$0 Embeddings (OpenAI vs. free & open source)
TLDRThe video discusses the cheapest and best ways to generate embeddings, highlighting OpenAI's Text Embedding Ada 2 for its affordability and performance. It also explores open-source alternatives for self-hosting and offline use, covering various models' benefits for different tasks like clustering, classification, and search. The video introduces the concept of multimodal embeddings, emphasizing the potential of comparing different media types in the same vector space. It provides a technical overview of using Hugging Face's Inference API and transformers.js library for generating embeddings, discussing the importance of model selection, tokenization, and vector normalization.
Takeaways
- 📈 OpenAI's text embedding Ada 2 is cost-effective, charging 0.0001 per 1000 tokens as of June 13th, 2023.
- 🔍 There are open-source alternatives to OpenAI for generating embeddings, which can be self-hosted and used offline.
- 🤔 Different embedding models have various benefits depending on the use case, such as input size limits, dimension size, and the type of tasks they're designed for.
- 💡 Embeddings can be utilized for a variety of purposes beyond search, including clustering, classification, re-ranking, and retrieval.
- 📊 The video discusses the use of hugging face's mteb (massive text embedding Benchmark) leaderboard as a reference for comparing different embedding models.
- 🔢 The importance of understanding tokenization processes like BPE (byte pair encoding) and WordPiece when working with embeddings, as they affect the input sequence length and model performance.
- 🚀 The potential of multimodal models that generate embeddings for both text and images, enabling comparison between different media types within the same vector space.
- 🛠️ The practical demonstration of generating embeddings using TypeScript node.js and transformers.js, including handling API calls and local model deployment.
- 📚 A reference to brilliant.org for learning computer science and math, particularly neural networks, to understand the underlying mechanisms of AI technologies.
- 🔑 The necessity of using API tokens for accessing hugging face's inference API and managing access through environment variables for security purposes.
- 🎯 The demonstration of calculating similarity scores between embeddings and the importance of understanding model performance thresholds for determining similarity.
Q & A
What is the primary focus of the video?
-The primary focus of the video is to discuss the cheapest and best ways to generate embeddings, comparing OpenAI's model with open source alternatives and exploring self-hosting options.
What is the cost of OpenAI's text embedding model as of June 13th, 2023?
-As of June 13th, 2023, OpenAI's text embedding model costs 0.0001 per 1000 tokens.
What is the main advantage of using open source embedding models over OpenAI's model?
-The main advantage of using open source embedding models is the possibility of self-hosting, avoiding vendor lock-in, and working completely offline.
What are some use cases for embeddings mentioned in the video?
-Some use cases for embeddings mentioned in the video include search, clustering, classification, re-ranking, and retrieval.
What is the purpose of the hugging face inference API?
-The purpose of the hugging face inference API is to provide a way to generate embeddings and perform various tasks using machine learning models hosted by hugging face, without the need to run the models locally.
What is the significance of the mteb leaderboard?
-The mteb leaderboard is significant because it provides a comparison of different embedding models based on their performance in diverse tasks, helping users choose the most suitable model for their needs.
What is the main difference between the E5 small V2 and All Mini LM L6 V2 models?
-The main difference between the E5 small V2 and All Mini LM L6 V2 models is the number of dimensions in their embeddings; E5 small V2 has 384 dimensions, while All Mini LM L6 V2 has more dimensions and is five times faster than the E5 small V2.
What is the role of tokenizers in generating embeddings?
-Tokenizers play a crucial role in generating embeddings by converting text into tokens, which are the basic units of input for embedding models. Different tokenizers, such as BPE and WordPiece, may produce different token sequences for the same text, affecting the resulting embeddings.
How does the video demonstrate the process of generating embeddings using the hugging face inference API?
-The video demonstrates the process of generating embeddings using the hugging face inference API by showing how to install the required packages, use the API to generate embeddings for given text, and handle the received embeddings in the code.
What is the potential downside of using a model with a higher number of dimensions for embeddings?
-A potential downside of using a model with a higher number of dimensions for embeddings is that it may require more computational resources and memory, leading to increased processing time and potentially larger memory requirements.
Outlines
💡 Exploring Open AI and Text Embeddings
The paragraph discusses the popularity of Open AI's text embedding model, Ada 2, due to its affordability and performance. It raises the question of whether there are better, open-source alternatives for generating embeddings, especially for those who wish to avoid vendor lock-in or work offline. The video aims to uncover such models and explore their benefits for different use cases, including their applicability beyond text, such as for images and audio.
