Загрузка видео...

Не удалось загрузить видео

На главную

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...

femke_plantinga's profile picture

Femke Plantinga

12,619 subscribers

29,660 просмотров • 1 год назад •via X (Twitter)

ОбразованиеНаука и технологииИгры
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

Traditional Chunking can lose context between chunks. (Let's explore a better way!) Enter Late Chunking… Here's how it works: Traditional Chunking • Split the text into chunks • Embed each chunk separately Late Chunking • Embed the entire text first • Split it into chunks after the embedding Advantages of Late Chunking • Maintains connections between segments • Reduces the need for complex chunking strategies • Cost-effective: extremely similar cost to regular chunking methods Late Chunking is a promising alternative to traditional methods like ColBERT and naive chunking. It's particularly useful for applications where the documents are long, and context needs to be retained across many pages of text when retrieving information. Want to learn more? • Blog post: • Notebook: Special thanks to Daniel Williams for his invaluable collaboration on this one! 🔥
1:19

Traditional Chunking can lose context between chunks. (Let's explore a better way!) Enter Late Chunking… Here's how it works: Traditional Chunking • Split the text into chunks • Embed each chunk separately Late Chunking • Embed the entire text first • Split it into chunks after the embedding Advantages of Late Chunking • Maintains connections between segments • Reduces the need for complex chunking strategies • Cost-effective: extremely similar cost to regular chunking methods Late Chunking is a promising alternative to traditional methods like ColBERT and naive chunking. It's particularly useful for applications where the documents are long, and context needs to be retained across many pages of text when retrieving information. Want to learn more? • Blog post: • Notebook: Special thanks to Daniel Williams for his invaluable collaboration on this one! 🔥

Femke Plantinga

19,792 просмотров • 1 год назад

Traditional chunking: cheap but dumb. ColBERT: smart but expensive. 𝗟𝗮𝘁𝗲 𝗰𝗵𝘂𝗻𝗸𝗶𝗻𝗴: the solution we've been waiting for. Here’s a quick evolution of chunking strategies: → 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 (the basics we all started with) • Token Chunking - split by token count • Sentence Chunking - split by sentence boundaries • Document-Based Chunking - split by sections/paragraphs → 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 (when things got sophisticated) • Semantic Chunking - split by meaning • LLM-Based Chunking - let the model decide But each chunking method separates text at defined points, meaning context is lost within the document from one chunk to the next. → 𝗘𝗻𝘁𝗲𝗿 𝗟𝗮𝘁𝗲 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 (the game changer) Traditional approach: Chunk first → Embed each chunk separately Late chunking approach: Embed the entire document → Then chunk with context preserved 𝗪𝗵𝘆 𝗰𝗵𝗼𝗼𝘀𝗲 𝗹𝗮𝘁𝗲 𝗰𝗵𝘂𝗻𝗸𝗶𝗻𝗴? When you chunk first, each piece loses its contextual relationship to the rest of the document. It's like reading a book by randomly picking paragraphs - you miss the flow. With late chunking, every chunk maintains awareness of its neighbors because the embedding happens at the document level first. Mean pooling is done on segments AFTER the full context is embedded. Jina AI tested and saw significant improvements in retrieval quality - chunks that were previously disconnected now maintain their semantic relationships. As documents get longer and context windows expand, late chunking might just become the new standard for high-quality retrieval systems. 𝗪𝗵𝗮𝘁 𝗱𝗼 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗺𝗮𝗸𝗲 𝘁𝗵𝗶𝘀 𝘄𝗼𝗿𝗸? No modifications to your retrieval pipeline are needed. 1. Long context embedding models (8192+ tokens) 2. Chunking logic that tracks token spans 3. Less than 30 lines of code to implement All you need is to switch the order at which you chunk and embed. Embed FIRST, then chunk, not the other way around. Dive deeper into late chunking:
1:19

