Type that defines the filter used in the similaritySearchVectorWithScore and maxMarginalRelevanceSearch methods. It includes limit, filter and a flag to include embeddings.
Add documents to the Convex table. It first converts the documents to vectors using the embeddings and then calls the addVectors method.
Documents to be added.
Promise that resolves when the documents have been added.
Add vectors and their corresponding documents to the Convex table.
Vectors to be added.
Corresponding documents to be added.
Promise that resolves when the vectors and documents have been added.
OptionalkOrFields: number | Partial<VectorStoreRetrieverInput<ConvexVectorStore<DataModel, TableName, IndexName, TextFieldName, EmbeddingFieldName, MetadataFieldName, InsertMutation, GetQuery>>>Optionalfilter: { Optionalcallbacks: CallbacksOptionaltags: string[]Optionalmetadata: Record<string, unknown>Optionalverbose: booleanOptionalmaxReturn documents selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to the query AND diversity among selected documents.
Text to look up documents similar to.
Optionalk: numberOptionalfilter: { Optional_callbacks: CallbacksSimilarity search on the vectors stored in the Convex table. It returns a list of documents and their corresponding similarity scores.
Query vector for the similarity search.
Number of nearest neighbors to return.
Optionalfilter: { Optional filter to be applied.
Promise that resolves to a list of documents and their corresponding similarity scores.
Optionalk: numberOptionalfilter: { Optional_callbacks: CallbacksStaticfromStatic method to create an instance of ConvexVectorStore from a list of documents. It first converts the documents to vectors and then adds them to the Convex table.
List of documents to be converted to vectors.
Embeddings to be used for conversion.
Database configuration for Convex.
Promise that resolves to a new instance of ConvexVectorStore.
StaticfromStatic method to create an instance of ConvexVectorStore from a list of texts. It first converts the texts to vectors and then adds them to the Convex table.
List of texts to be converted to vectors.
Metadata for the texts.
Embeddings to be used for conversion.
Database configuration for Convex.
Promise that resolves to a new instance of ConvexVectorStore.
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.