viruPVS/langchain-docs huggingface dataset:
langchain_community.utilities.gitlab.GitLabAPIWrapper¶
class langchain_community.utilities.gitlab.GitLabAPIWrapper[source]¶
Bases: BaseModel
Wrapper for GitLab 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 gitlab_base_branch: Optional[str] = None¶
The base branch in the GitLab repository, used for comparisons.
Usually ‘main’ or ‘master’. Defaults to ‘main’.
param gitlab_branch: Optional[str] = None¶
The specific branch in the GitLab repository where the bot will make
its commits. Defaults to ‘main’.
param gitlab_personal_access_token: Optional[str] = None¶
Personal access token for the GitLab service, used for authentication.
param gitlab_repository: Optional[str] = None¶
The name of the GitLab repository, in the form {username}/{repo-name}.
comment_on_issue(comment_query: str) → str[source]¶
Adds a comment to a gitlab issue
Parameters:
comment_query(str): a string which contains the issue number,
two newlines, and the comment.
for example: “1
Working on it now”
adds the comment “working on it now” to issue 1
Returns:str: A success or failure message
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
create_file(file_query: str) → str[source]¶
Creates a new file on the gitlab repo
Parameters:
file_query(str): a string which contains the file path
and the file contents. The file path is the first line
in the string, and the contents are the rest of the string.
For example, “hello_world.md
# Hello World!”
Returns:str: A success or failure message
create_pull_request(pr_query: str) → str[source]¶
Makes a pull request from the bot’s branch to the base branch
Parameters:
pr_query(str): a string which contains the PR title
and the PR body. The title is the first line
in the string, and the body are the rest of the string.
For example, “Updated README
made changes to add info”
Returns:str: A success or failure message
delete_file(file_path: str) → str[source]¶
Deletes a file from the repo
:param file_path: Where the file is
:type file_path: str
Returns
Success or failure message
:type file_path: str
Returns
Success or failure message
Return type
str
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_issue(issue_number: int) → Dict[str, Any][source]¶
Fetches a specific issue and its first 10 comments
:param issue_number: The number for the gitlab issue
:type issue_number: int
Returns
A dictionary containing the issue’s title,
body, and comments as a string
Return type
dict
get_issues() → str[source]¶
Fetches all open issues from the repo
Returns
A plaintext report containing the number of issues
and each issue’s title and number.
Return type
str
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¶
parse_issues(issues: List[Issue]) → List[dict][source]¶
Extracts title and number from each Issue and puts them in a dictionary
:param issues: A list of gitlab Issue objects
:type issues: List[Issue]
Returns
A dictionary of issue titles and numbers
Return type
List[dict]
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¶
read_file(file_path: str) → str[source]¶
Reads a file from the gitlab repo
:param file_path: the file path
:type file_path: str
Returns
The file decoded as a string
Return type
str
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¶
update_file(file_query: str) → str[source]¶
Updates a file with new content.
:param file_query: Contains the file path and the file contents.
The old file contents is wrapped in OLD <<<< and >>>> OLD
The new file contents is wrapped in NEW <<<< and >>>> NEW
For example:
test/hello.txt
OLD <<<<
Hello Earth!
>>>> OLD
NEW <<<<
Hello Mars!
>>>> NEW
Returns
A success or failure message
langchain_community.utilities.metaphor_search.MetaphorSearchAPIWrapper¶
class langchain_community.utilities.metaphor_search.MetaphorSearchAPIWrapper[source]¶
Bases: BaseModel
Wrapper for Metaphor Search 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 k: int = 10¶
param metaphor_api_key: str [Required]¶
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
results(query: str, num_results: int, include_domains: Optional[List[str]] = None, exclude_domains: Optional[List[str]] = None, start_crawl_date: Optional[str] = None, end_crawl_date: Optional[str] = None, start_published_date: Optional[str] = None, end_published_date: Optional[str] = None, use_autoprompt: Optional[bool] = None) → List[Dict][source]¶
Run query through Metaphor Search and return metadata.
Parameters
query – The query to search for.
num_results – The number of results to return.
include_domains – A list of domains to include in the search. Only one of include_domains and exclude_domains should be defined.
exclude_domains – A list of domains to exclude from the search. Only one of include_domains and exclude_domains should be defined.
start_crawl_date – If specified, only pages we crawled after start_crawl_date will be returned.
end_crawl_date – If specified, only pages we crawled before end_crawl_date will be returned.
start_published_date – If specified, only pages published after start_published_date will be returned.
end_published_date – If specified, only pages published before end_published_date will be returned.
use_autoprompt – If true, we turn your query into a more Metaphor-friendly query. Adds latency.
