diff --git a/.semversioner/next-release/patch-20260501174720978257.json b/.semversioner/next-release/patch-20260501174720978257.json
new file mode 100644
index 0000000..0317955
--- /dev/null
+++ b/.semversioner/next-release/patch-20260501174720978257.json
@@ -0,0 +1,4 @@
+{
+ "type": "patch",
+ "description": "Add type-checking to Jupyter Notebooks, update Pyright"
+}
diff --git a/benchmark_qed/autoe/utils/stats.py b/benchmark_qed/autoe/utils/stats.py
index 0c89960..b3851b8 100644
--- a/benchmark_qed/autoe/utils/stats.py
+++ b/benchmark_qed/autoe/utils/stats.py
@@ -550,6 +550,9 @@ def _apply_correction_and_build_result(
p_values = [c[3] for c in comparisons_raw]
_, corrected_pvals, _, _ = multipletests(p_values, alpha=alpha, method=correction)
+ if corrected_pvals is None:
+ msg = f"Invalid correction method: {correction}"
+ raise ValueError(msg)
comparisons = [
PairwiseComparison(
diff --git a/docs/notebooks/assertion_gen.ipynb b/docs/notebooks/assertion_gen.ipynb
index 514d01b..52d00e3 100644
--- a/docs/notebooks/assertion_gen.ipynb
+++ b/docs/notebooks/assertion_gen.ipynb
@@ -44,6 +44,7 @@
"metadata": {},
"outputs": [],
"source": [
+ "import asyncio\n",
"import logging\n",
"import os\n",
"\n",
@@ -173,13 +174,16 @@
"metadata": {},
"outputs": [],
"source": [
+ "from graphrag_storage.file_storage import FileStorage\n",
+ "\n",
"from benchmark_qed.autoq.question_gen.data_questions.assertion_gen.local_claim_assertion_gen import (\n",
" LocalClaimAssertionGenerator,\n",
")\n",
"\n",
"# Load existing data-local questions from disk\n",
- "existing_local_questions = load_questions(\n",
- " f\"{OUTPUT_QUESTIONS_PATH}/data_local_questions/selected_questions.json\"\n",
+ "local_question_storage = FileStorage(f\"{OUTPUT_QUESTIONS_PATH}/data_local_questions/\")\n",
+ "existing_local_questions = asyncio.run(\n",
+ " load_questions(local_question_storage, \"selected_questions.json\")\n",
")\n",
"\n",
"print(f\"Loaded {len(existing_local_questions)} existing data-local questions\")\n",
@@ -209,25 +213,29 @@
")\n",
"\n",
"# Generate assertions for all questions with claims\n",
- "await local_assertion_generator.agenerate_assertions_for_questions(\n",
- " questions_with_claims\n",
+ "asyncio.run(\n",
+ " local_assertion_generator.agenerate_assertions_for_questions(questions_with_claims)\n",
")\n",
"\n",
"# Combine back with questions that had no claims\n",
"updated_local_questions = questions_with_claims + questions_without_claims\n",
"\n",
"# Save updated questions with assertions\n",
- "save_questions(\n",
- " updated_local_questions,\n",
- " f\"{OUTPUT_QUESTIONS_PATH}/data_local_questions/\",\n",
- " \"selected_questions_with_assertions\",\n",
+ "asyncio.run(\n",
+ " save_questions(\n",
+ " updated_local_questions,\n",
+ " local_question_storage,\n",
+ " \"selected_questions_with_assertions\",\n",
+ " )\n",
")\n",
"\n",
"# Show summary\n",
"print(\"\\n=== SUMMARY ===\")\n",
"print(f\"Processed {len(questions_with_claims)} questions with claims\")\n",
"total_assertions = sum(\n",
- " len(q.attributes.get(\"assertions\", [])) for q in questions_with_claims\n",
+ " len(q.attributes.get(\"assertions\", []))\n",
+ " for q in questions_with_claims\n",
+ " if q.attributes is not None\n",
")\n",
"print(f\"Total assertions generated: {total_assertions}\")\n",
"print(\n",
@@ -366,8 +374,9 @@
")\n",
"\n",
"# Load existing data-global questions from disk\n",
- "existing_global_questions = load_questions(\n",
- " f\"{OUTPUT_QUESTIONS_PATH}/data_global_questions/selected_questions.json\"\n",
+ "global_question_storage = FileStorage(f\"{OUTPUT_QUESTIONS_PATH}/data_global_questions/\")\n",
+ "existing_global_questions = asyncio.run(\n",
+ " load_questions(global_question_storage, \"selected_questions.json\")\n",
")\n",
"\n",
"print(f\"Loaded {len(existing_global_questions)} existing data-global questions\")\n",
@@ -406,28 +415,34 @@
")\n",
"\n",
"# Generate assertions for ALL questions with claims\n",
- "await global_assertion_generator.agenerate_assertions_for_questions(\n",
- " questions_with_claims\n",
+ "asyncio.run(\n",
+ " global_assertion_generator.agenerate_assertions_for_questions(questions_with_claims)\n",
