support get sqlpass api by configuration

optimize drilldown part logic

app_emea_ar.py

@@ -44,6 +44,8 @@ def emea_ar_data_extract():
     output_extract_data_folder = r"./data/emea_ar/output/extract_data/docs/"
     output_mapping_data_folder = r"./data/emea_ar/output/mapping_data/docs/"
     drilldown_folder = r"./data/emea_ar/output/drilldown/"
+    db_mapping_document_folder = r"./data/emea_ar/output/db_mapping/document/"
+    db_mapping_provider_folder = r"./data/emea_ar/output/db_mapping/provider/"
     extract_way = "text"
     
     os.makedirs(pdf_folder, exist_ok=True)
@@ -51,12 +53,16 @@ def emea_ar_data_extract():
     os.makedirs(output_extract_data_folder, exist_ok=True)
     os.makedirs(output_mapping_data_folder, exist_ok=True)
     os.makedirs(drilldown_folder, exist_ok=True)
+    os.makedirs(db_mapping_document_folder, exist_ok=True)
+    os.makedirs(db_mapping_provider_folder, exist_ok=True)
     
     clean_folder(pdf_folder)
     clean_folder(output_pdf_text_folder)
     clean_folder(output_extract_data_folder)
     clean_folder(output_mapping_data_folder)
     clean_folder(drilldown_folder)
+    clean_folder(db_mapping_document_folder)
+    clean_folder(db_mapping_provider_folder)
     
     re_run_extract_data = False
     re_run_mapping_data = False
@@ -69,7 +75,8 @@ def emea_ar_data_extract():
                                           output_extract_data_folder=output_extract_data_folder,
                                           output_mapping_data_folder=output_mapping_data_folder,
                                           extract_way=extract_way,
-                                          drilldown_folder=drilldown_folder)
+                                          drilldown_folder=drilldown_folder,
+                                          compare_with_provider=False)
         doc_data_from_gpt, annotation_list = emea_ar_parsing.extract_data(re_run=re_run_extract_data)
         doc_mapping_data = emea_ar_parsing.mapping_data(
             data_from_gpt=doc_data_from_gpt, re_run=re_run_mapping_data

configuration/aus_prospectus/datapoint_keyword.json

@@ -1,6 +1,6 @@
 {
   "total_annual_dollar_based_charges": {"english": ["total annual dollar based charges", "total annual dollar based charges ($)","total annual dollar"]},
-  "management_fee_and_costs": {"english": ["management fees and cost", "Plus other investment fees and costs"]},
+  "management_fee_and_costs": {"english": ["management fees and cost", "management fees and costs", "Plus other investment fees and costs"]},
   "management_fee": {"english": ["management fee", "management fees","investment management fees","management fees and cost", "investment option management costs", "investment option management costs1", "Plus other investment fees and costs"]},
   "performance_fee": {"english": ["performance fee", "performance fees"]},
   "performance_fee_costs": {"english": ["performance fee costs", "performance fees costs"]},

core/auz_nz/hybrid_solution_script.py

@@ -32,22 +32,24 @@ from openai import AzureOpenAI
 
 ABB_JSON = dict()
 
-def get_abb_json():
+def get_abb_json(doc_source: str = "aus_prospectus"):
     global ABB_JSON
-    with open("abbreviation_records.json", "r") as file:
-        # Load the JSON and convert keys to lowercase
-        ABB_JSON = {key.lower(): value for key, value in json.load(file).items()}
+    if len(ABB_JSON.keys()) == 0:
+        with open(f"./configuration/{doc_source}/abbreviation_records.json", "r") as file:
+            # Load the JSON and convert keys to lowercase
+            ABB_JSON = {key.lower(): value for key, value in json.load(file).items()}
 
-def get_abbre_format_str(fundname):
+def get_abbre_format_str(fundname, doc_source: str = "aus_prospectus"):
     """Replaces abbreviations in a fund name with their expanded forms."""
     # Convert fund name to lowercase while matching
     f_list = fundname.lower().split()
+    get_abb_json(doc_source)
     updated_doc_fname_words = [ABB_JSON.get(word, word).lower() for word in f_list]
     return " ".join(updated_doc_fname_words)
 
-def replace_abbrevs_in_fundnames(fund_names_list):
+def replace_abbrevs_in_fundnames(fund_names_list, doc_source: str = "aus_prospectus"):
     """Replaces abbreviations in a list of fund names."""
-    return [get_abbre_format_str(fund_name) for fund_name in fund_names_list]
+    return [get_abbre_format_str(fund_name, doc_source) for fund_name in fund_names_list]
 
 
 ### STEP 2 - Remove Stopwords
@@ -438,7 +440,7 @@ def format_response(doc_id, pred_fund, db_fund, clean_pred_name, clean_db_name,
     return dt
 
