+S96-EL~1.PDF
+S97-Self-sensing GFRP-reinforced concrete beams containing carbon nanotube-nano carbon black composite fillers.pdf
+S98-MI~1.PDF
+S99-Commercial and recycled carbon-based fillers and fibers for self-sensing cement-based composites.pdf
+Self-sensing enhancement in smart ultra-high performance concrete composites via multi-scale carbon black.pdf
+"Self-sensing performance of cementitious composites with functional fillers at macro, micro and nano scales.pdf"
+Self‐Sensing Cementitious Composites with Hierarchical Carbon Fiber‐Carbon Nanotube Composite Fillers.pdf
+Silane treatment of bagasse fiber for reinforcement of cementitious composites.pdf
+Silane-treated carbon fiber for reinforcing cement.pdf
+The effect of silane surface treatment on the mechanical properties of UHPFRC.pdf
+document.pdf
diff --git a/papers/1-s2.0-S095006181732278X-main.pdf b/papers/1-s2.0-S095006181732278X-main.pdf
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+{"run_id": "ca70d974-88ac-4f0f-9855-e373dc760b22", "ts": 1770940738435, "inputs": {"question": "what is smart concerete?", "top_k": 8, "n_sentences": 4, "w_tfidf": 0.3, "w_bm25": 0.3, "w_emb": 0.4, "use_llm": false, "model": "gpt-5", "temperature": 0.2}, "retrieval": {"hits": [{"doc": "S30-Smart Graphite–Cement Composite for Roadway-Integrated Weigh-In-Motion Sensing.pdf", "page": "?", "score_tfidf": 0.5267006307534708, "score_bm25": 1.0, "score_dense": 0.7098788388270614, "combo_score": 0.7419617247568657}, {"doc": "S59-Modifying self-sensing cement-based composites through multiscale composition.pdf", "page": "?", "score_tfidf": 0.854222338923855, "score_bm25": 0.44663238855471943, "score_dense": 0.8151288517793684, "combo_score": 0.7163079589553197}, {"doc": "S26-TE~1.PDF", "page": "?", "score_tfidf": 0.9277266170577227, "score_bm25": 0.6934450500586189, "score_dense": 0.549131738611858, "combo_score": 0.7060041955796457}, {"doc": "S26-TE~1.PDF", "page": "?", "score_tfidf": 1.0, "score_bm25": 0.701547923740148, "score_dense": 0.4087956025663952, "combo_score": 0.6739826181486025}, {"doc": "S17-Cross tension and compression loading and large-scale testing of strain and damage sensing smart concrete.pdf", "page": "?", "score_tfidf": 0.6655686811753331, "score_bm25": 0.3671680772098779, "score_dense": 0.8849731734043732, "combo_score": 0.6638102968773125}, {"doc": "1-s2.0-S2090447920301593-main.pdf", "page": "?", "score_tfidf": 0.8886339821047743, "score_bm25": 0.6855310749255774, "score_dense": 0.3834955073599744, "combo_score": 0.6256477200530952}, {"doc": "S2-Characterization-of-smart-brass-fiber-reinforced-co_2020_Construction-and-Bu.pdf", "page": "?", "score_tfidf": 0.26213070431581836, "score_bm25": 0.48998856253275425, "score_dense": 0.9840270254762131, "combo_score": 0.619246590245057}, {"doc": "S32-Self-stress-sensing-smart-concrete-containing-fine-stee_2019_Construction-an.pdf", "page": "?", "score_tfidf": 0.2748116414810267, "score_bm25": 0.5296382441464794, "score_dense": 0.8987706060064166, "combo_score": 0.6008432080908185}], "latency_ms_retriever": 192}, "output": {"final_answer": "**Answer:** smart concrete (made from the UHPC containing both FSSAs and steel fibers) under compression in comparison with that of smart concretes made from the UHPCs containing other fillers and (2) to investigate the self-stress sensing capacity of a smart concrete anchorage, made from the UHPC containing both FSSAs and steel fibers, for monitoring the level of prestressing stress. (S32) infrastructure is built to support the growing population, it is essential that it is smart infrastructure to improve the lives Therefore, infrastructures are at the beginning of tures age and slowly deteriorate. (S59) This optimal mix was used to fabricate the smart composite plate, discussed in what follows. (S30) more smart cities, as the world population increases and more people move to urban environments. (S59)\n\n**Citations:** S30; S59; S26; S17; 1", "used_sentences": [{"sent": "smart concrete (made from the UHPC containing both FSSAs and steel fibers) under compression in comparison with that of smart concretes made from the UHPCs containing other fillers and (2) to investigate the self-stress sensing capacity of a smart concrete anchorage, made from the UHPC containing both FSSAs and steel fibers, for monitoring the level of prestressing stress.", "doc": "S32", "page": "?"}, {"sent": "infrastructure is built to support the growing population, it is essential that it is smart infrastructure to improve the lives Therefore, infrastructures are at the beginning of tures age and slowly deteriorate.", "doc": "S59", "page": "?"}, {"sent": "This optimal mix was used to fabricate the smart composite plate, discussed in what follows.", "doc": "S30", "page": "?"}, {"sent": "more smart cities, as the world population increases and more people move to urban environments.", "doc": "S59", "page": "?"}]}, "latency_ms_total": 888, "latency_ms_llm": null, "openai": null}
+{"run_id": "2a85d7bb-da8c-4710-ad2c-7e6aa024f641", "ts": 1770997604572, "inputs": {"question": "what is smart concrete", "top_k": 8, "n_sentences": 4, "w_tfidf": 0.3, "w_bm25": 0.3, "w_emb": 0.4, "use_llm": false, "model": "gpt-5", "temperature": 0.2}, "retrieval": {"hits": [{"doc": "S17-Cross tension and compression loading and large-scale testing of strain and damage sensing smart concrete.pdf", "page": "?", "score_tfidf": 0.7812515257335579, "score_bm25": 0.7352663039823301, "score_dense": 0.8767695341602232, "combo_score": 0.8056631625788557}, {"doc": "S74-Strain sensitivity of steel-fiber-reinforced industrial smart concrete.pdf", "page": "?", "score_tfidf": 0.7180607005519931, "score_bm25": 0.6877152952515094, "score_dense": 0.945573646308848, "combo_score": 0.79996225726459}, {"doc": "S17-Cross tension and compression loading and large-scale testing of strain and damage sensing smart concrete.pdf", "page": "?", "score_tfidf": 0.6496146887635347, "score_bm25": 0.6763484673337841, "score_dense": 1.0, "combo_score": 0.7977889468291957}, {"doc": "S2-Characterization-of-smart-brass-fiber-reinforced-co_2020_Construction-and-Bu.pdf", "page": "?", "score_tfidf": 0.7738965578242346, "score_bm25": 0.6927134383580094, "score_dense": 0.8814222037615043, "combo_score": 0.792551880359275}, {"doc": "S2-Characterization-of-smart-brass-fiber-reinforced-co_2020_Construction-and-Bu.pdf", "page": "?", "score_tfidf": 0.7103253005978989, "score_bm25": 0.6959447697763587, "score_dense": 0.9214132211426111, "combo_score": 0.7904463095693218}, {"doc": "S32-Self-stress-sensing-smart-concrete-containing-fine-stee_2019_Construction-an.pdf", "page": "?", "score_tfidf": 0.7179642554332893, "score_bm25": 0.7249796891508764, "score_dense": 0.8798064697010471, "combo_score": 0.7848057712556685}, {"doc": "S17-Cross tension and compression loading and large-scale testing of strain and damage sensing smart concrete.pdf", "page": "?", "score_tfidf": 0.9153664701009494, "score_bm25": 0.5209537951422858, "score_dense": 0.8750282780116994, "combo_score": 0.7809073907776503}, {"doc": "S32-Self-stress-sensing-smart-concrete-containing-fine-stee_2019_Construction-an.pdf", "page": "?", "score_tfidf": 0.7547700812189673, "score_bm25": 0.7115274938964362, "score_dense": 0.8204715448416257, "combo_score": 0.7680778904712713}], "latency_ms_retriever": 384}, "output": {"final_answer": "**Answer:** Smart concrete can be used in the important structures (hospitals, power plants, schools etc.) and in the critical structural elements such as first floor columns. (S17) concrete can keep the cost of smart concrete at competitive levels for The smart concrete can timely detect structural degradation and enable asset management by collect ing data for repair and maintenance. (S17) smart concrete (made from the UHPC containing both FSSAs and steel fibers) under compression in comparison with that of smart concretes made from the UHPCs containing other fillers and (2) to investigate the self-stress sensing capacity of a smart concrete anchorage, made from the UHPC containing both FSSAs and steel fibers, for monitoring the level of prestressing stress. (S32) The crack length sensitivity of smart concrete was 1 and crack length and change in electrical resistance of smart concrete had a strong linear relationship. (S2)\n\n**Citations:** S17; S74; S2; S32", "used_sentences": [{"sent": "Smart concrete can be used in the important structures (hospitals, power plants, schools etc.) and in the critical structural elements such as first floor columns.", "doc": "S17", "page": "?"