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¶
run(query: str) → str[source]¶
Run query through Google Trends with Serpapi
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.arxiv.ArxivAPIWrapper¶
class langchain_community.utilities.arxiv.ArxivAPIWrapper[source]¶
Bases: BaseModel
Wrapper around ArxivAPI.
To use, you should have the arxiv python package installed.
https://lukasschwab.me/arxiv.py/index.html
This wrapper will use the Arxiv API to conduct searches and
fetch document summaries. By default, it will return the document summaries
of the top-k results.
If the query is in the form of arxiv identifier
(see https://info.arxiv.org/help/find/index.html), it will return the paper
corresponding to the arxiv identifier.
It limits the Document content by doc_content_chars_max.
Set doc_content_chars_max=None if you don’t want to limit the content size.
top_k_results¶
number of the top-scored document used for the arxiv tool
ARXIV_MAX_QUERY_LENGTH¶
the cut limit on the query used for the arxiv tool.
load_max_docs¶
a limit to the number of loaded documents
load_all_available_meta¶
if True: the metadata of the loaded Documents contains all available
meta info (see https://lukasschwab.me/arxiv.py/index.html#Result),
if False: the metadata contains only the published date, title,
authors and summary.
doc_content_chars_max¶
an optional cut limit for the length of a document’s
content
Example
from langchain_community.utilities.arxiv import ArxivAPIWrapper
arxiv = ArxivAPIWrapper(
    top_k_results = 3,
    ARXIV_MAX_QUERY_LENGTH = 300,
    load_max_docs = 3,
    load_all_available_meta = False,
    doc_content_chars_max = 40000
)
arxiv.run("tree of thought llm)


)
arxiv.run("tree of thought llm)
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 ARXIV_MAX_QUERY_LENGTH: int = 300¶
param arxiv_exceptions: Any = None¶
param doc_content_chars_max: Optional[int] = 4000¶
param load_all_available_meta: bool = False¶
param load_max_docs: int = 100¶
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


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_summaries_as_docs(query: str) → List[Document][source]¶
Performs an arxiv search and returns list of
documents, with summaries as the content.
If an error occurs or no documents found, error text
is returned instead. Wrapper for
https://lukasschwab.me/arxiv.py/index.html#Search
Parameters
query – a plaintext search query
is_arxiv_identifier(query: str) → bool[source]¶
Check if a query is an arxiv identifier.
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(query: str) → List[Document][source]¶
Run Arxiv search and get the article texts plus the article meta information.
See https://lukasschwab.me/arxiv.py/index.html#Search
Returns: a list of documents with the document.page_content in text format
Performs an arxiv search, downloads the top k results as PDFs, loads
them as Documents, and returns them in a List.
Parameters
query – a plaintext search query
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]¶
Performs an arxiv search and A single string
with the publish date, title, authors, and summary
for each article separated by two newlines.
If an error occurs or no documents found, error text
is returned instead. Wrapper for
https://lukasschwab.me/arxiv.py/index.html#Search
Parameters
query – a plaintext search query
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 ArxivAPIWrapper¶


classmethod validate(value: Any) → Model¶
Examples using ArxivAPIWrapper¶
ArXiv


langchain_community.utilities.clickup.Member¶
class langchain_community.utilities.clickup.Member(id: int, username: str, email: str, initials: str)[source]¶
Component class for a member.
Attributes
id
username
email
initials
Methods
__init__(id, username, email, initials)
from_data(data)
__init__(id: int, username: str, email: str, initials: str) → None¶
classmethod from_data(data: Dict) → Member[source]¶


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.clickup.fetch_list_id¶
langchain_community.utilities.clickup.fetch_list_id(space_id: int, folder_id: int, access_token: str) → Optional[int][source]¶
Fetch the list id.


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¶


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¶
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.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.powerbi.json_to_md¶
langchain_community.utilities.powerbi.json_to_md(json_contents: List[Dict[str, Union[str, float, int]]], table_name: Optional[str] = None) → str[source]¶
Converts a JSON object to a markdown table.


