1805 lines
89 KiB
Python
1805 lines
89 KiB
Python
import os
|
|
import json
|
|
import json_repair
|
|
import re
|
|
import fitz
|
|
import pandas as pd
|
|
from traceback import print_exc
|
|
from utils.gpt_utils import chat
|
|
from utils.pdf_util import PDFUtil
|
|
from utils.sql_query_util import query_document_fund_mapping, query_investment_by_provider
|
|
from utils.logger import logger
|
|
from utils.biz_utils import add_slash_to_text_as_regex, clean_text, \
|
|
get_most_similar_name, remove_abundant_data, replace_special_table_header
|
|
|
|
|
|
class DataExtraction:
|
|
def __init__(
|
|
self,
|
|
doc_source: str,
|
|
doc_id: str,
|
|
pdf_file: str,
|
|
output_data_folder: str,
|
|
page_text_dict: dict,
|
|
datapoint_page_info: dict,
|
|
datapoints: list,
|
|
document_mapping_info_df: pd.DataFrame,
|
|
extract_way: str = "text",
|
|
output_image_folder: str = None,
|
|
) -> None:
|
|
self.doc_source = doc_source
|
|
self.doc_id = doc_id
|
|
self.pdf_file = pdf_file
|
|
self.configuration_folder = f"./configuration/{doc_source}/"
|
|
self.instruction_folder = f"./instructions/{doc_source}/"
|
|
if output_data_folder is None or len(output_data_folder) == 0:
|
|
output_data_folder = r"/data/emea_ar/output/extract_data/docs/"
|
|
os.makedirs(output_data_folder, exist_ok=True)
|
|
|
|
self.output_data_json_folder = os.path.join(output_data_folder, "json/")
|
|
os.makedirs(self.output_data_json_folder, exist_ok=True)
|
|
|
|
self.output_data_excel_folder = os.path.join(output_data_folder, "excel/")
|
|
os.makedirs(self.output_data_excel_folder, exist_ok=True)
|
|
|
|
if page_text_dict is None or len(page_text_dict.keys()) == 0:
|
|
self.page_text_dict = self.get_pdf_page_text_dict()
|
|
else:
|
|
self.page_text_dict = page_text_dict
|
|
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)
|
|
else:
|
|
self.document_mapping_info_df = document_mapping_info_df
|
|
|
|
self.fund_name_list = self.document_mapping_info_df["FundName"].unique().tolist()
|
|
|
|
# get document type by DocumentType in self.document_mapping_info_df
|
|
self.document_type = int(self.document_mapping_info_df["DocumentType"].iloc[0])
|
|
self.investment_objective_pages = []
|
|
if self.document_type == 1:
|
|
self.investment_objective_pages = self.get_investment_objective_pages()
|
|
|
|
self.provider_mapping_df = self.get_provider_mapping()
|
|
if len(self.provider_mapping_df) == 0:
|
|
self.provider_fund_name_list = []
|
|
else:
|
|
self.provider_fund_name_list = (
|
|
self.provider_mapping_df["FundName"].unique().tolist()
|
|
)
|
|
self.document_category, self.document_production = self.get_document_category_production()
|
|
self.datapoint_page_info = self.get_datapoint_page_info(datapoint_page_info)
|
|
self.page_nums_with_datapoints = self.get_page_nums_from_datapoint_page_info()
|
|
self.datapoints = datapoints
|
|
self.instructions_config = self.get_instructions_config()
|
|
self.datapoint_level_config = self.get_datapoint_level()
|
|
self.datapoint_type_config = self.get_datapoint_type()
|
|
self.datapoint_name_config = self.get_datapoint_name()
|
|
self.datapoint_reported_name_config, self.non_english_reported_name_config = \
|
|
self.get_datapoint_reported_name()
|
|
self.extract_way = extract_way
|
|
self.output_image_folder = output_image_folder
|
|
|
|
def get_document_category_production(self):
|
|
document_category = None
|
|
document_production = None
|
|
if self.doc_source == "aus_prospectus":
|
|
first_4_page_text = ""
|
|
for page_index, page_text in self.page_text_dict.items():
|
|
if page_index > 3:
|
|
break
|
|
first_4_page_text += page_text + "\n"
|
|
first_4_page_text = clean_text(first_4_page_text)
|
|
document_category_prompt_file = os.path.join(self.instruction_folder, "document_category_prompts.json")
|
|
with open(document_category_prompt_file, "r", encoding="utf-8") as f:
|
|
document_category_prompt = "\n".join(json.load(f).get("prompts", []))
|
|
if len(document_category_prompt) > 0:
|
|
prompts = f"Context: \n{first_4_page_text}\n\Instructions: \n{document_category_prompt}"
|
|
result, with_error = chat(
|
|
prompt=prompts, response_format={"type": "json_object"}, max_tokens=1000
|
|
)
|
|
response = result.get("response", "")
|
|
if not with_error:
|
|
try:
|
|
data = json.loads(response)
|
|
document_category = data.get("document_category", None)
|
|
document_production = data.get("document_production", None)
|
|
except:
|
|
pass
|
|
|
|
return document_category, document_production
|
|
|
|
def get_datapoint_page_info(self, datapoint_page_info: dict) -> dict:
|
|
"""
|
|
If document source is aus_propectus and document category is MIS
|
|
then remove the administration_fee from datapoint_page_info
|
|
"""
|
|
if self.doc_source == "aus_prospectus" and self.document_category.upper() == "MIS":
|
|
if "administration_fees" in list(datapoint_page_info.keys()):
|
|
datapoint_page_info.pop("administration_fees")
|
|
return datapoint_page_info
|
|
|
|
def get_investment_objective_pages(self):
|
|
investment_objective_pages = []
|
|
if self.document_type == 1:
|
|
objective_strategy_regex_config_file = os.path.join(self.configuration_folder, "objective_strategy_regex.json")
|
|
with open(objective_strategy_regex_config_file, "r", encoding="utf-8") as f:
|
|
objective_strategy_regex_config = json.load(f)
|
|
objective_start_regex = objective_strategy_regex_config.get("objective_strategy", {}).get("start", "")
|
|
|
|
if objective_start_regex is not None and len(objective_start_regex) > 0:
|
|
for page_index, text in self.page_text_dict.items():
|
|
if re.search(objective_start_regex, text, re.I):
|
|
investment_objective_pages.append(page_index)
|
|
if len(investment_objective_pages) > 0:
|
|
investment_objective_pages.sort()
|
|
return investment_objective_pages
|
|
|
|
def get_datapoint_reported_name(self):
|
|
language_config_file = os.path.join(self.configuration_folder, "language.json")
|
|
self.language_config = {}
|
|
with open(language_config_file, "r", encoding="utf-8") as file:
|
|
self.language_config = json.load(file)
|
|
|
|
self.language_id = self.document_mapping_info_df["Language"].iloc[0]
|
|
self.language = self.language_config.get(self.language_id, None)
|
|
|
|
datapoint_reported_name_config_file = os.path.join(self.configuration_folder, "datapoint_reported_name.json")
|
|
all_datapoint_reported_name = {}
|
|
with open(datapoint_reported_name_config_file, "r", encoding="utf-8") as file:
|
|
all_datapoint_reported_name = json.load(file)
|
|
|
|
non_english_reported_name_config = {}
|
|
datapoint_reported_name_config = {}
|
|
common_language = "english"
|
|
for datapoint, language_reported_name in all_datapoint_reported_name.items():
|
|
reported_name_list = language_reported_name.get(common_language, [])
|
|
|
|
if self.language != "english":
|
|
reported_name_list.extend(language_reported_name.get(self.language, []))
|
|
non_english_reported_name_config[datapoint] = language_reported_name.get(self.language, [])
|
|
# remove duplicate reported name
|
|
reported_name_list = list(set(reported_name_list))
|
|
# sort the reported name
|
|
reported_name_list.sort()
|
|
datapoint_reported_name_config[datapoint] = reported_name_list
|
|
return datapoint_reported_name_config, non_english_reported_name_config
|
|
|
|
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_mapping_list = []
|
|
for provider_id in provider_id_list:
|
|
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 get_pdf_image_base64(self, page_index: int) -> dict:
|
|
pdf_util = PDFUtil(self.pdf_file)
|
|
return pdf_util.extract_image_from_page(page_index=page_index,
|
|
output_folder=self.output_image_folder)
|
|
def get_instructions_config(self) -> dict:
|
|
instructions_config_file = os.path.join(self.instruction_folder, "data_extraction_prompts_config.json")
|
|
with open(instructions_config_file, "r", encoding="utf-8") as f:
|
|
instructions_config = json.load(f)
|
|
return instructions_config
|
|
|
|
def get_datapoint_level(self) -> dict:
|
|
datapoint_level_file = os.path.join(self.configuration_folder, "datapoint_level.json")
|
|
with open(datapoint_level_file, "r", encoding="utf-8") as f:
|
|
datapoint_level = json.load(f)
|
|
return datapoint_level
|
|
|
|
def get_datapoint_type(self) -> dict:
|
|
datapoint_type_file = os.path.join(self.configuration_folder, "datapoint_type.json")
|
|
with open(datapoint_type_file, "r", encoding="utf-8") as f:
|
|
datapoint_type = json.load(f)
|
|
return datapoint_type
|
|
|
|
def get_datapoint_name(self) -> dict:
|
|
datapoint_name_file = os.path.join(self.configuration_folder, "datapoint_name.json")
|
|
with open(datapoint_name_file, "r", encoding="utf-8") as f:
|
|
datapoint_name = json.load(f)
|
|
return datapoint_name
|
|
|
|
def get_pdf_page_text_dict(self) -> dict:
|
|
pdf_util = PDFUtil(self.pdf_file)
|
|
success, text, page_text_dict = pdf_util.extract_text()
|
|
return page_text_dict
|
|
|
|
def get_page_nums_from_datapoint_page_info(self) -> list:
|
|
page_nums_with_datapoints = []
|
|
for datapoint, page_nums in self.datapoint_page_info.items():
|
|
if datapoint == "doc_id":
|
|
continue
|
|
page_nums_with_datapoints.extend(page_nums)
|
|
