Defines the filter type used in search and delete operations. Can be an object for structured conditions or a string for simpler filtering.
Embeddings interface for generating vector embeddings from text queries, enabling vector-based similarity searches.
Optional
indexMethod to add an array of documents to the Tigris database.
An array of Document instances to be added to the Tigris database.
Optional
options: string[] | { Optional parameter that can either be an array of string IDs or an object with a property 'ids' that is an array of string IDs.
A Promise that resolves when the documents have been added to the Tigris database.
Method to add vectors to the Tigris database.
An array of vectors to be added to the Tigris database.
An array of Document instances corresponding to the vectors.
Optional
options: string[] | { Optional parameter that can either be an array of string IDs or an object with a property 'ids' that is an array of string IDs.
A Promise that resolves when the vectors have been added to the Tigris database.
Creates a VectorStoreRetriever
instance with flexible configuration options.
Optional
kOrFields: number | Partial<VectorStoreRetrieverInput<TigrisVectorStore>>If a number is provided, it sets the k
parameter (number of items to retrieve).
Optional
filter: string | objectOptional filter criteria to limit the items retrieved based on the specified filter type.
Optional
callbacks: CallbacksOptional callbacks that may be triggered at specific stages of the retrieval process.
Optional
tags: string[]Tags to categorize or label the VectorStoreRetriever
. Defaults to an empty array if not provided.
Optional
metadata: Record<string, unknown>Additional metadata as key-value pairs to add contextual information for the retrieval process.
Optional
verbose: booleanIf true
, enables detailed logging for the retrieval process. Defaults to false
.
VectorStoreRetriever
instance based on the provided parameters.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.
Searches for documents similar to a text query by embedding the query and performing a similarity search on the resulting vector.
Text query for finding similar documents.
Optional
k: numberNumber of similar results to return. Defaults to 4.
Optional
filter: string | objectOptional filter based on FilterType
.
Optional
_callbacks: CallbacksOptional callbacks for monitoring search progress
A promise resolving to an array of DocumentInterface
instances representing similar documents.
Method to perform a similarity search in the Tigris database and return the k most similar vectors along with their similarity scores.
The query vector.
The number of most similar vectors to return.
Optional
filter: objectOptional filter object to apply during the search.
A Promise that resolves to 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.
Text query for finding similar documents.
Optional
k: numberNumber of similar results to return. Defaults to 4.
Optional
filter: string | objectOptional filter based on FilterType
.
Optional
_callbacks: CallbacksOptional callbacks for monitoring search progress
A promise resolving to an array of tuples, each containing a document and its similarity score.
Static
fromStatic method to create a new instance of TigrisVectorStore from an array of Document instances.
An array of Document instances to be added to the Tigris database.
An instance of Embeddings to be used for embedding the documents.
An instance of TigrisLibArgs to be used for configuring the Tigris database.
A Promise that resolves to a new instance of TigrisVectorStore.
Static
fromStatic method to create a new instance of TigrisVectorStore from an existing index.
An instance of Embeddings to be used for embedding the documents.
An instance of TigrisLibArgs to be used for configuring the Tigris database.
A Promise that resolves to a new instance of TigrisVectorStore.
Static
fromStatic method to create a new instance of TigrisVectorStore from an array of texts.
An array of texts to be converted into Document instances and added to the Tigris database.
Either an array of metadata objects or a single metadata object to be associated with the texts.
An instance of Embeddings to be used for embedding the texts.
An instance of TigrisLibArgs to be used for configuring the Tigris database.
A Promise that resolves to a new instance of TigrisVectorStore.
Class for managing and operating vector search applications with Tigris, an open-source Serverless NoSQL Database and Search Platform.