 
-def final_function_to_match(doc_id, pred_list, db_list, provider_name):
+def final_function_to_match(doc_id, pred_list, db_list, provider_name, doc_source: str = "aus_prospectus"):
     final_result = {}
     df_data = []
     unmatched_pred_list = pred_list.copy()
@@ -456,12 +458,16 @@ def final_function_to_match(doc_id, pred_list, db_list, provider_name):
                                             step0_matched_db_name_cosine= all_matched_fund_names_[0], step0_matched_db_name_jacc= all_matched_fund_names_[1], step0_matched_db_name_leven= all_matched_fund_names_[2],
                                             step0_cosine=all_scores_[0], step0_jaccard=all_scores_[1], step0_levenshtein=all_scores_[2], 
                                             llm_flag=False))
-                unmatched_db_list.remove(db_list[matched_index])
-                unmatched_pred_list.remove(pred_list[index])
+                if db_list[matched_index] in unmatched_db_list:
+                    unmatched_db_list.remove(db_list[matched_index])
+                # unmatched_db_list.remove(db_list[matched_index])
+                if pred_list[index] in unmatched_pred_list:
+                    unmatched_pred_list.remove(pred_list[index])
+                # unmatched_pred_list.remove(pred_list[index])
             else:
                 ### STEP-1 Abbreviation replacement
-                cleaned_pred_name1 = replace_abbrevs_in_fundnames([pred_fund])[0]
-                cleaned_db_list1 = replace_abbrevs_in_fundnames(db_list)
+                cleaned_pred_name1 = replace_abbrevs_in_fundnames([pred_fund], doc_source)[0]
+                cleaned_db_list1 = replace_abbrevs_in_fundnames(db_list, doc_source)
                 # print("--> ",cleaned_db_list1, cleaned_pred_name1)
                 step1_result, matched_index, all_scores1_, all_matched_fund_names1_ = get_fund_match_final_score(cleaned_db_list1, cleaned_pred_name1)
                 # print(f"\nStep 1 - Abbreviation Replacement Result: {step1_result}")
@@ -477,8 +483,12 @@ def final_function_to_match(doc_id, pred_list, db_list, provider_name):
                                             step1_pred_name=cleaned_pred_name1, step1_db_name=cleaned_db_list1,
                                             step1_matched_db_name_cosine= all_matched_fund_names1_[0], step1_matched_db_name_jacc= all_matched_fund_names1_[1], step1_matched_db_name_leven= all_matched_fund_names1_[2],
                                             step1_cosine=all_scores1_[0], step1_jaccard=all_scores1_[1], step1_levenshtein=all_scores1_[2], llm_flag=False))
-                    unmatched_db_list.remove(db_list[matched_index])
-                    unmatched_pred_list.remove(pred_list[index])
+                    if db_list[matched_index] in unmatched_db_list:
+                        unmatched_db_list.remove(db_list[matched_index])
+                    # unmatched_db_list.remove(db_list[matched_index])
+                    if pred_list[index] in unmatched_pred_list:
+                        unmatched_pred_list.remove(pred_list[index])
+                    # unmatched_pred_list.remove(pred_list[index])
                 else:
                     ### STEP-2 Remove Stopwords
                     cleaned_pred_name2 = remove_stopwords_nltk([cleaned_pred_name1])[0]
@@ -501,8 +511,12 @@ def final_function_to_match(doc_id, pred_list, db_list, provider_name):
                                             step2_pred_name=cleaned_pred_name2, step2_db_name=cleaned_db_list2,
                                             step2_matched_db_name_cosine= all_matched_fund_names2_[0], step2_matched_db_name_jacc= all_matched_fund_names2_[1], step2_matched_db_name_leven= all_matched_fund_names2_[2],
                                             step2_cosine=all_scores2_[0], step2_jaccard=all_scores2_[1], step2_levenshtein=all_scores2_[2],llm_flag=False))
-                        unmatched_db_list.remove(db_list[matched_index])
-                        unmatched_pred_list.remove(pred_list[index])
+                        if db_list[matched_index] in unmatched_db_list:
+                            unmatched_db_list.remove(db_list[matched_index])
+                        # unmatched_db_list.remove(db_list[matched_index])
+                        if pred_list[index] in unmatched_pred_list:
+                            unmatched_pred_list.remove(pred_list[index])
+                        # unmatched_pred_list.remove(pred_list[index])
                     else:
                         ### STEP-3 Special Character Removal
                         cleaned_pred_name3 = remove_special_characters([cleaned_pred_name2])[0]
@@ -527,8 +541,12 @@ def final_function_to_match(doc_id, pred_list, db_list, provider_name):
                                     step3_pred_name=cleaned_pred_name3, step3_db_name=cleaned_db_list3,
                                     step3_matched_db_name_cosine= all_matched_fund_names3_[0], step3_matched_db_name_jacc= all_matched_fund_names3_[1], step3_matched_db_name_leven= all_matched_fund_names3_[2],
                                     step3_cosine=all_scores3_[0], step3_jaccard=all_scores3_[1], step3_levenshtein=all_scores3_[2],llm_flag=False))
-                            unmatched_db_list.remove(db_list[matched_index])
-                            unmatched_pred_list.remove(pred_list[index])
+                            if db_list[matched_index] in unmatched_db_list:
+                                unmatched_db_list.remove(db_list[matched_index])
+                            # unmatched_db_list.remove(db_list[matched_index])
+                            if pred_list[index] in unmatched_pred_list:
+                                unmatched_pred_list.remove(pred_list[index])
+                            # unmatched_pred_list.remove(pred_list[index])
                         else:
                             ### STEP-4 Common Words Removal
                             cleaned_db_list4, _ = remove_common_words(cleaned_db_list3)
@@ -565,8 +583,12 @@ def final_function_to_match(doc_id, pred_list, db_list, provider_name):
                                 # print("unmatched_pred_list: ",unmatched_pred_list)
                                 # print("db_list[matched_index]: ",db_list[matched_index])
                                 # print("pred_list[index]: ",pred_list[index])
-                                unmatched_db_list.remove(db_list[matched_index])
-                                unmatched_pred_list.remove(pred_list[index])
+                                if db_list[matched_index] in unmatched_db_list:
+                                    unmatched_db_list.remove(db_list[matched_index])
+                                # unmatched_db_list.remove(db_list[matched_index])
+                                if pred_list[index] in unmatched_pred_list:
+                                    unmatched_pred_list.remove(pred_list[index])
+                                # unmatched_pred_list.remove(pred_list[index])
                             else:
                                 df_data.append(format_response(doc_id, pred_list[index], db_list[matched_index], cleaned_pred_name4, 
                                     db_list[matched_index],
@@ -595,11 +617,11 @@ def final_function_to_match(doc_id, pred_list, db_list, provider_name):
     # print("==>>> DB LIST: ",unmatched_db_list)
     # print("==>>> PRED LIST: ",unmatched_pred_list)
     if len(unmatched_pred_list)!=0:
-        cleaned_unmatched_pred_list = replace_abbrevs_in_fundnames(unmatched_pred_list)
+        cleaned_unmatched_pred_list = replace_abbrevs_in_fundnames(unmatched_pred_list, doc_source)
         cleaned_unmatched_pred_list = remove_stopwords_nltk(cleaned_unmatched_pred_list)
         cleaned_unmatched_pred_list = remove_special_characters(cleaned_unmatched_pred_list)
         
-        cleaned_unmatched_db_list = replace_abbrevs_in_fundnames(unmatched_db_list)
+        cleaned_unmatched_db_list = replace_abbrevs_in_fundnames(unmatched_db_list, doc_source)
         cleaned_unmatched_db_list = remove_stopwords_nltk(cleaned_unmatched_db_list)
         cleaned_unmatched_db_list = remove_special_characters(cleaned_unmatched_db_list)
         prompt_context = f"""

core/data_extraction.py

@@ -969,7 +969,9 @@ class DataExtraction:
                 if datapoint_name == "performance_fee":
                     datapoint_name = "performance fees"
                 else:
-                    datapoint_name = datapoint_name.upper()
+                    datapoint_name = self.datapoint_name_config.get(datapoint_name, "")
+                    if len(datapoint_name) == 0:
+                        datapoint_name = datapoint.upper()
                 reported_name = f"The {datapoint_name} reported name could be:\n{joined_reported_name}"
                 
             instructions.append(reported_name)

core/data_mapping.py

@@ -1,6 +1,7 @@
 import os
 import json
 import pandas as pd
+from copy import deepcopy
 from utils.biz_utils import get_most_similar_name, remove_common_word
 from utils.sql_query_util import (
     query_document_fund_mapping,
@@ -18,14 +19,18 @@ class DataMapping:
         raw_document_data_list: list,
         document_mapping_info_df: pd.DataFrame,
         output_data_folder: str,
-        doc_source: str = "emea_ar"
+        doc_source: str = "emea_ar",
+        compare_with_provider: bool = True
     ):
         self.doc_id = doc_id
         self.datapoints = datapoints
         self.doc_source = doc_source
+        self.compare_with_provider = compare_with_provider
         self.raw_document_data_list = raw_document_data_list
         if document_mapping_info_df is None or len(document_mapping_info_df) == 0:
-            self.document_mapping_info_df = query_document_fund_mapping(doc_id, rerun=False)
+            self.document_mapping_info_df = query_document_fund_mapping(
+                doc_id, rerun=False
+            )
         else:
             self.document_mapping_info_df = document_mapping_info_df
 
