Overview The Embeddings module provides base classes and interfaces for generating vector embeddings from text. Embeddings are used to convert text into numerical vectors for semantic search and retrieval.
BaseEmbedding Abstract base class that all embedding implementations extend.
import { BaseEmbedding } from "@llamaindex/core/embeddings" ;
Properties Number of texts to embed in a single batch
Metadata about the embedding model Show EmbeddingInfo Fields
Dimensionality of the embedding vectors
Maximum tokens the model can process
Tokenizer used by the model
Methods Get embedding for a single text string abstract getTextEmbedding ( text : string ): Promise < number [] >
Get embeddings for multiple texts (batch processing) getTextEmbeddings ( texts : string []): Promise < Array < number [] >>
Array of embedding vectors, one per input text
Get embeddings for texts with batching and progress tracking getTextEmbeddingsBatch ( texts : string [], options ?: BaseEmbeddingOptions ): Promise < Array < number [] >>
Embedding options Show BaseEmbeddingOptions
Whether to log progress to console
progressCallback
(current: number, total: number) => void
Callback function for progress updates
Get embedding for a query (supports multi-modal content) getQueryEmbedding ( query : MessageContentDetail ): Promise < number [] | null >
query
MessageContentDetail
required
Query content (text or multi-modal)
The query embedding vector, or null if text extraction fails
Calculate similarity between two embedding vectors similarity ( embedding1 : number [], embedding2 : number [], mode ?: SimilarityType ): number
mode
SimilarityType
default: "DEFAULT"
Similarity metric: DEFAULT, DOT_PRODUCT, EUCLIDEAN, or COSINE
Similarity score between the two vectors
Usage Examples Basic Embedding import { OpenAIEmbedding } from "@llamaindex/openai" ; const embedModel = new OpenAIEmbedding ({ model: "text-embedding-3-small" }); const embedding = await embedModel . getTextEmbedding ( "LlamaIndex is a data framework for LLM applications" ); console . log ( embedding . length ); // 1536
Batch Embedding with Progress const texts = [ "First document" , "Second document" , "Third document" ]; const embeddings = await embedModel . getTextEmbeddingsBatch ( texts , { logProgress: true , progressCallback : ( current , total ) => { console . log ( `Progress: ${ current } / ${ total } ` ); } }); console . log ( embeddings . length ); // 3
Embedding Nodes The BaseEmbedding class extends TransformComponent and can embed nodes directly:
import { Document } from "@llamaindex/core/schema" ; const documents = [ new Document ({ text: "Document 1" }), new Document ({ text: "Document 2" }) ]; // Transform adds embeddings to nodes const embeddedNodes = await embedModel . transform ( documents ); console . log ( embeddedNodes [ 0 ]. embedding ); // number[]
Similarity Calculation const embedding1 = await embedModel . getTextEmbedding ( "cat" ); const embedding2 = await embedModel . getTextEmbedding ( "dog" ); const embedding3 = await embedModel . getTextEmbedding ( "car" ); const similarity1 = embedModel . similarity ( embedding1 , embedding2 ); const similarity2 = embedModel . similarity ( embedding1 , embedding3 ); console . log ( similarity1 > similarity2 ); // true (cat and dog are more similar)
Similarity Types enum SimilarityType { DEFAULT = "cosine" , DOT_PRODUCT = "dot_product" , EUCLIDEAN = "euclidean" , COSINE = "cosine" }
Token Truncation Embeddings automatically truncate text that exceeds the model’s token limit:
const embedModel = new OpenAIEmbedding ({ model: "text-embedding-3-small" }); // Set embedding info with max tokens embedModel . embedInfo = { dimensions: 1536 , maxTokens: 8191 , tokenizer: Tokenizers . CL100K_BASE }; // Long text will be automatically truncated const longText = "..." . repeat ( 10000 ); const embedding = await embedModel . getTextEmbedding ( longText );