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¶


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.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.


Get parameters for SerpAPI.
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) → dict[source]¶
Run query through SerpAPI and return the raw result.
run(query: str, **kwargs: Any) → str[source]¶
Run query through SerpAPI 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.


Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
Examples using SerpAPIWrapper¶
SerpAPI
Bittensor
AutoGPT


langchain_community.utilities.opaqueprompts.desanitize¶
langchain_community.utilities.opaqueprompts.desanitize(sanitized_text: str, secure_context: bytes) → str[source]¶
Restore the original sensitive data from the sanitized text.
Parameters
sanitized_text – Sanitized text.
secure_context – Secure context returned by the sanitize function.
Returns
De-sanitized text.


langchain_community.utilities.redis.get_client¶
langchain_community.utilities.redis.get_client(redis_url: str, **kwargs: Any) → RedisType[source]¶
Get a redis client from the connection url given. This helper accepts
urls for Redis server (TCP with/without TLS or UnixSocket) as well as
Redis Sentinel connections.
Redis Cluster is not supported.
Before creating a connection the existence of the database driver is checked
an and ValueError raised otherwise
To use, you should have the redis python package installed.
Example
from langchain_community.utilities.redis import get_client
redis_client = get_client(
    redis_url="redis://username:password@localhost:6379"
    index_name="my-index",
    embedding_function=embeddings.embed_query,
)
To use a redis replication setup with multiple redis server and redis sentinels
set “redis_url” to “redis+sentinel://” scheme. With this url format a path is
needed holding the name of the redis service within the sentinels to get the
correct redis server connection. The default service name is “mymaster”. The
optional second part of the path is the redis db number to connect to.
An optional username or password is used for booth connections to the rediserver
and the sentinel, different passwords for server and sentinel are not supported.
And as another constraint only one sentinel instance can be given:
Example
from langchain_community.utilities.redis import get_client
redis_client = get_client(
    redis_url="redis+sentinel://username:password@sentinelhost:26379/mymaster/0"
    index_name="my-index",
    embedding_function=embeddings.embed_query,
)


langchain_community.utilities.requests.TextRequestsWrapper¶
class langchain_community.utilities.requests.TextRequestsWrapper[source]¶
Bases: BaseModel
Lightweight wrapper around requests library.
The main purpose of this wrapper is to always return a text output.
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 auth: Optional[Any] = None¶
param headers: Optional[Dict[str, str]] = None¶
async adelete(url: str, **kwargs: Any) → str[source]¶
DELETE the URL and return the text asynchronously.
async aget(url: str, **kwargs: Any) → str[source]¶
GET the URL and return the text asynchronously.
async apatch(url: str, data: Dict[str, Any], **kwargs: Any) → str[source]¶
PATCH the URL and return the text asynchronously.
async apost(url: str, data: Dict[str, Any], **kwargs: Any) → str[source]¶
POST to the URL and return the text asynchronously.
async aput(url: str, data: Dict[str, Any], **kwargs: Any) → str[source]¶
PUT the URL and return the text 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
delete(url: str, **kwargs: Any) → str[source]¶
DELETE the URL and return the text.
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(url: str, **kwargs: Any) → str[source]¶
GET the URL and return the text.


