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chore: import upstream snapshot with attribution
2026-07-13 12:32:26 +08:00

66 lines
2.5 KiB
Python

from __future__ import annotations
from typing import Any
import json_repair
from yuxi.models.chat import select_model
from .base import GraphExtractor
DEFAULT_TRIPLE_EXTRACTION_PROMPT = """请从下面文本中抽取实体和实体关系,返回严格 JSON,不要输出解释。
JSON 格式:
{
"relations": [
{
"source": {"text": "实体文本", "label": "实体类型", "attributes": [{"text": "属性值", "label": "属性名称"}]},
"target": {"text": "实体文本", "label": "实体类型", "attributes": [{"text": "属性值", "label": "属性名称"}]},
"text": "关系显示文本",
"label": "关系类型"
}
]
}
"""
SCHEMA_INSTRUCTION = """抽取 Schema 约束:
{schema}
"""
class LLMGraphExtractor(GraphExtractor):
extractor_type = "llm"
def validate_options(self) -> None:
if not self.options.get("model_spec"):
raise ValueError("LLM 抽取器需要 model_spec")
if self.options.get("prompt"):
raise ValueError("LLM 图谱抽取器不支持自定义完整 Prompt,请使用 schema 配置抽取约束")
concurrency_count = self.options.get("concurrency_count", 1)
try:
concurrency_count = int(concurrency_count)
except (TypeError, ValueError) as exc:
raise ValueError("LLM 抽取器 concurrency_count 必须是整数") from exc
if concurrency_count < 1 or concurrency_count > 1000:
raise ValueError("LLM 抽取器 concurrency_count 必须在 1 到 1000 之间")
if self.options.get("model_params") is not None and not isinstance(self.options["model_params"], dict):
raise ValueError("LLM 抽取器 model_params 必须是对象")
async def extract(self, text: str, *, chunk_metadata: dict[str, Any] | None = None) -> dict[str, Any]:
self.validate_options()
model = select_model(
model_spec=self.options["model_spec"],
timeout=60.0,
model_params=self.options.get("model_params") or {},
)
prompt = self._build_prompt(text)
response = await model.call(prompt, stream=False)
parsed = json_repair.loads(response.content if response else "")
return parsed
def _build_prompt(self, text: str) -> str:
extraction_prompt = DEFAULT_TRIPLE_EXTRACTION_PROMPT
schema = str(self.options.get("schema") or "").strip()
if schema:
extraction_prompt = f"{extraction_prompt}\n{SCHEMA_INSTRUCTION.format(schema=schema)}"
return f"{extraction_prompt}\n\n文本:\n{text}"