")\n",
"\n",
"# Combine back with questions that had no claims\n",
"updated_global_questions = questions_with_claims + questions_without_claims\n",
"\n",
"# Save updated questions with assertions\n",
- "save_questions(\n",
- " updated_global_questions,\n",
- " f\"{OUTPUT_QUESTIONS_PATH}/data_global_questions/\",\n",
- " \"selected_questions_with_assertions\",\n",
+ "asyncio.run(\n",
+ " save_questions(\n",
+ " updated_global_questions,\n",
+ " global_question_storage,\n",
+ " \"selected_questions_with_assertions\",\n",
+ " )\n",
")\n",
"\n",
"# Show summary\n",
"print(\"\\n=== SUMMARY ===\")\n",
"print(f\"Processed {len(questions_with_claims)} questions with claims\")\n",
"total_assertions = sum(\n",
- " len(q.attributes.get(\"assertions\", [])) for q in questions_with_claims\n",
+ " len(q.attributes.get(\"assertions\", []))\n",
+ " for q in questions_with_claims\n",
+ " if q.attributes is not None\n",
")\n",
"total_map_assertions = sum(\n",
- " len(q.attributes.get(\"map_assertions\", [])) for q in questions_with_claims\n",
+ " len(q.attributes.get(\"map_assertions\", []))\n",
+ " for q in questions_with_claims\n",
+ " if q.attributes is not None\n",
")\n",
"print(f\"Total map assertions generated: {total_map_assertions}\")\n",
"print(f\"Total final assertions generated: {total_assertions}\")\n",
@@ -438,8 +453,10 @@
"# Show per-question breakdown\n",
"print(\"\\n=== PER-QUESTION BREAKDOWN ===\")\n",
"for i, q in enumerate(questions_with_claims, 1):\n",
- " assertions = q.attributes.get(\"assertions\", [])\n",
- " map_assertions = q.attributes.get(\"map_assertions\", [])\n",
+ " assertions = q.attributes.get(\"assertions\", []) if q.attributes is not None else []\n",
+ " map_assertions = (\n",
+ " q.attributes.get(\"map_assertions\", []) if q.attributes is not None else []\n",
+ " )\n",
" print(\n",
" f\"{i}. [{len(map_assertions)} map → {len(assertions)} final] {q.text[:60]}...\"\n",
" )"
diff --git a/docs/notebooks/autod.ipynb b/docs/notebooks/autod.ipynb
index 654ff8e..ab53628 100644
--- a/docs/notebooks/autod.ipynb
+++ b/docs/notebooks/autod.ipynb
@@ -18,9 +18,11 @@
"metadata": {},
"outputs": [],
"source": [
+ "import asyncio\n",
"import logging\n",
"import os\n",
"\n",
+ "from graphrag_storage.file_storage import FileStorage\n",
"from pydantic import SecretStr\n",
"\n",
"from benchmark_qed.autod.data_processor.embedding import TextEmbedder\n",
@@ -101,14 +103,17 @@
"metadata": {},
"outputs": [],
"source": [
- "documents = create_documents(\n",
- " input_path=INPUT_DATA_PATH,\n",
- " input_type=\"json\",\n",
- " text_tag=TEXT_COLUMN,\n",
- " metadata_tags=METADATA_COLUMNS,\n",
- " encoding=JSON_ENCODING,\n",
+ "documents = asyncio.run(\n",
+ " create_documents(\n",
+ " input_path=INPUT_DATA_PATH,\n",
+ " input_type=\"json\",\n",
+ " text_tag=TEXT_COLUMN,\n",
+ " metadata_tags=METADATA_COLUMNS,\n",
+ " encoding=JSON_ENCODING,\n",
+ " )\n",
")\n",
- "document_df = save_documents(documents, OUTPUT_DATA_PATH)\n",
+ "output_storage = FileStorage(OUTPUT_DATA_PATH)\n",
+ "document_df = asyncio.run(save_documents(documents, output_storage))\n",
"print(f\"Document count: {len(document_df)}\")\n",
"document_df.head()"
]
@@ -144,14 +149,16 @@
" )\n",
")\n",
"\n",
- "text_units = await create_text_units(\n",
- " documents=documents,\n",
- " metadata_tags=METADATA_COLUMNS,\n",
- " text_splitter=text_splitter,\n",
- " text_embedder=text_embedder,\n",
- " embed_text=True,\n",
+ "text_units = asyncio.run(\n",
+ " create_text_units(\n",
+ " documents=documents,\n",
+ " metadata_tags=METADATA_COLUMNS,\n",
+ " text_splitter=text_splitter,\n",
+ " text_embedder=text_embedder,\n",
+ " embed_text=True,\n",
+ " )\n",
")\n",
- "text_unit_df = save_text_units(text_units, OUTPUT_DATA_PATH)\n",
+ "text_unit_df = asyncio.run(save_text_units(text_units, output_storage))\n",
"print(f\"Text unit count: {len(text_unit_df)}\")\n",
"text_unit_df.head()"
]
@@ -273,8 +280,10 @@
" concurrent_coroutines=32,\n",
")\n",
"\n",
- "summary_result = await summarizer.asummarize(\n",
- " text_units=sampled_text_units,\n",
+ "summary_result = asyncio.run(\n",
