正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Open-source retrieval-augmented generation engine that combines advanced RAG techniques with agentic capabilities for grounded, traceable AI answers.

GitHub Projects Community

320,471 subscribers

20,985 次观看 • 5 个月前 •via X (Twitter)

教育科学技术

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

R2R from is an open-source agentic retrieval system that transforms RAG with multi-step reasoning across your data and the web.

R2R from is an open-source agentic retrieval system that transforms RAG with multi-step reasoning across your data and the web.

Y Combinator

23,251 次观看 • 1 年前

Vision-based(Colapli) RAG is becoming popular, so we built a platform to compare: - Simple OCR RAG - VisionRAG - Colpali - Hybrid Colpali 🚀 Introducing VARAG – the Vision-First RAG Engine (Vision Augmented Retrieval and Generation).

Vision-based(Colapli) RAG is becoming popular, so we built a platform to compare: - Simple OCR RAG - VisionRAG - Colpali - Hybrid Colpali 🚀 Introducing VARAG – the Vision-First RAG Engine (Vision Augmented Retrieval and Generation).

Adithya S K

96,933 次观看 • 2 年前

Vector databases are a key part of many LLM applications that need search or data retrieval, for example with Retrieval Augmented Generation (RAG). Learn how they work + how to use them in our new short course, taught by Weaviate AI Database's Sebastia(N_) Witalec ✊🏽✊🏾✊🏿!

Vector databases are a key part of many LLM applications that need search or data retrieval, for example with Retrieval Augmented Generation (RAG). Learn how they work + how to use them in our new short course, taught by Weaviate AI Database's Sebastia(N_) Witalec ✊🏽✊🏾✊🏿!

Andrew Ng

420,683 次观看 • 3 年前

We're unlocking next-level AI capabilities with the launch of our Retrieval Augmentation Generation (RAG) architecture! 🌟 By integrating a sophisticated information retrieval system, RAG empowers our Large Language Model (LLM), Qwen, with an extra layer of context, allowing it to access industry-specific insights or a company's proprietary data on demand. At the heart of RAG's knowledgebase lies the Vector Engine – a powerhouse of semantic recall ensuring all data is meticulously preserved and readily accessible. Plus, with Alibaba Cloud's suite of vector database solutions, we're at the forefront of augmented data retrieval, offering unprecedented precision and speed. Get ready to transform your data strategy with RAG! #AlibabaCloud #AI #Innovation #DataStrategy #VectorEngine #RAG #AIandBigDataSummit2024 #AIForward

We're unlocking next-level AI capabilities with the launch of our Retrieval Augmentation Generation (RAG) architecture! 🌟 By integrating a sophisticated information retrieval system, RAG empowers our Large Language Model (LLM), Qwen, with an extra layer of context, allowing it to access industry-specific insights or a company's proprietary data on demand. At the heart of RAG's knowledgebase lies the Vector Engine – a powerhouse of semantic recall ensuring all data is meticulously preserved and readily accessible. Plus, with Alibaba Cloud's suite of vector database solutions, we're at the forefront of augmented data retrieval, offering unprecedented precision and speed. Get ready to transform your data strategy with RAG! #AlibabaCloud #AI #Innovation #DataStrategy #VectorEngine #RAG #AIandBigDataSummit2024 #AIForward

Alibaba Cloud

9,636,416 次观看 • 2 年前

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:

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 次观看 • 2 年前

Check it out: An entire lesson from BloomTech's AI for Developer Productivity course! Fundamentals of RAG (Retrieval-Augmented-Generation). How we enhance accuracy and reliability of generative AI models. This is the foundation we build on to give AI important context.

Check it out: An entire lesson from BloomTech's AI for Developer Productivity course! Fundamentals of RAG (Retrieval-Augmented-Generation). How we enhance accuracy and reliability of generative AI models. This is the foundation we build on to give AI important context.

Austen Allred

79,529 次观看 • 2 年前

RAG Pipeline: a five-stage retrieval-augmented generation flow, from query to streamed answer with citations. each citation pings its source chunk when it appears in the answer. hover either end and the other lights up in the same hue.

