198044d033
* Add ty type checker to CI and fix type errors - Add ty (Astral's fast Python type checker) to GitHub CI workflow - Fix type annotations across all RAG apps: - Update load_data return types from list[str] to list[dict[str, Any]] - Fix base_rag_example.py to properly handle dict format from create_text_chunks - Fix type errors in leann-core: - chunking_utils.py: Add explicit type annotations - cli.py: Fix return type annotations for PDF extraction functions - interactive_utils.py: Fix readline import type handling - Fix type errors in apps: - wechat_history.py: Fix return type annotations - document_rag.py, code_rag.py: Replace **kwargs with explicit arguments - Add ty configuration to pyproject.toml This resolves the bug introduced in PR #157 where create_text_chunks() changed to return list[dict] but callers were not updated. * Fix remaining ty type errors - Fix slack_mcp_reader.py channel parameter can be None - Fix embedding_compute.py ContextProp type issue - Fix searcher_base.py method override signatures - Fix chunking_utils.py chunk_text assignment - Fix slack_rag.py and twitter_rag.py return types - Fix email.py and image_rag.py method overrides * Fix multimodal benchmark scripts type errors - Fix undefined LeannRetriever -> LeannMultiVector - Add proper type casts for HuggingFace Dataset iteration - Cast task config values to correct types - Add type annotations for dataset row dicts * Enable ty check for multimodal scripts in CI All type errors in multimodal scripts have been fixed, so we can now include them in the CI type checking. * Fix all test type errors and enable ty check on tests - Fix test_basic.py: search() takes str not list - Fix test_cli_prompt_template.py: add type: ignore for Mock assignments - Fix test_prompt_template_persistence.py: match BaseSearcher.search signature - Fix test_prompt_template_e2e.py: add type narrowing asserts after skip - Fix test_readme_examples.py: use explicit kwargs instead of **model_args - Fix metadata_filter.py: allow Optional[MetadataFilters] - Update CI to run ty check on tests * Format code with ruff * Format searcher_base.py
444 lines
15 KiB
Python
444 lines
15 KiB
Python
#!/usr/bin/env python3
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"""
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Modular script to reproduce NDCG results for ViDoRe v2 benchmark.
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This script uses the interface from leann_multi_vector.py to:
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1. Download ViDoRe v2 datasets
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2. Build indexes (LEANN or Fast-Plaid)
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3. Perform retrieval
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4. Evaluate using NDCG metrics
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Usage:
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# Evaluate all ViDoRe v2 tasks
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python vidore_v2_benchmark.py --model colqwen2 --tasks all
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# Evaluate specific task
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python vidore_v2_benchmark.py --model colqwen2 --task Vidore2ESGReportsRetrieval
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# Use Fast-Plaid index
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python vidore_v2_benchmark.py --model colqwen2 --use-fast-plaid --fast-plaid-index-path ./indexes/vidore_fastplaid
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# Rebuild index
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python vidore_v2_benchmark.py --model colqwen2 --rebuild-index
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"""
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import argparse
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import json
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import os
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from typing import Any, Optional, cast
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from datasets import Dataset, load_dataset
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from leann_multi_vector import (
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ViDoReBenchmarkEvaluator,
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_ensure_repo_paths_importable,
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)
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_ensure_repo_paths_importable(__file__)
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# Language name to dataset language field value mapping
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# Dataset uses ISO 639-3 + ISO 15924 format (e.g., "eng-Latn")
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LANGUAGE_MAPPING = {
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"english": "eng-Latn",
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"french": "fra-Latn",
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"spanish": "spa-Latn",
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"german": "deu-Latn",
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}
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# ViDoRe v2 task configurations
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# Prompts match MTEB task metadata prompts
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VIDORE_V2_TASKS = {
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"Vidore2ESGReportsRetrieval": {
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"dataset_path": "vidore/esg_reports_v2",
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"revision": "0542c0d03da0ec1c8cbc517c8d78e7e95c75d3d3",
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"languages": ["french", "spanish", "english", "german"],
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"prompt": {"query": "Find a screenshot that relevant to the user's question."},
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},
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"Vidore2EconomicsReportsRetrieval": {
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"dataset_path": "vidore/economics_reports_v2",
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"revision": "b3e3a04b07fbbaffe79be49dabf92f691fbca252",
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"languages": ["french", "spanish", "english", "german"],
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"prompt": {"query": "Find a screenshot that relevant to the user's question."},
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},
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"Vidore2BioMedicalLecturesRetrieval": {
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"dataset_path": "vidore/biomedical_lectures_v2",
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"revision": "a29202f0da409034d651614d87cd8938d254e2ea",
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"languages": ["french", "spanish", "english", "german"],
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"prompt": {"query": "Find a screenshot that relevant to the user's question."},
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},
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"Vidore2ESGReportsHLRetrieval": {
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"dataset_path": "vidore/esg_reports_human_labeled_v2",
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"revision": "6d467dedb09a75144ede1421747e47cf036857dd",
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# Note: This dataset doesn't have language filtering - all queries are English
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"languages": None, # No language filtering needed
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"prompt": {"query": "Find a screenshot that relevant to the user's question."},
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},
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}
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def load_vidore_v2_data(
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dataset_path: str,
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revision: Optional[str] = None,
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split: str = "test",
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language: Optional[str] = None,
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):
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"""
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Load ViDoRe v2 dataset.
