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- **Description:** docs update following the changes introduced in #15879 <!-- Thank you for contributing to LangChain! Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified. Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes if applicable, - **Dependencies:** any dependencies required for this change, - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/ If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. -->
62 lines
2.3 KiB
Python
62 lines
2.3 KiB
Python
from typing import Any, Dict, Optional, Type
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from langchain_core.callbacks import CallbackManagerForToolRun
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
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from langchain_community.chat_models import ChatOpenAI
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from langchain_community.tools.amadeus.base import AmadeusBaseTool
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class ClosestAirportSchema(BaseModel):
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"""Schema for the AmadeusClosestAirport tool."""
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location: str = Field(
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description=(
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" The location for which you would like to find the nearest airport "
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" along with optional details such as country, state, region, or "
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" province, allowing for easy processing and identification of "
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" the closest airport. Examples of the format are the following:\n"
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" Cali, Colombia\n "
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" Lincoln, Nebraska, United States\n"
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" New York, United States\n"
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" Sydney, New South Wales, Australia\n"
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" Rome, Lazio, Italy\n"
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" Toronto, Ontario, Canada\n"
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)
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)
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class AmadeusClosestAirport(AmadeusBaseTool):
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"""Tool for finding the closest airport to a particular location."""
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name: str = "closest_airport"
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description: str = (
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"Use this tool to find the closest airport to a particular location."
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)
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args_schema: Type[ClosestAirportSchema] = ClosestAirportSchema
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llm: Optional[BaseLanguageModel] = Field(default=None)
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"""Tool's llm used for calculating the closest airport. Defaults to `ChatOpenAI`."""
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@root_validator(pre=True)
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def set_llm(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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if not values.get("llm"):
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# For backward-compatibility
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values["llm"] = ChatOpenAI(temperature=0)
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return values
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def _run(
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self,
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location: str,
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run_manager: Optional[CallbackManagerForToolRun] = None,
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) -> str:
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content = (
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f" What is the nearest airport to {location}? Please respond with the "
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" airport's International Air Transport Association (IATA) Location "
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' Identifier in the following JSON format. JSON: "iataCode": "IATA '
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' Location Identifier" '
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)
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return self.llm.invoke(content)
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