Constructor to initialize the required fields
The fields required for initializing the TimeWeightedVectorStoreRetriever
Optional callbacksOptional metadataOptional tagsOptional verboseNOTE: When adding documents to a vector store, use addDocuments via retriever instead of directly to the vector store. This is because it is necessary to process the document in prepareDocuments.
The documents to add to vector store in the retriever
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.
Set the memory stream of documents.
The new memory stream of documents.
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 isGenerated using TypeDoc
TimeWeightedVectorStoreRetriever retrieves documents based on their time-weighted relevance. ref: https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/retrievers/time_weighted_retriever.py