mirror of
https://github.com/hwchase17/langchain
synced 2024-11-11 19:11:02 +00:00
fa5d49f2c1
ran ```bash g grep -l "langchain.vectorstores" | xargs -L 1 sed -i '' "s/langchain\.vectorstores/langchain_community.vectorstores/g" g grep -l "langchain.document_loaders" | xargs -L 1 sed -i '' "s/langchain\.document_loaders/langchain_community.document_loaders/g" g grep -l "langchain.chat_loaders" | xargs -L 1 sed -i '' "s/langchain\.chat_loaders/langchain_community.chat_loaders/g" g grep -l "langchain.document_transformers" | xargs -L 1 sed -i '' "s/langchain\.document_transformers/langchain_community.document_transformers/g" g grep -l "langchain\.graphs" | xargs -L 1 sed -i '' "s/langchain\.graphs/langchain_community.graphs/g" g grep -l "langchain\.memory\.chat_message_histories" | xargs -L 1 sed -i '' "s/langchain\.memory\.chat_message_histories/langchain_community.chat_message_histories/g" gco master libs/langchain/tests/unit_tests/*/test_imports.py gco master libs/langchain/tests/unit_tests/**/test_public_api.py ```
264 lines
8.8 KiB
Python
264 lines
8.8 KiB
Python
from __future__ import annotations # allows pydantic model to reference itself
|
|
|
|
import re
|
|
from typing import Any, List, Optional, Union
|
|
|
|
from langchain_community.graphs.networkx_graph import NetworkxEntityGraph
|
|
|
|
from langchain_experimental.cpal.constants import Constant
|
|
from langchain_experimental.pydantic_v1 import (
|
|
BaseModel,
|
|
Field,
|
|
PrivateAttr,
|
|
root_validator,
|
|
validator,
|
|
)
|
|
|
|
|
|
class NarrativeModel(BaseModel):
|
|
"""
|
|
Represent the narrative input as three story elements.
|
|
"""
|
|
|
|
story_outcome_question: str
|
|
story_hypothetical: str
|
|
story_plot: str # causal stack of operations
|
|
|
|
@validator("*", pre=True)
|
|
def empty_str_to_none(cls, v: str) -> Union[str, None]:
|
|
"""Empty strings are not allowed"""
|
|
if v == "":
|
|
return None
|
|
return v
|
|
|
|
|
|
class EntityModel(BaseModel):
|
|
name: str = Field(description="entity name")
|
|
code: str = Field(description="entity actions")
|
|
value: float = Field(description="entity initial value")
|
|
depends_on: List[str] = Field(default=[], description="ancestor entities")
|
|
|
|
# TODO: generalize to multivariate math
|
|
# TODO: acyclic graph
|
|
|
|
class Config:
|
|
validate_assignment = True
|
|
|
|
@validator("name")
|
|
def lower_case_name(cls, v: str) -> str:
|
|
v = v.lower()
|
|
return v
|
|
|
|
|
|
class CausalModel(BaseModel):
|
|
attribute: str = Field(description="name of the attribute to be calculated")
|
|
entities: List[EntityModel] = Field(description="entities in the story")
|
|
|
|
# TODO: root validate each `entity.depends_on` using system's entity names
|
|
|
|
|
|
class EntitySettingModel(BaseModel):
|
|
"""
|
|
Initial conditions for an entity
|
|
|
|
{"name": "bud", "attribute": "pet_count", "value": 12}
|
|
"""
|
|
|
|
name: str = Field(description="name of the entity")
|
|
attribute: str = Field(description="name of the attribute to be calculated")
|
|
value: float = Field(description="entity's attribute value (calculated)")
|
|
|
|
@validator("name")
|
|
def lower_case_transform(cls, v: str) -> str:
|
|
v = v.lower()
|
|
return v
|
|
|
|
|
|
class SystemSettingModel(BaseModel):
|
|
"""
|
|
Initial global conditions for the system.
|
|
|
|
{"parameter": "interest_rate", "value": .05}
|
|
"""
|
|
|
|
parameter: str
|
|
value: float
|
|
|
|
|
|
class InterventionModel(BaseModel):
|
|
"""
|
|
aka initial conditions
|
|
|
|
>>> intervention.dict()
|
|
{
|
|
entity_settings: [
|
|
{"name": "bud", "attribute": "pet_count", "value": 12},
|
|
{"name": "pat", "attribute": "pet_count", "value": 0},
|
|
],
|
|
system_settings: None,
|
|
}
|
|
"""
|
|
|
|
entity_settings: List[EntitySettingModel]
|
|
system_settings: Optional[List[SystemSettingModel]] = None
|
|
|
|
@validator("system_settings")
|
|
def lower_case_name(cls, v: str) -> Union[str, None]:
|
|
if v is not None:
|
|
raise NotImplementedError("system_setting is not implemented yet")
|
|
return v
|
|
|
|
|
|
class QueryModel(BaseModel):
|
|
"""translate a question about the story outcome into a programmatic expression"""
|
|
|
|
question: str = Field(alias=Constant.narrative_input.value) # input
|
|
expression: str # output, part of llm completion
|
|
llm_error_msg: str # output, part of llm completion
|
|
_result_table: str = PrivateAttr() # result of the executed query
|
|
|
|
|
|
class ResultModel(BaseModel):
|
|
question: str = Field(alias=Constant.narrative_input.value) # input
|
|
_result_table: str = PrivateAttr() # result of the executed query
|
|
|
|
|
|
class StoryModel(BaseModel):
|
|
causal_operations: Any = Field(required=True)
|
|
intervention: Any = Field(required=True)
|
|
query: Any = Field(required=True)
|
|
_outcome_table: Any = PrivateAttr(default=None)
|
|
_networkx_wrapper: Any = PrivateAttr(default=None)
|
|
|
|
def __init__(self, **kwargs: Any):
|
|
super().__init__(**kwargs)
|
|
self._compute()
|
|
|
|
# TODO: when langchain adopts pydantic.v2 replace w/ `__post_init__`
|
|
# misses hints github.com/pydantic/pydantic/issues/1729#issuecomment-1300576214
|
|
|
|
# TODO: move away from `root_validator` since it is deprecated in pydantic v2
|
|
# and causes mypy type-checking failures (hence the `type: ignore`)
|
|
@root_validator # type: ignore[call-overload]
|
|
def check_intervention_is_valid(cls, values: dict) -> dict:
|
|
valid_names = [e.name for e in values["causal_operations"].entities]
|
|
for setting in values["intervention"].entity_settings:
|
|
if setting.name not in valid_names:
|
|
error_msg = f"""
|
|
Hypothetical question has an invalid entity name.
