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"""Question answering over a graph.""" | |
from __future__ import annotations | |
from typing import Any, Dict, List | |
from pydantic import Field | |
from langchain.chains.base import Chain | |
from langchain.chains.graph_qa.prompts import ENTITY_EXTRACTION_PROMPT, PROMPT | |
from langchain.chains.llm import LLMChain | |
from langchain.graphs.networkx_graph import NetworkxEntityGraph, get_entities | |
from langchain.llms.base import BaseLLM | |
from langchain.prompts.base import BasePromptTemplate | |
class GraphQAChain(Chain): | |
"""Chain for question-answering against a graph.""" | |
graph: NetworkxEntityGraph = Field(exclude=True) | |
entity_extraction_chain: LLMChain | |
qa_chain: LLMChain | |
input_key: str = "query" #: :meta private: | |
output_key: str = "result" #: :meta private: | |
def input_keys(self) -> List[str]: | |
"""Return the input keys. | |
:meta private: | |
""" | |
return [self.input_key] | |
def output_keys(self) -> List[str]: | |
"""Return the output keys. | |
:meta private: | |
""" | |
_output_keys = [self.output_key] | |
return _output_keys | |
def from_llm( | |
cls, | |
llm: BaseLLM, | |
qa_prompt: BasePromptTemplate = 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, str]) -> Dict[str, Any]: | |
"""Extract entities, look up info and answer question.""" | |
question = inputs[self.input_key] | |
entity_string = self.entity_extraction_chain.run(question) | |
self.callback_manager.on_text( | |
"Entities Extracted:", end="\n", verbose=self.verbose | |
) | |
self.callback_manager.on_text( | |
entity_string, color="green", end="\n", verbose=self.verbose | |
) | |
entities = get_entities(entity_string) | |
context = "" | |
for entity in entities: | |
triplets = self.graph.get_entity_knowledge(entity) | |
context += "\n".join(triplets) | |
self.callback_manager.on_text("Full Context:", end="\n", verbose=self.verbose) | |
self.callback_manager.on_text( | |
context, color="green", end="\n", verbose=self.verbose | |
) | |
result = self.qa_chain({"question": question, "context": context}) | |
return {self.output_key: result[self.qa_chain.output_key]} | |