In the rapidly evolving field of AI, we’re seeing remarkable advancements in how machines understand and generate human-like responses. This article will explore the core concepts of Multi-Modal Language Models (MMLs), tokenization, Retrieval-Augmented Generation (RAG), and their implementation in JavaScript, especially with the LangChainJS framework.
What Are MMLs (Multi-Modal Language Models)?
Multi-Modal Language Models (MMLs) are AI systems that process and generate content across different types of data or modalities — such as text, images, audio, or video. Unlike traditional language models that only work with text, MMLs are designed to handle multiple forms of input simultaneously.
For instance, OpenAI’s GPT-4 Vision model can understand both text and images, enabling it to describe pictures, answer questions based on visual inputs, or even generate content like captions for images. This ability allows MMLs to perform tasks like image captioning, visual question answering, and multi-modal conversational AI, all powered by a unified transformer model.
MMLs typically consist of several components:
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Modality Encoders: These components transform raw input data (like images or audio) into feature vectors. For example, Vision Transformers (ViT) and CLIP are popular encoders for visual data.
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Input Projector: This step aligns non-text features with the language model’s embedding space, typically through attention mechanisms or linear transformations.
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Language Model (LLM): A large transformer model (such as GPT-3, GPT-4, or T5) that processes the combined input data.
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Output Projector/Generator: This is used to generate outputs in different modalities, such as converting text to images using models like Stable Diffusion.
By integrating these components, MMLs enable models to generate and process information in a more contextually rich way, offering a variety of real-world applications.
Understanding Tokenization in Language Models
Tokenization is a crucial step in preparing text for machine learning models. It refers to the process of breaking down raw text into smaller units called tokens, which the model can then process. These tokens could be entire words, subwords, or even individual characters.
For example, the sentence “Hello, world!” might be tokenized into ["Hello", ",", "world", "!"]. Once tokenized, each token is converted into a unique integer ID, which the model uses to look up an embedding vector. These vectors are then fed into the neural network.
Tokenization is necessary because models cannot process raw text directly. They require numerical representations to perform mathematical operations. Depending on the approach, tokenization can be done at different levels:
Word-level tokenization: Splitting text into words (simple but can lead to large vocabularies).
Subword tokenization (e.g., BPE, WordPiece): Splits words into smaller, meaningful chunks, which helps in handling out-of-vocabulary words.
Character-level tokenization: Breaks down text into individual characters, which ensures flexibility but results in longer input sequences.
Overall, tokenization serves as the foundation for a machine’s ability to understand human language, turning text into a machine-readable format.
How RAG (Retrieval-Augmented Generation) Enhances Language Models
Retrieval-Augmented Generation (RAG) is an innovative technique designed to augment the capabilities of traditional LLMs by incorporating external information during the generation process. In a RAG pipeline, the language model retrieves relevant documents or data from an external knowledge base and uses this information to generate more accurate and relevant responses.
The process works as follows:
Retriever Phase: The model uses the input (such as a question or prompt) to search a large external corpus of data (e.g., Wikipedia, databases, etc.) for relevant information. This retrieval can be based on semantic similarity between the query and the documents.
Generation Phase: Once the relevant data is retrieved, the language model incorporates this external information into its response generation process.
By combining a traditional LLM with external retrieval, RAG models can answer more complex and factual questions that might be outside the scope of the model’s pre-trained knowledge.
In short, RAG allows the language model to dynamically pull in additional, relevant information during the generation process, enhancing its ability to provide up-to-date, factually accurate answers.
How RAG Works Internally
At a high level, the process of using RAG in an AI pipeline includes these steps:
Query Processing: The input is transformed into a format suitable for retrieval.
External Retrieval: The model fetches relevant documents or information from a pre-built knowledge base, such as a vector store of document embeddings.
Context Integration: The retrieved documents are combined with the original input and provided to the language model for generating an answer.
Generation: The LLM processes the integrated context and generates a response based on both its internal knowledge and the external data retrieved.
This setup enables the LLM to provide highly relevant, contextually aware answers even when the information required is outside of its training data.
Implementing LLMs and RAG in JavaScript: LangChainJS
While Python is the go-to language for AI and machine learning, it’s possible to build LLM and RAG pipelines using JavaScript, especially for web applications. JavaScript frameworks like LangChainJS allow developers to create sophisticated AI models using JavaScript and Node.js.
LangChainJS is a powerful framework for building applications that integrate LLMs with external data sources. It supports the creation of RAG-like pipelines, enabling tasks such as document retrieval, vector search, and chain-of-thought reasoning — all within a JavaScript environment.
LangChainJS enables you to:
Load and Index Documents: You can load various types of documents (e.g., PDFs, text files, web pages) into a database, making them searchable.
Text Splitter: Documents can be split into smaller chunks, which are then embedded into a vector store for efficient retrieval.
Retriever: The retriever searches for relevant documents based on a query and passes them to the LLM for generating responses.
For instance, here's a basic LangChainJS example where you load and split a PDF, create a vector store, and search for relevant passages:
import { PDFLoader } from "langchain/document_loaders/fs/pdf";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { OpenAIEmbeddings } from "@langchain/openai";
// Load and split a PDF document
const loader = new PDFLoader("./data/lecture-notes.pdf");
const rawDocs = await loader.load();
const splitter = new RecursiveCharacterTextSplitter({ chunkSize: 128 });
const splitDocs = await splitter.splitDocuments(rawDocs);
// Create an embedding model and a memory vector store
const embeddings = new OpenAIEmbeddings();
const vectorstore = new MemoryVectorStore(embeddings);
// Embed and add the document chunks to the store
await vectorstore.addDocuments(splitDocs);
// Perform a similarity search on the vector store
const query = "What is deep learning?";
const retrievedDocs = await vectorstore.similaritySearch(query, 4);
const pageContents = retrievedDocs.map(doc => doc.pageContent);
console.log(pageContents);
// e.g. ["piece of research in machine learning", "using a learning algorithm", ...]
In this example, LangChainJS is used to load a PDF document, split it into smaller chunks, embed it into a vector store, and perform a similarity search. The results can then be fed into a language model for generating answers.
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The integration of Multi-Modal Language Models (MMLs), tokenization, and Retrieval-Augmented Generation (RAG) techniques is pushing the boundaries of AI. By enabling models to understand and generate text alongside other modalities (like images or audio), MMLs are driving more intelligent, context-aware AI systems.