| | |
| | |
| | """ |
| | 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 |
| |
|
| |
|
| |
|
| |
|
