$0 Embeddings (OpenAI vs. free & open source)

Rabbit Hole Syndrome
25 Jun 202384:41

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 embedding models to avoid vendor lock-in and work offline. The video covers various models, including those for specific tasks like search and clustering, and introduces the concept of multimodal embeddings. It provides a comprehensive guide on choosing the right model for different use cases and offers insights into the future of embeddings, emphasizing the potential of multimodal models.

Takeaways

  • 📈 OpenAI's text embedding model, Ada 2, is cost-effective but there are other open-source models that may be better suited for specific use cases.
  • 💡 Self-hosting embedding models can prevent vendor lock-in and allow for offline use, which is essential for some applications.
  • 🛠️ The video discusses various open-source embedding models, their benefits, and how they can be used for tasks like search, clustering, classification, and re-ranking.
  • 📊 The hugging face inference API allows for the generation of embeddings through a simple API call, with the potential for offline use by fetching and caching models locally.
  • 🔍 The video introduces the concept of multimodal embeddings, which can represent different media types such as text and images in the same vector space.
  • 📚 The script provides a brief overview of the technical aspects of embeddings, including how they relate content and the importance of dimensions and sequence length.
  • 🔧 The use of TypeScript and Node.js is demonstrated for generating embeddings, with the potential for running the same code in the browser.
  • 🔄 The video emphasizes the importance of choosing the right model based on the specific requirements of the task at hand, such as input size limits and output dimensions.
  • 🔍 The Massive Text Embedding Benchmark (MTEB) by Hugging Face is highlighted as a resource for comparing the performance of different embedding models.
  • 🛠️ The video provides practical guidance on how to use embeddings in real-world applications, including the use of vector databases for similarity searches.
  • 🔗 The video concludes with a look at the future of embeddings, particularly the potential of multimodal models that can handle different data types in a unified way.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is the comparison of different methods to generate embeddings, focusing on open AI and open source alternatives.

  • What is the cost of OpenAI's text embedding as of June 13th, 2023?

    -As of June 13th, 2023, OpenAI's text embedding costs 0.0001 per 1000 tokens.

  • 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 significance of the MTEB (Massive Text Embedding Benchmark) project by Hugging Face?

    -The MTEB project by Hugging Face is significant as it provides a benchmark for measuring the performance of text embedding models on diverse embedding tasks, offering a standardized way to evaluate and compare different models.

  • What is the role of tokenizers in the embedding generation process?

    -Tokenizers play a crucial role in the embedding generation process by converting text into tokens that the embedding models can understand. They help in breaking down words or phrases into smaller units that the models can process.

  • What are the advantages of using open source embedding models over closed-source ones like OpenAI's?

    -Open source embedding models offer advantages such as避免了供应商锁定（vendor lock-in），允许自我托管（self-hosting），完全离线工作（working completely offline），以及可能在成本上更加经济（potentially more cost-effective）.

  • What is the difference between the 'sentence-transformers' and 'transformers' tags in the Hugging Face model hub?

    -The 'sentence-transformers' tag indicates that the model should be run through the Sentence Transformers framework, which is designed to output a single embedding for an entire sentence. In contrast, models without this tag may produce multiple embeddings for each token in the sentence, requiring additional processing to aggregate them into a single embedding.

  • How does the video demonstrate the process of generating embeddings locally using Node.js?

    -The video demonstrates generating embeddings locally using Node.js by showcasing the use of the 'transformers.js' library, which allows for the local generation of embeddings without relying on an external API like OpenAI.

  • What is the role of the 'Onyx runtime' in transformers.js?

    -The 'Onyx runtime' is an inference engine used by transformers.js. It enables the running of machine learning models in various environments, including web browsers and Node.js servers, by utilizing technologies like WebAssembly and, potentially, web GPU.

  • What is the significance of the 'E5 small V2' model in the context of the video?

    -The 'E5 small V2' model is highlighted in the video as an efficient and lower-dimensionality model for generating embeddings. It is noted for its performance and smaller size, which can be beneficial for applications where computational resources and memory are limited.

Outlines

00:00

💡 Introduction to Text Embeddings and Open AI

The paragraph discusses the increasing popularity of Open AI's text embeddings, particularly the Ada 2 model, due to its affordability. 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 explore different models, their benefits, and various use cases for embeddings beyond just search, such as clustering and classification.

05:00

🌐 Exploring Open Source Embedding Models

This section delves into the world of open source embedding models, highlighting the availability of models like Sentence-BERT (SBERT) and the Expert system. It emphasizes the importance of understanding the different capabilities of each model, such as input size limits, output dimensions, and task-specific design. The video also discusses the versatility of embeddings across various data types like images and audio, and how they can be used for tasks like Google's reverse image search.

10:00

🛠️ Building with Node.js and TypeScript

The speaker introduces the decision to use TypeScript for the video, explaining the reasons including the audience's familiarity with JavaScript and the desire to showcase AI concepts in different programming languages. The video outlines the basic setup for a TypeScript project, including the package.json file and the index.ts entry point. It also briefly touches on the concept of embeddings and their application in real-world scenarios like chatbots and knowledge bases.

