{ "cells": [ { "cell_type": "markdown", "id": "bdsw9d71s7r", "metadata": {}, "source": [ "# Phase 8 — RAG Pipeline Evaluation\n", "\n", "This notebook measures **how good the pipeline actually is** across a fixed test set\n", "of 15 Apple finance questions with ground-truth reference answers.\n", "\n", "## Why evaluation matters\n", "\n", "| Without evaluation | With evaluation |\n", "|-------------------|----------------|\n", "| \"It seems to work on my test query\" | Quantified score across 15 diverse questions |\n", "| No way to compare retrieval configs | Side-by-side metric table for any two configs |\n", "| Hallucinations go undetected | Faithfulness score flags low-grounding answers |\n", "| Chunking/embedding changes are a guess | A/B test shows whether a change actually helped |\n", "\n", "## Metrics (all computed locally — no LLM judge, no API key)\n", "\n", "| Metric | What it measures | How |\n", "|--------|-----------------|-----|\n", "| **Faithfulness** | Are answer sentences entailed by the context? | NLI (reuses Phase 7 AnswerVerifier) |\n", "| **Answer Relevancy** | Is the answer on-topic for the question? | Cosine sim between question and answer embeddings |\n", "| **Context Precision** | What fraction of retrieved chunks are actually useful? | NLI: does chunk entail reference answer? |\n", "| **Context Recall** | What fraction of reference claims are in the retrieved context? | NLI: is each reference sentence covered by any chunk? |\n", "| **Answer F1** | Token-overlap with reference answer | SQuAD-style precision/recall F1 |\n", "| **Retrieval Hit Rate** | Did we retrieve the known-relevant chunks? | Set intersection (requires chunk IDs) |\n", "\n", "## Grade thresholds\n", "\n", "| Score | Grade |\n", "|-------|-------|\n", "| ≥ upper threshold | PASS ✓ |\n", "| ≥ lower threshold | REVIEW ⚠ |\n", "| < lower threshold | FAIL ✗ |\n", "\n", "## Steps\n", "| STEP | Content |\n", "|------|---------|\n", "| 1 | Setup — load retriever, LLM, embeddings |\n", "| 2 | Test dataset — inspect the 15 Apple finance questions |\n", "| 3 | Retrieval-only evaluation — context precision & recall without LLM |\n", "| 4 | Faithfulness — NLI grounding of generated answers |\n", "| 5 | Answer relevancy — cosine similarity check |\n", "| 6 | Answer F1 — token overlap vs reference |\n", "| 7 | Full pipeline evaluation — all metrics on all 15 samples |\n", "| 8 | Category breakdown — which question types are weakest? |\n", "| 9 | Configuration comparison — rerank vs no-rerank |\n", "| 10 | Scorecard & recommendations |" ] }, { "cell_type": "markdown", "id": "rk8bvbvqe29", "metadata": {}, "source": [ "## STEP 1 — Setup" ] }, { "cell_type": "code", "execution_count": 1, "id": "8qm4olusaow", "metadata": { "execution": { "iopub.execute_input": "2026-06-22T17:08:56.828200Z", "iopub.status.busy": "2026-06-22T17:08:56.828106Z", "iopub.status.idle": "2026-06-22T17:09:21.141119Z", "shell.execute_reply": "2026-06-22T17:09:21.140436Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:38:57,705 INFO Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1782148143.101273 3740606 port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", "I0000 00:00:1782148143.150197 3740606 cpu_feature_guard.cc:227] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1782148144.206073 3740606 port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:39:04,765 INFO Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:39:04,766 INFO NumExpr defaulting to 8 threads.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:39:06,147 INFO Load pretrained SentenceTransformer: sentence-transformers/all-MiniLM-L6-v2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:39:10,171 INFO Connected to 'financial_docs' (1234 vectors)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:39:12,886 INFO Use pytorch device: cpu\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:39:13,374 INFO Reranker loaded: cross-encoder/ms-marco-MiniLM-L-6-v2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:39:13,378 INFO Connected to 'financial_docs' (1234 vectors)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Collection: 1234 vectors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:39:13,707 INFO LLM backend: Ollama model=llama3.2:1b\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/pushkardeshpand/Documents/Morningstar RAG Pipeline/src/rag_chain.py:156: LangChainDeprecationWarning: The class `Ollama` was deprecated in LangChain 0.3.1 and will be removed in 1.0.0. An updated version of the class exists in the `langchain-ollama package and should be used instead. To use it run `pip install -U `langchain-ollama` and import as `from `langchain_ollama import OllamaLLM``.\n", " return Ollama(model=chosen)\n", "2026-06-22 22:39:13,733 INFO Connected to 'financial_docs' (1234 vectors)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:39:16,871 INFO Use pytorch device: cpu\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:39:17,471 INFO Reranker loaded: cross-encoder/ms-marco-MiniLM-L-6-v2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:39:17,493 INFO RAG chain LLM: Ollama(llama3.2:1b)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:39:17,535 INFO Load pretrained SentenceTransformer: sentence-transformers/all-MiniLM-L6-v2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "LLM: Ollama(llama3.2:1b)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Embedding model: all-MiniLM-L6-v2 loaded\n" ] } ], "source": [ "import sys\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "from pathlib import Path\n", "\n", "PROJECT_ROOT = Path(\".