| |
| |
| """ |
| Reference: |
| - [graphrag](https://github.com/microsoft/graphrag) |
| """ |
|
|
| import logging |
| import re |
| from typing import Any, Callable |
| from dataclasses import dataclass |
| import tiktoken |
|
|
| from graphrag.general.extractor import Extractor, ENTITY_EXTRACTION_MAX_GLEANINGS, DEFAULT_ENTITY_TYPES |
| from graphrag.general.graph_prompt import GRAPH_EXTRACTION_PROMPT, CONTINUE_PROMPT, LOOP_PROMPT |
| from graphrag.utils import ErrorHandlerFn, perform_variable_replacements |
| from rag.llm.chat_model import Base as CompletionLLM |
| import networkx as nx |
| from rag.utils import num_tokens_from_string |
|
|
| DEFAULT_TUPLE_DELIMITER = "<|>" |
| DEFAULT_RECORD_DELIMITER = "##" |
| DEFAULT_COMPLETION_DELIMITER = "<|COMPLETE|>" |
|
|
|
|
| @dataclass |
| class GraphExtractionResult: |
| """Unipartite graph extraction result class definition.""" |
|
|
| output: nx.Graph |
| source_docs: dict[Any, Any] |
|
|
|
|
| class GraphExtractor(Extractor): |
| """Unipartite graph extractor class definition.""" |
|
|
| _join_descriptions: bool |
| _tuple_delimiter_key: str |
| _record_delimiter_key: str |
| _entity_types_key: str |
| _input_text_key: str |
| _completion_delimiter_key: str |
| _entity_name_key: str |
| _input_descriptions_key: str |
| _extraction_prompt: str |
| _summarization_prompt: str |
| _loop_args: dict[str, Any] |
| _max_gleanings: int |
| _on_error: ErrorHandlerFn |
|
|
| 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, |
| tuple_delimiter_key: str | None = None, |
| record_delimiter_key: str | None = None, |
| input_text_key: str | None = None, |
| entity_types_key: str | None = None, |
| completion_delimiter_key: str | None = None, |
| join_descriptions=True, |
| max_gleanings: int | None = None, |
| on_error: ErrorHandlerFn | None = None, |
| ): |
| super().__init__(llm_invoker, language, entity_types, get_entity, set_entity, get_relation, set_relation) |
| """Init method definition.""" |
| |
| self._llm = llm_invoker |
| self._join_descriptions = join_descriptions |
| self._input_text_key = input_text_key or "input_text" |
| self._tuple_delimiter_key = tuple_delimiter_key or "tuple_delimiter" |
| self._record_delimiter_key = record_delimiter_key or "record_delimiter" |
| self._completion_delimiter_key = ( |
| completion_delimiter_key or "completion_delimiter" |
| ) |
| self._entity_types_key = entity_types_key or "entity_types" |
| self._extraction_prompt = GRAPH_EXTRACTION_PROMPT |
| self._max_gleanings = ( |
| max_gleanings |
| if max_gleanings is not None |
| else ENTITY_EXTRACTION_MAX_GLEANINGS |
| ) |
| self._on_error = on_error or (lambda _e, _s, _d: None) |
| self.prompt_token_count = num_tokens_from_string(self._extraction_prompt) |
|
|
| |
| encoding = tiktoken.get_encoding("cl100k_base") |
| yes = encoding.encode("YES") |
| no = encoding.encode("NO") |
| self._loop_args = {"logit_bias": {yes[0]: 100, no[0]: 100}, "max_tokens": 1} |
|
|
| |
| self._prompt_variables = { |
| "entity_types": entity_types, |
| self._tuple_delimiter_key: DEFAULT_TUPLE_DELIMITER, |
| self._record_delimiter_key: DEFAULT_RECORD_DELIMITER, |
| self._completion_delimiter_key: DEFAULT_COMPLETION_DELIMITER, |
| self._entity_types_key: ",".join(DEFAULT_ENTITY_TYPES), |
| } |
|
|
| 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] |
| variables = { |
| **self._prompt_variables, |
| self._input_text_key: content, |
| } |
| try: |
| gen_conf = {"temperature": 0.3} |
| hint_prompt = perform_variable_replacements(self._extraction_prompt, variables=variables) |
| response = self._chat(hint_prompt, [{"role": "user", "content": "Output:"}], gen_conf) |
| token_count += num_tokens_from_string(hint_prompt + response) |
|
|
| results = response or "" |
| history = [{"role": "system", "content": hint_prompt}, {"role": "user", "content": response}] |
|
|
| |
| for i in range(self._max_gleanings): |
| text = perform_variable_replacements(CONTINUE_PROMPT, history=history, variables=variables) |
| history.append({"role": "user", "content": text}) |
| response = self._chat("", history, gen_conf) |
| token_count += num_tokens_from_string("\n".join([m["content"] for m in history]) + response) |
| results += response or "" |
|
|
| |
| if i >= self._max_gleanings - 1: |
| break |
| history.append({"role": "assistant", "content": response}) |
| history.append({"role": "user", "content": LOOP_PROMPT}) |
| continuation = self._chat("", history, {"temperature": 0.8}) |
| token_count += num_tokens_from_string("\n".join([m["content"] for m in history]) + response) |
| if continuation != "YES": |
| break |
|
|
| record_delimiter = variables.get(self._record_delimiter_key, DEFAULT_RECORD_DELIMITER) |
| tuple_delimiter = variables.get(self._tuple_delimiter_key, DEFAULT_TUPLE_DELIMITER) |
| records = [re.sub(r"^\(|\)$", "", r.strip()) for r in results.split(record_delimiter)] |
| records = [r for r in records if r.strip()] |
| maybe_nodes, maybe_edges = self._entities_and_relations(chunk_key, records, tuple_delimiter) |
| return maybe_nodes, maybe_edges, token_count |
| except Exception as e: |
| logging.exception("error extracting graph") |
| return e, None, None |
|
|
|
|
|
|
|
|
