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New short course with Google Cloud, which Nikita Namjoshi and I teach: Understanding and Applying Text Embeddings with Vertex AI! Come learn about text embeddings, a key part of a GenAI developer’s toolkit. You'll learn to apply embeddings to classification, outlier detection, text clustering, and semantic search. We also... show more

Andrew Ng

1,764,556 subscribers

160,823 次观看 • 2 年前 •via X (Twitter)

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10 条评论

Blueberry ML 的头像

Blueberry ML2 年前

@googlecloud Andrew just like to say you are a remarkable teacher .. just finishing your ml and dl specialization … so clear and easy to follow … a pleasure to study … Thank you for sharing your knowledge and making AI accessible to a wider audience

Rudhra 的头像

Rudhra2 年前

@googlecloud Thank you 😊

Alvin Tsoi 的头像

Alvin Tsoi2 年前

@googlecloud Thanks!

AITechPulse 的头像

AITechPulse2 年前

@googlecloud Many thanks!

Arsalan Ali 的头像

Arsalan Ali2 年前

@googlecloud

Orlando Agostinho 的头像

Orlando Agostinho2 年前

@googlecloud Really thanks

Patricio M 的头像

Patricio M2 年前

@googlecloud Andrew thanks for the whole series of courses, all of them useful and interesting.

(QC_λ) dougdotcon | Douglas M. 的头像

(QC_λ) dougdotcon | Douglas M.2 年前

@googlecloud Thank you for that

Alex 的头像

Alex2 年前

@googlecloud @memdotai mem it

Rotex Hawk 🦇🔊 的头像

Rotex Hawk 🦇🔊2 年前

@googlecloud Thank you. Great course but there are too many unanswered questions. We need a part two on how to train the model and deploy it. Unfortunately in the pipeline def `supported_models = {'BISON'}` and I used llama so there is no way to further experiment.

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Build powerful, offline AI features with EmbeddingGemma. Our new 308M parameter text embedding model enables on-device semantic search, RAG, and more. Learn how to get started ↓

Build powerful, offline AI features with EmbeddingGemma. Our new 308M parameter text embedding model enables on-device semantic search, RAG, and more. Learn how to get started ↓

Google AI Developers

37,027 次观看 • 10 个月前

How do computers understand data? With semantic search! Instead of just matching keywords, it understands context using vector embeddings. Here’s how: 1) Convert data (text, images, etc.) into vectors (embeddings) 2) Store these vectors in a vector database 3) Search by meaning, not just the keywords Semantic search makes finding data across formats easier. Learn more in this blog post by Leonie, my all-time favorite:

How do computers understand data? With semantic search! Instead of just matching keywords, it understands context using vector embeddings. Here’s how: 1) Convert data (text, images, etc.) into vectors (embeddings) 2) Store these vectors in a vector database 3) Search by meaning, not just the keywords Semantic search makes finding data across formats easier. Learn more in this blog post by Leonie, my all-time favorite:

Femke Plantinga

23,911 次观看 • 1 年前

We just released "Large Language Models with Semantic Search”, built with Cohere, and taught by Jay Alammar and Luis Serrano. Search is a key part of many applications. Say, you need to retrieve documents or products in response to a user query; how can LLMs help? You’ll learn about (i) Embeddings, to retrieve a collection of documents loosely related to a query, and (ii) LLM assisted re-ranking, to rank them precisely according to relevance. You’ll also go through code showing how to tie all this together to build a complete search system for retrieving relevant Wikipedia articles. Please check it out!

We just released "Large Language Models with Semantic Search”, built with Cohere, and taught by Jay Alammar and Luis Serrano. Search is a key part of many applications. Say, you need to retrieve documents or products in response to a user query; how can LLMs help? You’ll learn about (i) Embeddings, to retrieve a collection of documents loosely related to a query, and (ii) LLM assisted re-ranking, to rank them precisely according to relevance. You’ll also go through code showing how to tie all this together to build a complete search system for retrieving relevant Wikipedia articles. Please check it out!

