Execute a DAX command and return the result asynchronously.
get_schemas() → str[source]#
Get the available schema’s.
get_table_info(table_names: Optional[Union[List[str], str]] = None) → str[source]#
Get information about specified tables.
get_table_names() → Iterable[str][source]#
Get names of tables available.
run(command: str) → Any[source]#
Execute a DAX command and return a json representing the results.
property headers: Dict[str, str]#
Get the token.
property request_url: str#
Get the request url.
property table_info: str#
Information about all tables in the database.
pydantic model langchain.utilities.PubMedAPIWrapper[source]#
Wrapper around PubMed API.
This wrapper will use the PubMed API to conduct searches and fetch
document summaries. By default, it will return the document summaries
of the top-k results of an input search.
Parameters
top_k_results – number of the top-scored document used for the PubMed tool
load_max_docs – a limit to the number of loaded documents
load_all_available_meta – 
if True: the metadata of the loaded Documents gets all available meta info(see https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ESearch)
if False: the metadata gets only the most informative fields.
field doc_content_chars_max: int = 2000#
field email: str = 'your_email@example.com'#
field load_all_available_meta: bool = False#
field load_max_docs: int = 25#
field top_k_results: int = 3#
load(query: str) → List[dict][source]#
Search PubMed for documents matching the query.
Return a list of dictionaries containing the document metadata.
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/utilities.html