Aquiles-RAG-TS-Client

JavaScript/TypeScript client to interact with the Aquiles-RAG service.

Installation

npm i @aquiles-ai/aquiles-rag-client

Features

• ✅ Full TypeScript support
• ✅ Asynchronous client based on Promises
• ✅ Automatic text chunking
• ✅ Support for custom metadata
• ✅ Reranking functions
• ✅ Vector index management

Basic Usage

import { AsyncAquilesRAG } from '@aquiles-ai/aquiles-rag-client';
import OpenAI from "openai";

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

async function getEmbedding(text: string): Promise<number[]> {
  if (!text) return [];

  const resp = await openai.embeddings.create({
    model: "text-embedding-3-small",
    input: text,
    encoding_format: "float",
  });

  const emb = (resp as any)?.data?.[0]?.embedding;
  if (!Array.isArray(emb)) throw new Error("Invalid embedding");

  return emb.map(Number);
}

async function main() {
  // Initialize the client
  const client = new AsyncAquilesRAG({
    host: 'http://127.0.0.1:5500',
    apiKey: 'dummy-api-key',
    timeout: 30000,
  });

  try {
    // Create an index
    console.log('Creating index...');
    await client.createIndex('my_index', 1536, 'FLOAT32', true);
    console.log('✓ Index created successfully');

    // Send document to RAG
    console.log('Sending document to RAG...');
    const text = `
      Artificial Intelligence is a field of computer science that focuses on 
      creating systems capable of performing tasks that normally require human intelligence.
      RAG (Retrieval Augmented Generation) is a technique that combines information retrieval
      with text generation to produce more accurate and grounded responses.
    `.repeat(5);

    const results = await client.sendRAG(
      getEmbedding,
      'my_index',
      'ai_document',
      text,
      {
        dtype: 'FLOAT32',
      }
    );

    console.log(`✓ Successfully sent ${results.length} chunks`);
    
    // Show results details
    results.forEach((result, idx) => {
      if (result.error) {
        console.log(`  Chunk ${idx + 1}: ❌ Error - ${result.error}`);
      } else {
        console.log(`  Chunk ${idx + 1}: ✓ ${result.status} - Key: ${result.key}`);
      }
    });

    // Perform a query
    console.log('\nPerforming query...');
    const queryEmbedding = await getEmbedding('RAG (Retrieval Augmented Generation)');

    const queryResults = await client.query('my_index', queryEmbedding, {
      topK: 5,
      cosineDistanceThreshold: 0.5,
    });

    console.log(`✓ Query results: Found ${queryResults.length} results`);
    
    if (queryResults.length === 0) {
      console.log('  No results found. Try adjusting the cosineDistanceThreshold.');
    } else {
      queryResults.forEach((result, idx) => {
        console.log(`\n  Result ${idx + 1}:`);
        console.log(`    Name: ${result.name_chunk}`);
        console.log(`    Score: ${result.score}`);
        console.log(`    Text: ${result.raw_text.substring(0, 150)}...`);
        
        if (result.metadata) {
          console.log(`    Metadata:`, JSON.stringify(result.metadata, null, 6));
        }
        
        if (result.embedding_model) {
          console.log(`    Model: ${result.embedding_model}`);
        }
      });
    }

    // Rerank results (if available)
    //if (queryResults.length > 0) {
    //  console.log('\nReranking results...');
    //  try {
    //    const reranked = await client.reranker('What is RAG?', queryResults);
    //    console.log(`✓ ${reranked.length} results reranked`);
        
    //    reranked.forEach((result, idx) => {
    //      console.log(`\n  Reranked ${idx + 1}:`);
    //      console.log(`    Score: ${result.score || 'N/A'}`);
    //      console.log(`    Content: ${JSON.stringify(result).substring(0, 100)}...`);
    //    });
    //  } catch (error) {
    //    console.log('  Reranking not available or failed:', (error as Error).message);
    //  }
    //}

    // Drop index (optional)
    // console.log('\nDropping index...');
    // const dropResult = await client.dropIndex('my_index', true);
    // console.log('✓ Index deleted:', dropResult);

  } catch (error) {
    console.error('❌ Error during execution:', error);
    if (error instanceof Error) {
      console.error('   Message:', error.message);
      console.error('   Stack:', error.stack);
    }
  }
}

main();

API

Constructor

new AsyncAquilesRAG(options?: AquilesRAGOptions)

Options:

• host: Base server URL (default: http://127.0.0.1:5500)
• apiKey: API key for authentication (optional)
• timeout: Timeout in milliseconds (default: 30000)

Methods

createIndex(indexName, embeddingsDim, dtype, deleteIfExists)

Creates a new vector index.

Parameters:

• indexName (string): Unique index name
• embeddingsDim (number): Embedding dimensionality (default: 768)
• dtype ('FLOAT32' | 'FLOAT64' | 'FLOAT16'): Data type (default: 'FLOAT32')
• deleteIfExists (boolean): Delete existing index (default: false)

query(index, embedding, options)

Queries the vector index.

Parameters:

• index (string): Index name
• embedding (number[]): Query embedding vector
• options (object):
  • dtype: Data type
  • topK: Number of results (default: 5)
  • cosineDistanceThreshold: Distance threshold (default: 0.6)
  • embeddingModel: Model identifier
  • metadata: Metadata filters

sendRAG(embeddingFunc, index, nameChunk, rawText, options)

Sends a document to RAG by splitting it into chunks.

Parameters:

• embeddingFunc (function): Function that generates embeddings
• index (string): Index name
• nameChunk (string): Base name for chunks
• rawText (string): Full text to process
• options (object):
  • dtype: Data type
  • embeddingModel: Model identifier
  • metadata: Document metadata

dropIndex(indexName, deleteDocs)

Deletes an index.

Parameters:

• indexName (string): Index name
• deleteDocs (boolean): Delete documents (default: false)

reranker(query, docs)

Reranks results by relevance.

Parameters:

• query (string): Original query
• docs (array | object): Results to rerank

Allowed Metadata

interface ChunkMetadata {
  author?: string;          // Document author
  language?: string;        // ISO 639-1 code (e.g., "EN", "ES")
  topics?: string[];        // List of topics
  source?: string;          // Content source
  created_at?: string;      // ISO 8601 date
  extra?: Record<string, any>; // Additional metadata
}

Utility Functions

chunkTextByWords(text, chunkSize)

Splits text into chunks by words.

import { chunkTextByWords } from '@aquiles-ai/aquiles-rag-client';

const chunks = chunkTextByWords('Your long text...', 600);

extractTextFromChunk(chunk)

Extracts text from a chunk with different formats.

import { extractTextFromChunk } from '@aquiles-ai/aquiles-rag-client';

const text = extractTextFromChunk(result);

Development

# Build
npm run build

# Run example
npm test

License

Apache 2.0