langchain.utilities.pubmed.PubMedAPIWrapper

class langchain.utilities.pubmed.PubMedAPIWrapper[source]

Bases: BaseModel

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

  • MAX_QUERY_LENGTH – maximum length of the query. Default is 300 characters.

  • doc_content_chars_max – maximum length of the document content. Content will be truncated if it exceeds this length. Default is 2000 characters.

  • max_retry – maximum number of retries for a request. Default is 5.

  • sleep_time – time to wait between retries. Default is 0.2 seconds.

  • email – email address to be used for the PubMed API.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param MAX_QUERY_LENGTH: int = 300
param base_url_efetch: str = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?'
param base_url_esearch: str = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?'
param doc_content_chars_max: int = 2000
param email: str = 'your_email@example.com'
param max_retry: int = 5
param sleep_time: float = 0.2
param top_k_results: int = 3
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) Model

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) Model

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters
  • include – fields to include in new model

  • exclude – fields to exclude from new model, as with values this takes precedence over include

  • update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

  • deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) DictStrAny

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) Model
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) unicode

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

lazy_load(query: str) Iterator[dict][source]

Search PubMed for documents matching the query. Return an iterator of dictionaries containing the document metadata.

lazy_load_docs(query: str) Iterator[Document][source]
load(query: str) List[dict][source]

Search PubMed for documents matching the query. Return a list of dictionaries containing the document metadata.

load_docs(query: str) List[Document][source]
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model
classmethod parse_obj(obj: Any) Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model
retrieve_article(uid: str, webenv: str) dict[source]
run(query: str) str[source]

Run PubMed search and get the article meta information. See https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ESearch It uses only the most informative fields of article meta information.

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode
classmethod update_forward_refs(**localns: Any) None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) Model