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RAG works well with static data, but what if data constantly changes? ⚡️ Give your agent access to up-to-date data from any source with the Graphiti Temporal Graph Framework. ⏰ ➡️ Check out Cole Medin's awesome Graphiti walkthrough.

Daniel Chalef

2,444 subscribers

71,021 次观看 • 1 年前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

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Chris Leone 的头像

Chris Leone1 年前

@cole_medin Cole knows AI.

HUDI 的头像

HUDI2 年前

🤔How to generate profit from your data? To unlock data monetization, we started with data ownership and crafted a fully decentralized data ecosystem: 1. Data Wallet (DataMask): Securely store and manage your data. 2. Data Connector (Connectors): Import data from Web2 giants like Google, Facebook, Amazon, Netflix, LinkedIn, TikTok, etc. 3. Data Protocol (IPDW): Enable peer-to-peer data sharing for monetization. Our roadmap ahead: 1. Aim for a critical mass of Data Wallet users (target: 1 million). 2. Develop a Data Exchange to bridge these data assets with the existing data market. The dice are cast. Reclaim your data sovereignty: join the data revolution 🦾🐸🏴‍☠️ #DataOwnership #DecentralizedData #JoinTheRevolution

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Introducing RAGs, a Streamlit app that allows you to create and customize your own RAG agent and then use it over your own data, all with natural language 🔥 Directly inspired by OpenAI GPTs, you can converse with an agent to help you do search/retrieval over any data you specify. The app contains three main pages: 🏠 Home Page : Have a “builder agent” build your RAG agent through natural language (you specify the data). ⚙️ RAG Config: Look at configured parameters 🤖 Use your RAG agent! Check out details below 👇 Blog: Repo:

Introducing RAGs, a Streamlit app that allows you to create and customize your own RAG agent and then use it over your own data, all with natural language 🔥 Directly inspired by OpenAI GPTs, you can converse with an agent to help you do search/retrieval over any data you specify. The app contains three main pages: 🏠 Home Page : Have a “builder agent” build your RAG agent through natural language (you specify the data). ⚙️ RAG Config: Look at configured parameters 🤖 Use your RAG agent! Check out details below 👇 Blog: Repo:

LlamaIndex 🦙

475,732 次观看 • 2 年前

Most underrated data source in a company: your AI agent's conversations. Your agent talks to more customers than any employee, but the data it generates goes nowhere. Latitude.so changes that, see how:

Most underrated data source in a company: your AI agent's conversations. Your agent talks to more customers than any employee, but the data it generates goes nowhere. Latitude.so changes that, see how:

cesar.wtf

1,344,530 次观看 • 1 个月前

Remove your data from Google listings with this simple trick. This will not remove your data from the original source but will make it a bit harder to find.

Remove your data from Google listings with this simple trick. This will not remove your data from the original source but will make it a bit harder to find.

Ryan M. Montgomery

286,107 次观看 • 1 年前

New JavaScript short course: Build a full-stack web application that uses RAG in JavaScript RAG Web Apps with LlamaIndex, taught by Laurie Voss, VP of Developer Relations at LlamaIndex 🦙 and npm co-founder. - Build a RAG application for querying your own data - Develop tools to interact with multiple data sources using an agent that intelligently selects the right tool for your queries - Create a full-stack web app that can chat with your data - Dig further into production-ready techniques, like how to persist your data so you aren’t constantly reindexing, and try the create-llama command line tool from LlamaIndex You can sign up here:

New JavaScript short course: Build a full-stack web application that uses RAG in JavaScript RAG Web Apps with LlamaIndex, taught by Laurie Voss, VP of Developer Relations at LlamaIndex 🦙 and npm co-founder. - Build a RAG application for querying your own data - Develop tools to interact with multiple data sources using an agent that intelligently selects the right tool for your queries - Create a full-stack web app that can chat with your data - Dig further into production-ready techniques, like how to persist your data so you aren’t constantly reindexing, and try the create-llama command line tool from LlamaIndex You can sign up here:

Andrew Ng

218,284 次观看 • 2 年前

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!