\").resolve().parent\n", "VECTORSTORE_DIR = PROJECT_ROOT / \"data\" / \"vectorstore\"\n", "if str(PROJECT_ROOT) not in sys.path:\n", " sys.path.insert(0, str(PROJECT_ROOT))\n", "\n", "# ── Retriever ────────────────────────────────────────────────────────────────\n", "from src.retriever import FinancialRetriever\n", "\n", "retriever_rerank = FinancialRetriever(\n", " vectorstore_dir = VECTORSTORE_DIR,\n", " rerank = True,\n", ")\n", "retriever_dense = FinancialRetriever(\n", " vectorstore_dir = VECTORSTORE_DIR,\n", " rerank = False, # dense-only, used in config comparison\n", ")\n", "print(f\"Collection: {retriever_rerank.collection.count()} vectors\")\n", "\n", "# ── LLM ──────────────────────────────────────────────────────────────────────\n", "from src.rag_chain import get_llm, build_rag_chain, llm_info\n", "\n", "llm = get_llm()\n", "chain = build_rag_chain(llm=llm, rerank=True)\n", "print(f\"LLM: {llm_info(llm)}\")\n", "\n", "# ── Embedding model (for answer relevancy) ───────────────────────────────────\n", "from langchain_huggingface import HuggingFaceEmbeddings\n", "\n", "embed_model = HuggingFaceEmbeddings(\n", " model_name = \"sentence-transformers/all-MiniLM-L6-v2\",\n", " model_kwargs = {\"device\": \"cpu\"},\n", " encode_kwargs = {\"normalize_embeddings\": True},\n", ")\n", "print(\"Embedding model: all-MiniLM-L6-v2 loaded\")" ] }, { "cell_type": "markdown", "id": "g8yw8exy6ro", "metadata": {}, "source": [ "## STEP 2 — Test dataset\n", "\n", "15 Apple finance questions with reference answers drawn from the FY2024 10-K.\n", "Categories: revenue, profitability, segment, geographic, expense, balance_sheet, risk." ] }, { "cell_type": "code", "execution_count": 2, "id": "jwrb9ttb82d", "metadata": { "execution": { "iopub.execute_input": "2026-06-22T17:09:21.142922Z", "iopub.status.busy": "2026-06-22T17:09:21.142689Z", "iopub.status.idle": "2026-06-22T17:09:21.155483Z", "shell.execute_reply": "2026-06-22T17:09:21.155131Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Apple Finance Test Set — 15 samples\n", "----------------------------------------\n", " balance_sheet 1 sample\n", " expense 1 sample\n", " geographic 2 samples\n", " profitability 4 samples\n", " revenue 1 sample\n", " risk 1 sample\n", " segment 5 samples\n", "\n", " # Category Question\n", "--------------------------------------------------------------------------------\n", " 1 revenue What were Apple's total net sales for fiscal year 2024?\n", " 2 profitability What was Apple's total gross margin in fiscal year 2024?\n", " 3 profitability What was Apple's net income for fiscal year 2024?\n", " 4 profitability What was Apple's diluted earnings per share for FY2024?\n", " 5 profitability What was Apple's operating income for fiscal year 2024?\n", " 6 segment What was iPhone revenue for fiscal year 2024?\n", " 7 segment How much did Apple's Services segment generate in FY2024?\n", " 8 segment What was Apple's Mac revenue for fiscal year 2024?\n", " 9 segment What were Apple's Wearables, Home and Accessories net sales in FY2024?\n", " 10 segment What was Apple's iPad revenue for fiscal year 2024?\n", " 11 geographic What was Apple's revenue in the Americas geographic segment in FY2024?\n", " 12 geographic What was Apple's Greater China revenue in FY2024?\n", " 13 expense What were Apple's research and development expenses in FY2024?\n", " 14 balance_sheet How much cash and marketable securities did Apple hold at the end of FY2024?\n", " 15 risk What supply chain risk factors does Apple cite in its 10-K?\n", "\n", "Example reference answer (sample 1):\n", " Q: What were Apple's total net sales for fiscal year 2024?\n", " A: Apple's total net sales for fiscal year 2024 were $391,035 million.\n" ] } ], "source": [ "from src.evaluator import AppleFinanceTestSet, EvalSample\n", "\n", "samples = AppleFinanceTestSet.get_samples()\n", "print(AppleFinanceTestSet.summary())\n", "print()\n", "\n", "# Inspect all questions\n", "print(f\"{'#':>3} {'Category':<15} Question\")\n", "print(\"-\" * 80)\n", "for i, s in enumerate(samples, 1):\n", " print(f\"{i:>3} {s.category:<15} {s.question}\")\n", "\n", "print()\n", "# Show one reference answer in full\n", "print(\"Example reference answer (sample 1):\")\n", "print(f\" Q: {samples[0].question}\")\n", "print(f\" A: {samples[0].reference_answer}\")" ] }, { "cell_type": "markdown", "id": "jw6pkmj7a7", "metadata": {}, "source": [ "## STEP 3 — Retrieval-only evaluation\n", "\n", "Before involving the LLM, check that **retrieval** finds relevant chunks.\n", "Context Precision and Context Recall use NLI to compare retrieved chunks\n", "against the reference answer — no LLM generation needed.\n", "\n", "**Why run this separately?**\n", "If retrieval is bad (low recall) the LLM has nothing to work with — the\n", "problem is upstream of generation. Fixing retrieval will improve all other\n", "metrics automatically." ] }, { "cell_type": "code", "execution_count": 3, "id": "16pussvjah1", "metadata": { "execution": { "iopub.execute_input": "2026-06-22T17:09:21.156856Z", "iopub.status.busy": "2026-06-22T17:09:21.156770Z", "iopub.status.idle": "2026-06-22T17:10:50.551744Z", "shell.execute_reply": "2026-06-22T17:10:50.551515Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:39:23,022 INFO Use pytorch device: cpu\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "NLI model ready. Label mapping: {0: 'contradiction', 1: 'entailment', 2: 'neutral'}\n", "Entailment index: 1\n", "\n", " # Category Precision Recall Question\n", "-------------------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5cecb51fcc16472380d87b1738d129d1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Batches: 0%| | 0/1 [00:003} {'Category':<14} {'Precision':>10} {'Recall':>8} Question\")\n", "print(\"-\" * 