Type of vector store implementing VectorStoreInterface.
Initializes a new instance of VectorStoreRetriever with the specified configuration.
This constructor configures the retriever to interact with a given VectorStore
and supports different retrieval strategies, including similarity search and maximal
marginal relevance (MMR) search. Various options allow customization of the number
of documents retrieved per query, filtering based on conditions, and fine-tuning
MMR-specific parameters.
Configuration options for setting up the retriever:
vectorStore (required): The VectorStore instance implementing VectorStoreInterface
that will be used to store and retrieve document embeddings. This is the core component
of the retriever, enabling vector-based similarity and MMR searches.
k (optional): Specifies the number of documents to retrieve per search query. If not
provided, defaults to 4. This count determines the number of most relevant documents returned
for each search operation, balancing performance with comprehensiveness.
searchType (optional): Defines the search approach used by the retriever, allowing for
flexibility between two methods:
"similarity" (default): A similarity-based search, retrieving documents with high vector
similarity to the query. This type prioritizes relevance and is often used when diversity
among results is less critical."mmr": Maximal Marginal Relevance search, which combines relevance with diversity. MMR
is useful for scenarios where varied content is essential, as it selects results that
both match the query and introduce content diversity.filter (optional): A filter of type FilterType, defined by the vector store, that allows
for refined and targeted search results. This filter applies specified conditions to limit
which documents are eligible for retrieval, offering control over the scope of results.
searchKwargs (optional, applicable only if searchType is "mmr"): Additional settings
for configuring MMR-specific behavior. These parameters allow further tuning of the MMR
search process:
fetchK: The initial number of documents fetched from the vector store before the MMR
algorithm is applied. Fetching a larger set enables the algorithm to select a more
diverse subset of documents.lambda: A parameter controlling the relevance-diversity balance, where 0 emphasizes
diversity and 1 prioritizes relevance. Intermediate values provide a blend of the two,
allowing customization based on the importance of content variety relative to query relevance.OptionalcallbacksOptional callbacks to handle various events in the retrieval process.
OptionalfilterOptional filter applied to search results, defined by the FilterType of the vector store.
Allows for refined, targeted results by restricting the returned documents based
on specified filter criteria.
Specifies the number of documents to retrieve for each search query. Defaults to 4 if not specified, providing a basic result count for similarity or MMR searches.
OptionalmetadataMetadata to provide additional context or information about the retrieval operation.
OptionalnameOptionalsearchAdditional options specific to maximal marginal relevance (MMR) search, applicable
only if searchType is set to "mmr".
Includes:
fetchK: The initial number of documents fetched before applying the MMR algorithm,
allowing for a larger selection from which to choose the most diverse results.lambda: A parameter between 0 and 1 to adjust the relevance-diversity balance,
where 0 prioritizes diversity and 1 prioritizes relevance.Determines the type of search operation to perform on the vector store.
"similarity" (default): Conducts a similarity search based purely on vector similarity
to the query."mmr": Executes a maximal marginal relevance (MMR) search, balancing relevance and
diversity in the retrieved results.OptionaltagsTags to label or categorize the retrieval operation.
The instance of VectorStore used for storing and retrieving document embeddings.
This vector store must implement the VectorStoreInterface to be compatible
with the retriever’s operations.
OptionalverboseIf set to true, enables verbose logging for the retrieval process.
Adds an array of documents to the vector store, embedding them as part of the storage process.
This method delegates document embedding and storage to the addDocuments
method of the underlying vector store.
An array of documents to embed and add to the vector store.
Optionaloptions: AddDocumentOptionsOptional settings to customize document addition.
A promise that resolves to an array of document IDs or void,
depending on the vector store's implementation.
Convert a runnable to a tool. Return a new instance of RunnableToolLike
which contains the runnable, name, description and schema.
Optionaldescription?: stringThe description of the tool. Falls back to the description on the Zod schema if not provided, or undefined if neither are provided.
Optionalname?: stringThe name of the tool. If not provided, it will default to the name of the runnable.
The Zod schema for the input of the tool. Infers the Zod type from the input type of the runnable.
An instance of RunnableToolLike which is a runnable that can be used as a tool.
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.
Optionaloptions: Partial<RunnableConfig<Record<string, any>>> | Partial<RunnableConfig<Record<string, any>>>[]Either a single call options object to apply to each batch call or an array for each call.
OptionalbatchOptions: RunnableBatchOptions & { An array of RunOutputs, or mixed RunOutputs and errors if batchOptions.returnExceptions is set
Optionaloptions: Partial<RunnableConfig<Record<string, any>>> | Partial<RunnableConfig<Record<string, any>>>[]OptionalbatchOptions: RunnableBatchOptions & { Optionaloptions: Partial<RunnableConfig<Record<string, any>>> | Partial<RunnableConfig<Record<string, any>>>[]OptionalbatchOptions: RunnableBatchOptionsBind arguments to a Runnable, returning a new Runnable.
A new RunnableBinding that, when invoked, will apply the bound args.
Optional_: RunnableConfig<Record<string, any>>The query string to retrieve relevant documents for.