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 次观看 • 8 个月前

Open-sourcing Introspect: MIT-licensed Deep-Research for your internal data! Works with spreadsheets, databases, PDFs, and web search. Has a remarkably simple architecture – Sonnet agent armed with recursive tool calling and 3 default tools. Best for use-cases where you want to combine insights from SQL with unstructured data + data from the web. Launching a bunch of products built on top of of this soon, but wanted to open-source our core engine to give back to the community! 2 min demo below, links in comments

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71,549 次观看 • 1 年前

You can now protect against sybil attacks without requiring users to give up personal data. Alongside Billions Network, we've developed a POC any company can set up where users claim rewards after verification - without sharing personal data with the company. Check out the demo below👇

You can now protect against sybil attacks without requiring users to give up personal data. Alongside Billions Network, we've developed a POC any company can set up where users claim rewards after verification - without sharing personal data with the company. Check out the demo below👇

KRNL Labs

94,578 次观看 • 10 个月前

1/ 🧠Humans are the best robot data source! 2/ 👓Human egocentric video is rich in quantity, but poor in quality. 3/ Beyond scaling data, smarter representation and architecture matter just as much. 4/ Want an open-source framework to train your own learn-from-human-data robot policy? 🚀We introduce HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos⬇️ ✦ Zero-Shot Human-to-Robot Transfer ✦ Robot-Data-Free ✦ Just 30 min of data per task ✦ Collect by Anyone, Anytime, Anywhere ✦ Deploy on Any Robot, Any Camera, Any Environment ✦ Open-Source & Easy-to-Implement Let's squeeze every bit of signal out of human data! 🌐 Website: 📄 Paper: 💻 Code: 📹 Video: 🧵 1/n

1/ 🧠Humans are the best robot data source! 2/ 👓Human egocentric video is rich in quantity, but poor in quality. 3/ Beyond scaling data, smarter representation and architecture matter just as much. 4/ Want an open-source framework to train your own learn-from-human-data robot policy? 🚀We introduce HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos⬇️ ✦ Zero-Shot Human-to-Robot Transfer ✦ Robot-Data-Free ✦ Just 30 min of data per task ✦ Collect by Anyone, Anytime, Anywhere ✦ Deploy on Any Robot, Any Camera, Any Environment ✦ Open-Source & Easy-to-Implement Let's squeeze every bit of signal out of human data! 🌐 Website: 📄 Paper: 💻 Code: 📹 Video: 🧵 1/n

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We figured out a new way to work with infra data: not old static dashboards, not “AI on every click” chaos. But generative UI with structure. Cased generates a controller, validates it, then runs real data: fast & predictable. AI does the thinking, UX stays fast. Check it out:

Ted Nyman

24,473 次观看 • 7 个月前

Here's Box AI doing data extraction on a financial press release. With AI, you'll be able to pull out any data you want from any piece of information in the world. Still in the early stages but it's getting better every day with each model breakthrough.

Here's Box AI doing data extraction on a financial press release. With AI, you'll be able to pull out any data you want from any piece of information in the world. Still in the early stages but it's getting better every day with each model breakthrough.

Aaron Levie

34,900 次观看 • 1 年前

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Andrew Ng

244,329 次观看 • 2 年前

Traditional data pipelines don't work for RAG applications. There are 3 issues with them: 1. Traditional data engineering solutions are optimized to handle structured data. RAG applications rely primarily on unstructured data. 2. The connector ecosystem to load data from unstructured data sources is very immature. 3. Traditional solutions do not offer any way to transform unstructured data into an optimized vector search index. The goal of a RAG Pipeline is to solve these problems. The number one objective is to create a reliable vector search index using factual knowledge and relevant context. This sounds easy, but it's one of the biggest challenges we face when building RAG applications. At a high level, there are four different stages in the architecture of a RAG pipeline: 1. Ingestion: Here is where the pipeline loads the information from the data source. 2. Extraction: Where the pipeline processes the input data and decides how to retrieve the text contained inside them. 3. Transform: Where the pipeline chunks the data and generates document embeddings. 4. Load: Where the pipeline creates a search index in a vector database and loads the document embeddings. There are different rabbit holes at each one of these stages. Here are three of them: 1. Ingesting data once is simple. The hard part is refreshing the vector database whenever the original data source changes. 2. Extracting the content of a plain text document is simple. The hard part is to extract content from complex documents containing tables, images, or cross-references. 3. A simple continual chunking strategy with an overlap is simple. The hard part is to find the optimal strategy for your specific knowledge base and the way you are planning to query it. In the attached video, I'll show you how you can build an enterprise-grade RAG Pipeline that solves every one of the above problems. I'll use Vectorize. They partnered with me on this post. You can use them to build RAG pipelines optimized for accurate context retrieval. If you have a few documents lying around, set up a free account and give it a try.

