mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-08 00:28:47 +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),
}
```
63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
"""Util that calls WolframAlpha."""
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from typing import Any, Dict, Optional
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from langchain_core.pydantic_v1 import BaseModel, root_validator
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from langchain_core.utils import get_from_dict_or_env
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class WolframAlphaAPIWrapper(BaseModel):
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"""Wrapper for Wolfram Alpha.
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Docs for using:
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1. Go to wolfram alpha and sign up for a developer account
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2. Create an app and get your APP ID
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3. Save your APP ID into WOLFRAM_ALPHA_APPID env variable
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4. pip install wolframalpha
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"""
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wolfram_client: Any #: :meta private:
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wolfram_alpha_appid: Optional[str] = None
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class Config:
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extra = "forbid"
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@root_validator(pre=True)
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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wolfram_alpha_appid = get_from_dict_or_env(
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values, "wolfram_alpha_appid", "WOLFRAM_ALPHA_APPID"
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)
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values["wolfram_alpha_appid"] = wolfram_alpha_appid
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try:
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import wolframalpha
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except ImportError:
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raise ImportError(
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"wolframalpha is not installed. "
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"Please install it with `pip install wolframalpha`"
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)
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client = wolframalpha.Client(wolfram_alpha_appid)
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values["wolfram_client"] = client
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return values
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def run(self, query: str) -> str:
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"""Run query through WolframAlpha and parse result."""
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res = self.wolfram_client.query(query)
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try:
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assumption = next(res.pods).text
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answer = next(res.results).text
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except StopIteration:
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return "Wolfram Alpha wasn't able to answer it"
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if answer is None or answer == "":
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# We don't want to return the assumption alone if answer is empty
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return "No good Wolfram Alpha Result was found"
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else:
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return f"Assumption: {assumption} \nAnswer: {answer}"
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