Traditional chunking: cheap but dumb. ColBERT: smart but expensive. 𝗟𝗮𝘁𝗲 𝗰𝗵𝘂𝗻𝗸𝗶𝗻𝗴: the solution we've been waiting for. Here’s a quick evolution of chunking strategies: → 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 (the basics we all started with) • Token Chunking - split by token count • Sentence Chunking - split by sentence boundaries • Document-Based Chunking - split by sections/paragraphs → 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 (when things got sophisticated) • Semantic Chunking - split by meaning • LLM-Based Chunking - let the model decide But each chunking method separates text at defined points, meaning context is lost within the document from one chunk to the next. → 𝗘𝗻𝘁𝗲𝗿 𝗟𝗮𝘁𝗲 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 (the game changer) Traditional approach: Chunk first → Embed each chunk separately Late chunking approach: Embed the entire document → Then chunk with context preserved 𝗪𝗵𝘆 𝗰𝗵𝗼𝗼𝘀𝗲 𝗹𝗮𝘁𝗲 𝗰𝗵𝘂𝗻𝗸𝗶𝗻𝗴? When you chunk first, each piece loses its contextual relationship to the rest of the document. It's like reading a book by randomly picking paragraphs - you miss the flow. With late chunking, every chunk maintains awareness of its neighbors because the embedding happens at the document level first. Mean pooling is done on segments AFTER the full context is embedded. Jina AI tested and saw significant improvements in retrieval quality - chunks that were previously disconnected now maintain their semantic relationships. As documents get longer and context windows expand, late chunking might just become the new standard for high-quality retrieval systems. 𝗪𝗵𝗮𝘁 𝗱𝗼 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗺𝗮𝗸𝗲 𝘁𝗵𝗶𝘀 𝘄𝗼𝗿𝗸? No modifications to your retrieval pipeline are needed. 1. Long context embedding models (8192+ tokens) 2. Chunking logic that tracks token spans 3. Less than 30 lines of code to implement All you need is to switch the order at which you chunk and embed. Embed FIRST, then chunk, not the other way around. Dive deeper into late chunking:

Femke Plantinga

125,305 просмотров • 11 месяцев назад

Chunking your text data is a crucial step when building a RAG app ✂️ 1. You avoid hitting the token limit 2. Smaller chunks make the retriever more accurate I cover a few chunking methods and suggest a few frameworks that offer this (LlamaIndex 🦙, , deepset, makers of Haystack)
4:24

Chunking your text data is a crucial step when building a RAG app ✂️ 1. You avoid hitting the token limit 2. Smaller chunks make the retriever more accurate I cover a few chunking methods and suggest a few frameworks that offer this (LlamaIndex 🦙, , deepset, makers of Haystack)

Erika Shorten

28,451 просмотров • 2 лет назад

Researchers built a new RAG approach that: - does not need a vector DB. - does not embed data. - involves no chunking. - performs no similarity search. And it hit 98.7% accuracy on a financial benchmark (SOTA). Here's the core problem with RAG that this new approach solves: Traditional RAG chunks documents, embeds them into vectors, and retrieves based on semantic similarity. But similarity ≠ relevance. When you ask "What were the debt trends in 2023?", a vector search returns chunks that look similar. But the actual answer might be buried in some Appendix, referenced on some page, in a section that shares zero semantic overlap with your query. Traditional RAG would likely never find it. PageIndex (open-source) solves this. Instead of chunking and embedding, PageIndex builds a hierarchical tree structure from your documents, like an intelligent table of contents. Then it uses reasoning to traverse that tree. For instance, the model doesn't ask: "What text looks similar to this query?" Instead, it asks: "Based on this document's structure, where would a human expert look for this answer?" That's a fundamentally different approach with: - No arbitrary chunking that breaks context. - No vector DB infrastructure to maintain. - Traceable retrieval to see exactly why it chose a specific section. - The ability to see in-document references ("see Table 5.3") the way a human would. But here's the deeper issue that it solves. Vector search treats every query as independent. But documents have structure and logic, like sections that reference other sections and context that builds across pages. PageIndex respects that structure instead of flattening it into embeddings. Do note that this approach may not make sense in every use case since traditional vector search is still fast, simple, and works well for many applications. But for professional documents that require domain expertise and multi-step reasoning, this tree-based, reasoning-first approach shines. For instance, PageIndex achieved 98.7% accuracy on FinanceBench, significantly outperforming traditional vector-based RAG systems on complex financial document analysis. Everything is fully open-source, so you can see the full implementation in GitHub and try it yourself. I have shared the GitHub repo in the replies!
0:24