Andrew Ng

597,097 次观看 • 3 年前

New short course: Building Multimodal Search and RAG", by Weaviate AI Database's Sebastia(N_) Witalec ✊🏽✊🏾✊🏿. Contrastive learning is used to train models to map vectors into an embedding space by pulling similar concepts closer together and pushing dissimilar concepts away from each other. This technique is also used to train multimodal embedding models that capture semantic similarity across different modalities like text, images, and audio. These multimodal embeddings can be used to build multimodal search and RAG systems. In this course, you'll learn how contrastive learning works, and how to add multimodality to RAG – so your models can draw on diverse, relevant context to answer questions. For example, a query about a financial report might synthesize information from text snippets, graphs, tables, and slides. You will also learn how visual instruction tuning lets you integrate image understanding into language models, and build a multi-vector recommender system using Weaviate’s open-source vector database. Please sign up here:

New short course: Building Multimodal Search and RAG", by Weaviate AI Database's Sebastia(N_) Witalec ✊🏽✊🏾✊🏿. Contrastive learning is used to train models to map vectors into an embedding space by pulling similar concepts closer together and pushing dissimilar concepts away from each other. This technique is also used to train multimodal embedding models that capture semantic similarity across different modalities like text, images, and audio. These multimodal embeddings can be used to build multimodal search and RAG systems. In this course, you'll learn how contrastive learning works, and how to add multimodality to RAG – so your models can draw on diverse, relevant context to answer questions. For example, a query about a financial report might synthesize information from text snippets, graphs, tables, and slides. You will also learn how visual instruction tuning lets you integrate image understanding into language models, and build a multi-vector recommender system using Weaviate’s open-source vector database. Please sign up here:

Andrew Ng

104,371 次观看 • 2 年前

New short course on Building Applications with Vector Databases, taught by Pinecone’s Tim Tully! At the heart of a vector database is the ability to store a collection of vectors and then query against that, meaning input a new vector and find similar ones. This is useful for many AI applications. In this course, you'll learn how to use vector databases to build: (i) Semantic Search: Create a text search tool that goes beyond keyword matching, and instead focuses on the meaning of content. (ii) RAG (retrieval augmented generation): Enhance your LLM output by incorporating context from sources the model wasn't trained on. (iii) Recommender System: Combine semantic search and RAG to recommend topics, and demonstrate it with a news article recommender. (iv) Hybrid Search: Build an application that finds items using both images and descriptive text -- by combining both sparse and dense vector representations of the data -- using an eCommerce dataset as an example. (v) Image Similarity: Use image vector embeddings to create an app to compare facial features, using a database of public figures to determine the likeness between them. (vi) Anomaly Detection: Build an anomaly detection app that identifies unusual patterns in network communication logs. I hope you’ll enjoy learning how to build all these types of applications! Please sign up here:

New short course on Building Applications with Vector Databases, taught by Pinecone’s Tim Tully! At the heart of a vector database is the ability to store a collection of vectors and then query against that, meaning input a new vector and find similar ones. This is useful for many AI applications. In this course, you'll learn how to use vector databases to build: (i) Semantic Search: Create a text search tool that goes beyond keyword matching, and instead focuses on the meaning of content. (ii) RAG (retrieval augmented generation): Enhance your LLM output by incorporating context from sources the model wasn't trained on. (iii) Recommender System: Combine semantic search and RAG to recommend topics, and demonstrate it with a news article recommender. (iv) Hybrid Search: Build an application that finds items using both images and descriptive text -- by combining both sparse and dense vector representations of the data -- using an eCommerce dataset as an example. (v) Image Similarity: Use image vector embeddings to create an app to compare facial features, using a database of public figures to determine the likeness between them. (vi) Anomaly Detection: Build an anomaly detection app that identifies unusual patterns in network communication logs. I hope you’ll enjoy learning how to build all these types of applications! Please sign up here:

Andrew Ng

137,091 次观看 • 2 年前

LangChain: Chat with Your Data, a new free short course created with Harrison Chase, is now available! In this 1 hour course, you’ll learn how to build one of the most requested LLM-based applications: Answering questions using information from a document or collection of documents (often called Retrieval Augmented Generation). You'll also learn how to use vector stores and embeddings to retrieve document chunks relevant to a query. I hope you enjoy the course!