}, {"sent": "concrete can keep the cost of smart concrete at competitive levels for The smart concrete can timely detect structural degradation and enable asset management by collect ing data for repair and maintenance.", "doc": "S17", "page": "?"}, {"sent": "smart concrete (made from the UHPC containing both FSSAs and steel fibers) under compression in comparison with that of smart concretes made from the UHPCs containing other fillers and (2) to investigate the self-stress sensing capacity of a smart concrete anchorage, made from the UHPC containing both FSSAs and steel fibers, for monitoring the level of prestressing stress.", "doc": "S32", "page": "?"}, {"sent": "The crack length sensitivity of smart concrete was 1 and crack length and change in electrical resistance of smart concrete had a strong linear relationship.", "doc": "S2", "page": "?"}]}, "latency_ms_total": 705, "latency_ms_llm": null, "openai": null}
+{"run_id": "41de8e5a-b695-4346-a034-e9f5800a3e89", "ts": 1771365967079, "inputs": {"question": "How does CNT aspect ratio influence stress gauge factor in cementitious composites?", "top_k": 8, "n_sentences": 4, "w_tfidf": 0.3, "w_bm25": 0.3, "w_emb": 0.4, "use_llm": false, "model": "gpt-5", "temperature": 0.2}, "retrieval": {"hits": [{"doc": "S18-Nano graphite platelets-enabled piezoresistive cementitious composites for structural health monitoring.pdf", "page": "?", "score_tfidf": 1.0, "score_bm25": 0.7794814256275794, "score_dense": 0.8332826939992174, "combo_score": 0.8671575052879608}, {"doc": "S56-Self-sensing cementitious composites incorporated with botryoid hybrid nano-carbon materials for smart infrastructures.pdf", "page": "?", "score_tfidf": 0.5827333666080915, "score_bm25": 0.9521720694653736, "score_dense": 0.9928929853239911, "combo_score": 0.857628824951636}, {"doc": "S82-SY~1.PDF", "page": "?", "score_tfidf": 0.6121307001579496, "score_bm25": 0.9633116527382539, "score_dense": 0.9117923147094122, "combo_score": 0.8373496317526259}, {"doc": "S84-TA~1.PDF", "page": "?", "score_tfidf": 0.77437341842884, "score_bm25": 0.8154877958180937, "score_dense": 0.8295768393098243, "combo_score": 0.8087890999980099}, {"doc": "S82-SY~1.PDF", "page": "?", "score_tfidf": 0.5333269983258244, "score_bm25": 0.8552344241838286, "score_dense": 0.9230234415989417, "combo_score": 0.7857778033924725}, {"doc": "S73-Development of self-sensing cement-based sensor using recycled fine waste glass aggregates coated with carbon nanotube.pdf", "page": "?", "score_tfidf": 0.500479376942504, "score_bm25": 0.8370845217719457, "score_dense": 0.9461316510102153, "combo_score": 0.779721830018421}, {"doc": "S64-Electrical Properties of Cement-Based Composites with Carbon Nanotubes, Graphene, and Graphite Nanofibers.pdf", "page": "?", "score_tfidf": 0.5919524504874675, "score_bm25": 0.8129635639261724, "score_dense": 0.8924387626618611, "combo_score": 0.7784503093888364}, {"doc": "S27-Effect of aspect ratio on strain sensing capacity of carbon fiber reinforced cement composites.pdf", "page": "?", "score_tfidf": 0.5683764285284425, "score_bm25": 0.8795702256398016, "score_dense": 0.8596545285876335, "combo_score": 0.7782458076855266}], "latency_ms_retriever": 111}, "output": {"final_answer": "**Answer:** Copper-nickel or nickel-chrome alloy-based strain gauges have a gauge factor of about 2 [27], which is much smaller than that of the cement composites with In addition, the gauge factor obtained in this study is quite similar to the value obtained in previous studies [12]. (S64) The same is more prominent when the dosage of CNT in the smart composite is in lower bound in Sensitivity of smart cement composites incorporated with pristine MWCNTs at 0.5 wt% is higher than cement paste incorporated with OH-MWCNTs and less than COOH-MWCNTs. (S82) was obtained from the average values of the composites under two stress Overall, the cement mortar modified with CNT-coated waste glass showed excellent piezoresistivity, whose sensitivity was dozens of times higher than that of commercially available strain gauge. (S73) However the gage factor was influenced by the maximum stresses Fiber’s aspect ratio is directly related to the calculated gage factors, which were higher as longer fiber was included. (S27)\n\n**Citations:** S18; S56; S82; S84; S73", "used_sentences": [{"sent": "Copper-nickel or nickel-chrome alloy-based strain gauges have a gauge factor of about 2 [27], which is much smaller than that of the cement composites with In addition, the gauge factor obtained in this study is quite similar to the value obtained in previous studies [12].", "doc": "S64", "page": "?"}, {"sent": "The same is more prominent when the dosage of CNT in the smart composite is in lower bound in Sensitivity of smart cement composites incorporated with pristine MWCNTs at 0.5 wt% is higher than cement paste incorporated with OH-MWCNTs and less than COOH-MWCNTs.", "doc": "S82", "page": "?"}, {"sent": "was obtained from the average values of the composites under two stress Overall, the cement mortar modified with CNT-coated waste glass showed excellent piezoresistivity, whose sensitivity was dozens of times higher than that of commercially available strain gauge.", "doc": "S73", "page": "?"}, {"sent": "However the gage factor was influenced by the maximum stresses Fiber’s aspect ratio is directly related to the calculated gage factors, which were higher as longer fiber was included.", "doc": "S27", "page": "?"}]}, "latency_ms_total": 350, "latency_ms_llm": null, "openai": null}
+{"run_id": "a8cd2714-ba1c-4c50-945c-8818c3214a57", "ts": 1771368325807, "inputs": {"question": "What conductive filler type typically yields the highest gauge factor in cement-based composites?", "top_k": 8, "n_sentences": 4, "w_tfidf": 0.3, "w_bm25": 0.3, "w_emb": 0.4, "use_llm": false, "model": "gpt-5", "temperature": 0.2}, "retrieval": {"hits": [{"doc": "S82-SY~1.PDF", "page": "?", "score_tfidf": 0.9240008154272813, "score_bm25": 0.9058841901194752, "score_dense": 0.8167872813418199, "combo_score": 0.8756804142007549}, {"doc": "S18-Nano graphite platelets-enabled piezoresistive cementitious composites for structural health monitoring.pdf", "page": "?", "score_tfidf": 0.8395026805096776, "score_bm25": 0.814694884457461, "score_dense": 0.9445213507651488, "combo_score": 0.8740678097962011}, {"doc": "S55-Electro-mechanical self-sensing response of ultra-high-performance fiber-reinforced concrete in tension.pdf", "page": "?", "score_tfidf": 1.0, "score_bm25": 1.0, "score_dense": 0.6644415097846281, "combo_score": 0.8657766039138513}, {"doc": "S9-Electro-mechanical-self-sensing-response-of-ultra-high-_2018_Composites-Part.pdf", "page": "?", "score_tfidf": 1.0, "score_bm25": 