@@ -44,7 +49,9 @@ class DataMapping:
     def set_mapping_data_by_db(self, document_mapping_info_df: pd.DataFrame):
         logger.info("Setting document mapping data")
         if document_mapping_info_df is None or len(document_mapping_info_df) == 0:
-            self.document_mapping_info_df = query_document_fund_mapping(self.doc_id, rerun=False)
+            self.document_mapping_info_df = query_document_fund_mapping(
+                self.doc_id, rerun=False
+            )
         else:
             self.document_mapping_info_df = document_mapping_info_df
         if len(self.document_mapping_info_df) == 0:
@@ -92,26 +99,27 @@ class DataMapping:
     def get_provider_mapping(self):
         if len(self.document_mapping_info_df) == 0:
             return pd.DataFrame()
-        provider_id_list = (
-            self.document_mapping_info_df["ProviderId"].unique().tolist()
-        )
+        provider_id_list = self.document_mapping_info_df["ProviderId"].unique().tolist()
         provider_mapping_list = []
         for provider_id in provider_id_list:
-            provider_mapping_list.append(query_investment_by_provider(provider_id, rerun=False))
+            provider_mapping_list.append(
+                query_investment_by_provider(provider_id, rerun=False)
+            )
         provider_mapping_df = pd.concat(provider_mapping_list)
         provider_mapping_df = provider_mapping_df.drop_duplicates()
         provider_mapping_df.reset_index(drop=True, inplace=True)
         return provider_mapping_df
 
     def mapping_raw_data_entrance(self):
-        if self.doc_source == "emear_ar":
+        if self.doc_source == "emea_ar":
             return self.mapping_raw_data()
         elif self.doc_source == "aus_prospectus":
-            return self.mapping_raw_data_aus()
+            return self.mapping_raw_data_generic()
         else:
             return self.mapping_raw_data()
+        # return self.mapping_raw_data_generic()
 
-    def mapping_raw_data_aus(self):
+    def mapping_raw_data_generic(self):
         logger.info(f"Mapping raw data for AUS Prospectus document {self.doc_id}")
         mapped_data_list = []
         # Generate raw name based on fund name and share name by integrate_share_name
@@ -128,7 +136,9 @@ class DataMapping:
                 raw_share_name = raw_data.get("share_name", "")
                 raw_data_keys = list(raw_data.keys())
                 if len(raw_share_name) > 0:
-                    integrated_share_name = self.integrate_share_name(raw_fund_name, raw_share_name)
+                    integrated_share_name = self.integrate_share_name(
+                        raw_fund_name, raw_share_name
+                    )
                     if integrated_share_name not in share_raw_name_list:
                         share_raw_name_list.append(integrated_share_name)
                     for datapoint in self.datapoints:
@@ -144,7 +154,7 @@ class DataMapping:
                                 "investment_type": 1,
                                 "investment_id": "",
                                 "investment_name": "",
-                                "similarity": 0
+                                "similarity": 0,
                             }
                             mapped_data_list.append(mapped_data)
                 else:
@@ -162,19 +172,23 @@ class DataMapping:
                                 "value": raw_data[datapoint],
                                 "investment_type": 33,
                                 "investment_id": "",
-                                "investment_name": ""
+                                "investment_name": "",
                             }
                             mapped_data_list.append(mapped_data)
         # Mapping raw data with database
-        iter_count = 30
+        iter_count = 60
         fund_match_result = {}
         if len(fund_raw_name_list) > 0:
-            fund_match_result = self.get_raw_name_db_match_result(fund_raw_name_list, "fund", iter_count)
-            logger.info(f"Fund match result: \n{fund_match_result}")
+            fund_match_result = self.get_raw_name_db_match_result(
+                fund_raw_name_list, "fund", iter_count
+            )
+            # logger.info(f"Fund match result: \n{fund_match_result}")
         share_match_result = {}
         if len(share_raw_name_list) > 0:
-            share_match_result = self.get_raw_name_db_match_result(share_raw_name_list, "share", iter_count)
-            logger.info(f"Share match result: \n{share_match_result}")
+            share_match_result = self.get_raw_name_db_match_result(
+                share_raw_name_list, "share", iter_count
+            )
+            # logger.info(f"Share match result: \n{share_match_result}")
 
         for mapped_data in mapped_data_list:
             investment_type = mapped_data["investment_type"]
@@ -182,9 +196,14 @@ class DataMapping:
             if investment_type == 33:
                 if fund_match_result.get(raw_name) is not None:
                     matched_db_fund_name = fund_match_result[raw_name]
-                    if matched_db_fund_name is not None and len(matched_db_fund_name) > 0:
+                    if (
+                        matched_db_fund_name is not None
+                        and len(matched_db_fund_name) > 0
+                    ):
                         # get FundId from self.doc_fund_mapping
-                        find_fund_df = self.doc_fund_mapping[self.doc_fund_mapping["FundName"] == matched_db_fund_name]
+                        find_fund_df = self.doc_fund_mapping[
+                            self.doc_fund_mapping["FundName"] == matched_db_fund_name
+                        ]
                         if find_fund_df is not None and len(find_fund_df) > 0:
                             fund_id = find_fund_df["FundId"].values[0]
                             mapped_data["investment_id"] = fund_id
@@ -193,9 +212,15 @@ class DataMapping:
             if investment_type == 1:
                 if share_match_result.get(raw_name) is not None:
                     matched_db_share_name = share_match_result[raw_name]
-                    if matched_db_share_name is not None and len(matched_db_share_name) > 0:
+                    if (
+                        matched_db_share_name is not None
+                        and len(matched_db_share_name) > 0
+                    ):
                         # get SecId from self.doc_fund_class_mapping
-                        find_share_df = self.doc_fund_class_mapping[self.doc_fund_class_mapping["ShareClassName"] == matched_db_share_name]
+                        find_share_df = self.doc_fund_class_mapping[
+                            self.doc_fund_class_mapping["ShareClassName"]
+                            == matched_db_share_name
+                        ]
                         if find_share_df is not None and len(find_share_df) > 0:
                             share_id = find_share_df["SecId"].values[0]
                             mapped_data["investment_id"] = share_id
@@ -205,26 +230,64 @@ class DataMapping:
         self.output_mapping_file(mapped_data_list)
         return mapped_data_list
 
-    def get_raw_name_db_match_result(self, raw_name_list, investment_type: str, iter_count: int = 30):
+    def get_raw_name_db_match_result(
+        self, raw_name_list, investment_type: str, iter_count: int = 30
+    ):
         # split raw_name_list into several parts which each part is with 30 elements
         # The reason to split is to avoid invoke token limitation issues from CahtGPT
-        raw_name_list_parts = [raw_name_list[i:i + iter_count] 
-                               for i in range(0, len(raw_name_list), iter_count)]
+        raw_name_list_parts = [
+            raw_name_list[i : i + iter_count]
+            for i in range(0, len(raw_name_list), iter_count)
+        ]
         all_match_result = {}
+        doc_fund_name_list = deepcopy(self.doc_fund_name_list)
+        doc_share_name_list = deepcopy(self.doc_share_name_list)
         for raw_name_list in raw_name_list_parts:
             if investment_type == "fund":
-                match_result = final_function_to_match(doc_id=self.doc_id,
-                                                       pred_list=raw_name_list,
-                                                       db_list=self.doc_fund_name_list,
-                                                       provider_name=self.provider_name)
+                match_result, doc_fund_name_list = self.get_final_function_to_match(
+                    raw_name_list, doc_fund_name_list
+                )
             else:
-                match_result = final_function_to_match(doc_id=self.doc_id,
-                                                       pred_list=raw_name_list,
-                                                       db_list=self.doc_share_name_list,
-                                                       provider_name=self.provider_name)
+                match_result, doc_share_name_list = self.get_final_function_to_match(
+                    raw_name_list, doc_share_name_list
+                )
             all_match_result.update(match_result)
         return all_match_result
 
