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.
2024-03-08 20:33:22 +00:00
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"""Util that invokes the Passio Nutrition AI API.
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"""
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from datetime import datetime, timedelta
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from typing import Any, Callable, Dict, Optional, final
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import requests
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from langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator
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from langchain_core.utils import get_from_dict_or_env
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class NoDiskStorage:
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2024-04-29 21:40:23 +00:00
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"""Mixin to prevent storing on disk."""
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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.
2024-03-08 20:33:22 +00:00
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@final
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def __getstate__(self) -> None:
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raise AttributeError("Do not store on disk.")
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@final
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def __setstate__(self, state: Any) -> None:
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raise AttributeError("Do not store on disk.")
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try:
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from tenacity import (
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retry,
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retry_if_result,
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stop_after_attempt,
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wait_exponential,
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wait_random,
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)
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except ImportError:
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# No retries if tenacity is not installed.
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def retry_fallback(
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f: Callable[..., Any], *args: Any, **kwargs: Any
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) -> Callable[..., Any]:
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return f
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def stop_after_attempt_fallback(n: int) -> None:
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return None
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def wait_random_fallback(a: float, b: float) -> None:
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return None
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def wait_exponential_fallback(
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multiplier: float = 1, min: float = 0, max: float = float("inf")
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) -> None:
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return None
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def is_http_retryable(rsp: requests.Response) -> bool:
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2024-04-29 21:40:23 +00:00
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"""Check if a HTTP response is retryable."""
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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.
2024-03-08 20:33:22 +00:00
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return bool(rsp) and rsp.status_code in [408, 425, 429, 500, 502, 503, 504]
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class ManagedPassioLifeAuth(NoDiskStorage):
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2024-04-29 21:40:23 +00:00
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"""Manage the token for the NutritionAI API."""
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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.
2024-03-08 20:33:22 +00:00
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_access_token_expiry: Optional[datetime]
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def __init__(self, subscription_key: str):
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self.subscription_key = subscription_key
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self._last_token = None
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self._access_token_expiry = None
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self._access_token = None
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self._customer_id = None
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@property
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def headers(self) -> dict:
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if not self.is_valid_now():
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self.refresh_access_token()
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return {
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"Authorization": f"Bearer {self._access_token}",
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"Passio-ID": self._customer_id,
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}
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def is_valid_now(self) -> bool:
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return (
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self._access_token is not None
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and self._customer_id is not None
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and self._access_token_expiry is not None
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and self._access_token_expiry > datetime.now()
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)
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@retry(
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retry=retry_if_result(is_http_retryable),
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stop=stop_after_attempt(4),
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wait=wait_random(0, 0.3) + wait_exponential(multiplier=1, min=0.1, max=2),
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)
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def _http_get(self, subscription_key: str) -> requests.Response:
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return requests.get(
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f"https://api.passiolife.com/v2/token-cache/napi/oauth/token/{subscription_key}"
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)
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def refresh_access_token(self) -> None:
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"""Refresh the access token for the NutritionAI API."""
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rsp = self._http_get(self.subscription_key)
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if not rsp:
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raise ValueError("Could not get access token")
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self._last_token = token = rsp.json()
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self._customer_id = token["customer_id"]
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self._access_token = token["access_token"]
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self._access_token_expiry = (
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datetime.now()
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+ timedelta(seconds=token["expires_in"])
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- timedelta(seconds=5)
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)
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# 5 seconds: approximate time for a token refresh to be processed.
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DEFAULT_NUTRITIONAI_API_URL = (
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"https://api.passiolife.com/v2/products/napi/food/search/advanced"
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)
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class NutritionAIAPI(BaseModel):
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"""Wrapper for the Passio Nutrition AI API."""
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nutritionai_subscription_key: str
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nutritionai_api_url: str = Field(default=DEFAULT_NUTRITIONAI_API_URL)
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more_kwargs: dict = Field(default_factory=dict)
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auth_: ManagedPassioLifeAuth
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@retry(
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retry=retry_if_result(is_http_retryable),
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stop=stop_after_attempt(4),
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wait=wait_random(0, 0.3) + wait_exponential(multiplier=1, min=0.1, max=2),
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)
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def _http_get(self, params: dict) -> requests.Response:
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return requests.get(
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self.nutritionai_api_url,
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headers=self.auth_.headers,
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params=params, # type: ignore
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)
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def _api_call_results(self, search_term: str) -> dict:
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"""Call the NutritionAI API and return the results."""
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rsp = self._http_get({"term": search_term, **self.more_kwargs})
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if not rsp:
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raise ValueError("Could not get NutritionAI API results")
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rsp.raise_for_status()
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return rsp.json()
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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 endpoint exists in environment."""
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nutritionai_subscription_key = get_from_dict_or_env(
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values, "nutritionai_subscription_key", "NUTRITIONAI_SUBSCRIPTION_KEY"
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)
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values["nutritionai_subscription_key"] = nutritionai_subscription_key
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nutritionai_api_url = get_from_dict_or_env(
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values,
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"nutritionai_api_url",
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"NUTRITIONAI_API_URL",
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DEFAULT_NUTRITIONAI_API_URL,
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)
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values["nutritionai_api_url"] = nutritionai_api_url
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values["auth_"] = ManagedPassioLifeAuth(nutritionai_subscription_key)
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return values
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def run(self, query: str) -> Optional[Dict]:
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"""Run query through NutrtitionAI API and parse result."""
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results = self._api_call_results(query)
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if results and len(results) < 1:
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return None
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return results
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