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