Traditional data pipelines don't work for RAG applications. There are 3 issues with them: 1. Traditional data engineering solutions are optimized to handle structured data. RAG applications rely primarily on unstructured data. 2. The connector ecosystem to load data from unstructured data sources is very immature. 3. Traditional solutions do not offer any way to transform unstructured data into an optimized vector search index. The goal of a RAG Pipeline is to solve these problems. The number one objective is to create a reliable vector search index using factual knowledge and relevant context. This sounds easy, but it's one of the biggest challenges we face when building RAG applications. At a high level, there are four different stages in the architecture of a RAG pipeline: 1. Ingestion: Here is where the pipeline loads the information from the data source. 2. Extraction: Where the pipeline processes the input data and decides how to retrieve the text contained inside them. 3. Transform: Where the pipeline chunks the data and generates document embeddings. 4. Load: Where the pipeline creates a search index in a vector database and loads the document embeddings. There are different rabbit holes at each one of these stages. Here are three of them: 1. Ingesting data once is simple. The hard part is refreshing the vector database whenever the original data source changes. 2. Extracting the content of a plain text document is simple. The hard part is to extract content from complex documents containing tables, images, or cross-references. 3. A simple continual chunking strategy with an overlap is simple. The hard part is to find the optimal strategy for your specific knowledge base and the way you are planning to query it. In the attached video, I'll show you how you can build an enterprise-grade RAG Pipeline that solves every one of the above problems. I'll use Vectorize. They partnered with me on this post. You can use them to build RAG pipelines optimized for accurate context retrieval. If you have a few documents lying around, set up a free account and give it a try.

Santiago

40,441 次观看 • 1 年前

We can only trust X 🚨 META/ Facebook planning to build data centers in China, store their data in China, and then GIVE THE CHINESE GOVERNMENT ACCESS to the data “In this document, Facebook is talking about making Chinese user data available to the Chinese government because they're going to store that data in China. Is that correct?” “Correct” “When you store that data in China, Americans who exchange messages or other information with Chinese Facebook users, that would mean the Chinese government could get access to the American data as well, is that correct? “Through the POP servers? Potentially.” “And Facebook was willing to take that risk?” “Yes. There was a lot of discussion about this and ultimately yes.” “I mean, this is, this is extraordinary. This is exactly contrary to what Facebook has represented for years here. They're willing to build data centers, store data in China. They are willing explicitly to give the Chinese government access to it”

We can only trust X 🚨 META/ Facebook planning to build data centers in China, store their data in China, and then GIVE THE CHINESE GOVERNMENT ACCESS to the data “In this document, Facebook is talking about making Chinese user data available to the Chinese government because they're going to store that data in China. Is that correct?” “Correct” “When you store that data in China, Americans who exchange messages or other information with Chinese Facebook users, that would mean the Chinese government could get access to the American data as well, is that correct? “Through the POP servers? Potentially.” “And Facebook was willing to take that risk?” “Yes. There was a lot of discussion about this and ultimately yes.” “I mean, this is, this is extraordinary. This is exactly contrary to what Facebook has represented for years here. They're willing to build data centers, store data in China. They are willing explicitly to give the Chinese government access to it”

Wall Street Apes

80,854 次观看 • 1 年前

As promised, I built a small local RAG agent to demonstrate how Model Context Protocol (𝐌𝐂𝐏) works in practice when connecting LLMs to tools and data. The agent can read, search, and list my notes; but the functionality itself isn’t the point, the protocol is. Instead of writing Gemini specific tool integrations, I connected the model to an 𝐌𝐂𝐏 server. This is why 𝐌𝐂𝐏 is highly relevant for production AI systems: • 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐚𝐭𝐢𝐨𝐧: one protocol instead of custom connectors per data source. • 𝐏𝐨𝐫𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲: the same MCP server works with Gemini, Claude, or any MCP compliant client. • 𝐒𝐚𝐟𝐞𝐭𝐲: models can only access explicitly exposed capabilities. 𝐌𝐂𝐏 shifts agent development from integration heavy to capability driven. Demo link in the first comment.

