Source code for langchain.chains.graph_qa.base

"""Question answering over a graph."""
from __future__ import annotations

from typing import Any, Dict, List, Optional

from langchain_community.graphs.networkx_graph import NetworkxEntityGraph, get_entities
from langchain_core.callbacks.manager import CallbackManagerForChainRun
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain_core.pydantic_v1 import Field

from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts import ENTITY_EXTRACTION_PROMPT, GRAPH_QA_PROMPT
from langchain.chains.llm import LLMChain


[docs]class GraphQAChain(Chain): """Chain for question-answering against a graph. *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: NetworkxEntityGraph = Field(exclude=True) entity_extraction_chain: LLMChain qa_chain: LLMChain input_key: str = "query" #: :meta private: output_key: str = "result" #: :meta private: @property def input_keys(self) -> List[str]: """Input keys. :meta private: """ return [self.input_key] @property def output_keys(self) -> List[str]: """Output keys. :meta private: """ _output_keys = [self.output_key] return _output_keys
[docs] @classmethod def from_llm( cls, llm: BaseLanguageModel, qa_prompt: BasePromptTemplate = GRAPH_QA_PROMPT, entity_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT, **kwargs: Any, ) -> GraphQAChain: """Initialize from LLM.""" qa_chain = LLMChain(llm=llm, prompt=qa_prompt) entity_chain = LLMChain(llm=llm, prompt=entity_prompt) return cls( qa_chain=qa_chain, entity_extraction_chain=entity_chain, **kwargs, )
def _call( self, inputs: Dict[str, Any], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, str]: """Extract entities, look up info and answer question.""" _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() question = inputs[self.input_key] entity_string = self.entity_extraction_chain.run(question) _run_manager.on_text("Entities Extracted:", end="\n", verbose=self.verbose) _run_manager.on_text( entity_string, color="green", end="\n", verbose=self.verbose ) entities = get_entities(entity_string) context = "" all_triplets = [] for entity in entities: all_triplets.extend(self.graph.get_entity_knowledge(entity)) context = "\n".join(all_triplets) _run_manager.on_text("Full Context:", end="\n", verbose=self.verbose) _run_manager.on_text(context, color="green", end="\n", verbose=self.verbose) result = self.qa_chain( {"question": question, "context": context}, callbacks=_run_manager.get_child(), ) return {self.output_key: result[self.qa_chain.output_key]}