+ " summarizer.asummarize(\n",
+ " text_units=sampled_text_units,\n",
+ " )\n",
")\n",
"print(f\"Summary: {summary_result.summary}\")"
]
diff --git a/docs/notebooks/autoe.ipynb b/docs/notebooks/autoe.ipynb
index 31a2d05..823515f 100644
--- a/docs/notebooks/autoe.ipynb
+++ b/docs/notebooks/autoe.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"id": "c0caa0d3",
"metadata": {},
"outputs": [],
@@ -49,7 +49,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"id": "bf9ea340",
"metadata": {},
"outputs": [],
@@ -61,18 +61,10 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"id": "351b3cad",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "cannot find .env file\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%load_ext dotenv\n",
"%dotenv"
@@ -316,9 +308,12 @@
"\n",
"all_summary_df = pd.concat(all_summaries, ignore_index=True)\n",
"print_df(\n",
- " all_summary_df[\n",
- " [\"question_set\", \"criteria\", \"reference\", \"generated\", \"mean\", \"std\"]\n",
- " ].reset_index(drop=True),\n",
+ " cast(\n",
+ " pd.DataFrame,\n",
+ " all_summary_df[\n",
+ " [\"question_set\", \"criteria\", \"reference\", \"generated\", \"mean\", \"std\"]\n",
+ " ].reset_index(drop=True),\n",
+ " ),\n",
" \"Reference Scores Summary\",\n",
")\n",
"all_summary_df.to_csv(output_dir / \"reference_scores_summary.csv\", index=False)"
@@ -340,7 +335,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"id": "225c147f",
"metadata": {},
"outputs": [],
@@ -383,485 +378,7 @@
"execution_count": null,
"id": "207cbce9",
"metadata": {},
- "outputs": [
- {
- "data": {
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- "
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- ],
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- "output_type": "display_data"
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- "
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- " Overall Assertion Scores Summary by Question Set and RAG Method \n",
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- "┃ question_… ┃ rag_method ┃ total_ass… ┃ successfu… ┃ failed_a… ┃ overall_a… ┃ avg_ques… ┃ total_que… ┃ top_k_us… ┃\n",
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- "│ data_link │ vector_ra… │ 134 │ 121 │ 13 │ 0.9029850… │ 0.922333… │ 50 │ all │\n",
- "│ data_link │ vector_ra… │ 134 │ 112 │ 22 │ 0.8358208… │ 0.854333… │ 50 │ all │\n",
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- "│ data_local │ vector_ra… │ 67 │ 65 │ 2 │ 0.9701492… │ 0.973333… │ 50 │ all │\n",
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"source": [
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{
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"id": "68190555",
"metadata": {},
"outputs": [],
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- "text/plain": [
- " Global assertions: \u001b[1;36m401\u001b[0m/\u001b[1;36m856\u001b[0m passed \u001b[1m(\u001b[0m\u001b[1;36m46.8\u001b[0m%\u001b[1m)\u001b[0m\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- " Global pass rate (per-question avg): 47.8%\n",
- "
\n"
- ],
- "text/plain": [
- " Global pass rate \u001b[1m(\u001b[0mper-question avg\u001b[1m)\u001b[0m: \u001b[1;36m47.8\u001b[0m%\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "\n",
- "Step 2: Supporting + discovery for 401 passed assertions...\n",
- "
\n"
- ],
- "text/plain": [
- "\n",
- "\u001b[1mStep \u001b[0m\u001b[1;36m2\u001b[0m\u001b[1m: Supporting + discovery for \u001b[0m\u001b[1;36m401\u001b[0m\u001b[1m passed assertions\u001b[0m\u001b[1;33m...\u001b[0m\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "16d7ee89404a4bf5b739f95a0ac6a1af",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Output()"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"# Run hierarchical assertion evaluation for all RAG methods\n",
"# This handles scoring, aggregation, comparison, and significance tests\n",
@@ -1191,17 +537,7 @@
"execution_count": null,
"id": "081adf2e",
"metadata": {},
- "outputs": [
- {
- "ename": "",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31mnotebook controller is DISPOSED. \n",
- "\u001b[1;31mView Jupyter log for further details."