1.0, "score_dense": 0.6644415097846281, "combo_score": 0.8657766039138513}, {"doc": "S82-SY~1.PDF", "page": "?", "score_tfidf": 0.7695538381891827, "score_bm25": 0.9075974860667206, "score_dense": 0.8938054098991277, "combo_score": 0.8606675612364221}, {"doc": "S68-EF~1.PDF", "page": "?", "score_tfidf": 0.7905695293702641, "score_bm25": 0.9550429518584995, "score_dense": 0.7471749173138648, "combo_score": 0.822553711294175}, {"doc": "Revolutionizing infrastructure The evolving landscape of electricity-based multifunctional concrete from concept to practice.pdf", "page": "?", "score_tfidf": 0.6715129139774919, "score_bm25": 0.8176725081731818, "score_dense": 0.8958990980313201, "combo_score": 0.8051152658577301}, {"doc": "S34-Self-sensing-ultra-high-performance-concrete-fo_2021_Sensors-and-Actuators-A.pdf", "page": "?", "score_tfidf": 0.7597570291002825, "score_bm25": 0.826967011443848, "score_dense": 0.7791221580219055, "combo_score": 0.7876660753720014}], "latency_ms_retriever": 268}, "output": {"final_answer": "**Answer:** The highest gauge factor of the cementitious composites filled with 5 vol% of NGPs is 156, which is far more than 2–3 of metal foil strain gauge. (S18) The highest gauge factor of 451.26 is obtained from cement based smart nanocomposites with 0.5 wt% of COOH-MWCNTs fabricated with The gauge factor of smart cement composites reported in literature is presented in Table 5. (S82) results indicated that the electrical resistance of the brass-coated steel fiber reinforced cement mortar gradually increases with the increase of the splitting load, and obtained that the highest gauge factor of cement-based composite is 5195, which is nearly 2600 times higher than that of metal strain gages with the gage factor of 2. (S34) repeatability on piezoresistive response, highest gauge factor, highest stress sensitivity and good compressive strength were observed in composites containing carbon blacks with the lowest structure (125 cm3/100 g) and the highest resistivity (3.3–3.9 Ω (3) The increase in CBN aggregates structure, surface area and con ductivity improved the conductive network inside the composite and reduced its internal capacitance. (S68)\n\n**Citations:** [S82](/file=papers/S82-SY~1.PDF); [S18](/file=papers/S18-Nano%20graphite%20platelets-enabled%20piezoresistive%20cementitious%20composites%20for%20structural%20health%20monitoring.pdf); [S55](/file=papers/S55-Electro-mechanical%20self-sensing%20response%20of%20ultra-high-performance%20fiber-reinforced%20concrete%20in%20tension.pdf); [S9](/file=papers/S9-Electro-mechanical-self-sensing-response-of-ultra-high-_2018_Composites-Part.pdf); [S68](/file=papers/S68-EF~1.PDF)", "used_sentences": [{"sent": "The highest gauge factor of the cementitious composites filled with 5 vol% of NGPs is 156, which is far more than 2–3 of metal foil strain gauge.", "doc": "S18", "page": "?"}, {"sent": "The highest gauge factor of 451.26 is obtained from cement based smart nanocomposites with 0.5 wt% of COOH-MWCNTs fabricated with The gauge factor of smart cement composites reported in literature is presented in Table 5.", "doc": "S82", "page": "?"}, {"sent": "results indicated that the electrical resistance of the brass-coated steel fiber reinforced cement mortar gradually increases with the increase of the splitting load, and obtained that the highest gauge factor of cement-based composite is 5195, which is nearly 2600 times higher than that of metal strain gages with the gage factor of 2.", "doc": "S34", "page": "?"}, {"sent": "repeatability on piezoresistive response, highest gauge factor, highest stress sensitivity and good compressive strength were observed in composites containing carbon blacks with the lowest structure (125 cm3/100 g) and the highest resistivity (3.3–3.9 Ω (3) The increase in CBN aggregates structure, surface area and con ductivity improved the conductive network inside the composite and reduced its internal capacitance.", "doc": "S68", "page": "?"}]}, "latency_ms_total": 1051, "latency_ms_llm": null, "openai": null}
+{"run_id": "f59b7a00-d1eb-42bc-8579-fd65acd682b6", "ts": 1771368900253, "inputs": {"question": "What conductive filler type typically yields the highest gauge factor in cement-based\ncomposites?", "top_k": 8, "n_sentences": 4, "w_tfidf": 0.3, "w_bm25": 0.3, "w_emb": 0.4, "use_llm": false, "model": "gpt-5", "temperature": 0.2}, "retrieval": {"hits": [{"doc": "S82-SY~1.PDF", "page": "?", "score_tfidf": 0.9240008154272813, "score_bm25": 0.9058841901194752, "score_dense": 0.8167872813418199, "combo_score": 0.8756804142007549}, {"doc": "S18-Nano graphite platelets-enabled piezoresistive cementitious composites for structural health monitoring.pdf", "page": "?", "score_tfidf": 0.8395026805096776, "score_bm25": 0.814694884457461, "score_dense": 0.9445213507651488, "combo_score": 0.8740678097962011}, {"doc": "S55-Electro-mechanical self-sensing response of ultra-high-performance fiber-reinforced concrete in tension.pdf", "page": "?", "score_tfidf": 1.0, "score_bm25": 1.0, "score_dense": 0.6644415097846281, "combo_score": 0.8657766039138513}, {"doc": "S9-Electro-mechanical-self-sensing-response-of-ultra-high-_2018_Composites-Part.pdf", "page": "?", "score_tfidf": 1.0, "score_bm25": 1.0, "score_dense": 0.6644415097846281, "combo_score": 0.8657766039138513}, {"doc": "S82-SY~1.PDF", "page": "?", "score_tfidf": 0.7695538381891827, "score_bm25": 0.9075974860667206, "score_dense": 0.8938054098991277, "combo_score": 0.8606675612364221}, {"doc": "S68-EF~1.PDF", "page": "?", "score_tfidf": 0.7905695293702641, "score_bm25": 0.9550429518584995, "score_dense": 0.7471749173138648, "combo_score": 0.822553711294175}, {"doc": "Revolutionizing infrastructure The evolving landscape of electricity-based multifunctional concrete from concept to practice.pdf", "page": "?", "score_tfidf": 0.6715129139774919, "score_bm25": 0.8176725081731818, "score_dense": 0.8958990980313201, "combo_score": 0.8051152658577301}, {"doc": "S34-Self-sensing-ultra-high-performance-concrete-fo_2021_Sensors-and-Actuators-A.pdf", "page": "?", "score_tfidf": 0.7597570291002825, "score_bm25": 0.826967011443848, "score_dense": 0.7791221580219055, "combo_score": 0.7876660753720014}], "latency_ms_retriever": 285}, "output": {"final_answer": "**Answer:** The highest gauge factor of the cementitious composites filled with 5 vol% of NGPs is 156, which is far more than 2–3 of metal foil strain gauge. (S18) The highest gauge factor of 451.26 is obtained from cement based smart nanocomposites with 0.5 wt% of COOH-MWCNTs fabricated with The gauge factor of smart cement composites reported in literature is presented in Table 5. (S82) results indicated that the electrical resistance of the brass-coated steel fiber reinforced cement mortar gradually increases with the increase of the splitting load, and obtained that the highest gauge factor of cement-based composite is 5195, which is nearly 2600 times higher than that of metal strain gages with the gage factor of 2. (S34) repeatability on piezoresistive response, highest gauge factor, highest stress sensitivity and good compressive strength were observed in composites containing carbon blacks with the lowest structure (125 cm3/100 g) and the highest resistivity (3.3–3.9 Ω (3) The increase in CBN aggregates structure, surface area and con ductivity improved the conductive network inside the composite and reduced its internal capacitance. (S68)\n\n**Citations:** [S82](/file=papers/S82-SY~1.PDF); [S18](/file=papers/S18-Nano%20graphite%20platelets-enabled%20piezoresistive%20cementitious%20composites%20for%20structural%20health%20monitoring.pdf); [S55](/file=papers/S55-Electro-mechanical%20self-sensing%20response%20of%20ultra-high-performance%20fiber-reinforced%20concrete%20in%20tension.pdf); [S9](/file=papers/S9-Electro-mechanical-self-sensing-response-of-ultra-high-_2018_Composites-Part.pdf); [S68](/file=papers/S68-EF~1.PDF)", "used_sentences": [{"sent": "The highest gauge factor of the cementitious composites filled with 5 vol% of NGPs is 156, which is far more than 2–3 of metal foil strain gauge.", "doc": "S18", "page": "?"