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Returns:
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corpus: dict mapping corpus_id to PIL Image
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queries: dict mapping query_id to query text
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qrels: dict mapping query_id to dict of {corpus_id: relevance_score}
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"""
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print(f"Loading dataset: {dataset_path} (split={split}, language={language})")
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# Load queries - cast to Dataset since we know split returns Dataset not DatasetDict
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query_ds = cast(Dataset, load_dataset(dataset_path, "queries", split=split, revision=revision))
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# Check if dataset has language field before filtering
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has_language_field = len(query_ds) > 0 and "language" in query_ds.column_names
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if language and has_language_field:
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# Map language name to dataset language field value (e.g., "english" -> "eng-Latn")
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dataset_language = LANGUAGE_MAPPING.get(language, language)
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query_ds_filtered = query_ds.filter(lambda x: x.get("language") == dataset_language)
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# Check if filtering resulted in empty dataset
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if len(query_ds_filtered) == 0:
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print(
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f"Warning: No queries found after filtering by language '{language}' (mapped to '{dataset_language}')."
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)
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# Try with original language value (dataset might use simple names like 'english')
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print(f"Trying with original language value '{language}'...")
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query_ds_filtered = query_ds.filter(lambda x: x.get("language") == language)
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if len(query_ds_filtered) == 0:
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# Try to get a sample to see actual language values
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try:
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sample_ds = cast(
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Dataset,
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load_dataset(dataset_path, "queries", split=split, revision=revision),
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)
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if len(sample_ds) > 0 and "language" in sample_ds.column_names:
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sample_langs = set(sample_ds["language"])
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print(f"Available language values in dataset: {sample_langs}")
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except Exception:
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pass
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else:
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print(
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f"Found {len(query_ds_filtered)} queries using original language value '{language}'"
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)
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query_ds = query_ds_filtered
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queries: dict[str, str] = {}
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for row in query_ds:
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row_dict = cast(dict[str, Any], row)
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query_id = f"query-{split}-{row_dict['query-id']}"
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queries[query_id] = row_dict["query"]
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# Load corpus (images) - cast to Dataset
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corpus_ds = cast(Dataset, load_dataset(dataset_path, "corpus", split=split, revision=revision))
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corpus: dict[str, Any] = {}
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for row in corpus_ds:
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row_dict = cast(dict[str, Any], row)
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corpus_id = f"corpus-{split}-{row_dict['corpus-id']}"
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# Extract image from the dataset row
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if "image" in row_dict:
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corpus[corpus_id] = row_dict["image"]
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elif "page_image" in row_dict:
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corpus[corpus_id] = row_dict["page_image"]
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else:
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raise ValueError(
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f"No image field found in corpus. Available fields: {list(row_dict.keys())}"
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)
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# Load qrels (relevance judgments) - cast to Dataset
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qrels_ds = cast(Dataset, load_dataset(dataset_path, "qrels", split=split, revision=revision))
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qrels: dict[str, dict[str, int]] = {}
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for row in qrels_ds:
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row_dict = cast(dict[str, Any], row)
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query_id = f"query-{split}-{row_dict['query-id']}"
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corpus_id = f"corpus-{split}-{row_dict['corpus-id']}"
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if query_id not in qrels:
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qrels[query_id] = {}
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qrels[query_id][corpus_id] = int(row_dict["score"])
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print(
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f"Loaded {len(queries)} queries, {len(corpus)} corpus items, {len(qrels)} query-relevance mappings"
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)
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# Filter qrels to only include queries that exist
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qrels = {qid: rel_docs for qid, rel_docs in qrels.items() if qid in queries}
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# Filter out queries without any relevant documents (matching MTEB behavior)
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# This is important for correct NDCG calculation
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qrels_filtered = {qid: rel_docs for qid, rel_docs in qrels.items() if len(rel_docs) > 0}
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queries_filtered = {
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qid: query_text for qid, query_text in queries.items() if qid in qrels_filtered
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}
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print(
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f"After filtering queries without positives: {len(queries_filtered)} queries, {len(qrels_filtered)} query-relevance mappings"
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)
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return corpus, queries_filtered, qrels_filtered
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def evaluate_task(
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task_name: str,
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model_name: str,
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index_path: str,
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use_fast_plaid: bool = False,
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fast_plaid_index_path: Optional[str] = None,
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language: Optional[str] = None,
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rebuild_index: bool = False,
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top_k: int = 100,