- ]
- }
- ],
+ "outputs": [],
"source": [
"# The pipeline automatically prints and saves a significance summary table.\n",
"# You can also load it from CSV for further analysis:\n",
@@ -1214,17 +550,7 @@
"execution_count": null,
"id": "b064b8a4",
"metadata": {},
- "outputs": [
- {
- "ename": "",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31mnotebook controller is DISPOSED. \n",
- "\u001b[1;31mView Jupyter log for further details."
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Alternatively, you can run significance tests separately and build the summary\n",
"# yourself using summarize_significance_results(). This is useful when you have\n",
diff --git a/docs/notebooks/autoq.ipynb b/docs/notebooks/autoq.ipynb
index 84b7170..36042b7 100644
--- a/docs/notebooks/autoq.ipynb
+++ b/docs/notebooks/autoq.ipynb
@@ -32,6 +32,7 @@
"metadata": {},
"outputs": [],
"source": [
+ "import asyncio\n",
"import json\n",
"import logging\n",
"import os\n",
@@ -233,20 +234,22 @@
"source": [
"from benchmark_qed.autod.sampler.sample_gen import acreate_clustered_sample\n",
"\n",
- "clustered_sample = await acreate_clustered_sample(\n",
- " input_path=INPUT_DATA_PATH,\n",
- " output_path=OUTPUT_DATA_PATH,\n",
- " text_embedder=text_embedder,\n",
- " num_clusters=NUM_CLUSTERS,\n",
- " num_samples_per_cluster=NUM_SAMPLES_PER_CLUSTER,\n",
- " input_type=\"json\",\n",
- " text_tag=TEXT_COLUMN,\n",
- " metadata_tags=METADATA_COLUMNS,\n",
- " chunk_size=CHUNK_SIZE,\n",
- " chunk_overlap=CHUNK_OVERLAP,\n",
- " file_encoding=FILE_ENCODING,\n",
- " token_encoding=ENCODING_MODEL,\n",
- " random_seed=RANDOM_SEED,\n",
+ "clustered_sample = asyncio.run(\n",
+ " acreate_clustered_sample(\n",
+ " input_path=INPUT_DATA_PATH,\n",
+ " output_path=OUTPUT_DATA_PATH,\n",
+ " text_embedder=text_embedder,\n",
+ " num_clusters=NUM_CLUSTERS,\n",
+ " num_samples_per_cluster=NUM_SAMPLES_PER_CLUSTER,\n",
+ " input_type=\"json\",\n",
+ " text_tag=TEXT_COLUMN,\n",
+ " metadata_tags=METADATA_COLUMNS,\n",
+ " chunk_size=CHUNK_SIZE,\n",
+ " chunk_overlap=CHUNK_OVERLAP,\n",
+ " file_encoding=FILE_ENCODING,\n",
+ " token_encoding=ENCODING_MODEL,\n",
+ " random_seed=RANDOM_SEED,\n",
+ " )\n",
")\n",
"print(\n",
" f\"Sampled {len(clustered_sample.sample_texts)} samples from {len(clustered_sample.text_units)} text units in {len(clustered_sample.documents)} documents.\"\n",
@@ -268,6 +271,8 @@
"metadata": {},
"outputs": [],
"source": [
+ "from graphrag_storage.file_storage import FileStorage\n",
+ "\n",
"from benchmark_qed.autoq.config import (\n",
" AssertionConfig,\n",
" GlobalAssertionConfig,\n",
@@ -332,27 +337,36 @@
" assertion_config=assertion_config,\n",
")\n",
"\n",
- "data_local_question_results = await data_local_generator.agenerate(\n",
- " num_questions=NUM_QUESTIONS,\n",
- " oversample_factor=OVERSAMPLE_FACTOR,\n",
+ "data_local_question_results = asyncio.run(\n",
+ " data_local_generator.agenerate(\n",
+ " num_questions=NUM_QUESTIONS,\n",
+ " oversample_factor=OVERSAMPLE_FACTOR,\n",
+ " )\n",
")\n",
+ "data_local_storage = FileStorage(f\"{OUTPUT_QUESTIONS_PATH}/data_local_questions/\")\n",
"\n",
"# save both candidate questions and the final selected questions\n",
- "save_questions(\n",
- " data_local_question_results.selected_questions,\n",
- " f\"{OUTPUT_QUESTIONS_PATH}/data_local_questions/\",\n",
- " \"selected_questions\",\n",
- ")\n",
- "save_questions(\n",
- " data_local_question_results.selected_questions,\n",
- " f\"{OUTPUT_QUESTIONS_PATH}/data_local_questions/\",\n",
- " \"selected_questions_text\",\n",
- " question_text_only=True,\n",
- ")\n",
- "save_questions(\n",
- " data_local_question_results.candidate_questions,\n",
- " f\"{OUTPUT_QUESTIONS_PATH}/data_local_questions/\",\n",
- " \"candidate_questions\",\n",
+ "asyncio.run(\n",
+ " save_questions(\n",
+ " data_local_question_results.selected_questions,\n",