}, {"sent": "The highest gauge factor of 451.26 is obtained from cement based smart nanocomposites with 0.5 wt% of COOH-MWCNTs fabricated with The gauge factor of smart cement composites reported in literature is presented in Table 5.", "doc": "S82", "page": "?"}, {"sent": "results indicated that the electrical resistance of the brass-coated steel fiber reinforced cement mortar gradually increases with the increase of the splitting load, and obtained that the highest gauge factor of cement-based composite is 5195, which is nearly 2600 times higher than that of metal strain gages with the gage factor of 2.", "doc": "S34", "page": "?"}, {"sent": "repeatability on piezoresistive response, highest gauge factor, highest stress sensitivity and good compressive strength were observed in composites containing carbon blacks with the lowest structure (125 cm3/100 g) and the highest resistivity (3.3–3.9 Ω (3) The increase in CBN aggregates structure, surface area and con ductivity improved the conductive network inside the composite and reduced its internal capacitance.", "doc": "S68", "page": "?"}]}, "latency_ms_total": 1040, "latency_ms_llm": null, "openai": null}
+{"run_id": "5ada8d82-1291-4814-891b-8e51bdc95738", "ts": 1771369393305, "inputs": {"question": "What conductive filler type typically yields the highest gauge factor in cement-based\ncomposites?", "top_k": 8, "n_sentences": 4, "w_tfidf": 0.3, "w_bm25": 0.3, "w_emb": 0.4, "use_llm": false, "model": "gpt-5", "temperature": 0.2}, "retrieval": {"hits": [{"doc": "S82-SY~1.PDF", "page": "?", "score_tfidf": 0.9240008154272813, "score_bm25": 0.9058841901194752, "score_dense": 0.8167872813418199, "combo_score": 0.8756804142007549}, {"doc": "S18-Nano graphite platelets-enabled piezoresistive cementitious composites for structural health monitoring.pdf", "page": "?", "score_tfidf": 0.8395026805096776, "score_bm25": 0.814694884457461, "score_dense": 0.9445213507651488, "combo_score": 0.8740678097962011}, {"doc": "S55-Electro-mechanical self-sensing response of ultra-high-performance fiber-reinforced concrete in tension.pdf", "page": "?", "score_tfidf": 1.0, "score_bm25": 1.0, "score_dense": 0.6644415097846281, "combo_score": 0.8657766039138513}, {"doc": "S9-Electro-mechanical-self-sensing-response-of-ultra-high-_2018_Composites-Part.pdf", "page": "?", "score_tfidf": 1.0, "score_bm25": 1.0, "score_dense": 0.6644415097846281, "combo_score": 0.8657766039138513}, {"doc": "S82-SY~1.PDF", "page": "?", "score_tfidf": 0.7695538381891827, "score_bm25": 0.9075974860667206, "score_dense": 0.8938054098991277, "combo_score": 0.8606675612364221}, {"doc": "S68-EF~1.PDF", "page": "?", "score_tfidf": 0.7905695293702641, "score_bm25": 0.9550429518584995, "score_dense": 0.7471749173138648, "combo_score": 0.822553711294175}, {"doc": "Revolutionizing infrastructure The evolving landscape of electricity-based multifunctional concrete from concept to practice.pdf", "page": "?", "score_tfidf": 0.6715129139774919, "score_bm25": 0.8176725081731818, "score_dense": 0.8958990980313201, "combo_score": 0.8051152658577301}, {"doc": "S34-Self-sensing-ultra-high-performance-concrete-fo_2021_Sensors-and-Actuators-A.pdf", "page": "?", "score_tfidf": 0.7597570291002825, "score_bm25": 0.826967011443848, "score_dense": 0.7791221580219055, "combo_score": 0.7876660753720014}], "latency_ms_retriever": 295}, "output": {"final_answer": "**Answer:** The highest gauge factor of the cementitious composites filled with 5 vol% of NGPs is 156, which is far more than 2–3 of metal foil strain gauge. (S18) The highest gauge factor of 451.26 is obtained from cement based smart nanocomposites with 0.5 wt% of COOH-MWCNTs fabricated with The gauge factor of smart cement composites reported in literature is presented in Table 5. (S82) results indicated that the electrical resistance of the brass-coated steel fiber reinforced cement mortar gradually increases with the increase of the splitting load, and obtained that the highest gauge factor of cement-based composite is 5195, which is nearly 2600 times higher than that of metal strain gages with the gage factor of 2. (S34) repeatability on piezoresistive response, highest gauge factor, highest stress sensitivity and good compressive strength were observed in composites containing carbon blacks with the lowest structure (125 cm3/100 g) and the highest resistivity (3.3–3.9 Ω (3) The increase in CBN aggregates structure, surface area and con ductivity improved the conductive network inside the composite and reduced its internal capacitance. (S68)\n\n**Citations:** S82; S18; S55; S9; S68", "used_sentences": [{"sent": "The highest gauge factor of the cementitious composites filled with 5 vol% of NGPs is 156, which is far more than 2–3 of metal foil strain gauge.", "doc": "S18", "page": "?"}, {"sent": "The highest gauge factor of 451.26 is obtained from cement based smart nanocomposites with 0.5 wt% of COOH-MWCNTs fabricated with The gauge factor of smart cement composites reported in literature is presented in Table 5.", "doc": "S82", "page": "?"}, {"sent": "results indicated that the electrical resistance of the brass-coated steel fiber reinforced cement mortar gradually increases with the increase of the splitting load, and obtained that the highest gauge factor of cement-based composite is 5195, which is nearly 2600 times higher than that of metal strain gages with the gage factor of 2.", "doc": "S34", "page": "?"}, {"sent": "repeatability on piezoresistive response, highest gauge factor, highest stress sensitivity and good compressive strength were observed in composites containing carbon blacks with the lowest structure (125 cm3/100 g) and the highest resistivity (3.3–3.9 Ω (3) The increase in CBN aggregates structure, surface area and con ductivity improved the conductive network inside the composite and reduced its internal capacitance.", "doc": "S68", "page": "?"}]}, "latency_ms_total": 1048, "latency_ms_llm": null, "openai": null}
+{"run_id": "3bef3e6b-b4cb-4531-b287-3685d9b0bd74", "ts": 1771370791775, "inputs": {"question": "How does CNT length influence stress gauge factor performance?", "top_k": 8, "n_sentences": 4, "w_tfidf": 0.3, "w_bm25": 0.3, "w_emb": 0.4, "use_llm": false, "model": "gpt-5", "temperature": 0.2}, "retrieval": {"hits": [{"doc": "S45-Insitu synthesizing carbon nanotubes on cement to develop self-sensing cementitious composites.pdf", "page": "?", "score_tfidf": 0.6676194053845611, "score_bm25": 0.9539704442079809, "score_dense": 0.9001611250512749, "combo_score": 0.8465414048982725}, {"doc": "S45-Insitu synthesizing carbon nanotubes on cement to develop self-sensing cementitious composites.pdf", "page": "?", "score_tfidf": 0.6246525781011704, "score_bm25": 0.937502666778104, "score_dense": 0.9025056585907169, "combo_score": 0.8296488369000691}, {"doc": "S82-SY~1.PDF", "page": "?", "score_tfidf": 0.741241413314952, "score_bm25": 1.0, "score_dense": 0.6662800559668925, "combo_score": 0.7888844463812426}, {"doc": "S45-Insitu synthesizing carbon nanotubes on cement to develop self-sensing cementitious composites.pdf", "page": "?", "score_tfidf": 0.5308932095950486, "score_bm25": 