176 lines
6.8 KiB
Python
176 lines
6.8 KiB
Python
import asyncio
|
||
import json
|
||
import time
|
||
from typing import List, Union
|
||
|
||
from .global_logger import logger
|
||
from . import prompt_template
|
||
from .knowledge_lib import INVALID_ENTITY
|
||
from src.llm_models.utils_model import LLMRequest
|
||
from json_repair import repair_json
|
||
|
||
|
||
def _extract_json_from_text(text: str):
|
||
# sourcery skip: assign-if-exp, extract-method
|
||
"""从文本中提取JSON数据的高容错方法"""
|
||
if text is None:
|
||
logger.error("输入文本为None")
|
||
return []
|
||
|
||
try:
|
||
fixed_json = repair_json(text)
|
||
if isinstance(fixed_json, str):
|
||
parsed_json = json.loads(fixed_json)
|
||
else:
|
||
parsed_json = fixed_json
|
||
|
||
# 如果是列表，直接返回
|
||
if isinstance(parsed_json, list):
|
||
return parsed_json
|
||
|
||
# 如果是字典且只有一个项目，可能包装了列表
|
||
if isinstance(parsed_json, dict):
|
||
# 如果字典只有一个键，并且值是列表，返回那个列表
|
||
if len(parsed_json) == 1:
|
||
value = list(parsed_json.values())[0]
|
||
if isinstance(value, list):
|
||
return value
|
||
return parsed_json
|
||
|
||
# 其他情况，尝试转换为列表
|
||
logger.warning(f"解析的JSON不是预期格式: {type(parsed_json)}, 内容: {parsed_json}")
|
||
return []
|
||
|
||
except Exception as e:
|
||
logger.error(f"JSON提取失败: {e}, 原始文本: {text[:100] if text else 'None'}...")
|
||
return []
|
||
|
||
|
||
def _entity_extract(llm_req: LLMRequest, paragraph: str) -> List[str]:
|
||
# sourcery skip: reintroduce-else, swap-if-else-branches, use-named-expression
|
||
"""对段落进行实体提取，返回提取出的实体列表（JSON格式）"""
|
||
entity_extract_context = prompt_template.build_entity_extract_context(paragraph)
|
||
|
||
# 使用 asyncio.run 来运行异步方法
|
||
try:
|
||
# 如果当前已有事件循环在运行，使用它
|
||
loop = asyncio.get_running_loop()
|
||
future = asyncio.run_coroutine_threadsafe(llm_req.generate_response_async(entity_extract_context), loop)
|
||
response, _ = future.result()
|
||
except RuntimeError:
|
||
# 如果没有运行中的事件循环，直接使用 asyncio.run
|
||
response, _ = asyncio.run(llm_req.generate_response_async(entity_extract_context))
|
||
|
||
# 添加调试日志
|
||
logger.debug(f"LLM返回的原始响应: {response}")
|
||
|
||
entity_extract_result = _extract_json_from_text(response)
|
||
|
||
# 检查返回的是否为有效的实体列表
|
||
if not isinstance(entity_extract_result, list):
|
||
if not isinstance(entity_extract_result, dict):
|
||
raise ValueError(f"实体提取结果格式错误，期望列表但得到: {type(entity_extract_result)}")
|
||
|
||
# 尝试常见的键名
|
||
for key in ["entities", "result", "data", "items"]:
|
||
if key in entity_extract_result and isinstance(entity_extract_result[key], list):
|
||
entity_extract_result = entity_extract_result[key]
|
||
break
|
||
else:
|
||
# 如果找不到合适的列表，抛出异常
|
||
raise ValueError(f"实体提取结果格式错误，期望列表但得到: {type(entity_extract_result)}")
|
||
# 过滤无效实体
|
||
entity_extract_result = [
|
||
entity
|
||
for entity in entity_extract_result
|
||
