viruPVS/langchain-docs huggingface dataset:
langchain_community.utilities.clickup.CUList¶
class langchain_community.utilities.clickup.CUList(folder_id: float, name: str, content: Optional[str] = None, due_date: Optional[int] = None, due_date_time: Optional[bool] = None, priority: Optional[int] = None, assignee: Optional[int] = None, status: Optional[str] = None)[source]¶
Component class for a list.
Attributes
assignee
content
due_date
due_date_time
priority
status
folder_id
name
Methods
__init__(folder_id, name[, content, ...])
from_data(data)
__init__(folder_id: float, name: str, content: Optional[str] = None, due_date: Optional[int] = None, due_date_time: Optional[bool] = None, priority: Optional[int] = None, assignee: Optional[int] = None, status: Optional[str] = None) → None¶
classmethod from_data(data: dict) → CUList[source]¶
langchain_community.utilities.arcee.DALMFilterType¶
class langchain_community.utilities.arcee.DALMFilterType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Filter types available for a DALM retrieval as enumerator.
fuzzy_search = 'fuzzy_search'¶
strict_search = 'strict_search'¶
langchain_community.utilities.bibtex.BibtexparserWrapper¶
class langchain_community.utilities.bibtex.BibtexparserWrapper[source]¶
Bases: BaseModel
Wrapper around bibtexparser.
To use, you should have the bibtexparser python package installed.
https://bibtexparser.readthedocs.io/en/master/
This wrapper will use bibtexparser to load a collection of references from
a bibtex file and fetch document summaries.
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.
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
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¶
get_metadata(entry: Mapping[str, Any], load_extra: bool = False) → Dict[str, Any][source]¶
Get metadata for the given entry.
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().
load_bibtex_entries(path: str) → List[Dict[str, Any]][source]¶
Load bibtex entries from the bibtex file at the given path.
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
langchain_community.utilities.jira.JiraAPIWrapper¶
class langchain_community.utilities.jira.JiraAPIWrapper[source]¶
Bases: BaseModel
Wrapper for Jira 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 confluence: Any = None¶
param jira_api_token: Optional[str] = None¶
param jira_instance_url: Optional[str] = None¶
param jira_username: Optional[str] = None¶
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
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¶
issue_create(query: str) → str[source]¶
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().
other(query: str) → str[source]¶
page_create(query: str) → str[source]¶
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
parse_issues(issues: Dict) → List[dict][source]¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_obj(obj: Any) → Model¶
parse_projects(projects: List[dict]) → List[dict][source]¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
project() → str[source]¶
run(mode: str, query: str) → str[source]¶
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¶
search(query: str) → str[source]¶
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¶
Examples using JiraAPIWrapper¶
Jira
langchain_community.utilities.nasa.NasaAPIWrapper¶
class langchain_community.utilities.nasa.NasaAPIWrapper[source]¶
Bases: BaseModel
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.
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.
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
get_media(query: str) → str[source]¶
get_media_metadata_location(query: str) → str[source]¶
get_media_metadata_manifest(query: str) → str[source]¶
get_video_captions_location(query: str) → str[source]¶
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().
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¶
run(mode: str, query: str) → str[source]¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
langchain_community.utilities.google_search.GoogleSearchAPIWrapper¶
class langchain_community.utilities.google_search.GoogleSearchAPIWrapper[source]¶
Bases: BaseModel
Wrapper for Google Search API.
Adapted from: Instructions adapted from https://stackoverflow.com/questions/
37083058/
programmatically-searching-google-in-python-using-custom-search
TODO: DOCS for using it
1. Install google-api-python-client
- If you don’t already have a Google account, sign up.
- If you have never created a Google APIs Console project,
read the Managing Projects page and create a project in the Google API Console.
- Install the library using pip install google-api-python-client
2. Enable the Custom Search API
- Navigate to the APIs & Services→Dashboard panel in Cloud Console.
- Click Enable APIs and Services.
- Search for Custom Search API and click on it.
- Click Enable.
URL for it: https://console.cloud.google.com/apis/library/customsearch.googleapis
.com
3. To create an API key:
- Navigate to the APIs & Services → Credentials panel in Cloud Console.
- Select Create credentials, then select API key from the drop-down menu.
- The API key created dialog box displays your newly created key.
- You now have an API_KEY
Alternatively, you can just generate an API key here:
https://developers.google.com/custom-search/docs/paid_element#api_key
4. Setup Custom Search Engine so you can search the entire web
- Create a custom search engine here: https://programmablesearchengine.google.com/.
- In What to search to search, pick the Search the entire Web option.
After search engine is created, you can click on it and find Search engine ID
on the Overview page.
Create a new model by parsing and validating input data from keyword arguments.
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 google_api_key: Optional[str] = None¶
param google_cse_id: Optional[str] = None¶
param k: int = 10¶
param siterestrict: bool = False¶
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().
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¶
results(query: str, num_results: int, search_params: Optional[Dict[str, str]] = None) → List[Dict][source]¶
Run query through GoogleSearch and return metadata.