🌐 Introduction to Embeddings and Node.js Setup
This section introduces the concept of embeddings and their various applications, such as search, clustering, and classification. It emphasizes the importance of choosing the right model based on input size limits, output dimensions, and task types. The video presents a TypeScript Node.js project setup, explaining the reasons for using TypeScript and JavaScript, and provides a basic project structure for the upcoming demonstrations.
📚 Understanding Sentence Transformers and Hugging Face
The paragraph delves into the resources for open-source embeddings, particularly Sentence Transformers and Hugging Face. It explains that Sentence Transformers is a framework for generating sentence embeddings, while Hugging Face is a hub for machine learning models and datasets. The video outlines the process of exploring and selecting appropriate models from these platforms based on specific tasks and performance benchmarks.
🔍 Deep Dive into Embedding Models and Use Cases
This section provides an in-depth look at various embedding models, their specializations, and use cases. It discusses general-purpose models, search-specific models, multilingual models, and multimodal models. The importance of understanding the output formats and compatibility with different similarity calculation methods is highlighted. The Massive Text Embedding Benchmark (MTEB) is introduced as a valuable resource for evaluating and comparing models.
🧠 Tokenization and Model Selection
The paragraph discusses the concept of tokenization and its impact on model input sequence length. It explains how different tokenizers, like BPE and WordPiece, work and their significance in model performance. The video emphasizes the importance of selecting the right model based on the task, input size, and desired output dimensions, using the MTEB leaderboard as a reference for comparison.
🚀 Building with Hugging Face Inference API
This section covers the process of generating embeddings using the Hugging Face Inference API. It explains the steps for installing the necessary packages, setting up the API with an access token, and using the API to generate embeddings for text inputs. The video demonstrates how to handle the API's output and emphasizes the potential of using different models available on the Hugging Face platform.
🤖 Local Embedding Generation with Transformers.js
The paragraph introduces the Transformers.js library, which allows for local generation of embeddings without reliance on an external API. It explains the setup for using Transformers.js in a Node.js environment, including project structure and installation of dependencies. The video highlights the capabilities of the library and its potential for offline and server-side applications.
🌟 The Future of Embeddings: Multimodal Models
The final section discusses the emerging trend of multimodal embeddings, which enable the comparison of different media types within the same vector space. It introduces the CLIP model as an example of such technology and touches on the potential of these models for various applications. The video encourages viewers to explore this area further and anticipates a future where multimodal embeddings become increasingly important in AI and machine learning.
Mindmap
Keywords
💡Embeddings
💡OpenAI
💡Self-hosting
💡Open source
💡Tokenization
💡Vector space
💡Hugging Face
💡Inference API
💡Model benchmarking
💡Multimodal models
Highlights
Exploring the cheapest and best ways to generate embeddings, with a focus on open AI and open source alternatives.
Open AI's text embedding Ada 2 is highly cost-effective at $0.0001 per 1000 tokens, but there may be better alternatives.
Considering self-hosting and offline use cases for embedding models, and avoiding vendor lock-in.
Introducing a video series on embeddings, covering background, open source models, use cases, and comparisons.
Discussing the versatility of embeddings for various tasks such as search, clustering, classification, and re-ranking.
Exploring the benefits and limitations of different embedding models based on input size, dimension size, and task types.
Using TypeScript for the video demonstration, offering a different perspective from the Python-dominated AI field.
Providing a quick refresher on what embeddings are and their applications in relating content.
Discussing the capabilities of embeddings for different data types, including text, images, and audio.
Introducing Expert.net as a primary source for information on sentence transformers and embedding models.
Highlighting Hugging Face as a central hub for machine learning models, datasets, and tooling.
Exploring the Massive Text Embedding Benchmark (MTEB) project by Hugging Face for evaluating embedding models.
Discussing the importance of understanding the tokenizers used by different models, such as BPE and WordPiece.
Analyzing the Hugging Face inference API as a way to generate embeddings without downloading models.
Considering the trade-offs between model complexity, input sequence length, and embedding performance.
Demonstrating the process of generating embeddings locally using Transformers.js for offline use.
Discussing the potential of multimodal embeddings that can represent different media types in the same vector space.