LangChain: Chat with Your Data, a new free short course created with Harrison Chase, is now available! In this 1 hour course, you’ll learn how to build one of the most requested LLM-based applications: Answering questions using information from a document or collection of documents (often called Retrieval Augmented Generation). You'll also learn how to use vector stores and embeddings to retrieve document chunks relevant to a query. I hope you enjoy the course!

Andrew Ng

384,282 次观看 • 3 年前

New short course Multimodal RAG: Chat with Videos, developed with Intel and taught by vasudevlal! In this course, you’ll work with LLaVA (Large Language and Vision Assistant), a Large Vision Language Model (LVLM) that can process both images and text. For example, given an image of a person doing a handstand on a skateboard at the beach, LLaVA doesn't just caption the scene, it’s able to predict possible outcomes, like the person losing balance or falling off. By understanding not just what's in a video frame, but what might happen next, your application can provide more insightful answers to questions about video. You'll build a full multimodal RAG pipeline that can chat about video content: - Use the BridgeTower model to create joint text-image embeddings in a 512-dimensional multimodal semantic space. - Learn video processing techniques to extract keyframes, generate transcripts using Whisper, and create captions. - Use the LanceDB vector database to store and retrieve high-dimensional multimodal embeddings. - Integrate the LLaVA model, combining CLIP's (Contrastive Language Image Pretraining) vision transformer with Llama, for advanced visual-textual reasoning. Your final system will ingest video data, generate embeddings for frames and text, perform similarity searches for relevant content, and use the retrieved multimodal context to inform LVLM-based response generation. The result is a system capable of answering nuanced questions about video content, effectively chatting about the video it has processed. Please sign up here!

New short course Multimodal RAG: Chat with Videos, developed with Intel and taught by vasudevlal! In this course, you’ll work with LLaVA (Large Language and Vision Assistant), a Large Vision Language Model (LVLM) that can process both images and text. For example, given an image of a person doing a handstand on a skateboard at the beach, LLaVA doesn't just caption the scene, it’s able to predict possible outcomes, like the person losing balance or falling off. By understanding not just what's in a video frame, but what might happen next, your application can provide more insightful answers to questions about video. You'll build a full multimodal RAG pipeline that can chat about video content: - Use the BridgeTower model to create joint text-image embeddings in a 512-dimensional multimodal semantic space. - Learn video processing techniques to extract keyframes, generate transcripts using Whisper, and create captions. - Use the LanceDB vector database to store and retrieve high-dimensional multimodal embeddings. - Integrate the LLaVA model, combining CLIP's (Contrastive Language Image Pretraining) vision transformer with Llama, for advanced visual-textual reasoning. Your final system will ingest video data, generate embeddings for frames and text, perform similarity searches for relevant content, and use the retrieved multimodal context to inform LVLM-based response generation. The result is a system capable of answering nuanced questions about video content, effectively chatting about the video it has processed. Please sign up here!

Andrew Ng

107,825 次观看 • 1 年前

This is, by far, one of the best uses of modern AI. If you don't use embeddings when querying your database, you are definitely leaving a lot on the table. In this video, I'll show you how to run semantic searches using OpenAI and PostgreSQL. It's all thanks to Pgai, an open-source PostgreSQL extension: Here's what will happen: 1. We'll create a simple table with news articles 2. We'll generate embeddings for those articles 3. We'll run queries on top of those embeddings For this video, I generated the embeddings using a simple query, but pgai Vectorizer would do the same automatically as new information makes it into the database. This is awesome! If you have a PostgreSQL database with data you are searching over, you should start experimenting with semantic searches immediately. For most use cases, a combination of full-text search + semantic search is the best approach. If you don't have a PostgreSQL database around, you can try free for 30 days using Timescale: Thanks to the TimescaleDB (by Tiger Data) team for partnering with me on this post!