+ " data_local_storage,\n",
+ " \"selected_questions\",\n",
+ " )\n",
+ ")\n",
+ "asyncio.run(\n",
+ " save_questions(\n",
+ " data_local_question_results.selected_questions,\n",
+ " data_local_storage,\n",
+ " \"selected_questions_text\",\n",
+ " question_text_only=True,\n",
+ " )\n",
+ ")\n",
+ "asyncio.run(\n",
+ " save_questions(\n",
+ " data_local_question_results.candidate_questions,\n",
+ " data_local_storage,\n",
+ " \"candidate_questions\",\n",
+ " )\n",
")"
]
},
@@ -379,8 +393,11 @@
"# Please note that we load all the candidate local questions (not just the selected ones) as that gives us a bigger pool of local questions to aggregate from.\n",
"# If you have previously run the data local question generation step, you can load the candidate questions from disk instead of re-running the data local question generation step as the below example.\n",
"# Otherwise, you can use data_local_question_results.candidate_questions directly\n",
- "local_questions = load_questions(\n",
- " f\"{OUTPUT_QUESTIONS_PATH}/data_local_questions/candidate_questions.json\"\n",
+ "local_questions = asyncio.run(\n",
+ " load_questions(\n",
+ " data_local_storage,\n",
+ " \"candidate_questions.json\",\n",
+ " )\n",
")\n",
"print(f\"Loaded {len(local_questions)} candidate local questions.\")\n",
"\n",
@@ -395,27 +412,36 @@
" assertion_config=assertion_config,\n",
")\n",
"\n",
- "data_global_question_results = await data_global_generator.agenerate(\n",
- " num_questions=NUM_QUESTIONS,\n",
- " oversample_factor=OVERSAMPLE_FACTOR,\n",
+ "data_global_question_results = asyncio.run(\n",
+ " data_global_generator.agenerate(\n",
+ " num_questions=NUM_QUESTIONS,\n",
+ " oversample_factor=OVERSAMPLE_FACTOR,\n",
+ " )\n",
")\n",
+ "data_global_storage = FileStorage(f\"{OUTPUT_QUESTIONS_PATH}/data_global_questions/\")\n",
"\n",
"# save both candidate questions and the final selected questions\n",
- "save_questions(\n",
- " data_global_question_results.selected_questions,\n",
- " f\"{OUTPUT_QUESTIONS_PATH}/data_global_questions/\",\n",
- " \"selected_questions\",\n",
- ")\n",
- "save_questions(\n",
- " data_global_question_results.selected_questions,\n",
- " f\"{OUTPUT_QUESTIONS_PATH}/data_global_questions/\",\n",
- " \"selected_questions_text\",\n",
- " question_text_only=True,\n",
- ")\n",
- "save_questions(\n",
- " data_global_question_results.candidate_questions,\n",
- " f\"{OUTPUT_QUESTIONS_PATH}/data_global_questions/\",\n",
- " \"candidate_questions\",\n",
+ "asyncio.run(\n",
+ " save_questions(\n",
+ " data_global_question_results.selected_questions,\n",
+ " data_global_storage,\n",
+ " \"selected_questions\",\n",
+ " )\n",
+ ")\n",
+ "asyncio.run(\n",
+ " save_questions(\n",
+ " data_global_question_results.selected_questions,\n",
+ " data_global_storage,\n",
+ " \"selected_questions_text\",\n",
+ " question_text_only=True,\n",
+ " )\n",
+ ")\n",
+ "asyncio.run(\n",
+ " save_questions(\n",
+ " data_global_question_results.candidate_questions,\n",
+ " data_global_storage,\n",
+ " \"candidate_questions\",\n",
+ " )\n",
")"
]
},
@@ -498,8 +524,8 @@
"# Load candidate local questions (result from the data local question generation step)\n",
"# Linked questions are generated from local questions that share named entities.\n",
"# We use candidate local questions to have a larger pool for entity linking.\n",
- "local_questions = load_questions(\n",
- " f\"{OUTPUT_QUESTIONS_PATH}/data_local_questions/candidate_questions.json\"\n",
+ "local_questions = asyncio.run(\n",
+ " load_questions(data_local_storage, \"candidate_questions.json\")\n",
")\n",
"print(\n",
" f\"Loaded {len(local_questions)} candidate local questions for linked question generation.\"\n",
@@ -526,27 +552,36 @@
")\n",
"\n",
"# Generate linked questions (using higher oversample factor due to validation filtering)\n",