Researchers built a new RAG approach that: - does not need a vector DB. - does not embed data. - involves no chunking. - performs no similarity search. And it hit 98.7% accuracy on a financial benchmark (SOTA). Here's the core problem with RAG that this new approach solves: Traditional RAG chunks documents, embeds them into vectors, and retrieves based on semantic similarity. But similarity ≠ relevance. When you ask "What were the debt trends in 2023?", a vector search returns chunks that look similar. But the actual answer might be buried in some Appendix, referenced on some page, in a section that shares zero semantic overlap with your query. Traditional RAG would likely never find it. PageIndex (open-source) solves this. Instead of chunking and embedding, PageIndex builds a hierarchical tree structure from your documents, like an intelligent table of contents. Then it uses reasoning to traverse that tree. For instance, the model doesn't ask: "What text looks similar to this query?" Instead, it asks: "Based on this document's structure, where would a human expert look for this answer?" That's a fundamentally different approach with: - No arbitrary chunking that breaks context. - No vector DB infrastructure to maintain. - Traceable retrieval to see exactly why it chose a specific section. - The ability to see in-document references ("see Table 5.3") the way a human would. But here's the deeper issue that it solves. Vector search treats every query as independent. But documents have structure and logic, like sections that reference other sections and context that builds across pages. PageIndex respects that structure instead of flattening it into embeddings. Do note that this approach may not make sense in every use case since traditional vector search is still fast, simple, and works well for many applications. But for professional documents that require domain expertise and multi-step reasoning, this tree-based, reasoning-first approach shines. For instance, PageIndex achieved 98.7% accuracy on FinanceBench, significantly outperforming traditional vector-based RAG systems on complex financial document analysis. Everything is fully open-source, so you can see the full implementation in GitHub and try it yourself. I have shared the GitHub repo in the replies!

Avi Chawla

971,934 просмотров • 5 месяцев назад

Finally, a RAG solution that works with complex documents! Real-world documents can be messy, filled with text, tables, images, and intricate flow charts. Traditional parsing and chunking methods struggle to handle these. So, what’s the solution? We need smart techniques that can intuitively chunk relevant context and understand what’s inside each chunk, whether it's text, images, or diagrams. In this video, I’ll walk you through a breakthrough technique for extracting structured information from complex documents. It's unlike any other technique you've seen before.✨ It takes any unstructured (text, tables, images, flow-charts) input and parses it into a JSON format that LLMs can easily process. I used EyeLevel.AI's GroundX platform for this – a powerful tool that allows you to build a RAG application in just 3 steps. It also comes with a nice Python SDK and can be easily deployed on-premise (K8s cluster)! Try it yourself:
6:45

Finally, a RAG solution that works with complex documents! Real-world documents can be messy, filled with text, tables, images, and intricate flow charts. Traditional parsing and chunking methods struggle to handle these. So, what’s the solution? We need smart techniques that can intuitively chunk relevant context and understand what’s inside each chunk, whether it's text, images, or diagrams. In this video, I’ll walk you through a breakthrough technique for extracting structured information from complex documents. It's unlike any other technique you've seen before.✨ It takes any unstructured (text, tables, images, flow-charts) input and parses it into a JSON format that LLMs can easily process. I used EyeLevel.AI's GroundX platform for this – a powerful tool that allows you to build a RAG application in just 3 steps. It also comes with a nice Python SDK and can be easily deployed on-premise (K8s cluster)! Try it yourself:

Akshay 🚀

102,660 просмотров • 1 год назад

ColPali is changing the game for PDF retrieval by eliminating the need for OCR and chunking methods 🚀 Inspired by ColBERT’s success with text, ColPali splits an image of a document into patches, which are then processed through a vision LLM called PaliGemma. The embeddings for each patch retain contextual information, similarly to text embeddings in methods like ColBERT. During retrieval, user queries are embedded in the same space, and then compared to document patches using the MaxSim operator. ColPali recipe POC Weaviate AI Database: ColPali paper: As always, shoutout to the awesome Daniel Williams for the help! 💙
2:50

ColPali is changing the game for PDF retrieval by eliminating the need for OCR and chunking methods 🚀 Inspired by ColBERT’s success with text, ColPali splits an image of a document into patches, which are then processed through a vision LLM called PaliGemma. The embeddings for each patch retain contextual information, similarly to text embeddings in methods like ColBERT. During retrieval, user queries are embedded in the same space, and then compared to document patches using the MaxSim operator. ColPali recipe POC Weaviate AI Database: ColPali paper: As always, shoutout to the awesome Daniel Williams for the help! 💙

Victoria Slocum

67,833 просмотров • 1 год назад

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:
1:05

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 просмотров • 1 год назад

visualizing text splitting & chunking strategies ChunkViz .com I thought I remembered a tool to visualize text chunking, but I couldn't find it, so I built one I didn't realize it would be so visually pleasing to tinker with 4 different splitters featured
4:06

visualizing text splitting & chunking strategies ChunkViz .com I thought I remembered a tool to visualize text chunking, but I couldn't find it, so I built one I didn't realize it would be so visually pleasing to tinker with 4 different splitters featured

Greg Kamradt

116,225 просмотров • 2 лет назад

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:
0:44

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 просмотров • 2 лет назад

An underrated issue with document parsing for RAG / agent use cases is dealing with multi-page tables - sometimes a big table spills over into multiple pages. This breaks chunking algorithms that generally operate at the page-level or smaller, and causes LLMs to lose the full view of the data. With LlamaParse Continuous Mode (in beta), you can now parse a document with multi-page tables and join them into a single table! This means you can now: 💡 Do contiguous chunking for RAG use cases OR 💡 Parse the table for text-to-SQL Check out our blog post highlighting this feature. Huge shoutout to Pierre-Loic Doulcet and Sacha Bron : Signup here: It's in beta, let us know your feedback!
2:22

An underrated issue with document parsing for RAG / agent use cases is dealing with multi-page tables - sometimes a big table spills over into multiple pages. This breaks chunking algorithms that generally operate at the page-level or smaller, and causes LLMs to lose the full view of the data. With LlamaParse Continuous Mode (in beta), you can now parse a document with multi-page tables and join them into a single table! This means you can now: 💡 Do contiguous chunking for RAG use cases OR 💡 Parse the table for text-to-SQL Check out our blog post highlighting this feature. Huge shoutout to Pierre-Loic Doulcet and Sacha Bron : Signup here: It's in beta, let us know your feedback!

Jerry Liu

24,245 просмотров • 1 год назад

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!
2:40

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

107,548 просмотров • 1 год назад

60hz! real time chunking on an so101 with LeRobot. Not looking too bad. Bit of jitter throughout but no mode switching across chunks
0:20

60hz! real time chunking on an so101 with LeRobot. Not looking too bad. Bit of jitter throughout but no mode switching across chunks

Jack Vial

82,627 просмотров • 5 месяцев назад

Learn to optimize RAG for cost and performance in our new short course, Prompt Compression and Query Optimization, created with MongoDB and taught by Richmond Alake. This course teaches you to combine traditional database capabilities with vector search using MongoDB for RAG. You'll learn these techniques: - Vector search: For semantic matching of user queries - Filtering using metadata: Pre- and post-filtering to narrow search results - Projections: Selecting only necessary fields to minimize data returned - Boosting: Reranking results to improve relevance - Prompt compression: Using a small LLM to compress context, significantly reducing token count and processing costs These methods address scaling, performance, and security challenges in large-scale RAG applications. You can sign up here:
3:12

