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I built an entire RAG application without writing any code. This is one of the easiest ways to start building AI applications. I used Langflow. It's open-source. It's a visual interface for building and deploying AI applications. I built a couple of workflows to show you how it works,... and I'm impressed! Check the video I recorded. I built two workflows: 1. The first one loads data into a vector store database (Astra DB). 2. The second asks questions from that data using OpenAI's models. Langchain does the heavy lifting behind the scenes, but having a visual interface makes this 10 times easier. Here is Langflow's GitHub repository: In the video, I used their pre-release version. The development experience for building AI applications is improving tremendously! What a time to be a builder!show more

Santiago

453,325 subscribers

263,077 Aufrufe • vor 2 Jahren •via X (Twitter)

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10 Kommentare

Profilbild von Santiago

Santiagovor 2 Jahren

Next week, I'm publishing a video where I'll create this same application starting from zero. There's something good that: I'll explain every step and the thinking process as I built the application. That's how I learn, and I'm sure it will be very helpful to others too. I'll publish this video on my YouTube channel. If you want to subscribe there, here is the link:

Profilbild von Altcoin Jim

Altcoin Jimvor 2 Jahren

Santiago, are you aware that RAG has already been supercharged / greatly improved upon by @origin_trail ? The next wave in AI is Decentralized Retrieval Augmented Generation #dRAG - a combo of neural AI (LLMs) & symbolic AI (knowledge graphs) via knowledge graphs & blockchain. dRAG utilises trusted, verifiable knowledge sources with provenance and is a ground-breaking development that the world is asleep on. Here are some working examples for massive enterprises already. Would love your thoughts..?

Profilbild von Hesam

Hesamvor 2 Jahren

It’s surprising how a prominent and complex task such as this is so simplified by langchain and visual interfaces, basically by layer upon layer of abstraction Software development needed such approach long time ago, but nothing is a good motivator as public demand

Profilbild von Santiago

Santiagovor 2 Jahren

Yes. I'm sure we will continue to see this happening. The development ecosystem will continue to improve over the coming months. Pretty exciting!

Profilbild von Green Apple

Green Applevor 2 Jahren

Is it possible to do local LLM here instead of chatGPT? Ie then the workflow would be different right.. -Local LLM -Local Vector DB - Web APP( or chat window) running locally in system

Profilbild von Santiago

Santiagovor 2 Jahren

Yes, you can do local models too.

Profilbild von Shikhar Srivastava

Shikhar Srivastavavor 2 Jahren

Dude, you are absolutely dominating my "Must Try Later" Bookmark folder on X with these useful posts 😂

Profilbild von Santiago

Santiagovor 2 Jahren

There’s so much happening!

Profilbild von Jinani

Jinanivor 2 Jahren

Langflow or Flowise, which would be better for nocode builders?

Profilbild von Santiago

Santiagovor 2 Jahren

I’ve tried both. Honestly, whatever you like the most. They are great tools.

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I recorded a video on how you can build a complete RAG application without writing any code. I used Langflow, an open-source project that sits on top of Langchain. Langflow hides all the complexity and makes the process 10x easier. This is a step-by-step tutorial.

I recorded a video on how you can build a complete RAG application without writing any code. I used Langflow, an open-source project that sits on top of Langchain. Langflow hides all the complexity and makes the process 10x easier. This is a step-by-step tutorial.

Santiago

96,267 Aufrufe • vor 2 Jahren

99% of AI applications are cool-looking demos. Impressive, but don't get fooled by the hype. It takes a lot to build enterprise-grade products that deliver real value. I have at least three weekly conversations with companies that want to use a Large Language Model with their data. The demand is huge! Here is one idea about what you can do to help. The use cases that most of these companies want to solve are similar: They have an extensive knowledge base and want to build a simple application that uses that information to answer questions. In other words, they need help building Retrieval Augmented Generation (RAG) applications they can use in many different scenarios: 1. To train new employees 2. To help their support team 3. To search old meetings and documents 4. To help with their research However, building these systems is not straightforward. Yes, there's a lot of information online, but there aren't enough people who know how to create solutions that work. Here is the idea: Today, you can build an enterprise-grade RAG application without writing code. A couple of MIT PhDs with 10+ years of experience building AI applications created . It's a no-code platform for building applications using Large Language Models. They are partnering with me on this post. You can use Stack AI to create, test, and deploy an end-to-end production-ready AI system. It's SOC-2, HIPAA, and GDPR compliant and offers SSO, role management, access control, and on-premise deployments. Of course, you can use the platform with any LLM on the market now. It's the whole nine yards for building AI applications. Check them out here: 2023 was about models. 2024 is about the tools using these models to build production-ready applications. That's where I'd start.