85)\n", "\n", "precision_scores = []\n", "recall_scores = []\n", "\n", "for i, sample in enumerate(samples, 1):\n", " # Retrieve chunks\n", " chunks = retriever_rerank.retrieve(\n", " sample.question,\n", " n_results = 5,\n", " filters = sample.filters or None,\n", " )\n", "\n", " if not chunks:\n", " print(f\"{i:>3} {sample.category:<14} {'n/a':>10} {'n/a':>8} \"\n", " f\"{sample.question[:45]} [no chunks]\")\n", " continue\n", "\n", " context_texts = [c[\"text\"] for c in chunks]\n", "\n", " # ── Context Precision: fraction of chunks useful for the reference answer\n", " from src.verifier import AnswerVerifier\n", " verifier = AnswerVerifier()\n", " ref_sentences = verifier.split_sentences(sample.reference_answer)\n", "\n", " useful = 0\n", " for chunk in chunks:\n", " pairs = [(chunk[\"text\"], s) for s in ref_sentences]\n", " scores = nli.predict(pairs, apply_softmax=True)\n", " if max(float(s[ENTAIL_IDX]) for s in scores) >= _PRECISION_RECALL_ENTAIL_THRESHOLD:\n", " useful += 1\n", " precision = useful / len(chunks)\n", "\n", " # ── Context Recall: fraction of reference claims covered by any chunk\n", " covered = 0\n", " for sent in ref_sentences:\n", " pairs = [(ctx, sent) for ctx in context_texts]\n", " scores = nli.predict(pairs, apply_softmax=True)\n", " if max(float(s[ENTAIL_IDX]) for s in scores) >= _PRECISION_RECALL_ENTAIL_THRESHOLD:\n", " covered += 1\n", " recall = covered / len(ref_sentences) if ref_sentences else 0.0\n", "\n", " precision_scores.append(precision)\n", " recall_scores.append(recall)\n", "\n", " p_icon = \"✓\" if precision >= 0.70 else (\"⚠\" if precision >= 0.50 else \"✗\")\n", " r_icon = \"✓\" if recall >= 0.70 else (\"⚠\" if recall >= 0.50 else \"✗\")\n", " print(f\"{i:>3} {sample.category:<14} \"\n", " f\"{p_icon} {precision:>7.1%} {r_icon} {recall:>6.1%} \"\n", " f\"{sample.question[:45]}\")\n", "\n", "print(\"-\" * 85)\n", "if precision_scores:\n", " avg_p = sum(precision_scores) / len(precision_scores)\n", " avg_r = sum(recall_scores) / len(recall_scores)\n", " print(f\"{'AVG':>3} {'':14} {avg_p:>8.1%} {avg_r:>7.1%}\")\n", " print(f\"\\nContext Precision: {avg_p:.1%} | Context Recall: {avg_r:.1%}\")" ] }, { "cell_type": "markdown", "id": "ee415kqh2sn", "metadata": {}, "source": [ "## STEP 4 — Faithfulness (NLI grounding of generated answers)\n", "\n", "For each sample, generate an answer and check what fraction of its sentences\n", "are entailed by the retrieved context. This is the Phase 7 grounding check\n", "run systematically across the full test set.\n", "\n", "**Note:** This step runs the LLM for all 15 questions. Expect ~1–3 min with\n", "a local Ollama model, ~5–10 min with HuggingFace flan-t5-large." ] }, { "cell_type": "code", "execution_count": 4, "id": "c1i36mianjm", "metadata": { "execution": { "iopub.execute_input": "2026-06-22T17:10:50.556022Z", "iopub.status.busy": "2026-06-22T17:10:50.555603Z", "iopub.status.idle": "2026-06-22T17:17:20.686572Z", "shell.execute_reply": "2026-06-22T17:17:20.685890Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:40:50,609 INFO Loading NLI model: cross-encoder/nli-deberta-v3-small\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:40:52,518 INFO Use pytorch device: cpu\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:40:52,809 INFO NLI model ready.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " # Grounding Grade Question\n", "---------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b49ce452aee74c5887e8458337f51d45", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Batches: 0%| | 0/1 [00:003} {'Grounding':>10} {'Grade':<8} Question\")\n", "print(\"-\" * 75)\n", "\n", "for i, sample in enumerate(samples, 1):\n", " # Retrieve\n", " chunks = retriever_rerank.retrieve(\n", " sample.question,\n", " n_results = 5,\n", " filters = sample.filters or None,\n", " )\n", "\n", " # Generate\n", " answer = chain.invoke({\n", " \"query\" : sample.question,\n", " \"filters\": sample.filters or None,\n", " })\n", "\n", " # Faithfulness\n", " result = verifier.verify(answer, chunks)\n", " score = result[\"grounding_score\"]\n", "\n", " generated_answers[sample.question] = answer\n", " retrieved_chunks_[sample.question] = chunks\n", " faithfulness_scores.append(score)\n", "\n", " grade = \"PASS\" if score >= 0.80 else (\"REVIEW\" if score >= 0.60 else \"FAIL\")\n", " icon = \"✓\" if grade == \"PASS\" else (\"⚠\" if grade == \"REVIEW\" else \"✗\")\n", " print(f\"{i:>3} {icon} {score:>8.1%} {grade:<8} {sample.question[:55]}\")\n", "\n", "avg_faith = sum(faithfulness_scores) / len(faithfulness_scores)\n", "print(\"-\" * 75)\n", "print(f\"{'AVG':>3} {avg_faith:>8.1%}\")\n", "print(f\"\\nMean Faithfulness: {avg_faith:.1%}\")" ] }, { "cell_type": "markdown", "id": "4jkuvvlnekl", "metadata": {}, "source": [ "## STEP 5 — Answer Relevancy\n", "\n", "Cosine similarity between the question embedding and the answer embedding.\n", "A well-focused answer that directly addresses the question scores high.\n", "An answer that goes off-topic (e.g. answers a different question, repeats\n", "boilerplate) scores low.\n", "\n", "**Limitation:** This is a proxy metric. Two semantically distinct sentences\n", "about Apple financials may have similar embeddings even if one doesn't answer\n", "the question. Treat scores below 0.50 as a signal to investigate." ] }, { "cell_type": "code", "execution_count": 5, "id": "s7c7jg60ke", "metadata": { "execution": { "iopub.execute_input": "2026-06-22T17:17:20.690568Z", "iopub.status.busy": "2026-06-22T17:17:20.690328Z", "iopub.status.idle": "2026-06-22T17:17:21.181185Z", "shell.execute_reply": "2026-06-22T17:17:21.180670Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " # Relevancy Grade Question\n", "---------------------------------------------------------------------------\n", " 1 ⚠ 0.709 REVIEW What were Apple's total net sales for fiscal year 2024?