Parameters
query – The query to search for.
num_results – The number of results to return.
search_params – Parameters to be passed on search
Returns
snippet - The description of the result.
title - The title of the result.
link - The link to the result.
Return type
A list of dictionaries with the following keys
Return type
A list of dictionaries with the following keys
run(query: str) → str[source]¶
Run query through GoogleSearch and parse result.
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¶
Examples using GoogleSearchAPIWrapper¶
Google Search
Bittensor
Set env var OPENAI_API_KEY or load from a .env file:
Agents
WebResearchRetriever
Message Memory in Agent backed by a database
Memory in Agent
Shared memory across agents and tools
langchain_community.utilities.spark_sql.SparkSQL¶
class langchain_community.utilities.spark_sql.SparkSQL(spark_session: Optional[SparkSession] = None, catalog: Optional[str] = None, schema: Optional[str] = None, ignore_tables: Optional[List[str]] = None, include_tables: Optional[List[str]] = None, sample_rows_in_table_info: int = 3)[source]¶
SparkSQL is a utility class for interacting with Spark SQL.
Initialize a SparkSQL object.
Parameters
spark_session – A SparkSession object.
If not provided, one will be created.
catalog – The catalog to use.
If not provided, the default catalog will be used.
schema – The schema to use.
If not provided, the default schema will be used.
ignore_tables – A list of tables to ignore.
If not provided, all tables will be used.
include_tables – A list of tables to include.
If not provided, all tables will be used.
sample_rows_in_table_info – The number of rows to include in the table info.
Defaults to 3.
Methods
__init__([spark_session, catalog, schema, ...])
Initialize a SparkSQL object.
from_uri(database_uri[, engine_args])
Creating a remote Spark Session via Spark connect.
get_table_info([table_names])
get_table_info_no_throw([table_names])
Get information about specified tables.
get_usable_table_names()
Get names of tables available.
run(command[, fetch])
run_no_throw(command[, fetch])
Execute a SQL command and return a string representing the results.
Execute a SQL command and return a string representing the results.
__init__(spark_session: Optional[SparkSession] = None, catalog: Optional[str] = None, schema: Optional[str] = None, ignore_tables: Optional[List[str]] = None, include_tables: Optional[List[str]] = None, sample_rows_in_table_info: int = 3)[source]¶
Initialize a SparkSQL object.
Parameters
spark_session – A SparkSession object.
If not provided, one will be created.
catalog – The catalog to use.
If not provided, the default catalog will be used.
schema – The schema to use.
If not provided, the default schema will be used.
ignore_tables – A list of tables to ignore.
If not provided, all tables will be used.
include_tables – A list of tables to include.
If not provided, all tables will be used.
sample_rows_in_table_info – The number of rows to include in the table info.
Defaults to 3.
classmethod from_uri(database_uri: str, engine_args: Optional[dict] = None, **kwargs: Any) → SparkSQL[source]¶
Creating a remote Spark Session via Spark connect.
For example: SparkSQL.from_uri(“sc://localhost:15002”)
get_table_info(table_names: Optional[List[str]] = None) → str[source]¶
get_table_info_no_throw(table_names: Optional[List[str]] = None) → str[source]¶
Get information about specified tables.
Follows best practices as specified in: Rajkumar et al, 2022
(https://arxiv.org/abs/2204.00498)
If sample_rows_in_table_info, the specified number of sample rows will be
appended to each table description. This can increase performance as
demonstrated in the paper.
demonstrated in the paper.
get_usable_table_names() → Iterable[str][source]¶
Get names of tables available.
run(command: str, fetch: str = 'all') → str[source]¶
run_no_throw(command: str, fetch: str = 'all') → str[source]¶
Execute a SQL command and return a string representing the results.
If the statement returns rows, a string of the results is returned.
If the statement returns no rows, an empty string is returned.
If the statement throws an error, the error message is returned.
Examples using SparkSQL¶
Spark SQL
langchain_community.utilities.serpapi.SerpAPIWrapper¶
class langchain_community.utilities.serpapi.SerpAPIWrapper[source]¶
Bases: BaseModel
Wrapper around SerpAPI.
To use, you should have the google-search-results python package installed,
and the environment variable SERPAPI_API_KEY set with your API key, or pass
serpapi_api_key as a named parameter to the constructor.
Example
from langchain_community.utilities import SerpAPIWrapper
serpapi = SerpAPIWrapper()
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 aiosession: Optional[aiohttp.client.ClientSession] = None¶
param params: dict = {'engine': 'google', 'gl': 'us', 'google_domain': 'google.com', 'hl': 'en'}¶
param serpapi_api_key: Optional[str] = None¶
async aresults(query: str) → dict[source]¶
Use aiohttp to run query through SerpAPI and return the results async.
async arun(query: str, **kwargs: Any) → str[source]¶
Run query through SerpAPI and parse result async.
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
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¶
get_params(query: str) → Dict[str, str][source]¶
Get parameters for SerpAPI.