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),
}
```
## Add Passio Nutrition AI Food Search Tool to Community Package
### Description
We propose adding a new tool to the `community` package, enabling
integration with Passio Nutrition AI for food search functionality. This
tool will provide a simple interface for retrieving nutrition facts
through the Passio Nutrition AI API, simplifying user access to
nutrition data based on food search queries.
### Implementation Details
- **Class Structure:** Implement `NutritionAI`, extending `BaseTool`. It
includes an `_run` method that accepts a query string and, optionally, a
`CallbackManagerForToolRun`.
- **API Integration:** Use `NutritionAIAPI` for the API wrapper,
encapsulating all interactions with the Passio Nutrition AI and
providing a clean API interface.
- **Error Handling:** Implement comprehensive error handling for API
request failures.
### Expected Outcome
- **User Benefits:** Enable easy querying of nutrition facts from Passio
Nutrition AI, enhancing the utility of the `langchain_community` package
for nutrition-related projects.
- **Functionality:** Provide a straightforward method for integrating
nutrition information retrieval into users' applications.
### Dependencies
- `langchain_core` for base tooling support
- `pydantic` for data validation and settings management
- Consider `requests` or another HTTP client library if not covered by
`NutritionAIAPI`.
### Tests and Documentation
- **Unit Tests:** Include tests that mock network interactions to ensure
tool reliability without external API dependency.
- **Documentation:** Create an example notebook in
`docs/docs/integrations/tools/passio_nutrition_ai.ipynb` showing usage,
setup, and example queries.
### Contribution Guidelines Compliance
- Adhere to the project's linting and formatting standards (`make
format`, `make lint`, `make test`).
- Ensure compliance with LangChain's contribution guidelines,
particularly around dependency management and package modifications.
### Additional Notes
- Aim for the tool to be a lightweight, focused addition, not
introducing significant new dependencies or complexity.
- Potential future enhancements could include caching for common queries
to improve performance.
### Twitter Handle
- Here is our Passio AI [twitter handle](https://twitter.com/@passio_ai)
where we announce our products.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
