"""
CPAL Chain and its subchains
"""
from __future__ import annotations
import json
from typing import Any, ClassVar, Dict, List, Optional, Type
from langchain.base_language import BaseLanguageModel
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.output_parsers import PydanticOutputParser
from langchain_core.callbacks.manager import CallbackManagerForChainRun
from langchain_core.prompts.prompt import PromptTemplate
from langchain_experimental import pydantic_v1 as pydantic
from langchain_experimental.cpal.constants import Constant
from langchain_experimental.cpal.models import (
CausalModel,
InterventionModel,
NarrativeModel,
QueryModel,
StoryModel,
)
from langchain_experimental.cpal.templates.univariate.causal import (
template as causal_template,
)
from langchain_experimental.cpal.templates.univariate.intervention import (
template as intervention_template,
)
from langchain_experimental.cpal.templates.univariate.narrative import (
template as narrative_template,
)
from langchain_experimental.cpal.templates.univariate.query import (
template as query_template,
)
class _BaseStoryElementChain(Chain):
chain: LLMChain
input_key: str = Constant.narrative_input.value #: :meta private:
output_key: str = Constant.chain_answer.value #: :meta private:
pydantic_model: ClassVar[Optional[Type[pydantic.BaseModel]]] = (
None #: :meta private:
)
template: ClassVar[Optional[str]] = None #: :meta private:
@classmethod
def parser(cls) -> PydanticOutputParser:
"""Parse LLM output into a pydantic object."""
if cls.pydantic_model is None:
raise NotImplementedError(
f"pydantic_model not implemented for {cls.__name__}"
)
return PydanticOutputParser(pydantic_object=cls.pydantic_model)
@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
@classmethod
def from_univariate_prompt(
cls,
llm: BaseLanguageModel,
**kwargs: Any,
) -> Any:
return cls(
chain=LLMChain(
llm=llm,
prompt=PromptTemplate(
input_variables=[Constant.narrative_input.value],
template=kwargs.get("template", cls.template),
partial_variables={
"format_instructions": cls.parser().get_format_instructions()
},
),
),
**kwargs,
)
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
completion = self.chain.run(inputs[self.input_key])
pydantic_data = self.__class__.parser().parse(completion)
return {
Constant.chain_data.value: pydantic_data,
Constant.chain_answer.value: None,
}
[docs]class NarrativeChain(_BaseStoryElementChain):
"""Decompose the narrative into its story elements.
- causal model
- query
- intervention
"""
pydantic_model: ClassVar[Type[pydantic.BaseModel]] = NarrativeModel
template: ClassVar[str] = narrative_template
[docs]class CausalChain(_BaseStoryElementChain):
"""Translate the causal narrative into a stack of operations."""
pydantic_model: ClassVar[Type[pydantic.BaseModel]] = CausalModel
template: ClassVar[str] = causal_template
[docs]class InterventionChain(_BaseStoryElementChain):
"""Set the hypothetical conditions for the causal model."""
pydantic_model: ClassVar[Type[pydantic.BaseModel]] = InterventionModel
template: ClassVar[str] = intervention_template
[docs]class QueryChain(_BaseStoryElementChain):
"""Query the outcome table using SQL.
*Security note*: This class implements an AI technique that generates SQL code.
If those SQL commands are executed, it's critical to ensure they use credentials
that are narrowly-scoped to only include the permissions this chain needs.
Failure to do so may result in data corruption or loss, since this chain may
attempt commands like `DROP TABLE` or `INSERT` if appropriately prompted.
The best way to guard against such negative outcomes is to (as appropriate)
limit the permissions granted to the credentials used with this chain.
"""
pydantic_model: ClassVar[Type[pydantic.BaseModel]] = QueryModel
template: ClassVar[str] = query_template # TODO: incl. table schema
[docs]class CPALChain(_BaseStoryElementChain):
"""Causal program-aided language (CPAL) chain implementation.
*Security note*: The building blocks of this class include the implementation
of an AI technique that generates SQL code. If those SQL commands
are executed, it's critical to ensure they use credentials that
are narrowly-scoped to only include the permissions this chain needs.
Failure to do so may result in data corruption or loss, since this chain may
attempt commands like `DROP TABLE` or `INSERT` if appropriately prompted.