\n", " 2 ✓ 0.918 PASS What was Apple's total gross margin in fiscal year 2024\n", " 3 ✓ 0.907 PASS What was Apple's net income for fiscal year 2024?\n", " 4 ✓ 0.910 PASS What was Apple's diluted earnings per share for FY2024?\n", " 5 ✓ 0.807 PASS What was Apple's operating income for fiscal year 2024?\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 6 ⚠ 0.738 REVIEW What was iPhone revenue for fiscal year 2024?\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 7 ✓ 0.800 PASS How much did Apple's Services segment generate in FY202\n", " 8 ✓ 0.876 PASS What was Apple's Mac revenue for fiscal year 2024?\n", " 9 ✓ 0.858 PASS What were Apple's Wearables, Home and Accessories net s\n", " 10 ✓ 0.900 PASS What was Apple's iPad revenue for fiscal year 2024?\n", " 11 ✗ 0.166 FAIL What was Apple's revenue in the Americas geographic seg\n", " 12 ⚠ 0.677 REVIEW What was Apple's Greater China revenue in FY2024?\n", " 13 ✗ 0.472 FAIL What were Apple's research and development expenses in \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 14 ✓ 0.892 PASS How much cash and marketable securities did Apple hold \n", " 15 ⚠ 0.750 REVIEW What supply chain risk factors does Apple cite in its 1\n", "---------------------------------------------------------------------------\n", "AVG 0.759\n", "\n", "Lowest-relevancy answers (investigate these):\n", "\n", " Score=0.166 Q: What was Apple's revenue in the Americas geographic segment in FY2024?\n", " A: The provided documents do not contain enough information to answer this question. [5]\n", "\n", " Score=0.472 Q: What were Apple's research and development expenses in FY2024?\n", " A: According to [2] and [3], Research and Development (R&D) expense increased from $29,915 in [2023] to $31,370 in [2024].\n", "\n", " Score=0.677 Q: What was Apple's Greater China revenue in FY2024?\n", " A: [1] Apple 10-K FY2024 (filed 2024-11-01)\n", "Greater China net sales decreased during 2024 compared to 2023 due primarily to lower net sales of iPhone and iPad. The weakness in the renminbi relative to th\n" ] } ], "source": [ "import numpy as np\n", "\n", "relevancy_scores = []\n", "\n", "print(f\"{'#':>3} {'Relevancy':>10} {'Grade':<8} Question\")\n", "print(\"-\" * 75)\n", "\n", "for i, sample in enumerate(samples, 1):\n", " answer = generated_answers.get(sample.question, \"\")\n", " if not answer.strip():\n", " print(f\"{i:>3} {'n/a':>10} {sample.question[:55]} [no answer]\")\n", " relevancy_scores.append(0.0)\n", " continue\n", "\n", " q_vec = np.array(embed_model.embed_query(sample.question))\n", " a_vec = np.array(embed_model.embed_query(answer))\n", " denom = np.linalg.norm(q_vec) * np.linalg.norm(a_vec)\n", " score = float(np.clip(np.dot(q_vec, a_vec) / denom, 0, 1)) if denom else 0.0\n", " relevancy_scores.append(score)\n", "\n", " grade = \"PASS\" if score >= 0.75 else (\"REVIEW\" if score >= 0.50 else \"FAIL\")\n", " icon = \"✓\" if grade == \"PASS\" else (\"⚠\" if grade == \"REVIEW\" else \"✗\")\n", " print(f\"{i:>3} {icon} {score:>8.3f} {grade:<8} {sample.question[:55]}\")\n", "\n", "avg_rel = sum(relevancy_scores) / len(relevancy_scores)\n", "print(\"-\" * 75)\n", "print(f\"{'AVG':>3} {avg_rel:>8.3f}\")\n", "\n", "# Show worst-scoring answers for inspection\n", "print(\"\\nLowest-relevancy answers (investigate these):\")\n", "ranked = sorted(zip(relevancy_scores, samples), key=lambda x: x[0])\n", "for score, sample in ranked[:3]:\n", " answer = generated_answers.get(sample.question, \"\")\n", " print(f\"\\n Score={score:.3f} Q: {sample.question}\")\n", " print(f\" A: {answer[:200]}\")" ] }, { "cell_type": "markdown", "id": "87tjjs0g5n8", "metadata": {}, "source": [ "## STEP 6 — Answer F1 (token overlap vs reference)\n", "\n", "Token-level F1 between the generated answer and the reference answer.\n", "This is the same metric used in SQuAD — it is normalised (lowercase,\n", "punctuation removed) and measures how much vocabulary overlaps.\n", "\n", "**What this catches:** Answers that completely miss the key numbers or terms\n", "in the reference answer will score very low. An answer that says \"revenue\n", "increased significantly\" when the reference says \"$391,035 million\" will\n", "score near 0 on the number tokens.\n", "\n", "**What it misses:** Paraphrases that use different but correct words score\n", "lower than they should. F1 is a recall-precision tradeoff over tokens, not\n", "meaning." ] }, { "cell_type": "code", "execution_count": 6, "id": "485d5h1svt7", "metadata": { "execution": { "iopub.execute_input": "2026-06-22T17:17:21.184430Z", "iopub.status.busy": "2026-06-22T17:17:21.183395Z", "iopub.status.idle": "2026-06-22T17:17:21.192526Z", "shell.execute_reply": "2026-06-22T17:17:21.191922Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " # F1 Grade Question\n", "---------------------------------------------------------------------------\n", " 1 ✗ 0.204 FAIL What were Apple's total net sales for fiscal year 2024?\n", " 2 ✓ 0.743 PASS What was Apple's total gross margin in fiscal year 2024\n", " 3 ⚠ 0.333 REVIEW What was Apple's net income for fiscal year 2024?\n", " 4 ✓ 0.558 PASS What was Apple's diluted earnings per share for FY2024?\n", " 5 ✗ 0.295 FAIL What was Apple's operating income for fiscal year 2024?\n", " 6 ✗ 0.254 FAIL What was iPhone revenue for fiscal year 2024?\n", " 7 ⚠ 0.492 REVIEW How much did Apple's Services segment generate in FY202\n", " 8 ⚠ 0.373 REVIEW What was Apple's Mac revenue for fiscal year 2024?