|
|
`{setting.name}` not in `{valid_names}`
|
|
"""
|
|
raise ValueError(error_msg)
|
|
return values
|
|
|
|
def _block_back_door_paths(self) -> None:
|
|
# stop intervention entities from depending on others
|
|
intervention_entities = [
|
|
entity_setting.name for entity_setting in self.intervention.entity_settings
|
|
]
|
|
for entity in self.causal_operations.entities:
|
|
if entity.name in intervention_entities:
|
|
entity.depends_on = []
|
|
entity.code = "pass"
|
|
|
|
def _set_initial_conditions(self) -> None:
|
|
for entity_setting in self.intervention.entity_settings:
|
|
for entity in self.causal_operations.entities:
|
|
if entity.name == entity_setting.name:
|
|
entity.value = entity_setting.value
|
|
|
|
def _make_graph(self) -> None:
|
|
self._networkx_wrapper = NetworkxEntityGraph()
|
|
for entity in self.causal_operations.entities:
|
|
for parent_name in entity.depends_on:
|
|
self._networkx_wrapper._graph.add_edge(
|
|
parent_name, entity.name, relation=entity.code
|
|
)
|
|
|
|
# TODO: is it correct to drop entities with no impact on the outcome (?)
|
|
self.causal_operations.entities = [
|
|
entity
|
|
for entity in self.causal_operations.entities
|
|
if entity.name in self._networkx_wrapper.get_topological_sort()
|
|
]
|
|
|
|
def _sort_entities(self) -> None:
|
|
# order the sequence of causal actions
|
|
sorted_nodes = self._networkx_wrapper.get_topological_sort()
|
|
self.causal_operations.entities.sort(key=lambda x: sorted_nodes.index(x.name))
|
|
|
|
def _forward_propagate(self) -> None:
|
|
try:
|
|
import pandas as pd
|
|
except ImportError as e:
|
|
raise ImportError(
|
|
"Unable to import pandas, please install with `pip install pandas`."
|
|
) from e
|
|
entity_scope = {
|
|
entity.name: entity for entity in self.causal_operations.entities
|
|
}
|
|
for entity in self.causal_operations.entities:
|
|
if entity.code == "pass":
|
|
continue
|
|
else:
|
|
# gist.github.com/dean0x7d/df5ce97e4a1a05be4d56d1378726ff92
|
|
exec(entity.code, globals(), entity_scope)
|
|
row_values = [entity.dict() for entity in entity_scope.values()]
|
|
self._outcome_table = pd.DataFrame(row_values)
|
|
|
|
def _run_query(self) -> None:
|
|
def humanize_sql_error_msg(error: str) -> str:
|
|
pattern = r"column\s+(.*?)\s+not found"
|
|
col_match = re.search(pattern, error)
|
|
if col_match:
|
|
return (
|
|
"SQL error: "
|
|
+ col_match.group(1)
|
|
+ " is not an attribute in your story!"
|
|
)
|
|
else:
|
|
return str(error)
|
|
|
|
if self.query.llm_error_msg == "":
|
|
try:
|
|
import duckdb
|
|
|
|
df = self._outcome_table # noqa
|
|
query_result = duckdb.sql(self.query.expression).df()
|
|
self.query._result_table = query_result
|
|
except duckdb.BinderException as e:
|
|
self.query._result_table = humanize_sql_error_msg(str(e))
|
|
except ImportError as e:
|
|
raise ImportError(
|
|
"Unable to import duckdb, please install with `pip install duckdb`."
|
|
) from e
|
|
except Exception as e:
|
|
self.query._result_table = str(e)
|
|
else:
|
|
msg = "LLM maybe failed to translate question to SQL query."
|
|
raise ValueError(
|
|
{
|
|
"question": self.query.question,
|
|
"llm_error_msg": self.query.llm_error_msg,
|
|
"msg": msg,
|
|
}
|
|
)
|
|
|
|
def _compute(self) -> Any:
|
|
self._block_back_door_paths()
|
|
self._set_initial_conditions()
|
|
self._make_graph()
|
|
self._sort_entities()
|
|
self._forward_propagate()
|
|
self._run_query()
|
|
|
|
def print_debug_report(self) -> None:
|
|
report = {
|
|
"outcome": self._outcome_table,
|
|
"query": self.query.dict(),
|
|
"result": self.query._result_table,
|
|
}
|
|
from pprint import pprint
|
|
|
|
pprint(report)
|