GET the URL and return the text.
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¶
patch(url: str, data: Dict[str, Any], **kwargs: Any) → str[source]¶
PATCH the URL and return the text.
post(url: str, data: Dict[str, Any], **kwargs: Any) → str[source]¶
POST to the URL and return the text.
put(url: str, data: Dict[str, Any], **kwargs: Any) → str[source]¶
PUT the URL and return the text.
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¶
property requests: langchain_community.utilities.requests.Requests¶
Examples using TextRequestsWrapper¶
Requests
JSON
OpenAPI
Tool Input Schema


langchain_community.utilities.google_jobs.GoogleJobsAPIWrapper¶
class langchain_community.utilities.google_jobs.GoogleJobsAPIWrapper[source]¶
Bases: BaseModel
Wrapper for SerpApi’s Google Scholar API
You can create SerpApi.com key by signing up at: https://serpapi.com/users/sign_up.
The wrapper uses the SerpApi.com python package:
https://serpapi.com/integrations/python
To use, you should have the environment variable SERPAPI_API_KEY
set with your API key, or pass serp_api_key as a named parameter
to the constructor.
Example:
from langchain_community.utilities import GoogleJobsAPIWrapper
google_Jobs = GoogleJobsAPIWrapper()
google_Jobs.run(‘langchain’)
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 serp_api_key: Optional[pydantic.types.SecretStr] = None¶
Constraints
type = string
writeOnly = True
format = password
param serp_search_engine: Any = 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


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¶
run(query: str) → str[source]¶
Run query through Google Trends with Serpapi
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.requests.Requests¶
class langchain_community.utilities.requests.Requests[source]¶
Bases: BaseModel
Wrapper around requests to handle auth and async.
The main purpose of this wrapper is to handle authentication (by saving
headers) and enable easy async methods on the same base object.
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 auth: Optional[Any] = None¶
param headers: Optional[Dict[str, str]] = None¶
adelete(url: str, **kwargs: Any) → AsyncGenerator[ClientResponse, None][source]¶
DELETE the URL and return the text asynchronously.
aget(url: str, **kwargs: Any) → AsyncGenerator[ClientResponse, None][source]¶
GET the URL and return the text asynchronously.
apatch(url: str, data: Dict[str, Any], **kwargs: Any) → AsyncGenerator[ClientResponse, None][source]¶
PATCH the URL and return the text asynchronously.
apost(url: str, data: Dict[str, Any], **kwargs: Any) → AsyncGenerator[ClientResponse, None][source]¶
POST to the URL and return the text asynchronously.
aput(url: str, data: Dict[str, Any], **kwargs: Any) → AsyncGenerator[ClientResponse, None][source]¶
PUT the URL and return the text 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.


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
delete(url: str, **kwargs: Any) → Response[source]¶
DELETE the URL and return the text.
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(url: str, **kwargs: Any) → Response[source]¶
GET the URL and return the text.


GET the URL and return the text.
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¶
patch(url: str, data: Dict[str, Any], **kwargs: Any) → Response[source]¶
PATCH the URL and return the text.
post(url: str, data: Dict[str, Any], **kwargs: Any) → Response[source]¶
POST to the URL and return the text.
put(url: str, data: Dict[str, Any], **kwargs: Any) → Response[source]¶
PUT the URL and return the text.
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 Requests¶
Natural Language APIs


langchain_community.utilities.google_places_api.GooglePlacesAPIWrapper¶
class langchain_community.utilities.google_places_api.GooglePlacesAPIWrapper[source]¶
Bases: BaseModel
Wrapper around Google Places API.
To use, you should have the googlemaps python package installed,an API key for the google maps platform,
and the environment variable ‘’GPLACES_API_KEY’’
set with your API key , or pass ‘gplaces_api_key’
as a named parameter to the constructor.
By default, this will return the all the results on the input query.You can use the top_k_results argument to limit the number of results.
Example
from langchain_community.utilities import GooglePlacesAPIWrapper
gplaceapi = GooglePlacesAPIWrapper()
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 gplaces_api_key: Optional[str] = None¶
param top_k_results: Optional[int] = 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