This is, by far, one of the best uses of modern AI. If you don't use embeddings when querying your database, you are definitely leaving a lot on the table. In this video, I'll show you how to run semantic searches using OpenAI and PostgreSQL. It's all thanks to Pgai, an open-source PostgreSQL extension: Here's what will happen: 1. We'll create a simple table with news articles 2. We'll generate embeddings for those articles 3. We'll run queries on top of those embeddings For this video, I generated the embeddings using a simple query, but pgai Vectorizer would do the same automatically as new information makes it into the database. This is awesome! If you have a PostgreSQL database with data you are searching over, you should start experimenting with semantic searches immediately. For most use cases, a combination of full-text search + semantic search is the best approach. If you don't have a PostgreSQL database around, you can try free for 30 days using Timescale: Thanks to the TimescaleDB (by Tiger Data) team for partnering with me on this post!

Santiago

110,133 次观看 • 1 年前

New short course: Open Source Models with Hugging Face 🤗, taught by Maria Khalusova, Marc Sun, and Younes Belkada! Hugging Face has been a game changer by letting you quickly grab any of hundreds of thousands of already-trained open source models to assemble into new applications. This course teaches you best practices for building this way, including how to search and choose among models. You’ll learn to use the Transformers library and walk through multiple models for text, audio, and image processing, including zero-shot image segmentation, zero-shot audio classification, and speech recognition. You'll also learn to use multimodal models for visual question answering, image search, and image captioning. Finally, you’ll learn how to demo what you build locally, on the cloud, or via an API using Gradio and Hugging Face Spaces. You can sign up here:

New short course: Open Source Models with Hugging Face 🤗, taught by Maria Khalusova, Marc Sun, and Younes Belkada! Hugging Face has been a game changer by letting you quickly grab any of hundreds of thousands of already-trained open source models to assemble into new applications. This course teaches you best practices for building this way, including how to search and choose among models. You’ll learn to use the Transformers library and walk through multiple models for text, audio, and image processing, including zero-shot image segmentation, zero-shot audio classification, and speech recognition. You'll also learn to use multimodal models for visual question answering, image search, and image captioning. Finally, you’ll learn how to demo what you build locally, on the cloud, or via an API using Gradio and Hugging Face Spaces. You can sign up here:

Andrew Ng

224,538 次观看 • 2 年前

🌍 New Tutorial Alert: Visualizing Google's AlphaEarth Satellite Embeddings in 3D! 🛰️🏙️ Google DeepMind has released AlphaEarth, a groundbreaking satellite embedding dataset on Google Earth Engine. It includes annual global embeddings from 2017 to 2024, where each pixel covers a 10×10 meter area. In this tutorial, you'll learn how to: ✅ Access and visualize AlphaEarth embeddings in 3D ✅ Leverage them for machine learning and change detection 🎥 Watch the tutorial: 📘 Try the notebook: #GeoAI #geospatial #AI #Python #DeepLearning

🌍 New Tutorial Alert: Visualizing Google's AlphaEarth Satellite Embeddings in 3D! 🛰️🏙️ Google DeepMind has released AlphaEarth, a groundbreaking satellite embedding dataset on Google Earth Engine. It includes annual global embeddings from 2017 to 2024, where each pixel covers a 10×10 meter area. In this tutorial, you'll learn how to: ✅ Access and visualize AlphaEarth embeddings in 3D ✅ Leverage them for machine learning and change detection 🎥 Watch the tutorial: 📘 Try the notebook: #GeoAI #geospatial #AI #Python #DeepLearning

Qiusheng Wu

26,015 次观看 • 1 年前

Traditional (SQL) databases rely primarily on keyword-based searches to retrieve information. These searches match the exact words or phrases in your query to the text stored in the database. While effective for many applications, this method has limitations when it comes to understanding context or finding relevant information that doesn’t include the exact keywords. Hybrid search combines the strengths of traditional keyword-based BM25 search with the advanced capabilities of semantic search. To effectively implement a hybrid search, a vector database is essential. Vector databases go beyond just words; they understand the meaning behind the data. They transform data such as text, images, or audio into numerical representations called vectors. These vector embeddings enable the database to find similar items, even if they don't share exact keywords. When you integrate hybrid search with Retrieval-Augmented Generation (RAG) systems, you can achieve higher accuracy in retrieved context and better output in generated responses. Learn more about RAG systems in this video with Victoria Slocum:

Traditional (SQL) databases rely primarily on keyword-based searches to retrieve information. These searches match the exact words or phrases in your query to the text stored in the database. While effective for many applications, this method has limitations when it comes to understanding context or finding relevant information that doesn’t include the exact keywords. Hybrid search combines the strengths of traditional keyword-based BM25 search with the advanced capabilities of semantic search. To effectively implement a hybrid search, a vector database is essential. Vector databases go beyond just words; they understand the meaning behind the data. They transform data such as text, images, or audio into numerical representations called vectors. These vector embeddings enable the database to find similar items, even if they don't share exact keywords. When you integrate hybrid search with Retrieval-Augmented Generation (RAG) systems, you can achieve higher accuracy in retrieved context and better output in generated responses. Learn more about RAG systems in this video with Victoria Slocum:

Femke Plantinga

140,618 次观看 • 2 年前

Here is a killer way to use embeddings: I built a notebook to show you how to do anomaly detection on images. Anomaly detection is one of the most common problems in the real world. Learn how to implement anomaly detection, and you'll be able to solve a ton of use cases that happen every day: • Predicting equipment failures • Identifying defective products • Identifying tumors in medical images • Spotting counterfeit products • Detecting fraud • Flagging fake reviews • Detecting bot traffic My notebook focuses on images. It uses: • Langchain • OpenClip embeddings • Oracle vector store This is the way my example works: • You start with a collection of similar images • You can then check whether a new image "belongs" to that collection I generate embeddings and use Oracle 26ai to store them and retrieve similar images on demand.

Here is a killer way to use embeddings: I built a notebook to show you how to do anomaly detection on images. Anomaly detection is one of the most common problems in the real world. Learn how to implement anomaly detection, and you'll be able to solve a ton of use cases that happen every day: • Predicting equipment failures • Identifying defective products • Identifying tumors in medical images • Spotting counterfeit products • Detecting fraud • Flagging fake reviews • Detecting bot traffic My notebook focuses on images. It uses: • Langchain • OpenClip embeddings • Oracle vector store This is the way my example works: • You start with a collection of similar images • You can then check whether a new image "belongs" to that collection I generate embeddings and use Oracle 26ai to store them and retrieve similar images on demand.

Santiago

24,527 次观看 • 4 个月前

Tokenization -- turning text into a sequence of integers -- is a key part of generative AI, and most API providers charge per million tokens. How does tokenization work? Learn the details of tokenization and RAG optimization in Retrieval Optimization: From Tokenization to Vector Quantization, created in collaboration with Qdrant and taught by its Developer Relations Lead, Kacper Łukawski. This course focuses on Retrieval augmented generation (RAG), which has two steps: First, a retriever finds relevant information; then, the generator uses what’s retrieved as context to produce a response. You’ll learn to optimize the first step (the retriever) by understanding how tokenization works and how it impacts the relevance of your search. In addition, you will also learn to measure and improve retrieval quality, speed, and memory. In detail, you’ll: - Learn about the internal workings of the embedding models and how your text turns into vectors. - Understand how several tokenizers, such as Byte-Pair Encoding, WordPiece, Unigram, and SentencePiece work. - Explore common challenges with tokenizers, such as unknown tokens, domain-specific identifiers, and numerical values, that can negatively affect your vector search. - Understand how to measure the quality of your search across relevance, ranking, and score-related metrics. - Understand how the main parameters in "HNSW", a graph-based algorithm, affect the relevance and speed of vector search, and how to tune its parameters. - Experiment with the three major quantization methods – product, scalar, and binary – and learn how they impact memory requirements, search quality, and speed. By the end of this course, you’ll have a solid understanding of how tokenization functions and how to optimize vector search in your RAG systems. Please sign up here!

