mirror of
https://github.com/hwchase17/langchain
synced 2024-11-18 09:25:54 +00:00
ed58eeb9c5
Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
"""Util that calls WolframAlpha."""
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from typing import Any, Dict, Optional
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from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
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from langchain_core.utils import get_from_dict_or_env
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class WolframAlphaAPIWrapper(BaseModel):
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"""Wrapper for Wolfram Alpha.
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Docs for using:
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1. Go to wolfram alpha and sign up for a developer account
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2. Create an app and get your APP ID
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3. Save your APP ID into WOLFRAM_ALPHA_APPID env variable
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4. pip install wolframalpha
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"""
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wolfram_client: Any #: :meta private:
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wolfram_alpha_appid: Optional[str] = None
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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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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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wolfram_alpha_appid = get_from_dict_or_env(
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values, "wolfram_alpha_appid", "WOLFRAM_ALPHA_APPID"
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)
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values["wolfram_alpha_appid"] = wolfram_alpha_appid
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try:
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import wolframalpha
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except ImportError:
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raise ImportError(
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"wolframalpha is not installed. "
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"Please install it with `pip install wolframalpha`"
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)
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client = wolframalpha.Client(wolfram_alpha_appid)
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values["wolfram_client"] = client
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return values
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def run(self, query: str) -> str:
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"""Run query through WolframAlpha and parse result."""
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res = self.wolfram_client.query(query)
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try:
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assumption = next(res.pods).text
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answer = next(res.results).text
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except StopIteration:
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return "Wolfram Alpha wasn't able to answer it"
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if answer is None or answer == "":
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# We don't want to return the assumption alone if answer is empty
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return "No good Wolfram Alpha Result was found"
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else:
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return f"Assumption: {assumption} \nAnswer: {answer}"
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