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

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20,985 次观看 • 4 个月前 •via X (Twitter)

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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.

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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).

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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 ✊🏽✊🏾✊🏿!

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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

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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:

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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.

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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.

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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 ⤵️

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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:

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28,210 次观看 • 1 年前

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