Learn to optimize RAG for cost and performance in our new short course, Prompt Compression and Query Optimization, created with MongoDB and taught by Richmond Alake. This course teaches you to combine traditional database capabilities with vector search using MongoDB for RAG. You'll learn these techniques: - Vector search: For semantic matching of user queries - Filtering using metadata: Pre- and post-filtering to narrow search results - Projections: Selecting only necessary fields to minimize data returned - Boosting: Reranking results to improve relevance - Prompt compression: Using a small LLM to compress context, significantly reducing token count and processing costs These methods address scaling, performance, and security challenges in large-scale RAG applications. You can sign up here:

Andrew Ng

71,699 просмотров • 2 лет назад

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:
3:25

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 просмотров • 2 лет назад

New short course on advanced retrieval for RAG (retrieval augmented generation)! RAG fetches relevant documents to give context to an LLM. In Advanced Retrieval for AI with Chroma, taught by Chroma founder anton 🇺🇸, you’ll learn: (i) Query expansion using an LLM to rewrite and improve a query, by either generating either additional relevant queries or a hypothetical answer to the query. (ii) Reranking using a cross-encoder - a model trained to measure similarity between two inputs presented simultaneously. Reranking reorders retrieved documents based on the cross-encoder similarity measure. (iii) Constructing and training an Embedding Adaptor, which is a model that adapts the embedding values to be more relevant to your use case. Each of these techniques can help you build much better RAG systems. Please sign up for the course here:
2:36

New short course on advanced retrieval for RAG (retrieval augmented generation)! RAG fetches relevant documents to give context to an LLM. In Advanced Retrieval for AI with Chroma, taught by Chroma founder anton 🇺🇸, you’ll learn: (i) Query expansion using an LLM to rewrite and improve a query, by either generating either additional relevant queries or a hypothetical answer to the query. (ii) Reranking using a cross-encoder - a model trained to measure similarity between two inputs presented simultaneously. Reranking reorders retrieved documents based on the cross-encoder similarity measure. (iii) Constructing and training an Embedding Adaptor, which is a model that adapts the embedding values to be more relevant to your use case. Each of these techniques can help you build much better RAG systems. Please sign up for the course here:

Andrew Ng

191,219 просмотров • 2 лет назад

If you turn the volume up you can hear how I’m chunking this 5 iron
0:20

If you turn the volume up you can hear how I’m chunking this 5 iron

Zag

13,087 просмотров • 4 месяцев назад

Everyone knows action chunking is great for imitation learning. It turns out that we can extend its success to RL to better leverage prior data for improved exploration and online sample efficiency! The recipe to achieve this is incredibly simple. 🧵 1/N
0:25

Everyone knows action chunking is great for imitation learning. It turns out that we can extend its success to RL to better leverage prior data for improved exploration and online sample efficiency! The recipe to achieve this is incredibly simple. 🧵 1/N

Qiyang (Colin) Li

48,231 просмотров • 1 год назад

Ohhhh look at me I’m Scottie Scheffler I can make pars after chunking it into the jungle I’m the best golfer in the world
0:40

Ohhhh look at me I’m Scottie Scheffler I can make pars after chunking it into the jungle I’m the best golfer in the world

Fore Play

1,013,177 просмотров • 11 месяцев назад

Vision RAG with vector database is all you need. It uses vision language model to embed pages of PDF as directly vectors, without the tedious chunking process. 100% Opensource code.
0:38

Vision RAG with vector database is all you need. It uses vision language model to embed pages of PDF as directly vectors, without the tedious chunking process. 100% Opensource code.

Shubham Saboo

106,731 просмотров • 1 год назад

Just chunking... 😭
1:11

Just chunking... 😭

Aunty Debbie 🇿🇦 ♏

547,478 просмотров • 1 год назад