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# Source code for langchain.chains.graph_qa.falkordb

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

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

    from langchain_community.graphs import FalkorDBGraph
    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.chains.base import Chain
    from langchain.chains.graph_qa.prompts import CYPHER_GENERATION_PROMPT, CYPHER_QA_PROMPT
    from langchain.chains.llm import LLMChain

    INTERMEDIATE_STEPS_KEY = "intermediate_steps"

    def extract_cypher(text: str) -> str:
        """
        Extract Cypher code from a text.
        Args:
            text: Text to extract Cypher code from.

        Returns:
            Cypher code extracted from the text.
        """
        # 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):
        """Chain for question-answering against a graph by generating Cypher statements.

        *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: 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
        """Number of results to return from the query"""
        return_intermediate_steps: bool = False
        """Whether or not to return the intermediate steps along with the final answer."""
        return_direct: bool = False
        """Whether or not to return the result of querying the graph directly."""

        @property
        def input_keys(self) -> List[str]:
            """Return the input keys.

            :meta private:
            """
            return [self.input_key]

        @property
        def output_keys(self) -> List[str]:
            """Return the output keys.

            :meta private:
            """
            _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:
            """Initialize from LLM."""
            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]:
            """Generate Cypher statement, use it to look up in db and answer question."""
            _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