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first_stage_k: int = 500,
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k_values: Optional[list[int]] = None,
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output_dir: Optional[str] = None,
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):
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"""
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Evaluate a single ViDoRe v2 task.
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"""
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print(f"\n{'=' * 80}")
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print(f"Evaluating task: {task_name}")
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print(f"{'=' * 80}")
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# Get task config
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if task_name not in VIDORE_V2_TASKS:
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raise ValueError(f"Unknown task: {task_name}. Available: {list(VIDORE_V2_TASKS.keys())}")
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task_config = VIDORE_V2_TASKS[task_name]
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dataset_path = str(task_config["dataset_path"])
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revision = str(task_config["revision"])
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# Determine language
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if language is None:
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# Use first language if multiple available
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languages = cast(Optional[list[str]], task_config.get("languages"))
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if languages is None:
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# Task doesn't support language filtering (e.g., Vidore2ESGReportsHLRetrieval)
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language = None
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elif len(languages) == 1:
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language = languages[0]
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else:
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language = None
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# Initialize k_values if not provided
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if k_values is None:
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k_values = [1, 3, 5, 10, 100]
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# Load data
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corpus, queries, qrels = load_vidore_v2_data(
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dataset_path=dataset_path,
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revision=revision,
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split="test",
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language=language,
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)
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# Check if we have any queries
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if len(queries) == 0:
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print(
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f"\nWarning: No queries found for task {task_name} with language {language}. Skipping evaluation."
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)
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# Return zero scores
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scores = {}
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for k in k_values:
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scores[f"ndcg_at_{k}"] = 0.0
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scores[f"map_at_{k}"] = 0.0
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scores[f"recall_at_{k}"] = 0.0
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scores[f"precision_at_{k}"] = 0.0
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scores[f"mrr_at_{k}"] = 0.0
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return scores
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# Initialize evaluator
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evaluator = ViDoReBenchmarkEvaluator(
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model_name=model_name,
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use_fast_plaid=use_fast_plaid,
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top_k=top_k,
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first_stage_k=first_stage_k,
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k_values=k_values,
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)
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# Build or load index
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index_path_full = index_path if not use_fast_plaid else fast_plaid_index_path
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if index_path_full is None:
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index_path_full = f"./indexes/{task_name}_{model_name}"
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if use_fast_plaid:
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index_path_full = f"./indexes/{task_name}_{model_name}_fastplaid"
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index_or_retriever, corpus_ids_ordered = evaluator.build_index_from_corpus(
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corpus=corpus,
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index_path=index_path_full,
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rebuild=rebuild_index,
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)
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# Search queries
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task_prompt = cast(Optional[dict[str, str]], task_config.get("prompt"))
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results = evaluator.search_queries(
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queries=queries,
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corpus_ids=corpus_ids_ordered,
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index_or_retriever=index_or_retriever,
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fast_plaid_index_path=fast_plaid_index_path,
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task_prompt=task_prompt,
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)
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# Evaluate
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scores = evaluator.evaluate_results(results, qrels, k_values=k_values)
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# Print results
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print(f"\n{'=' * 80}")
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print(f"Results for {task_name}:")
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print(f"{'=' * 80}")
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for metric, value in scores.items():
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if isinstance(value, (int, float)):
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print(f" {metric}: {value:.5f}")
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# Save results
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if output_dir:
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os.makedirs(output_dir, exist_ok=True)
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results_file = os.path.join(output_dir, f"{task_name}_results.json")
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scores_file = os.path.join(output_dir, f"{task_name}_scores.json")
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with open(results_file, "w") as f:
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json.dump(results, f, indent=2)
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print(f"\nSaved results to: {results_file}")
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with open(scores_file, "w") as f:
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json.dump(scores, f, indent=2)
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print(f"Saved scores to: {scores_file}")
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return scores
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def main():
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parser = argparse.ArgumentParser(
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description="Evaluate ViDoRe v2 benchmark using LEANN/Fast-Plaid indexing"
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)
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parser.add_argument(
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"--model",
