RT Avi Chawla (@_avichawla)6 no-code LLM/RAG/Agent builder tools for AI engineers.Production-grade and 100% open-source!(find the GitHub repos in the replies) https://t.co/It07fQRBL7 ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/SvYt7PiJQxAvi Chawla (@_avichawla):6 no-code LLM/RAG/Agent builder tools for AI engineers.Production-grade and 100% open-source!(find the GitHub repos in the replies) https://t.co/It07fQRBL7 ...

1️⃣ Sim AIA drag-and-drop UI to build AI agent workflows!Sim AI is a lightweight, user-friendly platform that makes creating AI agent workflows accessible to everyone.Supports all major LLMs, MCP servers, vectorDBs, etc.100% open-source with 18k starsThe video below depicts a finance assistance app created with Sim and connected to Telegram in minutes.The Finance Agent uses Firecrawl for web searches and accesses stock data via Alpha Vantage's API through MCP servers.🔗 https://t.co/z4LPCV4SwH2️⃣ Transformer ...

6 no-code LLM/RAG/Agent builder tools for AI engineers.Production-grade and 100% open-source!(find the GitHub repos in the replies) https://t.co/It07fQRBL7 ...

RT Avi Chawla (@_avichawla)5 Agentic AI design patterns, explained visually!Agentic behaviors allow LLMs to refine their output by incorporating self-evaluation, planning, and collaboration!The visual depicts the 5 most popular design patterns for building AI Agents.1️⃣ Reflection patternThe AI reviews its own work to spot mistakes and iterate until it produces the final response.2️⃣ Tool use patternTools allow LLMs to gather more information by:- Querying a vector database- Executing Python scripts- Invoki ...

5 Agentic AI design patterns, explained visually!Agentic behaviors allow LLMs to refine their output by incorporating self-evaluation, planning, and collaboration!The visual depicts the 5 most popular design patterns for building AI Agents.1️⃣ Reflection patternThe AI reviews its own work to spot mistakes and iterate until it produces the final response.2️⃣ Tool use patternTools allow LLMs to gather more information by:- Querying a vector database- Executing Python scripts- Invoking APIs, etc.This is helpfu ...

Microsoft.Google.AWS.Everyone's solving the same problem for Agents:How to build a real-time context layer for Agents across dozens of data sources?Airweave is an open-source context retrieval layer that solves this!Learn how this layer differs from RAG below: https://t.co/hqycgs5eoqAvi Chawla (@_avichawla):You are in an AI engineer interview at Google.The interviewer asks:"Our data is spread across several sources (Gmail, Drive, etc.)How would you build a unified query engine over it?"You: "I'll embed ever ...

AI Engineering & RAG - The document discusses building a unified query engine over data spread across several sources using vector DB and RAG (Retrieval-Augmented Generation) [1] - It presents a scenario of an AI engineer interview at Google, focusing on querying data from sources like Gmail and Drive [1] - The author shares tutorials and insights on DS (Data Science), ML (Machine Learning), LLMs (Large Language Models), and RAGs [1]

Airweave GitHub repo: https://t.co/iU6P0KoaRf(don't forget to star it ⭐) ...

You are in an AI engineer interview at Google.The interviewer asks:"Our data is spread across several sources (Gmail, Drive, etc.)How would you build a unified query engine over it?"You: "I'll embed everything in a vector DB and do RAG."Interview over!Here's what you missed:Devs treat context retrieval like a weekend project.Their mental model is simple: "Just embed the data, store them in vector DB, and call it a day."This works beautifully for static sources.But the problem is that no real-world workflow ...