"""
Question answering over an RDF or OWL graph using SPARQL.
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain_core.callbacks.manager import CallbackManagerForChainRun
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts.base import BasePromptTemplate
from langchain_core.prompts.prompt import PromptTemplate
from langchain_core.pydantic_v1 import Field
from langchain_community.chains.graph_qa.prompts import SPARQL_QA_PROMPT
from langchain_community.graphs import NeptuneRdfGraph
INTERMEDIATE_STEPS_KEY = "intermediate_steps"
SPARQL_GENERATION_TEMPLATE = """
Task: Generate a SPARQL SELECT statement for querying a graph database.
For instance, to find all email addresses of John Doe, the following
query in backticks would be suitable:
```
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT ?email
WHERE {{
?person foaf:name "John Doe" .
?person foaf:mbox ?email .
}}
```
Instructions:
Use only the node types and properties provided in the schema.
Do not use any node types and properties that are not explicitly provided.
Include all necessary prefixes.
Examples:
Schema:
{schema}
Note: Be as concise as possible.
Do not include any explanations or apologies in your responses.
Do not respond to any questions that ask for anything else than
for you to construct a SPARQL query.
Do not include any text except the SPARQL query generated.
The question is:
{prompt}"""
SPARQL_GENERATION_PROMPT = PromptTemplate(
input_variables=["schema", "prompt"], template=SPARQL_GENERATION_TEMPLATE
)
[docs]class NeptuneSparqlQAChain(Chain):
"""Chain for question-answering against a Neptune graph
by generating SPARQL 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 = NeptuneSparqlQAChain.from_llm(
llm=llm,
graph=graph
)
response = chain.invoke(query)
"""
graph: NeptuneRdfGraph = Field(exclude=True)
sparql_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 [self.input_key]
@property
def output_keys(self) -> List[str]:
_output_keys = [self.output_key]
return _output_keys
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
*,
qa_prompt: BasePromptTemplate = SPARQL_QA_PROMPT,
sparql_prompt: BasePromptTemplate = SPARQL_GENERATION_PROMPT,
examples: Optional[str] = None,
**kwargs: Any,
) -> NeptuneSparqlQAChain:
"""Initialize from LLM."""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
template_to_use = SPARQL_GENERATION_TEMPLATE
if examples:
template_to_use = template_to_use.replace(
"Examples:", "Examples: " + examples
)
sparql_prompt = PromptTemplate(
input_variables=["schema", "prompt"], template=template_to_use
)
sparql_generation_chain = LLMChain(llm=llm, prompt=sparql_prompt)
return cls( # type: ignore[call-arg]
qa_chain=qa_chain,
sparql_generation_chain=sparql_generation_chain,
examples=examples,
**kwargs,
)
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
"""
Generate SPARQL query, use it to retrieve a response from the gdb and answer
the question.
"""
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
callbacks = _run_manager.get_child()
prompt = inputs[self.input_key]
intermediate_steps: List = []
generated_sparql = self.sparql_generation_chain.run(
{"prompt": prompt, "schema": self.graph.get_schema}, callbacks=callbacks
)
# Extract SPARQL
generated_sparql = extract_sparql(generated_sparql)
_run_manager.on_text("Generated SPARQL:", end="\n", verbose=self.verbose)
_run_manager.on_text(
generated_sparql, color="green", end="\n", verbose=self.verbose
)
intermediate_steps.append({"query": generated_sparql})
context = self.graph.query(generated_sparql)
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(
{"prompt": prompt, "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