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.
fetch_place_details(place_id: str) → Optional[str][source]¶
format_place_details(place_details: Dict[str, Any]) → Optional[str][source]¶
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 Places search and get k number of places that exists that match.
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.sql_database.SQLDatabase¶
class langchain_community.utilities.sql_database.SQLDatabase(engine: Engine, schema: Optional[str] = None, metadata: Optional[MetaData] = None, ignore_tables: Optional[List[str]] = None, include_tables: Optional[List[str]] = None, sample_rows_in_table_info: int = 3, indexes_in_table_info: bool = False, custom_table_info: Optional[dict] = None, view_support: bool = False, max_string_length: int = 300)[source]¶
SQLAlchemy wrapper around a database.
Create engine from database URI.
Attributes
dialect
Return string representation of dialect to use.
table_info
Information about all tables in the database.
Methods
__init__(engine[, schema, metadata, ...])
Create engine from database URI.
from_cnosdb([url, user, password, tenant, ...])
Class method to create an SQLDatabase instance from a CnosDB connection.
from_databricks(catalog, schema[, host, ...])
Class method to create an SQLDatabase instance from a Databricks connection.
from_uri(database_uri[, engine_args])
Construct a SQLAlchemy engine from URI.
get_table_info([table_names])
Get information about specified tables.
get_table_info_no_throw([table_names])
Get information about specified tables.
get_table_names()
Get names of tables available.
get_usable_table_names()
Get names of tables available.
run(command[, fetch])
Execute a SQL command and return a string representing the results.
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__(engine: Engine, schema: Optional[str] = None, metadata: Optional[MetaData] = None, ignore_tables: Optional[List[str]] = None, include_tables: Optional[List[str]] = None, sample_rows_in_table_info: int = 3, indexes_in_table_info: bool = False, custom_table_info: Optional[dict] = None, view_support: bool = False, max_string_length: int = 300)[source]¶
Create engine from database URI.
classmethod from_cnosdb(url: str = '127.0.0.1:8902', user: str = 'root', password: str = '', tenant: str = 'cnosdb', database: str = 'public') → SQLDatabase[source]¶
Class method to create an SQLDatabase instance from a CnosDB connection.
This method requires the ‘cnos-connector’ package. If not installed, it
can be added using pip install cnos-connector.
Parameters
url (str) – The HTTP connection host name and port number of the CnosDB
service, excluding “http://” or “https://”, with a default value
of “127.0.0.1:8902”.
user (str) – The username used to connect to the CnosDB service, with a
default value of “root”.
password (str) – The password of the user connecting to the CnosDB service,
with a default value of “”.
tenant (str) – The name of the tenant used to connect to the CnosDB service,
with a default value of “cnosdb”.
database (str) – The name of the database in the CnosDB tenant.
Returns
An instance of SQLDatabase configured with the provided
CnosDB connection details.
Return type
SQLDatabase


CnosDB connection details.
Return type
SQLDatabase
classmethod from_databricks(catalog: str, schema: str, host: Optional[str] = None, api_token: Optional[str] = None, warehouse_id: Optional[str] = None, cluster_id: Optional[str] = None, engine_args: Optional[dict] = None, **kwargs: Any) → SQLDatabase[source]¶
Class method to create an SQLDatabase instance from a Databricks connection.
This method requires the ‘databricks-sql-connector’ package. If not installed,
it can be added using pip install databricks-sql-connector.
Parameters
catalog (str) – The catalog name in the Databricks database.
schema (str) – The schema name in the catalog.
host (Optional[str]) – The Databricks workspace hostname, excluding
‘https://’ part. If not provided, it attempts to fetch from the
environment variable ‘DATABRICKS_HOST’. If still unavailable and if
running in a Databricks notebook, it defaults to the current workspace
hostname. Defaults to None.
api_token (Optional[str]) – The Databricks personal access token for
accessing the Databricks SQL warehouse or the cluster. If not provided,
it attempts to fetch from ‘DATABRICKS_TOKEN’. If still unavailable
and running in a Databricks notebook, a temporary token for the current
user is generated. Defaults to None.
warehouse_id (Optional[str]) – The warehouse ID in the Databricks SQL. If
provided, the method configures the connection to use this warehouse.
Cannot be used with ‘cluster_id’. Defaults to None.
cluster_id (Optional[str]) – The cluster ID in the Databricks Runtime. If
provided, the method configures the connection to use this cluster.