Returns
title - The title of the page
url - The url
author - Author of the content, if applicable. Otherwise, None.
published_date - Estimated date published
in YYYY-MM-DD format. Otherwise, None.
Return type
A list of dictionaries with the following keys
Return type
A list of dictionaries with the following keys
async results_async(query: str, num_results: int, include_domains: Optional[List[str]] = None, exclude_domains: Optional[List[str]] = None, start_crawl_date: Optional[str] = None, end_crawl_date: Optional[str] = None, start_published_date: Optional[str] = None, end_published_date: Optional[str] = None, use_autoprompt: Optional[bool] = None) → List[Dict][source]¶
Get results from the Metaphor Search API asynchronously.
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 MetaphorSearchAPIWrapper¶
Metaphor Search
langchain_community.utilities.wolfram_alpha.WolframAlphaAPIWrapper¶
class langchain_community.utilities.wolfram_alpha.WolframAlphaAPIWrapper[source]¶
Bases: BaseModel
Wrapper for Wolfram Alpha.
Docs for using:
Go to wolfram alpha and sign up for a developer account
Create an app and get your APP ID
Save your APP ID into WOLFRAM_ALPHA_APPID env variable
pip install wolframalpha
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 wolfram_alpha_appid: 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¶
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(query: str) → str[source]¶
run(query: str) → str[source]¶
Run query through WolframAlpha 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 WolframAlphaAPIWrapper¶
Wolfram Alpha
langchain_community.utilities.google_serper.GoogleSerperAPIWrapper¶
class langchain_community.utilities.google_serper.GoogleSerperAPIWrapper[source]¶
Bases: BaseModel
Wrapper around the Serper.dev Google Search API.
You can create a free API key at https://serper.dev.
To use, you should have the environment variable SERPER_API_KEY
set with your API key, or pass serper_api_key as a named parameter
to the constructor.
Example
from langchain_community.utilities import GoogleSerperAPIWrapper
google_serper = GoogleSerperAPIWrapper()
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 gl: str = 'us'¶
param hl: str = 'en'¶
param k: int = 10¶
param serper_api_key: Optional[str] = None¶
param tbs: Optional[str] = None¶
param type: Literal['news', 'search', 'places', 'images'] = 'search'¶
async aresults(query: str, **kwargs: Any) → Dict[source]¶
Run query through GoogleSearch.
async arun(query: str, **kwargs: Any) → str[source]¶
Run query through GoogleSearch 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
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, **kwargs: Any) → Dict[source]¶
Run query through GoogleSearch.
run(query: str, **kwargs: Any) → 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.
langchain_community.utilities.vertexai.create_retry_decorator¶
langchain_community.utilities.vertexai.create_retry_decorator(llm: BaseLLM, *, max_retries: int = 1, run_manager: Optional[Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]] = None) → Callable[[Any], Any][source]¶
Creates a retry decorator for Vertex / Palm LLMs.
langchain_community.utilities.powerbi.PowerBIDataset¶
class langchain_community.utilities.powerbi.PowerBIDataset[source]¶
Bases: BaseModel
Create PowerBI engine from dataset ID and credential or token.
Use either the credential or a supplied token to authenticate.
If both are supplied the credential is used to generate a token.
The impersonated_user_name is the UPN of a user to be impersonated.
If the model is not RLS enabled, this will be ignored.
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.ClientSession] = None¶
param credential: Optional[TokenCredential] = None¶
param dataset_id: str [Required]¶
param group_id: Optional[str] = None¶
param impersonated_user_name: Optional[str] = None¶
param sample_rows_in_table_info: int = 1¶
Constraints
exclusiveMinimum = 0
maximum = 10
param schemas: Dict[str, str] [Optional]¶
param table_names: List[str] [Required]¶
param token: Optional[str] = None¶
async aget_table_info(table_names: Optional[Union[List[str], str]] = None) → str[source]¶
Get information about specified tables.
async arun(command: str) → Any[source]¶
Execute a DAX command and return the result asynchronously.
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_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.
get_table_names() → Iterable[str][source]¶
Get names of tables available.
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(command: str) → Any[source]¶
Execute a DAX command and return a json representing the results.
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¶
langchain_community.utilities.merriam_webster.MerriamWebsterAPIWrapper¶
class langchain_community.utilities.merriam_webster.MerriamWebsterAPIWrapper[source]¶
Bases: BaseModel
Wrapper for Merriam-Webster.
Docs for using:
Go to https://www.dictionaryapi.com/register/index and register an
developer account with a key for the Collegiate Dictionary
Get your API Key from https://www.dictionaryapi.com/account/my-keys
Save your API Key into MERRIAM_WEBSTER_API_KEY env variable
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 merriam_webster_api_key: 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
run(query: str) → str[source]¶
Run query through Merriam-Webster API and return a formatted 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¶