#
# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import json
import os
from collections import defaultdict
from api.db import LLMType
from api.db.services.llm_service import LLMBundle
from api.db.services.knowledgebase_service import KnowledgebaseService
from api.settings import retrievaler
from api.utils import get_uuid
from rag.nlp import tokenize, search
from rag.utils.es_conn import ELASTICSEARCH
from ranx import evaluate
import pandas as pd
from tqdm import tqdm
class Benchmark:
def __init__(self, kb_id):
e, kb = KnowledgebaseService.get_by_id(kb_id)
self.similarity_threshold = kb.similarity_threshold
self.vector_similarity_weight = kb.vector_similarity_weight
self.embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING, llm_name=kb.embd_id, lang=kb.language)
def _get_benchmarks(self, query, dataset_idxnm, count=16):
req = {"question": query, "size": count, "vector": True, "similarity": self.similarity_threshold}
sres = retrievaler.search(req, search.index_name(dataset_idxnm), self.embd_mdl)
return sres
def _get_retrieval(self, qrels, dataset_idxnm):
run = defaultdict(dict)
query_list = list(qrels.keys())
for query in query_list:
sres = self._get_benchmarks(query, dataset_idxnm)
sim, _, _ = retrievaler.rerank(sres, query, 1 - self.vector_similarity_weight,
self.vector_similarity_weight)
for index, id in enumerate(sres.ids):
run[query][id] = sim[index]
return run
def embedding(self, docs, batch_size=16):
vects = []
cnts = [d["content_with_weight"] for d in docs]
for i in range(0, len(cnts), batch_size):
vts, c = self.embd_mdl.encode(cnts[i: i + batch_size])
vects.extend(vts.tolist())
assert len(docs) == len(vects)
for i, d in enumerate(docs):
v = vects[i]
d["q_%d_vec" % len(v)] = v
return docs
def ms_marco_index(self, file_path, index_name):
qrels = defaultdict(dict)
texts = defaultdict(dict)
docs = []
filelist = os.listdir(file_path)
for dir in filelist:
data = pd.read_parquet(os.path.join(file_path, dir))
for i in tqdm(range(len(data)), colour="green", desc="Indexing:" + dir):
query = data.iloc[i]['query']
for rel, text in zip(data.iloc[i]['passages']['is_selected'], data.iloc[i]['passages']['passage_text']):
d = {
"id": get_uuid()
}
tokenize(d, text, "english")
docs.append(d)
texts[d["id"]] = text
qrels[query][d["id"]] = int(rel)
if len(docs) >= 32:
docs = self.embedding(docs)
ELASTICSEARCH.bulk(docs, search.index_name(index_name))
docs = []
docs = self.embedding(docs)
ELASTICSEARCH.bulk(docs, search.index_name(index_name))
return qrels, texts
def trivia_qa_index(self, file_path, index_name):
qrels = defaultdict(dict)
texts = defaultdict(dict)
docs = []
filelist = os.listdir(file_path)
for dir in filelist:
data = pd.read_parquet(os.path.join(file_path, dir))
for i in tqdm(range(len(data)), colour="green", desc="Indexing:" + dir):
query = data.iloc[i]['question']
for rel, text in zip(data.iloc[i]["search_results"]['rank'],
data.iloc[i]["search_results"]['search_context']):
d = {
"id": get_uuid()
}
tokenize(d, text, "english")
docs.append(d)
texts[d["id"]] = text
qrels[query][d["id"]] = int(rel)
if len(docs) >= 32:
docs = self.embedding(docs)
ELASTICSEARCH.bulk(docs, search.index_name(index_name))
docs = []
docs = self.embedding(docs)
ELASTICSEARCH.bulk(docs, search.index_name(index_name))
return qrels, texts
def miracl_index(self, file_path, corpus_path, index_name):
corpus_total = {}
for corpus_file in os.listdir(corpus_path):
tmp_data = pd.read_json(os.path.join(corpus_path, corpus_file), lines=True)
for index, i in tmp_data.iterrows():
corpus_total[i['docid']] = i['text']
topics_total = {}
for topics_file in os.listdir(os.path.join(file_path, 'topics')):
if 'test' in topics_file:
continue
tmp_data = pd.read_csv(os.path.join(file_path, 'topics', topics_file), sep='\t', names=['qid', 'query'])
for index, i in tmp_data.iterrows():
topics_total[i['qid']] = i['query']
qrels = defaultdict(dict)
texts = defaultdict(dict)
docs = []
for qrels_file in os.listdir(os.path.join(file_path, 'qrels')):
if 'test' in qrels_file:
continue
tmp_data = pd.read_csv(os.path.join(file_path, 'qrels', qrels_file), sep='\t',
names=['qid', 'Q0', 'docid', 'relevance'])
for i in tqdm(range(len(tmp_data)), colour="green", desc="Indexing:" + qrels_file):
query = topics_total[tmp_data.iloc[i]['qid']]
text = corpus_total[tmp_data.iloc[i]['docid']]
rel = tmp_data.iloc[i]['relevance']
d = {
"id": get_uuid()
}
tokenize(d, text, 'english')
docs.append(d)
texts[d["id"]] = text
qrels[query][d["id"]] = int(rel)
if len(docs) >= 32:
docs = self.embedding(docs)
ELASTICSEARCH.bulk(docs, search.index_name(index_name))
docs = []
docs = self.embedding(docs)
ELASTICSEARCH.bulk(docs, search.index_name(index_name))
return qrels, texts
def save_results(self, qrels, run, texts, dataset, file_path):
keep_result = []
run_keys = list(run.keys())
for run_i in tqdm(range(len(run_keys)), desc="Calculating ndcg@10 for single query"):
key = run_keys[run_i]
keep_result.append({'query': key, 'qrel': qrels[key], 'run': run[key],
'ndcg@10': evaluate({key: qrels[key]}, {key: run[key]}, "ndcg@10")})
keep_result = sorted(keep_result, key=lambda kk: kk['ndcg@10'])
with open(os.path.join(file_path, dataset + 'result.md'), 'w', encoding='utf-8') as f:
f.write('## Score For Every Query\n')
for keep_result_i in keep_result:
f.write('### query: ' + keep_result_i['query'] + ' ndcg@10:' + str(keep_result_i['ndcg@10']) + '\n')
scores = [[i[0], i[1]] for i in keep_result_i['run'].items()]
scores = sorted(scores, key=lambda kk: kk[1])
for score in scores[:10]:
f.write('- text: ' + str(texts[score[0]]) + '\t qrel: ' + str(score[1]) + '\n')
print(os.path.join(file_path, dataset + '_result.md'), 'Saved!')
