| |
| |
| """ |
| Reference: |
| - [graphrag](https://github.com/microsoft/graphrag) |
| """ |
| import logging |
| import re |
| from typing import Any, Callable |
| from dataclasses import dataclass |
| from graphrag.general.extractor import Extractor, ENTITY_EXTRACTION_MAX_GLEANINGS |
| from graphrag.light.graph_prompt import PROMPTS |
| from graphrag.utils import pack_user_ass_to_openai_messages, split_string_by_multi_markers |
| from rag.llm.chat_model import Base as CompletionLLM |
| import networkx as nx |
| from rag.utils import num_tokens_from_string |
|
|
|
|
| @dataclass |
| class GraphExtractionResult: |
| """Unipartite graph extraction result class definition.""" |
|
|
| output: nx.Graph |
| source_docs: dict[Any, Any] |
|
|
|
|
| class GraphExtractor(Extractor): |
|
|
| _max_gleanings: int |
|
|
| def __init__( |
| self, |
| llm_invoker: CompletionLLM, |
| language: str | None = "English", |
| entity_types: list[str] | None = None, |
| get_entity: Callable | None = None, |
| set_entity: Callable | None = None, |
| get_relation: Callable | None = None, |
| set_relation: Callable | None = None, |
| example_number: int = 2, |
| max_gleanings: int | None = None, |
| ): |
| super().__init__(llm_invoker, language, entity_types, get_entity, set_entity, get_relation, set_relation) |
| """Init method definition.""" |
| self._max_gleanings = ( |
| max_gleanings |
| if max_gleanings is not None |
| else ENTITY_EXTRACTION_MAX_GLEANINGS |
| ) |
| self._example_number = example_number |
| examples = "\n".join( |
| PROMPTS["entity_extraction_examples"][: int(self._example_number)] |
| ) |
|
|
| example_context_base = dict( |
| tuple_delimiter=PROMPTS["DEFAULT_TUPLE_DELIMITER"], |
| record_delimiter=PROMPTS["DEFAULT_RECORD_DELIMITER"], |
| completion_delimiter=PROMPTS["DEFAULT_COMPLETION_DELIMITER"], |
| entity_types=",".join(self._entity_types), |
| language=self._language, |
| ) |
| |
| examples = examples.format(**example_context_base) |
|
|
| self._entity_extract_prompt = PROMPTS["entity_extraction"] |
| self._context_base = dict( |
| tuple_delimiter=PROMPTS["DEFAULT_TUPLE_DELIMITER"], |
| record_delimiter=PROMPTS["DEFAULT_RECORD_DELIMITER"], |
| completion_delimiter=PROMPTS["DEFAULT_COMPLETION_DELIMITER"], |
| entity_types=",".join(self._entity_types), |
| examples=examples, |
| language=self._language, |
| ) |
|
|
| self._continue_prompt = PROMPTS["entiti_continue_extraction"] |
| self._if_loop_prompt = PROMPTS["entiti_if_loop_extraction"] |
|
|
| self._left_token_count = llm_invoker.max_length - num_tokens_from_string( |
| self._entity_extract_prompt.format( |
| **self._context_base, input_text="{input_text}" |
| ).format(**self._context_base, input_text="") |
| ) |
| self._left_token_count = max(llm_invoker.max_length * 0.6, self._left_token_count) |
|
|
| def _process_single_content(self, chunk_key_dp: tuple[str, str]): |
| token_count = 0 |
| chunk_key = chunk_key_dp[0] |
| content = chunk_key_dp[1] |
| hint_prompt = self._entity_extract_prompt.format( |
| **self._context_base, input_text="{input_text}" |
| ).format(**self._context_base, input_text=content) |
|
|
| try: |
| gen_conf = {"temperature": 0.8} |
| final_result = self._chat(hint_prompt, [{"role": "user", "content": "Output:"}], gen_conf) |
| token_count += num_tokens_from_string(hint_prompt + final_result) |
| history = pack_user_ass_to_openai_messages("Output:", final_result, self._continue_prompt) |
| for now_glean_index in range(self._max_gleanings): |
| glean_result = self._chat(hint_prompt, history, gen_conf) |
| history.extend([{"role": "assistant", "content": glean_result}, {"role": "user", "content": self._continue_prompt}]) |
| token_count += num_tokens_from_string("\n".join([m["content"] for m in history]) + hint_prompt + self._continue_prompt) |
| final_result += glean_result |
| if now_glean_index == self._max_gleanings - 1: |
| break |
|
|
| if_loop_result = self._chat(self._if_loop_prompt, history, gen_conf) |
| token_count += num_tokens_from_string("\n".join([m["content"] for m in history]) + if_loop_result + self._if_loop_prompt) |
| if_loop_result = if_loop_result.strip().strip('"').strip("'").lower() |
| if if_loop_result != "yes": |
| break |
|
|
| records = split_string_by_multi_markers( |
| final_result, |
| [self._context_base["record_delimiter"], self._context_base["completion_delimiter"]], |
| ) |
| rcds = [] |
| for record in records: |
| record = re.search(r"\((.*)\)", record) |
| if record is None: |
| continue |
| rcds.append(record.group(1)) |
| records = rcds |
| maybe_nodes, maybe_edges = self._entities_and_relations(chunk_key, records, self._context_base["tuple_delimiter"]) |
| return maybe_nodes, maybe_edges, token_count |
| except Exception as e: |
| logging.exception("error extracting graph") |
| return e, None, None |
|
|
