"""Question answering over a graph."""
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):
"""Question-answering against Ontotext GraphDB
https://graphdb.ontotext.com/ by generating SPARQL queries.
*Security note*: Make sure that the database connection uses credentials
that are narrowly-scoped to only include necessary permissions.
Failure to do so may result in data corruption or loss, since the calling
code may attempt commands that would result in deletion, mutation
of data if appropriately prompted or reading sensitive data if such
data is present in the database.
The best way to guard against such negative outcomes is to (as appropriate)
limit the permissions granted to the credentials used with this tool.
See https://python.langchain.com/docs/security for more information.
"""
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:
"""Initialize from LLM."""
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]:
"""
Generate a SPARQL query, use it to retrieve a response from GraphDB and answer
the question.
"""
_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.")
