Optional callbacksOptional filterOptional metadataOptional nameOptional searchOptional tagsOptional verboseOverride the default addDocuments method to embed the documents twice,
once using the larger embeddings model, and then again using the default
embedding model linked to the vector store.
An array of documents to add to the vector store.
Optional options: AddDocumentOptionsAn optional object containing additional options for adding documents.
A promise that resolves to an array of the document IDs that were added to the vector store.
Assigns new fields to the dict output of this runnable. Returns a new runnable.
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<RunnableConfig> | Partial<RunnableConfig>[]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<RunnableConfig> | Partial<RunnableConfig>[]Optional batchOptions: RunnableBatchOptions & { Optional options: Partial<RunnableConfig> | Partial<RunnableConfig>[]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: BaseCallbackConfig | CallbacksOptional configuration object for the retrieval process.
A promise that resolves to an array of Document objects.
Optional options: RunnableConfigReturn a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input.
Pick keys from the dict output of this runnable. Returns a new runnable.
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<RunnableConfig>A readable stream that is also an iterable.
Generate a stream of events emitted by the internal steps of the runnable.
Use to create an iterator over StreamEvents that provide real-time information about the progress of the runnable, including StreamEvents from intermediate results.
A StreamEvent is a dictionary with the following schema:
event: string - Event names are of the format: on_[runnable_type]_(start|stream|end).name: string - The name of the runnable that generated the event.run_id: string - Randomly generated ID associated with the given execution of
the runnable that emitted the event. A child runnable that gets invoked as part of the execution of a
parent runnable is assigned its own unique ID.tags: string[] - The tags of the runnable that generated the event.metadata: Record<string, any> - The metadata of the runnable that generated the event.data: Record<string, any>Below is a table that illustrates some events that might be emitted by various chains. Metadata fields have been omitted from the table for brevity. Chain definitions have been included after the table.
| event | name | chunk | input | output |
|---|---|---|---|---|
| on_llm_start | [model name] | {'input': 'hello'} | ||
| on_llm_stream | [model name] | 'Hello' OR AIMessageChunk("hello") | ||
| on_llm_end | [model name] | 'Hello human!' | ||
| on_chain_start | format_docs | |||
| on_chain_stream | format_docs | "hello world!, goodbye world!" | ||
| on_chain_end | format_docs | [Document(...)] | "hello world!, goodbye world!" | |
| on_tool_start | some_tool | {"x": 1, "y": "2"} | ||
| on_tool_stream | some_tool | {"x": 1, "y": "2"} | ||
| on_tool_end | some_tool | {"x": 1, "y": "2"} | ||
| on_retriever_start | [retriever name] | {"query": "hello"} | ||
| on_retriever_chunk | [retriever name] | {documents: [...]} | ||
| on_retriever_end | [retriever name] | {"query": "hello"} | {documents: [...]} | |
| on_prompt_start | [template_name] | {"question": "hello"} | ||
| on_prompt_end | [template_name] | {"question": "hello"} | ChatPromptValue(messages: [SystemMessage, ...]) |
Optional streamOptions: Omit<LogStreamCallbackHandlerInput, "autoClose">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<RunnableConfig>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.
Bind lifecycle listeners to a Runnable, returning a new Runnable. The Run object contains information about the run, including its id, type, input, output, error, startTime, endTime, and any tags or metadata added to the run.
The object containing the callback functions.
Optional onCalled after the runnable finishes running, with the Run object.
Optional config: RunnableConfigOptional onCalled if the runnable throws an error, with the Run object.
Optional config: RunnableConfigOptional onCalled before the runnable starts running, with the Run object.
Optional config: RunnableConfigAdd 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
A retriever that uses two sets of embeddings to perform adaptive retrieval. Based off of the "Matryoshka embeddings: faster OpenAI vector search using Adaptive Retrieval" blog post https://supabase.com/blog/matryoshka-embeddings.
This class performs "Adaptive Retrieval" for searching text embeddings efficiently using the Matryoshka Representation Learning (MRL) technique. It retrieves documents similar to a query embedding in two steps:
First-pass: Uses a lower dimensional sub-vector from the MRL embedding for an initial, fast, but less accurate search.
Second-pass: Re-ranks the top results from the first pass using the full, high-dimensional embedding for higher accuracy.
This code implements MRL embeddings for efficient vector search by combining faster, lower-dimensional initial search with accurate, high-dimensional re-ranking.