community[patch]: Add Passio Nutrition AI Food Search Tool to Community Package (#18278)

## 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.

libs/community/langchain_community/tools/passio_nutrition_ai/__init__.py

@@ -0,0 +1,5 @@
+"""Passio Nutrition AI API toolkit."""
+
+from langchain_community.tools.passio_nutrition_ai.tool import NutritionAI
+
+__all__ = ["NutritionAI"]

libs/community/langchain_community/tools/passio_nutrition_ai/tool.py

@@ -0,0 +1,38 @@
+"""Tool for the Passio Nutrition AI API."""
+
+from typing import Dict, Optional, Type
+
+from langchain_core.callbacks import CallbackManagerForToolRun
+from langchain_core.pydantic_v1 import BaseModel, Field
+from langchain_core.tools import BaseTool
+
+from langchain_community.utilities.passio_nutrition_ai import NutritionAIAPI
+
+
+class NutritionAIInputs(BaseModel):
+    """Inputs to the Passio Nutrition AI tool."""
+
+    query: str = Field(
+        description="A query to look up using Passio Nutrition AI, usually a few words."
+    )
+
+
+class NutritionAI(BaseTool):
+    """Tool that queries the Passio Nutrition AI API."""
+
+    name: str = "nutritionai_advanced_search"
+    description: str = (
+        "A wrapper around the Passio Nutrition AI. "
+        "Useful to retrieve nutrition facts. "
+        "Input should be a search query string."
+    )
+    api_wrapper: NutritionAIAPI
+    args_schema: Type[BaseModel] = NutritionAIInputs
+
+    def _run(
+        self,
+        query: str,
+        run_manager: Optional[CallbackManagerForToolRun] = None,
+    ) -> Optional[Dict]:
+        """Use the tool."""
+        return self.api_wrapper.run(query)