page_nums_with_datapoints = list(set(page_nums_with_datapoints))
|
|
# sort the page numbers
|
|
page_nums_with_datapoints.sort()
|
|
return page_nums_with_datapoints
|
|
|
|
def extract_data(self) -> dict:
|
|
logger.info(f"Extracting data from document {self.doc_id}, extract way: {self.extract_way}")
|
|
if self.extract_way == "text":
|
|
data_list = self.extract_data_by_text()
|
|
elif self.extract_way == "image":
|
|
data_list = self.extract_data_by_image()
|
|
else:
|
|
data_list = self.extract_data_by_text()
|
|
if self.doc_source == "aus_prospectus":
|
|
data_list = self.post_supplement_data(data_list)
|
|
# data_list = remove_abundant_data(data_list)
|
|
self.output_data_to_file(data_list)
|
|
return data_list
|
|
|
|
def post_supplement_data(self, data_list: list) -> list:
|
|
"""
|
|
data_dict = {"doc_id": self.doc_id}
|
|
data_dict["page_index"] = page_num
|
|
data_dict["datapoints"] = ", ".join(page_datapoints)
|
|
data_dict["page_text"] = page_text
|
|
data_dict["instructions"] = instructions
|
|
data_dict["raw_answer"] = response
|
|
data_dict["extract_data"] = data
|
|
data_dict["extract_way"] = original_way
|
|
data_dict["prompt_token"] = result.get("prompt_token", 0)
|
|
data_dict["completion_token"] = result.get("completion_token", 0)
|
|
data_dict["total_token"] = result.get("total_token", 0)
|
|
"""
|
|
data_list = self.supplement_minimum_initial_investment(data_list)
|
|
data_list, datapoint_list_with_production_name = self.post_adjust_for_value_with_production_name(data_list)
|
|
data_list = self.remove_duplicate_data(data_list)
|
|
if "management_fee" not in datapoint_list_with_production_name and "management_fee_and_costs" not in datapoint_list_with_production_name:
|
|
data_list = self.post_adjust_management_fee_costs(data_list)
|
|
|
|
data_list = self.check_administration_fees(data_list)
|
|
return data_list
|
|
|
|
def check_administration_fees(self, data_list: list):
|
|
"""
|
|
If document source is aus_prospectus and document category is MIS, then remove the administration fees from data_list
|
|
"""
|
|
if self.doc_source == "aus_prospectus" and self.document_category.upper() == "MIS":
|
|
for data_dict in data_list:
|
|
extract_data = data_dict.get("extract_data", {})
|
|
data = extract_data.get("data", [])
|
|
remove_items = []
|
|
for data_item in data:
|
|
keys = list(data_item.keys())
|
|
if "administration_fees" in keys:
|
|
data_item.pop("administration_fees")
|
|
keys = [key for key in list(data_item.keys()) if key not in ["fund_name", "share_name"]]
|
|
if len(keys) == 0:
|
|
remove_items.append(data_item)
|
|
|
|
for remove_item in remove_items:
|
|
try:
|
|
if remove_item in extract_data["data"]:
|
|
extract_data["data"].remove(remove_item)
|
|
except:
|
|
pass
|
|
return data_list
|
|
|
|
def post_adjust_for_value_with_production_name(self, data_list: list):
|
|
"""
|
|
If some datapoint with production name, then each fund/ share class in the same document for the datapoint should be with same value.
|
|
"""
|
|
raw_name_dict = self.get_raw_name_dict(data_list)
|
|
raw_name_list = list(raw_name_dict.keys())
|
|
raw_name_as_production_name = None
|
|
for raw_name in raw_name_list:
|
|
if raw_name.lower() in self.document_production.lower():
|
|
raw_name_as_production_name = raw_name
|
|
break
|
|
datapoint_list_with_production_name = []
|
|
if raw_name_as_production_name is None:
|
|
return data_list, datapoint_list_with_production_name
|
|
|
|
raw_name_dict.pop(raw_name_as_production_name)
|
|
|
|
for data_dict in data_list:
|
|
extract_data = data_dict.get("extract_data", {})
|
|
data = extract_data.get("data", [])
|
|
remove_item_list = []
|
|
new_dp_item_list = []
|
|
for data_item in data:
|
|
keys = list(data_item.keys())
|
|
fund_name = data_item.get("fund_name", "")
|
|
share_name = data_item.get("share_name", "")
|
|
raw_name = self.get_raw_name(fund_name, share_name)
|
|
if raw_name.lower() in self.document_production.lower():
|
|
dp_keys = [key for key in keys if key not in ["fund_name", "share_name"]]
|
|
for dp_key in dp_keys:
|
|
if dp_key not in datapoint_list_with_production_name:
|
|
datapoint_list_with_production_name.append(dp_key)
|
|
remove_item_list.append(data_item)
|
|
for v_raw_name, v_dict in raw_name_dict.items():
|
|
v_fund_name = v_dict.get("fund_name", "")
|
|
v_share_name = v_dict.get("share_name", "")
|
|
if len(share_name) > 0:
|
|
new_dp_item = {"fund_name": v_fund_name, "share_name": v_share_name}
|
|
else:
|
|
new_dp_item = {"fund_name": v_fund_name}
|
|
for dp_key in dp_keys:
|
|
new_dp_item[dp_key] = data_item.get(dp_key, "")
|
|
new_dp_item["source"] = "from_production_name"
|
|
new_dp_item_list.append(new_dp_item)
|
|
for remove_item in remove_item_list:
|
|
if remove_item in extract_data["data"]:
|
|
extract_data["data"].remove(remove_item)
|
|
if len(new_dp_item_list) > 0:
|
|
extract_data["data"].extend(new_dp_item_list)
|
|
|
|
if len(datapoint_list_with_production_name) == 0:
|
|
return data_list, datapoint_list_with_production_name
|
|
for data_dict in data_list:
|
|
extract_data = data_dict.get("extract_data", {})
|
|
data = extract_data.get("data", [])
|
|
remove_item_list = []
|
|
for data_item in data:
|
|
source = data_item.get("source", "")
|
|
if source == "from_production_name":
|
|
continue
|
|
keys = list(data_item.keys())
|
|
dp_keys = [key for key in keys if key not in ["fund_name", "share_name"]]
|
|
for dp_key in dp_keys:
|
|
if dp_key in datapoint_list_with_production_name:
|
|
data_item.pop(dp_key)
|
|
keys = list(data_item.keys())
|
|
dp_keys = [key for key in keys if key not in ["fund_name", "share_name"]]
|
|
if len(dp_keys) == 0:
|
|
remove_item_list.append(data_item)
|
|
for remove_item in remove_item_list:
|
|
if remove_item in extract_data["data"]:
|
|
extract_data["data"].remove(remove_item)
|
|
return data_list, datapoint_list_with_production_name
|
|
|
|
def remove_duplicate_data(self, data_list: list):
|
|
"""
|
|
The purpose is to remove duplicate data in the different pages.
|
|
Reason:
|
|
1. Some pdf documents have multiple pages for the same data
|
|
2. Usually, the first data is the latest data, and the others is the older data.
|
|
3. That's why we need to remove the duplicate data in the different pages.
|
|
"""
|
|
handled_data_dict_list = []
|
|
for data_dict in data_list:
|
|
extract_data = data_dict.get("extract_data", {})
|
|
data = extract_data.get("data", [])
|
|
for data_item in data:
|
|
keys = list(data_item.keys())
|
|
fund_name = data_item.get("fund_name", "")
|
|
share_name = data_item.get("share_name", "")
|
|
raw_name = self.get_raw_name(fund_name, share_name)
|
|
dp_keys = [key for key in keys if key not in ["fund_name", "share_name"]]
|
|
# sort the keys
|
|
dp_keys.sort()
|
|
additional_dp_keys = [dp_key for dp_key in dp_keys
|
|
if dp_key not in ["management_fee", "management_fee_and_costs"]]
|
|
if len(additional_dp_keys) == 0:
|
|
continue
|
|
for c_data_dict in data_list:
|
|
if c_data_dict in handled_data_dict_list:
|
|
continue
|
|
if c_data_dict == data_dict:
|
|
continue
|
|
c_extract_data = c_data_dict.get("extract_data", {})
|
|
c_data = c_extract_data.get("data", [])
|
|
remove_c_items = []
|
|
for c_data_item in c_data:
|
|
c_keys = list(c_data_item.keys())
|
|
c_fund_name = c_data_item.get("fund_name", "")
|
|
c_share_name = c_data_item.get("share_name", "")
|
|
c_raw_name = self.get_raw_name(c_fund_name, c_share_name)
|
|
if raw_name != c_raw_name:
|
|
continue
|
|
c_dp_keys = [key for key in c_keys if key not in ["fund_name", "share_name"]]
|
|
c_dp_keys.sort()
|
|
if dp_keys == c_dp_keys:
|
|
remove_c_items.append(c_data_item)
|
|
for remove_c_item in remove_c_items:
|
|
if remove_c_item in c_data:
|
|
c_data.remove(remove_c_item)
|
|
handled_data_dict_list.append(data_dict)
|
|
return data_list
|
|
|
|
def get_raw_name(self, fund_name: str, share_name: str) -> str:
|
|
raw_name = ""
|
|
if len(fund_name) == 0:
|
|
return raw_name
|
|
if fund_name == share_name:
|
|
raw_name = fund_name
|
|
elif len(share_name) > 0 and share_name.startswith(fund_name):
|
|
raw_name = share_name
|
|
else:
|
|
raw_name = f"{fund_name} {share_name}".strip()
|
|
return raw_name
|
|
|
|
def get_raw_name_dict(self, data_list: list) -> dict:
|
|
raw_name_dict = {}
|
|
for data_dict in data_list:
|
|
extract_data = data_dict.get("extract_data", {})
|
|
data = extract_data.get("data", [])
|
|
for data_item in data:
|
|
fund_name = data_item.get("fund_name", "")
|
|
share_name = data_item.get("share_name", "")
|
|
raw_name = self.get_raw_name(fund_name, share_name)
|
|
if len(raw_name) == 0:
|
|
continue
|
|
# if isinstance(self.document_production, str) and \
|
|
# raw_name.lower() in self.document_production.lower():
|
|
# continue
|
|
if raw_name not in list(raw_name_dict.keys()):
|
|
raw_name_dict[raw_name] = {"fund_name": fund_name, "share_name": share_name}
|
|
return raw_name_dict
|
|
|
|
def post_adjust_management_fee_costs(self, data_list: list):
|
|
"""
|
|
Adjust the management fee and management fee and costs
|
|
Because maybe the management fee and costs disclose in the first pages,
|
|
and the management fee disclose in the next pages.