provided, the method configures the connection to use this cluster.
Cannot be used with ‘warehouse_id’. If running in a Databricks notebook
and both ‘warehouse_id’ and ‘cluster_id’ are None, it uses the ID of the
cluster the notebook is attached to. Defaults to None.
engine_args (Optional[dict]) – The arguments to be used when connecting
Databricks. Defaults to None.
**kwargs (Any) – Additional keyword arguments for the from_uri method.
Returns
An instance of SQLDatabase configured with the providedDatabricks connection details.
Return type
SQLDatabase
Raises
ValueError – If ‘databricks-sql-connector’ is not found, or if both
    ‘warehouse_id’ and ‘cluster_id’ are provided, or if neither
    ‘warehouse_id’ nor ‘cluster_id’ are provided and it’s not executing
    inside a Databricks notebook.
classmethod from_uri(database_uri: str, engine_args: Optional[dict] = None, **kwargs: Any) → SQLDatabase[source]¶
Construct a SQLAlchemy engine from URI.
get_table_info(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.
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)


(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.
get_table_names() → Iterable[str][source]¶
Get names of tables available.
get_usable_table_names() → Iterable[str][source]¶
Get names of tables available.
run(command: str, fetch: Union[Literal['all'], Literal['one']] = '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.
run_no_throw(command: str, fetch: Union[Literal['all'], Literal['one']] = '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 SQLDatabase¶
Rebuff
SQL Database
Multiple Retrieval Sources
Set env var OPENAI_API_KEY or load from a .env file
Vector SQL Retriever with MyScale
SQL
sql_db.md


langchain_community.utilities.anthropic.get_num_tokens_anthropic¶
langchain_community.utilities.anthropic.get_num_tokens_anthropic(text: str) → int[source]¶
Get the number of tokens in a string of text.


langchain_community.utilities.openapi.OpenAPISpec¶
class langchain_community.utilities.openapi.OpenAPISpec[source]¶
OpenAPI Model that removes mis-formatted parts of the spec.
Attributes
base_url
Get the base url.
openapi
Methods
__init__()
from_file(path)
Get an OpenAPI spec from a file path.
from_spec_dict(spec_dict)
Get an OpenAPI spec from a dict.
from_text(text)
Get an OpenAPI spec from a text.
from_url(url)
Get an OpenAPI spec from a URL.
get_cleaned_operation_id(operation, path, method)
Get a cleaned operation id from an operation id.
get_methods_for_path(path)
Return a list of valid methods for the specified path.
get_operation(path, method)
Get the operation object for a given path and HTTP method.
get_parameters_for_operation(operation)
Get the components for a given operation.
get_parameters_for_path(path)
get_referenced_schema(ref)
Get a schema (or nested reference) or err.
get_request_body_for_operation(operation)
Get the request body for a given operation.
get_schema(schema)
parse_obj(obj)
__init__()¶
classmethod from_file(path: Union[str, Path]) → OpenAPISpec[source]¶
Get an OpenAPI spec from a file path.
classmethod from_spec_dict(spec_dict: dict) → OpenAPISpec[source]¶
Get an OpenAPI spec from a dict.
classmethod from_text(text: str) → OpenAPISpec[source]¶
Get an OpenAPI spec from a text.
classmethod from_url(url: str) → OpenAPISpec[source]¶
Get an OpenAPI spec from a URL.
static get_cleaned_operation_id(operation: Operation, path: str, method: str) → str[source]¶


