fix benchmark issue (#3324)

### What problem does this PR solve?



### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

rag/benchmark.py

 from ranx import evaluate
 import pandas as pd
 from tqdm import tqdm
+from ranx import Qrels, Run
 
 
 class Benchmark:
         query_list = list(qrels.keys())
         for query in query_list:
 
-            ranks = retrievaler.retrieval(query, self.embd_mdl, dataset_idxnm.replace("ragflow_", ""),
-                                          [self.kb.id], 0, 30,
+            ranks = retrievaler.retrieval(query, self.embd_mdl,
+                                          dataset_idxnm, [self.kb.id], 1, 30,
                            0.0, self.vector_similarity_weight)
             for c in ranks["chunks"]:
                 if "vector" in c:
                 for rel, text in zip(data.iloc[i]['passages']['is_selected'], data.iloc[i]['passages']['passage_text']):
                     d = {
                         "id": get_uuid(),
-                        "kb_id": self.kb.id
+                        "kb_id": self.kb.id,
+                        "docnm_kwd": "xxxxx",
+                        "doc_id": "ksksks"
                     }
                     tokenize(d, text, "english")
                     docs.append(d)
                 for rel, text in zip(data.iloc[i]["search_results"]['rank'],
                                      data.iloc[i]["search_results"]['search_context']):
                     d = {
-                        "id": get_uuid()
+                        "id": get_uuid(),
+                        "kb_id": self.kb.id,
+                        "docnm_kwd": "xxxxx",
+                        "doc_id": "ksksks"
                     }
                     tokenize(d, text, "english")
                     docs.append(d)
                 text = corpus_total[tmp_data.iloc[i]['docid']]
                 rel = tmp_data.iloc[i]['relevance']
                 d = {
-                    "id": get_uuid()
+                    "id": get_uuid(),
+                    "kb_id": self.kb.id,
+                    "docnm_kwd": "xxxxx",
+                    "doc_id": "ksksks"
                 }
                 tokenize(d, text, 'english')
                 docs.append(d)
         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")})
+                                'ndcg@10': evaluate(Qrels({key: qrels[key]}), Run({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')
         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"]))
+            print(dataset, evaluate(Qrels(qrels), Run(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"]))
+            print(dataset, evaluate((qrels), Run(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',
                                                  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"]))
+                print(dataset, evaluate(Qrels(qrels), Run(run), ["ndcg@10", "map@5", "mrr"]))
                 self.save_results(qrels, run, texts, dataset, file_path)