def __call__(self, dataset, file_path, miracl_corpus=''):
if dataset == "ms_marco_v1.1":
qrels, texts = self.ms_marco_index(file_path, "benchmark_ms_marco_v1.1")
run = self._get_retrieval(qrels, "benchmark_ms_marco_v1.1")
print(dataset, evaluate(qrels, run, ["ndcg@10", "map@5", "mrr"]))
self.save_results(qrels, run, texts, dataset, file_path)
if dataset == "trivia_qa":
qrels, texts = self.trivia_qa_index(file_path, "benchmark_trivia_qa")
run = self._get_retrieval(qrels, "benchmark_trivia_qa")
print(dataset, evaluate(qrels, run, ["ndcg@10", "map@5", "mrr"]))
self.save_results(qrels, run, texts, dataset, file_path)
if dataset == "miracl":
for lang in ['ar', 'bn', 'de', 'en', 'es', 'fa', 'fi', 'fr', 'hi', 'id', 'ja', 'ko', 'ru', 'sw', 'te', 'th',
'yo', 'zh']:
if not os.path.isdir(os.path.join(file_path, 'miracl-v1.0-' + lang)):
print('Directory: ' + os.path.join(file_path, 'miracl-v1.0-' + lang) + ' not found!')
continue
if not os.path.isdir(os.path.join(file_path, 'miracl-v1.0-' + lang, 'qrels')):
print('Directory: ' + os.path.join(file_path, 'miracl-v1.0-' + lang, 'qrels') + 'not found!')
continue
if not os.path.isdir(os.path.join(file_path, 'miracl-v1.0-' + lang, 'topics')):
print('Directory: ' + os.path.join(file_path, 'miracl-v1.0-' + lang, 'topics') + 'not found!')
continue
if not os.path.isdir(os.path.join(miracl_corpus, 'miracl-corpus-v1.0-' + lang)):
print('Directory: ' + os.path.join(miracl_corpus, 'miracl-corpus-v1.0-' + lang) + ' not found!')
continue
qrels, texts = self.miracl_index(os.path.join(file_path, 'miracl-v1.0-' + lang),
os.path.join(miracl_corpus, 'miracl-corpus-v1.0-' + lang),
"benchmark_miracl_" + lang)
run = self._get_retrieval(qrels, "benchmark_miracl_" + lang)
print(dataset, evaluate(qrels, run, ["ndcg@10", "map@5", "mrr"]))
self.save_results(qrels, run, texts, dataset, file_path)
if __name__ == '__main__':
print('*****************RAGFlow Benchmark*****************')
kb_id = input('Please input kb_id:\n')
ex = Benchmark(kb_id)
dataset = input(
'RAGFlow Benchmark Support:\n\tms_marco_v1.1:<https://huggingface.co/datasets/microsoft/ms_marco>\n\ttrivia_qa:<https://huggingface.co/datasets/mandarjoshi/trivia_qa>\n\tmiracl:<https://huggingface.co/datasets/miracl/miracl>\nPlease input dataset choice:\n')
if dataset in ['ms_marco_v1.1', 'trivia_qa']:
if dataset == "ms_marco_v1.1":
print("Notice: Please provide the ms_marco_v1.1 dataset only. ms_marco_v2.1 is not supported!")
dataset_path = input('Please input ' + dataset + ' dataset path:\n')
ex(dataset, dataset_path)
elif dataset == 'miracl':
dataset_path = input('Please input ' + dataset + ' dataset path:\n')
corpus_path = input('Please input ' + dataset + '-corpus dataset path:\n')
ex(dataset, dataset_path, miracl_corpus=corpus_path)
else:
print("Dataset: ", dataset, "not supported!")