viruPVS/langchain-docs huggingface dataset:
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.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
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.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
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
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.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]¶