Source code for langchain_community.chains.graph_qa.hugegraph
"""在图上的问答。"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain_core.callbacks import CallbackManagerForChainRun
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain_core.pydantic_v1 import Field
from langchain_community.chains.graph_qa.prompts import (
CYPHER_QA_PROMPT,
GREMLIN_GENERATION_PROMPT,
)
from langchain_community.graphs.hugegraph import HugeGraph
[docs]class HugeGraphQAChain(Chain):
"""用生成gremlin语句来针对图形进行问答的链。
*安全注意事项*:确保数据库连接使用的凭据仅限于包括必要权限。如果未能这样做,可能会导致数据损坏或丢失,因为调用代码可能会尝试执行会导致删除、变异数据(如果适当提示)或读取敏感数据(如果数据库中存在此类数据)的命令。防范这些负面结果的最佳方法是(根据需要)限制授予此工具使用的凭据的权限。
有关更多信息,请参见 https://python.langchain.com/docs/security。"""
graph: HugeGraph = Field(exclude=True)
gremlin_generation_chain: LLMChain
qa_chain: LLMChain
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
@property
def input_keys(self) -> List[str]:
"""输入键。
:元数据 私有:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""输出键。
:元数据 私有:
"""
_output_keys = [self.output_key]
return _output_keys
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
*,
qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT,
gremlin_prompt: BasePromptTemplate = GREMLIN_GENERATION_PROMPT,
**kwargs: Any,
) -> HugeGraphQAChain:
"""从LLM初始化。"""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
gremlin_generation_chain = LLMChain(llm=llm, prompt=gremlin_prompt)
return cls(
qa_chain=qa_chain,
gremlin_generation_chain=gremlin_generation_chain,
**kwargs,
)
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
"""生成 gremlin 语句,使用它在数据库中查找并回答问题。"""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
question = inputs[self.input_key]
generated_gremlin = self.gremlin_generation_chain.run(
{"question": question, "schema": self.graph.get_schema}, callbacks=callbacks
)
_run_manager.on_text("Generated gremlin:", end="\n", verbose=self.verbose)
_run_manager.on_text(
generated_gremlin, color="green", end="\n", verbose=self.verbose
)
context = self.graph.query(generated_gremlin)
_run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
_run_manager.on_text(
str(context), color="green", end="\n", verbose=self.verbose
)
result = self.qa_chain(
{"question": question, "context": context},
callbacks=callbacks,
)
return {self.output_key: result[self.qa_chain.output_key]}