mirror of
https://github.com/hwchase17/langchain
synced 2024-11-02 09:40:22 +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."""
|
|
from typing import Any, Dict, Optional
|
|
|
|
from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
|
|
from langchain_core.utils import get_from_dict_or_env
|
|
|
|
|
|
class WolframAlphaAPIWrapper(BaseModel):
|
|
"""Wrapper for Wolfram Alpha.
|
|
|
|
Docs for using:
|
|
|
|
1. Go to wolfram alpha and sign up for a developer account
|
|
2. Create an app and get your APP ID
|
|
3. Save your APP ID into WOLFRAM_ALPHA_APPID env variable
|
|
4. pip install wolframalpha
|
|
|
|
"""
|
|
|
|
wolfram_client: Any #: :meta private:
|
|
wolfram_alpha_appid: Optional[str] = None
|
|
|
|
class Config:
|
|
"""Configuration for this pydantic object."""
|
|
|
|
extra = Extra.forbid
|
|
|
|
@root_validator()
|
|
def validate_environment(cls, values: Dict) -> Dict:
|
|
"""Validate that api key and python package exists in environment."""
|
|
wolfram_alpha_appid = get_from_dict_or_env(
|
|
values, "wolfram_alpha_appid", "WOLFRAM_ALPHA_APPID"
|
|
)
|
|
values["wolfram_alpha_appid"] = wolfram_alpha_appid
|
|
|
|
try:
|
|
import wolframalpha
|
|
|
|
except ImportError:
|
|
raise ImportError(
|
|
"wolframalpha is not installed. "
|
|
"Please install it with `pip install wolframalpha`"
|
|
)
|
|
client = wolframalpha.Client(wolfram_alpha_appid)
|
|
values["wolfram_client"] = client
|
|
|
|
return values
|
|
|
|
def run(self, query: str) -> str:
|
|
"""Run query through WolframAlpha and parse result."""
|
|
res = self.wolfram_client.query(query)
|
|
|
|
try:
|
|
assumption = next(res.pods).text
|
|
answer = next(res.results).text
|
|
except StopIteration:
|
|
return "Wolfram Alpha wasn't able to answer it"
|
|
|
|
if answer is None or answer == "":
|
|
# We don't want to return the assumption alone if answer is empty
|
|
return "No good Wolfram Alpha Result was found"
|
|
else:
|
|
return f"Assumption: {assumption} \nAnswer: {answer}"
|