"""在图上的问答。"""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Dict, List, Optional
if TYPE_CHECKING:
import rdflib
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain_core.callbacks.manager import CallbackManager, CallbackManagerForChainRun
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts.base import BasePromptTemplate
from langchain_core.pydantic_v1 import Field
from langchain_community.chains.graph_qa.prompts import (
GRAPHDB_QA_PROMPT,
GRAPHDB_SPARQL_FIX_PROMPT,
GRAPHDB_SPARQL_GENERATION_PROMPT,
)
from langchain_community.graphs import OntotextGraphDBGraph
[docs]class OntotextGraphDBQAChain(Chain):
"""针对Ontotext GraphDB进行问答，通过生成SPARQL查询。
*安全提示*：确保数据库连接使用的凭据范围狭窄，仅包括必要的权限。如果未这样做，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。防范这种负面结果的最佳方法是（根据情况）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: OntotextGraphDBGraph = Field(exclude=True)
sparql_generation_chain: LLMChain
sparql_fix_chain: LLMChain
max_fix_retries: int
qa_chain: LLMChain
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
@property
def input_keys(self) -> List[str]:
return [self.input_key]
@property
def output_keys(self) -> List[str]:
_output_keys = [self.output_key]
return _output_keys
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
*,
sparql_generation_prompt: BasePromptTemplate = GRAPHDB_SPARQL_GENERATION_PROMPT,
sparql_fix_prompt: BasePromptTemplate = GRAPHDB_SPARQL_FIX_PROMPT,
max_fix_retries: int = 5,
qa_prompt: BasePromptTemplate = GRAPHDB_QA_PROMPT,
**kwargs: Any,
) -> OntotextGraphDBQAChain:
"""从LLM初始化。"""
sparql_generation_chain = LLMChain(llm=llm, prompt=sparql_generation_prompt)
sparql_fix_chain = LLMChain(llm=llm, prompt=sparql_fix_prompt)
max_fix_retries = max_fix_retries
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
return cls(
qa_chain=qa_chain,
sparql_generation_chain=sparql_generation_chain,
sparql_fix_chain=sparql_fix_chain,
max_fix_retries=max_fix_retries,
**kwargs,
)
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
"""生成一个SPARQL查询，使用它从GraphDB检索响应并回答问题。
"""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
prompt = inputs[self.input_key]
ontology_schema = self.graph.get_schema
sparql_generation_chain_result = self.sparql_generation_chain.invoke(
{"prompt": prompt, "schema": ontology_schema}, callbacks=callbacks
)
generated_sparql = sparql_generation_chain_result[
self.sparql_generation_chain.output_key
]
generated_sparql = self._get_prepared_sparql_query(
_run_manager, callbacks, generated_sparql, ontology_schema
)
query_results = self._execute_query(generated_sparql)
qa_chain_result = self.qa_chain.invoke(
{"prompt": prompt, "context": query_results}, callbacks=callbacks
)
result = qa_chain_result[self.qa_chain.output_key]
return {self.output_key: result}
def _get_prepared_sparql_query(
self,
_run_manager: CallbackManagerForChainRun,
callbacks: CallbackManager,
generated_sparql: str,
ontology_schema: str,
) -> str:
try:
return self._prepare_sparql_query(_run_manager, generated_sparql)
except Exception as e:
retries = 0
error_message = str(e)
self._log_invalid_sparql_query(
_run_manager, generated_sparql, error_message
)
while retries < self.max_fix_retries:
try:
sparql_fix_chain_result = self.sparql_fix_chain.invoke(
{
"error_message": error_message,
"generated_sparql": generated_sparql,
"schema": ontology_schema,
},
callbacks=callbacks,
)
generated_sparql = sparql_fix_chain_result[
self.sparql_fix_chain.output_key
]
return self._prepare_sparql_query(_run_manager, generated_sparql)
except Exception as e:
retries += 1
parse_exception = str(e)
self._log_invalid_sparql_query(
_run_manager, generated_sparql, parse_exception
)
raise ValueError("The generated SPARQL query is invalid.")
def _prepare_sparql_query(
self, _run_manager: CallbackManagerForChainRun, generated_sparql: str
) -> str:
from rdflib.plugins.sparql import prepareQuery
prepareQuery(generated_sparql)
self._log_prepared_sparql_query(_run_manager, generated_sparql)
return generated_sparql
def _log_prepared_sparql_query(
self, _run_manager: CallbackManagerForChainRun, generated_query: str
) -> None:
_run_manager.on_text("Generated SPARQL:", end="\n", verbose=self.verbose)
_run_manager.on_text(
generated_query, color="green", end="\n", verbose=self.verbose
)
def _log_invalid_sparql_query(
self,
_run_manager: CallbackManagerForChainRun,
generated_query: str,
error_message: str,
) -> None:
_run_manager.on_text("Invalid SPARQL query: ", end="\n", verbose=self.verbose)
_run_manager.on_text(
generated_query, color="red", end="\n", verbose=self.verbose
)
_run_manager.on_text(
"SPARQL Query Parse Error: ", end="\n", verbose=self.verbose
)
_run_manager.on_text(
error_message, color="red", end="\n\n", verbose=self.verbose
)
def _execute_query(self, query: str) -> List[rdflib.query.ResultRow]:
try:
return self.graph.query(query)
except Exception:
raise ValueError("Failed to execute the generated SPARQL query.")