Source code for langchain.chains.graph_qa.neptune_cypher

from __future__ import annotations

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

from langchain_community.graphs import BaseNeptuneGraph
from langchain_core.callbacks import CallbackManagerForChainRun
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts.base import BasePromptTemplate
from langchain_core.pydantic_v1 import Field

from langchain.chains.base import Chain
from langchain.chains.graph_qa.prompts import (
from langchain.chains.llm import LLMChain
from langchain.chains.prompt_selector import ConditionalPromptSelector

INTERMEDIATE_STEPS_KEY = "intermediate_steps"

[docs]def trim_query(query: str) -> str: """Trim the query to only include Cypher keywords.""" keywords = ( "CALL", "CREATE", "DELETE", "DETACH", "LIMIT", "MATCH", "MERGE", "OPTIONAL", "ORDER", "REMOVE", "RETURN", "SET", "SKIP", "UNWIND", "WITH", "WHERE", "//", ) lines = query.split("\n") new_query = "" for line in lines: if line.strip().upper().startswith(keywords): new_query += line + "\n" return new_query
[docs]def extract_cypher(text: str) -> str: """Extract Cypher code from text using Regex.""" # 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]def use_simple_prompt(llm: BaseLanguageModel) -> bool: """Decides whether to use the simple prompt""" if llm._llm_type and "anthropic" in llm._llm_type: # type: ignore return True # Bedrock anthropic if hasattr(llm, "model_id") and "anthropic" in llm.model_id: # type: ignore return True return False
PROMPT_SELECTOR = ConditionalPromptSelector( default_prompt=NEPTUNE_OPENCYPHER_GENERATION_PROMPT, conditionals=[(use_simple_prompt, NEPTUNE_OPENCYPHER_GENERATION_SIMPLE_PROMPT)], )
[docs]class NeptuneOpenCypherQAChain(Chain): """Chain for question-answering against a Neptune graph by generating openCypher 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. Example: .. code-block:: python chain = NeptuneOpenCypherQAChain.from_llm( llm=llm, graph=graph ) response = """ graph: BaseNeptuneGraph = 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 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.""" extra_instructions: Optional[str] = None """Extra instructions by the appended to the query generation prompt.""" @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
[docs] @classmethod def from_llm( cls, llm: BaseLanguageModel, *, qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT, cypher_prompt: Optional[BasePromptTemplate] = None, extra_instructions: Optional[str] = None, **kwargs: Any, ) -> NeptuneOpenCypherQAChain: """Initialize from LLM.""" qa_chain = LLMChain(llm=llm, prompt=qa_prompt) _cypher_prompt = cypher_prompt or PROMPT_SELECTOR.get_prompt(llm) cypher_generation_chain = LLMChain(llm=llm, prompt=_cypher_prompt) return cls( qa_chain=qa_chain, cypher_generation_chain=cypher_generation_chain, extra_instructions=extra_instructions, **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.get_schema, "extra_instructions": self.extra_instructions or "", }, callbacks=callbacks, ) # Extract Cypher code if it is wrapped in backticks generated_cypher = extract_cypher(generated_cypher) generated_cypher = trim_query(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}) context = self.graph.query(generated_cypher) 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