Base LLM abstract interface.
It should take in a prompt and return a string.
Pass a sequence of prompts to a model and return generations.
This method should make use of batched calls for models that expose a batched API.
Use this method when you want to:
Asynchronously pass a sequence of prompts to a model and return generations.
This method should make use of batched calls for models that expose a batched API.
Use this method when you want to:
Return a dictionary of the LLM.
Save the LLM.
Whether to cache the response.
Whether to log the tool's progress.
Callbacks for this call and any sub-calls (e.g. a Chain calling an LLM).
Optional list of tags associated with the retriever.
Optional metadata associated with the retriever.
Optional encoder to use for counting tokens.
If verbose is None, set it.
Model wrapper that returns outputs formatted to match the given schema.
Return the ordered IDs of the tokens in a text.
Get the number of tokens present in the text.
Get the number of tokens in the messages.
Return True as this class is serializable.
Get the namespace of the LangChain object.
Return a unique identifier for this class for serialization purposes.
Convert the graph to a JSON-serializable format.
Serialize a "not implemented" object.
Get a JSON schema that represents the input to the Runnable.
Get a JSON schema that represents the output of the Runnable.
The type of config this Runnable accepts specified as a Pydantic model.
Get a JSON schema that represents the config of the Runnable.
Return a list of prompts used by this Runnable.
Pipe Runnable objects.
Pick keys from the output dict of this Runnable.
Merge the Dict input with the output produced by the mapping argument.
Run invoke in parallel on a list of inputs.
Run ainvoke in parallel on a list of inputs.
Stream all output from a Runnable, as reported to the callback system.
Generate a stream of events.
Bind arguments to a Runnable, returning a new Runnable.
Bind lifecycle listeners to a Runnable, returning a new Runnable.
Bind async lifecycle listeners to a Runnable.
Bind input and output types to a Runnable, returning a new Runnable.
Create a new Runnable that retries the original Runnable on exceptions.
Map a function to multiple iterables.
Add fallbacks to a Runnable, returning a new Runnable.
Create a BaseTool from a Runnable.