|
|
According to biz rule, if can't find management fee when found management fee and costs, the management fee should be same as management fee and costs.
|
|
if can't find management fee and costs when found management fee, the management fee and costs should be same as management fee.
|
|
This function is to adjust the management fee and management fee and costs according to this case.
|
|
"""
|
|
management_fee_costs_list = []
|
|
management_fee_list = []
|
|
complex_rule_keywords = "Recoverable expenses \nEstimated other indirect costs"
|
|
for data_dict in data_list:
|
|
extract_data = data_dict.get("extract_data", {})
|
|
exist_complex_rule_keywords = False
|
|
page_text = data_dict.get("page_text", "")
|
|
if complex_rule_keywords in page_text:
|
|
exist_complex_rule_keywords = True
|
|
data = extract_data.get("data", [])
|
|
for data_item in data:
|
|
keys = list(data_item.keys())
|
|
fund_name = data_item.get("fund_name", "")
|
|
share_name = data_item.get("share_name", "")
|
|
if fund_name == "" or share_name == "":
|
|
continue
|
|
if "management_fee" in keys:
|
|
management_fee = data_item.get("management_fee", -1)
|
|
if management_fee != -1:
|
|
found = False
|
|
for mf in management_fee_list:
|
|
mf_fund_name = mf.get("fund_name", "")
|
|
mf_share_name = mf.get("share_name", "")
|
|
if (mf_fund_name == fund_name and mf_share_name == share_name) or \
|
|
(len(mf_fund_name) > 0 and len(mf_share_name) > 0 and mf_fund_name == mf_share_name and
|
|
(mf_share_name.endswith(share_name) or share_name.endswith(mf_share_name))):
|
|
if exist_complex_rule_keywords and \
|
|
("interposed_vehicle_performance_fee_cost" in keys or "recoverable_expenses" in keys):
|
|
mfc["management_fee"] = management_fee
|
|
found = True
|
|
break
|
|
else:
|
|
mf_value = mf.get("management_fee", -1)
|
|
if mf_value != -1 and mf_value >= management_fee:
|
|
mf["management_fee"] = management_fee
|
|
found = True
|
|
break
|
|
if not found:
|
|
management_fee_list.append({"fund_name": fund_name,
|
|
"share_name": share_name,
|
|
"management_fee": management_fee})
|
|
if "management_fee_and_costs" in keys:
|
|
management_fee_costs = data_item.get("management_fee_and_costs", -1)
|
|
if management_fee_costs != -1:
|
|
found = False
|
|
for mfc in management_fee_costs_list:
|
|
mfc_fund_name = mfc.get("fund_name", "")
|
|
mfc_share_name = mfc.get("share_name", "")
|
|
if (mfc_fund_name == fund_name and mfc_share_name == share_name) or \
|
|
(len(mfc_fund_name) > 0 and len(mfc_share_name) > 0 and mfc_fund_name == mfc_share_name and
|
|
(mfc_share_name.endswith(share_name) or share_name.endswith(mfc_share_name))):
|
|
if exist_complex_rule_keywords and \
|
|
("interposed_vehicle_performance_fee_cost" in keys or "recoverable_expenses" in keys):
|
|
mfc["management_fee_and_costs"] = management_fee_costs
|
|
found = True
|
|
break
|
|
else:
|
|
mfc_value = mfc.get("management_fee_and_costs", -1)
|
|
if mfc_value != -1 and mfc_value <= management_fee_costs:
|
|
mfc["management_fee_and_costs"] = management_fee_costs
|
|
found = True
|
|
break
|
|
if not found:
|
|
management_fee_costs_list.append({"fund_name": fund_name,
|
|
"share_name": share_name,
|
|
"management_fee_and_costs": management_fee_costs})
|
|
for data_dict in data_list:
|
|
extract_data = data_dict.get("extract_data", {})
|
|
data = extract_data.get("data", [])
|
|
for data_item in data:
|
|
keys = list(data_item.keys())
|
|
fund_name = data_item.get("fund_name", "")
|
|
share_name = data_item.get("share_name", "")
|
|
if fund_name == "" or share_name == "":
|
|
continue
|
|
if "management_fee" in keys:
|
|
for mf in management_fee_list:
|
|
if mf.get("fund_name", "") == fund_name and mf.get("share_name", "") == share_name:
|
|
data_item["management_fee"] = mf.get("management_fee", -1)
|
|
break
|
|
if "management_fee_and_costs" in keys:
|
|
for mfc in management_fee_costs_list:
|
|
if mfc.get("fund_name", "") == fund_name and mfc.get("share_name", "") == share_name:
|
|
data_item["management_fee_and_costs"] = mfc.get("management_fee_and_costs", -1)
|
|
break
|
|
return data_list
|
|
|
|
def supplement_minimum_initial_investment(self, data_list: list):
|
|
"""
|
|
Minimum initial investment should be same as from every fund/ share class in the same document.
|
|
This function is to supplement the minimum initial investment to each fund/ share class in the same document.
|
|
"""
|
|
exist_minimum_initial_investment = False
|
|
minimum_initial_investment = -1
|
|
mii_dict = None
|
|
for data_dict in data_list:
|
|
extract_data = data_dict.get("extract_data", {})
|
|
data = extract_data.get("data", [])
|
|
remove_items = []
|
|
for data_item in data:
|
|
keys = list(data_item.keys())
|
|
if "minimum_initial_investment" in keys:
|
|
exist_minimum_initial_investment = True
|
|
if minimum_initial_investment == -1:
|
|
minimum_initial_investment = data_item.get("minimum_initial_investment", -1)
|
|
if mii_dict is None:
|
|
mii_dict = data_dict
|
|
remove_items.append(data_item)
|
|
for data_item in remove_items:
|
|
try:
|
|
data_dict["extract_data"]["data"].remove(data_item)
|
|
except:
|
|
pass
|
|
if exist_minimum_initial_investment and minimum_initial_investment != -1:
|
|
# get all of funds in data_list
|
|
fund_name_list = []
|
|
for iter_data_dict in data_list:
|
|
extract_data = iter_data_dict.get("extract_data", {})
|
|
data = extract_data.get("data", [])
|
|
for data_item in data:
|
|
keys = list(data_item.keys())
|
|
if "fund_name" in keys:
|
|
fund_name = data_item.get("fund_name", "")
|
|
if len(fund_name) > 0 and fund_name not in fund_name_list:
|
|
fund_name_list.append(fund_name)
|
|
# rewrite mii_dict, set each fund name with same minimum_initial_investment value
|
|
new_mii_data_list = []
|
|
for fund_name in fund_name_list:
|
|
new_data_dict = {"fund_name": fund_name, "minimum_initial_investment": minimum_initial_investment}
|
|
new_mii_data_list.append(new_data_dict)
|
|
|
|
mii_dict["extract_data"]["data"].extend(new_mii_data_list)
|
|
return data_list
|
|
|
|
def extract_data_by_text(self) -> dict:
|
|
"""
|
|
keys are
|
|
doc_id, page_index, datapoint, value, raw_fund_name, fund_id, fund_name, raw_share_name, share_id, share_name
|
|
"""
|
|
data_list = []
|
|
pdf_page_count = len(self.page_text_dict.keys())
|
|
handled_page_num_list = []
|
|
|
|
previous_page_num = -1
|
|
previous_page_datapoints = []
|
|
previous_page_fund_name = None
|
|
for page_num, page_text in self.page_text_dict.items():
|
|
# if page_num != 18:
|
|
# continue
|
|
if page_num in handled_page_num_list:
|
|
continue
|
|
page_datapoints = self.get_datapoints_by_page_num(page_num)
|
|
if len(page_datapoints) == 0:
|
|
continue
|
|
if previous_page_num == page_num - 1 and \
|
|
previous_page_datapoints == page_datapoints and \
|
|
previous_page_fund_name is not None:
|
|
# Transfer previous page fund name to be the pre-fix of page text
|
|
# The purpose is to get fund name if the first records without fund name
|
|
# example document: 431073795, page index 1727 to 1728
|
|
logger.info(f"Transfer previous page fund name: {previous_page_fund_name} to be the pre-fix of page text")
|
|
page_text = f"\nThe last fund name of previous PDF page: {previous_page_fund_name}\n{page_text}"
|
|
else:
|
|
previous_page_fund_name = None
|
|
|
|
page_text = replace_special_table_header(page_text)
|
|
extract_data = self.extract_data_by_page(
|
|
page_num,
|
|
page_text,
|
|
page_datapoints,
|
|
need_exclude=False,
|
|
exclude_data=None,
|
|
previous_page_last_fund=previous_page_fund_name
|
|
)
|
|
data_list.append(extract_data)
|
|
|
|
|
|
page_data_list = extract_data.get("extract_data", {}).get("data", [])
|
|
if len(page_data_list) > 0:
|
|
previous_page_num = page_num
|
|
previous_page_fund_name = page_data_list[-1].get("fund_name", "")
|
|
previous_page_datapoints = page_datapoints
|
|
extract_way = extract_data.get("extract_way", "text")
|
|
current_page_data_count = len(page_data_list)
|
|
if current_page_data_count > 0:
|
|
count = 1
|
|
# some pdf documents have multiple pages for the same data
|
|
# and the next page may without table header with data point keywords.
|
|
# the purpose is try to get data from the next page
|
|
if extract_way == "text":
|
|
current_text = page_text
|
|
else:
|
|
current_text = ""
|
|
|
|
while True:
|
|
try:
|
|
next_page_num = page_num + count
|
|
if next_page_num >= pdf_page_count:
|
|
break
|
|
if self.document_type == 1 and next_page_num in self.investment_objective_pages:
|
|
break
|
|
next_datapoints = page_datapoints
|
|
if next_page_num in self.page_nums_with_datapoints:
|
|
should_continue = False
|
|
next_datapoints = self.get_datapoints_by_page_num(next_page_num)
|
|
if len(next_datapoints) == 0:
|
|
should_continue = True
|
|
else:
|
|
for next_datapoint in next_datapoints:
|
|
if self.doc_source == "aus_prospectus":
|
|
if next_datapoint in page_datapoints:
|
|
should_continue = False
|
|
break
|
|
else:
|
|
if next_datapoint not in page_datapoints:
|
|
should_continue = True
|
|
break
|
|
if should_continue:
|
|
next_datapoints.extend(page_datapoints)
|
|
# remove duplicate datapoints
|
|
next_datapoints = list(set(next_datapoints))
|
|
if not should_continue:
|
|
break
|
|
if extract_way == "text":
|
|
next_page_text = self.page_text_dict.get(next_page_num, "")
|
|
with_same_structure_table = self.is_next_page_with_same_structure_table(
|
|
current_text, next_page_text
|
|
)
|
|
if not with_same_structure_table:
|
|
break
|
|
next_page_text = replace_special_table_header(next_page_text)
|
|
target_text = current_text + next_page_text
|
|
else:
|
|
target_text = ""
|
|
|
|
# try to get data by current page_datapoints
|
|
logger.info(f"Try to get data from next page {next_page_num}")
|
|
next_page_extract_data = self.extract_data_by_page(
|
|
next_page_num,
|
|
target_text,
|
|
next_datapoints,
|
|
need_exclude=True,
|
|
exclude_data=page_data_list,
|
|
previous_page_last_fund=previous_page_fund_name
|
|
)
|
|
next_page_data_list = next_page_extract_data.get(
|
|
"extract_data", {}
|
|
).get("data", [])
|
|
# print(f"next_page_data_list: {next_page_num}")
|
|
# print(next_page_data_list)
|
|
if next_page_data_list is not None and len(next_page_data_list) > 0:
|
|
for current_page_data in page_data_list:
|
|
if current_page_data in next_page_data_list:
|
|
next_page_data_list.remove(current_page_data)
|
|
if len(next_page_data_list) == 0:
|
|
break
|
|
next_page_extract_data["extract_data"][
|
|
"data"
|
|
] = next_page_data_list
|
|
data_list.append(next_page_extract_data)
|
|
previous_page_num = next_page_num
|
|
previous_page_fund_name = next_page_data_list[-1].get("fund_name", "")
|
|
handled_page_num_list.append(next_page_num)
|
|
exist_current_page_datapoint = False
|
|
for next_page_data in next_page_data_list:
|
|
for page_datapoint in page_datapoints:
|
|
if page_datapoint in list(next_page_data.keys()):
|
|
exist_current_page_datapoint = True
|
|
break
|
|
if exist_current_page_datapoint:
|
|
break
|
|
if not exist_current_page_datapoint:
|
|
break
|
|
else:
|
|
data_list.append(next_page_extract_data)
|
|
break
|
|
count += 1
|
|
except Exception as e:
|
|
logger.error(f"Error in extracting data from next page: {e}")
|
|
break
|
|
|
|
# self.output_data_to_file(data_list)
|
|
return data_list
|
|
|
|
def is_next_page_with_same_structure_table(self, current_page_text: str, next_page_text: str) -> bool:
|
|
with_same_structure_table = False
|
|
compare_table_structure_prompts_file = os.path.join(self.instruction_folder, "compare_table_structure_prompts.json")
|
|
with open(compare_table_structure_prompts_file, "r", encoding="utf-8") as f:
|
|
compare_table_structure_prompts = "\n".join(json.load(f).get("prompts", []))
|
|
if len(compare_table_structure_prompts) > 0:
|
|
prompts = f"Context: \ncurrent page contents:\n{current_page_text}\nnext page contents:\n{next_page_text}\nInstructions:\n{compare_table_structure_prompts}\n"
|
|
result, with_error = chat(
|
|
prompt=prompts, response_format={"type": "json_object"}, max_tokens=100
|
|
)
|
|
response = result.get("response", "")
|
|
if not with_error:
|
|
try:
|
|
data = json.loads(response)
|
|
answer = data.get("answer", "No")
|
|
if answer.lower() == "yes":
|
|
with_same_structure_table = True
|
|
except:
|
|
pass
|
|
|
|
return with_same_structure_table
|
|
|
|
def extract_data_by_image(self) -> dict:
|
|
"""
|
|
keys are
|
|
doc_id, page_index, datapoint, value, raw_fund_name, fund_id, fund_name, raw_share_name, share_id, share_name
|
|
"""
|
|
data_list = []
|
|
pdf_page_count = len(self.page_text_dict.keys())
|
|
handled_page_num_list = []
|
|
for page_num, page_text in self.page_text_dict.items():
|
|
if page_num in handled_page_num_list:
|
|
continue
|
|
page_datapoints = self.get_datapoints_by_page_num(page_num)
|
|
if len(page_datapoints) == 0:
|
|
continue
|
|
|
|
extract_data = self.extract_data_by_page_image(page_num=page_num,
|
|
page_datapoints=page_datapoints)
|
|
data_list.append(extract_data)
|
|
|
|
page_data_list = extract_data.get("extract_data", {}).get("data", [])
|
|
|
|
current_page_data_count = len(page_data_list)
|
|
if current_page_data_count > 0:
|
|
count = 1
|
|
|
|
while count < 3:
|
|
try:
|
|
next_page_num = page_num + count
|
|
if next_page_num >= pdf_page_count:
|
|
break
|
|
next_datapoints = page_datapoints
|
|
if next_page_num in self.page_nums_with_datapoints:
|
|
should_continue = False
|
|
next_datapoints = self.get_datapoints_by_page_num(next_page_num)
|
|
if len(next_datapoints) == 0:
|
|
should_continue = True
|
|
else:
|
|
for next_datapoint in next_datapoints:
|
|
if next_datapoint not in page_datapoints:
|
|
should_continue = True
|
|
break
|
|
next_datapoints.extend(page_datapoints)
|
|
# remove duplicate datapoints
|
|
next_datapoints = list(set(next_datapoints))
|
|
if not should_continue:
|
|
break
|
|
# try to get data by current page_datapoints
|
|
next_page_extract_data = self.extract_data_by_page_image(
|
|
page_num=next_page_num,
|
|
page_datapoints=next_datapoints,
|
|
need_extract_text=False
|
|
)
|
|
next_page_data_list = next_page_extract_data.get(
|
|
"extract_data", {}
|
|
).get("data", [])
|
|
|
|
if next_page_data_list is not None and len(next_page_data_list) > 0:
|
|
data_list.append(next_page_extract_data)
|
|
handled_page_num_list.append(next_page_num)
|
|
exist_current_page_datapoint = False
|
|
for next_page_data in next_page_data_list:
|
|
for page_datapoint in page_datapoints:
|
|
if page_datapoint in list(next_page_data.keys()):
|
|
exist_current_page_datapoint = True
|
|
break
|
|
if exist_current_page_datapoint:
|
|
break
|
|
if not exist_current_page_datapoint:
|
|
break
|
|
else:
|
|
break
|
|
count += 1
|
|
except Exception as e:
|
|
logger.error(f"Error in extracting data from next page: {e}")
|
|
break
|
|
|
|
# self.output_data_to_file(data_list)
|
|
|
|
return data_list
|
|
|
|
def output_data_to_file(self, data_list: list) -> None:
|
|
json_data_file = os.path.join(
|
|
self.output_data_json_folder, f"{self.doc_id}.json"
|
|
)
|
|
with open(json_data_file, "w", encoding="utf-8") as f:
|
|
json.dump(data_list, f, ensure_ascii=False, indent=4)
|
|
|
|
data_df = pd.DataFrame(data_list)
|
|
data_df.reset_index(drop=True, inplace=True)
|
|
excel_data_file = os.path.join(
|
|
self.output_data_excel_folder, f"{self.doc_id}.xlsx"
|
|
)
|
|
with pd.ExcelWriter(excel_data_file) as writer:
|
|
data_df.to_excel(writer, sheet_name="extract_data", index=False)
|
|
|
|
def extract_data_by_page(
|
|
self,
|
|
page_num: int,
|
|
page_text: str,
|
|
page_datapoints: list,
|
|
need_exclude: bool = False,
|
|
exclude_data: list = None,
|
|
previous_page_last_fund: str = None) -> dict:
|
|
# If can't find numberic value, e.g. 1.25 or 3,88
|
|
# apply Vision ChatGPT to extract data
|
|
exist_data_point_value_text = False
|
|
for datapoint in page_datapoints:
|
|
if self.datapoint_type_config.get(datapoint, "") == "text":
|
|
exist_data_point_value_text = True
|
|
break
|
|
exist_numeric_value = False
|
|
special_code_all = []
|
|
page_text_line_count = 100
|
|
if not exist_data_point_value_text:
|
|
special_code_regex = r"\x10|\x11|\x12|\x13|\x14|\x15|\x16|\x17|\x18|\x19|\x1a|\x1b|\x1c|\x1d|\x1e|\x1f"
|
|
special_code_all = [code for code in re.findall(special_code_regex, page_text)
|
|
if code != "\n"]
|
|
page_text_line_count = len(page_text.split("\n"))
|
|
numeric_regex = r"\d+(\.|\,)\d+"
|
|
exist_numeric_value = (re.search(numeric_regex, page_text) is not None)
|
|
if not exist_data_point_value_text and (not exist_numeric_value or
|
|
page_text_line_count < 3 or
|
|
len(special_code_all) > 100):
|
|
logger.info(f"Can't find numberic value in page {page_num}, apply Vision ChatGPT to extract data")
|
|
return self.extract_data_by_page_image(
|
|
page_num=page_num,
|
|
page_datapoints=page_datapoints,
|
|
need_exclude=False,
|
|
exclude_data=None,
|
|
previous_page_last_fund=previous_page_last_fund,
|
|
need_extract_text=False)
|
|
else:
|
|
return self.extract_data_by_page_text(
|
|
page_num=page_num,
|
|
page_text=page_text,
|
|
page_datapoints=page_datapoints,
|
|
need_exclude=need_exclude,
|
|
exclude_data=exclude_data,
|
|
previous_page_last_fund=previous_page_last_fund
|
|
)
|
|
|
|
def extract_data_by_page_text(
|
|
self,
|
|
page_num: int,
|
|
page_text: str,
|
|
page_datapoints: list,
|
|
need_exclude: bool = False,
|
|
exclude_data: list = None,
|
|
previous_page_last_fund: str = None,
|
|
original_way: str = "text"
|
|
) -> dict:
|
|
"""
|
|
keys are
|
|
doc_id, page_index, datapoint, value, raw_fund_name, fund_id, fund_name, raw_share_name, share_id, share_name
|
|
"""
|
|
logger.info(f"Extracting data from page {page_num}")
|
|
if self.document_type == 1:
|
|
# pre_context = f"The document type is prospectus. \nThe fund names in this document are {', '.join(self.fund_name_list)}."
