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In Knowledge Graphs for RAG, our latest short course, you’ll explore how knowledge graphs work, how to build with them, and create better retrieval augmented generation applications. Start today for free:

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DeepLearning.AI

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13,987 görüntüleme • 2 yıl önce •via X (Twitter)

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Knowledge graphs are really cool 🧠 What’s even cooler is LLMs + knowledge graphs backed by a graph db (NebulaGraph) 🔥 This presents an entirely new stack for retrieval-augmented generation (separate from vector db + top-k)! Now possible with LlamaIndex 🦙 👇
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Knowledge graphs are really cool 🧠 What’s even cooler is LLMs + knowledge graphs backed by a graph db (NebulaGraph) 🔥 This presents an entirely new stack for retrieval-augmented generation (separate from vector db + top-k)! Now possible with LlamaIndex 🦙 👇

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RAG is not Memory for AI Agents. 5 AI memory engines to build agents that maintain long-term context and learn continuously: (Last 2 released just this month) 1. Zep builds and queries temporally-aware knowledge graphs that evolve with every interaction. 100% Opensource.
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RAG is not Memory for AI Agents. 5 AI memory engines to build agents that maintain long-term context and learn continuously: (Last 2 released just this month) 1. Zep builds and queries temporally-aware knowledge graphs that evolve with every interaction. 100% Opensource.

Unwind AI

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Today SciPhi is open-sourcing Triplex, a SOTA LLM for knowledge graph construction. Triplex is so small that it can be used with SciPhi's R2R to build knowledge graphs directly from your laptop. Triplex outperforms few-shot prompted gpt-4o at 1/60th the inference cost.
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Today SciPhi is open-sourcing Triplex, a SOTA LLM for knowledge graph construction. Triplex is so small that it can be used with SciPhi's R2R to build knowledge graphs directly from your laptop. Triplex outperforms few-shot prompted gpt-4o at 1/60th the inference cost.

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RAG is not Memory. AI agents need long-term memory to maintain context and learn continuously. 5 frameworks for AI agent memory. 100% opensource. 1. Graphiti builds temporally-aware knowledge graphs for AI agents that change over time with evolving relationships and context.
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RAG is not Memory. AI agents need long-term memory to maintain context and learn continuously. 5 frameworks for AI agent memory. 100% opensource. 1. Graphiti builds temporally-aware knowledge graphs for AI agents that change over time with evolving relationships and context.

Unwind AI

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How do we approach design? Try it for yourself. Want to join our discord for more insider knowledge ask in the comments.
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How do we approach design? Try it for yourself. Want to join our discord for more insider knowledge ask in the comments.

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Introducing knowledge bases for Poe bots! Starting today, bot creators can now add knowledge bases to their bots, to create more unique experiences grounded in any sources they provide, complete with citations. (1/5)
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Introducing knowledge bases for Poe bots! Starting today, bot creators can now add knowledge bases to their bots, to create more unique experiences grounded in any sources they provide, complete with citations. (1/5)

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Check out these common #OnlineSafety myths and facts to brush up on your knowledge this #SaferInternetDay! 💭 Want to learn more? 👇 Explore our full library of FREE guides for parents, carers and educators today >>
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Check out these common #OnlineSafety myths and facts to brush up on your knowledge this #SaferInternetDay! 💭 Want to learn more? 👇 Explore our full library of FREE guides for parents, carers and educators today >>

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GraphRAG-UI GraphRAG-UI is a user-friendly interface for GraphRAG, a powerful tool that uses the Retrieval-Augmented Generation (RAG) approach to index and query large text data. This project supports the latest version graphrag-0.3.3 and aims to provide a convenient management and interaction method for GraphRAG, supporting the configuration of local large language models like Ollama, making it easier for users to leverage.
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GraphRAG-UI GraphRAG-UI is a user-friendly interface for GraphRAG, a powerful tool that uses the Retrieval-Augmented Generation (RAG) approach to index and query large text data. This project supports the latest version graphrag-0.3.3 and aims to provide a convenient management and interaction method for GraphRAG, supporting the configuration of local large language models like Ollama, making it easier for users to leverage.

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Brilliant open-source tool from Anthropic Visualize internal LLM pathways through open attribution graphs. → Attribution graphs reveal which neurons and features drive each output token, turning the LLM’s black box into a clear map. They open-sourced a library that generates attribution graphs to trace how LLMs arrive at outputs. These graphs reveal internal activation flows and feature influences in popular open-weights models. 🧠 Attribution Graphs Graphs link model activations to output tokens. Nodes represent neurons or features. Edges show influence strength. Researchers inspect paths to see which components drive each decision. 🛠️ Toolchain and Frontend Circuit-tracer library works with models like Gemma-2-2b and Llama-3.2-1b. Demo notebooks illustrate multi-step reasoning examples. Neuronpedia hosts an interactive UI for graph exploration, annotation, and sharing. 🔍 Hypothesis Testing Users tweak feature values in graphs, regenerate outputs, and observe changes. This lets researchers validate whether specific circuits control behaviors like language translation or reasoning chains.
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Brilliant open-source tool from Anthropic Visualize internal LLM pathways through open attribution graphs. → Attribution graphs reveal which neurons and features drive each output token, turning the LLM’s black box into a clear map. They open-sourced a library that generates attribution graphs to trace how LLMs arrive at outputs. These graphs reveal internal activation flows and feature influences in popular open-weights models. 🧠 Attribution Graphs Graphs link model activations to output tokens. Nodes represent neurons or features. Edges show influence strength. Researchers inspect paths to see which components drive each decision. 🛠️ Toolchain and Frontend Circuit-tracer library works with models like Gemma-2-2b and Llama-3.2-1b. Demo notebooks illustrate multi-step reasoning examples. Neuronpedia hosts an interactive UI for graph exploration, annotation, and sharing. 🔍 Hypothesis Testing Users tweak feature values in graphs, regenerate outputs, and observe changes. This lets researchers validate whether specific circuits control behaviors like language translation or reasoning chains.

