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    Class FakeVectorStore

    Class that extends VectorStore to store vectors in memory. Provides methods for adding documents, performing similarity searches, and creating instances from texts, documents, or an existing index.

    Hierarchy (View Summary)

    Index

    Constructors

    constructor

    Properties

    embeddings FilterType lc_kwargs lc_serializable memoryVectors similarity

    Accessors

    lc_aliases lc_attributes lc_id lc_secrets lc_serializable_keys

    Methods

    _vectorstoreType addDocuments addVectors asRetriever delete maxMarginalRelevanceSearch? similaritySearch similaritySearchVectorWithScore similaritySearchWithScore toJSON toJSONNotImplemented fromDocuments fromExistingIndex fromTexts lc_name

    Constructors

    Properties

    embeddings: EmbeddingsInterface

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

    FilterType: (doc: Document) => boolean

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

    lc_kwargs: SerializedFields
    lc_serializable: boolean = false
    memoryVectors: MemoryVector[] = []
    similarity: (a: number[], b: number[]) => number

    Type Declaration

      • (a: number[], b: number[]): number

      • Returns the average of cosine distances between vectors a and b

        Parameters

        • a: number[]

          first vector

        • b: number[]

          second vector

        Returns number

    Accessors

    • get lc_aliases(): undefined | { [key: string]: string }

      A 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.

      Returns undefined | { [key: string]: string }

    • get lc_attributes(): undefined | SerializedFields

      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.

      Returns undefined | SerializedFields

    • get lc_id(): string[]

      The final serialized identifier for the module.

      Returns string[]

    • get lc_secrets(): undefined | { [key: string]: string }

      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.

      Returns undefined | { [key: string]: string }

    • get lc_serializable_keys(): undefined | string[]

      A manual list of keys that should be serialized. If not overridden, all fields passed into the constructor will be serialized.

      Returns undefined | string[]

    Methods

    Abstract_vectorstoreType

    • _vectorstoreType(): string

      Returns a string representing the type of vector store, which subclasses must implement to identify their specific vector storage type.

      Returns string

      A string indicating the vector store type.

    • Method to add documents to the memory vector store. It extracts the text from each document, generates embeddings for them, and adds the resulting vectors to the store.

      Parameters

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

        Array of Document instances to be added to the store.

      Returns Promise<void>

      Promise that resolves when all documents have been added.

    • Method to add vectors to the memory vector store. It creates MemoryVector instances for each vector and document pair and adds them to the store.

      Parameters

      • vectors: number[][]

        Array of vectors to be added to the store.

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

        Array of Document instances corresponding to the vectors.

      Returns Promise<void>

      Promise that resolves when all vectors have been added.

    • Creates a VectorStoreRetriever instance with flexible configuration options.

      Parameters

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

        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: (doc: Document) => boolean

        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<FakeVectorStore>

      • A configured VectorStoreRetriever instance based on the provided parameters.

      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 },
      });

    • Deletes documents from the vector store based on the specified parameters.

      Parameters

      • Optional_params: Record<string, any>

        Flexible key-value pairs defining conditions for document deletion.

      Returns Promise<void>

      A promise that resolves once the deletion is complete.

    • 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<(doc: Document) => boolean>

        Options for configuring a maximal marginal relevance (MMR) search.

        MMR search optimizes for both similarity to the query and diversity among the results, balancing the retrieval of relevant documents with variation in the content returned.

        Fields:

        • fetchK (optional): The initial number of documents to retrieve from the vector store before applying the MMR algorithm. This larger set provides a pool of documents from which the algorithm can select the most diverse results based on relevance to the query.

        • filter (optional): A filter of type FilterType to refine the search results, allowing additional conditions to target specific subsets of documents.

        • k: The number of documents to return in the final results. This is the primary count of documents that are most relevant to the query.

        • lambda (optional): A value between 0 and 1 that determines the balance between relevance and diversity:

          • A lambda of 0 emphasizes diversity, maximizing content variation.
          • A lambda of 1 emphasizes similarity to the query, focusing on relevance. Values between 0 and 1 provide a mix of relevance and diversity.
        • OptionalfetchK?: number
        • Optionalfilter?: FilterType
        • k: number
        • Optionallambda?: number
      • _callbacks: undefined | Callbacks

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

      • List of documents selected by maximal marginal relevance.

    • Searches for documents similar to a text query by embedding the query and performing a similarity search on the resulting vector.

      Parameters

      • query: string

        Text query for finding similar documents.

      • k: number = 4

        Number of similar results to return. Defaults to 4.

      • filter: undefined | ((doc: Document) => boolean) = undefined

        Optional filter based on FilterType.

      • _callbacks: undefined | Callbacks = undefined

        Optional callbacks for monitoring search progress

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

      A promise resolving to an array of DocumentInterface instances representing similar documents.

    • Method to perform a similarity search in the memory vector store. It calculates the similarity between the query vector and each vector in the store, sorts the results by similarity, and returns the top k results along with their scores.

      Parameters

      • query: number[]

        Query vector to compare against the vectors in the store.

      • k: number

        Number of top results to return.

      • Optionalfilter: (doc: Document) => boolean

        Optional filter function to apply to the vectors before performing the search.

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

      Promise that resolves with an array of tuples, each containing a Document and its similarity score.

    • 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 | ((doc: Document) => boolean) = undefined

        Optional filter based on FilterType.

      • _callbacks: undefined | Callbacks = undefined

        Optional callbacks for monitoring search progress

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

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

    • Static method to create a FakeVectorStore instance from an array of Document instances. It adds the documents to the store.

      Parameters

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

        Array of Document instances to be added to the store.

      • embeddings: EmbeddingsInterface

        Embeddings instance used to generate embeddings for the documents.

      • OptionaldbConfig: FakeVectorStoreArgs

        Optional FakeVectorStoreArgs to configure the FakeVectorStore instance.

      Returns Promise<FakeVectorStore>

      Promise that resolves with a new FakeVectorStore instance.

    • Static method to create a FakeVectorStore instance from an existing index. It creates a new FakeVectorStore instance without adding any documents or vectors.

      Parameters

      • embeddings: EmbeddingsInterface

        Embeddings instance used to generate embeddings for the documents.

      • OptionaldbConfig: FakeVectorStoreArgs

        Optional FakeVectorStoreArgs to configure the FakeVectorStore instance.

      Returns Promise<FakeVectorStore>

      Promise that resolves with a new FakeVectorStore instance.

    • Static method to create a FakeVectorStore instance from an array of texts. It creates a Document for each text and metadata pair, and adds them to the store.

      Parameters

      • texts: string[]

        Array of texts to be added to the store.

      • metadatas: object | object[]

        Array or single object of metadata corresponding to the texts.

      • embeddings: EmbeddingsInterface

        Embeddings instance used to generate embeddings for the texts.

      • OptionaldbConfig: FakeVectorStoreArgs

        Optional FakeVectorStoreArgs to configure the FakeVectorStore instance.

      Returns Promise<FakeVectorStore>

      Promise that resolves with a new FakeVectorStore instance.

    • The name of the serializable. Override to provide an alias or to preserve the serialized module name in minified environments.

      Implemented as a static method to support loading logic.

      Returns string

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