Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.base.Runnable[~langchain.schema.runnable.utils.Input, ~langchain.schema.runnable.utils.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
property InputType: Type[langchain.schema.runnable.utils.Input]¶
property OutputType: Type[langchain.schema.runnable.utils.Output]¶
property args: dict¶
The tool’s input arguments.
property input_schema: Type[pydantic.main.BaseModel]¶
The tool’s input schema.
property is_single_input: bool¶
Whether the tool only accepts a single input.
property output_schema: Type[pydantic.main.BaseModel]¶
Examples using Tool¶
DataForSeo
Google Serper
SerpAPI
Google Search
Zep Memory
Dynamodb Chat Message History
SageMaker Tracking
Document Comparison
Natural Language APIs
Github
Bittensor
Pydantic compatibility
Comparing Chain Outputs
Agents
AutoGPT
BabyAGI with Tools
Plug-and-Plai
Wikibase Agent
SalesGPT - Your Context-Aware AI Sales Assistant With Knowledge Base
Custom Agent with PlugIn Retrieval
Agent Debates with Tools
Message Memory in Agent backed by a database
Memory in Agent
Multi-Input Tools
Defining Custom Tools
Self-ask with search
ReAct document store
OpenAI Multi Functions Agent