File size: 7,001 Bytes
6054f54 a7642c6 b6ce919 8bc2fc9 8de8827 b6ce919 6054f54 eae0334 6054f54 758538f 6054f54 758538f 6054f54 b71b66c 5f0746a b71b66c 5f0746a b71b66c 6054f54 6a49fcd 6054f54 6a49fcd 6054f54 4efca68 6d597a0 4efca68 6054f54 5f0746a b71b66c 77dc93a 5f0746a b71b66c 6054f54 b71b66c 6054f54 140db5b 6054f54 140db5b b6ce919 6054f54 758538f b6ce919 8bc2fc9 b6ce919 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 |
#
# 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 collections
import os
import re
import traceback
from typing import Any
from concurrent.futures import ThreadPoolExecutor
from dataclasses import dataclass
from graphrag.extractor import Extractor
from graphrag.mind_map_prompt import MIND_MAP_EXTRACTION_PROMPT
from graphrag.utils import ErrorHandlerFn, perform_variable_replacements
from rag.llm.chat_model import Base as CompletionLLM
import markdown_to_json
from functools import reduce
from rag.utils import num_tokens_from_string
@dataclass
class MindMapResult:
"""Unipartite Mind Graph result class definition."""
output: dict
class MindMapExtractor(Extractor):
_input_text_key: str
_mind_map_prompt: str
_on_error: ErrorHandlerFn
def __init__(
self,
llm_invoker: CompletionLLM,
prompt: str | None = None,
input_text_key: str | None = None,
on_error: ErrorHandlerFn | None = None,
):
"""Init method definition."""
# TODO: streamline construction
self._llm = llm_invoker
self._input_text_key = input_text_key or "input_text"
self._mind_map_prompt = prompt or MIND_MAP_EXTRACTION_PROMPT
self._on_error = on_error or (lambda _e, _s, _d: None)
def _key(self, k):
return re.sub(r"\*+", "", k)
def _be_children(self, obj: dict, keyset: set):
if isinstance(obj, str):
obj = [obj]
if isinstance(obj, list):
keyset.update(obj)
obj = [re.sub(r"\*+", "", i) for i in obj]
return [{"id": i, "children": []} for i in obj if i]
arr = []
for k, v in obj.items():
k = self._key(k)
if k and k not in keyset:
keyset.add(k)
arr.append(
{
"id": k,
"children": self._be_children(v, keyset)
}
)
return arr
def __call__(
self, sections: list[str], prompt_variables: dict[str, Any] | None = None
) -> MindMapResult:
"""Call method definition."""
if prompt_variables is None:
prompt_variables = {}
try:
res = []
max_workers = int(os.environ.get('MINDMAP_EXTRACTOR_MAX_WORKERS', 12))
with ThreadPoolExecutor(max_workers=max_workers) as exe:
threads = []
token_count = max(self._llm.max_length * 0.8, self._llm.max_length - 512)
texts = []
cnt = 0
for i in range(len(sections)):
section_cnt = num_tokens_from_string(sections[i])
if cnt + section_cnt >= token_count and texts:
threads.append(exe.submit(self._process_document, "".join(texts), prompt_variables))
texts = []
cnt = 0
texts.append(sections[i])
cnt += section_cnt
if texts:
threads.append(exe.submit(self._process_document, "".join(texts), prompt_variables))
for i, _ in enumerate(threads):
res.append(_.result())
if not res:
return MindMapResult(output={"id": "root", "children": []})
merge_json = reduce(self._merge, res)
if len(merge_json) > 1:
keys = [re.sub(r"\*+", "", k) for k, v in merge_json.items() if isinstance(v, dict)]
keyset = set(i for i in keys if i)
merge_json = {
"id": "root",
"children": [
{
"id": self._key(k),
"children": self._be_children(v, keyset)
}
for k, v in merge_json.items() if isinstance(v, dict) and self._key(k)
]
}
else:
k = self._key(list(merge_json.keys())[0])
merge_json = {"id": k, "children": self._be_children(list(merge_json.items())[0][1], {k})}
except Exception as e:
logging.exception("error mind graph")
self._on_error(
e,
traceback.format_exc(), None
)
merge_json = {"error": str(e)}
return MindMapResult(output=merge_json)
def _merge(self, d1, d2):
for k in d1:
if k in d2:
if isinstance(d1[k], dict) and isinstance(d2[k], dict):
self._merge(d1[k], d2[k])
elif isinstance(d1[k], list) and isinstance(d2[k], list):
d2[k].extend(d1[k])
else:
d2[k] = d1[k]
else:
d2[k] = d1[k]
return d2
def _list_to_kv(self, data):
for key, value in data.items():
if isinstance(value, dict):
self._list_to_kv(value)
elif isinstance(value, list):
new_value = {}
for i in range(len(value)):
if isinstance(value[i], list) and i > 0:
new_value[value[i - 1]] = value[i][0]
data[key] = new_value
else:
continue
return data
def _todict(self, layer: collections.OrderedDict):
to_ret = layer
if isinstance(layer, collections.OrderedDict):
to_ret = dict(layer)
try:
for key, value in to_ret.items():
to_ret[key] = self._todict(value)
except AttributeError:
pass
return self._list_to_kv(to_ret)
def _process_document(
self, text: str, prompt_variables: dict[str, str]
) -> str:
variables = {
**prompt_variables,
self._input_text_key: text,
}
text = perform_variable_replacements(self._mind_map_prompt, variables=variables)
gen_conf = {"temperature": 0.5}
response = self._chat(text, [{"role": "user", "content": "Output:"}], gen_conf)
response = re.sub(r"```[^\n]*", "", response)
logging.debug(response)
logging.debug(self._todict(markdown_to_json.dictify(response)))
return self._todict(markdown_to_json.dictify(response))
|