+    def get_final_function_to_match(self, raw_name_list, db_name_list):
+        if len(db_name_list) == 0:
+            match_result = {}
+            for raw_name in raw_name_list:
+                match_result[raw_name] = ""
+        else:
+            match_result = final_function_to_match(
+                doc_id=self.doc_id,
+                pred_list=raw_name_list,
+                db_list=db_name_list,
+                provider_name=self.provider_name,
+                doc_source=self.doc_source
+            )
+            matched_name_list = list(match_result.values())
+            db_name_list = self.remove_matched_names(db_name_list, matched_name_list)
+        return match_result, db_name_list
+
+    def remove_matched_names(self, target_name_list: list, matched_name_list: list):
+        if len(matched_name_list) == 0:
+            return target_name_list
+        
+        matched_name_list = list(set(matched_name_list))
+        matched_name_list = [
+            value for value in matched_name_list if value is not None and len(value) > 0
+        ]
+        for matched_name in matched_name_list:
+            if (
+                matched_name is not None
+                and len(matched_name) > 0
+                and matched_name in target_name_list
+            ):
+                target_name_list.remove(matched_name)
+        return target_name_list
+
     def mapping_raw_data(self):
         """
         doc_id, page_index, datapoint, value,
@@ -245,9 +308,14 @@ class DataMapping:
                 if raw_fund_name is None or len(raw_fund_name) == 0:
                     continue
                 raw_share_name = raw_data.get("share_name", "")
-                if len(self.doc_fund_name_list) == 0 and len(self.provider_fund_name_list) == 0:
+                if (
+                    len(self.doc_fund_name_list) == 0
+                    and len(self.provider_fund_name_list) == 0
+                ):
                     if len(raw_share_name) > 0:
-                        integrated_share_name = self.integrate_share_name(raw_fund_name, raw_share_name)
+                        integrated_share_name = self.integrate_share_name(
+                            raw_fund_name, raw_share_name
+                        )
                         raw_data_keys = list(raw_data.keys())
                         for datapoint in self.datapoints:
                             if datapoint in raw_data_keys:
@@ -262,7 +330,7 @@ class DataMapping:
                                     "investment_type": 1,
                                     "investment_id": "",
                                     "investment_name": "",
-                                    "similarity": 0
+                                    "similarity": 0,
                                 }
                                 mapped_data_list.append(mapped_data)
                     else:
@@ -279,13 +347,15 @@ class DataMapping:
                                     "value": raw_data[datapoint],
                                     "investment_type": 33,
                                     "investment_id": "",
-                                    "investment_name": ""
+                                    "investment_name": "",
                                 }
                                 mapped_data_list.append(mapped_data)
                 else:
                     raw_name = ""
                     if raw_share_name is not None and len(raw_share_name) > 0:
-                        raw_name = self.integrate_share_name(raw_fund_name, raw_share_name)
+                        raw_name = self.integrate_share_name(
+                            raw_fund_name, raw_share_name
+                        )
                         if mapped_share_cache.get(raw_name) is not None:
                             investment_info = mapped_share_cache[raw_name]
                         else:
@@ -298,14 +368,20 @@ class DataMapping:
                                 )
                                 fund_id = fund_info["id"]
                                 mapped_fund_cache[raw_fund_name] = fund_info
-                            investment_info = self.matching_with_database(
-                                raw_name=raw_name, 
-                                raw_share_name=raw_share_name, 
-                                raw_fund_name=raw_fund_name,
-                                parent_id=fund_id, 
-                                matching_type="share",
-                                process_cache=process_cache
-                            )
+                            investment_info = {}
+                            if len(fund_id) > 0:
+                                investment_info = self.mapping_unique_raw_data(fund_id=fund_id,
+                                                                               raw_fund_name=raw_fund_name,
+                                                                               raw_data_list=raw_data_list)
+                            if investment_info.get("id", None) is None or len(investment_info.get("id", "")) == 0:
+                                investment_info = self.matching_with_database(
+                                    raw_name=raw_name,
+                                    raw_share_name=raw_share_name,
+                                    raw_fund_name=raw_fund_name,
+                                    parent_id=fund_id,
+                                    matching_type="share",
+                                    process_cache=process_cache,
+                                )
                             mapped_share_cache[raw_name] = investment_info
                     elif raw_fund_name is not None and len(raw_fund_name) > 0:
                         raw_name = raw_fund_name
@@ -322,7 +398,7 @@ class DataMapping:
                             "id": "",
                             "legal_name": "",
                             "investment_type": -1,
-                            "similarity": 0
+                            "similarity": 0,
                         }
 
                     raw_data_keys = list(raw_data.keys())
@@ -339,13 +415,35 @@ class DataMapping:
                                 "investment_type": investment_info["investment_type"],
                                 "investment_id": investment_info["id"],
                                 "investment_name": investment_info["legal_name"],
-                                "similarity": investment_info["similarity"]
+                                "similarity": investment_info["similarity"],
                             }
                             mapped_data_list.append(mapped_data)
 
         self.output_mapping_file(mapped_data_list)
         return mapped_data_list
     
+    def mapping_unique_raw_data(self, fund_id: str, raw_fund_name: str, raw_data_list: list):
+        share_count = 0
+        for raw_data in raw_data_list:
+            fund_name = raw_data.get("fund_name", "")
+            share_name = raw_data.get("share_name", "")
+            if fund_name == raw_fund_name and  share_name is not None and len(share_name) > 0:
+                share_count += 1
+                if share_count > 1:
+                    break
+        data_info = {}
+        if share_count == 1:
+            doc_compare_mapping = self.doc_fund_class_mapping[
+                    self.doc_fund_class_mapping["FundId"] == fund_id
+                ]
+            if len(doc_compare_mapping) == 1:
+                data_info["id"] = doc_compare_mapping["SecId"].values[0]
+                data_info["legal_name"] = doc_compare_mapping["ShareClassName"].values[0]
+                data_info["investment_type"] = 1
+                data_info["similarity"] = 1
+        return data_info
+        
+
     def output_mapping_file(self, mapped_data_list: list):
         json_data_file = os.path.join(
             self.output_data_json_folder, f"{self.doc_id}.json"
@@ -390,7 +488,7 @@ class DataMapping:
         raw_fund_name: str = None,
         parent_id: str = None,
         matching_type: str = "fund",
-        process_cache: dict = {}
+        process_cache: dict = {},
     ):
         if len(self.doc_fund_name_list) == 0 and len(self.provider_fund_name_list) == 0:
             data_info["id"] = ""
@@ -417,8 +515,9 @@ class DataMapping:
                 doc_compare_mapping = self.doc_fund_class_mapping[
                     self.doc_fund_class_mapping["FundId"] == parent_id
                 ]
-                provider_compare_mapping = self.provider_fund_class_mapping\
-                        [self.provider_fund_class_mapping["FundId"] == parent_id]
+                provider_compare_mapping = self.provider_fund_class_mapping[
+                    self.provider_fund_class_mapping["FundId"] == parent_id
+                ]
                 if len(doc_compare_mapping) == 0:
                     if len(provider_compare_mapping) == 0:
                         doc_compare_name_list = self.doc_share_name_list
@@ -436,8 +535,9 @@ class DataMapping:
                         doc_compare_mapping["ShareClassName"].unique().tolist()
                     )
 
