Source code for langchain.chains.graph_qa.sparql

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 (
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 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 ={"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 = {"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