class AzionVectorStoreExample usage:
// Initialize the vector store
const vectorStore = new AzionVectorStore(embeddings, {
dbName: "mydb",
tableName: "documents"
});
// Setup database with hybrid search and metadata columns
await vectorStore.setupDatabase({
columns: ["topic", "language"],
mode: "hybrid"
});
// OR: Initialize using the static create method
const vectorStore = await AzionVectorStore.initialize(embeddings, {
dbName: "mydb",
tableName: "documents"
}, {
columns: ["topic", "language"],
mode: "hybrid"
});
By default, the columns are not expanded, meaning that the metadata is stored in a single column:
// Setup database with hybrid search and metadata columns
await vectorStore.setupDatabase({
columns: ["*"],
mode: "hybrid"
});
// Add documents to the vector store
await vectorStore.addDocuments([
new Document({
pageContent: "Australia is known for its unique wildlife",
metadata: { topic: "nature", language: "en" }
})
]);
// Perform similarity search
const results = await vectorStore.similaritySearch(
"coral reefs in Australia",
2, // Return top 2 results
{ filter: [{ operator: "=", column: "topic", string: "biology" }] } // Optional AzionFilter
);
// Perform full text search
const ftResults = await vectorStore.fullTextSearch(
"Sydney Opera House",
1, // Return top result
{ filter: [{ operator: "=", column: "language", string: "en" }] } // Optional AzionFilter
);Name of the database to search
The embeddings generated for the input texts.
Whether the metadata is contained in a single column or multiple columns
Returns a string representing the type of vector store, which subclasses must implement to identify their specific vector storage type.
Adds an array of documents to the collection. The documents are first
converted to vectors using the embedDocuments method of the
embeddings instance.
Adds an array of vectors and corresponding documents to the collection. The vectors and documents are batch inserted into the database.
Creates a VectorStoreRetriever instance with flexible configuration options.
Performs a full-text search on the vector store and returns the top 'k' similar documents.
Performs a hybrid search on the vector store and returns the top 'k' similar documents.
Performs a similarity search on the vector store and returns the top 'k' similar documents.
Deletes rows from the Cassandra table that match the specified WHERE clause conditions.
Return documents selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to the query AND diversity among selected documents.
Sets up the database and tables.
Searches for documents similar to a text query by embedding the query and performing a similarity search on the resulting vector.
Performs a similarity search on the vectors in the collection. The search is performed using the given query vector and returns the top k most similar vectors along with their corresponding documents and similarity scores.
Searches for documents similar to a text query by embedding the query, and returns results with similarity scores.
Creates an instance of AnalyticDBVectorStore from an array of texts
and corresponding metadata. The texts are first converted to Document
instances before being added to the collection.
Initializes the llama_cpp model for usage in the chat models wrapper.
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.