AzionVectorStore

Example 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
);