\n", " 9 ✓ 0.588 PASS What were Apple's Wearables, Home and Accessories net s\n", " 10 ⚠ 0.424 REVIEW What was Apple's iPad revenue for fiscal year 2024?\n", " 11 ✗ 0.000 FAIL What was Apple's revenue in the Americas geographic seg\n", " 12 ⚠ 0.329 REVIEW What was Apple's Greater China revenue in FY2024?\n", " 13 ✓ 0.579 PASS What were Apple's research and development expenses in \n", " 14 ✗ 0.262 FAIL How much cash and marketable securities did Apple hold \n", " 15 ✗ 0.194 FAIL What supply chain risk factors does Apple cite in its 1\n", "---------------------------------------------------------------------------\n", "AVG 0.375\n", "\n", "Lowest-F1 answers — generated vs reference:\n", "\n", " F1=0.000 Q: What was Apple's revenue in the Americas geographic segment in FY2024?\n", " Generated : The provided documents do not contain enough information to answer this question. [5]\n", " Reference : Apple's Americas net sales were $167,045 million in fiscal year 2024, up from $162,560 million in fiscal year 2023.\n", " Missing tokens (in ref, not in answer): ['045', '162', '167', '2023', '2024', '560', 'americas', 'apple', 'fiscal', 'from']\n", " Extra tokens (in answer, not in ref): ['5', 'answer', 'contain', 'do', 'documents', 'enough', 'information', 'not', 'provided', 'question']\n", "\n", " F1=0.194 Q: What supply chain risk factors does Apple cite in its 10-K?\n", " Generated : Based on the provided documents, Apple cites the following supply chain risk factors:\n", "\n", "* The Company's retail operations are subject to many factors t\n", " Reference : Apple cites several supply chain risks in its 10-K: dependence on single-source or limited-source suppliers for certain components, concentration of m\n", " Missing tokens (in ref, not in answer): ['10', 'asia', 'certain', 'china', 'component', 'components', 'concentration', 'dependence', 'disasters', 'disputes']\n", " Extra tokens (in answer, not in ref): ['4', 'a', 'ability', 'activity', 'additionally', 'adverse', 'adversely', 'an', 'any', 'are']\n" ] } ], "source": [ "import re\n", "\n", "def normalise(text: str) -> str:\n", " text = text.lower()\n", " text = re.sub(r\"[^a-z0-9\\s]\", \" \", text)\n", " return \" \".join(text.split())\n", "\n", "def token_f1(generated: str, reference: str) -> float:\n", " gen = set(normalise(generated).split())\n", " ref = set(normalise(reference).split())\n", " if not gen or not ref:\n", " return 0.0\n", " common = gen & ref\n", " if not common:\n", " return 0.0\n", " p = len(common) / len(gen)\n", " r = len(common) / len(ref)\n", " return 2 * p * r / (p + r)\n", "\n", "f1_scores = []\n", "\n", "print(f\"{'#':>3} {'F1':>8} {'Grade':<8} Question\")\n", "print(\"-\" * 75)\n", "\n", "for i, sample in enumerate(samples, 1):\n", " answer = generated_answers.get(sample.question, \"\")\n", " score = token_f1(answer, sample.reference_answer)\n", " f1_scores.append(score)\n", "\n", " grade = \"PASS\" if score >= 0.50 else (\"REVIEW\" if score >= 0.30 else \"FAIL\")\n", " icon = \"✓\" if grade == \"PASS\" else (\"⚠\" if grade == \"REVIEW\" else \"✗\")\n", " print(f\"{i:>3} {icon} {score:>6.3f} {grade:<8} {sample.question[:55]}\")\n", "\n", "avg_f1 = sum(f1_scores) / len(f1_scores)\n", "print(\"-\" * 75)\n", "print(f\"{'AVG':>3} {avg_f1:>6.3f}\")\n", "\n", "# Detailed look at lowest F1 — what is the answer vs reference?\n", "print(\"\\nLowest-F1 answers — generated vs reference:\")\n", "ranked_f1 = sorted(zip(f1_scores, samples), key=lambda x: x[0])\n", "for score, sample in ranked_f1[:2]:\n", " answer = generated_answers.get(sample.question, \"\")\n", " ref = sample.reference_answer\n", " gen_toks = set(normalise(answer).split())\n", " ref_toks = set(normalise(ref).split())\n", " missing = ref_toks - gen_toks\n", " extra = gen_toks - ref_toks\n", " print(f\"\\n F1={score:.3f} Q: {sample.question}\")\n", " print(f\" Generated : {answer[:150]}\")\n", " print(f\" Reference : {ref[:150]}\")\n", " print(f\" Missing tokens (in ref, not in answer): {sorted(missing)[:10]}\")\n", " print(f\" Extra tokens (in answer, not in ref): {sorted(extra)[:10]}\")" ] }, { "cell_type": "markdown", "id": "2tgdggnhhv4", "metadata": {}, "source": [ "## STEP 7 — Full pipeline evaluation\n", "\n", "Run all metrics together on all 15 samples using `RAGEvaluator`.\n", "This is the primary entrypoint for a full benchmark run.\n", "\n", "> **Runtime note:** This re-runs LLM generation + NLI for all 15 samples.\n", "> If STEP 4 already ran, the answers are cached in `generated_answers` —\n", "> but `RAGEvaluator.evaluate_dataset()` re-generates independently so you\n", "> get a clean, reproducible measurement." ] }, { "cell_type": "code", "execution_count": 7, "id": "4bc5mpvwojt", "metadata": { "execution": { "iopub.execute_input": "2026-06-22T17:17:21.195175Z", "iopub.status.busy": "2026-06-22T17:17:21.194800Z", "iopub.status.idle": "2026-06-23T04:56:16.063177Z", "shell.execute_reply": "2026-06-23T04:56:16.062709Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:47:21,196 INFO Loading NLI model: cross-encoder/nli-deberta-v3-small\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:47:23,418 INFO Use pytorch device: cpu\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:47:23,647 INFO Loading NLI model: cross-encoder/nli-deberta-v3-small\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:47:26,054 INFO Use pytorch device: cpu\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:47:26,270 INFO NLI model ready.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:47:26,270 INFO RAGEvaluator ready.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-22 22:47:26,271 INFO [ 1/15] What were Apple's total net sales for fiscal year 2024?