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92 lines
2.9 KiB
Python
92 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 CYPHER_QA_PROMPT, NGQL_GENERATION_PROMPT
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from langchain.chains.llm import LLMChain
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from langchain.graphs.nebula_graph import NebulaGraph
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from langchain.prompts.base import BasePromptTemplate
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class NebulaGraphQAChain(Chain):
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"""Chain for question-answering against a graph by generating nGQL statements."""
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graph: NebulaGraph = Field(exclude=True)
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ngql_generation_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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*,
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qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT,
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ngql_prompt: BasePromptTemplate = NGQL_GENERATION_PROMPT,
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**kwargs: Any,
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) -> NebulaGraphQAChain:
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"""Initialize from LLM."""
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qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
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ngql_generation_chain = LLMChain(llm=llm, prompt=ngql_prompt)
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return cls(
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qa_chain=qa_chain,
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ngql_generation_chain=ngql_generation_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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"""Generate nGQL statement, use it to look up in db and answer question."""
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_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
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callbacks = _run_manager.get_child()
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question = inputs[self.input_key]
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generated_ngql = self.ngql_generation_chain.run(
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{"question": question, "schema": self.graph.get_schema}, callbacks=callbacks
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)
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_run_manager.on_text("Generated nGQL:", end="\n", verbose=self.verbose)
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_run_manager.on_text(
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generated_ngql, color="green", end="\n", verbose=self.verbose
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)
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context = self.graph.query(generated_ngql)
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_run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
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_run_manager.on_text(
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str(context), color="green", end="\n", verbose=self.verbose
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)
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result = self.qa_chain(
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{"question": question, "context": context},
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callbacks=callbacks,
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)
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return {self.output_key: result[self.qa_chain.output_key]}
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