-                if len(provider_compare_mapping) == 0 or \
-                    len(provider_compare_mapping) < len(doc_compare_mapping):
+                if len(provider_compare_mapping) == 0 or len(
+                    provider_compare_mapping
+                ) < len(doc_compare_mapping):
                     provider_compare_name_list = doc_compare_name_list
                     provider_compare_mapping = doc_compare_mapping
                 else:
@@ -464,11 +564,15 @@ class DataMapping:
                     share_name=raw_share_name,
                     fund_name=raw_fund_name,
                     matching_type=matching_type,
-                    process_cache=process_cache)
+                    process_cache=process_cache,
+                )
                 if matching_type == "fund":
                     threshold = 0.7
                 else:
-                    threshold = 0.9
+                    if self.compare_with_provider:
+                        threshold = 0.9
+                    else:
+                        threshold = 0.6
                 if max_similarity is not None and max_similarity >= threshold:
                     data_info["id"] = doc_compare_mapping[
                         doc_compare_mapping[compare_name_dp] == max_similarity_name
@@ -479,38 +583,44 @@ class DataMapping:
             if data_info.get("id", None) is None or len(data_info.get("id", "")) == 0:
                 # set pre_common_word_list, reason: the document mapping for same fund maybe different with provider mapping
                 # the purpose is to get the most common word list, to improve the similarity.
-                max_similarity_name, max_similarity = get_most_similar_name(
-                    raw_name, 
-                    provider_compare_name_list, 
-                    share_name=raw_share_name,
-                    fund_name=raw_fund_name,
-                    matching_type=matching_type, 
-                    pre_common_word_list=pre_common_word_list,
-                    process_cache=process_cache
-                )
-                threshold = 0.7
-                if matching_type == "share":
-                    threshold = 0.5
-                round_similarity = 0
-                if max_similarity is not None and isinstance(max_similarity, float):
-                    round_similarity = round(max_similarity, 1)
-                if round_similarity is not None and round_similarity >= threshold:
-                    data_info["id"] = provider_compare_mapping[
-                        provider_compare_mapping[compare_name_dp] == max_similarity_name
-                    ][compare_id_dp].values[0]
-                    data_info["legal_name"] = max_similarity_name
-                    data_info["similarity"] = max_similarity
-                else:
-                    if len(doc_compare_name_list) == 1:
-                        data_info["id"] = doc_compare_mapping[
-                            doc_compare_mapping[compare_name_dp] == doc_compare_name_list[0]
+                if self.compare_with_provider:
+                    max_similarity_name, max_similarity = get_most_similar_name(
+                        raw_name,
+                        provider_compare_name_list,
+                        share_name=raw_share_name,
+                        fund_name=raw_fund_name,
+                        matching_type=matching_type,
+                        pre_common_word_list=pre_common_word_list,
+                        process_cache=process_cache,
+                    )
+                    threshold = 0.7
+                    if matching_type == "share":
+                        threshold = 0.5
+                    round_similarity = 0
+                    if max_similarity is not None and isinstance(max_similarity, float):
+                        round_similarity = round(max_similarity, 1)
+                    if round_similarity is not None and round_similarity >= threshold:
+                        data_info["id"] = provider_compare_mapping[
+                            provider_compare_mapping[compare_name_dp] == max_similarity_name
                         ][compare_id_dp].values[0]
-                        data_info["legal_name"] = doc_compare_name_list[0]
-                        data_info["similarity"] = 1
+                        data_info["legal_name"] = max_similarity_name
+                        data_info["similarity"] = max_similarity
                     else:
-                        data_info["id"] = ""
-                        data_info["legal_name"] = ""
-                        data_info["similarity"] = 0
+                        if len(doc_compare_name_list) == 1:
+                            data_info["id"] = doc_compare_mapping[
+                                doc_compare_mapping[compare_name_dp]
+                                == doc_compare_name_list[0]
+                            ][compare_id_dp].values[0]
+                            data_info["legal_name"] = doc_compare_name_list[0]
+                            data_info["similarity"] = 1
+                        else:
+                            data_info["id"] = ""
+                            data_info["legal_name"] = ""
+                            data_info["similarity"] = 0
+                else:
+                    data_info["id"] = ""
+                    data_info["legal_name"] = ""
+                    data_info["similarity"] = 0
             data_info["investment_type"] = investment_type
         else:
             data_info["id"] = ""

