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88 lines
2.9 KiB
Python
88 lines
2.9 KiB
Python
"""Question answering over a graph."""
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from __future__ import annotations
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from typing import Any, Dict, List, Optional
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from pydantic import Field
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from langchain.base_language import BaseLanguageModel
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from langchain.callbacks.manager import CallbackManagerForChainRun
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from langchain.chains.base import Chain
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from langchain.chains.graph_qa.prompts import ENTITY_EXTRACTION_PROMPT, PROMPT
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from langchain.chains.llm import LLMChain
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from langchain.graphs.networkx_graph import NetworkxEntityGraph, get_entities
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from langchain.prompts.base import BasePromptTemplate
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class GraphQAChain(Chain):
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"""Chain for question-answering against a graph."""
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graph: NetworkxEntityGraph = Field(exclude=True)
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entity_extraction_chain: LLMChain
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qa_chain: LLMChain
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input_key: str = "query" #: :meta private:
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output_key: str = "result" #: :meta private:
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@property
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def input_keys(self) -> List[str]:
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"""Return the input keys.
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:meta private:
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"""
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return [self.input_key]
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@property
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def output_keys(self) -> List[str]:
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"""Return the output keys.
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:meta private:
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"""
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_output_keys = [self.output_key]
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return _output_keys
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@classmethod
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def from_llm(
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cls,
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llm: BaseLanguageModel,
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qa_prompt: BasePromptTemplate = PROMPT,
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entity_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT,
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**kwargs: Any,
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) -> GraphQAChain:
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"""Initialize from LLM."""
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qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
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entity_chain = LLMChain(llm=llm, prompt=entity_prompt)
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return cls(
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qa_chain=qa_chain,
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entity_extraction_chain=entity_chain,
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**kwargs,
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)
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def _call(
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self,
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inputs: Dict[str, Any],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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"""Extract entities, look up info and answer question."""
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_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
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question = inputs[self.input_key]
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entity_string = self.entity_extraction_chain.run(question)
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_run_manager.on_text("Entities Extracted:", end="\n", verbose=self.verbose)
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_run_manager.on_text(
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entity_string, color="green", end="\n", verbose=self.verbose
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)
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entities = get_entities(entity_string)
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context = ""
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for entity in entities:
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triplets = self.graph.get_entity_knowledge(entity)
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context += "\n".join(triplets)
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_run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
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_run_manager.on_text(context, color="green", end="\n", verbose=self.verbose)
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result = self.qa_chain(
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{"question": question, "context": context},
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callbacks=_run_manager.get_child(),
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)
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return {self.output_key: result[self.qa_chain.output_key]}
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