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Introducing Elysia Playground An interactive playground to experiment with Elysia directly from your browser A new tutorial of 21 pages, With an easy-to-understand guide to help you learn, and build your first Elysia app There has never been a better time to learn Elysia

SaltyAom

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57,785 views • 9 months ago •via X (Twitter)

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Good morning, Elysia AI You can talk to Elysia documentation, starting today

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Dressed in a lovely outfit prepared together with ROG, the elegant Elysia makes her graceful entrance — bringing heartfelt gifts and hoping to brighten your gaming journey. 👉 #HonkaiImpact3rd #ROGxHI3 #Elysia

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Elysia gets hit with a lethal injection. It started out as a trace of that syringe attack animation from Honkai to just deciding to do my own thing with it. #ryona #リョナ

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elysia trolled at first to be a version of herself that doesn't know mei and mei was SO sad... oh my god 😭

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34,711 views • 3 days ago

How do you build an end-to-end agentic RAG app? Lucky for you, you can just run two commands: ‘pip install elysia-ai’ and ’elysia start’ I wrote a massive blog post detailing all the things we built into this 𝗼𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: But if you don't have time to read though that, here’s the TLDR version 🔽 Instead of the typical "text in, text out" approach, Elysia uses a decision tree architecture where intelligent agents determine the best tools to use, evaluate results, and decide whether to continue or complete their tasks. It's an AI that actually thinks through problems step-by-step in a controllable, user-understandable format. The three main things that set Elysia apart: 1️⃣ 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲𝘀 𝘄𝗶𝘁𝗵 𝗦𝗺𝗮𝗿𝘁 𝗔𝗴𝗲𝗻𝘁𝘀: Each node has a decision agent with global context awareness. They evaluate past actions, current state, and future possibilities to choose the optimal tool. Plus, they can handle errors intelligently – if something fails, they'll try a different approach rather than just giving up. 2️⃣ 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗗𝗶𝘀𝗽𝗹𝗮𝘆𝘀: Elysia chooses from seven display formats – tables, e-commerce cards, tickets, conversations, documents, charts, and more. It analyzes your data structure and automatically picks the most appropriate way to present information. 3️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲: Unlike traditional RAG systems that perform blind searches, Elysia analyzes your collections first. It understands your data structure, creates summaries, generates metadata, and uses this knowledge to handle complex queries intelligently. The frontend displays the entire decision tree as it's traversed, showing you exactly why it made each choice. No more black-box AI systems – you get complete transparency into the reasoning process. We built Elysia to be the successor to Verba, taking everything we learned about RAG applications and pushing it to the next level. It's not just about retrieving and generating anymore – 𝗶𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝗰𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗔𝗜 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀 𝘁𝗵𝗮𝘁 𝘁𝗿𝘂𝗹𝘆 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗮𝗻𝗱 𝗽𝗿𝗲𝘀𝗲𝗻𝘁 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆. GitHub: Demo: Get started:

How do you build an end-to-end agentic RAG app? Lucky for you, you can just run two commands: ‘pip install elysia-ai’ and ’elysia start’ I wrote a massive blog post detailing all the things we built into this 𝗼𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: But if you don't have time to read though that, here’s the TLDR version 🔽 Instead of the typical "text in, text out" approach, Elysia uses a decision tree architecture where intelligent agents determine the best tools to use, evaluate results, and decide whether to continue or complete their tasks. It's an AI that actually thinks through problems step-by-step in a controllable, user-understandable format. The three main things that set Elysia apart: 1️⃣ 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲𝘀 𝘄𝗶𝘁𝗵 𝗦𝗺𝗮𝗿𝘁 𝗔𝗴𝗲𝗻𝘁𝘀: Each node has a decision agent with global context awareness. They evaluate past actions, current state, and future possibilities to choose the optimal tool. Plus, they can handle errors intelligently – if something fails, they'll try a different approach rather than just giving up. 2️⃣ 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗗𝗶𝘀𝗽𝗹𝗮𝘆𝘀: Elysia chooses from seven display formats – tables, e-commerce cards, tickets, conversations, documents, charts, and more. It analyzes your data structure and automatically picks the most appropriate way to present information. 3️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲: Unlike traditional RAG systems that perform blind searches, Elysia analyzes your collections first. It understands your data structure, creates summaries, generates metadata, and uses this knowledge to handle complex queries intelligently. The frontend displays the entire decision tree as it's traversed, showing you exactly why it made each choice. No more black-box AI systems – you get complete transparency into the reasoning process. We built Elysia to be the successor to Verba, taking everything we learned about RAG applications and pushing it to the next level. It's not just about retrieving and generating anymore – 𝗶𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝗰𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗔𝗜 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀 𝘁𝗵𝗮𝘁 𝘁𝗿𝘂𝗹𝘆 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗮𝗻𝗱 𝗽𝗿𝗲𝘀𝗲𝗻𝘁 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆. GitHub: Demo: Get started:

Victoria Slocum

12,091 views • 11 months ago

This is the main point why Elysia exists Not performance, not fancy stuffs This problem is very real and I learnt it a hard way Type signature from your actual code IS the documentation not the other way around We spent ~3 years to perfect this

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SaltyAom

93,386 views • 5 months ago

‘pip install elysia’ and ‘elysia start’ That’s literally all it takes to get the most advanced open source agentic RAG app running on your data. We just released 𝗘𝗹𝘆𝘀𝗶𝗮, our open source, agentic RAG framework and an app so cool needed a cool video to go with it. Watch the full video: In the video, we go through these components of Elysia: 1️⃣ 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Instead of giving agents access to all tools at once, Elysia uses a pre-defined web of nodes with corresponding actions. Each decision agent has global context awareness. 2️⃣ 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗗𝗮𝘁𝗮 𝗗𝗶𝘀𝗽𝗹𝗮𝘆𝘀: Seven different data display formats including tables, e-commerce product cards, GitHub tickets, and charts. The system automatically choses the best display format. 3️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲: Unlike naive RAG systems that perform blind vector searches, Elysia analyzes your collections to understand data structure and meaning before performing queries. 𝗢𝘁𝗵𝗲𝗿 𝗖𝗼𝗼𝗹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: • 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗦𝘆𝘀𝘁𝗲𝗺: Uses positive examples as few-shot demonstrations for smaller, faster models • 𝗖𝗵𝘂𝗻𝗸-𝗢𝗻-𝗗𝗲𝗺𝗮𝗻𝗱: Dynamically chunks documents at query time instead of pre-chunking • 𝗠𝘂𝗹𝘁𝗶-𝗠𝗼𝗱𝗲𝗹 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆: Routes different tasks to appropriate model sizes based on complexity …And also how to get started with your own data! The entire project is open source and designed with customization in mind. You can use it as-is for effective data searching, or install the Python package to create custom tools for whatever agentic AI purposes you need. Big kudos to Edward for the vision, filming, and editing this masterpiece

‘pip install elysia’ and ‘elysia start’ That’s literally all it takes to get the most advanced open source agentic RAG app running on your data. We just released 𝗘𝗹𝘆𝘀𝗶𝗮, our open source, agentic RAG framework and an app so cool needed a cool video to go with it. Watch the full video: In the video, we go through these components of Elysia: 1️⃣ 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Instead of giving agents access to all tools at once, Elysia uses a pre-defined web of nodes with corresponding actions. Each decision agent has global context awareness. 2️⃣ 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗗𝗮𝘁𝗮 𝗗𝗶𝘀𝗽𝗹𝗮𝘆𝘀: Seven different data display formats including tables, e-commerce product cards, GitHub tickets, and charts. The system automatically choses the best display format. 3️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲: Unlike naive RAG systems that perform blind vector searches, Elysia analyzes your collections to understand data structure and meaning before performing queries. 𝗢𝘁𝗵𝗲𝗿 𝗖𝗼𝗼𝗹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: • 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗦𝘆𝘀𝘁𝗲𝗺: Uses positive examples as few-shot demonstrations for smaller, faster models • 𝗖𝗵𝘂𝗻𝗸-𝗢𝗻-𝗗𝗲𝗺𝗮𝗻𝗱: Dynamically chunks documents at query time instead of pre-chunking • 𝗠𝘂𝗹𝘁𝗶-𝗠𝗼𝗱𝗲𝗹 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆: Routes different tasks to appropriate model sizes based on complexity …And also how to get started with your own data! The entire project is open source and designed with customization in mind. You can use it as-is for effective data searching, or install the Python package to create custom tools for whatever agentic AI purposes you need. Big kudos to Edward for the vision, filming, and editing this masterpiece

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