Optionalconfig: Callbacks | BaseCallbackConfigOptional configuration object for the retrieval process.
A promise that resolves to an array of Document objects.
Use .invoke() instead. Will be removed in 0.3.0.
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.
Executes a retrieval operation.
The query string used to search for relevant documents.
Optionaloptions: RunnableConfig<Record<string, any>>(optional) Configuration options for the retrieval run, which may include callbacks, tags, and metadata.
A promise that resolves to an array of DocumentInterface instances
representing the most relevant documents to the query.
Return 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.
Optionaloptions: Partial<RunnableConfig<Record<string, any>>>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.
ATTENTION This reference table is for the V2 version of the schema.
+----------------------+-----------------------------+------------------------------------------+
| event | input | output/chunk |
+======================+=============================+==========================================+
| on_chat_model_start | {"messages": BaseMessage[]} | |
+----------------------+-----------------------------+------------------------------------------+
| on_chat_model_stream | | AIMessageChunk("hello") |
+----------------------+-----------------------------+------------------------------------------+
| on_chat_model_end | {"messages": BaseMessage[]} | AIMessageChunk("hello world") |
+----------------------+-----------------------------+------------------------------------------+
| on_llm_start | {'input': 'hello'} | |
+----------------------+-----------------------------+------------------------------------------+
| on_llm_stream | | 'Hello' |
+----------------------+-----------------------------+------------------------------------------+
| on_llm_end | 'Hello human!' | |
+----------------------+-----------------------------+------------------------------------------+
| on_chain_start | | |
+----------------------+-----------------------------+------------------------------------------+
| on_chain_stream | | "hello world!" |
+----------------------+-----------------------------+------------------------------------------+
| on_chain_end | [Document(...)] | "hello world!, goodbye world!" |
+----------------------+-----------------------------+------------------------------------------+
| on_tool_start | {"x": 1, "y": "2"} | |
+----------------------+-----------------------------+------------------------------------------+
| on_tool_end | | {"x": 1, "y": "2"} |
+----------------------+-----------------------------+------------------------------------------+
| on_retriever_start | {"query": "hello"} | |
+----------------------+-----------------------------+------------------------------------------+
| on_retriever_end | {"query": "hello"} | [Document(...), ..] |
+----------------------+-----------------------------+------------------------------------------+
| on_prompt_start | {"question": "hello"} | |
+----------------------+-----------------------------+------------------------------------------+
| on_prompt_end | {"question": "hello"} | ChatPromptValue(messages: BaseMessage[]) |
+----------------------+-----------------------------+------------------------------------------+
The "on_chain_*" events are the default for Runnables that don't fit one of the above categories.
In addition to the standard events above, users can also dispatch custom events.
Custom events will be only be surfaced with in the v2 version of the API!
A custom event has following format:
+-----------+------+------------------------------------------------------------+
| Attribute | Type | Description |
+===========+======+============================================================+
| name | str | A user defined name for the event. |
+-----------+------+------------------------------------------------------------+
| data | Any | The data associated with the event. This can be anything. |
+-----------+------+------------------------------------------------------------+
Here's an example:
import { RunnableLambda } from "@langchain/core/runnables";
import { dispatchCustomEvent } from "@langchain/core/callbacks/dispatch";
// Use this import for web environments that don't support "async_hooks"
// and manually pass config to child runs.
// import { dispatchCustomEvent } from "@langchain/core/callbacks/dispatch/web";
const slowThing = RunnableLambda.from(async (someInput: string) => {
// Placeholder for some slow operation
await new Promise((resolve) => setTimeout(resolve, 100));
await dispatchCustomEvent("progress_event", {
message: "Finished step 1 of 2",
});
await new Promise((resolve) => setTimeout(resolve, 100));
return "Done";
});
const eventStream = await slowThing.streamEvents("hello world", {
version: "v2",
});
for await (const event of eventStream) {
if (event.event === "on_custom_event") {
console.log(event);
}
}
OptionalstreamOptions: Omit<EventStreamCallbackHandlerInput, "autoClose">OptionalstreamOptions: Omit<EventStreamCallbackHandlerInput, "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.
Optionaloptions: Partial<RunnableConfig<Record<string, any>>>OptionalstreamOptions: 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.
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.
OptionalonCalled after the runnable finishes running, with the Run object.
Optionalconfig: RunnableConfig<Record<string, any>>OptionalonCalled if the runnable throws an error, with the Run object.
Optionalconfig: RunnableConfig<Record<string, any>>OptionalonCalled before the runnable starts running, with the Run object.
Optionalconfig: RunnableConfig<Record<string, any>>Add retry logic to an existing runnable.
Optionalfields: { OptionalonOptionalstopA new RunnableRetry that, when invoked, will retry according to the parameters.
Staticis
Class for retrieving documents from a
VectorStorebased on vector similarity or maximal marginal relevance (MMR).VectorStoreRetrieverextendsBaseRetriever, implementing methods for adding documents to the underlying vector store and performing document retrieval with optional configurations.VectorStoreRetriever
Implements
VectorStoreRetrieverInterface