A path to the module that contains the class, eg. ["langchain", "llms"] Usually should be the same as the entrypoint the class is exported from.
Optional callbacksOptional metadataOptional tagsOptional verboseProtected childProtected idProtected lc_Protected Optional childKProtected Optional parentKProtected Optional parentA map of aliases for constructor args. Keys are the attribute names, e.g. "foo". Values are the alias that will replace the key in serialization. This is used to eg. make argument names match Python.
A map of additional attributes to merge with constructor args. Keys are the attribute names, e.g. "foo". Values are the attribute values, which will be serialized. These attributes need to be accepted by the constructor as arguments.
The final serialized identifier for the module.
A map of secrets, which will be omitted from serialization. Keys are paths to the secret in constructor args, e.g. "foo.bar.baz". Values are the secret ids, which will be used when deserializing.
Internal method that handles batching and configuration for a runnable It takes a function, input values, and optional configuration, and returns a promise that resolves to the output values.
The function to be executed for each input value.
Optional options: Partial<BaseCallbackConfig>[]Optional runManagers: (undefined | CallbackManagerForChainRun)[]Optional batchOptions: RunnableBatchOptionsOptional options: Partial<BaseCallbackConfig & { Optional batchOptions: RunnableBatchOptionsA promise that resolves to the output values.
TODO: This should be an abstract method, but we'd like to avoid breaking changes to people currently using subclassed custom retrievers. Change it on next major release.
Default streaming implementation. Subclasses should override this method if they support streaming output.
Optional options: Partial<BaseCallbackConfig>Adds documents to the docstore and vectorstores. If a retriever is provided, it will be used to add documents instead of the vectorstore.
The documents to add
Optional config: { Optional addBoolean of whether to add documents to docstore.
This can be false if and only if ids are provided. You may want
to set this to False if the documents are already in the docstore
and you don't want to re-add them.
Optional ids?: string[]Optional list of ids for documents. If provided should be the same length as the list of documents. Can provided if parent documents are already in the document store and you don't want to re-add to the docstore. If not provided, random UUIDs will be used as ids.
Default implementation of batch, which calls invoke N times. Subclasses should override this method if they can batch more efficiently.
Array of inputs to each batch call.
Optional options: Partial<BaseCallbackConfig> | Partial<BaseCallbackConfig>[]Either a single call options object to apply to each batch call or an array for each call.
Optional batchOptions: RunnableBatchOptions & { An array of RunOutputs, or mixed RunOutputs and errors if batchOptions.returnExceptions is set
Optional options: Partial<BaseCallbackConfig> | Partial<BaseCallbackConfig>[]Optional batchOptions: RunnableBatchOptions & { Optional options: Partial<BaseCallbackConfig> | Partial<BaseCallbackConfig>[]Optional batchOptions: RunnableBatchOptionsBind arguments to a Runnable, returning a new Runnable.
A new RunnableBinding that, when invoked, will apply the bound args.
Main method used to retrieve relevant documents. It takes a query
string and an optional configuration object, and returns a promise that
resolves to an array of Document objects. This method handles the
retrieval process, including starting and ending callbacks, and error
handling.
The query string to retrieve relevant documents for.
Optional config: Callbacks | BaseCallbackConfigOptional configuration object for the retrieval process.
A promise that resolves to an array of Document objects.
Optional options: BaseCallbackConfigReturn a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input.
Create a new runnable sequence that runs each individual runnable in series, piping the output of one runnable into another runnable or runnable-like.
A runnable, function, or object whose values are functions or runnables.
A new runnable sequence.
Stream output in chunks.
Optional options: Partial<BaseCallbackConfig>A readable stream that is also an iterable.
Stream all output from a runnable, as reported to the callback system. This includes all inner runs of LLMs, Retrievers, Tools, etc. Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run. The jsonpatch ops can be applied in order to construct state.
Optional options: Partial<BaseCallbackConfig>Optional streamOptions: Omit<LogStreamCallbackHandlerInput, "autoClose">Default implementation of transform, which buffers input and then calls stream. Subclasses should override this method if they can start producing output while input is still being generated.
Bind config to a Runnable, returning a new Runnable.
New configuration parameters to attach to the new runnable.
A new RunnableBinding with a config matching what's passed.
Create a new runnable from the current one that will try invoking other passed fallback runnables if the initial invocation fails.
Other runnables to call if the runnable errors.
A new RunnableWithFallbacks.
Add retry logic to an existing runnable.
Optional fields: { Optional onOptional stopA new RunnableRetry that, when invoked, will retry according to the parameters.
Static isStatic lc_Protected _callOptional options: Partial<BaseCallbackConfig> & { Protected _getProtected _separateProtected _transformHelper method to transform an Iterator of Input values into an Iterator of
Output values, with callbacks.
Use this to implement stream() or transform() in Runnable subclasses.
Optional runManager: CallbackManagerForChainRunOptional options: Partial<BaseCallbackConfig>Optional options: BaseCallbackConfig & { Generated using TypeDoc
A type of document retriever that splits input documents into smaller chunks while separately storing and preserving the original documents. The small chunks are embedded, then on retrieval, the original "parent" documents are retrieved.
This strikes a balance between better targeted retrieval with small documents and the more context-rich larger documents.