\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Running full evaluation on 15 samples...\n", "(Each sample: retrieve → generate → 6 metrics)\n", "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c00871ce5a164feeba227481d4dcf3f6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Batches: 0%| | 0/1 [00:006} {'Relev':>6} {'CPrec':>6} {'CRec':>6} \"\n", " f\"{'F1':>6} {'Agg':>6} {'N':>3}\")\n", "print(\"-\" * 75)\n", "\n", "for cat, scores in breakdown.items():\n", " n = scores.get(\"n_samples\", 0)\n", " print(\n", " f\"{cat:<15} \"\n", " f\"{scores.get('faithfulness', 0):>6.2f} \"\n", " f\"{scores.get('answer_relevancy', 0):>6.2f} \"\n", " f\"{scores.get('context_precision', 0):>6.2f} \"\n", " f\"{scores.get('context_recall', 0):>6.2f} \"\n", " f\"{scores.get('answer_f1', 0):>6.2f} \"\n", " f\"{scores.get('aggregate_score', 0):>6.2f} \"\n", " f\"{n:>3}\"\n", " )\n", "\n", "print()\n", "# Identify weakest category\n", "weakest = min(breakdown, key=lambda c: breakdown[c].get(\"aggregate_score\", 1.0))\n", "print(f\"Weakest category: {weakest} \"\n", " f\"(aggregate={breakdown[weakest].get('aggregate_score', 0):.2f})\")\n", "print(f\"→ Consider adding more '{weakest}' documents to the vectorstore \"\n", " f\"or tuning retrieval filters for this category.\")" ] }, { "cell_type": "markdown", "id": "9d7ngdjmsak", "metadata": {}, "source": [ "## STEP 9 — Configuration comparison\n", "\n", "Compare two retrieval configs on the same test set:\n", "\n", "| Config | Description |\n", "|--------|-------------|\n", "| **rerank** | Dense ANN (top-20) → cross-encoder rerank → top-5 |\n", "| **dense-only** | Dense ANN top-5, no reranking |\n", "\n", "Use `evaluate_dataset()` on a small subset (5 questions) for speed.\n", "The full 15-sample run would take ~2× longer." ] }, { "cell_type": "code", "execution_count": 9, "id": "2ou4b75239y", "metadata": { "execution": { "iopub.execute_input": "2026-06-23T04:56:16.074024Z", "iopub.status.busy": "2026-06-23T04:56:16.073790Z", "iopub.status.idle": "2026-06-23T05:02:17.203282Z", "shell.execute_reply": "2026-06-23T05:02:17.200323Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:16,168 INFO Connected to 'financial_docs' (1234 vectors)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 15a13d70-d71b-41a0-a305-91a74c1bbb5e)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:16,294 WARNING '(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 15a13d70-d71b-41a0-a305-91a74c1bbb5e)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Retrying in 1s [Retry 1/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:16,295 WARNING Retrying in 1s [Retry 1/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:16,748 WARNING Retrying (Retry(total=1, connect=2, read=2, redirect=None, status=None)) after connection broken by 'SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)'))': /batch/\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:16,779 WARNING Retrying (Retry(total=0, connect=2, read=2, redirect=None, status=None)) after connection broken by 'SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)'))': /batch/\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:16,824 INFO Backing off send_request(...) for 0.3s (requests.exceptions.SSLError: HTTPSConnectionPool(host='us.i.posthog.com', port=443): Max retries exceeded with url: /batch/ (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)'))))\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:17,201 WARNING Retrying (Retry(total=1, connect=2, read=2, redirect=None, status=None)) after connection broken by 'SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)'))': /batch/\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:17,234 WARNING Retrying (Retry(total=0, connect=2, read=2, redirect=None, status=None)) after connection broken by 'SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)'))': /batch/\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:17,263 INFO Backing off send_request(...) for 1.2s (requests.exceptions.SSLError: HTTPSConnectionPool(host='us.i.posthog.com', port=443): Max retries exceeded with url: /batch/ (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)'))))\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 44f3691e-12d7-4294-b79c-5acb6b9f133c)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:17,334 WARNING '(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 44f3691e-12d7-4294-b79c-5acb6b9f133c)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Retrying in 2s [Retry 2/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:17,335 WARNING Retrying in 2s [Retry 2/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:18,467 WARNING Retrying (Retry(total=1, connect=2, read=2, redirect=None, status=None)) after connection broken by 'SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)'))': /batch/\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:18,496 WARNING Retrying (Retry(total=0, connect=2, read=2, redirect=None, status=None)) after connection broken by 'SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)'))': /batch/\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:18,522 INFO Backing off send_request(...) for 3.4s (requests.exceptions.SSLError: HTTPSConnectionPool(host='us.i.posthog.com', port=443): Max retries exceeded with url: /batch/ (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)'))))\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 2f284e64-2abd-42d7-b81b-5b1b886cc773)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:19,362 WARNING '(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 2f284e64-2abd-42d7-b81b-5b1b886cc773)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Retrying in 4s [Retry 