instructions/aus_prospectus/data_extraction_prompts_config.json

@@ -61,21 +61,6 @@
 			"---Example End---",
 			"The output should be:",
 			"{\"data\": [{\"fund name\": \"Allan Gray Australian Equity Fund\", \"share name\": \"Class A\", \"management_fee_and_costs\": 1.19, \"management_fee\": 0.77, \"administration_fees\": 0.42}]",
-			"- 5. Reverse order of data columns from table text in PDF:",
-			"For this case, 1. the columns order is reversed, \n2. The fund name is in the end of row with number value in front of fund name.",
-			"---Example 1 Start---",
-			"Transaction\ncosts\n(gross)1\nBuy-sell\nspreads\nTransaction\ncosts (net)\nEquals\ninvestment fees and\ncosts\nThe investment fees and\ncosts are made up of\nPlus\nother\ninvestment\nfees and\ncosts\nPerformance\nfee\n% pa\nEntry %/\nExit %\n% pa\n% pa\n% pa\nReady-made portfolios\nSimple choice\n0.04\n0.10/0.10\n0.00\n0.62\n0.55\n0.07\nMLC Stable\n0.05\n0.10/0.10\n0.02\n0.80\n0.65\n0.15\nMLC Conservative Balanced",
-			"---Example 1 End---",
-			"For this case, Management fees and costs = Management fees with same reported name: Plus\nother\ninvestment\nfees and\ncosts",
-			"The output should be: ",
-			"{\"data\": [{\"fund name\": \"MLC Stable\", \"share name\": \"MLC Stable\", \"buy_spread\": 0.10, \"sell_spread\": 0.10, \"management_fee_and_costs\": 0.55, \"management_fee\": 0.55, \"performance_fee\": 0.07}, {\"fund name\": \"MLC Conservative Balanced\", \"share name\": \"MLC Conservative Balanced\", \"buy_spread\": 0.10, \"sell_spread\": 0.10, \"management_fee_and_costs\": 0.65, \"management_fee\": 0.65, \"performance_fee\": 0.15}]",
-			"\n",
-			"---Example 2 Start---",
-			"\nTotal\nTransaction Costs\nPerformance Fees\nManagement fees and costs\nIndirect Fee\nManagement fees\nMLC diversified investment\noption\n1.49% p.a.\n0.01% p.a.\n0.06% p.a.\n0.07% p.a.\n1.35% p.a.\nMLC Horizon 2\nIncome Portfolio\n",
-			"---Example 2 End---",
-			"For this case, Management fees and costs = Management fees + Indirect Fee.",
-			"The output should be:",
-			"{\"data\": [{\"fund name\": \"MLC Horizon 2 Income Portfolio\", \"share name\": \"MLC Horizon 2 Income Portfolio\", \"management_fee_and_costs\": 1.42, \"management_fee\": 1.35, \"indirect_costs\": 0.07, \"performance_fee\": 0.06}]",
 			"- 6. Please ignore these words as fund names, it means never extract these words as fund names. They are:",
 			"\"Ready-made portfolios\", \"Simple choice\", \"Build-your-own portfolio\"."
 		],
@@ -136,7 +121,7 @@
 		"special_rule": {
 			"management_fee_and_costs": [
 				"If there are multiple Management fee and costs reported names, here is the priority rule:",
-				"- With \"Total Management fees and costs (gross)\" and \"Total Management fees and costs (net)\", pick up the values from \"Total Management fees and costs (net)\".",
+				"A. With \"Total Management fees and costs (gross)\" and \"Total Management fees and costs (net)\", pick up the values from \"Total Management fees and costs (net)\".",
 				"---Example Start---",
 				"\n Investment option \nInvestment option \nmanagement \ncosts1  \n% p.a. \n(A)\nLifeplan \nadministration fee \n(gross)2  \n% p.a. \n(B)\nLifeplan \nadministration fee \n(net)  \n% p.a. \n(C)\nTotal Management \nfees and costs \n(gross)  \n% p.a. \n(A + B)\nTotal Management \nfees and costs  \n(net)  \n% p.a. \n(A + C)\nAllan Gray Australian Equity Fund \u2013 Class A\n0.77\n0.60\n0.42\n1.37\n1.19\n",
 				"---Example End---",
@@ -144,19 +129,24 @@
 				"{\"data\": [{\"fund name\": \"Allan Gray Australian Equity Fund\", \"share name\": \"Class A\", \"management_fee_and_costs\": 1.19, \"management_fee\": 0.77, \"administration_fees\": 0.42}]",
 				"\n",
 				"If there are multiple Management fee and costs sub-columns, here is the rule:",
-				"- With \"Management fees\" and \"Indirect fee\", sum the values from these two columns: \"Management fees\" + \"Indirect fee\".",
+				"B. With \"Management fees\" and \"Indirect fee\", sum the values from these two columns: \"Management fees\" + \"Indirect fee\".",
 				"---Example Start---",
-				"\nTotal\nTransaction Costs\nPerformance Fees\nManagement fees and costs\nIndirect Fee\nManagement fees\nMLC diversified investment\noption\n1.49% p.a.\n0.01% p.a.\n0.06% p.a.\n0.07% p.a.\n1.35% p.a.\nMLC Horizon 2\nIncome Portfolio\n",
+				"\n\nManagement fees \nManagement fees and costs \nIndirect Fee \nPerformance Fees \nTransaction Costs \nTotal \nMLC diversified investment \noption \nMLC Horizon 2 \nIncome Portfolio \n1.35% p.a. \n0.07% p.a. \n0.06% p.a. \n0.01% p.a. \n1.49% p.a. \n",
 				"---Example End---",
 				"The output should be:",
 				"{\"data\": [{\"fund name\": \"MLC Horizon 2 Income Portfolio\", \"share name\": \"MLC Horizon 2 Income Portfolio\", \"management_fee_and_costs\": 1.42, \"management_fee\": 1.35, \"indirect_costs\": 0.07, \"performance_fee\": 0.06}]",
 				"\n",
-				"- With \"Management fees\" and \"Administration fee\", sum the values from these two columns: \"Management fees\" + \"Administration fee\".",
-				"---Example Start---",
-				"\nTotal\nTransaction Costs\nPerformance Fees\nManagement fees and costs\nAdministration Fee\nManagement fees\nMLC diversified investment\noption\n1.62% p.a.\n0.02% p.a.\n0.03% p.a.\n0.09% p.a.\n1.58% p.a.\nMLC Horizon 4 Balanced\nPortfolio\n",
-				"---Example End---",
+				"C. If only find \"Management fees and costs\", please output the relevant as data point key: \"management_fee_and_costs\", instead of \"management_fee\".",
+				"---Example 1 Start---",
+				"The fees and costs for managing \nyour investment \nManagement fees and costs \n1 \n• \nSPDR World: 0.30% per annum of net asset \nvalue. This is reduced to 0.18% per annum of net \nasset value with effect from 14 February 2022.",
+				"---Example 1 End---",
 				"The output should be:",
-				"{\"data\": [{\"fund name\": \"MLC Horizon 4 Balanced Portfolio\", \"share name\": \"MLC Horizon 4 Balanced Portfolio\", \"management_fee_and_costs\": 1.67, \"management_fee\": 1.58, \"administration_fees\": 0.09, \"performance_fee\": 0.03}]"
+				"{\"data\": [{\"fund name\": \"SPDR World\", \"share name\": \"SPDR World\", \"management_fee_and_costs\": 0.18}]",
+				"---Example 2 Start---",
+				"Management Fees and Costs \n\nAs at the date of this PDS, Management Fees and Costs will be capped at: \n\n• 0.18% pa of net asset value for SPDR World \n\n• 0.21% pa of net asset value for SPDR World (Hedged) \n\n",
+				"---Example 2 End---",
+				"The output should be:",
+				"{\"data\": [{\"fund name\": \"SPDR World\", \"share name\": \"SPDR World\", \"management_fee_and_costs\": 0.18}, {\"fund name\": \"SPDR World (Hedged)\", \"share name\": \"SPDR World (Hedged)\", \"management_fee_and_costs\": 0.21}]"
 			],
 			"buy_spread": [
 				"Please don't extract data by the reported names for buy_spread or sell_spread, they are: ",
@@ -263,7 +253,7 @@
 			"date_of_last_hwm_reset_value": ["29 March 2023", "18 April 2024", "19 October 2021"],
 			"date_of_last_performance_fee_restructure_value": ["12 August 2022", "15 March 2024", "11 November 2023"],
 			"high_water_mark_type_value": ["Total Return", "Excess Return", "Both TR & ER"],
-			"minimum_initial_investment_value": [0, 5, 12],
+			"minimum_initial_investment_value": [0, 5000, 10000],
 			"recoverable_expenses_value": [0.12, 0.05, 0.06],
 			"indirect_costs_value": [0.12, 0.16, 0.02]
 		},

main.py

@@ -31,11 +31,14 @@ class EMEA_AR_Parsing:
         output_mapping_data_folder: str = r"/data/emea_ar/output/mapping_data/docs/",
         extract_way: str = "text",
         drilldown_folder: str = r"/data/emea_ar/output/drilldown/",
+        compare_with_provider: bool = True
     ) -> None:
         self.doc_id = doc_id
         self.doc_source = doc_source
         self.pdf_folder = pdf_folder
         os.makedirs(self.pdf_folder, exist_ok=True)
+        self.compare_with_provider = compare_with_provider
+        
         self.pdf_file = self.download_pdf()
         self.document_mapping_info_df = query_document_fund_mapping(doc_id, rerun=False)
 
@@ -76,7 +79,7 @@ class EMEA_AR_Parsing:
             self.pdf_file,
             self.document_mapping_info_df,
             self.doc_source,
-            output_pdf_text_folder
+            output_pdf_text_folder,
         )
         self.page_text_dict = self.filter_pages.page_text_dict
 
@@ -87,7 +90,9 @@ class EMEA_AR_Parsing:
             drilldown_folder = r"/data/emea_ar/output/drilldown/"
         os.makedirs(drilldown_folder, exist_ok=True)
         self.drilldown_folder = drilldown_folder
-        misc_config_file = os.path.join(f"./configuration/{doc_source}/", "misc_config.json")
+        misc_config_file = os.path.join(
+            f"./configuration/{doc_source}/", "misc_config.json"
+        )
         if os.path.exists(misc_config_file):
             with open(misc_config_file, "r", encoding="utf-8") as f:
                 misc_config = json.load(f)
@@ -249,6 +254,14 @@ class EMEA_AR_Parsing:
                 except Exception as e:
                     logger.error(f"Error: {e}")
                 annotation_list = annotation_list_df.to_dict(orient="records")
+                try:
+                    drilldown_json_file = os.path.join(
+                        drilldown_data_folder, f"{doc_id}_drilldown.json"
+                    )
+                    with open(drilldown_json_file, "w", encoding="utf-8") as f:
+                        json.dump(annotation_list, f, ensure_ascii=False, indent=4)
+                except Exception as e:
+                    logger.error(f"Error: {e}")
         return annotation_list
 
     def mapping_data(self, data_from_gpt: list, re_run: bool = False) -> list:
@@ -278,7 +291,8 @@ class EMEA_AR_Parsing:
             data_from_gpt,
             self.document_mapping_info_df,
             self.output_mapping_data_folder,
-            self.doc_source
+            self.doc_source,
+            compare_with_provider=self.compare_with_provider
         )
         return data_mapping.mapping_raw_data_entrance()
 
