Загрузка видео...

Не удалось загрузить видео

На главную

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

GithubProjects's profile picture

GitHub Projects Community

320,471 subscribers

20,985 просмотров • 1 месяц назад •via X (Twitter)

ОбразованиеНаука и технологии
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

R2R from is an open-source agentic retrieval system that transforms RAG with multi-step reasoning across your data and the web.
0:44

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 просмотров • 1 год назад

The future of documentation is AI native and personalized. Answers grounded in your source of truth, powered by agentic retrieval, and includes citations for deeper dives. Start by typing cmd + i.
0:24

The future of documentation is AI native and personalized. Answers grounded in your source of truth, powered by agentic retrieval, and includes citations for deeper dives. Start by typing cmd + i.

Mintlify

54,726 просмотров • 11 месяцев назад

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).
2:19

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

95,370 просмотров • 1 год назад

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 ✊🏽✊🏾✊🏿!
2:24

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

420,673 просмотров • 2 лет назад

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.
1:00:27

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

79,486 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

🆕 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 ⤵️
0:47

🆕 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

19,765 просмотров • 1 год назад

Building a reliable RAG system doesn’t stop at retrieval and generation, you need observability too. In the Retrieval Augmented Generation course, you'll explore how LLM observability platforms can help you: - Trace prompts through each step of the pipeline - Log and evaluate component behavior - Run experiments and monitor system performance over time The lesson uses Phoenix (by Arize) as an open-source example, but the techniques apply broadly, and you'll also learn where traditional tools like Grafana or Datadog fit in. 🧠 Learn how to monitor and improve your RAG systems in the full course:
1:15

Building a reliable RAG system doesn’t stop at retrieval and generation, you need observability too. In the Retrieval Augmented Generation course, you'll explore how LLM observability platforms can help you: - Trace prompts through each step of the pipeline - Log and evaluate component behavior - Run experiments and monitor system performance over time The lesson uses Phoenix (by Arize) as an open-source example, but the techniques apply broadly, and you'll also learn where traditional tools like Grafana or Datadog fit in. 🧠 Learn how to monitor and improve your RAG systems in the full course:

DeepLearning.AI

14,702 просмотров • 9 месяцев назад

YC F24's Circlemind 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.
3:26

YC F24's Circlemind 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

22,927 просмотров • 1 год назад

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.
0:23

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

91,045 просмотров • 8 месяцев назад

🔮 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 ⬇️
0:49

🔮 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

18,565 просмотров • 2 лет назад

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.
0:38

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 🚀

168,465 просмотров • 1 год назад

HelixDB () makes RAG and AI retrieval easier to build and manage with an ultra-performant, open source graph-vector database. Congrats on the launch, George and Xav! Check it out on GitHub:
0:42

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

Y Combinator

67,242 просмотров • 1 год назад

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.
3:45

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

28,171 просмотров • 1 год назад

🚀 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:
2:49

🚀 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

42,281 просмотров • 1 год назад

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!
1:43

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

655,437 просмотров • 2 лет назад

The wait is over - PAI Pro is officially open for early access. It brings Utopai’s cinematic AI capabilities into your coding agent, equipping you with advanced skills for image generation, video generation, and workflow orchestration to bring your story to life. Even fishy ones.
2:07

The wait is over - PAI Pro is officially open for early access. It brings Utopai’s cinematic AI capabilities into your coding agent, equipping you with advanced skills for image generation, video generation, and workflow orchestration to bring your story to life. Even fishy ones.

Utopai Studios

952,190 просмотров • 17 дней назад

Onyx just hit #1 on GitHub trending. Open source AI platform — self-hostable, works with every major LLM provider, and ships with: - Agentic RAG - Deep research mode - Custom agents - Web search - Code execution - Voice mode - Image generation - 50+ connectors out of the box This is what a self-hosted AI stack is starting to look like.
0:29

Onyx just hit #1 on GitHub trending. Open source AI platform — self-hostable, works with every major LLM provider, and ships with: - Agentic RAG - Deep research mode - Custom agents - Web search - Code execution - Voice mode - Image generation - 50+ connectors out of the box This is what a self-hosted AI stack is starting to look like.

0xMarioNawfal

75,491 просмотров • 2 месяцев назад

SOMEONE BUILT AN AI-POWERED SEARCH ENGINE THAT INDEXES YOUR ENTIRE IMESSAGE DATABASE LOCALLY USING CURSOR-STYLE AGENTIC SEARCH, IMAGE EMBEDDINGS, AND CLAUDE FOR ORCHESTRATION. YOUR WHOLE MESSAGE HISTORY. FULLY SEARCHABLE. RUNS ON YOUR MAC. OPEN SOURCE.
1:10

SOMEONE BUILT AN AI-POWERED SEARCH ENGINE THAT INDEXES YOUR ENTIRE IMESSAGE DATABASE LOCALLY USING CURSOR-STYLE AGENTIC SEARCH, IMAGE EMBEDDINGS, AND CLAUDE FOR ORCHESTRATION. YOUR WHOLE MESSAGE HISTORY. FULLY SEARCHABLE. RUNS ON YOUR MAC. OPEN SOURCE.

0xMarioNawfal

67,323 просмотров • 3 месяцев назад

Build an Agentic RAG Agent to Chat with Multiple Sources 🔥 Agno makes it possible. 100% Open Source Link to Agno's GitHub repo in the comments.
1:01

Build an Agentic RAG Agent to Chat with Multiple Sources 🔥 Agno makes it possible. 100% Open Source Link to Agno's GitHub repo in the comments.

Tinz Twins

22,840 просмотров • 1 год назад