|
|
# if pre_context in page_text:
|
|
# page_text = page_text.replace(pre_context, "\n").strip()
|
|
pre_context = ""
|
|
if len(self.investment_objective_pages) > 0:
|
|
# Get the page number of the most recent investment objective at the top of the current page.
|
|
diff_pages = [page_num - investment_objective_page for investment_objective_page
|
|
in self.investment_objective_pages
|
|
if investment_objective_page <= page_num]
|
|
if len(diff_pages) > 0 and diff_pages[-1] < 5:
|
|
top_nearest_investment_objective_page = self.investment_objective_pages[len(diff_pages) - 1]
|
|
top_nearest_investment_objective_text = self.page_text_dict.get(top_nearest_investment_objective_page, "")
|
|
if top_nearest_investment_objective_text in page_text:
|
|
page_text = page_text.replace(top_nearest_investment_objective_text, "").strip()
|
|
pre_context = f"\nThe most recent investment objective page text which maybe with fund name is: \n{top_nearest_investment_objective_text}.\n"
|
|
# If can't find previous investment objective text, add the fund names to be the pre-fix of page text
|
|
page_text = f"{pre_context}\n{page_text}".strip()
|
|
|
|
instructions = self.get_instructions_by_datapoints(
|
|
page_text,
|
|
page_datapoints,
|
|
need_exclude,
|
|
exclude_data,
|
|
extract_way="text"
|
|
)
|
|
result, with_error = chat(
|
|
prompt=instructions, response_format={"type": "json_object"}
|
|
)
|
|
response = result.get("response", "")
|
|
if with_error:
|
|
logger.error(f"Error in extracting tables from page")
|
|
data_dict = {"doc_id": self.doc_id}
|
|
data_dict["page_index"] = page_num
|
|
data_dict["datapoints"] = ", ".join(page_datapoints)
|
|
data_dict["page_text"] = page_text
|
|
data_dict["instructions"] = instructions
|
|
data_dict["raw_answer"] = response
|
|
data_dict["extract_data"] = {"data": []}
|
|
data_dict["extract_way"] = original_way
|
|
data_dict["prompt_token"] = result.get("prompt_token", 0)
|
|
data_dict["completion_token"] = result.get("completion_token", 0)
|
|
data_dict["total_token"] = result.get("total_token", 0)
|
|
return data_dict
|
|
try:
|
|
data = json.loads(response)
|
|
except:
|
|
try:
|
|
# if occur error, perhaps the output length is over 4K tokens
|
|
# split the context to two parts and try to get data from the two parts
|
|
# Attention: after deploy ChatGPT4o 2024-08-16 version, the max token length is 16K,
|
|
# need not to split the context.
|
|
# data = self.chat_by_split_context(
|
|
# page_text, page_datapoints, need_exclude, exclude_data
|
|
# )
|
|
if len(data.get("data", [])) == 0:
|
|
data = json_repair.loads(response)
|
|
except:
|
|
data = {"data": []}
|
|
try:
|
|
data = self.validate_data(extract_data_info=data,
|
|
page_text=page_text,
|
|
previous_page_last_fund=previous_page_last_fund)
|
|
except:
|
|
pass
|
|
|
|
data_dict = {"doc_id": self.doc_id}
|
|
data_dict["page_index"] = page_num
|
|
data_dict["datapoints"] = ", ".join(page_datapoints)
|
|
data_dict["page_text"] = page_text
|
|
data_dict["instructions"] = instructions
|
|
data_dict["raw_answer"] = response
|
|
data_dict["extract_data"] = data
|
|
data_dict["extract_way"] = original_way
|
|
data_dict["prompt_token"] = result.get("prompt_token", 0)
|
|
data_dict["completion_token"] = result.get("completion_token", 0)
|
|
data_dict["total_token"] = result.get("total_token", 0)
|
|
return data_dict
|
|
|
|
def extract_data_by_page_image(
|
|
self,
|
|
page_num: int,
|
|
page_datapoints: list,
|
|
need_exclude: bool = False,
|
|
exclude_data: list = None,
|
|
previous_page_last_fund: str = None,
|
|
need_extract_text: bool = False
|
|
) -> dict:
|
|
"""
|
|
keys are
|
|
doc_id, page_index, datapoint, value, raw_fund_name, fund_id, fund_name, raw_share_name, share_id, share_name
|
|
"""
|
|
if need_extract_text:
|
|
logger.info(f"Extracting data from page {page_num} with extracting text as single step.")
|
|
page_text = self.get_image_text(page_num)
|
|
if page_text is None or len(page_text) == 0:
|
|
data_dict = {"doc_id": self.doc_id}
|
|
data_dict["page_index"] = page_num
|
|
data_dict["datapoints"] = ", ".join(page_datapoints)
|
|
data_dict["page_text"] = ""
|
|
data_dict["instructions"] = ""
|
|
data_dict["raw_answer"] = ""
|
|
data_dict["extract_data"] = {"data": []}
|
|
data_dict["extract_way"] = "image"
|
|
data_dict["prompt_token"] = 0
|
|
data_dict["completion_token"] = 0
|
|
data_dict["total_token"] = 0
|
|
return data_dict
|
|
else:
|
|
if previous_page_last_fund is not None and len(previous_page_last_fund) > 0:
|
|
logger.info(f"Transfer previous page fund name: {previous_page_last_fund} to be the pre-fix of page text")
|
|
page_text = f"\nThe last fund name of previous PDF page: {previous_page_last_fund}\n{page_text}"
|
|
return self.extract_data_by_page_text(
|
|
page_num=page_num,
|
|
page_text=page_text,
|
|
page_datapoints=page_datapoints,
|
|
need_exclude=need_exclude,
|
|
exclude_data=exclude_data,
|
|
previous_page_last_fund=previous_page_last_fund,
|
|
original_way="image"
|
|
)
|
|
else:
|
|
logger.info(f"Extracting data from page {page_num} without extracting text as single step.")
|
|
return self.extract_data_by_pure_image(
|
|
page_num=page_num,
|
|
page_datapoints=page_datapoints,
|
|
need_exclude=need_exclude,
|
|
exclude_data=exclude_data,
|
|
previous_page_last_fund=previous_page_last_fund
|
|
)
|
|
|
|
def extract_data_by_pure_image(
|
|
self,
|
|
page_num: int,
|
|
page_datapoints: list,
|
|
need_exclude: bool = False,
|
|
exclude_data: list = None,
|
|
previous_page_last_fund: str = None
|
|
) -> dict:
|
|
"""
|
|
keys are
|
|
doc_id, page_index, datapoint, value, raw_fund_name, fund_id, fund_name, raw_share_name, share_id, share_name
|
|
"""
|
|
image_base64 = self.get_pdf_image_base64(page_num)
|
|
instructions = self.get_instructions_by_datapoints(
|
|
previous_page_last_fund,
|
|
page_datapoints,
|
|
need_exclude=need_exclude,
|
|
exclude_data=exclude_data,
|
|
extract_way="image"
|
|
)
|
|
result, with_error = chat(
|
|
prompt=instructions, response_format={"type": "json_object"}, image_base64=image_base64
|
|
)
|
|
response = result.get("response", "")
|
|
if with_error:
|
|
logger.error(f"Error in extracting tables from page")
|
|
data_dict = {"doc_id": self.doc_id}
|
|
data_dict["page_index"] = page_num
|
|
data_dict["datapoints"] = ", ".join(page_datapoints)
|
|
data_dict["page_text"] = ""
|
|
data_dict["instructions"] = instructions
|
|
data_dict["raw_answer"] = response
|
|
data_dict["extract_data"] = {"data": []}
|
|
data_dict["extract_way"] = "image"
|
|
data_dict["prompt_token"] = result.get("prompt_token", 0)
|
|
data_dict["completion_token"] = result.get("completion_token", 0)
|
|
data_dict["total_token"] = result.get("total_token", 0)
|
|
return data_dict
|
|
try:
|
|
data = json.loads(response)
|
|
except:
|
|
try:
|
|
data = json_repair.loads(response)
|
|
except:
|
|
data = {"data": []}
|
|
try:
|
|
data = self.validate_data(data, None, previous_page_last_fund)
|
|
except:
|
|
pass
|
|
|
|
data_dict = {"doc_id": self.doc_id}
|
|
data_dict["page_index"] = page_num
|
|
data_dict["datapoints"] = ", ".join(page_datapoints)
|
|
data_dict["page_text"] = ""
|
|
data_dict["instructions"] = instructions
|
|
data_dict["raw_answer"] = response
|
|
data_dict["extract_data"] = data
|
|
data_dict["extract_way"] = "image"
|
|
data_dict["prompt_token"] = result.get("prompt_token", 0)
|
|
data_dict["completion_token"] = result.get("completion_token", 0)
|
|
data_dict["total_token"] = result.get("total_token", 0)
|
|
return data_dict
|
|
|
|
def get_image_text(self, page_num: int) -> str:
|
|
image_base64 = self.get_pdf_image_base64(page_num)
|
|
instructions = self.instructions_config.get("get_image_text", "\n")
|
|
logger.info(f"Get text from image of page {page_num}")
|
|
result, with_error = chat(
|
|
