Praison AI

Embeddings Module

Generate text embeddings for semantic search and similarity

Generate vector embeddings for semantic search, similarity matching, and RAG applications.

Diagram

Embedding Flow

📝 Text

🔢 Embed

📊 Vector

🔍 Search

📐 Similarity

Quick Start

  1. Generate Single Embedding
    typescript
    import { embed } from 'praisonai';

const result = embed("Hello, world!");

console.log(result.embedding);    // number[]
console.log(result.dimensions);   // 1536
console.log(result.model);        // 'text-embedding-3-small'
  2. Generate Batch Embeddings
    typescript
    import { embeddings } from 'praisonai';

const result = embeddings([
  "First document",
  "Second document",
  "Third document"
]);

console.log(result.embeddings.length);  // 3

Embedding Functions

Sync Functions

typescript
import { embed, embedding, embeddings } from 'praisonai';

// Single text
const single = embed("Hello world");

// Alias for embed
const single2 = embedding("Hello world");

// Multiple texts
const batch = embeddings(["Text 1", "Text 2", "Text 3"]);

Async Functions

typescript
import { aembed, aembedding, aembeddings } from 'praisonai';

// Single text (async)
const single = await aembed("Hello world");

// Alias for aembed
const single2 = await aembedding("Hello world");

// Multiple texts (async)
const batch = await aembeddings(["Text 1", "Text 2", "Text 3"]);

Configuration

typescript
import { setEmbeddingConfig, getDimensions } from 'praisonai';

// Set global config
setEmbeddingConfig({
  model: 'text-embedding-3-large',
  dimensions: 3072,
  apiKey: process.env.OPENAI_API_KEY
});

// Get dimensions for a model
const dims = getDimensions('text-embedding-3-small');  // 1536

Configuration Options

OptionTypeDefaultDescription
modelstring'text-embedding-3-small'Embedding model
dimensionsnumber1536Vector dimensions
apiKeystringundefinedAPI key (uses env var if not set)
baseUrlstringundefinedCustom API base URL

Supported Models

ModelDimensionsProvider
text-embedding-3-small1536OpenAI
text-embedding-3-large3072OpenAI
text-embedding-ada-0021536OpenAI
embed-english-v3.01024Cohere
embed-multilingual-v3.01024Cohere

Similarity Functions

typescript
import { cosineSimilarity, euclideanDistance, normalize } from 'praisonai';

const vec1 = [0.1, 0.2, 0.3];
const vec2 = [0.2, 0.3, 0.4];

// Cosine similarity (0-1, higher = more similar)
const similarity = cosineSimilarity(vec1, vec2);
console.log(similarity);  // ~0.99

// Euclidean distance (lower = more similar)
const distance = euclideanDistance(vec1, vec2);
console.log(distance);  // ~0.17

// Normalize vector to unit length
const normalized = normalize(vec1);

Result Types

EmbeddingResult

typescript
interface EmbeddingResult {
  embedding: number[];      // Vector array
  model: string;            // Model used
  dimensions: number;       // Vector dimensions
  usage?: {
    promptTokens: number;
    totalTokens: number;
  };
}

BatchEmbeddingResult

typescript
interface BatchEmbeddingResult {
  embeddings: number[][];   // Array of vectors
  model: string;            // Model used
  dimensions: number;       // Vector dimensions
  usage?: {
    promptTokens: number;
    totalTokens: number;
  };
}

Common Patterns

Semantic Search

typescript
import { embed, embeddings, cosineSimilarity } from 'praisonai';

// Index documents
const docs = ["AI is amazing", "Machine learning rocks", "TypeScript is typed"];
const docVectors = embeddings(docs);

// Search
const query = embed("artificial intelligence");
const scores = docVectors.embeddings.map((vec, i) => ({
  doc: docs[i],
  score: cosineSimilarity(query.embedding, vec)
}));

// Sort by similarity
scores.sort((a, b) => b.score - a.score);
console.log(scores[0]);  // Most similar document

Document Clustering

typescript
import { embeddings, cosineSimilarity } from 'praisonai';

const docs = ["Doc 1", "Doc 2", "Doc 3"];
const vectors = embeddings(docs);

// Build similarity matrix
const matrix = vectors.embeddings.map((v1, i) =>
  vectors.embeddings.map((v2, j) =>
    cosineSimilarity(v1, v2)
  )
);

RAG Context

typescript
import { embed, cosineSimilarity } from 'praisonai';

async function findRelevantContext(query: string, chunks: string[]) {
  const queryVec = await aembed(query);
  const chunkVecs = await aembeddings(chunks);

  return chunks
    .map((chunk, i) => ({
      chunk,
      score: cosineSimilarity(queryVec.embedding, chunkVecs.embeddings[i])
    }))
    .sort((a, b) => b.score - a.score)
    .slice(0, 3);  // Top 3 chunks
}

API Reference

Embeddings

Embeddings module reference

Best Practices

Use batch embeddings for multiple texts

Call embeddings() once with an array instead of calling embed() multiple times.

Cache embeddings for static content

Store embeddings in a database to avoid regenerating them for unchanged content.

Choose the right model

Use text-embedding-3-small for speed, text-embedding-3-large for accuracy.

Related

Knowledge Base

Build knowledge bases with embeddings

 RAG Agent

Retrieval-augmented generation