Tony20100/langchain_full huggingface dataset:
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
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.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
classmethod from_function(func: Optional[Callable], name: str, description: str, return_direct: bool = False, args_schema: Optional[Type[BaseModel]] = None, coroutine: Optional[Callable[[...], Awaitable[Any]]] = None, **kwargs: Any) → Tool¶
Initialize tool from a function.
Parameters
func (Optional[Callable]) – 
name (str) – 
description (str) – 
return_direct (bool) –
description (str) – 
return_direct (bool) – 
args_schema (Optional[Type[BaseModel]]) – 
coroutine (Optional[Callable[[...], Awaitable[Any]]]) – 
kwargs (Any) – 
Return type
Tool
classmethod from_llm_and_method(llm: BaseLanguageModel, path: str, method: str, spec: OpenAPISpec, requests: Optional[Requests] = None, verbose: bool = False, return_intermediate_steps: bool = False, **kwargs: Any) → NLATool[source]¶
Instantiate the tool from the specified path and method.
Parameters
llm (BaseLanguageModel) – 
path (str) – 
method (str) – 
spec (OpenAPISpec) – 
requests (Optional[Requests]) – 
verbose (bool) – 
return_intermediate_steps (bool) – 
kwargs (Any) – 
Return type
NLATool
classmethod from_open_api_endpoint_chain(chain: OpenAPIEndpointChain, api_title: str) → NLATool[source]¶
Convert an endpoint chain to an API endpoint tool.
Parameters
chain (OpenAPIEndpointChain) – 
api_title (str) – 
Return type
NLATool
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
get_graph(config: Optional[RunnableConfig] = None) → Graph¶
Return a graph representation of this runnable.
Parameters
config (Optional[RunnableConfig]) – 
Return type
Graph
get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
The tool’s input schema.
Parameters
config (Optional[RunnableConfig]) – 
Return type
Type[BaseModel]
config (Optional[RunnableConfig]) – 
Return type
Type[BaseModel]
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
Return type
List[str]
get_name(suffix: Optional[str] = None, *, name: Optional[str] = None) → str¶
Get the name of the runnable.
Parameters
suffix (Optional[str]) – 
name (Optional[str]) – 
Return type
str
get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate output to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic output schema that depends on which
configuration the runnable is invoked with.
This method allows to get an output schema for a specific configuration.
Parameters
config (Optional[RunnableConfig]) – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
Return type
Type[BaseModel]
get_prompts(config: Optional[RunnableConfig] = None) → List[BasePromptTemplate]¶
Parameters
config (Optional[RunnableConfig]) – 
Return type
List[BasePromptTemplate]
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
Transform a single input into an output. Override to implement.
Parameters
input (Union[str, Dict]) – The input to the runnable.
config (Optional[RunnableConfig]) – A config to use when invoking the runnable.
langchain_community.agent_toolkits.github.toolkit.GetIssue¶
class langchain_community.agent_toolkits.github.toolkit.GetIssue[source]¶
Bases: BaseModel
Schema for operations that require an issue number as input.
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 issue_number: int = 0¶
Issue number as an integer, e.g. 42
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
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
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 (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) –
langchain_community.agent_toolkits.load_tools.load_tools¶
langchain_community.agent_toolkits.load_tools.load_tools(tool_names: List[str], llm: Optional[BaseLanguageModel] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, allow_dangerous_tools: bool = False, **kwargs: Any) → List[BaseTool][source]¶
Load tools based on their name.
Tools allow agents to interact with various resources and services like
APIs, databases, file systems, etc.
Please scope the permissions of each tools to the minimum required for the
application.
For example, if an application only needs to read from a database,
the database tool should not be given write permissions. Moreover
consider scoping the permissions to only allow accessing specific
tables and impose user-level quota for limiting resource usage.
Please read the APIs of the individual tools to determine which configuration
they support.
See [Security](https://python.langchain.com/docs/security) for more information.
Parameters
tool_names (List[str]) – name of tools to load.
llm (Optional[BaseLanguageModel]) – An optional language model, may be needed to initialize certain tools.