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type=str,
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default="colqwen2",
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choices=["colqwen2", "colpali"],
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help="Model to use",
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)
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parser.add_argument(
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"--task",
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type=str,
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default=None,
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help="Specific task to evaluate (or 'all' for all tasks)",
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)
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parser.add_argument(
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"--tasks",
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type=str,
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default="all",
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help="Tasks to evaluate: 'all' or comma-separated list",
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)
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parser.add_argument(
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"--index-path",
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type=str,
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default=None,
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help="Path to LEANN index (auto-generated if not provided)",
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)
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parser.add_argument(
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"--use-fast-plaid",
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action="store_true",
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help="Use Fast-Plaid instead of LEANN",
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)
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parser.add_argument(
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"--fast-plaid-index-path",
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type=str,
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default=None,
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help="Path to Fast-Plaid index (auto-generated if not provided)",
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)
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parser.add_argument(
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"--rebuild-index",
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action="store_true",
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help="Rebuild index even if it exists",
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)
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parser.add_argument(
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"--language",
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type=str,
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default=None,
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help="Language to evaluate (default: first available)",
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)
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parser.add_argument(
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"--top-k",
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type=int,
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default=100,
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help="Top-k results to retrieve",
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)
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parser.add_argument(
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"--first-stage-k",
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type=int,
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default=500,
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help="First stage k for LEANN search",
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)
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parser.add_argument(
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"--k-values",
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type=str,
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default="1,3,5,10,100",
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help="Comma-separated k values for evaluation (e.g., '1,3,5,10,100')",
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)
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parser.add_argument(
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"--output-dir",
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type=str,
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default="./vidore_v2_results",
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help="Output directory for results",
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)
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args = parser.parse_args()
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# Parse k_values
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k_values = [int(k.strip()) for k in args.k_values.split(",")]
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# Determine tasks to evaluate
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if args.task:
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tasks_to_eval = [args.task]
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elif args.tasks.lower() == "all":
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tasks_to_eval = list(VIDORE_V2_TASKS.keys())
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else:
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tasks_to_eval = [t.strip() for t in args.tasks.split(",")]
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print(f"Tasks to evaluate: {tasks_to_eval}")
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# Evaluate each task
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all_scores = {}
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for task_name in tasks_to_eval:
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try:
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scores = evaluate_task(
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task_name=task_name,
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model_name=args.model,
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index_path=args.index_path,
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use_fast_plaid=args.use_fast_plaid,
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fast_plaid_index_path=args.fast_plaid_index_path,
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language=args.language,
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rebuild_index=args.rebuild_index,
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top_k=args.top_k,
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first_stage_k=args.first_stage_k,
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k_values=k_values,
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output_dir=args.output_dir,
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)
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all_scores[task_name] = scores
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except Exception as e:
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print(f"\nError evaluating {task_name}: {e}")
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import traceback
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traceback.print_exc()
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continue
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# Print summary
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if all_scores:
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print(f"\n{'=' * 80}")
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print("SUMMARY")
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print(f"{'=' * 80}")
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for task_name, scores in all_scores.items():
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print(f"\n{task_name}:")
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# Print main metrics
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for metric in ["ndcg_at_5", "ndcg_at_10", "ndcg_at_100", "map_at_10", "recall_at_10"]:
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if metric in scores:
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print(f" {metric}: {scores[metric]:.5f}")
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if __name__ == "__main__":
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main()
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