3/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:19,363 WARNING Retrying in 4s [Retry 3/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:21,959 WARNING Retrying (Retry(total=1, connect=2, read=2, redirect=None, status=None)) after connection broken by 'SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)'))': /batch/\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:21,991 WARNING Retrying (Retry(total=0, connect=2, read=2, redirect=None, status=None)) after connection broken by 'SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)'))': /batch/\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:22,021 ERROR Giving up send_request(...) after 4 tries (requests.exceptions.SSLError: HTTPSConnectionPool(host='us.i.posthog.com', port=443): Max retries exceeded with url: /batch/ (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)'))))\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 74bfe53a-a1a3-4010-9105-3105637ab158)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:23,394 WARNING '(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 74bfe53a-a1a3-4010-9105-3105637ab158)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Retrying in 8s [Retry 4/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:23,395 WARNING Retrying in 8s [Retry 4/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 47721c63-c331-471c-9de7-1b23c0a5d484)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:31,423 WARNING '(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 47721c63-c331-471c-9de7-1b23c0a5d484)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Retrying in 8s [Retry 5/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:31,423 WARNING Retrying in 8s [Retry 5/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: e87aa0f4-7df0-4ea7-b11e-ae36ad663611)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:39,455 WARNING '(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: e87aa0f4-7df0-4ea7-b11e-ae36ad663611)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 349217f5-660c-49e0-9f11-55ced07c42a7)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:39,575 WARNING '(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 349217f5-660c-49e0-9f11-55ced07c42a7)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Retrying in 1s [Retry 1/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:39,576 WARNING Retrying in 1s [Retry 1/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 16b087fc-fc72-419c-b627-7578c67952c0)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:40,607 WARNING '(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 16b087fc-fc72-419c-b627-7578c67952c0)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Retrying in 2s [Retry 2/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:40,608 WARNING Retrying in 2s [Retry 2/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 9a1975e3-24f6-41b5-b551-6e5488d0ab7b)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:42,641 WARNING '(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 9a1975e3-24f6-41b5-b551-6e5488d0ab7b)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Retrying in 4s [Retry 3/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:42,642 WARNING Retrying in 4s [Retry 3/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: aea3e272-51f0-4346-996d-6eca3fa9c17f)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:46,671 WARNING '(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: aea3e272-51f0-4346-996d-6eca3fa9c17f)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Retrying in 8s [Retry 4/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:46,672 WARNING Retrying in 8s [Retry 4/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: e7964190-675b-4fd1-ba3a-c993ceb651aa)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:54,705 WARNING '(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: e7964190-675b-4fd1-ba3a-c993ceb651aa)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Retrying in 8s [Retry 5/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:26:54,712 WARNING Retrying in 8s [Retry 5/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 0c948c58-9dd6-4104-afec-24ca02668bec)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:27:02,748 WARNING '(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 0c948c58-9dd6-4104-afec-24ca02668bec)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/README.md (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: ca2aec36-eaea-4e3d-99b3-fa92253eb47b)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/./README.md\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:27:02,892 WARNING '(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/README.md (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: ca2aec36-eaea-4e3d-99b3-fa92253eb47b)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/./README.md\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Retrying in 1s [Retry 1/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:27:02,893 WARNING Retrying in 1s [Retry 1/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/README.md (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 9d049c64-df19-41a9-b435-30d20f72ee51)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/./README.md\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:27:03,931 WARNING '(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/README.md (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 9d049c64-df19-41a9-b435-30d20f72ee51)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/./README.md\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Retrying in 2s [Retry 2/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:27:03,931 WARNING