- "data_linked_question_results = await data_linked_generator.agenerate(\n",
- " num_questions=NUM_QUESTIONS,\n",
- " oversample_factor=LINKED_OVERSAMPLE_FACTOR,\n",
+ "data_linked_question_results = asyncio.run(\n",
+ " data_linked_generator.agenerate(\n",
+ " num_questions=NUM_QUESTIONS,\n",
+ " oversample_factor=LINKED_OVERSAMPLE_FACTOR,\n",
+ " )\n",
")\n",
+ "data_linked_storage = FileStorage(f\"{OUTPUT_QUESTIONS_PATH}/data_linked_questions/\")\n",
"\n",
"# Save both candidate questions and the final selected questions\n",
- "save_questions(\n",
- " data_linked_question_results.selected_questions,\n",
- " f\"{OUTPUT_QUESTIONS_PATH}/data_linked_questions/\",\n",
- " \"selected_questions\",\n",
+ "asyncio.run(\n",
+ " save_questions(\n",
+ " data_linked_question_results.selected_questions,\n",
+ " data_linked_storage,\n",
+ " \"selected_questions\",\n",
+ " )\n",
")\n",
- "save_questions(\n",
- " data_linked_question_results.selected_questions,\n",
- " f\"{OUTPUT_QUESTIONS_PATH}/data_linked_questions/\",\n",
- " \"selected_questions_text\",\n",
- " question_text_only=True,\n",
+ "asyncio.run(\n",
+ " save_questions(\n",
+ " data_linked_question_results.selected_questions,\n",
+ " data_linked_storage,\n",
+ " \"selected_questions_text\",\n",
+ " question_text_only=True,\n",
+ " )\n",
")\n",
- "save_questions(\n",
- " data_linked_question_results.candidate_questions,\n",
- " f\"{OUTPUT_QUESTIONS_PATH}/data_linked_questions/\",\n",
- " \"candidate_questions\",\n",
+ "asyncio.run(\n",
+ " save_questions(\n",
+ " data_linked_question_results.candidate_questions,\n",
+ " data_linked_storage,\n",
+ " \"candidate_questions\",\n",
+ " )\n",
")\n",
"\n",
"print(\n",
@@ -601,15 +636,17 @@
" concurrent_coroutines=CONCURRENT_REQUESTS,\n",
")\n",
"\n",
- "activity_context = await activity_generator.agenerate(\n",
- " num_personas=NUM_PERSONAS,\n",
- " num_tasks=NUM_TASKS_PER_PERSONA,\n",
- " num_entities_per_task=NUM_ENTITIES_PER_TASK,\n",
- " oversample_factor=OVERSAMPLE_FACTOR,\n",
- " use_representative_samples_only=True, # if True, we will only use a subset of representative samples from the clustered texts to generate activity context (for efficiency). If False, we will use all the samples in the clustered texts.\n",
+ "activity_context = asyncio.run(\n",
+ " activity_generator.agenerate(\n",
+ " num_personas=NUM_PERSONAS,\n",
+ " num_tasks=NUM_TASKS_PER_PERSONA,\n",
+ " num_entities_per_task=NUM_ENTITIES_PER_TASK,\n",
+ " oversample_factor=OVERSAMPLE_FACTOR,\n",
+ " use_representative_samples_only=True, # if True, we will only use a subset of representative samples from the clustered texts to generate activity context (for efficiency). If False, we will use all the samples in the clustered texts.\n",
+ " )\n",
")\n",
- "\n",
- "save_activity_context(activity_context, f\"{OUTPUT_QUESTIONS_PATH}/context/\")"
+ "activity_storage = FileStorage(f\"{OUTPUT_QUESTIONS_PATH}/context/\")\n",
+ "asyncio.run(save_activity_context(activity_context, activity_storage))"
]
},
{
@@ -652,27 +689,38 @@
" random_seed=RANDOM_SEED,\n",
")\n",
"\n",
- "activity_local_question_results = await activity_local_generator.agenerate(\n",
- " num_questions=NUM_QUESTIONS,\n",
- " oversample_factor=OVERSAMPLE_FACTOR,\n",
+ "activity_local_question_results = asyncio.run(\n",
+ " activity_local_generator.agenerate(\n",
+ " num_questions=NUM_QUESTIONS,\n",
+ " oversample_factor=OVERSAMPLE_FACTOR,\n",
+ " )\n",
+ ")\n",
+ "activity_local_storage = FileStorage(\n",
+ " f\"{OUTPUT_QUESTIONS_PATH}/activity_local_questions/\"\n",
")\n",
"\n",
"# save both candidate questions and the final selected questions\n",
- "save_questions(\n",
- " activity_local_question_results.selected_questions,\n",
- " f\"{OUTPUT_QUESTIONS_PATH}/activity_local_questions/\",\n",
- " \"selected_questions\",\n",