prompt=instructions, response_format={"type": "json_object"}, image_base64=image_base64
|
|
)
|
|
response = result.get("response", "")
|
|
text = ""
|
|
if with_error:
|
|
logger.error(f"Can't get text from current image")
|
|
try:
|
|
data = json.loads(response)
|
|
except:
|
|
try:
|
|
data = json_repair.loads(response)
|
|
except:
|
|
pass
|
|
text = data.get("text", "")
|
|
# print(text)
|
|
return text
|
|
|
|
def validate_data(self,
|
|
extract_data_info: dict,
|
|
page_text: str,
|
|
previous_page_last_fund: str=None) -> dict:
|
|
"""
|
|
Validate data by the rules
|
|
1. Each data should be with fund name
|
|
2. For share level data, it should be with share name
|
|
"""
|
|
data_list = extract_data_info.get("data", [])
|
|
if len(data_list) == 0:
|
|
return extract_data_info
|
|
remove_list = []
|
|
performance_fee_regex = r"Amount\s+of\s+the\s+performance\s+fees|Performance\s+Fees\s+amounts|Performance\s+fees\s+amounts|Commissioni\s+di\s+performance|Performance\s+Fee\s+|Performance\s+fees\s+charged"
|
|
nav_regex = r"based\s+on\s+(the\s+)?NAV|on\s+the\s+Share\s+Class\s+NAV|NAV\s+of\s+performance\s+fee|of\s+the\s+average\s+Net\s+Asset\s+Value|Attivi\s+in\s+gestione|Performance\s+Fee\s+of\s+NAV\s+in|share\s+class\s+dealing\s+NAV"
|
|
if page_text is not None and len(page_text) > 0:
|
|
performance_fee_search = re.search(performance_fee_regex, page_text)
|
|
nav_search = re.search(nav_regex, page_text)
|
|
else:
|
|
performance_fee_search = None
|
|
nav_search = None
|
|
for data in data_list:
|
|
if (data.get("performance_fee", None) is not None and
|
|
performance_fee_search is not None and
|
|
nav_search is not None):
|
|
data.pop("performance_fee")
|
|
keys = [key for key in list(data.keys())
|
|
if key not in ["fund name", "share name"]]
|
|
if len(keys) == 0:
|
|
remove_list.append(data)
|
|
continue
|
|
fund_name = data.get("fund name", "").strip()
|
|
if fund_name == "":
|
|
remove_list.append(data)
|
|
continue
|
|
|
|
# Clean fund name start
|
|
if previous_page_last_fund is not None and len(previous_page_last_fund) > 0:
|
|
previous_page_last_fund = previous_page_last_fund.strip()
|
|
if fund_name.startswith(previous_page_last_fund) and fund_name != previous_page_last_fund:
|
|
modified_fund_name = fund_name.replace(previous_page_last_fund, "").strip()
|
|
if len(modified_fund_name.split()) > 1:
|
|
fund_name = modified_fund_name
|
|
fund_name = self.get_fund_name(fund_name, "Fund")
|
|
fund_name = self.get_fund_name(fund_name, "Bond")
|
|
|
|
remove_prefix_list = ["Market Specific Equity Sub-Funds",
|
|
"International and Regional Equity Sub-Funds",
|
|
"Equity Sub-Funds"]
|
|
for remove_item in remove_prefix_list:
|
|
if fund_name.startswith(remove_item):
|
|
fund_name = fund_name.replace(remove_item, "").strip()
|
|
|
|
data["fund name"] = fund_name
|
|
# Clean fund name end
|
|
|
|
keys = list(data.keys())
|
|
for key in keys:
|
|
if self.datapoint_level_config.get(key, "") == "share_level":
|
|
if data.get("share name", "") == "":
|
|
include_key_words = False
|
|
if key == "ter" and page_text is not None and len(page_text) > 0:
|
|
ter_regex = r"TER\s+in\s+\%|TER\s*\%"
|
|
ter_search = re.search(ter_regex, page_text)
|
|
if ter_search is not None:
|
|
include_key_words = True
|
|
if not include_key_words:
|
|
is_share_name = self.check_fund_name_as_share(fund_name)
|
|
if not is_share_name:
|
|
remove_list.append(data)
|
|
break
|
|
data["share name"] = fund_name
|
|
if data.get(key, "") == "":
|
|
data.pop(key)
|
|
for remove_data in remove_list:
|
|
if remove_data in data_list:
|
|
data_list.remove(remove_data)
|
|
# check performance_fee
|
|
for data in data_list:
|
|
performance_fee = data.get("performance_fee", None)
|
|
if performance_fee is not None:
|
|
try:
|
|
performance_fee = float(performance_fee)
|
|
if (performance_fee > 3 and performance_fee % 2.5 == 0) or \
|
|
performance_fee > 10:
|
|
data.pop("performance_fee")
|
|
except:
|
|
data.pop("performance_fee")
|
|
remove_list = []
|
|
for data in data_list:
|
|
keys = [key for key in list(data.keys())
|
|
if key not in ["fund name", "share name"]]
|
|
if len(keys) == 0:
|
|
remove_list.append(data)
|
|
for remove_data in remove_list:
|
|
if remove_data in data_list:
|
|
data_list.remove(remove_data)
|
|
# update "fund name" to be "fund_name"
|
|
# update "share name" to be "share_name"
|
|
new_data_list = []
|
|
multi_over_3_share_regex = r"([A-Z]{1,}\,\s){3,}"
|
|
exist_multi_over_3_share = False
|
|
for data in data_list:
|
|
fund_name = data.get("fund name", "").strip()
|
|
if len(fund_name) == 0:
|
|
continue
|
|
raw_share_name = data.get("share name", "")
|
|
if not exist_multi_over_3_share:
|
|
multi_over_3_share_search = re.search(multi_over_3_share_regex, raw_share_name)
|
|
if multi_over_3_share_search is not None:
|
|
exist_multi_over_3_share = True
|
|
if exist_multi_over_3_share:
|
|
share_name_list = self.split_multi_share_name(raw_share_name)
|
|
else:
|
|
share_name_list = [raw_share_name]
|
|
if len(share_name_list) > 0:
|
|
for share_name in share_name_list:
|
|
new_data = {}
|
|
new_data["fund_name"] = fund_name
|
|
if share_name != "":
|
|
new_data["share_name"] = share_name
|
|
ter = data.get("ter", None)
|
|
if ter is not None:
|
|
new_data["ter"] = ter
|
|
performance_fee = data.get("performance fees", None)
|
|
if performance_fee is not None:
|
|
new_data["performance_fee"] = performance_fee
|
|
|
|
for key, value in data.items():
|
|
if key not in ["fund name", "share name", "ter", "performance fees"]:
|
|
new_data[key] = value
|
|
new_data_list.append(new_data)
|
|
|
|
extract_data_info["data"] = new_data_list
|
|
return extract_data_info
|
|
|
|
def split_multi_share_name(self, raw_share_name: str) -> list:
|
|
"""
|
|
Some document, e.g. 481482392
|
|
Exist multi share name as table header, e.g. "Class A, B, E, M, N, P, R, U"
|
|
For this case, need split the share name to be ["Class A", "Class B", "Class E",
|
|
"Class M", "Class N", "Class P", "Class R", "Class U"]
|
|
"""
|
|
multi_over_2_share_regex = r"([A-Z]{1,}\,\s){2,}"
|
|
multi_over_2_share_search = re.search(multi_over_2_share_regex, raw_share_name)
|
|
share_name_list = [raw_share_name]
|
|
if multi_over_2_share_search is not None:
|
|
multi_share_splits = [share_name.strip() for share_name in raw_share_name.split(",")
|
|
if len(share_name.strip()) > 0]
|
|
first_share_name = multi_share_splits[0]
|
|
first_share_name_split = first_share_name.split()
|
|
share_name_prefix = None
|
|
if len(first_share_name_split) == 2:
|
|
share_name_prefix = first_share_name_split[0]
|
|
if share_name_prefix is not None and len(share_name_prefix) > 0:
|
|
new_share_name_list = []
|
|
for split in multi_share_splits:
|
|
if split == first_share_name:
|
|
new_share_name_list.append(split)
|
|
else:
|
|
new_share_name_list.append(f"{share_name_prefix} {split}")
|
|
share_name_list = new_share_name_list
|
|
else:
|
|
share_name_list = multi_share_splits
|
|
else:
|
|
share_name_list = multi_share_splits
|
|
return share_name_list
|
|
|
|
def get_fund_name(self, fund_name: str, fund_feature: str):
|
|
if not fund_name.endswith(fund_feature):
|
|
return fund_name
|
|
# to avoid split funds to fund s
|
|
fund_feature = fund_feature + " "
|
|
fund_name_split = fund_name.split(fund_feature)
|
|
if len(fund_name_split) > 1:
|
|
last_fund = fund_name_split[-1].strip()
|
|
if len(last_fund) == 0:
|
|
last_fund = fund_name_split[-2].strip()
|
|
fund_name = f"{last_fund} {fund_feature}"
|
|
return fund_name
|
|
|
|
def check_fund_name_as_share(self, fund_name: str) -> bool:
|
|
"""
|
|
Check if the fund name is the same as share name
|
|
"""
|
|
if len(fund_name) == 0 == 0:
|
|
return False
|
|
share_name_list = self.document_mapping_info_df["ShareClassName"].unique().tolist()
|
|
if len(share_name_list) == 0:
|
|