0.8582121001475044, "score_dense": 0.9269541782395806, "combo_score": 0.7875132642185981}, {"doc": "S78-Mechanical and self-sensing properties of concrete reinforced with carbon nanofibres.pdf", "page": "?", "score_tfidf": 0.5208098986734895, "score_bm25": 0.9410350103051379, "score_dense": 0.8201862554113928, "combo_score": 0.7666279748581454}, {"doc": "S85-Performance of cement-based sensors with CNT for strain sensing.pdf", "page": "?", "score_tfidf": 0.5896459104834435, "score_bm25": 0.8911648142453857, "score_dense": 0.8056640829983435, "combo_score": 0.7665088506179862}, {"doc": "S56-Self-sensing cementitious composites incorporated with botryoid hybrid nano-carbon materials for smart infrastructures.pdf", "page": "?", "score_tfidf": 0.6068468728468565, "score_bm25": 0.8431882902106639, "score_dense": 0.8075942121140879, "combo_score": 0.7580482337628912}, {"doc": "S85-Performance of cement-based sensors with CNT for strain sensing.pdf", "page": "?", "score_tfidf": 0.40988956584361097, "score_bm25": 0.7456437722269638, "score_dense": 1.0, "combo_score": 0.7466600014211724}], "latency_ms_retriever": 255}, "output": {"final_answer": "**Answer:** (c) Gauge factor and stress sensitivity of SSCCs with different CNT@Cem contents. (S45) Gauge factor of 0·50% CNT paste at 28 d against loading rate (negative values correspond to unloading cycles) for three different maximum axial loads (the stress levels applied were 1·875, 3·75 and 5·625 MPa, for 3, 6 and 9 kN, respectively) with CNT for strain sensing Downloaded by [ UNIVERSITY OF CAMBRIDGE] on [01/02/23]. (S85) 7c, the gauge factor of the control was determined as 21.5 and increased to 748 for the CNT@Cem-25 sample. (S45) (c) Gauge factor and stress sensitivity of SSCCs with different CNT@Cem contents. (S45)\n\n**Citations:** S45; S82; S78; S85", "used_sentences": [{"sent": "(c) Gauge factor and stress sensitivity of SSCCs with different CNT@Cem contents.", "doc": "S45", "page": "?"}, {"sent": "Gauge factor of 0·50% CNT paste at 28 d against loading rate (negative values correspond to unloading cycles) for three different maximum axial loads (the stress levels applied were 1·875, 3·75 and 5·625 MPa, for 3, 6 and 9 kN, respectively) with CNT for strain sensing Downloaded by [ UNIVERSITY OF CAMBRIDGE] on [01/02/23].", "doc": "S85", "page": "?"}, {"sent": "7c, the gauge factor of the control was determined as 21.5 and increased to 748 for the CNT@Cem-25 sample.", "doc": "S45", "page": "?"}, {"sent": "(c) Gauge factor and stress sensitivity of SSCCs with different CNT@Cem contents.", "doc": "S45", "page": "?"}]}, "latency_ms_total": 1149, "latency_ms_llm": null, "openai": null}
diff --git a/rag_artifacts/tfidf_matrix.joblib b/rag_artifacts/tfidf_matrix.joblib
new file mode 100644
index 0000000000000000000000000000000000000000..0cdcee7aa8c3afbbb693312ca394596b97d58798
--- /dev/null
+++ b/rag_artifacts/tfidf_matrix.joblib
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:67ae7fdedbd93f5f69f3ef8ef14c8113fada46ebcd4b11a3c8b6ac06a59278b7
+size 22115315
diff --git a/rag_artifacts/tfidf_vectorizer.joblib b/rag_artifacts/tfidf_vectorizer.joblib
new file mode 100644
index 0000000000000000000000000000000000000000..638d4bb74cb03b8c7de9bc69acd6b02bf216af97
--- /dev/null
+++ b/rag_artifacts/tfidf_vectorizer.joblib
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6df0b9f3418f8abc32feaef1e85bd4f6c0cc5ec395ae2464c635a046ca114f57
+size 7520233
diff --git a/rag_eval_metrics.py b/rag_eval_metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..49115eb68de130220ca3f2dff4cafee89c1eb3b9
--- /dev/null
+++ b/rag_eval_metrics.py
@@ -0,0 +1,727 @@
+#!/usr/bin/env python3
+"""
+rag_eval_metrics.py
+
+Evaluate RAG retrieval quality by comparing app logs (JSONL) with a gold file (CSV).
+
+Extended to also evaluate answer quality using:
+- Lexical similarity: BLEU, ROUGE-1/2/L
+- Semantic similarity: BERTScore (Recall, F1)
+
+If nltk / rouge-score / bert-score are missing, the script still runs and
+returns NaN for these metrics instead of crashing.
+
+Also uses robust CSV reading to handle non-UTF8 encodings (cp1252/latin1).
+"""
+
+import argparse
+import json
+import os
+import sys
+from pathlib import Path
+from typing import Dict, List, Tuple, Any, Optional
+
+import pandas as pd
+import numpy as np
+
+# ----------------------------- Small Utils ----------------------------- #
+
+def filename_key(s: str) -> str:
+ s = (s or "").strip().replace("\\", "/").split("/")[-1]
+ return s.casefold()
+
+def re_split_sc(s: str) -> List[str]:
+ import re
+ return re.split(r"[;,]", s)
+
+def _pick_last_non_empty(hit_lists) -> List[dict]:
+ """
+ Robustly select the last non-empty hits list from a pandas Series or iterable.
+
+ This fixes the KeyError that happens when using reversed() directly on a Series
+ with a non-range index.
+ """
+ # Convert pandas Series or other iterables to a plain Python list
+ try:
+ values = list(hit_lists.tolist())
+ except AttributeError:
+ values = list(hit_lists)
+
+ # Walk from last to first, return first non-empty list-like
+ for lst in reversed(values):
+ if isinstance(lst, (list, tuple)) and len(lst) > 0:
+ return lst
+
+ # If everything was empty / NaN
+ return []
+
+def _read_csv_robust(path: Path) -> pd.DataFrame:
+ """
+ Try multiple encodings so we don't crash on Windows-1252 / Latin-1 CSVs.
+ """
+ encodings = ["utf-8", "utf-8-sig", "cp1252", "latin1"]
+ last_err = None
+ for enc in encodings:
+ try:
+ return pd.read_csv(path, encoding=enc)
+ except UnicodeDecodeError as e:
+ last_err = e
+ continue
+ # If all fail, re-raise the last error
+ raise last_err if last_err is not None else ValueError(
+ "Failed to read CSV with fallback encodings."
+ )
+
+# ----------------------------- IO Helpers ----------------------------- #
+
+def read_logs(jsonl_path: Path) -> pd.DataFrame:
+ """
+ Read RAG JSONL logs and aggregate by question.
+
+ Returns a DataFrame with columns:
+ - question: original question text (last occurrence)
+ - hits: list of dicts {doc, page} for retrieval
+ - answer: final answer text logged for that question
+ """
+ rows = []
+ if (not jsonl_path.exists()) or jsonl_path.stat().st_size == 0:
+ return pd.DataFrame(columns=["question", "hits", "answer"])
+
+ with open(jsonl_path, "r", encoding="utf-8") as f:
+ for line in f:
+ line = line.strip()
+ if not line:
+ continue
+ try:
+ rec = json.loads(line)
+ except Exception:
+ continue
+
+ # Extract question
+ q = (((rec.get("inputs") or {}).get("question")) or "").strip()
+
+ # Extract retrieval hits (if present)
+ retr = (rec.get("retrieval") or {})
+ hits = retr.get("hits", [])
+ norm_hits = []
+ for h in hits or []:
+ doc = (h.get("doc") or "").strip()
+ page = str(h.get("page") or "").strip()
+
+ # Normalize page to int or None
+ try:
+ page_int = int(page)
+ except Exception:
+ page_int = None
+
+ norm_hits.append({"doc": doc, "page": page_int})
+
+ # Extract final answer text (if present)
+ out = (rec.get("output") or {})
+ ans = ((out.get("final_answer") or "")).strip()
+
+ rows.append({"question": q, "hits": norm_hits, "answer": ans})
+
+ df = pd.DataFrame(rows)
+ if df.empty:
+ return pd.DataFrame(columns=["question", "hits", "answer"])
+
+ # Group by normalized question text and keep last non-empty hits list and answer per question
+ df = (
+ df.groupby(df["question"].astype(str).str.casefold().str.strip(), as_index=False)
+ .agg({
+ "question": "last",
+ "hits": _pick_last_non_empty,
+ "answer": "last"
+ })
+ )
+ return df
+
+def read_gold(csv_path: Path) -> Tuple[pd.DataFrame, Dict[str, str]]:
+ """
+ Read gold CSV with retrieval labels and optional reference answers.