Get a cleaned operation id from an operation id.
get_methods_for_path(path: str) → List[str][source]¶
Return a list of valid methods for the specified path.
get_operation(path: str, method: str) → Operation[source]¶
Get the operation object for a given path and HTTP method.
get_parameters_for_operation(operation: Operation) → List[Parameter][source]¶
Get the components for a given operation.
get_parameters_for_path(path: str) → List[Parameter][source]¶
get_referenced_schema(ref: Reference) → Schema[source]¶
Get a schema (or nested reference) or err.
get_request_body_for_operation(operation: Operation) → Optional[RequestBody][source]¶
Get the request body for a given operation.
get_schema(schema: Union[Reference, Schema]) → Schema[source]¶
classmethod parse_obj(obj: dict) → OpenAPISpec[source]¶
Examples using OpenAPISpec¶
Natural Language APIs


langchain_community.utilities.openweathermap.OpenWeatherMapAPIWrapper¶
class langchain_community.utilities.openweathermap.OpenWeatherMapAPIWrapper[source]¶
Bases: BaseModel
Wrapper for OpenWeatherMap API using PyOWM.
Docs for using:
Go to OpenWeatherMap and sign up for an API key
Save your API KEY into OPENWEATHERMAP_API_KEY env variable
pip install pyowm
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 openweathermap_api_key: Optional[str] = None¶
param owm: Any = 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(location: str) → str[source]¶


run(location: str) → str[source]¶
Get the current weather information for a specified location.
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 OpenWeatherMapAPIWrapper¶
OpenWeatherMap


langchain_community.utilities.google_lens.GoogleLensAPIWrapper¶
class langchain_community.utilities.google_lens.GoogleLensAPIWrapper[source]¶
Bases: BaseModel
Wrapper for SerpApi’s Google Lens API
You can create SerpApi.com key by signing up at: https://serpapi.com/users/sign_up.
The wrapper uses the SerpApi.com python package:
https://serpapi.com/integrations/python
To use, you should have the environment variable SERPAPI_API_KEY
set with your API key, or pass serp_api_key as a named parameter
to the constructor.
Example:
from langchain_community.utilities import GoogleLensAPIWrapper
google_lens = GoogleLensAPIWrapper()
google_lens.run(‘langchain’)
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 serp_api_key: Optional[pydantic.types.SecretStr] = None¶
Constraints
type = string
writeOnly = True
format = password
param serp_search_engine: Any = 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


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_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 through Google Trends with Serpapi
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.clickup.fetch_first_id¶
langchain_community.utilities.clickup.fetch_first_id(data: dict, key: str) → Optional[int][source]¶
Fetch the first id from a dictionary.


langchain_community.utilities.tavily_search.TavilySearchAPIWrapper¶
class langchain_community.utilities.tavily_search.TavilySearchAPIWrapper[source]¶
Bases: BaseModel
Wrapper for Tavily 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 tavily_api_key: str [Required]¶
clean_results(results: List[Dict]) → List[Dict][source]¶
Clean results from Tavily Search API.
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¶


raw_results(query: str, max_results: Optional[int] = 5, search_depth: Optional[str] = 'advanced', include_domains: Optional[List[str]] = [], exclude_domains: Optional[List[str]] = [], include_answer: Optional[bool] = False, include_raw_content: Optional[bool] = False, include_images: Optional[bool] = False) → Dict[source]¶
async raw_results_async(query: str, max_results: Optional[int] = 5, search_depth: Optional[str] = 'advanced', include_domains: Optional[List[str]] = [], exclude_domains: Optional[List[str]] = [], include_answer: Optional[bool] = False, include_raw_content: Optional[bool] = False, include_images: Optional[bool] = False) → Dict[source]¶
Get results from the Tavily Search API asynchronously.
results(query: str, max_results: Optional[int] = 5, search_depth: Optional[str] = 'advanced', include_domains: Optional[List[str]] = [], exclude_domains: Optional[List[str]] = [], include_answer: Optional[bool] = False, include_raw_content: Optional[bool] = False, include_images: Optional[bool] = False) → List[Dict][source]¶
Run query through Tavily Search and return metadata.
Parameters
query – The query to search for.
max_results – The maximum number of results to return.
search_depth – The depth of the search. Can be “basic” or “advanced”.
include_domains – A list of domains to include in the search.
exclude_domains – A list of domains to exclude from the search.
include_answer – Whether to include the answer in the results.
include_raw_content – Whether to include the raw content in the results.
include_images – Whether to include images in the results.
Returns
The query that was searched for.