return False
|
|
max_similarity_name, max_similarity = get_most_similar_name(
|
|
text=fund_name,
|
|
name_list=share_name_list,
|
|
share_name=None,
|
|
fund_name=None,
|
|
matching_type="share",
|
|
process_cache=None)
|
|
if max_similarity >= 0.8:
|
|
return True
|
|
return False
|
|
|
|
def get_datapoints_by_page_num(self, page_num: int) -> list:
|
|
datapoints = []
|
|
|
|
for datapoint in self.datapoints:
|
|
if page_num in self.datapoint_page_info.get(datapoint, []):
|
|
datapoints.append(datapoint)
|
|
return datapoints
|
|
|
|
def get_instructions_by_datapoints(
|
|
self,
|
|
page_text: str,
|
|
datapoints: list,
|
|
need_exclude: bool = False,
|
|
exclude_data: list = None,
|
|
extract_way: str = "text",
|
|
) -> str:
|
|
"""
|
|
Get instructions to extract data from the page by the datapoints
|
|
Below is the instructions sections:
|
|
summary: string
|
|
reported_name by datapoints: dict
|
|
data_business_features: dict
|
|
common: list
|
|
investment_level by datapoints: dict
|
|
data_value_range by datapoints: dict
|
|
special_rule by datapoints: dict
|
|
special_cases: dict
|
|
common: list
|
|
title
|
|
contents
|
|
special_case by datapoints: list
|
|
title
|
|
contents
|
|
output_requirement
|
|
common: list
|
|
fund_level: list
|
|
share_level: dict
|
|
fund_name: list
|
|
share_name: list
|
|
ogc_value: list
|
|
ter_value: list
|
|
performance_fee_value: list
|
|
end
|
|
"""
|
|
instructions = []
|
|
if extract_way == "text":
|
|
instructions = [f"Context:\n{page_text}\n\nInstructions:\n"]
|
|
|
|
datapoint_name_list = []
|
|
for datapoint in datapoints:
|
|
datapoint_name = self.datapoint_name_config.get(datapoint, "")
|
|
datapoint_name_list.append(datapoint_name)
|
|
|
|
if extract_way == "text":
|
|
summary = self.instructions_config.get("summary", "\n")
|
|
elif extract_way == "image":
|
|
summary = self.instructions_config.get("summary_image", "\n")
|
|
if page_text is not None and len(page_text) > 0:
|
|
logger.info(f"Transfer previous page fund name: {page_text} to be the pre-fix of page text")
|
|
summary += f"\nThe last fund name of previous PDF page: {page_text}\n"
|
|
else:
|
|
summary = self.instructions_config.get("summary", "\n")
|
|
|
|
instructions.append(summary.format(", ".join(datapoint_name_list)))
|
|
instructions.append("\n")
|
|
|
|
if extract_way == "image":
|
|
image_features = self.instructions_config.get("image_features", [])
|
|
instructions.extend(image_features)
|
|
instructions.append("\n")
|
|
|
|
instructions.append("Datapoints Reported name:\n")
|
|
instructions.append("Please look for relevant reported names and similar variations in the context.\n")
|
|
reported_name_info_in_instructions = self.instructions_config.get("reported_name", {})
|
|
for datapoint in datapoints:
|
|
reported_name_list = self.datapoint_reported_name_config.get(datapoint, [])
|
|
if len(reported_name_list) == 0:
|
|
reported_name = reported_name_info_in_instructions.get(datapoint, "")
|
|
else:
|
|
joined_reported_name = ", ".join(reported_name_list)
|
|
datapoint_name = datapoint
|
|
if datapoint_name == "performance_fee":
|
|
datapoint_name = "performance fees"
|
|
else:
|
|
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)
|
|
instructions.append("\n")
|
|
instructions.append("\n")
|
|
|
|
if self.language != "english":
|
|
"""
|
|
"multilingual_reported_name": {
|
|
"describe": "Please be careful to extract relevant data by different reported names from multilingual Context.",
|
|
"regular_example_template": "{datapoint} Example {number}:\nLanguage: {language}\n---Context Start-----\n{fund_name}\n{share_name}\n{reported_name}\n{value}\n---Context End-----\nAnswer: {answer}",
|
|
"special_example_template_none": "{datapoint} Example {number}:\nLanguage: {language}\nValue is belong to \"-, *, **, N/A, N/A%, N/A %, NONE\", ignore it\n---Context Start-----\n{fund_name}\n{share_name}\n{reported_name} 2)\n-\n---Context End-----\nAnswer: {answer}",
|
|
"value_examples": ["1,98", "3.25", "2.16", "1,73", "4,53"]
|
|
"fund_example": "Fund 1",
|
|
"share_example": "Share 1"
|
|
}
|
|
"""
|
|
multilingual_reported_name_config = self.instructions_config.get("multilingual_reported_name", {})
|
|
describe = multilingual_reported_name_config.get("describe", "")
|
|
regular_example_template = multilingual_reported_name_config.get("regular_example_template", "")
|
|
special_example_template_none = multilingual_reported_name_config.get("special_example_template_none", "")
|
|
value_examples = multilingual_reported_name_config.get("value_examples", [])
|
|
fund_example = multilingual_reported_name_config.get("fund_example", "")
|
|
share_example = multilingual_reported_name_config.get("share_example", "")
|
|
instructions.append("Multilingual reported name:\n")
|
|
instructions.append(f"{describe}\n")
|
|
|
|
# set language the first char to be upper
|
|
language = self.language[0].upper() + self.language[1:]
|
|
for datapoint in datapoints:
|
|
mul_reported_name_list = self.non_english_reported_name_config.get(datapoint, [])
|
|
# shuffle the reported name list
|
|
mul_reported_name_list = list(set(mul_reported_name_list))
|
|
if len(mul_reported_name_list) == 0:
|
|
continue
|
|
datapoint_name = datapoint
|
|
if datapoint_name == "performance_fee":
|
|
datapoint_name = "performance fees"
|
|
else:
|
|
datapoint_name = datapoint_name.upper()
|
|
example_count = 1
|
|
none_value_example_count = 0
|
|
for mul_reported_name in mul_reported_name_list:
|
|
if datapoint in ["ter", "performance_fee"] and example_count >= 3:
|
|
break
|
|
value = value_examples[example_count % len(value_examples)]
|
|
answer = {"fund name": fund_example,
|
|
"share name": share_example,
|
|
datapoint: float(value.replace(",", "."))}
|
|
# transfer answer to string
|
|
answer = json.dumps(answer, ensure_ascii=False)
|
|
example = regular_example_template.format(
|
|
datapoint=datapoint_name,
|
|
number=example_count,
|
|
language=language,
|
|
fund_name=fund_example,
|
|
share_name=share_example,
|
|
reported_name=mul_reported_name,
|
|
value=value,
|
|
answer=answer,
|
|
)
|
|
instructions.append(example)
|
|
instructions.append("\n")
|
|
instructions.append("\n")
|
|
|
|
example_count += 1
|
|
if len(mul_reported_name.split()) > 1:
|
|
if none_value_example_count != 2:
|
|
none_value_example = special_example_template_none.format(
|
|
datapoint=datapoint_name,
|
|
number=example_count,
|
|
language=language,
|
|
fund_name=fund_example,
|
|
share_name=share_example,
|
|
reported_name=mul_reported_name,
|
|
answer = json.dumps({}, ensure_ascii=False)
|
|
)
|
|
instructions.append(none_value_example)
|
|
instructions.append("\n")
|
|
instructions.append("\n")
|
|
example_count += 1
|
|
none_value_example_count += 1
|
|
|
|
instructions.append("\n")
|
|
instructions.append("Data business features:\n")
|
|
data_business_features = self.instructions_config.get(
|
|
"data_business_features", {}
|
|
)
|
|
common = "\n".join(data_business_features.get("common", []))
|
|
instructions.append(common)
|
|
instructions.append("\n")
|
|
|
|
instructions.append("Datapoints investment level:\n")
|
|
investment_level_info = data_business_features.get("investment_level", {})
|
|
for datapoint in datapoints:
|
|
investment_level = investment_level_info.get(datapoint, "")
|
|
instructions.append(investment_level)
|
|
instructions.append("\n")
|
|
instructions.append("\n")
|
|
|
|
instructions.append("Datapoints value range:\n")
|
|
data_value_range_info = data_business_features.get("data_value_range", {})
|
|
for datapoint in datapoints:
|
|
data_value_range = data_value_range_info.get(datapoint, "")
|
|
instructions.append(data_value_range)
|
|
instructions.append("\n")
|
|
instructions.append("\n")
|
|
|
|
special_rule_info = data_business_features.get("special_rule", {})