15:01

📊 Understanding Embeddings and Their Applications

This paragraph discusses the concept of embeddings in more detail, explaining how they relate different pieces of content based on their underlying meanings. It describes the process of plotting similar texts together on a chart, with dissimilar texts far apart. The speaker also hints at the complexity of choosing the right embedding model for different tasks and sets the stage for discussing the evaluation and ranking of various models.

20:02

🔍 Deepening the Discussion on Embeddings

The speaker addresses the versatility of embeddings beyond text, including their use for images and audio. It explains how image embeddings can determine the similarity between images, similar to how text embeddings work. The paragraph also touches on the future of embeddings, hinting at exciting developments and encouraging viewers to stay informed about advancements in this field.

25:04

🤖 Multimodal Embeddings: The Future of AI

The final paragraph discusses the emerging field of multimodal embeddings, where models like CLIP enable the generation of embeddings from different media types within the same vector space. This innovation opens up possibilities for comparing various data types directly. The speaker also mentions additional research papers that further explore the concept of multimodal embeddings and their potential applications in the future of AI.

Mindmap

Keywords

💡Embeddings

Embeddings are a way to represent text, images, or other data types in a numerical form that can be understood by machine learning models. In the context of this video, they are used to determine the similarity between different pieces of content, such as paragraphs or images. The video discusses various models for generating embeddings and their applications in tasks like search, clustering, and classification.

💡OpenAI

OpenAI is an artificial intelligence research organization known for developing advanced AI models, including those for generating embeddings. The video mentions OpenAI's text embedding Ada 2, which is noted for its affordability and performance. The discussion around OpenAI sets the stage for comparing it with other open-source embedding models.

💡Self-hosting

Self-hosting refers to the practice of running software, services, or models on one's own servers rather than relying on external providers. In the video, the concept is important for those who want to host their own embedding models to avoid vendor lock-in, work offline, or have specific customization needs that external APIs might not support.

💡Open source

Open source refers to software or models that are freely available for use, modification, and distribution. The video emphasizes the benefits of open source models for generating embeddings, presenting them as cost-effective alternatives to proprietary solutions like OpenAI's offerings. It also suggests the flexibility and control they offer to users.

💡Tokenization

Tokenization is the process of breaking down text into individual elements, or tokens, that a machine learning model can understand. In the context of embeddings, it's a crucial step that affects the model's ability to capture the meaning and context of the text. The video discusses different tokenization methods, such as Byte Pair Encoding and WordPiece, and their impact on embedding generation.

💡Hugging Face

Hugging Face is a company and platform that provides a wide range of machine learning models and datasets, including those for generating embeddings. The video mentions Hugging Face as a hub for machine learning resources and as a source for various embedding models. It also discusses Hugging Face's Inference API and how it can be used to generate embeddings.

💡Model Benchmarking

Model benchmarking involves evaluating and comparing the performance of different machine learning models on specific tasks. The Massive Text Embedding Benchmark (MTEB) mentioned in the video is an example of this, providing a standardized way to measure and rank embedding models based on their performance in diverse tasks. This helps users choose the most suitable model for their needs.

💡Multimodal Models

Multimodal models are capable of handling and integrating data from multiple types, such as text and images. In the video, the concept is introduced with the CLIP model from OpenAI, which can generate embeddings for both images and text in the same vector space. This enables novel applications where different media types can be compared and analyzed together.

💡Zero-shot Learning

Zero-shot learning is a machine learning technique where a model is able to recognize or classify examples from classes it has not been explicitly trained on. The video touches on this concept in the context of multimodal models, where the model can understand and generate embeddings for different media types without being trained for each specific type.

💡Search and Retrieval

Search and retrieval is a key application of embeddings where the goal is to find and rank documents or other content based on their relevance to a query. The video discusses how different embedding models perform in this task, with some being specifically optimized for search and retrieval by training on datasets like question-answer pairs or search engine queries.

💡Vector Operations

Vector operations refer to the mathematical calculations performed on vectors, which in the context of embeddings, represent the numerical form of data. The video discusses how operations like dot product, cosine similarity, and Euclidean distance are used to compare the similarity of different embeddings. It also touches on the importance of these operations in databases designed for vector searches.

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 working offline with embedding models to avoid vendor lock-in and external API dependence.

Introduction to popular open source embedding models that can be self-hosted and run directly in the browser.

Understanding the different use cases for embeddings, such as search, clustering, classification, re-ranking, and retrieval.

Comparing various embedding models based on input size limits, dimension size, and task-specific design.

Using TypeScript for the demonstration, highlighting its advantages for JavaScript and TypeScript developers.

Exploring the potential of embeddings to relate content and determine the underlying meaning of text.

Discussing the capabilities of image embeddings and their applications, such as Google's reverse image search.

Introducing the concept of multimodal embeddings that can represent different media types in the same vector space.

Examining the future of embeddings, including the potential for AI to understand and compare audio, images, and text across multiple modalities.

Using Hugging Face's Inference API and transformers.js library for generating embeddings locally and in the browser.

Discussing the importance of model caching for reducing load times and improving user experience in web applications.

Highlighting the potential of quantized models for reducing file size and optimizing for embedded systems or browser use.

Providing practical guidance on selecting the right embedding model based on task requirements and performance benchmarks.

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