Returns
The query that was searched for.
follow_up_questions: A list of follow up questions.
response_time: The response time of the query.
answer: The answer to the query.
images: A list of images.
results: A list of dictionaries containing the results:
title: The title of the result.
url: The url of the result.
content: The content of the result.
score: The score of the result.
raw_content: The raw content of the result.
Return type
query
async results_async(query: str, max_results: Optional[int] = 5, search_depth: Optional[str] = 'advanced', include_domains: Optional[List[str]] = [], exclude_domains: Optional[List[str]] = [], include_answer: Optional[bool] = False, include_raw_content: Optional[bool] = False, include_images: Optional[bool] = False) → List[Dict][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¶
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.alpha_vantage.AlphaVantageAPIWrapper¶
class langchain_community.utilities.alpha_vantage.AlphaVantageAPIWrapper[source]¶
Bases: BaseModel
Wrapper for AlphaVantage API for Currency Exchange Rate.
Docs for using:
Go to AlphaVantage and sign up for an API key
Save your API KEY into ALPHAVANTAGE_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 alphavantage_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


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(from_currency: str, to_currency: str) → str[source]¶


run(from_currency: str, to_currency: str) → str[source]¶
Get the current exchange rate for a specified currency pair.
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¶
property standard_currencies: List[str]¶
Examples using AlphaVantageAPIWrapper¶
Alpha Vantage


langchain_community.utilities.outline.OutlineAPIWrapper¶
class langchain_community.utilities.outline.OutlineAPIWrapper[source]¶
Bases: BaseModel
Wrapper around OutlineAPI.
This wrapper will use the Outline API to query the documents of your instance.
By default it will return the document content of the top-k results.
It limits the document content by doc_content_chars_max.
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 doc_content_chars_max: int = 4000¶
param load_all_available_meta: bool = False¶
param outline_api_key: Optional[str] = None¶
param outline_instance_url: Optional[str] = None¶
param outline_search_endpoint: str = '/api/documents.search'¶
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


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¶


run(query: str) → List[Document][source]¶
Run Outline search and get the document content plus the meta information.
Returns: a list of documents.
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.serpapi.HiddenPrints¶
class langchain_community.utilities.serpapi.HiddenPrints[source]¶
Context manager to hide prints.
Methods
__init__()
__init__()¶


langchain_community.utilities.clickup.extract_dict_elements_from_component_fields¶
langchain_community.utilities.clickup.extract_dict_elements_from_component_fields(data: dict, component: Type[Component]) → dict[source]¶
Extract elements from a dictionary.
Parameters
data – The dictionary to extract elements from.
component – The component to extract elements from.
Returns
A dictionary containing the elements from the input dictionary that are also
in the component.


langchain_community.utilities.opaqueprompts.sanitize¶
langchain_community.utilities.opaqueprompts.sanitize(input: Union[str, Dict[str, str]]) → Dict[str, Union[str, Dict[str, str]]][source]¶
Sanitize input string or dict of strings by replacing sensitive data with
placeholders.
It returns the sanitized input string or dict of strings and the secure
context as a dict following the format:
{
“sanitized_input”: <sanitized input string or dict of strings>,
“secure_context”: <secure context>
}
The secure context is a bytes object that is needed to de-sanitize the response
from the LLM.
Parameters
input – Input string or dict of strings.
Returns
Sanitized input string or dict of strings and the secure context
as a dict following the format:
{
”sanitized_input”: <sanitized input string or dict of strings>,
“secure_context”: <secure context>
}
The secure_context needs to be passed to the desanitize function.
Raises
ValueError – If the input is not a string or dict of strings.
ImportError – If the opaqueprompts Python package is not installed.