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Here’s a pretty weird and surprising result - retrieval-augmented generation works unreasonably well for robot learning – but only when parameterized using difference vectors! We introduce Difference-Aware Retrieval Policies for Imitation Learning (DARP), a simple, semi-parametric RAG architecture for imitation learning that achieves gains of up to 200% over standard behavior cloning. No additional assumptions beyond BC, just a little architecture switch! The theory backing it up is pretty cool too and it works on real robots! :) Play with our website to understand better: 🧵(1/7)
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Here’s a pretty weird and surprising result - retrieval-augmented generation works unreasonably well for robot learning – but only when parameterized using difference vectors! We introduce Difference-Aware Retrieval Policies for Imitation Learning (DARP), a simple, semi-parametric RAG architecture for imitation learning that achieves gains of up to 200% over standard behavior cloning. No additional assumptions beyond BC, just a little architecture switch! The theory backing it up is pretty cool too and it works on real robots! :) Play with our website to understand better: 🧵(1/7)

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how it felt walking into my school’s Scholastic Book Fair with $20 and a thirst for knowledge
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You can now view Knowledge Base retrieval chunks directly in the Test LLM feature🌐 Get full visibility into how your Knowledge Base is being used. Instantly verify whether queries retrieve the right information — and fine-tune as needed.
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You can now view Knowledge Base retrieval chunks directly in the Test LLM feature🌐 Get full visibility into how your Knowledge Base is being used. Instantly verify whether queries retrieve the right information — and fine-tune as needed.

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Grateful to participate in the AFN Special Chiefs Assembly today. Partnership with First Nations is at the core of our mission to build. Only by working together can we build stronger communities and better futures for the next generation.
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Grateful to participate in the AFN Special Chiefs Assembly today. Partnership with First Nations is at the core of our mission to build. Only by working together can we build stronger communities and better futures for the next generation.

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What if your second brain could actually think with you? Today, we're proud to release Ryzome - where your knowledge comes alive with AI. So, what exactly is Ryzome? and how is it changing the way people think, create, and do work? Thread 👇[1/12]
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What if your second brain could actually think with you? Today, we're proud to release Ryzome - where your knowledge comes alive with AI. So, what exactly is Ryzome? and how is it changing the way people think, create, and do work? Thread 👇[1/12]

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How have I lived for 25 years without this knowledge???

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Finally! A RAG over code solution that actually works (open-source). Naive chunking used in RAG isn't suited for code. This is because codebases have long-range dependencies, cross-file references, etc., that independent text chunks just can't capture. Graph-Code is a graph-driven RAG system that solves this. It analyzes the Python codebase and builds knowledge graphs to enable natural language querying. Key features: - Deep code parsing to extract classes, functions, and relationships. - Uses Memgraph to store the codebase as a graph. - Parses pyproject to understand external dependencies. - Retrieves actual source code snippets for found functions. Find the repo in the replies!
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Finally! A RAG over code solution that actually works (open-source). Naive chunking used in RAG isn't suited for code. This is because codebases have long-range dependencies, cross-file references, etc., that independent text chunks just can't capture. Graph-Code is a graph-driven RAG system that solves this. It analyzes the Python codebase and builds knowledge graphs to enable natural language querying. Key features: - Deep code parsing to extract classes, functions, and relationships. - Uses Memgraph to store the codebase as a graph. - Parses pyproject to understand external dependencies. - Retrieves actual source code snippets for found functions. Find the repo in the replies!

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Treat the whole patient with confidence. We’ll help you create treatment plans for patients with complex comorbidities. Free access to the Comorbidities Manager for all NHS staff and learners, funded by @NHS_HealthEdEng NHS Knowledge for Healthcare #NHSBMJBP
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Treat the whole patient with confidence. We’ll help you create treatment plans for patients with complex comorbidities. Free access to the Comorbidities Manager for all NHS staff and learners, funded by @NHS_HealthEdEng NHS Knowledge for Healthcare #NHSBMJBP

BMJ Best Practice

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We’ve got you. We’ll help you create treatment plans for patients with complex comorbidities. Free access to the Comorbidities Manager for all NHS staff and learners. NHS England Workforce, Training and Education | #NHSBMJBP | NHS Knowledge for Healthcare
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We’ve got you. We’ll help you create treatment plans for patients with complex comorbidities. Free access to the Comorbidities Manager for all NHS staff and learners. NHS England Workforce, Training and Education | #NHSBMJBP | NHS Knowledge for Healthcare

BMJ Best Practice

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AI Agents can take your business to the next level, and Bird makes set up easier than ever. Dive into our Knowledge Base to learn how and explore a range of other valuable resources.
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AI Agents can take your business to the next level, and Bird makes set up easier than ever. Dive into our Knowledge Base to learn how and explore a range of other valuable resources.

Bird

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Today’s decentralized applications are fragmented across a chaotic landscape of disconnected blockchains. It’s time to unite them once and for all. Explore the three layers of Anoma to create one application layer for all Web3. One place for apps that just work everywhere.
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Today’s decentralized applications are fragmented across a chaotic landscape of disconnected blockchains. It’s time to unite them once and for all. Explore the three layers of Anoma to create one application layer for all Web3. One place for apps that just work everywhere.

Anoma

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