+
+ Returns:
+ - gold_df: rows with columns ['question', 'doc', 'page', 'answer', ...]
+ where 'question' is normalized (casefold+strip)
+ - gold_answers: dict mapping normalized question -> reference answer text
+ """
+ df = _read_csv_robust(csv_path)
+ cols = {c.lower().strip(): c for c in df.columns}
+
+ # --- question column ---
+ q_col = None
+ for cand in ["question", "query", "q"]:
+ if cand in cols:
+ q_col = cols[cand]
+ break
+ if q_col is None:
+ raise ValueError("Gold CSV must contain a 'question' column (case-insensitive).")
+
+ # --- possible relevant_docs (list-in-cell) column ---
+ rel_list_col = None
+ for cand in ["relevant_docs", "relevant", "docs"]:
+ if cand in cols:
+ rel_list_col = cols[cand]
+ break
+
+ # --- single-doc-per-row column ---
+ doc_col = None
+ for cand in ["doc", "document", "file", "doc_name"]:
+ if cand in cols:
+ doc_col = cols[cand]
+ break
+
+ # --- optional page column ---
+ page_col = None
+ for cand in ["page", "page_num", "page_number"]:
+ if cand in cols:
+ page_col = cols[cand]
+ break
+
+ # --- optional answer column (for QA metrics) ---
+ ans_col = None
+ for cand in ["answer", "reference_answer", "gold_answer"]:
+ if cand in cols:
+ ans_col = cols[cand]
+ break
+
+ rows = []
+
+ # Case 1: relevant_docs list column (no explicit doc_col)
+ if rel_list_col and doc_col is None:
+ for _, r in df.iterrows():
+ q_raw = str(r[q_col]).strip()
+ q_norm = q_raw.casefold().strip()
+ ans_raw = str(r[ans_col]).strip() if (ans_col and pd.notna(r[ans_col])) else ""
+
+ rel_val = str(r[rel_list_col]) if pd.notna(r[rel_list_col]) else ""
+ if not rel_val:
+ rows.append({
+ "question_raw": q_raw,
+ "question": q_norm,
+ "doc": None,
+ "page": np.nan,
+ "answer": ans_raw
+ })
+ continue
+
+ parts = [p.strip() for p in re_split_sc(rel_val)]
+ for d in parts:
+ rows.append({
+ "question_raw": q_raw,
+ "question": q_norm,
+ "doc": filename_key(d),
+ "page": np.nan,
+ "answer": ans_raw
+ })
+
+ # Case 2: doc/page columns (one relevant doc per row)
+ elif doc_col:
+ for _, r in df.iterrows():
+ q_raw = str(r[q_col]).strip()
+ q_norm = q_raw.casefold().strip()
+ ans_raw = str(r[ans_col]).strip() if (ans_col and pd.notna(r[ans_col])) else ""
+
+ d = str(r[doc_col]).strip() if pd.notna(r[doc_col]) else ""
+ p = r[page_col] if (page_col and pd.notna(r[page_col])) else np.nan
+
+ try:
+ p = int(p)
+ except Exception:
+ p = np.nan
+
+ rows.append({
+ "question_raw": q_raw,
+ "question": q_norm,
+ "doc": filename_key(d),
+ "page": p,
+ "answer": ans_raw
+ })
+
+ else:
+ raise ValueError("Gold CSV must contain either a 'doc' column or a 'relevant_docs' column.")
+
+ gold = pd.DataFrame(rows)
+
+ # Keep only rows with a valid doc (when docs exist)
+ gold["has_doc"] = gold["doc"].apply(lambda x: isinstance(x, str) and len(x) > 0)
+ if gold["has_doc"].any():
+ gold = gold[gold["has_doc"]].copy()
+ gold.drop(columns=["has_doc"], inplace=True, errors="ignore")
+
+ # Remove duplicates
+ gold = gold.drop_duplicates(subset=["question", "doc", "page"])
+
+ # Build question -> gold_answer map (normalized questions)
+ gold_answers: Dict[str, str] = {}
+ if "answer" in gold.columns:
+ tmp = (
+ gold[["question", "answer"]]
+ .dropna(subset=["answer"])
+ .drop_duplicates(subset=["question"])
+ )
+ gold_answers = dict(zip(tmp["question"], tmp["answer"]))
+
+ return gold, gold_answers
+
+# ----------------------------- Retrieval Metric Core ----------------------------- #
+
+def dcg_at_k(relevances: List[int]) -> float:
+ dcg = 0.0
+ for i, rel in enumerate(relevances, start=1):
+ if rel > 0:
+ dcg += 1.0 / np.log2(i + 1.0)
+ return float(dcg)
+
+def ndcg_at_k(relevances: List[int]) -> float:
+ dcg = dcg_at_k(relevances)
+ ideal = sorted(relevances, reverse=True)
+ idcg = dcg_at_k(ideal)
+ if idcg == 0.0:
+ return 0.0
+ return float(dcg / idcg)
+
+def compute_metrics_for_question(gold_docs, gold_pages, hits, k):
+ top = hits[:k] if hits else []
+ pred_docs = [filename_key(h.get("doc", "")) for h in top]
+ pred_pairs = [(filename_key(h.get("doc", "")), h.get("page", None)) for h in top]
+
+ # --- Doc-level metrics ---
+ gold_doc_set = set([d for d in gold_docs if isinstance(d, str) and d])
+
+ rel_bin_doc = [1 if d in gold_doc_set else 0 for d in pred_docs]
+ hitk_doc = 1 if any(rel_bin_doc) else 0
+ prec_doc = (sum(rel_bin_doc) / max(1, len(pred_docs))) if pred_docs else 0.0
+ rec_doc = (sum(rel_bin_doc) / max(1, len(gold_doc_set))) if gold_doc_set else 0.0
+ ndcg_doc = ndcg_at_k(rel_bin_doc)
+
+ # --- Page-level metrics (only if gold has page labels) ---
+ gold_pairs = set()
+ for d, p in zip(gold_docs, gold_pages):
+ if isinstance(d, str) and d and (p is not None) and (not (isinstance(p, float) and np.isnan(p))):
+ try:
+ p_int = int(p)
+ except Exception:
+ continue
+ gold_pairs.add((d, p_int))
+
+ if gold_pairs:
+ rel_bin_page = []
+ for (d, p) in pred_pairs:
+ if p is None or not isinstance(p, int):
+ rel_bin_page.append(0)
+ else:
+ rel_bin_page.append(1 if (d, p) in gold_pairs else 0)
+
+ hitk_page = 1 if any(rel_bin_page) else 0
+ prec_page = (sum(rel_bin_page) / max(1, len(pred_pairs))) if pred_pairs else 0.0
+ rec_page = (sum(rel_bin_page) / max(1, len(gold_pairs))) if gold_pairs else 0.0
+ ndcg_page = ndcg_at_k(rel_bin_page)
+ else:
+ hitk_page = prec_page = rec_page = ndcg_page = np.nan
+
+ return {
+ "hit@k_doc": hitk_doc,
+ "precision@k_doc": prec_doc,
+ "recall@k_doc": rec_doc,
+ "ndcg@k_doc": ndcg_doc,
+ "hit@k_page": hitk_page,
+ "precision@k_page": prec_page,
+ "recall@k_page": rec_page,
+ "ndcg@k_page": ndcg_page,
+ "n_gold_docs": int(len(gold_doc_set)),
+ "n_gold_doc_pages": int(len(gold_pairs)),
+ "n_pred": int(len(pred_docs))
+ }
+
+# ---------------------- Answer Quality Metrics (with fallbacks) ---------------------- #
+
+# Try to import optional libraries; if missing, we fall back to NaN metrics
+try:
+ from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
+ HAVE_NLTK = True
+except Exception:
+ sentence_bleu = None
+ SmoothingFunction = None
+ HAVE_NLTK = False
+
+try:
+ from rouge_score import rouge_scorer
+ HAVE_ROUGE = True
+except Exception:
+ rouge_scorer = None
+ HAVE_ROUGE = False
+
+try:
+ from bert_score import score as bert_score
+ HAVE_BERT = True
+except Exception:
+ bert_score = None
+ HAVE_BERT = False
+
+if HAVE_NLTK:
+ _SMOOTH = SmoothingFunction().method1
+else:
+ _SMOOTH = None
+
+if HAVE_ROUGE:
+ _ROUGE_SCORER = rouge_scorer.RougeScorer(
+ ["rouge1", "rouge2", "rougeL"], use_stemmer=True
+ )
+else:
+ _ROUGE_SCORER = None
+
+def _normalize_text_for_metrics(s: str) -> str:
+ import re
+ s = (s or "").strip().lower()
+ # remove simple markdown markers
+ s = re.sub(r"\*\*|\*", "", s)
+ # drop inline citations like (Doc.pdf, p.X)
+ s = re.sub(r"\([^)]*\)", " ", s)
+ s = re.sub(r"\s+", " ", s)
+ return s.strip()
+
+def compute_text_metrics(pred: str, ref: str) -> Dict[str, float]:
+ """
+ Compute lexical and semantic similarity metrics between prediction and reference:
+ - BLEU
+ - ROUGE-1/2/L (F-measure)
+ - BERTScore Recall, F1
+
+ If the required libraries (nltk, rouge-score, bert-score) are not installed,
+ returns NaN for all metrics.
