Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

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.

_avichawla's profile picture

Avi Chawla

62,316 subscribers

91,045 görüntüleme • 9 ay önce •via X (Twitter)

Bilim & Teknoloji
LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

Real-time knowledge bases are the future of RAG! Today, I'll show you how to build agents that can search across any app, database, or document store in real time. We'll be using Airweave, an open-source tool that builds live, bi-temporal knowledge bases so your agents always reason on the freshest facts. It seamlessly connects to tools like Notion, Google Drive, and SQL databases, transforming their contents into searchable knowledge. The best part? The entire setup runs locally inside a Docker container on my machine. You can also expose it via an API and an MCP server. In the video below, I provide the complete setup and a live demo of its capabilities. I've also shared a link to Airweave's GitHub repo in the next tweet!
9:03

Real-time knowledge bases are the future of RAG! Today, I'll show you how to build agents that can search across any app, database, or document store in real time. We'll be using Airweave, an open-source tool that builds live, bi-temporal knowledge bases so your agents always reason on the freshest facts. It seamlessly connects to tools like Notion, Google Drive, and SQL databases, transforming their contents into searchable knowledge. The best part? The entire setup runs locally inside a Docker container on my machine. You can also expose it via an API and an MCP server. In the video below, I provide the complete setup and a live demo of its capabilities. I've also shared a link to Airweave's GitHub repo in the next tweet!

Akshay 🚀

66,067 görüntüleme • 9 ay önce

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 görüntüleme • 1 yıl önce

Airweave (Airweave (YC X25)) is an open-source tool that lets agents search any app. It connects to apps, databases, or document stores and turns their contents into searchable knowledge bases for agents. Congrats on the launch, Lennert Jansen & Rauf!
1:45

Airweave (Airweave (YC X25)) is an open-source tool that lets agents search any app. It connects to apps, databases, or document stores and turns their contents into searchable knowledge bases for agents. Congrats on the launch, Lennert Jansen & Rauf!

Y Combinator

77,732 görüntüleme • 1 yıl önce

Startups have raised $1B+ to crack Enterprise AI search. Now someone open-sourced it for FREE. The video below shows a self-hosted Slack assistant that answers your questions by searching across all your company's tools in a single query. It's built on top of Airweave, an open-source context retrieval layer that makes all your tools searchable for Agents using semantic, keyword, and agentic search. Here's how the app works: - The app watches for questions in Slack. - It searches every connected tool at once using Airweave (Notion, GitHub, Jira, Linear, etc.) to find relevant context. - The Airweave engine ranks the results by relevance and returns references to the original docs. - An LLM generates the final response and sends it back to Slack with citations. The entire stack is self-hostable via Docker. Find the project in the replies!
0:26

Startups have raised $1B+ to crack Enterprise AI search. Now someone open-sourced it for FREE. The video below shows a self-hosted Slack assistant that answers your questions by searching across all your company's tools in a single query. It's built on top of Airweave, an open-source context retrieval layer that makes all your tools searchable for Agents using semantic, keyword, and agentic search. Here's how the app works: - The app watches for questions in Slack. - It searches every connected tool at once using Airweave (Notion, GitHub, Jira, Linear, etc.) to find relevant context. - The Airweave engine ranks the results by relevance and returns references to the original docs. - An LLM generates the final response and sends it back to Slack with citations. The entire stack is self-hostable via Docker. Find the project in the replies!

Avi Chawla

11,927 görüntüleme • 3 ay önce

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 görüntüleme • 1 yıl önce

Open-source retrieval-augmented generation engine that combines advanced RAG techniques with agentic capabilities for grounded, traceable AI answers.
0:25

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

GitHub Projects Community

20,985 görüntüleme • 2 ay önce

Introducing MCP for arXiv Let your research agents stand on the shoulders of giants Fast multi-turn retrieval, keyword search, and embedding search tools across millions of arXiv papers 🚀
0:39

Introducing MCP for arXiv Let your research agents stand on the shoulders of giants Fast multi-turn retrieval, keyword search, and embedding search tools across millions of arXiv papers 🚀

alphaXiv

276,073 görüntüleme • 3 ay önce

OptimAI Search is now on Agentic(.)Market - and we’re bringing the stack to Base. Builders can plug into live, citation-backed context across web, social, and crypto data through MCP, API, and SDK - then pay seamlessly with x402. No brittle workflows. No manual stitching. Just agent-native access to real-time intelligence at scale. And with custom domain agents on the marketplace, specialized knowledge becomes reusable. Search is becoming infrastructure. Build with it: 💡 💡
1:06

