"""
Question answering over an RDF or OWL graph using SPARQL.
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from langchain_community.graphs.rdf_graph import RdfGraph
from langchain_core.callbacks import CallbackManagerForChainRun
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts.base import BasePromptTemplate
from langchain_core.pydantic_v1 import Field
from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts import (
SPARQL_GENERATION_SELECT_PROMPT,
SPARQL_GENERATION_UPDATE_PROMPT,
SPARQL_INTENT_PROMPT,
SPARQL_QA_PROMPT,
)
from langchain.chains.llm import LLMChain
[docs]class GraphSparqlQAChain(Chain):
"""Question-answering against an RDF or OWL graph by generating SPARQL statements.
*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: RdfGraph = Field(exclude=True)
sparql_generation_select_chain: LLMChain
sparql_generation_update_chain: LLMChain
sparql_intent_chain: LLMChain
qa_chain: LLMChain
return_sparql_query: bool = False
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
sparql_query_key: str = "sparql_query" #: :meta private:
@property
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return the output keys.
:meta private:
"""
_output_keys = [self.output_key]
return _output_keys
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
*,
qa_prompt: BasePromptTemplate = SPARQL_QA_PROMPT,
sparql_select_prompt: BasePromptTemplate = SPARQL_GENERATION_SELECT_PROMPT,
sparql_update_prompt: BasePromptTemplate = SPARQL_GENERATION_UPDATE_PROMPT,
sparql_intent_prompt: BasePromptTemplate = SPARQL_INTENT_PROMPT,
**kwargs: Any,
) -> GraphSparqlQAChain:
"""Initialize from LLM."""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
sparql_generation_select_chain = LLMChain(llm=llm, prompt=sparql_select_prompt)
sparql_generation_update_chain = LLMChain(llm=llm, prompt=sparql_update_prompt)
sparql_intent_chain = LLMChain(llm=llm, prompt=sparql_intent_prompt)
return cls(
qa_chain=qa_chain,
sparql_generation_select_chain=sparql_generation_select_chain,
sparql_generation_update_chain=sparql_generation_update_chain,
sparql_intent_chain=sparql_intent_chain,
**kwargs,
)
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
"""
Generate SPARQL query, use it to retrieve a response from the gdb and answer
the question.
"""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
prompt = inputs[self.input_key]
_intent = self.sparql_intent_chain.run({"prompt": prompt}, callbacks=callbacks)
intent = _intent.strip()
if "SELECT" in intent and "UPDATE" not in intent:
sparql_generation_chain = self.sparql_generation_select_chain
intent = "SELECT"
elif "UPDATE" in intent and "SELECT" not in intent:
sparql_generation_chain = self.sparql_generation_update_chain
intent = "UPDATE"
else:
raise ValueError(
"I am sorry, but this prompt seems to fit none of the currently "
"supported SPARQL query types, i.e., SELECT and UPDATE."
)
_run_manager.on_text("Identified intent:", end="\n", verbose=self.verbose)
_run_manager.on_text(intent, color="green", end="\n", verbose=self.verbose)
generated_sparql = sparql_generation_chain.run(
{"prompt": prompt, "schema": self.graph.get_schema}, callbacks=callbacks
)
_run_manager.on_text("Generated SPARQL:", end="\n", verbose=self.verbose)
_run_manager.on_text(
generated_sparql, color="green", end="\n", verbose=self.verbose
)
if intent == "SELECT":
context = self.graph.query(generated_sparql)
_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(
{"prompt": prompt, "context": context},
callbacks=callbacks,
)
res = result[self.qa_chain.output_key]
elif intent == "UPDATE":
self.graph.update(generated_sparql)
res = "Successfully inserted triples into the graph."
else:
raise ValueError("Unsupported SPARQL query type.")
chain_result: Dict[str, Any] = {self.output_key: res}
if self.return_sparql_query:
chain_result[self.sparql_query_key] = generated_sparql
return chain_result