"""
Question answering over an RDF or OWL graph using SPARQL.
"""
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.base import BasePromptTemplate
from pydantic import Field
from langchain_community.chains.graph_qa.prompts import (
SPARQL_GENERATION_SELECT_PROMPT,
SPARQL_GENERATION_UPDATE_PROMPT,
SPARQL_INTENT_PROMPT,
SPARQL_QA_PROMPT,
)
from langchain_community.graphs.rdf_graph import RdfGraph
[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:
allow_dangerous_requests: bool = False
"""Forced user opt-in to acknowledge that the chain can make dangerous requests.
*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.
"""
def __init__(self, **kwargs: Any) -> None:
"""Initialize the chain."""
super().__init__(**kwargs)
if self.allow_dangerous_requests is not True:
raise ValueError(
"In order to use this chain, you must acknowledge that it can make "
"dangerous requests by setting `allow_dangerous_requests` to `True`."
"You must narrowly scope the permissions of the database connection "
"to only include necessary permissions. Failure to do so may result "
"in data corruption or loss or reading sensitive data if such data is "
"present in the database."
"Only use this chain if you understand the risks and have taken the "
"necessary precautions. "
"See https://python.langchain.com/docs/security for more information."
)
@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
