Designing software that today's AI coding agents can actually use.

A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization. - google/langextract

☁️ Convert any site to clean markdown & llms.txt. Boost your site's AI discoverability or generate LLM context for a project you're working with. - harlan-zw/mdream

“Don’t use chatbots as search engines” was great advice for several years... until it wasn’t. I wrote about how good OpenAI’s o3 was at using its Bing-backed search tool back …

Wanting to use your personal or organizational data in AI workflows, but it's stuck in PDFs and other document formats? Docling is here to helpDocling is an ...

Ready to become a certified Architect - Cloud Pak for Data? Register now and use code IBMTechYT20 for 20% off of your exam → https://ibm.biz/BdeXNRLearn more...

Contribute to FullStackRetrieval-com/RetrievalTutorials development by creating an account on GitHub.

To try everything Brilliant has to offer—free—for a full 30 days, visit https://brilliant.org/AdamLucek/ You’ll also get 20% off an annual premium subscript...

35% off our upcoming evals course: https://bit.ly/evals-aiModern IR Evaluation in the RAG Era w/ Nandan Thakur.Learn about future directions in RAG evaluatio...

Orion Weller presents new frontiers in information retrieval, focusing on how instruction following and reasoning capabilities from large language models can be integrated into retrieval systems. He introduces Promptriever, a fast embedder that can follow instructions, and Rank1, a powerful but slower reasoning reranker, demonstrating their ability to unlock new types of queries and significantly improve performance. 00:00 - New Frontiers in IR: Instruction Following and Reasoning 00:07 - Language Models (LLMs) & Their Key Capabilities 00:20 - Instruction Following 00:57 - Reasoning (Test-Time Compute) 01:41 - Bridging LLMs to Information Retrieval (IR) 01:52 - Evolution of Search (Google 1999 vs. Today) 02:17 - SearchGPT and Its Limitations 02:38 - Search Hasn't Changed Fundamentally 03:16 - Keyword Search (Traditional IR) 04:11 - Semantic Search (Modern IR) 04:38 - Instruction-Based Search (Proposed IR) 05:25 - Challenge: Reranking Alone Isn't Enough 06:02 - Prompt & Reasoning-Based Search (Advanced IR) 06:42 - What is an Instruction in IR? (Attributes & NLU) 07:31 - Call to Action: Prompt Retrievers Like LLMs 07:46 - Introducing Promptriever & Rank1 08:23 - Bi-Encoder vs. Cross-Encoder Architecture 09:10 - Can We Make Promptable Retrievers? (Promptriever's Idea) 10:08 - Generating Synthetic Instructions 10:34 - Promptriever Experimental Settings 11:20 - Promptriever Evaluation Data (FollowIR & InstructIR) 12:28 - Promptriever Instruction Following Results 12:59 - Promptriever Results: Out-of-Domain (OOD) with Generic Prompts 13:10 - Promptriever: Generic Prompt Examples 13:58 - Promptriever Performance with Generic Prompts (BEIR OOD) 14:44 - Promptriever: Robustness to Paraphrased Prompts 15:16 - Promptriever Summary 16:04 - Introducing Rank1 (Test-Time Compute for IR) 16:22 - Test-Time Compute in LLMs (O1 AIME example) 17:08 - What Does Test-Time Compute Look Like in IR? (Rank1 Example) 18:01 - Rank1 Evaluation Data (BRIGHT dataset) 18:50 - Rank1: Example of Model Reasoning (Leetcode Problem) 19:35 - Rank1 Results (BRIGHT, NevIR, mFollowIR) 20:15 - Rank1: Direct Comparison of Reasoning Chain 20:33 - Rank1: Finding New Relevant Documents (DL19/DL20) 21:05 - Re-judging Old Data (Explanation) 22:05 - Rank1 Summary 22:37 - The Goal: IR That Works Like LLMs 22:56 - Implications for Downstream Users 23:36 - Open Data/Open Source & Contact Info 23:45 - Q&A Session - Promptriever & Bi-Encoder 24:23 - Q&A Session - Operationalizing Promptriever 26:04 - Q&A Session - Cross-Encoder Integration 26:33 - Q&A Session - Meta-Search/Human-Provided Prompts 27:56 - Q&A Session - Rank1 vs. Frontier Reasoning Models 28:07 - Clarification on Rank1's Training Focus 28:30 - How Rank1 Compares to O3/Gemini 29:32 - Q&A Session - Fine-Tuning Rank1 30:19 - Q&A Session - Where to Find the Models 30:45 - Conclusion of Q&A

Introduction to GraphRAG

Our evals course with 35% discount code: https://bit.ly/evals-ai

Retrieval Augmented Generation based on SQLite. Contribute to ggozad/haiku.rag development by creating an account on GitHub.

Basic Memory is a knowledge management system that allows you to build a persistent semantic graph from conversations with AI assistants, stored in standard Markdown files on your computer. Integra...

