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langchain/libs/community/langchain_community/utilities/passio_nutrition_ai.py

174 lines
5.5 KiB
Python

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