OptimAI Search is now on Agentic(.)Market - and we’re bringing the stack to Base. Builders can plug into live, citation-backed context across web, social, and crypto data through MCP, API, and SDK - then pay seamlessly with x402. No brittle workflows. No manual stitching. Just agent-native access to real-time intelligence at scale. And with custom domain agents on the marketplace, specialized knowledge becomes reusable. Search is becoming infrastructure. Build with it: 💡 💡

OptimAI Network

58,040 görüntüleme • 1 ay önce

We've added native, low-latency RAG to Conversational AI — enabling your voice agents to access and use large knowledge bases in real time.
5:49

We've added native, low-latency RAG to Conversational AI — enabling your voice agents to access and use large knowledge bases in real time.

ElevenLabs

23,107 görüntüleme • 1 yıl önce

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!
9:28

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 görüntüleme • 7 ay önce

We shipped big changes for Cloudflare Developers AI Search! Every agent needs search and it shouldn't take a week to set up. AI Search is the search primitive for your agents. Create an instance, upload your data, and search it. Comes with hybrid retrieval with vector and BM25.
0:24

We shipped big changes for Cloudflare Developers AI Search! Every agent needs search and it shouldn't take a week to set up. AI Search is the search primitive for your agents. Create an instance, upload your data, and search it. Comes with hybrid retrieval with vector and BM25.

Anni Wang

12,975 görüntüleme • 2 ay önce

We built Open Gamma An Open Source Presentation Generator with Composio Tool Router and AI SDK - Generate Presentations in Google Slides instantly - Can research on the topic with web search - Can connect and fetch data from multiple data sources like Hubspot, Slack, Notion and more Completely free and open source link to the code:
1:00

We built Open Gamma An Open Source Presentation Generator with Composio Tool Router and AI SDK - Generate Presentations in Google Slides instantly - Can research on the topic with web search - Can connect and fetch data from multiple data sources like Hubspot, Slack, Notion and more Completely free and open source link to the code:

Karan Vaidya

40,455 görüntüleme • 6 ay önce

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 görüntüleme • 1 yıl önce

Introducing NVIDIA Nemotron 3 Ultra. A frontier smart open model built for long-running agents that need to plan, reason, use tools and keep working across complex coding, research and enterprise workflows. Up to 5x faster inference and up to 30% lower cost for agentic tasks. Learn more:
0:24

Introducing NVIDIA Nemotron 3 Ultra. A frontier smart open model built for long-running agents that need to plan, reason, use tools and keep working across complex coding, research and enterprise workflows. Up to 5x faster inference and up to 30% lower cost for agentic tasks. Learn more:

NVIDIA

227,607 görüntüleme • 29 gün önce

🧑‍🚀 Introducing...Mission Commander 🧑‍🚀 Your new guide to Data Universe, through an #SN1 Apex-powered chat interface which can now: 💡 Identify the optimal hashtags and data sources for your query 🧩 Automatically populate your query with the data labels ⚡ Suggest improvements and extensions to your query Real-time social media data with Gravity, visualised via Nebula, and explored through Mission Commander ♻️ #Bittensor's Constellation is forming ✨
1:21

🧑‍🚀 Introducing...Mission Commander 🧑‍🚀 Your new guide to Data Universe, through an #SN1 Apex-powered chat interface which can now: 💡 Identify the optimal hashtags and data sources for your query 🧩 Automatically populate your query with the data labels ⚡ Suggest improvements and extensions to your query Real-time social media data with Gravity, visualised via Nebula, and explored through Mission Commander ♻️ #Bittensor's Constellation is forming ✨

