Class ConvexVectorStore<DataModel, TableName, IndexName, TextFieldName, EmbeddingFieldName, MetadataFieldName, InsertMutation, GetQuery>

Class that is a wrapper around Convex storage and vector search. It is used to insert embeddings in Convex documents with a vector search index, and perform a vector search on them.

ConvexVectorStore does NOT implement maxMarginalRelevanceSearch.

Type Parameters

  • DataModel extends GenericDataModel
  • TableName extends TableNamesInDataModel<DataModel>
  • IndexName extends VectorIndexNames<NamedTableInfo<DataModel, TableName>>
  • TextFieldName extends FieldPaths<NamedTableInfo<DataModel, TableName>>
  • EmbeddingFieldName extends FieldPaths<NamedTableInfo<DataModel, TableName>>
  • MetadataFieldName extends FieldPaths<NamedTableInfo<DataModel, TableName>>
  • InsertMutation extends FunctionReference<"mutation", "internal", {
        document: object;
        table: string;
    }>
  • GetQuery extends FunctionReference<"query", "internal", {
        id: string;
    }, object | null>

Hierarchy

  • VectorStore
    • ConvexVectorStore

Constructors

constructor

Properties

FilterType embeddings

Methods

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

Constructors

constructor

Properties

FilterType

FilterType: {
    filter?: ((q: VectorFilterBuilder<GenericDocument, NamedVectorIndex<NamedTableInfo<DataModel, TableName>, IndexName>>) => FilterExpression<boolean>);
    includeEmbeddings?: boolean;
}

Type that defines the filter used in the similaritySearchVectorWithScore and maxMarginalRelevanceSearch methods. It includes limit, filter and a flag to include embeddings.

embeddings

embeddings: EmbeddingsInterface

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

Methods

addDocuments

  • addDocuments(documents): Promise<void>

  • Add documents to the Convex table. It first converts the documents to vectors using the embeddings and then calls the addVectors method.

    Parameters

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

      Documents to be added.

    Returns Promise<void>

    Promise that resolves when the documents have been added.

addVectors

  • addVectors(vectors, documents): Promise<void>

  • Add vectors and their corresponding documents to the Convex table.

    Parameters

    • vectors: number[][]

      Vectors to be added.

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

      Corresponding documents to be added.

    Returns Promise<void>

    Promise that resolves when the vectors and documents have been added.

asRetriever

  • asRetriever(kOrFields?, filter?, callbacks?, tags?, metadata?, verbose?): VectorStoreRetriever<ConvexVectorStore<DataModel, TableName, IndexName, TextFieldName, EmbeddingFieldName, MetadataFieldName, InsertMutation, GetQuery>>

  • Creates a VectorStoreRetriever instance with flexible configuration options.

    Parameters

    • OptionalkOrFields: number | Partial<VectorStoreRetrieverInput<ConvexVectorStore<DataModel, TableName, IndexName, TextFieldName, EmbeddingFieldName, MetadataFieldName, InsertMutation, GetQuery>>>

      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: {
          filter?: ((q: VectorFilterBuilder<GenericDocument, NamedVectorIndex<NamedTableInfo<DataModel, TableName>, IndexName>>) => FilterExpression<boolean>);
          includeEmbeddings?: boolean;
      }

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

      • Optionalfilter?: ((q: VectorFilterBuilder<GenericDocument, NamedVectorIndex<NamedTableInfo<DataModel, TableName>, IndexName>>) => FilterExpression<boolean>)
          • (q): FilterExpression<boolean>

          • Parameters

            Returns FilterExpression<boolean>

      • OptionalincludeEmbeddings?: boolean
    • 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<ConvexVectorStore<DataModel, TableName, IndexName, TextFieldName, EmbeddingFieldName, MetadataFieldName, InsertMutation, GetQuery>>

    • 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

  • delete(_params?): Promise<void>

  • 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.

OptionalmaxMarginalRelevanceSearch

  • maxMarginalRelevanceSearch(query, options, _callbacks): Promise<DocumentInterface<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<{
          filter?: ((q: VectorFilterBuilder<GenericDocument, NamedVectorIndex<NamedTableInfo<DataModel, TableName>, IndexName>>) => FilterExpression<boolean>);
          includeEmbeddings?: boolean;
      }>
    • _callbacks: undefined | Callbacks

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

    • List of documents selected by maximal marginal relevance.

similaritySearch

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

  • 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.

    • Optionalk: number

      Number of similar results to return. Defaults to 4.

    • Optionalfilter: {
          filter?: ((q: VectorFilterBuilder<GenericDocument, NamedVectorIndex<NamedTableInfo<DataModel, TableName>, IndexName>>) => FilterExpression<boolean>);
          includeEmbeddings?: boolean;
      }

      Optional filter based on FilterType.

      • Optionalfilter?: ((q: VectorFilterBuilder<GenericDocument, NamedVectorIndex<NamedTableInfo<DataModel, TableName>, IndexName>>) => FilterExpression<boolean>)
          • (q): FilterExpression<boolean>

          • Parameters

            Returns FilterExpression<boolean>

      • OptionalincludeEmbeddings?: boolean
    • Optional_callbacks: Callbacks

      Optional callbacks for monitoring search progress

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

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

similaritySearchVectorWithScore

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

  • Similarity search on the vectors stored in the Convex table. It returns a list of documents and their corresponding similarity scores.

    Parameters

    • query: number[]

      Query vector for the similarity search.

    • k: number

      Number of nearest neighbors to return.

    • Optionalfilter: {
          filter?: ((q: VectorFilterBuilder<GenericDocument, NamedVectorIndex<NamedTableInfo<DataModel, TableName>, IndexName>>) => FilterExpression<boolean>);
          includeEmbeddings?: boolean;
      }

      Optional filter to be applied.

      • Optionalfilter?: ((q: VectorFilterBuilder<GenericDocument, NamedVectorIndex<NamedTableInfo<DataModel, TableName>, IndexName>>) => FilterExpression<boolean>)
          • (q): FilterExpression<boolean>

          • Parameters

            Returns FilterExpression<boolean>

      • OptionalincludeEmbeddings?: boolean

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

    Promise that resolves to a list of documents and their corresponding similarity scores.

similaritySearchWithScore

  • similaritySearchWithScore(query, k?, filter?, _callbacks?): Promise<[DocumentInterface<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.

    • Optionalk: number

      Number of similar results to return. Defaults to 4.

    • Optionalfilter: {
          filter?: ((q: VectorFilterBuilder<GenericDocument, NamedVectorIndex<NamedTableInfo<DataModel, TableName>, IndexName>>) => FilterExpression<boolean>);
          includeEmbeddings?: boolean;
      }

      Optional filter based on FilterType.

      • Optionalfilter?: ((q: VectorFilterBuilder<GenericDocument, NamedVectorIndex<NamedTableInfo<DataModel, TableName>, IndexName>>) => FilterExpression<boolean>)
          • (q): FilterExpression<boolean>

          • Parameters

            Returns FilterExpression<boolean>

      • OptionalincludeEmbeddings?: boolean
    • Optional_callbacks: Callbacks

      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.

toJSON

  • toJSON(): Serialized

  • Returns Serialized

toJSONNotImplemented

StaticfromDocuments

StaticfromTexts

Settings

Member Visibility

On This Page

Constructors
Properties
Methods