RAG Pipeline: a five-stage retrieval-augmented generation flow, from query to streamed answer with citations. each citation pings its source chunk when it appears in the answer. hover either end and the other lights up in the same hue.

Jeet

30,487 次观看 • 1 个月前

YC F24's Smooth is an open-source RAG that uses knowledge graphs and PageRank for more accurate retrieval. Their RAG is up to 3x more accurate than vector databases.

YC F24's Smooth is an open-source RAG that uses knowledge graphs and PageRank for more accurate retrieval. Their RAG is up to 3x more accurate than vector databases.

Y Combinator

23,018 次观看 • 1 年前

🆕 Retrieval Engine by tackles the AI hallucination problem 🔥 RAG grounds AI responses in your docs (with citations!), reducing fabricated outputs that pretend to be accurate. 1️⃣ Upload docs 2️⃣ Ask questions 3️⃣ Get cited answers Built for when accuracy really matters. Try it now ⤵️

🆕 Retrieval Engine by tackles the AI hallucination problem 🔥 RAG grounds AI responses in your docs (with citations!), reducing fabricated outputs that pretend to be accurate. 1️⃣ Upload docs 2️⃣ Ask questions 3️⃣ Get cited answers Built for when accuracy really matters. Try it now ⤵️

io.net

19,849 次观看 • 1 年前

RAG can’t keep up with real-time data. Airweave builds live, bi-temporal knowledge bases so that your Agents always reason on the freshest facts. Supports fully agentic retrieval with semantic and keyword search, query expansion, and more across 30+ sources. 100% open-source.

RAG can’t keep up with real-time data. Airweave builds live, bi-temporal knowledge bases so that your Agents always reason on the freshest facts. Supports fully agentic retrieval with semantic and keyword search, query expansion, and more across 30+ sources. 100% open-source.

Avi Chawla

91,373 次观看 • 1 年前

Finally! A Text-to-SQL tool that actually works! Vanna is an open-source RAG framework for complex Text-to-SQL generation. It manages dynamic data and allows custom RAG model training for greater accuracy. 100% open-source.

Finally! A Text-to-SQL tool that actually works! Vanna is an open-source RAG framework for complex Text-to-SQL generation. It manages dynamic data and allows custom RAG model training for greater accuracy. 100% open-source.

Akshay 🚀

168,734 次观看 • 1 年前

🔮 7 AI Predictions for 2024 We spill the AI tea on these critical shifts: 🛡️ Cybersecurity 🖼️ Multimodal AI 🎨 Fine-tuning LLMs 🔁 LLMs commoditization 💡 Closed-source models 👨‍💻 Developer productivity 🔎 Retrieval-Augmented Generation Watch now ⬇️

🔮 7 AI Predictions for 2024 We spill the AI tea on these critical shifts: 🛡️ Cybersecurity 🖼️ Multimodal AI 🎨 Fine-tuning LLMs 🔁 LLMs commoditization 💡 Closed-source models 👨‍💻 Developer productivity 🔎 Retrieval-Augmented Generation Watch now ⬇️

App Economy Insights

18,594 次观看 • 2 年前

HelixDB () makes RAG and AI retrieval easier to build and manage with an ultra-performant, open source graph-vector database. Congrats on the launch, Lam Sambert and Xav! Check it out on GitHub:

HelixDB () makes RAG and AI retrieval easier to build and manage with an ultra-performant, open source graph-vector database. Congrats on the launch, Lam Sambert and Xav! Check it out on GitHub:

Y Combinator

67,993 次观看 • 1 年前

today i'm launching an open source RAG platform built on top of turbopuffer fast ingestion, fast retrieval, clean developer experience. one of the few AI infra products that genuinely feels modern. more soon 👀

today i'm launching an open source RAG platform built on top of turbopuffer fast ingestion, fast retrieval, clean developer experience. one of the few AI infra products that genuinely feels modern. more soon 👀

Yoginth

46,844 次观看 • 5 个月前

In this short video, I explain the concept of RAG (Retrieval-Augmented Generation) in a simple and easy-to-understand way. I hope it helps! Let me know your thoughts. The material is based on Prof. Tom Yeh's 'AI by Hand' resources.

