Source code for langchain_community.chains.graph_qa.falkordb

"""在图上的问答。"""
from __future__ import annotations

import re
from typing import Any, Dict, List, Optional

from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain_core.callbacks import CallbackManagerForChainRun
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain_core.pydantic_v1 import Field

from langchain_community.chains.graph_qa.prompts import (
    CYPHER_GENERATION_PROMPT,
    CYPHER_QA_PROMPT,
)
from langchain_community.graphs import FalkorDBGraph

INTERMEDIATE_STEPS_KEY = "intermediate_steps"


def extract_cypher(text: str) -> str:
    """从文本中提取Cypher代码。
参数：
    text：要从中提取Cypher代码的文本。

返回：
    从文本中提取的Cypher代码。
"""
    # The pattern to find Cypher code enclosed in triple backticks
    pattern = r"```(.*?)```"

    # Find all matches in the input text
    matches = re.findall(pattern, text, re.DOTALL)

    return matches[0] if matches else text


class FalkorDBQAChain(Chain):
    """用生成Cypher语句针对图形进行问答的链。

    *安全提示*：确保数据库连接使用的凭据范围狭窄，仅包括必要的权限。
        如果未能这样做，可能会导致数据损坏或丢失，因为调用代码可能会尝试执行会导致删除、变异数据或在适当提示的情况下读取敏感数据的命令，如果数据库中存在这样的数据。
        防范这种负面结果的最佳方法是（根据需要）限制授予此工具使用的凭据的权限。

        有关更多信息，请参见https://python.langchain.com/docs/security。"""

    graph: FalkorDBGraph = Field(exclude=True)
    cypher_generation_chain: LLMChain
    qa_chain: LLMChain
    input_key: str = "query"  #: :meta private:
    output_key: str = "result"  #: :meta private:
    top_k: int = 10
    """查询返回的结果数量"""
    return_intermediate_steps: bool = False
    """是否返回中间步骤以及最终答案。"""
    return_direct: bool = False
    """是否直接返回查询图形的结果。"""

    @property
    def input_keys(self) -> List[str]:
        """返回输入的键。

:元数据 私有:
"""
        return [self.input_key]

    @property
    def output_keys(self) -> List[str]:
        """返回输出键。

:元数据 私有:
"""
        _output_keys = [self.output_key]
        return _output_keys

    @property
    def _chain_type(self) -> str:
        return "graph_cypher_chain"

    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        *,
        qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT,
        cypher_prompt: BasePromptTemplate = CYPHER_GENERATION_PROMPT,
        **kwargs: Any,
    ) -> FalkorDBQAChain:
        """从LLM初始化。"""
        qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
        cypher_generation_chain = LLMChain(llm=llm, prompt=cypher_prompt)

        return cls(
            qa_chain=qa_chain,
            cypher_generation_chain=cypher_generation_chain,
            **kwargs,
        )

    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        """生成Cypher语句，使用它在数据库中查找并回答问题。"""
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        callbacks = _run_manager.get_child()
        question = inputs[self.input_key]

        intermediate_steps: List = []

        generated_cypher = self.cypher_generation_chain.run(
            {"question": question, "schema": self.graph.schema}, callbacks=callbacks
        )

        # Extract Cypher code if it is wrapped in backticks
        generated_cypher = extract_cypher(generated_cypher)

        _run_manager.on_text("Generated Cypher:", end="\n", verbose=self.verbose)
        _run_manager.on_text(
            generated_cypher, color="green", end="\n", verbose=self.verbose
        )

        intermediate_steps.append({"query": generated_cypher})

        # Retrieve and limit the number of results
        context = self.graph.query(generated_cypher)[: self.top_k]

        if self.return_direct:
            final_result = context
        else:
            _run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
            _run_manager.on_text(
                str(context), color="green", end="\n", verbose=self.verbose
            )

            intermediate_steps.append({"context": context})

            result = self.qa_chain(
                {"question": question, "context": context},
                callbacks=callbacks,
            )
            final_result = result[self.qa_chain.output_key]

        chain_result: Dict[str, Any] = {self.output_key: final_result}
        if self.return_intermediate_steps:
            chain_result[INTERMEDIATE_STEPS_KEY] = intermediate_steps

        return chain_result