Learn how to use vector search and embeddings to easily combine your data with large language models like GPT-4. You will first learn the concepts and then create three projects. ✏️ Course developed by Beau Carnes. 💻 Code: https://github.com/beaucarnes/vector-search-tutorial 🔗 Access MongoDB Atlas: https://cloud.mongodb.com/ 🏗️ MongoDB provided a grant to make this course possible. ⭐️ Contents ⭐️ ⌨️ (00:00) Introduction ⌨️ (01:18) What are vector embeddings? ⌨️ (02:39) What is vector search? ⌨️ (03:40) MongoDB Atlas vector search ⌨️ (04:30) Project 1: Semantic search for movie database ⌨️ (32:55) Project 2: RAG with Atlas Vector Search, LangChain, OpenAI ⌨️ (54:36) Project 3: Chatbot connected to your documentation 🎉 Thanks to our Champion and Sponsor supporters: 👾 davthecoder 👾 jedi-or-sith 👾 南宮千影 👾 Agustín Kussrow 👾 Nattira Maneerat 👾 Heather Wcislo 👾 Serhiy Kalinets 👾 Justin Hual 👾 Otis Morgan 👾 Oscar Rahnama -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news ❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp

A brief introduction to using llamafile (a single-file tool for working with large language models) and sqlite-vec (A SQLite extension for vector search) to build a Retrival Augmentation Generation (RAG) application. This was a live online event hosted on Dec 17th 2024 in the Mozilla AI Discord, join us for the next event at at https://discord.gg/Ve7WeCJFXk LINKS: - Doc w/ links to all mentioned projects/blog posts: https://docs.google.com/document/d/17GYLzlGUyJF9EDeaa1P-dFFZnkwxATnBcg5KnNgpvPE/edit?usp=sharing - Slides: https://docs.google.com/presentation/d/14Szda-VnZzepL-1U9Nb7sXQg_TTf56OQ-KtUIMQ5xug/edit?usp=sharing

a package and makitdown plugin that transforms pdf files into markdown for llm use. - InectGit/pdf-to-markdown

In this video I look at the new release from Qwen of their new Embedding and Reranking models which are start of the art and most importantly open weights mo...

A vector search SQLite extension that runs anywhere! - asg017/sqlite-vec

Agentic Document Extraction just got faster! We've improved the median document processing from 135 seconds to 8 seconds! Agentic Document Extraction sees documents visually and uses an iterative workflow to accurately extract text, figures, form fields, charts, and more to create an LLM-ready output. You can use our SDK to parse complex documents and get the extracted content in Markdown and JSON. You can then feed the output to an LLM, RAG application, or other downstream apps. You can also use our Playground to test out Agentic Document Extraction. Try out Agentic Document Extraction: - Playground: https://va.landing.ai/demo/doc-extraction - Library: https://github.com/landing-ai/agentic-doc Learn more: https://landing.ai/agentic-document-extraction

Ready to become a certified watsonx AI Assistant Engineer? Register now and use code IBMTechYT20 for 20% off of your exam → https://ibm.biz/BdngMV 🚀 Try GraphRAG now! Access the code here → https://ibm.biz/BdngaC Learn more about GraphRAG here → https://ibm.biz/BdngM9 🤖 Can AI turn text into structured knowledge? Discover how GraphRAG leverages knowledge graphs, graph databases, and Cypher queries to transform unstructured data into actionable insights. See how LLMs enable intelligent retrieval and automation, reshaping workflows across industries. 🚀 AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/BdngMU #knowledgegraph #cypher #ai

Learn how `sqlite-vec` turns SQLite into a fast, embedded vector search engine. With support for float32, int8, and bit vectors, optimized distance metrics, and native SQL integration, it's ideal for offline AI, semantic search, and lightweight ML apps. This post walks through how it works and why it's surprisingly powerful.

Looking for the best open-source embedding model for your RAG app? We share a comparison workflow so you can stop paying the OpenAI tax.

Vector databases are kind of everywhere these days. There is a big pool of VC's that are pouring money into the ecosystem too. But while all of that is happening, sqlite has also gotten support for it. In this episode we talk the Alex Garcia, the maintainer of this project, and discuss how the project got created on what the future has in store. 00:00 Introduction 00:40 Dataviz 04:39 Chromebook matters 10:30 Why sqlite rocks 17:32 Facebook and VR stuff 26:19 Datasette & Simon 38:31 Towards sqlite-vec 46:46 Getting attention 52:38 Current work Sqlite-vec Github repo: https://github.com/asg017/sqlite-vec Alex Garcia blog: https://alexgarcia.xyz/blog/2024/sqlite-vec-hybrid-search/index.html Datasette discord: https://discord.com/invite/ktd74dm5mw Sqlite-vec channel on Mozilla Discord: https://discord.gg/Ve7WeCJFXk We have a Discord these days, feel free to discuss the podcast with us there! https://discord.probabl.ai You can follow the podcast on most podcast players including apple podcasts, spotify and rss.com. - https://podcasts.apple.com/us/podcast/sample-space/id1739598572 - https://open.spotify.com/show/0BnwEHuyOlHgeZfselpn1n - https://rss.com/podcasts/sample-space/ This podcast is part of the open efforts over at probabl. To learn more you can check out website or reach out to us on social media. Website: https://probabl.ai/ Bluesky: https://bsky.app/profile/probabl.bsky.social LinkedIn: https://www.linkedin.com/company/probabl Twitter: https://x.com/probabl_ai #probabl

Vector search has become a foundational tool for modern applications — from powering recommendation engines to enabling semantic search in…

CocoIndex now supports knowledge graph with incremental processing. Build live knowledge for agents is super easy with CocoIndex!

Python tool for converting files and office documents to Markdown. - microsoft/markitdown