Tokenization -- turning text into a sequence of integers -- is a key part of generative AI, and most API providers charge per million tokens. How does tokenization work? Learn the details of tokenization and RAG optimization in Retrieval Optimization: From Tokenization to Vector Quantization, created in collaboration with Qdrant and taught by its Developer Relations Lead, Kacper Łukawski. This course focuses on Retrieval augmented generation (RAG), which has two steps: First, a retriever finds relevant information; then, the generator uses what’s retrieved as context to produce a response. You’ll learn to optimize the first step (the retriever) by understanding how tokenization works and how it impacts the relevance of your search. In addition, you will also learn to measure and improve retrieval quality, speed, and memory. In detail, you’ll: - Learn about the internal workings of the embedding models and how your text turns into vectors. - Understand how several tokenizers, such as Byte-Pair Encoding, WordPiece, Unigram, and SentencePiece work. - Explore common challenges with tokenizers, such as unknown tokens, domain-specific identifiers, and numerical values, that can negatively affect your vector search. - Understand how to measure the quality of your search across relevance, ranking, and score-related metrics. - Understand how the main parameters in "HNSW", a graph-based algorithm, affect the relevance and speed of vector search, and how to tune its parameters. - Experiment with the three major quantization methods – product, scalar, and binary – and learn how they impact memory requirements, search quality, and speed. By the end of this course, you’ll have a solid understanding of how tokenization functions and how to optimize vector search in your RAG systems. Please sign up here!

Andrew Ng

146,313 次观看 • 1 年前

How can business leaders, not just coders, build AI business applications with LLMs? Our new short course, created with Microsoft, is out! In How Business Thinkers Can Start Building AI Plugins With Semantic Kernel, taught by John Maeda, you'll learn to use Semantic Kernel and build an AI planner that can automatically select the plugins it needs to create multi-step plans with sophisticated logic. Please sign up here:

How can business leaders, not just coders, build AI business applications with LLMs? Our new short course, created with Microsoft, is out! In How Business Thinkers Can Start Building AI Plugins With Semantic Kernel, taught by John Maeda, you'll learn to use Semantic Kernel and build an AI planner that can automatically select the plugins it needs to create multi-step plans with sophisticated logic. Please sign up here:

Andrew Ng

331,720 次观看 • 3 年前

Brain signals and LLM embeddings converge for predicting every spoken or heard word. Beautiful research from Google AI They compared human brain activity during real conversations with internal embeddings from a speech-to-text LLM. Measured electrode signals in speech and language-related brain regions and matched them to the model’s word-level features. 🤖Key Highlights → Brain activity aligns linearly with LLM embeddings for real-life spoken conversations. → Sequence of comprehension: first speech sounds, then word meaning. → Sequence of production: planned meaning, then articulation, then hearing one’s own voice. → Consistent predictive coding (pre-onset anticipation, post-onset surprise) mirrors LLM next-word prediction. → Lower-tier auditory regions still show partial sensitivity to semantic information. 🤖 Model-Brain Alignment They observed a clear sequence: during comprehension, auditory cortex (superior temporal gyrus) showed strong correlation with speech embeddings, then language embeddings aligned with Broca’s area. During production, Broca’s area correlated with language embeddings before articulation, followed by motor cortex signals matching speech embeddings. This suggests that next-word prediction and higher-level meaning representation in the model parallel the brain’s approach. ⚙ So the study revealed a shared computational principle of predicting words in context. Even though the Transformer-based LLM processes words in parallel layers, the human brain processes them serially yet mirrors similar statistical regularities. This supports a “soft hierarchy” where both lower-level acoustic processing and higher-level semantic processing partially overlap in the brain.

Brain signals and LLM embeddings converge for predicting every spoken or heard word. Beautiful research from Google AI They compared human brain activity during real conversations with internal embeddings from a speech-to-text LLM. Measured electrode signals in speech and language-related brain regions and matched them to the model’s word-level features. 🤖Key Highlights → Brain activity aligns linearly with LLM embeddings for real-life spoken conversations. → Sequence of comprehension: first speech sounds, then word meaning. → Sequence of production: planned meaning, then articulation, then hearing one’s own voice. → Consistent predictive coding (pre-onset anticipation, post-onset surprise) mirrors LLM next-word prediction. → Lower-tier auditory regions still show partial sensitivity to semantic information. 🤖 Model-Brain Alignment They observed a clear sequence: during comprehension, auditory cortex (superior temporal gyrus) showed strong correlation with speech embeddings, then language embeddings aligned with Broca’s area. During production, Broca’s area correlated with language embeddings before articulation, followed by motor cortex signals matching speech embeddings. This suggests that next-word prediction and higher-level meaning representation in the model parallel the brain’s approach. ⚙ So the study revealed a shared computational principle of predicting words in context. Even though the Transformer-based LLM processes words in parallel layers, the human brain processes them serially yet mirrors similar statistical regularities. This supports a “soft hierarchy” where both lower-level acoustic processing and higher-level semantic processing partially overlap in the brain.