+ """
+ # If any of the libraries is missing, skip answer metrics
+ if not (HAVE_NLTK and HAVE_ROUGE and HAVE_BERT):
+ return {
+ "bleu": np.nan,
+ "rouge1": np.nan,
+ "rouge2": np.nan,
+ "rougeL": np.nan,
+ "bert_recall": np.nan,
+ "bert_f1": np.nan,
+ }
+
+ pred_n = _normalize_text_for_metrics(pred)
+ ref_n = _normalize_text_for_metrics(ref)
+
+ if not pred_n or not ref_n:
+ return {
+ "bleu": np.nan,
+ "rouge1": np.nan,
+ "rouge2": np.nan,
+ "rougeL": np.nan,
+ "bert_recall": np.nan,
+ "bert_f1": np.nan,
+ }
+
+ pred_tokens = pred_n.split()
+ ref_tokens = ref_n.split()
+
+ # BLEU (sentence-level with smoothing)
+ bleu = float(
+ sentence_bleu([ref_tokens], pred_tokens, smoothing_function=_SMOOTH)
+ )
+
+ # ROUGE via rouge-score (F-measure)
+ rs = _ROUGE_SCORER.score(ref_n, pred_n)
+ rouge1 = float(rs["rouge1"].fmeasure)
+ rouge2 = float(rs["rouge2"].fmeasure)
+ rougeL = float(rs["rougeL"].fmeasure)
+
+ # BERTScore (semantic similarity)
+ P, R, F1 = bert_score([pred_n], [ref_n], lang="en", rescale_with_baseline=True)
+ bert_recall = float(R.mean().item())
+ bert_f1 = float(F1.mean().item())
+
+ return {
+ "bleu": bleu,
+ "rouge1": rouge1,
+ "rouge2": rouge2,
+ "rougeL": rougeL,
+ "bert_recall": bert_recall,
+ "bert_f1": bert_f1,
+ }
+
+# ----------------------------- Orchestration ----------------------------- #
+
+# === Dark blue and accent colors ===
+COLOR_TITLE = "\033[94m" # light blue for titles
+COLOR_TEXT = "\033[34m" # dark blue
+COLOR_ACCENT = "\033[36m" # cyan for metrics
+COLOR_RESET = "\033[0m"
+
+def _fmt(x: Any) -> str:
+ try:
+ return f"{float(x):.3f}"
+ except Exception:
+ return "-"
+
+def main():
+ ap = argparse.ArgumentParser()
+ ap.add_argument("--gold_csv", required=True, type=str)
+ ap.add_argument("--logs_jsonl", required=True, type=str)
+ ap.add_argument("--k", type=int, default=8)
+ ap.add_argument("--out_dir", type=str, default="rag_artifacts")
+ args = ap.parse_args()
+
+ out_dir = Path(args.out_dir)
+ out_dir.mkdir(parents=True, exist_ok=True)
+
+ gold_path = Path(args.gold_csv)
+ logs_path = Path(args.logs_jsonl)
+
+ if not gold_path.exists():
+ print(
+ f"{COLOR_TEXT}❌ gold.csv not found at {gold_path}{COLOR_RESET}",
+ file=sys.stderr,
+ )
+ sys.exit(0)
+ if not logs_path.exists() or logs_path.stat().st_size == 0:
+ print(
+ f"{COLOR_TEXT}❌ logs JSONL not found or empty at {logs_path}{COLOR_RESET}",
+ file=sys.stderr,
+ )
+ sys.exit(0)
+
+ # Read gold (retrieval + QA answers)
+ try:
+ gold, gold_answers = read_gold(gold_path)
+ except Exception as e:
+ print(
+ f"{COLOR_TEXT}❌ Failed to read gold: {e}{COLOR_RESET}",
+ file=sys.stderr,
+ )
+ sys.exit(0)
+
+ # Read logs (with robust aggregation)
+ try:
+ logs = read_logs(logs_path)
+ except Exception as e:
+ print(
+ f"{COLOR_TEXT}❌ Failed to read logs: {e}{COLOR_RESET}",
+ file=sys.stderr,
+ )
+ sys.exit(0)
+
+ if gold.empty:
+ print(
+ f"{COLOR_TEXT}❌ Gold file contains no usable rows.{COLOR_RESET}",
+ file=sys.stderr,
+ )
+ sys.exit(0)
+ if logs.empty:
+ print(
+ f"{COLOR_TEXT}❌ Logs file contains no usable entries.{COLOR_RESET}",
+ file=sys.stderr,
+ )
+ sys.exit(0)
+
+ # Build gold dict: normalized_question -> list of (doc, page)
+ gdict: Dict[str, List[Tuple[str, Optional[int]]]] = {}
+ for _, r in gold.iterrows():
+ q = str(r["question"]).strip() # already normalized in read_gold
+ d = r["doc"]
+ p = r["page"] if "page" in r else np.nan
+ gdict.setdefault(q, []).append((d, p))
+
+ # Normalize log questions for join
+ logs["q_norm"] = logs["question"].astype(str).str.casefold().str.strip()
+
+ perq_rows = []
+ not_in_logs, not_in_gold = [], []
+
+ # For each gold question, compute metrics using logs
+ for q_norm, pairs in gdict.items():
+ row = logs[logs["q_norm"] == q_norm]
+ gdocs = [d for (d, _) in pairs]
+ gpages = [p for (_, p) in pairs]
+
+ if row.empty:
+ # No logs for this gold question → zero retrieval and no answer metrics
+ not_in_logs.append(q_norm)
+ base_metrics = {
+ "hit@k_doc": 0,
+ "precision@k_doc": 0.0,
+ "recall@k_doc": 0.0,
+ "ndcg@k_doc": 0.0,
+ "hit@k_page": np.nan,
+ "precision@k_page": np.nan,
+ "recall@k_page": np.nan,
+ "ndcg@k_page": np.nan,
+ "n_gold_docs": int(len(set([d for d in gdocs if isinstance(d, str) and d]))),
+ "n_gold_doc_pages": int(
+ len(
+ [
+ (d, p)
+ for (d, p) in zip(gdocs, gpages)
+ if isinstance(d, str) and d and pd.notna(p)
+ ]
+ )
+ ),
+ "n_pred": 0,
+ }
+
+ txt_metrics = {
+ "bleu": np.nan,
+ "rouge1": np.nan,
+ "rouge2": np.nan,
+ "rougeL": np.nan,
+ "bert_recall": np.nan,
+ "bert_f1": np.nan,
+ }
+
+ perq_rows.append(
+ {
+ "question": q_norm,
+ "covered_in_logs": 0,
+ **base_metrics,
+ **txt_metrics,
+ }
+ )
+ continue
+
+ # Use aggregated hits from read_logs
+ hits = row.iloc[0]["hits"] or []
+ base_metrics = compute_metrics_for_question(gdocs, gpages, hits, args.k)
+
+ # Answer text: predicted vs. gold
+ pred_answer = str(row.iloc[0].get("answer", "")).strip()
+ gold_answer = str(gold_answers.get(q_norm, "")).strip()
+
+ if gold_answer and pred_answer:
+ txt_metrics = compute_text_metrics(pred_answer, gold_answer)
+ else:
+ txt_metrics = {
+ "bleu": np.nan,
+ "rouge1": np.nan,
+ "rouge2": np.nan,