In this short video, I explain the concept of RAG (Retrieval-Augmented Generation) in a simple and easy-to-understand way. I hope it helps! Let me know your thoughts. The material is based on Prof. Tom Yeh's 'AI by Hand' resources.

Cornelius Adejoro

28,210 次观看 • 2 年前

How to Make AI Models More Useful? Discover the potential of AI and blockchain, and how features like RAG and TEEs can help NEAR unlock its full potential to build the world's best truly open AI. TL;DR - RAG (Retrieval-Augmented Generation) enhances AI agents by personalizing responses with relevant context. - Blockchain ensures transactions are processed transparently, efficiently, and securely. - NEAR Protocol is designed for AI, featuring a sharding architecture that supports high transaction volumes at low fees without compromising performance. It also offers privacy, user-owned data, and monetization opportunities. Study the NEAR stack, explore how to make AI models useful:

How to Make AI Models More Useful? Discover the potential of AI and blockchain, and how features like RAG and TEEs can help NEAR unlock its full potential to build the world's best truly open AI. TL;DR - RAG (Retrieval-Augmented Generation) enhances AI agents by personalizing responses with relevant context. - Blockchain ensures transactions are processed transparently, efficiently, and securely. - NEAR Protocol is designed for AI, featuring a sharding architecture that supports high transaction volumes at low fees without compromising performance. It also offers privacy, user-owned data, and monetization opportunities. Study the NEAR stack, explore how to make AI models useful:

NEARWEEK

23,509 次观看 • 1 年前

New short course on sophisticated RAG (Retrieval Augmented Generation) techniques is out! Taught by Jerry Liu and Anupam Datta of LlamaIndex 🦙 and TruEra , this teaches advanced techniques that help your LLM generate good answers. Topics include: - Sentence-window retrieval, which retrieves not just the most relevant sentence, but a window of sentences around it for higher quality context. - Auto-merging retrieval, which organizes your document into a hierarchical tree structure, where each parent node's text is split among its child nodes. Based on the relevance of the child nodes to a user query, this lets you better decide whether the entire parent node should be provided as context to the LLM. - Evaluation methodology for separately evaluating the quality of the key steps of RAG (context relevance, answer relevance, groundedness) so that you can perform error analysis, identify which part of your pipeline needs work, and tune components systematically. Please check out the course!

New short course on sophisticated RAG (Retrieval Augmented Generation) techniques is out! Taught by Jerry Liu and Anupam Datta of LlamaIndex 🦙 and TruEra , this teaches advanced techniques that help your LLM generate good answers. Topics include: - Sentence-window retrieval, which retrieves not just the most relevant sentence, but a window of sentences around it for higher quality context. - Auto-merging retrieval, which organizes your document into a hierarchical tree structure, where each parent node's text is split among its child nodes. Based on the relevance of the child nodes to a user query, this lets you better decide whether the entire parent node should be provided as context to the LLM. - Evaluation methodology for separately evaluating the quality of the key steps of RAG (context relevance, answer relevance, groundedness) so that you can perform error analysis, identify which part of your pipeline needs work, and tune components systematically. Please check out the course!

Andrew Ng

655,873 次观看 • 2 年前

🚀 Amazon Nova Premier, our most capable teacher model for creating custom distilled models, is now available on Amazon Bedrock! Built for complex tasks like Retrieval-Augmented Generation (RAG), function calling, and agentic coding, its one-million-token context window enables analysis of large datasets while being the most cost-effective proprietary model in its intelligence tier. Learn more:

🚀 Amazon Nova Premier, our most capable teacher model for creating custom distilled models, is now available on Amazon Bedrock! Built for complex tasks like Retrieval-Augmented Generation (RAG), function calling, and agentic coding, its one-million-token context window enables analysis of large datasets while being the most cost-effective proprietary model in its intelligence tier. Learn more:

Amazon Science

42,314 次观看 • 1 年前