Rohan Paul

15,213 次观看 • 1 年前

How do professional RAG applications chunk their text? Let’s cover some Advanced Chunking Techniques. In our latest video, we cover simple chunking methods like splitting documents into sentences or sections. But these methods often miss out on ensuring each chunk has independent meaning. Semantic chunking solved exactly this! By measuring the semantic similarity between sentences using vector embeddings, we can combine similar sentences into meaningful chunks. With LLM-based chunking, large language models help break down text effectively, although it can be slow and costly. And what about the newest Late Chunking? Which keeps context intact across chunks—more on that soon. 👀 In this video, we cover these advanced techniques in detail. Watch it to learn more. A big shoutout to Daniel Williams for helping create this video! 💚

How do professional RAG applications chunk their text? Let’s cover some Advanced Chunking Techniques. In our latest video, we cover simple chunking methods like splitting documents into sentences or sections. But these methods often miss out on ensuring each chunk has independent meaning. Semantic chunking solved exactly this! By measuring the semantic similarity between sentences using vector embeddings, we can combine similar sentences into meaningful chunks. With LLM-based chunking, large language models help break down text effectively, although it can be slow and costly. And what about the newest Late Chunking? Which keeps context intact across chunks—more on that soon. 👀 In this video, we cover these advanced techniques in detail. Watch it to learn more. A big shoutout to Daniel Williams for helping create this video! 💚

Femke Plantinga

29,660 次观看 • 1 年前

Function calling is a powerful way to extend the capabilities of LLMs and AI agents by letting them use external tools. Our new short course Function calling and Data Extraction with LLMs, created with @NexusflowX and taught by Jiantao Jiao and Venkat, demonstrates how to prompt LLMs to form calls to external functions. You'll work with NexusRavenV2-13B, a 13B parameter open-source model that excels in function calling tasks while still being small enough to host locally. Learn to use function calling to extract structured data from unstructured text and access web APIs, and build an end-to-end application that processes customer service transcripts. You'll learn how to build LLM-powered applications that can analyze feedback, automate data entry, and enhance search. Please get started here:

Function calling is a powerful way to extend the capabilities of LLMs and AI agents by letting them use external tools. Our new short course Function calling and Data Extraction with LLMs, created with @NexusflowX and taught by Jiantao Jiao and Venkat, demonstrates how to prompt LLMs to form calls to external functions. You'll work with NexusRavenV2-13B, a 13B parameter open-source model that excels in function calling tasks while still being small enough to host locally. Learn to use function calling to extract structured data from unstructured text and access web APIs, and build an end-to-end application that processes customer service transcripts. You'll learn how to build LLM-powered applications that can analyze feedback, automate data entry, and enhance search. Please get started here:

Andrew Ng

110,630 次观看 • 2 年前

I wrote a AI "text adventure game" (MUD) in the past two weeks. We had a "hackweek" for prototyping AI game play. Story is AI generated text. Text generator is fed the theme and previous rooms and rules for exit generation. It returns a JSON string of exits and their descriptions. Which the game then parses and sends to AI image generator and room graph system. There's also a AI written battle story generator, which gets your inventory as input. AI figures how to use your weapons and consumables. And returns JSON for managing inventory state (broken weapons, consumables, etc).

I wrote a AI "text adventure game" (MUD) in the past two weeks. We had a "hackweek" for prototyping AI game play. Story is AI generated text. Text generator is fed the theme and previous rooms and rules for exit generation. It returns a JSON string of exits and their descriptions. Which the game then parses and sends to AI image generator and room graph system. There's also a AI written battle story generator, which gets your inventory as input. AI figures how to use your weapons and consumables. And returns JSON for managing inventory state (broken weapons, consumables, etc).

Sebastian Aaltonen @ SIGGRAPH

72,311 次观看 • 7 个月前