- ")\n",
- "save_questions(\n",
- " activity_local_question_results.selected_questions,\n",
- " f\"{OUTPUT_QUESTIONS_PATH}/activity_local_questions/\",\n",
- " \"selected_questions_text\",\n",
- " question_text_only=True,\n",
- ")\n",
- "save_questions(\n",
- " activity_local_question_results.candidate_questions,\n",
- " f\"{OUTPUT_QUESTIONS_PATH}/activity_local_questions/\",\n",
- " \"candidate_questions\",\n",
+ "asyncio.run(\n",
+ " save_questions(\n",
+ " activity_local_question_results.selected_questions,\n",
+ " activity_local_storage,\n",
+ " \"selected_questions\",\n",
+ " )\n",
+ ")\n",
+ "asyncio.run(\n",
+ " save_questions(\n",
+ " activity_local_question_results.selected_questions,\n",
+ " activity_local_storage,\n",
+ " \"selected_questions_text\",\n",
+ " question_text_only=True,\n",
+ " )\n",
+ ")\n",
+ "asyncio.run(\n",
+ " save_questions(\n",
+ " activity_local_question_results.candidate_questions,\n",
+ " activity_local_storage,\n",
+ " \"candidate_questions\",\n",
+ " )\n",
")"
]
},
@@ -713,34 +761,45 @@
" random_seed=RANDOM_SEED,\n",
")\n",
"\n",
- "activity_global_question_results = await activity_global_generator.agenerate(\n",
- " num_questions=NUM_QUESTIONS,\n",
- " oversample_factor=OVERSAMPLE_FACTOR,\n",
+ "activity_global_question_results = asyncio.run(\n",
+ " activity_global_generator.agenerate(\n",
+ " num_questions=NUM_QUESTIONS,\n",
+ " oversample_factor=OVERSAMPLE_FACTOR,\n",
+ " )\n",
+ ")\n",
+ "activity_global_storage = FileStorage(\n",
+ " f\"{OUTPUT_QUESTIONS_PATH}/activity_global_questions/\"\n",
")\n",
"\n",
"# save both candidate questions and the final selected questions\n",
- "save_questions(\n",
- " activity_global_question_results.selected_questions,\n",
- " f\"{OUTPUT_QUESTIONS_PATH}/activity_global_questions/\",\n",
- " \"selected_questions\",\n",
- ")\n",
- "save_questions(\n",
- " activity_global_question_results.selected_questions,\n",
- " f\"{OUTPUT_QUESTIONS_PATH}/activity_global_questions/\",\n",
- " \"selected_questions_text\",\n",
- " question_text_only=True,\n",
- ")\n",
- "save_questions(\n",
- " activity_global_question_results.candidate_questions,\n",
- " f\"{OUTPUT_QUESTIONS_PATH}/activity_global_questions/\",\n",
- " \"candidate_questions\",\n",
+ "asyncio.run(\n",
+ " save_questions(\n",
+ " activity_global_question_results.selected_questions,\n",
+ " activity_global_storage,\n",
+ " \"selected_questions\",\n",
+ " )\n",
+ ")\n",
+ "asyncio.run(\n",
+ " save_questions(\n",
+ " activity_global_question_results.selected_questions,\n",
+ " activity_global_storage,\n",
+ " \"selected_questions_text\",\n",
+ " question_text_only=True,\n",
+ " )\n",
+ ")\n",
+ "asyncio.run(\n",
+ " save_questions(\n",
+ " activity_global_question_results.candidate_questions,\n",
+ " activity_global_storage,\n",
+ " \"candidate_questions\",\n",
+ " )\n",
")"
]
}
],
"metadata": {
"kernelspec": {
- "display_name": "benchmark-qed",
+ "display_name": "benchmark-qed (3.12.12)",
"language": "python",
"name": "python3"
},
@@ -754,7 +813,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.6"
+ "version": "3.12.12"
}
},
"nbformat": 4,
diff --git a/docs/notebooks/retrieval_metrics.ipynb b/docs/notebooks/retrieval_metrics.ipynb
index c01518a..e0fd10b 100644
--- a/docs/notebooks/retrieval_metrics.ipynb
+++ b/docs/notebooks/retrieval_metrics.ipynb
@@ -44,10 +44,12 @@
"metadata": {},
"outputs": [],
"source": [
+ "import asyncio\n",
"import json\n",
"import logging\n",
"import os\n",
"from pathlib import Path\n",
+ "from typing import Any\n",
"\n",
"import pandas as pd\n",
"import tiktoken\n",
@@ -234,6 +236,8 @@
"metadata": {},
"outputs": [],
"source": [
+ "from benchmark_qed.autoe.retrieval import save_cluster_references_to_json\n",
+ "\n",
"# Load text units from parquet file\n",
"text_df = pd.read_parquet(REFERENCE_DATA_PATH / \"text_units.parquet\")\n",