langchain_community.utilities.redis.TokenEscaper¶
class langchain_community.utilities.redis.TokenEscaper(escape_chars_re: Optional[Pattern] = None)[source]¶
Escape punctuation within an input string.
Attributes
DEFAULT_ESCAPED_CHARS
Methods
__init__([escape_chars_re])
escape(value)
__init__(escape_chars_re: Optional[Pattern] = None)[source]¶
escape(value: str) → str[source]¶


langchain_community.utilities.twilio.TwilioAPIWrapper¶
class langchain_community.utilities.twilio.TwilioAPIWrapper[source]¶
Bases: BaseModel
Messaging Client using Twilio.
To use, you should have the twilio python package installed,
and the environment variables TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN, and
TWILIO_FROM_NUMBER, or pass account_sid, auth_token, and from_number as
named parameters to the constructor.
Example
from langchain_community.utilities.twilio import TwilioAPIWrapper
twilio = TwilioAPIWrapper(
    account_sid="ACxxx",
    auth_token="xxx",
    from_number="+10123456789"
)
twilio.run('test', '+12484345508')
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 account_sid: Optional[str] = None¶
Twilio account string identifier.
param auth_token: Optional[str] = None¶
Twilio auth token.
param from_number: Optional[str] = None¶
A Twilio phone number in [E.164](https://www.twilio.com/docs/glossary/what-e164)
format, an
[alphanumeric sender ID](https://www.twilio.com/docs/sms/send-messages#use-an-alphanumeric-sender-id),
or a [Channel Endpoint address](https://www.twilio.com/docs/sms/channels#channel-addresses)
that is enabled for the type of message you want to send. Phone numbers or
[short codes](https://www.twilio.com/docs/sms/api/short-code) purchased from
Twilio also work here. You cannot, for example, spoof messages from a private
cell phone number. If you are using messaging_service_sid, this parameter
must be empty.


cell phone number. If you are using messaging_service_sid, this parameter
must be empty.
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¶


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(body: str, to: str) → str[source]¶
Run body through Twilio and respond with message sid.
Parameters
body – The text of the message you want to send. Can be up to 1,600
characters in length.
to – The destination phone number in
[E.164](https://www.twilio.com/docs/glossary/what-e164) format for
SMS/MMS or
[Channel user address](https://www.twilio.com/docs/sms/channels#channel-addresses)
for other 3rd-party channels.


for other 3rd-party channels.
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 TwilioAPIWrapper¶
Twilio


langchain_community.utilities.redis.check_redis_module_exist¶
langchain_community.utilities.redis.check_redis_module_exist(client: RedisType, required_modules: List[dict]) → None[source]¶
Check if the correct Redis modules are installed.


langchain_community.utilities.golden_query.GoldenQueryAPIWrapper¶
class langchain_community.utilities.golden_query.GoldenQueryAPIWrapper[source]¶
Bases: BaseModel
Wrapper for Golden.
Docs for using:
Go to https://golden.com and sign up for an account
Get your API Key from https://golden.com/settings/api
Save your API Key into GOLDEN_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 golden_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


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 Golden Query API and return the JSON raw 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 GoldenQueryAPIWrapper¶
Golden Query
Golden


langchain_community.utilities.wikipedia.WikipediaAPIWrapper¶
class langchain_community.utilities.wikipedia.WikipediaAPIWrapper[source]¶
Bases: BaseModel
Wrapper around WikipediaAPI.
To use, you should have the wikipedia python package installed.
This wrapper will use the Wikipedia API to conduct searches and
fetch page summaries. By default, it will return the page summaries
of the top-k results.
It limits the Document content by doc_content_chars_max.
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 doc_content_chars_max: int = 4000¶
param lang: str = 'en'¶
param load_all_available_meta: bool = False¶
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