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 (
CYPHER_QA_PROMPT,
NEPTUNE_OPENCYPHER_GENERATION_PROMPT,
NEPTUNE_OPENCYPHER_GENERATION_SIMPLE_PROMPT,
)
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 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 https://python.langchain.com/docs/security for more information.
Example:
.. code-block:: python
chain = NeptuneOpenCypherQAChain.from_llm(
llm=llm,
graph=graph
)
response = chain.run(query)
"""
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 = self.cypher_generation_chain.run(
{
"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