"if \"short_id\" not in text_df.columns:\n",
@@ -245,9 +249,11 @@
"# embed text units if needed\n",
"# skip this if you already have embeddings in your corpus\n",
"print(f\"Embedding {len(corpus)} text units\")\n",
- "corpus = await text_embedder.embed_batch(\n",
- " text_units=corpus,\n",
- " batch_size=32,\n",
+ "corpus = asyncio.run(\n",
+ " text_embedder.embed_batch(\n",
+ " text_units=corpus,\n",
+ " batch_size=32,\n",
+ " )\n",
")\n",
"print(f\"Embedded {len(corpus)} text units\")\n",
"\n",
@@ -289,7 +295,7 @@
" print(f\"Loaded {len(questions)} questions\")\n",
"\n",
" # Generate cluster references using batch assessment\n",
- " batch_results = await cluster_rater.assess_batch(questions)\n",
+ " batch_results = asyncio.run(cluster_rater.assess_batch(questions))\n",
"\n",
" print(f\"Generated cluster references for {len(batch_results)} questions\")\n",
"\n",
@@ -300,7 +306,7 @@
" / f\"clusters_{NUM_CLUSTERS}\"\n",
" / \"reference.json\"\n",
" )\n",
- " save_cluster_references_to_json( # noqa: F821\n",
+ " save_cluster_references_to_json(\n",
" batch_results,\n",
" output_path,\n",
" include_clusters=True,\n",
@@ -415,8 +421,10 @@
" if NUM_QUESTIONS is not None:\n",
" retrieval_results = retrieval_results[:NUM_QUESTIONS]\n",
"\n",
- " relevance_results = await assess_batch_relevance(\n",
- " retrieval_results=retrieval_results, relevance_rater=relevance_rater\n",
+ " relevance_results = asyncio.run(\n",
+ " assess_batch_relevance(\n",
+ " retrieval_results=retrieval_results, relevance_rater=relevance_rater\n",
+ " )\n",
" )\n",
"\n",
" # save relevance results to json file\n",
@@ -622,7 +630,7 @@
"\n",
"for rag_method in RAG_METHODS:\n",
" for question_set in QUESTION_SETS:\n",
- " row_data = {\n",
+ " row_data: dict[str, Any] = {\n",
" \"Question_Set\": question_set,\n",
" \"RAG_Method\": rag_method,\n",
" }\n",
@@ -711,4 +719,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/pyproject.toml b/pyproject.toml
index 1383218..7cd0033 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -43,7 +43,7 @@ dev = [
"ipywidgets>=8.1.5",
"mkdocs-material>=9.6.10",
"poethepoet>=0.32.1",
- "pyright>=1.1.392.post0",
+ "pyright>=1.1.409",
"nest-asyncio>=1.6.0",
"py7zr>=0.22.0",
"pytest>=8.3.5",
@@ -57,6 +57,7 @@ dev = [
"mkdocs-exclude-search>=0.6.6",
"mkdocs-typer2>=0.1.4",
"semversioner>=2.0.6",
+ "nbqa>=1.9.1",
]
[build-system]
@@ -81,10 +82,20 @@ _ruff_check = 'ruff check . --preview'
_format_code = "ruff format . --preview"
_format_check = 'ruff format . --check --preview'
_check_typos = 'typos .'
-
+_typecheck_notebooks = 'nbqa pyright docs/notebooks'
typecheck = "pyright"
+check_notebooks = [
+ '_typecheck_notebooks',
+]
check_package_types = "pyright --verifytypes benchmark_qed --ignoreexternal"
-check = ['_check_typos', '_format_check', '_ruff_check', 'typecheck', 'check_package_types', ]
+check = [
+ '_check_typos',
+ '_format_check',
+ '_ruff_check',
+ 'typecheck',
+ 'check_notebooks',
+ 'check_package_types',
+]
fix = "ruff check --fix ."
fix_unsafe = "ruff check --preview --fix --unsafe-fixes ."
format = ['_sort_imports', '_format_code']
@@ -127,7 +138,7 @@ sequence = [
# https://github.com/microsoft/pyright/blob/9f81564a4685ff5c55edd3959f9b39030f590b2f/docs/configuration.md#sample-pyprojecttoml-file
[tool.pyright]
-include = ["benchmark_qed", "tests"]
+include = ["benchmark_qed", "tests", "docs/notebooks"]
exclude = ["**/__pycache__"]
[tool.pytest.ini_options]
diff --git a/uv.lock b/uv.lock
index 1ad5735..7aae456 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
version = 1
-revision = 2
+revision = 3
requires-python = ">=3.11"
resolution-markers = [
"python_full_version >= '3.13' and sys_platform == 'win32'",
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{ name = "yarl" },
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