mirror of
https://github.com/hwchase17/langchain.git
synced 2025-09-03 03:59:42 +00:00
bf5193bb99
Upgrade to using a literal for specifying the extra which is the
recommended approach in pydantic 2.
This works correctly also in pydantic v1.
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel, extra="forbid"):
x: int
Foo(x=5, y=1)
```
And
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel):
x: int
class Config:
extra = "forbid"
Foo(x=5, y=1)
```
## Enum -> literal using grit pattern:
```
engine marzano(0.1)
language python
or {
`extra=Extra.allow` => `extra="allow"`,
`extra=Extra.forbid` => `extra="forbid"`,
`extra=Extra.ignore` => `extra="ignore"`
}
```
Resorted attributes in config and removed doc-string in case we will
need to deal with going back and forth between pydantic v1 and v2 during
the 0.3 release. (This will reduce merge conflicts.)
## Sort attributes in Config:
```
engine marzano(0.1)
language python
function sort($values) js {
return $values.text.split(',').sort().join("\n");
}
class_definition($name, $body) as $C where {
$name <: `Config`,
$body <: block($statements),
$values = [],
$statements <: some bubble($values) assignment() as $A where {
$values += $A
},
$body => sort($values),
}
```
117 lines
4.1 KiB
Python
117 lines
4.1 KiB
Python
"""Toolkit for interacting with a Power BI dataset."""
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from __future__ import annotations
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from typing import TYPE_CHECKING, List, Optional, Union
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from langchain_core.callbacks import BaseCallbackManager
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.language_models.chat_models import BaseChatModel
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from langchain_core.prompts import PromptTemplate
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from langchain_core.prompts.chat import (
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ChatPromptTemplate,
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HumanMessagePromptTemplate,
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SystemMessagePromptTemplate,
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)
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from langchain_core.pydantic_v1 import Field
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from langchain_core.tools import BaseToolkit
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from langchain_community.tools import BaseTool
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from langchain_community.tools.powerbi.prompt import (
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QUESTION_TO_QUERY_BASE,
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SINGLE_QUESTION_TO_QUERY,
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USER_INPUT,
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)
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from langchain_community.tools.powerbi.tool import (
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InfoPowerBITool,
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ListPowerBITool,
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QueryPowerBITool,
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)
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from langchain_community.utilities.powerbi import PowerBIDataset
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if TYPE_CHECKING:
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from langchain.chains.llm import LLMChain
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class PowerBIToolkit(BaseToolkit):
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"""Toolkit for interacting with Power BI dataset.
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*Security Note*: This toolkit interacts with an external service.
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Control access to who can use this toolkit.
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Make sure that the capabilities given by this toolkit to the calling
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code are appropriately scoped to the application.
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See https://python.langchain.com/docs/security for more information.
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Parameters:
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powerbi: The Power BI dataset.
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llm: The language model to use.
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examples: Optional. The examples for the prompt. Default is None.
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max_iterations: Optional. The maximum iterations to run. Default is 5.
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callback_manager: Optional. The callback manager. Default is None.
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output_token_limit: Optional. The output token limit. Default is None.
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tiktoken_model_name: Optional. The TikToken model name. Default is None.
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"""
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powerbi: PowerBIDataset = Field(exclude=True)
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llm: Union[BaseLanguageModel, BaseChatModel] = Field(exclude=True)
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examples: Optional[str] = None
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max_iterations: int = 5
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callback_manager: Optional[BaseCallbackManager] = None
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output_token_limit: Optional[int] = None
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tiktoken_model_name: Optional[str] = None
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class Config:
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arbitrary_types_allowed = True
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def get_tools(self) -> List[BaseTool]:
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"""Get the tools in the toolkit."""
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return [
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QueryPowerBITool(
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llm_chain=self._get_chain(),
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powerbi=self.powerbi,
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examples=self.examples,
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max_iterations=self.max_iterations,
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output_token_limit=self.output_token_limit, # type: ignore[arg-type]
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tiktoken_model_name=self.tiktoken_model_name,
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),
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InfoPowerBITool(powerbi=self.powerbi),
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ListPowerBITool(powerbi=self.powerbi),
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]
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def _get_chain(self) -> LLMChain:
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"""Construct the chain based on the callback manager and model type."""
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from langchain.chains.llm import LLMChain
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if isinstance(self.llm, BaseLanguageModel):
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return LLMChain(
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llm=self.llm,
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callback_manager=self.callback_manager
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if self.callback_manager
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else None,
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prompt=PromptTemplate(
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template=SINGLE_QUESTION_TO_QUERY,
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input_variables=["tool_input", "tables", "schemas", "examples"],
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),
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)
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system_prompt = SystemMessagePromptTemplate(
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prompt=PromptTemplate(
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template=QUESTION_TO_QUERY_BASE,
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input_variables=["tables", "schemas", "examples"],
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)
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)
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human_prompt = HumanMessagePromptTemplate(
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prompt=PromptTemplate(
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template=USER_INPUT,
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input_variables=["tool_input"],
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)
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)
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return LLMChain(
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llm=self.llm,
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callback_manager=self.callback_manager if self.callback_manager else None,
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prompt=ChatPromptTemplate.from_messages([system_prompt, human_prompt]),
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)
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