The best way to guard against such negative outcomes is to (as appropriate)
limit the permissions granted to the credentials used with this chain.
"""
llm: BaseLanguageModel
narrative_chain: Optional[NarrativeChain] = None
causal_chain: Optional[CausalChain] = None
intervention_chain: Optional[InterventionChain] = None
query_chain: Optional[QueryChain] = None
_story: StoryModel = pydantic.PrivateAttr(default=None) # TODO: change name ?
[docs] @classmethod
def from_univariate_prompt(
cls,
llm: BaseLanguageModel,
**kwargs: Any,
) -> CPALChain:
"""instantiation depends on component chains
*Security note*: The building blocks of this class include the implementation
of an AI technique that generates SQL code. If those SQL commands
are executed, it's critical to ensure they use credentials that
are narrowly-scoped to only include the permissions this chain needs.
Failure to do so may result in data corruption or loss, since this chain may
attempt commands like `DROP TABLE` or `INSERT` if appropriately prompted.
The best way to guard against such negative outcomes is to (as appropriate)
limit the permissions granted to the credentials used with this chain.
"""
return cls(
llm=llm,
chain=LLMChain(
llm=llm,
prompt=PromptTemplate(
input_variables=["question", "query_result"],
template=(
"Summarize this answer '{query_result}' to this "
"question '{question}'? "
),
),
),
narrative_chain=NarrativeChain.from_univariate_prompt(llm=llm),
causal_chain=CausalChain.from_univariate_prompt(llm=llm),
intervention_chain=InterventionChain.from_univariate_prompt(llm=llm),
query_chain=QueryChain.from_univariate_prompt(llm=llm),
**kwargs,
)
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
**kwargs: Any,
) -> Dict[str, Any]:
# instantiate component chains
if self.narrative_chain is None:
self.narrative_chain = NarrativeChain.from_univariate_prompt(llm=self.llm)
if self.causal_chain is None:
self.causal_chain = CausalChain.from_univariate_prompt(llm=self.llm)
if self.intervention_chain is None:
self.intervention_chain = InterventionChain.from_univariate_prompt(
llm=self.llm
)
if self.query_chain is None:
self.query_chain = QueryChain.from_univariate_prompt(llm=self.llm)
# decompose narrative into three causal story elements
narrative = self.narrative_chain(inputs[Constant.narrative_input.value])[
Constant.chain_data.value
]
story = StoryModel(
causal_operations=self.causal_chain(narrative.story_plot)[
Constant.chain_data.value
],
intervention=self.intervention_chain(narrative.story_hypothetical)[
Constant.chain_data.value
],
query=self.query_chain(narrative.story_outcome_question)[
Constant.chain_data.value
],
)
self._story = story
def pretty_print_str(title: str, d: str) -> str:
return title + "\n" + d
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
_run_manager.on_text(
pretty_print_str("story outcome data", story._outcome_table.to_string()),
color="green",
end="\n\n",
verbose=self.verbose,
)
def pretty_print_dict(title: str, d: dict) -> str:
return title + "\n" + json.dumps(d, indent=4)
_run_manager.on_text(
pretty_print_dict("query data", story.query.dict()),
color="blue",
end="\n\n",
verbose=self.verbose,
)
if story.query._result_table.empty:
# prevent piping bad data into subsequent chains
raise ValueError(
(
"unanswerable, query and outcome are incoherent\n"
"\n"
"outcome:\n"
f"{story._outcome_table}\n"
"query:\n"
f"{story.query.dict()}"
)
)
else:
query_result = float(story.query._result_table.values[0][-1])
if False:
"""TODO: add this back in when demanded by composable chains"""
reporting_chain = self.chain
human_report = reporting_chain.run(
question=story.query.question, query_result=query_result
)
query_result = {
"query_result": query_result,
"human_report": human_report,
}
output = {
Constant.chain_data.value: story,
self.output_key: query_result,
**kwargs,
}
return output
[docs] def draw(self, **kwargs: Any) -> None:
"""
CPAL chain can draw its resulting DAG.
Usage in a jupyter notebook:
>>> from IPython.display import SVG
>>> cpal_chain.draw(path="graph.svg")
>>> SVG('graph.svg')
"""
self._story._networkx_wrapper.draw_graphviz(**kwargs)