As promised, I built a small local RAG agent to demonstrate how Model Context Protocol (𝐌𝐂𝐏) works in practice when connecting LLMs to tools and data. The agent can read, search, and list my notes; but the functionality itself isn’t the point, the protocol is. Instead of writing Gemini specific tool integrations, I connected the model to an 𝐌𝐂𝐏 server. This is why 𝐌𝐂𝐏 is highly relevant for production AI systems: • 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐚𝐭𝐢𝐨𝐧: one protocol instead of custom connectors per data source. • 𝐏𝐨𝐫𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲: the same MCP server works with Gemini, Claude, or any MCP compliant client. • 𝐒𝐚𝐟𝐞𝐭𝐲: models can only access explicitly exposed capabilities. 𝐌𝐂𝐏 shifts agent development from integration heavy to capability driven. Demo link in the first comment.

Sui_Builds

12,853 次观看 • 6 个月前

With various LLMs and AI agents being deployed, Nodepay’s infrastructure for real time data retrieval is crucial for delivering up-to-date insights for any inquiry, action or task instantly. Envision a world where any model or AI agent can access real time data ensuring the most precise results to give you a competitive advantage. This is Node Search - Nodepay’s decentralized AI answer engine that makes this possible, powered by 1.8M global user nodes and $NC Nodecoin

With various LLMs and AI agents being deployed, Nodepay’s infrastructure for real time data retrieval is crucial for delivering up-to-date insights for any inquiry, action or task instantly. Envision a world where any model or AI agent can access real time data ensuring the most precise results to give you a competitive advantage. This is Node Search - Nodepay’s decentralized AI answer engine that makes this possible, powered by 1.8M global user nodes and $NC Nodecoin

Nodepay

183,572 次观看 • 1 年前

🔥 Big news! Hugging Face + @cloudflare are teaming up to give AI devs instant access to enterprise WebRTC infrastructure worldwide. Build real-time voice & video AI apps with FastRTC and stream 10GB of data for free with your HF token. Check out the Llama 4 voice chat demo!

🔥 Big news! Hugging Face + @cloudflare are teaming up to give AI devs instant access to enterprise WebRTC infrastructure worldwide. Build real-time voice & video AI apps with FastRTC and stream 10GB of data for free with your HF token. Check out the Llama 4 voice chat demo!

Freddy A Boulton

30,493 次观看 • 1 年前

You can now give AI agents the ability to interact with ANY website. This new MCP server lets Claude, Cursor, or any AI agent navigate, click, fill forms, and extract data from sites without APIs. Just connect it and give it a URL + goal. Here’s how to set it up:

You can now give AI agents the ability to interact with ANY website. This new MCP server lets Claude, Cursor, or any AI agent navigate, click, fill forms, and extract data from sites without APIs. Just connect it and give it a URL + goal. Here’s how to set it up:

Alvaro Cintas

77,654 次观看 • 7 个月前

Does your team rely on Snowflake or BigQuery as your data warehouse? We built the step that every other onchain data provider skips. Transform first, deliver only what you need. dbt Connector → Datashare is now live. Query against 130+ chains ➡️ Transform with dbt on DuneSQL ➡️ Deliver only what you need to your warehouse One contract. Any dataset. Any transformation.

Does your team rely on Snowflake or BigQuery as your data warehouse? We built the step that every other onchain data provider skips. Transform first, deliver only what you need. dbt Connector → Datashare is now live. Query against 130+ chains ➡️ Transform with dbt on DuneSQL ➡️ Deliver only what you need to your warehouse One contract. Any dataset. Any transformation.

Dune

31,177 次观看 • 3 个月前