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#
# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import logging
import itertools
import re
import time
from dataclasses import dataclass
from typing import Any, Callable
import networkx as nx
from graphrag.general.extractor import Extractor
from rag.nlp import is_english
import editdistance
from graphrag.entity_resolution_prompt import ENTITY_RESOLUTION_PROMPT
from rag.llm.chat_model import Base as CompletionLLM
from graphrag.utils import perform_variable_replacements
DEFAULT_RECORD_DELIMITER = "##"
DEFAULT_ENTITY_INDEX_DELIMITER = "<|>"
DEFAULT_RESOLUTION_RESULT_DELIMITER = "&&"
@dataclass
class EntityResolutionResult:
"""Entity resolution result class definition."""
graph: nx.Graph
removed_entities: list
class EntityResolution(Extractor):
"""Entity resolution class definition."""
_resolution_prompt: str
_output_formatter_prompt: str
_record_delimiter_key: str
_entity_index_delimiter_key: str
_resolution_result_delimiter_key: str
def __init__(
self,
llm_invoker: CompletionLLM,
get_entity: Callable | None = None,
set_entity: Callable | None = None,
get_relation: Callable | None = None,
set_relation: Callable | None = None
):
super().__init__(llm_invoker, get_entity=get_entity, set_entity=set_entity, get_relation=get_relation, set_relation=set_relation)
"""Init method definition."""
self._llm = llm_invoker
self._resolution_prompt = ENTITY_RESOLUTION_PROMPT
self._record_delimiter_key = "record_delimiter"
self._entity_index_dilimiter_key = "entity_index_delimiter"
self._resolution_result_delimiter_key = "resolution_result_delimiter"
self._input_text_key = "input_text"
def __call__(self, graph: nx.Graph, prompt_variables: dict[str, Any] | None = None) -> EntityResolutionResult:
"""Call method definition."""
if prompt_variables is None:
prompt_variables = {}
# Wire defaults into the prompt variables
prompt_variables = {
**prompt_variables,
self._record_delimiter_key: prompt_variables.get(self._record_delimiter_key)
or DEFAULT_RECORD_DELIMITER,
self._entity_index_dilimiter_key: prompt_variables.get(self._entity_index_dilimiter_key)
or DEFAULT_ENTITY_INDEX_DELIMITER,
self._resolution_result_delimiter_key: prompt_variables.get(self._resolution_result_delimiter_key)
or DEFAULT_RESOLUTION_RESULT_DELIMITER,
}
nodes = graph.nodes
entity_types = list(set(graph.nodes[node].get('entity_type', '-') for node in nodes))
node_clusters = {entity_type: [] for entity_type in entity_types}
for node in nodes:
node_clusters[graph.nodes[node].get('entity_type', '-')].append(node)
candidate_resolution = {entity_type: [] for entity_type in entity_types}
for k, v in node_clusters.items():
candidate_resolution[k] = [(a, b) for a, b in itertools.combinations(v, 2) if self.is_similarity(a, b)]
gen_conf = {"temperature": 0.5}
resolution_result = set()
for candidate_resolution_i in candidate_resolution.items():
if candidate_resolution_i[1]:
try:
pair_txt = [
f'When determining whether two {candidate_resolution_i[0]}s are the same, you should only focus on critical properties and overlook noisy factors.\n']
for index, candidate in enumerate(candidate_resolution_i[1]):
pair_txt.append(
f'Question {index + 1}: name of{candidate_resolution_i[0]} A is {candidate[0]} ,name of{candidate_resolution_i[0]} B is {candidate[1]}')
sent = 'question above' if len(pair_txt) == 1 else f'above {len(pair_txt)} questions'
pair_txt.append(
f'\nUse domain knowledge of {candidate_resolution_i[0]}s to help understand the text and answer the {sent} in the format: For Question i, Yes, {candidate_resolution_i[0]} A and {candidate_resolution_i[0]} B are the same {candidate_resolution_i[0]}./No, {candidate_resolution_i[0]} A and {candidate_resolution_i[0]} B are different {candidate_resolution_i[0]}s. For Question i+1, (repeat the above procedures)')
pair_prompt = '\n'.join(pair_txt)
variables = {
**prompt_variables,
self._input_text_key: pair_prompt
}
text = perform_variable_replacements(self._resolution_prompt, variables=variables)
response = self._chat(text, [{"role": "user", "content": "Output:"}], gen_conf)
result = self._process_results(len(candidate_resolution_i[1]), response,
prompt_variables.get(self._record_delimiter_key,
DEFAULT_RECORD_DELIMITER),
prompt_variables.get(self._entity_index_dilimiter_key,
DEFAULT_ENTITY_INDEX_DELIMITER),
prompt_variables.get(self._resolution_result_delimiter_key,
DEFAULT_RESOLUTION_RESULT_DELIMITER))
for result_i in result:
resolution_result.add(candidate_resolution_i[1][result_i[0] - 1])
except Exception:
logging.exception("error entity resolution")
connect_graph = nx.Graph()
removed_entities = []
connect_graph.add_edges_from(resolution_result)
for sub_connect_graph in nx.connected_components(connect_graph):
sub_connect_graph = connect_graph.subgraph(sub_connect_graph)
remove_nodes = list(sub_connect_graph.nodes)
keep_node = remove_nodes.pop()
self._merge_nodes(keep_node, self._get_entity_(remove_nodes))
for remove_node in remove_nodes:
removed_entities.append(remove_node)
remove_node_neighbors = graph[remove_node]
remove_node_neighbors = list(remove_node_neighbors)
for remove_node_neighbor in remove_node_neighbors:
rel = self._get_relation_(remove_node, remove_node_neighbor)
if graph.has_edge(remove_node, remove_node_neighbor):
graph.remove_edge(remove_node, remove_node_neighbor)
if remove_node_neighbor == keep_node:
if graph.has_edge(keep_node, remove_node):
graph.remove_edge(keep_node, remove_node)
continue
if not rel:
continue
if graph.has_edge(keep_node, remove_node_neighbor):
self._merge_edges(keep_node, remove_node_neighbor, [rel])
else:
pair = sorted([keep_node, remove_node_neighbor])
graph.add_edge(pair[0], pair[1], weight=rel['weight'])
self._set_relation_(pair[0], pair[1],
dict(
src_id=pair[0],
tgt_id=pair[1],
weight=rel['weight'],
description=rel['description'],
keywords=[],
source_id=rel.get("source_id", ""),
metadata={"created_at": time.time()}
))
graph.remove_node(remove_node)
return EntityResolutionResult(
graph=graph,
removed_entities=removed_entities
)
def _process_results(
self,
records_length: int,
results: str,
record_delimiter: str,
entity_index_delimiter: str,
resolution_result_delimiter: str
) -> list:
ans_list = []
records = [r.strip() for r in results.split(record_delimiter)]
for record in records:
pattern_int = f"{re.escape(entity_index_delimiter)}(\d+){re.escape(entity_index_delimiter)}"
match_int = re.search(pattern_int, record)
res_int = int(str(match_int.group(1) if match_int else '0'))
if res_int > records_length:
continue
pattern_bool = f"{re.escape(resolution_result_delimiter)}([a-zA-Z]+){re.escape(resolution_result_delimiter)}"
match_bool = re.search(pattern_bool, record)
res_bool = str(match_bool.group(1) if match_bool else '')
if res_int and res_bool:
if res_bool.lower() == 'yes':
ans_list.append((res_int, "yes"))
return ans_list
def is_similarity(self, a, b):
if is_english(a) and is_english(b):
if editdistance.eval(a, b) <= min(len(a), len(b)) // 2:
return True
if len(set(a) & set(b)) > 0:
return True
return False
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