if (entity is not None) and (entity != "") and (entity not in INVALID_ENTITY)
|
||
]
|
||
|
||
if not entity_extract_result:
|
||
raise ValueError("实体提取结果为空")
|
||
|
||
return entity_extract_result
|
||
|
||
|
||
def _rdf_triple_extract(llm_req: LLMRequest, paragraph: str, entities: list) -> List[List[str]]:
|
||
"""对段落进行实体提取，返回提取出的实体列表（JSON格式）"""
|
||
rdf_extract_context = prompt_template.build_rdf_triple_extract_context(
|
||
paragraph, entities=json.dumps(entities, ensure_ascii=False)
|
||
)
|
||
|
||
# 使用 asyncio.run 来运行异步方法
|
||
try:
|
||
# 如果当前已有事件循环在运行，使用它
|
||
loop = asyncio.get_running_loop()
|
||
future = asyncio.run_coroutine_threadsafe(llm_req.generate_response_async(rdf_extract_context), loop)
|
||
response, _ = future.result()
|
||
except RuntimeError:
|
||
# 如果没有运行中的事件循环，直接使用 asyncio.run
|
||
response, _ = asyncio.run(llm_req.generate_response_async(rdf_extract_context))
|
||
|
||
# 添加调试日志
|
||
logger.debug(f"RDF LLM返回的原始响应: {response}")
|
||
|
||
rdf_triple_result = _extract_json_from_text(response)
|
||
|
||
# 检查返回的是否为有效的三元组列表
|
||
if not isinstance(rdf_triple_result, list):
|
||
if not isinstance(rdf_triple_result, dict):
|
||
raise ValueError(f"RDF三元组提取结果格式错误，期望列表但得到: {type(rdf_triple_result)}")
|
||
|
||
# 尝试常见的键名
|
||
for key in ["triples", "result", "data", "items"]:
|
||
if key in rdf_triple_result and isinstance(rdf_triple_result[key], list):
|
||
rdf_triple_result = rdf_triple_result[key]
|
||
break
|
||
else:
|
||
# 如果找不到合适的列表，抛出异常
|
||
raise ValueError(f"RDF三元组提取结果格式错误，期望列表但得到: {type(rdf_triple_result)}")
|
||
# 验证三元组格式
|
||
for triple in rdf_triple_result:
|
||
if (
|
||
not isinstance(triple, list)
|
||
or len(triple) != 3
|
||
or (triple[0] is None or triple[1] is None or triple[2] is None)
|
||
or "" in triple
|
||
):
|
||
raise ValueError("RDF提取结果格式错误")
|
||
|
||
return rdf_triple_result
|
||
|
||
|
||
def info_extract_from_str(
|
||
llm_client_for_ner: LLMRequest, llm_client_for_rdf: LLMRequest, paragraph: str
|
||
) -> Union[tuple[None, None], tuple[list[str], list[list[str]]]]:
|
||
try_count = 0
|
||
while True:
|
||
try:
|
||
entity_extract_result = _entity_extract(llm_client_for_ner, paragraph)
|
||
break
|
||
except Exception as e:
|
||
logger.warning(f"实体提取失败，错误信息：{e}")
|
||
try_count += 1
|
||
if try_count < 3:
|
||
logger.warning("将于5秒后重试")
|
||
time.sleep(5)
|
||
else:
|
||
logger.error("实体提取失败，已达最大重试次数")
|
||
return None, None
|
||
|
||
try_count = 0
|
||
while True:
|
||
try:
|
||
rdf_triple_extract_result = _rdf_triple_extract(llm_client_for_rdf, paragraph, entity_extract_result)
|
||
break
|
||
except Exception as e:
|
||
logger.warning(f"实体提取失败，错误信息：{e}")
|
||
try_count += 1
|
||
if try_count < 3:
|
||
logger.warning("将于5秒后重试")
|
||
time.sleep(5)
|
||
else:
|
||
logger.error("实体提取失败，已达最大重试次数")
|
||
return None, None
|
||
|
||
return entity_extract_result, rdf_triple_extract_result
|