This is a Pydantic model dynamically generated from the structure of any Runnable.
You can call .schema() on it to obtain a JSONSchema representation.# The input schema of the chain is the input schema of its first part, the prompt.chain.input_schema.schema()    {'title': 'PromptInput',     'type': 'object',     'properties': {'topic': {'title': 'Topic', 'type': 'string'}}}Output Schema‚ÄãA description of the outputs produced by a Runnable.
This is a Pydantic model dynamically generated from the structure of any Runnable.
In an effort to make it as easy as possible to create custom chains, we've implemented a "Runnable" protocol that most components implement. This is a standard interface with a few different methods, which makes it easy to define custom chains as well as making it possible to invoke them in a standard way. The standard interface exposed includes:
In an effort to make it as easy as possible to create custom chains, we've implemented a "Runnable" protocol that most components implement. This is a standard interface with a few different methods, which makes it easy to define custom chains as well as making it possible to invoke them in a standard way. The standard interface exposed includes: ->: This is a Pydantic model dynamically generated from the structure of any Runnable.
You can call .schema() on it to obtain a JSONSchema representation.# The input schema of the chain is the input schema of its first part, the prompt.chain.input_schema.schema()    {'title': 'PromptInput',     'type': 'object',     'properties': {'topic': {'title': 'Topic', 'type': 'string'}}}Output Schema‚ÄãA description of the outputs produced by a Runnable.
This is a Pydantic model dynamically generated from the structure of any Runnable.