RAG

Learn how Video-RAG boosts training-free and low-compute long-video understanding by pairing OCR, ASR, and open-vocabulary detection with any long-video LVLMs.

18 likes, 2 comments. "How to build local Multimodal RAG with Qwen3-VL | by NEXA Community member"

Video OCR, long video understanding, and video grounding

Learn how Video-RAG boosts training-free and low-compute long-video understanding by pairing OCR, ASR, and open-vocabulary detection with any long-video LVLMs.

Notes on context engineering and agent harnesses for video libraries: designing the structured representations to make media legible to LLMs. Since we started joining meetings from our computers, video has become the default way that organizations capture what happens at work. We’re at the point now where recording things

Bridging the gap between organisational data, implicit organisational reasoning, and AI reasoning with Knowledge Graphs purpose-built for AI.

Multimodal embeddings allow AI systems to search and reason across text, images, audio, and video in their native formats. This blog covers the key intuitions behind how this all works and walks through three practical implementations using Weaviate and Gemini.

The only viable successor to Kuzu. Built for agentic solutions in highly regulated industries.

We're excited to introduce langchain-azure-storage, the first official Azure Storage integration package built by Microsoft for LangChain 1.0. As part...

AI21 Maestro’s Structured RAG fixes RAG’s accuracy gaps with hybrid retrieval—delivering reliable, auditable answers for enterprise compliance and reporting.

Strategies and parameters you can tune to improve the performance of Retrieval-Augmented Generation (RAG) applications for production.

In this article, we will understand the architecture of the multi-agent research system that Anthropic built.

A Qdrant Star shares her hardwon lessons from her extensive opensource building

Jupyter notebooks testing different OCR models for document parsing (Dolphin, MonkeyOCR, Marker, Nanonets, ...) - AdemBoukhris457/Docs_Parsing_Techniques

From BM25 to RAG: Everything I learned about vector databases, embedding models, and vector search - and everything in between.

See how Exa used LangGraph and LangSmith to build a multi-agent web research system to process research queries

OpenAI self-leaked its Deep Research prompts and it's a goldmine of ideas! Let's analyze this in detail!

📦 Package Links & Code Examples:- 🐍 PyPI: https://pypi.org/project/graph_nd/- 📁 GitHub: https://github.com/zach-blumenfeld/graph-nd- 📚 Docs: https://grap...

Extract and generate a knowledge graph from educational curriculum information without writing a single line of code.

Weaviate `1.31` implements the MUVERA encoding algorithm for multi-vector embeddings. In this blog, we dive the algorithm in detail, including what MUVERA is, how it works, and whether it might make sense for you.

This repository shares end-to-end notebooks on how to use various Weaviate features and integrations! - weaviate/recipes

Fully functional agentic deep research app with step-by-step instructions (100% opensource)

Learn about vector search, a technique that uses mathematical representations of data to find similar items in large data sets.

Structuring legal information as a knowledge graph to increase the answer accuracy using a LangGraph agent

Choosing the best reranking model for your RAG-based QA system can be tricky. This blog post simplifies RAG reranking model selection, helping you pick the right one to optimize your system's performance.

Learn how to build better retrieval augmented generation (RAG) pipelines for LLMs, search, and recommendation. In this chapter we explore two-stage retrieval and the incredible accuracy of reranker models.