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Optional callback manager or list of callback handlers.
If not provided, default global callback manager will be used.
allow_dangerous_tools (bool) – Optional flag to allow dangerous tools.
Tools that contain some level of risk.
Please use with caution and read the documentation of these tools
to understand the risks and how to mitigate them.
Refer to https://python.langchain.com/docs/security
for more information.
Please note that this list may not be fully exhaustive.
It is your responsibility to understand which tools
you’re using and the risks associated with them.
kwargs (Any) – 
Returns
you’re using and the risks associated with them.
kwargs (Any) – 
Returns
List of tools.
Return type
List[BaseTool]
Examples using load_tools¶
AWS Lambda
Aim
Amazon API Gateway
ArXiv
Argilla
ChatGPT Plugins
ClearML
Comet
Comet Tracing
Dall-E Image Generator
DataForSEO
Dataherald
Eleven Labs Text2Speech
Flyte
Golden
Google
Google Drive
Google Finance
Google Jobs
GraphQL
How to track token usage in ChatModels
Hugging Face
Human as a tool
LLMonitor
MLX
MLflow
Memorize
OpenWeatherMap
Requests
SageMaker Tracking
SceneXplain
Search Tools
SearchApi
SearxNG Search API
SerpAPI
Serper - Google Search API
Stack Exchange
Streamlit
WandB Tracing
Weights & Biases
Wolfram Alpha
langchain_community.agent_toolkits.openapi.base.create_openapi_agent(llm: BaseLanguageModel, toolkit: OpenAPIToolkit, callback_manager: Optional[BaseCallbackManager] = None, prefix: str = "You are an agent designed to answer questions by making web requests to an API given the openapi spec.\n\nIf the question does not seem related to the API, return I don't know. Do not make up an answer.\nOnly use information provided by the tools to construct your response.\n\nFirst, find the base URL needed to make the request.\n\nSecond, find the relevant paths needed to answer the question. Take note that, sometimes, you might need to make more than one request to more than one path to answer the question.\n\nThird, find the required parameters needed to make the request. For GET requests, these are usually URL parameters and for POST requests, these are request body parameters.\n\nFourth, make the requests needed to answer the question. Ensure that you are sending the correct parameters to the request by checking which parameters are required. For parameters with a fixed set of values, please use the spec to look at which values are allowed.\n\nUse the exact parameter names as listed in the spec, do not make up any names or abbreviate the names of parameters.\nIf you get a not found error, ensure that you are using a path that actually exists in the spec.\n", suffix: str = 'Begin!\n\nQuestion: {input}\nThought: I should explore the spec to find the base server url for the API in the servers node.\n{agent_scratchpad}', format_instructions: Optional[str] = None, input_variables: Optional[List[str]] = None, max_iterations: Optional[int] = 15, max_execution_time: Optional[float] = None, early_stopping_method: str = 'force', verbose: bool = False, return_intermediate_steps: bool
Construct an OpenAPI agent from an LLM and tools.
Security Note: When creating an OpenAPI agent, check the permissionsand capabilities of the underlying toolkit.
For example, if the default implementation of OpenAPIToolkit
uses the RequestsToolkit which contains tools to make arbitrary
network requests against any URL (e.g., GET, POST, PATCH, PUT, DELETE),
Control access to who can submit issue requests using this toolkit and
what network access it has.
See https://python.langchain.com/docs/security for more information.
Parameters
llm (BaseLanguageModel) – 
toolkit (OpenAPIToolkit) – 
callback_manager (Optional[BaseCallbackManager]) – 
prefix (str) – 
suffix (str) – 
format_instructions (Optional[str]) – 
input_variables (Optional[List[str]]) – 
max_iterations (Optional[int]) – 
max_execution_time (Optional[float]) – 
early_stopping_method (str) – 
verbose (bool) – 
return_intermediate_steps (bool) – 
agent_executor_kwargs (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
AgentExecutor
Examples using create_openapi_agent¶
OpenAPI
langchain_community.agent_toolkits.github.toolkit.NoInput¶
class langchain_community.agent_toolkits.github.toolkit.NoInput[source]¶
Bases: BaseModel
Schema for operations that do not require any input.