99% of AI applications are cool-looking demos. Impressive, but don't get fooled by the hype. It takes a lot to build enterprise-grade products that deliver real value. I have at least three weekly conversations with companies that want to use a Large Language Model with their data. The demand is huge! Here is one idea about what you can do to help. The use cases that most of these companies want to solve are similar: They have an extensive knowledge base and want to build a simple application that uses that information to answer questions. In other words, they need help building Retrieval Augmented Generation (RAG) applications they can use in many different scenarios: 1. To train new employees 2. To help their support team 3. To search old meetings and documents 4. To help with their research However, building these systems is not straightforward. Yes, there's a lot of information online, but there aren't enough people who know how to create solutions that work. Here is the idea: Today, you can build an enterprise-grade RAG application without writing code. A couple of MIT PhDs with 10+ years of experience building AI applications created . It's a no-code platform for building applications using Large Language Models. They are partnering with me on this post. You can use Stack AI to create, test, and deploy an end-to-end production-ready AI system. It's SOC-2, HIPAA, and GDPR compliant and offers SSO, role management, access control, and on-premise deployments. Of course, you can use the platform with any LLM on the market now. It's the whole nine yards for building AI applications. Check them out here: 2023 was about models. 2024 is about the tools using these models to build production-ready applications. That's where I'd start.

Santiago

197,702 Aufrufe • vor 2 Jahren

Step-by-step instructions on how to build a RAG system using Oracle + Langchain. The time of the specialized vector store is over. Every major database management system now supports vectors. This is huge! It's one fewer system to worry about, and one fewer component to break. Oracle 26ai comes packed with AI-first features, and I decided to put a simple step-by-step guide on how to use it with Langchain. Check the attached video. You need a couple of things to follow along: 1. Create an Oracle 26ai database—you can do this for free online on the Oracle cloud. 2. Use the notebook linked below for the code. Here is a link to the GitHub Repository with the notebook: Thanks to the Oracle team for the collaboration on getting this video out and for hosting the notebook on their GitHub repository.

Step-by-step instructions on how to build a RAG system using Oracle + Langchain. The time of the specialized vector store is over. Every major database management system now supports vectors. This is huge! It's one fewer system to worry about, and one fewer component to break. Oracle 26ai comes packed with AI-first features, and I decided to put a simple step-by-step guide on how to use it with Langchain. Check the attached video. You need a couple of things to follow along: 1. Create an Oracle 26ai database—you can do this for free online on the Oracle cloud. 2. Use the notebook linked below for the code. Here is a link to the GitHub Repository with the notebook: Thanks to the Oracle team for the collaboration on getting this video out and for hosting the notebook on their GitHub repository.

Santiago

11,366 Aufrufe • vor 8 Monaten

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Santiago

101,473 Aufrufe • vor 1 Jahr

Follow this video to learn how to build a simple AI agent without writing any code. I'm using Langflow. In my opinion, it's the best tool I've found for building agents. It's open-source, comes with a huge list of reusable components, and provides an awesome experience for builders.

Follow this video to learn how to build a simple AI agent without writing any code. I'm using Langflow. In my opinion, it's the best tool I've found for building agents. It's open-source, comes with a huge list of reusable components, and provides an awesome experience for builders.

Santiago

73,897 Aufrufe • vor 1 Jahr

I built an agent that answers machine-learning questions. It's autonomous, and the best part is that I built the whole thing without writing a single line of Python code. Here is what I did and how I did it: Over a year ago, a friend and I built a site that publishes multi-choice questions. You get a new one every day. I decided to have GPT-3.5 answer questions. Here is what I needed to build: 1. Connect to the site's API to retrieve today's question 2. Extract the question and the potential choices 3. Connect to OpenAI's API and ask GPT-3.5 to answer the question 4. Parse the answer from the model 5. Submit the answer back to the API to get the score Not difficult. Likely several hours of work. But I didn't have to write any code. I built the whole thing by dragging and dropping components using Vellum is a YC-backed platform for developers to build LLM applications. They are the only ones I've seen offering this functionality. They sponsored this post, and their team helped me with all my questions while I built this. I created a workflow. The platform supports several node types to build whatever you have in mind. I show how I put the whole thing together in the attached video. The only code I had to write was a few lines of Jinja to parse and transform the API and the LLM results. There are three lessons I want to share from this experience: First, the best possible code is the one you didn't write. I'm a big fan of no-code tools because they help me materialize my ideas fast. They help product people, designers, and no coders collaborate on the solution. Second, Large Language Models are sensitive to how you prompt them. Small changes to prompts can make a big difference in results. This is more pronounced when you are building a multi-step workflow. Third, automated testing and evaluation for prompts is critical. There aren't many companies thinking about this. They'll have a hard time moving from a demo phase. The attached video will show you what I did.