Macrocosmos

13,230 görüntüleme • 1 yıl önce

Traditional (SQL) databases rely primarily on keyword-based searches to retrieve information. These searches match the exact words or phrases in your query to the text stored in the database. While effective for many applications, this method has limitations when it comes to understanding context or finding relevant information that doesn’t include the exact keywords. Hybrid search combines the strengths of traditional keyword-based BM25 search with the advanced capabilities of semantic search. To effectively implement a hybrid search, a vector database is essential. Vector databases go beyond just words; they understand the meaning behind the data. They transform data such as text, images, or audio into numerical representations called vectors. These vector embeddings enable the database to find similar items, even if they don't share exact keywords. When you integrate hybrid search with Retrieval-Augmented Generation (RAG) systems, you can achieve higher accuracy in retrieved context and better output in generated responses. Learn more about RAG systems in this video with Victoria Slocum:
0:44

Traditional (SQL) databases rely primarily on keyword-based searches to retrieve information. These searches match the exact words or phrases in your query to the text stored in the database. While effective for many applications, this method has limitations when it comes to understanding context or finding relevant information that doesn’t include the exact keywords. Hybrid search combines the strengths of traditional keyword-based BM25 search with the advanced capabilities of semantic search. To effectively implement a hybrid search, a vector database is essential. Vector databases go beyond just words; they understand the meaning behind the data. They transform data such as text, images, or audio into numerical representations called vectors. These vector embeddings enable the database to find similar items, even if they don't share exact keywords. When you integrate hybrid search with Retrieval-Augmented Generation (RAG) systems, you can achieve higher accuracy in retrieved context and better output in generated responses. Learn more about RAG systems in this video with Victoria Slocum:

Femke Plantinga

140,618 görüntüleme • 2 yıl önce

INCREDIBLE!! An MCP server to browse the web like humans! Bright Data MCP server provides 30+ powerful tools that allow AI agents to access, search, crawl, and interact with the web without getting blocked. 100% open-source, works at scale!
0:37

INCREDIBLE!! An MCP server to browse the web like humans! Bright Data MCP server provides 30+ powerful tools that allow AI agents to access, search, crawl, and interact with the web without getting blocked. 100% open-source, works at scale!

Akshay 🚀

90,404 görüntüleme • 1 yıl önce

Today we announced the new Einstein Copilot Search and Salesforce Vector Database in Data Cloud to power semantic search and retrieval augmented generation. This will enhance Einstein Copilot's ability to understand, generate outputs, and automate actions across a wide variety of use cases, contexts, and data/content types. LLMs can't be relied on to recall specific data they were trained on, so the way to make them work in the enterprise, where accuracy is paramount, is to feed them with the right data using hybrid keyword and vector search-powered RAG. By bringing all this seamlessly into our apps and Einstein Copilot UX as well as Hyperforce cloud infrastructure, we can offer high performance, low latency, addressing data privacy, security, and residency requirements🌟
3:06

Today we announced the new Einstein Copilot Search and Salesforce Vector Database in Data Cloud to power semantic search and retrieval augmented generation. This will enhance Einstein Copilot's ability to understand, generate outputs, and automate actions across a wide variety of use cases, contexts, and data/content types. LLMs can't be relied on to recall specific data they were trained on, so the way to make them work in the enterprise, where accuracy is paramount, is to feed them with the right data using hybrid keyword and vector search-powered RAG. By bringing all this seamlessly into our apps and Einstein Copilot UX as well as Hyperforce cloud infrastructure, we can offer high performance, low latency, addressing data privacy, security, and residency requirements🌟

Clara Shih

49,616 görüntüleme • 2 yıl önce

✨ Build real-time conversational agents with Gemini 3.1 Flash Live. Improve user interactions with technology that supports natural conversations across 90+ languages, accent changes, and multimodal inputs.
1:08

✨ Build real-time conversational agents with Gemini 3.1 Flash Live. Improve user interactions with technology that supports natural conversations across 90+ languages, accent changes, and multimodal inputs.

Google for Developers

17,001 görüntüleme • 2 ay önce

I built a Deepseek R1 RAG Reasoning Agent running locally on my computer. It's an Agentic RAG reasoning agent that can think, reason and fall back to web search if needed. 100% Opensource code with step-by-step tutorial.
1:50

I built a Deepseek R1 RAG Reasoning Agent running locally on my computer. It's an Agentic RAG reasoning agent that can think, reason and fall back to web search if needed. 100% Opensource code with step-by-step tutorial.

Shubham Saboo

124,490 görüntüleme • 1 yıl önce