Class ZepVectorStore

ZepVectorStore is a VectorStore implementation that uses the Zep long-term memory store as a backend.

If the collection does not exist, it will be created automatically.

Requires zep-js to be installed:

npm install @getzep/zep-js

Hierarchy

  • VectorStore
    • ZepVectorStore

Constructors

constructor

Properties

FilterType client collection embeddings

Methods

addDocuments addVectors asRetriever delete maxMarginalRelevanceSearch similaritySearch similaritySearchVectorWithScore similaritySearchWithScore toJSON toJSONNotImplemented fromDocuments fromTexts

Constructors

constructor

Properties

FilterType

FilterType: string | object

Defines the filter type used in search and delete operations. Can be an object for structured conditions or a string for simpler filtering.

client

client: ZepClient

The ZepClient instance used to interact with Zep's API.

collection

collection: DocumentCollection

The Zep document collection.

embeddings

embeddings: EmbeddingsInterface

Embeddings interface for generating vector embeddings from text queries, enabling vector-based similarity searches.

Methods

addDocuments

  • addDocuments(documents): Promise<string[]>

  • Adds documents to the collection. The documents are first embedded into vectors using the provided embedding model.

    Parameters

    • documents: Document<Record<string, any>>[]

      The documents to add.

    Returns Promise<string[]>

    • A promise that resolves with the UUIDs of the added documents.

addVectors

  • addVectors(vectors, documents): Promise<string[]>

  • Adds vectors and corresponding documents to the collection.

    Parameters

    • vectors: number[][]

      The vectors to add.

    • documents: Document<Record<string, any>>[]

      The corresponding documents to add.

    Returns Promise<string[]>

    • A promise that resolves with the UUIDs of the added documents.

asRetriever

  • asRetriever(kOrFields?, filter?, callbacks?, tags?, metadata?, verbose?): VectorStoreRetriever<ZepVectorStore>

  • Creates a VectorStoreRetriever instance with flexible configuration options.

    Parameters

    • OptionalkOrFields: number | Partial<VectorStoreRetrieverInput<ZepVectorStore>>

      If a number is provided, it sets the k parameter (number of items to retrieve).

      • If an object is provided, it should contain various configuration options.
    • Optionalfilter: string | object

      Optional filter criteria to limit the items retrieved based on the specified filter type.

    • Optionalcallbacks: Callbacks

      Optional callbacks that may be triggered at specific stages of the retrieval process.

    • Optionaltags: string[]

      Tags to categorize or label the VectorStoreRetriever. Defaults to an empty array if not provided.

    • Optionalmetadata: Record<string, unknown>

      Additional metadata as key-value pairs to add contextual information for the retrieval process.

    • Optionalverbose: boolean

      If true, enables detailed logging for the retrieval process. Defaults to false.

    Returns VectorStoreRetriever<ZepVectorStore>

    • A configured VectorStoreRetriever instance based on the provided parameters.

    Example

    Basic usage with a k value:

    const retriever = myVectorStore.asRetriever(5);

    Usage with a configuration object:

    const retriever = myVectorStore.asRetriever({
      k: 10,
      filter: myFilter,
      tags: ['example', 'test'],
      verbose: true,
      searchType: 'mmr',
      searchKwargs: { alpha: 0.5 },
    });

delete

maxMarginalRelevanceSearch

  • maxMarginalRelevanceSearch(query, options): Promise<Document<Record<string, any>>[]>

  • Return documents selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to the query AND diversity among selected documents.

    Parameters

    • query: string

      Text to look up documents similar to.

    • options: MaxMarginalRelevanceSearchOptions<string | object>

    Returns Promise<Document<Record<string, any>>[]>

    • List of documents selected by maximal marginal relevance.

similaritySearch

  • similaritySearch(query, k?, filter?, _callbacks?): Promise<Document<Record<string, any>>[]>

  • Performs a similarity search on the Zep collection.

    Parameters

    • query: string

      The query string to search for.

    • Optionalk: number = 4

      The number of results to return. Defaults to 4.

    • Optionalfilter: string | object = undefined

      An optional set of JSONPath filters to apply to the search.

    • Optional_callbacks: Callbacks = undefined

      Optional callbacks. Currently not implemented.

    Returns Promise<Document<Record<string, any>>[]>

    • A promise that resolves to an array of Documents that are similar to the query.

    Async

similaritySearchVectorWithScore

  • similaritySearchVectorWithScore(query, k, filter?): Promise<[Document<Record<string, any>>, number][]>

  • Performs a similarity search in the collection and returns the results with their scores.

    Parameters

    • query: number[]

      The query vector.

    • k: number

      The number of results to return.

    • Optionalfilter: Record<string, unknown>

      The filter to apply to the search. Zep only supports Record<string, unknown> as filter.

    Returns Promise<[Document<Record<string, any>>, number][]>

    • A promise that resolves with the search results and their scores.

similaritySearchWithScore

  • similaritySearchWithScore(query, k?, filter?, _callbacks?): Promise<[Document<Record<string, any>>, number][]>

  • Searches for documents similar to a text query by embedding the query, and returns results with similarity scores.

    Parameters

    • query: string

      Text query for finding similar documents.

    • k: number = 4

      Number of similar results to return. Defaults to 4.

    • filter: undefined | Record<string, unknown> = undefined

      Optional filter based on FilterType.

    • _callbacks: undefined = undefined

      Optional callbacks for monitoring search progress

    Returns Promise<[Document<Record<string, any>>, number][]>

    A promise resolving to an array of tuples, each containing a document and its similarity score.

toJSON

  • toJSON(): Serialized

  • Returns Serialized

toJSONNotImplemented

StaticfromDocuments

  • fromDocuments(docs, embeddings, zepConfig): Promise<ZepVectorStore>

  • Creates a new ZepVectorStore instance from an array of Documents. Each Document is added to a Zep collection.

    Parameters

    • docs: Document<Record<string, any>>[]

      The Documents to add.

    • embeddings: EmbeddingsInterface

      The embeddings to use for vectorizing the Document contents.

    • zepConfig: IZepConfig

      The configuration object for the Zep API.

    Returns Promise<ZepVectorStore>

    • A promise that resolves with the new ZepVectorStore instance.

StaticfromTexts

  • fromTexts(texts, metadatas, embeddings, zepConfig): Promise<ZepVectorStore>

  • Creates a new ZepVectorStore instance from an array of texts. Each text is converted into a Document and added to the collection.

    Parameters

    • texts: string[]

      The texts to convert into Documents.

    • metadatas: object | object[]

      The metadata to associate with each Document. If an array is provided, each element is associated with the corresponding Document. If an object is provided, it is associated with all Documents.

    • embeddings: EmbeddingsInterface

      The embeddings to use for vectorizing the texts.

    • zepConfig: IZepConfig

      The configuration object for the Zep API.

    Returns Promise<ZepVectorStore>

    • A promise that resolves with the new ZepVectorStore instance.

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