+ "rougeL": np.nan,
+ "bert_recall": np.nan,
+ "bert_f1": np.nan,
+ }
+
+ perq_rows.append(
+ {
+ "question": q_norm,
+ "covered_in_logs": 1,
+ **base_metrics,
+ **txt_metrics,
+ }
+ )
+
+ # Any log questions not in gold
+ gold_qs = set(gdict.keys())
+ for qn in logs["q_norm"].tolist():
+ if qn not in gold_qs:
+ not_in_gold.append(qn)
+
+ perq = pd.DataFrame(perq_rows)
+ covered = perq[perq["covered_in_logs"] == 1].copy()
+
+ agg = {
+ "questions_total_gold": int(len(gdict)),
+ "questions_covered_in_logs": int(covered.shape[0]),
+ "questions_missing_in_logs": int(len(not_in_logs)),
+ "questions_in_logs_not_in_gold": int(len(set(not_in_gold))),
+ "k": int(args.k),
+ "mean_hit@k_doc": float(covered["hit@k_doc"].mean()) if not covered.empty else 0.0,
+ "mean_precision@k_doc": float(covered["precision@k_doc"].mean()) if not covered.empty else 0.0,
+ "mean_recall@k_doc": float(covered["recall@k_doc"].mean()) if not covered.empty else 0.0,
+ "mean_ndcg@k_doc": float(covered["ndcg@k_doc"].mean()) if not covered.empty else 0.0,
+ "mean_hit@k_page": float(covered["hit@k_page"].dropna().mean())
+ if covered["hit@k_page"].notna().any()
+ else None,
+ "mean_precision@k_page": float(covered["precision@k_page"].dropna().mean())
+ if covered["precision@k_page"].notna().any()
+ else None,
+ "mean_recall@k_page": float(covered["recall@k_page"].dropna().mean())
+ if covered["recall@k_page"].notna().any()
+ else None,
+ "mean_ndcg@k_page": float(covered["ndcg@k_page"].dropna().mean())
+ if covered["ndcg@k_page"].notna().any()
+ else None,
+ "avg_gold_docs_per_q": float(perq["n_gold_docs"].mean()) if not perq.empty else 0.0,
+ "avg_preds_per_q": float(perq["n_pred"].mean()) if not perq.empty else 0.0,
+ "examples_missing_in_logs": list(not_in_logs[:10]),
+ "examples_in_logs_not_in_gold": list(dict.fromkeys(not_in_gold))[:10],
+ }
+
+ # Aggregate answer-quality metrics (lexical + semantic)
+ if "bleu" in covered.columns:
+ agg["mean_bleu"] = float(covered["bleu"].mean(skipna=True))
+ agg["mean_rouge1"] = float(covered["rouge1"].mean(skipna=True))
+ agg["mean_rouge2"] = float(covered["rouge2"].mean(skipna=True))
+ agg["mean_rougeL"] = float(covered["rougeL"].mean(skipna=True))
+ agg["mean_bert_recall"] = float(covered["bert_recall"].mean(skipna=True))
+ agg["mean_bert_f1"] = float(covered["bert_f1"].mean(skipna=True))
+
+ perq_path = out_dir / "metrics_per_question.csv"
+ agg_path = out_dir / "metrics_aggregate.json"
+
+ perq.to_csv(perq_path, index=False)
+ with open(agg_path, "w", encoding="utf-8") as f:
+ json.dump(agg, f, ensure_ascii=False, indent=2)
+
+ # === Console summary with color ===
+ print(f"{COLOR_TITLE}RAG Evaluation Summary{COLOR_RESET}")
+ print(f"{COLOR_TITLE}----------------------{COLOR_RESET}")
+ print(f"{COLOR_TEXT}Gold questions: {COLOR_ACCENT}{agg['questions_total_gold']}{COLOR_RESET}")
+ print(f"{COLOR_TEXT}Covered in logs: {COLOR_ACCENT}{agg['questions_covered_in_logs']}{COLOR_RESET}")
+ print(f"{COLOR_TEXT}Missing in logs: {COLOR_ACCENT}{agg['questions_missing_in_logs']}{COLOR_RESET}")
+ print(
+ f"{COLOR_TEXT}In logs but not in gold: "
+ f"{COLOR_ACCENT}{agg['questions_in_logs_not_in_gold']}{COLOR_RESET}"
+ )
+ print(f"{COLOR_TEXT}k = {COLOR_ACCENT}{agg['k']}{COLOR_RESET}\n")
+
+ print(
+ f"{COLOR_TEXT}Doc-level:{COLOR_RESET} "
+ f"{COLOR_ACCENT}Hit@k={_fmt(agg['mean_hit@k_doc'])} "
+ f"Precision@k={_fmt(agg['mean_precision@k_doc'])} "
+ f"Recall@k={_fmt(agg['mean_recall@k_doc'])} "
+ f"nDCG@k={_fmt(agg['mean_ndcg@k_doc'])}{COLOR_RESET}"
+ )
+
+ if agg.get("mean_hit@k_page") is not None:
+ print(
+ f"{COLOR_TEXT}Page-level:{COLOR_RESET} "
+ f"{COLOR_ACCENT}Hit@k={_fmt(agg['mean_hit@k_page'])} "
+ f"Precision@k={_fmt(agg['mean_precision@k_page'])} "
+ f"Recall={_fmt(agg['mean_recall@k_page'])} "
+ f"nDCG@k={_fmt(agg['mean_ndcg@k_page'])}{COLOR_RESET}"
+ )
+ else:
+ print(f"{COLOR_TEXT}Page-level: (no page labels in gold){COLOR_RESET}")
+
+ # Lexical metrics summary
+ if "mean_bleu" in agg:
+ print(
+ f"{COLOR_TEXT}Lexical (answer quality):{COLOR_RESET} "
+ f"{COLOR_ACCENT}BLEU={_fmt(agg.get('mean_bleu'))} "
+ f"ROUGE-1={_fmt(agg.get('mean_rouge1'))} "
+ f"ROUGE-2={_fmt(agg.get('mean_rouge2'))} "
+ f"ROUGE-L={_fmt(agg.get('mean_rougeL'))}{COLOR_RESET}"
+ )
+
+ # Semantic metrics summary
+ if "mean_bert_f1" in agg:
+ print(
+ f"{COLOR_TEXT}Semantic (BERTScore):{COLOR_RESET} "
+ f"{COLOR_ACCENT}Recall={_fmt(agg.get('mean_bert_recall'))} "
+ f"F1={_fmt(agg.get('mean_bert_f1'))}{COLOR_RESET}"
+ )
+
+ print()
+ print(
+ f"{COLOR_TEXT}Wrote per-question CSV → "
+ f"{COLOR_ACCENT}{perq_path}{COLOR_RESET}"
+ )
+ print(
+ f"{COLOR_TEXT}Wrote aggregate JSON → "
+ f"{COLOR_ACCENT}{agg_path}{COLOR_RESET}"
+ )
+
+if __name__ == "__main__":
+ main()
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3fd8cc2b1c2388dd6c2d8415bfc67592b4ef07fe
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,20 @@
+# Core
+gradio
+numpy
+pandas
+scikit-learn
+joblib
+
+# RAG / retrieval
+sentence-transformers
+rank-bm25
+PyMuPDF
+pypdf
+
+# OpenAI LLM (for optional LLM synthesis)
+openai
+
+# Evaluation metrics (lexical + semantic)
+nltk
+rouge-score
+bert-score