ragflow/graphrag/entity_resolution.py

#
#  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