@@ -334,6 +348,7 @@ def mapping_data(
         output_mapping_data_folder=output_mapping_folder,
         extract_way=extract_way,
         drilldown_folder=drilldown_folder,
+        compare_with_provider=False
     )
     doc_data_from_gpt, annotation_list = emea_ar_parsing.extract_data(
         re_run=re_run_extract_data
@@ -502,18 +517,29 @@ def batch_start_job(
                 writer, index=False, sheet_name="extract_data"
             )
 
-        if document_mapping_file is not None and len(document_mapping_file) > 0 and os.path.exists(document_mapping_file):
+        if (
+            doc_source == "aus_prospectus"
+            and document_mapping_file is not None
+            and len(document_mapping_file) > 0
+            and os.path.exists(document_mapping_file)
+        ):
             try:
-                merged_total_data_folder = os.path.join(output_mapping_total_folder, "merged/")
+                merged_total_data_folder = os.path.join(
+                    output_mapping_total_folder, "merged/"
+                )
                 os.makedirs(merged_total_data_folder, exist_ok=True)
                 data_file_base_name = os.path.basename(output_file)
-                output_merged_data_file_path = os.path.join(merged_total_data_folder, "merged_" + data_file_base_name)
-                merge_output_data_aus_prospectus(output_file, document_mapping_file, output_merged_data_file_path)
+                output_merged_data_file_path = os.path.join(
+                    merged_total_data_folder, "merged_" + data_file_base_name
+                )
+                merge_output_data_aus_prospectus(
+                    output_file, document_mapping_file, output_merged_data_file_path
+                )
             except Exception as e:
                 logger.error(f"Error: {e}")
 
         if calculate_metrics:
-            prediction_sheet_name = "total_mapping_data"
+            prediction_sheet_name = "data_in_doc_mapping"
             ground_truth_file = r"/data/emea_ar/ground_truth/data_extraction/mapping_data_info_73_documents.xlsx"
             ground_truth_sheet_name = "mapping_data"
             metrics_output_folder = r"/data/emea_ar/output/metrics/"
@@ -770,11 +796,11 @@ def test_auto_generate_instructions():
 
 
 def test_data_extraction_metrics():
-    data_type = "data_extraction"
+    data_type = "document_mapping_in_db"
     # prediction_file = r"/data/emea_ar/output/mapping_data/total/mapping_data_info_88_documents_by_image_20240920033929.xlsx"
-    prediction_file = r"/data/emea_ar/output/mapping_data/total/mapping_data_info_88_documents_by_text_20240922152517.xlsx"
+    prediction_file = r"/data/emea_ar/output/mapping_data/total/mapping_data_info_51_documents_by_text_20250127104008.xlsx"
     # prediction_file = r"/data/emea_ar/output/mapping_data/docs/by_text/excel/481475385.xlsx"
-    prediction_sheet_name = "mapping_data"
+    prediction_sheet_name = "data_in_doc_mapping"
     ground_truth_file = r"/data/emea_ar/ground_truth/data_extraction/mapping_data_info_73_documents.xlsx"
     ground_truth_sheet_name = "mapping_data"
     metrics_output_folder = r"/data/emea_ar/output/metrics/"
@@ -1015,9 +1041,9 @@ def batch_run_documents(
     page_filter_ground_truth_file = (
         r"/data/emea_ar/ground_truth/page_filter/datapoint_page_info_88_documents.xlsx"
     )
-    re_run_extract_data = False
-    re_run_mapping_data = False
-    force_save_total_data = True
+    re_run_extract_data = True
+    re_run_mapping_data = True
+    force_save_total_data = False
     calculate_metrics = False
 
     extract_way = "text"
@@ -1194,13 +1220,17 @@ def merge_output_data_aus_prospectus(
 ):
     # TODO: merge output data for aus prospectus, plan to realize it on 2025-01-16
     data_df = pd.read_excel(data_file_path, sheet_name="total_mapping_data")
-    document_mapping_df = pd.read_excel(document_mapping_file, sheet_name="document_mapping")
+    document_mapping_df = pd.read_excel(
+        document_mapping_file, sheet_name="document_mapping"
+    )
     # set doc_id to be string type
     data_df["doc_id"] = data_df["doc_id"].astype(str)
     document_mapping_df["DocumentId"] = document_mapping_df["DocumentId"].astype(str)
 
     doc_id_list = data_df["doc_id"].unique().tolist()
-    datapoint_keyword_config_file = r"./configuration/aus_prospectus/datapoint_name.json"
+    datapoint_keyword_config_file = (
+        r"./configuration/aus_prospectus/datapoint_name.json"
+    )
     with open(datapoint_keyword_config_file, "r", encoding="utf-8") as f:
         datapoint_keyword_config = json.load(f)
     datapoint_name_list = list(datapoint_keyword_config.keys())
@@ -1212,7 +1242,9 @@ def merge_output_data_aus_prospectus(
                 "EffectiveDate"
             ].values[0]
         )[0:10]
-        share_doc_data_df = data_df[(data_df["doc_id"] == doc_id) & (data_df["investment_type"] == 1)]
+        share_doc_data_df = data_df[
+            (data_df["doc_id"] == doc_id) & (data_df["investment_type"] == 1)
+        ]
         exist_raw_name_list = []
         for index, row in share_doc_data_df.iterrows():
             doc_id = str(row["doc_id"])
@@ -1228,7 +1260,9 @@ def merge_output_data_aus_prospectus(
             fund_id = ""
             fund_legal_name = ""
             if share_class_id != "":
-                record_row = document_mapping_df[document_mapping_df["FundClassId"] == share_class_id]
+                record_row = document_mapping_df[
+                    document_mapping_df["FundClassId"] == share_class_id
+                ]
                 if len(record_row) > 0:
                     fund_id = record_row["FundId"].values[0]
                     fund_legal_name = record_row["FundLegalName"].values[0]
@@ -1265,16 +1299,16 @@ def merge_output_data_aus_prospectus(
                 doc_data_list.append(data)
             # find data from total_data_list by raw_name
             for data in doc_data_list:
-                if (
-                    data["raw_name"] == raw_name
-                ):
+                if data["raw_name"] == raw_name:
                     update_key = datapoint
                     data[update_key] = value
                     if page_index not in data["page_index"]:
                         data["page_index"].append(page_index)
                     break
 
-        fund_doc_data_df = data_df[(data_df["doc_id"] == doc_id) & (data_df["investment_type"] == 33)]
+        fund_doc_data_df = data_df[
+            (data_df["doc_id"] == doc_id) & (data_df["investment_type"] == 33)
+        ]
         for index, row in fund_doc_data_df.iterrows():
             doc_id = str(row["doc_id"])
             page_index = int(row["page_index"])
@@ -1289,8 +1323,9 @@ def merge_output_data_aus_prospectus(
             exist = False
             if fund_id != "":
                 for data in doc_data_list:
-                    if (fund_id != "" and data["fund_id"] == fund_id) or \
-                        (data["raw_fund_name"] == raw_fund_name):
+                    if (fund_id != "" and data["fund_id"] == fund_id) or (
+                        data["raw_fund_name"] == raw_fund_name
+                    ):
                         update_key = datapoint
                         data[update_key] = value
                         if page_index not in data["page_index"]:
@@ -1323,6 +1358,7 @@ def merge_output_data_aus_prospectus(
 
