Type that defines the filter used in the similaritySearchVectorWithScore and maxMarginalRelevanceSearch methods. It includes limit, filter and a flag to include embeddings.
Optional filter?: ((q) => FilterExpression<boolean>)Optional includeA path to the module that contains the class, eg. ["langchain", "llms"] Usually should be the same as the entrypoint the class is exported from.
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.
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.
The final serialized identifier for the module.
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.
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.
Optional kOrFields: number | Partial<VectorStoreRetrieverInput<ConvexVectorStore<DataModel, TableName, IndexName, TextFieldName, EmbeddingFieldName, MetadataFieldName, InsertMutation, GetQuery>>>Optional filter: { Optional filter?: ((q) => FilterExpression<boolean>)Optional includeOptional callbacks: CallbacksOptional tags: string[]Optional metadata: Record<string, unknown>Optional verbose: booleanSimilarity 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.
Optional filter: { Optional filter to be applied.
Optional filter?: ((q) => FilterExpression<boolean>)Optional includePromise that resolves to a list of documents and their corresponding similarity scores.
Optional maxReturn 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.
Static fromStatic 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.
Static fromStatic 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.
Static lc_Generated using TypeDoc
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.