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"

[docs]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
[docs]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 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"
[docs] @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 = {"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