 
 if __name__ == "__main__":
+    # test_data_extraction_metrics()
     # data_file_path = r"/data/aus_prospectus/output/mapping_data/total/mapping_data_info_11_documents_by_text_20250116220811.xlsx"
     # document_mapping_file_path = r"/data/aus_prospectus/basic_information/11_documents/document_mapping.xlsx"
     # merged_total_data_folder = r'/data/aus_prospectus/output/mapping_data/total/merged/'
@@ -1350,10 +1386,16 @@ if __name__ == "__main__":
 
     doc_source = "aus_prospectus"
     if doc_source == "aus_prospectus":
-        document_sample_file = r"./sample_documents/aus_prospectus_100_documents_multi_fund_sample.txt"
+        # document_sample_file = (
+        #     r"./sample_documents/aus_prospectus_100_documents_multi_fund_sample.txt"
+        # )
+        document_sample_file = (
+            r"./sample_documents/aus_prospectus_17_documents_sample.txt"
+        )
         with open(document_sample_file, "r", encoding="utf-8") as f:
             special_doc_id_list = [doc_id.strip() for doc_id in f.readlines()]
-        document_mapping_file = r"/data/aus_prospectus/basic_information/from_2024_documents/aus_100_document_prospectus_multi_fund.xlsx"
+        # document_mapping_file = r"/data/aus_prospectus/basic_information/from_2024_documents/aus_100_document_prospectus_multi_fund.xlsx"
+        document_mapping_file = r"/data/aus_prospectus/basic_information/17_documents/aus_prospectus_17_documents_mapping.xlsx"
         # special_doc_id_list: list = [
         #     "539790009",
         #     "542300403",
@@ -1367,7 +1409,7 @@ if __name__ == "__main__":
         #     "555377021",
         #     "555654388",
         # ]
-        # special_doc_id_list: list = ["534287518"]
+        special_doc_id_list: list = ["377377369"]
         pdf_folder: str = r"/data/aus_prospectus/pdf/"
         output_pdf_text_folder: str = r"/data/aus_prospectus/output/pdf_text/"
         output_extract_data_child_folder: str = (
@@ -1397,7 +1439,61 @@ if __name__ == "__main__":
             drilldown_folder=drilldown_folder,
         )
     elif doc_source == "emea_ar":
-        special_doc_id_list = ["553242408"]
+        special_doc_id_list = [
+            "292989214",
+            "316237292",
+            "321733631",
+            "323390570",
+            "327956364",
+            "333207452",
+            "334718372",
+            "344636875",
+            "362246081",
+            "366179419",
+            "380945052",
+            "382366116",
+            "387202452",
+            "389171486",
+            "391456740",
+            "391736837",
+            "394778487",
+            "401684600",
+            "402113224",
+            "402181770",
+            "402397014",
+            "405803396",
+            "445102363",
+            "445256897",
+            "448265376",
+            "449555622",
+            "449623976",
+            "458291624",
+            "458359181",
+            "463081566",
+            "469138353",
+            "471641628",
+            "476492237",
+            "478585901",
+            "478586066",
+            "479042264",
+            "479793787",
+            "481475385",
+            "483617247",
+            "486378555",
+            "486383912",
+            "492121213",
+            "497497599",
+            "502693599",
+            "502821436",
+            "503194284",
+            "506559375",
+            "507967525",
+            "508854243",
+            "509845549",
+            "520879048",
+            "529925114",
+        ]
+        # special_doc_id_list = ["532438210"]
         batch_run_documents(
             doc_source=doc_source, special_doc_id_list=special_doc_id_list
         )

sample_documents/aus_prospectus_17_documents_sample.txt

@@ -0,0 +1,17 @@
+377377369
+397107472
+401212184
+409723592
+411062815
+412778803
+414751292
+462770987
+471206458
+391080133
+391080140
+410899007
+420339794
+441280757
+446324179
+454036250
+384508026

utils/pdf_util.py

@@ -543,7 +543,7 @@ class PDFUtil:
                 matching_val_area = page.search_for(text_block.replace('\n', '').replace('-', ''))
             if len(matching_val_area) == 0:
                 matching_val_area = page.search_for(text_block.replace('-\n', ''))
-        if len(matching_val_area) > 0 and len(text_block.strip().split()) == 1:
+        if len(matching_val_area) > 0 and len(text_block.strip().split()) < 3:
             new_matching_val_area = []
             for area in matching_val_area:
                 # get text by text_bbox

utils/sql_query_util.py

@@ -8,7 +8,7 @@ import dotenv
 dotenv.load_dotenv()
 
 
-def query_document_fund_mapping(doc_id, rerun=True, output_folder=r"/data/emea_ar/output/mapping/document/"):
+def query_document_fund_mapping(doc_id, rerun=True, output_folder=r"./data/emea_ar/output/db_mapping/document/"):
     count = 1
     while True:
         try:
@@ -27,10 +27,13 @@ def query_document_fund_mapping(doc_id, rerun=True, output_folder=r"/data/emea_a
                     by=["FundName", "ShareClassName"]
                 ).reset_index(drop=True)
                 if output_folder is not None and len(output_folder) > 0:
-                    os.makedirs(output_folder, exist_ok=True)
-                    output_file = os.path.join(output_folder, f"{doc_id}.xlsx")
-                    with pd.ExcelWriter(output_file) as writer:
-                        document_mapping_info_df.to_excel(writer, index=False)
+                    try:
+                        os.makedirs(output_folder, exist_ok=True)
+                        output_file = os.path.join(output_folder, f"{doc_id}.xlsx")
+                        with pd.ExcelWriter(output_file) as writer:
+                            document_mapping_info_df.to_excel(writer, index=False)
+                    except:
+                        pass
             return document_mapping_info_df
         except Exception as e:
             print(e)
@@ -40,7 +43,7 @@ def query_document_fund_mapping(doc_id, rerun=True, output_folder=r"/data/emea_a
             count += 1
 
 
-def query_investment_by_provider(company_id: str, rerun=True, output_folder=r"/data/emea_ar/output/mapping/provider/"):
+def query_investment_by_provider(company_id: str, rerun=True, output_folder=r"./data/emea_ar/output/db_mapping/provider/"):
     count = 1
     while True:
         try:
@@ -59,10 +62,13 @@ def query_investment_by_provider(company_id: str, rerun=True, output_folder=r"/d
                     .sort_values(by=['FundName', 'ShareClassName']) \
                     .reset_index(drop=True)
                 if output_folder is not None and len(output_folder) > 0:
-                    os.makedirs(output_folder, exist_ok=True)
-                    output_file = os.path.join(output_folder, f"{company_id}.xlsx")
-                    with pd.ExcelWriter(output_file) as writer:
-                        investment_by_provider_df.to_excel(writer, index=False)
+                    try:
+                        os.makedirs(output_folder, exist_ok=True)
+                        output_file = os.path.join(output_folder, f"{company_id}.xlsx")
+                        with pd.ExcelWriter(output_file) as writer:
+                            investment_by_provider_df.to_excel(writer, index=False)
+                    except:
+                        pass
             return investment_by_provider_df
         except Exception as e:
             print(e)
@@ -73,7 +79,7 @@ def query_investment_by_provider(company_id: str, rerun=True, output_folder=r"/d
 
 
 def query_data_by_biz_type(biztype: str, para, return_df: bool):
-    sqlpass_url = "https://api.morningstar.com/sqlpassapi/v1/sql"
+    sqlpass_url = os.getenv("SQL_PASS_URL")
     url = sqlpass_url + "?sqlName={0}&params={1}".format(biztype, str(para))
     headers = {"ApiKey": os.getenv("SQL_PASS_KEY")}
     if return_df: