ragflow/graphrag/entity_resolution.py
aniaan 8b8a2f2949
fix(nursery): Fix Closure Trap Issues in Trio Concurrent Tasks (#7106)
## Problem Description
Multiple files in the RAGFlow project contain closure trap issues when
using lambda functions with `trio.open_nursery()`. This problem causes
concurrent tasks created in loops to reference the same variable,
resulting in all tasks processing the same data (the data from the last
iteration) rather than each task processing its corresponding data from
the loop.

## Issue Details
When using a `lambda` to create a closure function and passing it to
`nursery.start_soon()` within a loop, the lambda function captures a
reference to the loop variable rather than its value. For example:

```python
# Problematic code
async with trio.open_nursery() as nursery:
    for d in docs:
        nursery.start_soon(lambda: doc_keyword_extraction(chat_mdl, d, topn))
```

In this pattern, when concurrent tasks begin execution, `d` has already
become the value after the loop ends (typically the last element),
causing all tasks to use the same data.

## Fix Solution
Changed the way concurrent tasks are created with `nursery.start_soon()`
by leveraging Trio's API design to directly pass the function and its
arguments separately:

```python
# Fixed code
async with trio.open_nursery() as nursery:
    for d in docs:
        nursery.start_soon(doc_keyword_extraction, chat_mdl, d, topn)
```

This way, each task uses the parameter values at the time of the
function call, rather than references captured through closures.

## Fixed Files
Fixed closure traps in the following files:

1. `rag/svr/task_executor.py`: 3 fixes, involving document keyword
extraction, question generation, and tag processing
2. `rag/raptor.py`: 1 fix, involving document summarization
3. `graphrag/utils.py`: 2 fixes, involving graph node and edge
processing
4. `graphrag/entity_resolution.py`: 2 fixes, involving entity resolution
and graph node merging
5. `graphrag/general/mind_map_extractor.py`: 2 fixes, involving document
processing
6. `graphrag/general/extractor.py`: 3 fixes, involving content
processing and graph node/edge merging
7. `graphrag/general/community_reports_extractor.py`: 1 fix, involving
community report extraction

## Potential Impact
This fix resolves a serious concurrency issue that could have caused:
- Data processing errors (processing duplicate data)
- Performance degradation (all tasks working on the same data)
- Inconsistent results (some data not being processed)

After the fix, all concurrent tasks should correctly process their
respective data, improving system correctness and reliability.
2025-04-18 18:00:20 +08:00

193 lines
9.0 KiB
Python

#
# 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
from dataclasses import dataclass
from typing import Any, Callable
import networkx as nx
import trio
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, chat_limiter, GraphChange
DEFAULT_RECORD_DELIMITER = "##"
DEFAULT_ENTITY_INDEX_DELIMITER = "<|>"
DEFAULT_RESOLUTION_RESULT_DELIMITER = "&&"
@dataclass
class EntityResolutionResult:
"""Entity resolution result class definition."""
graph: nx.Graph
change: GraphChange
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,
):
super().__init__(llm_invoker)
"""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"
async def __call__(self, graph: nx.Graph,
subgraph_nodes: set[str],
prompt_variables: dict[str, Any] | None = None,
callback: Callable | None = None) -> EntityResolutionResult:
"""Call method definition."""
if prompt_variables is None:
prompt_variables = {}
# Wire defaults into the prompt variables
self.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 = sorted(graph.nodes())
entity_types = sorted(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 (a in subgraph_nodes or b in subgraph_nodes) and self.is_similarity(a, b)]
num_candidates = sum([len(candidates) for _, candidates in candidate_resolution.items()])
callback(msg=f"Identified {num_candidates} candidate pairs")
resolution_result = set()
resolution_batch_size = 100
async with trio.open_nursery() as nursery:
for candidate_resolution_i in candidate_resolution.items():
if not candidate_resolution_i[1]:
continue
for i in range(0, len(candidate_resolution_i[1]), resolution_batch_size):
candidate_batch = candidate_resolution_i[0], candidate_resolution_i[1][i:i + resolution_batch_size]
nursery.start_soon(self._resolve_candidate, candidate_batch, resolution_result)
callback(msg=f"Resolved {num_candidates} candidate pairs, {len(resolution_result)} of them are selected to merge.")
change = GraphChange()
connect_graph = nx.Graph()
connect_graph.add_edges_from(resolution_result)
async with trio.open_nursery() as nursery:
for sub_connect_graph in nx.connected_components(connect_graph):
merging_nodes = list(sub_connect_graph)
nursery.start_soon(self._merge_graph_nodes, graph, merging_nodes, change)
# Update pagerank
pr = nx.pagerank(graph)
for node_name, pagerank in pr.items():
graph.nodes[node_name]["pagerank"] = pagerank
return EntityResolutionResult(
graph=graph,
change=change,
)
async def _resolve_candidate(self, candidate_resolution_i: tuple[str, list[tuple[str, str]]], resolution_result: set[str]):
gen_conf = {"temperature": 0.5}
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 = {
**self.prompt_variables,
self._input_text_key: pair_prompt
}
text = perform_variable_replacements(self._resolution_prompt, variables=variables)
logging.info(f"Created resolution prompt {len(text)} bytes for {len(candidate_resolution_i[1])} entity pairs of type {candidate_resolution_i[0]}")
async with chat_limiter:
response = await trio.to_thread.run_sync(lambda: self._chat(text, [{"role": "user", "content": "Output:"}], gen_conf))
logging.debug(f"_resolve_candidate chat prompt: {text}\nchat response: {response}")
result = self._process_results(len(candidate_resolution_i[1]), response,
self.prompt_variables.get(self._record_delimiter_key,
DEFAULT_RECORD_DELIMITER),
self.prompt_variables.get(self._entity_index_dilimiter_key,
DEFAULT_ENTITY_INDEX_DELIMITER),
self.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])
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)) > 1:
return True
return False