"""Question answering over a graph."""
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 pydantic import Field
from langchain_community.chains.graph_qa.prompts import (
CYPHER_QA_PROMPT,
KUZU_GENERATION_PROMPT,
)
from langchain_community.graphs.kuzu_graph import KuzuGraph
[docs]
def remove_prefix(text: str, prefix: str) -> str:
"""Remove a prefix from a text.
Args:
text: Text to remove the prefix from.
prefix: Prefix to remove from the text.
Returns:
Text with the prefix removed.
"""
if text.startswith(prefix):
return text[len(prefix) :]
return text
[docs]
class KuzuQAChain(Chain):
"""Question-answering against a graph by generating Cypher statements for Kùzu.
*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: KuzuGraph = Field(exclude=True)
cypher_generation_chain: LLMChain
qa_chain: LLMChain
input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private:
allow_dangerous_requests: bool = False
"""Forced user opt-in to acknowledge that the chain can make dangerous requests.
*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.
"""
def __init__(self, **kwargs: Any) -> None:
"""Initialize the chain."""
super().__init__(**kwargs)
if self.allow_dangerous_requests is not True:
raise ValueError(
"In order to use this chain, you must acknowledge that it can make "
"dangerous requests by setting `allow_dangerous_requests` to `True`."
"You must narrowly scope the permissions of the database connection "
"to only include necessary permissions. Failure to do so may result "
"in data corruption or loss or reading sensitive data if such data is "
"present in the database."
"Only use this chain if you understand the risks and have taken the "
"necessary precautions. "
"See https://python.langchain.com/docs/security for more information."
)
@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: Optional[BaseLanguageModel] = None,
*,
qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT,
cypher_prompt: BasePromptTemplate = KUZU_GENERATION_PROMPT,
cypher_llm: Optional[BaseLanguageModel] = None,
qa_llm: Optional[BaseLanguageModel] = None,
**kwargs: Any,
) -> KuzuQAChain:
"""Initialize from LLM."""
if not cypher_llm and not llm:
raise ValueError("Either `llm` or `cypher_llm` parameters must be provided")
if not qa_llm and not llm:
raise ValueError(
"Either `llm` or `qa_llm` parameters must be provided along with"
" `cypher_llm`"
)
if cypher_llm and qa_llm and llm:
raise ValueError(
"You can specify up to two of 'cypher_llm', 'qa_llm'"
", and 'llm', but not all three simultaneously."
)
qa_chain = LLMChain(
llm=qa_llm or llm, # type: ignore[arg-type]
prompt=qa_prompt,
)
cypher_generation_chain = LLMChain(
llm=cypher_llm or llm, # type: ignore[arg-type]
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, str]:
"""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]
generated_cypher = self.cypher_generation_chain.run(
{"question": question, "schema": self.graph.get_schema}, callbacks=callbacks
)
# Extract Cypher code if it is wrapped in triple backticks
# with the language marker "cypher"
generated_cypher = remove_prefix(extract_cypher(generated_cypher), "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
)
context = self.graph.query(generated_cypher)
_run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
_run_manager.on_text(
str(context), color="green", end="\n", verbose=self.verbose
)
result = self.qa_chain(
{"question": question, "context": context},
callbacks=callbacks,
)
return {self.output_key: result[self.qa_chain.output_key]}