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Santiago

309,825 Aufrufe • vor 2 Jahren

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Santiago

108,835 Aufrufe • vor 2 Jahren

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Santiago

39,101 Aufrufe • vor 2 Jahren

Here is a step-by-step introduction to building a workflow with a custom AI agent that uses MCP. I explain every component in the video: 1. Building the MCP server 2. Building the agent and an MCP client 3. Building a workflow that uses the agent The goal is simple: Generate a dialogue between two people and make one yell and the other answer with sarcasm. Kestra is sponsoring this video. They are an open-source orchestration platform (repo link below), and I used them to build the workflow that connects every component.

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Santiago

51,527 Aufrufe • vor 1 Jahr

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.

Santiago

230,594 Aufrufe • vor 1 Jahr

Python is the best language in the world. Today, you can build AI and data applications using Python alone! No HTML or JavaScript necessary. Take a look at Taipy, an open-source Python library to build end-to-end production applications. Star their repository: I recorded a quick video showing how to build a simple chat interface to talk to OpenAI GPT-3.5 using Taipy alone. Something important to keep in mind: Taipy's goal is not to replace web developers but to provide an alternative to those who need to build applications without web experience. If you are a data scientist or someone dealing with data, Taipy will simplify your life considerably. Thanks to the team behind Taipy for collaborating with me on this post. Here is a link to the full demo. Download this code and run it. It takes 30 seconds:

Python is the best language in the world. Today, you can build AI and data applications using Python alone! No HTML or JavaScript necessary. Take a look at Taipy, an open-source Python library to build end-to-end production applications. Star their repository: I recorded a quick video showing how to build a simple chat interface to talk to OpenAI GPT-3.5 using Taipy alone. Something important to keep in mind: Taipy's goal is not to replace web developers but to provide an alternative to those who need to build applications without web experience. If you are a data scientist or someone dealing with data, Taipy will simplify your life considerably. Thanks to the team behind Taipy for collaborating with me on this post. Here is a link to the full demo. Download this code and run it. It takes 30 seconds:

Santiago

359,020 Aufrufe • vor 2 Jahren

The Voice AI receptionist I'm having a conversation with in this video was built by Claude Code.. I installed VAPI skills and Claude handled the rest. It's important to mention, I was having dinner while Claude Built this for me. This is the future of work.

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Damola | AI & Automation

15,048 Aufrufe • vor 4 Monaten

Fable built me the Piranesi city building game that I faked in an AI video last year. The key mechanic the AI came up with is building a city of arches and waterways in cyclopedian ruins & inviting humans to live amongst them. This one impressed me. Play:

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Ethan Mollick

82,364 Aufrufe • vor 1 Monat

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willie

12,845 Aufrufe • vor 8 Monaten

This might be the best "AI Engineer" I've tried so far. I'm an old school developer who started 30 years ago. I feel very uncomfortable letting AI take control of my code, but for the sake of science, I spent two hours building an application that took me weeks to build a couple of years ago. I used Pythagora, a brand new tool backed by Y Combinator. They just released to the public. Keep in mind that I use AI every day to write code, but Pythagora is something different: it's a tool that leads, and uses you—the human—as the copilot. I go into more details in the video, but here is the TLDR; 1. Holy molly! We've made a ton of progress on this front! This is way better than Devin when I tested it a few months back. 2. Love the approach of generating a plan with sub-tasks before writing any code. 3. The tools never tries to do too much: it tackles every small task one at a time, and gives you instructions so you test everything. 4. It does exactly what you'd do when it gets stuck: writes a bunch of logs and uses those to correct itself. Pretty neat! 5. It's fast. It runs locally. It's an extension to Visual Studio Code. I'm impressed, but I don't think this tool is for me. I'm not the type of developer who's ready to relinquish control. I felt I had no connection with the code because I didn't write it. It was not my code. I know many people who don't care about this. I know many people who will get tremendous value out of Pythagora. I hope they keep pushing the limits, providing feedback, and helping this get to a point where old folks like me feel more comfortable using it. Don't take my word for it. The best thing you can do is to give it a try and see how you feel using it. Thanks to the team who built this, for all of the explanations and support, and especially, for sitting and listening to my dumb questions for 2 hours while I tested this.

Santiago

212,056 Aufrufe • vor 1 Jahr