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 no_input: str = ''¶
No input required, e.g. `` (empty string).
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
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
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 (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
langchain_community.agent_toolkits.azure_cognitive_services.AzureCognitiveServicesToolkit¶
class langchain_community.agent_toolkits.azure_cognitive_services.AzureCognitiveServicesToolkit[source]¶
Bases: BaseToolkit
Toolkit for Azure Cognitive Services.
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
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
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 (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
Parameters
by_alias (bool) – 
ref_template (unicode) – 
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
dumps_kwargs (Any) – 
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) – 
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) – 
Return type
Model
Examples using AzureCognitiveServicesToolkit¶
Azure Cognitive Services
langchain_community.agent_toolkits.load_tools.load_huggingface_tool¶
langchain_community.agent_toolkits.load_tools.load_huggingface_tool(task_or_repo_id: str, model_repo_id: Optional[str] = None, token: Optional[str] = None, remote: bool = False, **kwargs: Any) → BaseTool[source]¶
Loads a tool from the HuggingFace Hub.
Parameters
task_or_repo_id (str) – Task or model repo id.
model_repo_id (Optional[str]) – Optional model repo id.
token (Optional[str]) – Optional token.
remote (bool) – Optional remote. Defaults to False.
**kwargs (Any) – 
Returns
A tool.
Return type
BaseTool
Examples using load_huggingface_tool¶
Hugging Face
HuggingFace Hub Tools
Construct a Power BI agent from a Chat LLM and tools.
If you supply only a toolkit and no Power BI dataset, the same LLM is used for both.
Parameters
llm (BaseChatModel) – 
toolkit (Optional[PowerBIToolkit]) – 
powerbi (Optional[PowerBIDataset]) – 
callback_manager (Optional[BaseCallbackManager]) – 
output_parser (Optional[AgentOutputParser]) – 
prefix (str) – 
suffix (str) – 
examples (Optional[str]) – 
input_variables (Optional[List[str]]) – 
memory (Optional[BaseChatMemory]) – 
top_k (int) – 
verbose (bool) – 
agent_executor_kwargs (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
AgentExecutor
langchain_community.agent_toolkits.nasa.toolkit.NasaToolkit¶
class langchain_community.agent_toolkits.nasa.toolkit.NasaToolkit[source]¶
Bases: BaseToolkit
Nasa Toolkit.
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 tools: List[BaseTool] = []¶
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
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
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 (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
self (Model) – 
Returns
new model instance
Return type
Model
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.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
classmethod from_nasa_api_wrapper(nasa_api_wrapper: NasaAPIWrapper) → NasaToolkit[source]¶
Parameters
nasa_api_wrapper (NasaAPIWrapper) – 
Return type
NasaToolkit
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
get_tools() → List[BaseTool][source]¶
Get the tools in the toolkit.
Return type
List[BaseTool]
Parameters
obj (Any) – 
Return type
Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
b (Union[str, bytes]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
dumps_kwargs (Any) – 
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) – 
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) – 
Return type
Model
Examples using NasaToolkit¶
NASA
langchain_community.agent_toolkits.polygon.toolkit.PolygonToolkit¶
class langchain_community.agent_toolkits.polygon.toolkit.PolygonToolkit[source]¶
Bases: BaseToolkit
Polygon Toolkit.
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 tools: List[BaseTool] = []¶
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
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
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 (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
self (Model) – 
Returns
new model instance
Return type
Model
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.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
classmethod from_polygon_api_wrapper(polygon_api_wrapper: PolygonAPIWrapper) → PolygonToolkit[source]¶
Parameters
polygon_api_wrapper (PolygonAPIWrapper) – 
Return type
PolygonToolkit
get_tools() → List[BaseTool][source]¶
Get the tools in the toolkit.
Return type
List[BaseTool]
Parameters
obj (Any) – 
Return type
Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
b (Union[str, bytes]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
dumps_kwargs (Any) – 
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) – 
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) – 
Return type
Model
Examples using PolygonToolkit¶
Polygon IO Toolkit