Retrying in 2s [Retry 2/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/README.md (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: c2d2640e-aa28-4564-ae94-3984489eea3d)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/./README.md\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:27:05,958 WARNING '(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/README.md (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: c2d2640e-aa28-4564-ae94-3984489eea3d)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/./README.md\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Retrying in 4s [Retry 3/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:27:05,960 WARNING Retrying in 4s [Retry 3/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/README.md (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 368ac1dc-2256-45c2-b83d-370bdfa96f4d)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/./README.md\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:27:09,999 WARNING '(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/README.md (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 368ac1dc-2256-45c2-b83d-370bdfa96f4d)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/./README.md\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Retrying in 8s [Retry 4/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:27:10,000 WARNING Retrying in 8s [Retry 4/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/README.md (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 9ed9925d-3c54-4680-8e27-627a38778652)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/./README.md\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:27:18,300 WARNING '(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/README.md (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: 9ed9925d-3c54-4680-8e27-627a38778652)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/./README.md\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Retrying in 8s [Retry 5/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:27:18,300 WARNING Retrying in 8s [Retry 5/5].\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "'(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/README.md (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: e0bfd06a-9719-4d60-87f0-c85ef9462381)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/./README.md\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:27:26,331 WARNING '(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/README.md (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)')))\"), '(Request ID: e0bfd06a-9719-4d60-87f0-c85ef9462381)')' thrown while requesting HEAD https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2/resolve/main/./README.md\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:27:26,339 INFO Use pytorch device: cpu\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:27:26,371 INFO Reranker loaded: cross-encoder/ms-marco-MiniLM-L-6-v2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:27:26,405 INFO RAG chain LLM: Ollama(llama3.2:1b)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:27:26,408 INFO Connected to 'financial_docs' (1234 vectors)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:27:26,409 INFO RAG chain LLM: Ollama(llama3.2:1b)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-06-23 10:27:26,409 INFO Evaluating config: rerank\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Running configuration comparison on 5 samples per config...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5efe07c534a94804837261f11e4b19bb", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Batches: 0%| | 0/1 [00:00= hi else (\"REVIEW\" if score >= lo else \"FAIL\")\n", " icon = \"✓\" if grade == \"PASS\" else (\"⚠\" if grade == \"REVIEW\" else \"✗\")\n", " bar = \"█\" * int(score * 20)\n", " print(f\" {icon} {label:<22} {score:>6.1%} [{bar:<20}] {grade}\")\n", "\n", " if grade == \"FAIL\":\n", " if key == \"faithfulness\":\n", " recommendations.append(\n", " \"Faithfulness FAIL → LLM is hallucinating. \"\n", " \"Lower LLM temperature, add 'only use the context' to the prompt, \"\n", " \"or increase entail_threshold in guardrails.\"\n", " )\n", " elif key == \"answer_relevancy\":\n", " recommendations.append(\n", " \"Answer Relevancy FAIL → Answers are off-topic. \"\n", " \"Check that the prompt template doesn't allow LLM to ignore the question. \"\n", " \"Add 'Answer only the specific question asked' to the system prompt.\"\n", " )\n", " elif key == \"context_precision\":\n", " recommendations.append(\n", " \"Context Precision FAIL → Too many irrelevant chunks retrieved. \"\n", " \"Enable reranking, reduce n_results, or add metadata filters.\"\n", " )\n", " elif key == \"context_recall\":\n", " recommendations.append(\n", " \"Context Recall FAIL → Missing relevant chunks. \"\n", " \"Increase n_results, check that documents are fully indexed, \"\n", " \"or try a finance-specific embedding model (ProsusAI/finbert).\"\n", " )\n", " elif key == \"answer_f1\":\n", " recommendations.append(\n", " \"Answer F1 FAIL → Generated answers lack key terms/numbers from reference. \"\n", " \"Ensure the LLM is instructed to cite specific figures from context. \"\n", " \"Check that financial tables are being chunked correctly (not split).\"\n", " )\n", "\n", "print()\n", "print(f\" {'Aggregate Score':<22} {scores['aggregate_score']:>6.1%}\")\n", "print(\"=\" * 70)\n", "\n", "if recommendations:\n", " print(\"\\n Recommendations:\")\n", " for i, rec in enumerate(recommendations, 1):\n", " print(f\"\\n [{i}] {rec}\")\n", "else:\n", " print(\"\\n All metrics pass. 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