|
|
# The reason why apply special_rule_by_keywords is:
|
|
# 1. The special rule is very complex, prompsts are very long.
|
|
# 2. To load it by keywords, is to avoid for simple case, the prompts are too long.
|
|
complex_special_rule = data_business_features.get("sepcial_rule_by_keywords", "")
|
|
with_special_rule_title = False
|
|
for datapoint in datapoints:
|
|
find_complex_special_rule = False
|
|
if page_text is not None and len(page_text) > 0:
|
|
complex_special_rule_list = complex_special_rule.get(datapoint, [])
|
|
for complex_special_rule in complex_special_rule_list:
|
|
complex_keywords = complex_special_rule.get("keywords", [])
|
|
if len(complex_keywords) == 0:
|
|
continue
|
|
exist_keywords = False
|
|
for special_keywords in complex_keywords:
|
|
special_keywrods_regex = add_slash_to_text_as_regex(special_keywords)
|
|
if special_keywords in page_text or \
|
|
re.search(special_keywrods_regex, page_text) is not None:
|
|
exist_keywords = True
|
|
break
|
|
if exist_keywords:
|
|
complex_prompts_list = complex_special_rule.get("prompts", [])
|
|
if len(complex_prompts_list) > 0:
|
|
if not with_special_rule_title:
|
|
instructions.append("Special rule:\n")
|
|
with_special_rule_title = True
|
|
complex_prompts = "\n".join(complex_prompts_list)
|
|
instructions.append(complex_prompts)
|
|
instructions.append("\n\n")
|
|
find_complex_special_rule = True
|
|
if find_complex_special_rule:
|
|
continue
|
|
special_rule_list = special_rule_info.get(datapoint, [])
|
|
if len(special_rule_list) > 0:
|
|
if not with_special_rule_title:
|
|
instructions.append("Special rule:\n")
|
|
with_special_rule_title = True
|
|
special_rule = "\n".join(special_rule_list)
|
|
instructions.append(special_rule)
|
|
instructions.append("\n\n")
|
|
|
|
instructions.append("\n")
|
|
|
|
instructions.append("Special cases:\n")
|
|
special_cases = self.instructions_config.get("special_cases", {})
|
|
special_cases_common_list = special_cases.get("common", [])
|
|
special_cases_number = 1
|
|
for special_cases_common in special_cases_common_list:
|
|
title = special_cases_common.get("title", "")
|
|
title = f"{special_cases_number}. {title} "
|
|
special_cases_number += 1
|
|
instructions.append(title)
|
|
instructions.append("\n")
|
|
contents_list = special_cases_common.get("contents", [])
|
|
contents = "\n".join(contents_list)
|
|
instructions.append(contents)
|
|
instructions.append("\n\n")
|
|
|
|
for datapoint in datapoints:
|
|
special_case_list = special_cases.get(datapoint, [])
|
|
for special_case in special_case_list:
|
|
title = special_case.get("title", "")
|
|
title = f"{special_cases_number}. {title} "
|
|
special_cases_number += 1
|
|
instructions.append(title)
|
|
instructions.append("\n")
|
|
contents_list = special_case.get("contents", [])
|
|
contents = "\n".join(contents_list)
|
|
instructions.append(contents)
|
|
instructions.append("\n")
|
|
instructions.append("\n")
|
|
|
|
instructions.append("Output requirement:\n")
|
|
output_requirement = self.instructions_config.get("output_requirement", {})
|
|
output_requirement_common_list = output_requirement.get("common", [])
|
|
instructions.append("\n".join(output_requirement_common_list))
|
|
instructions.append("\n")
|
|
|
|
fund_datapoint_value_example = {}
|
|
fund_level_config = output_requirement.get("fund_level", {})
|
|
|
|
share_datapoint_value_example = {}
|
|
share_level_config = output_requirement.get("share_level", {})
|
|
|
|
example_list = []
|
|
dp_reported_name_config = output_requirement.get("dp_reported_name", {})
|
|
dp_reported_name = {}
|
|
for datapoint in datapoints:
|
|
investment_level = self.datapoint_level_config.get(datapoint, "")
|
|
if investment_level == "fund_level":
|
|
# fund_level_example_list = output_requirement.get("fund_level", [])
|
|
# for example in fund_level_example_list:
|
|
# try:
|
|
# sub_example_list = json.loads(example)
|
|
# except:
|
|
# sub_example_list = json_repair.loads(example)
|
|
# example_list.extend(sub_example_list)
|
|
fund_datapoint_value_example[datapoint] = fund_level_config.get(
|
|
f"{datapoint}_value", []
|
|
)
|
|
elif investment_level == "share_level":
|
|
share_datapoint_value_example[datapoint] = share_level_config.get(
|
|
f"{datapoint}_value", []
|
|
)
|
|
dp_reported_name[datapoint] = dp_reported_name_config.get(datapoint, "")
|
|
|
|
instructions.append(f"Example:\n")
|
|
|
|
fund_datapoint_list = list(fund_datapoint_value_example.keys())
|
|
if len(fund_datapoint_list) > 0:
|
|
fund_name_example_list = fund_level_config.get("fund_name", [])
|
|
for index in range(len(fund_name_example_list)):
|
|
example_dict = {
|
|
"fund name": fund_name_example_list[index],
|
|
}
|
|
for fund_datapoint in fund_datapoint_list:
|
|
fund_datapoint_values = fund_datapoint_value_example[fund_datapoint]
|
|
if index < len(fund_datapoint_values):
|
|
example_dict[fund_datapoint] = fund_datapoint_values[index]
|
|
example_list.append(example_dict)
|
|
|
|
share_datapoint_list = list(share_datapoint_value_example.keys())
|
|
if len(share_datapoint_list) > 0:
|
|
fund_name_example_list = share_level_config.get("fund_name", [])
|
|
share_name_example_list = share_level_config.get("share_name", [])
|
|
|
|
for index in range(len(fund_name_example_list)):
|
|
example_dict = {
|
|
"fund name": fund_name_example_list[index],
|
|
"share name": share_name_example_list[index],
|
|
}
|
|
for share_datapoint in share_datapoint_list:
|
|
share_datapoint_values = share_datapoint_value_example[share_datapoint]
|
|
if index < len(share_datapoint_values):
|
|
example_dict[share_datapoint] = share_datapoint_values[index]
|
|
example_list.append(example_dict)
|
|
example_data = {"data": example_list, "dp_reported_name": dp_reported_name}
|
|
instructions.append(json.dumps(example_data, ensure_ascii=False, indent=4))
|
|
instructions.append("\n")
|
|
instructions.append("\n")
|
|
|
|
end_list = self.instructions_config.get("end", [])
|
|
instructions.append("\n".join(end_list))
|
|
instructions.append("\n")
|
|
|
|
if need_exclude and exclude_data is not None and isinstance(exclude_data, list):
|
|
instructions.append("Please exclude below data from output:\n")
|
|
instructions.append(json.dumps(exclude_data, ensure_ascii=False, indent=4))
|
|
instructions.append("\n")
|
|
instructions.append("\n")
|
|
instructions.append("Answer:\n")
|
|
|
|
instructions_text = "".join(instructions)
|
|
return instructions_text
|
|
|
|
# def chat_by_split_context(self,
|
|
# page_text: str,
|
|
# page_datapoints: list,
|
|
# need_exclude: bool,
|
|
# exclude_data: list) -> list:
|
|
# """
|
|
# If occur error, split the context to two parts and try to get data from the two parts
|
|
# Relevant document: 503194284, page index 147
|
|
# """
|
|
# try:
|
|
# logger.info(f"Split context to get data to fix issue which output length is over 4K tokens")
|
|
# split_context = re.split(r"\n", page_text)
|
|
# split_context = [text.strip() for text in split_context
|
|
# if len(text.strip()) > 0]
|
|
# if len(split_context) < 10:
|
|
# return {"data": []}
|
|
|
|
# split_context_len = len(split_context)
|
|
# top_10_context = split_context[:10]
|
|
# rest_context = split_context[10:]
|
|
# header = "\n".join(top_10_context)
|
|
# half_len = split_context_len // 2
|
|
# # the member of half_len should not start with number
|
|
# # reverse iterate the list by half_len
|
|
# half_len_list = [i for i in range(half_len)]
|
|
|
|
# fund_name_line = ""
|
|
# half_line = rest_context[half_len].strip()
|
|
# max_similarity_fund_name, max_similarity = get_most_similar_name(
|
|
# half_line, self.provider_fund_name_list, matching_type="fund"
|
|
# )
|
|
# if max_similarity < 0.2:
|
|
# # get the fund name line text from the first half
|
|
# for index in reversed(half_len_list):
|
|
# line_text = rest_context[index].strip()
|
|
# if len(line_text) == 0:
|
|
# continue
|
|
# line_text_split = line_text.split()
|
|
# if len(line_text_split) < 3:
|
|
# continue
|
|
# first_word = line_text_split[0]
|
|
# if first_word.lower() == "class":
|
|
# continue
|
|
|
|
# max_similarity_fund_name, max_similarity = get_most_similar_name(
|
|
# line_text, self.provider_fund_name_list, matching_type="fund"
|
|
# )
|
|
# if max_similarity >= 0.2:
|
|
# fund_name_line = line_text
|
|
# break
|
|
# else:
|
|
# fund_name_line = half_line
|
|
# half_len += 1
|
|
# if fund_name_line == "":
|
|
# return {"data": []}
|
|
|
|
# logger.info(f"Split first part from 0 to {half_len}")
|
|
# split_first_part = "\n".join(split_context[:half_len])
|
|
# first_part = '\n'.join(split_first_part)
|
|
# first_instructions = self.get_instructions_by_datapoints(
|
|
# first_part, page_datapoints, need_exclude, exclude_data, extract_way="text"
|
|
# )
|
|
# response, with_error = chat(
|
|
# first_instructions, response_format={"type": "json_object"}
|
|
# )
|
|
# first_part_data = {"data": []}
|
|
# if not with_error:
|
|
# try:
|
|
# first_part_data = json.loads(response)
|
|
# except:
|
|
# first_part_data = json_repair.loads(response)
|
|
|
|
# logger.info(f"Split second part from {half_len} to {split_context_len}")
|
|
# split_second_part = "\n".join(split_context[half_len:])
|
|
# second_part = header + "\n" + fund_name_line + "\n" + split_second_part
|
|
# second_instructions = self.get_instructions_by_datapoints(
|
|
# second_part, page_datapoints, need_exclude, exclude_data, extract_way="text"
|
|
# )
|
|
# response, with_error = chat(
|
|
# second_instructions, response_format={"type": "json_object"}
|
|
# )
|
|
# second_part_data = {"data": []}
|
|
# if not with_error:
|
|
# try:
|
|
# second_part_data = json.loads(response)
|
|
# except:
|
|
# second_part_data = json_repair.loads(response)
|
|
|
|
# first_part_data_list = first_part_data.get("data", [])
|
|
# logger.info(f"First part data count: {len(first_part_data_list)}")
|
|
# second_part_data_list = second_part_data.get("data", [])
|
|
# logger.info(f"Second part data count: {len(second_part_data_list)}")
|
|
# for first_data in first_part_data_list:
|
|
# if first_data in second_part_data_list:
|
|
# second_part_data_list.remove(first_data)
|
|
# else:
|
|
# # if the first part data is with same fund name and share name,
|
|
# # remove the second part data
|
|
# first_data_dp = [key for key in list(first_data.keys())
|
|
# if key not in ["fund name", "share name"]]
|
|
# # order the data points
|
|
# first_data_dp.sort()
|
|
# first_fund_name = first_data.get("fund name", "")
|
|
# first_share_name = first_data.get("share name", "")
|
|
# if len(first_fund_name) > 0 and len(first_share_name) > 0:
|
|
# remove_second_list = []
|
|
# for second_data in second_part_data_list:
|
|
# second_fund_name = second_data.get("fund name", "")
|
|
# second_share_name = second_data.get("share name", "")
|
|
# if first_fund_name == second_fund_name and \
|
|
# first_share_name == second_share_name:
|
|
# second_data_dp = [key for key in list(second_data.keys())
|
|
# if key not in ["fund name", "share name"]]
|
|
# second_data_dp.sort()
|
|
# if first_data_dp == second_data_dp:
|
|
# remove_second_list.append(second_data)
|
|
# for remove_second in remove_second_list:
|
|
# if remove_second in second_part_data_list:
|
|
# second_part_data_list.remove(remove_second)
|
|
|
|
# data_list = first_part_data_list + second_part_data_list
|
|
# extract_data = {"data": data_list}
|
|
# return extract_data
|
|
# except Exception as e:
|
|
# logger.error(f"Error in split context: {e}")
|
|
# return {"data": []}
|