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152072f900
### What problem does this PR solve? #1594 ### Type of change - [x] New Feature (non-breaking change which adds functionality)
138 lines
4.6 KiB
Python
138 lines
4.6 KiB
Python
#
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# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import logging
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import traceback
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from concurrent.futures import ThreadPoolExecutor
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from dataclasses import dataclass
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from typing import Any
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from graphrag.mind_map_prompt import MIND_MAP_EXTRACTION_PROMPT
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from graphrag.utils import ErrorHandlerFn, perform_variable_replacements
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from rag.llm.chat_model import Base as CompletionLLM
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import markdown_to_json
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from functools import reduce
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from rag.utils import num_tokens_from_string
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@dataclass
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class MindMapResult:
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"""Unipartite Mind Graph result class definition."""
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output: dict
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class MindMapExtractor:
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_llm: CompletionLLM
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_input_text_key: str
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_mind_map_prompt: str
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_on_error: ErrorHandlerFn
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def __init__(
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self,
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llm_invoker: CompletionLLM,
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prompt: str | None = None,
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input_text_key: str | None = None,
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on_error: ErrorHandlerFn | None = None,
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):
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"""Init method definition."""
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# TODO: streamline construction
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self._llm = llm_invoker
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self._input_text_key = input_text_key or "input_text"
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self._mind_map_prompt = prompt or MIND_MAP_EXTRACTION_PROMPT
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self._on_error = on_error or (lambda _e, _s, _d: None)
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def __call__(
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self, sections: list[str], prompt_variables: dict[str, Any] | None = None
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) -> MindMapResult:
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"""Call method definition."""
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if prompt_variables is None:
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prompt_variables = {}
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try:
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exe = ThreadPoolExecutor(max_workers=12)
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threads = []
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token_count = self._llm.max_length * 0.7
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texts = []
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res = []
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cnt = 0
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for i in range(len(sections)):
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section_cnt = num_tokens_from_string(sections[i])
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if cnt + section_cnt >= token_count and texts:
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threads.append(exe.submit(self._process_document, "".join(texts), prompt_variables))
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texts = []
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cnt = 0
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texts.append(sections[i])
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cnt += section_cnt
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if texts:
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threads.append(exe.submit(self._process_document, "".join(texts), prompt_variables))
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for i, _ in enumerate(threads):
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res.append(_.result())
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merge_json = reduce(self._merge, res)
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merge_json = self._list_to_kv(merge_json)
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except Exception as e:
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logging.exception("error mind graph")
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self._on_error(
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e,
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traceback.format_exc(), None
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)
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return MindMapResult(output=merge_json)
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def _merge(self, d1, d2):
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for k in d1:
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if k in d2:
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if isinstance(d1[k], dict) and isinstance(d2[k], dict):
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self._merge(d1[k], d2[k])
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elif isinstance(d1[k], list) and isinstance(d2[k], list):
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d2[k].extend(d1[k])
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else:
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d2[k] = d1[k]
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else:
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d2[k] = d1[k]
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return d2
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def _list_to_kv(self, data):
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for key, value in data.items():
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if isinstance(value, dict):
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self._list_to_kv(value)
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elif isinstance(value, list):
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new_value = {}
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for i in range(len(value)):
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if isinstance(value[i], list):
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new_value[value[i - 1]] = value[i][0]
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data[key] = new_value
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else:
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continue
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return data
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def _process_document(
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self, text: str, prompt_variables: dict[str, str]
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) -> str:
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variables = {
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**prompt_variables,
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self._input_text_key: text,
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}
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text = perform_variable_replacements(self._mind_map_prompt, variables=variables)
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gen_conf = {"temperature": 0.5}
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response = self._llm.chat(text, [], gen_conf)
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print(response)
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print("---------------------------------------------------\n", markdown_to_json.dictify(response))
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return dict(markdown_to_json.dictify(response))
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