RAG

On the the engineering challenges and lessons learned from building Claude's Research system

After 1 hr research, Here are the best open source 'General agent' projects: - Suna: https://t.co/BRsQToXL9P - Deer-flow from Bytedance: https://t.co/4zwuRKaNFZ - Google-gemini-search: https://t.co/iFIMgBxfeg - Langchanin open deep search:

Explore how reranker for RAG systems by refining results, reducing hallucinations, and improving relevance and accuracy.

Breadcrumbs list

Learn how to fine-tune Cohere's reranker and generate synthetic data using DSPy!

LlamaIndex is a simple, flexible framework for building knowledge assistants using LLMs connected to your enterprise data.

Explore how semantic chunking enhances RAG systems by improving context, precision, and performance through optimized chunking strategies and advanced tools.

This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and cont...

Take a deep dive into Neo4j Aura Graph Analytics, and learn how to run PageRank, community detection, and other algorithms directly in your workflow.

Knowledge graphs are powerful tools for representing relationships between entities in a structured format. They are widely used in various…

It’s been a while since I last wrote a blog post, and I’m excited to be back!

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

In this blog post we will build a local Deep research agent using Qwen 3 8b model, LangGraph, Composio, and Ollama.

Visual RAG expands AI's ability to understand and utilize charts, graphs, and images, a critical skill as 65% of people are visual learners. Mastering this technology allows you to build truly multimodal AI systems that can reason about visual data, giving you a competitive edge in enterprise AI development and opening new possibilities for data-driven applications.

Explore the top Retrieval-Augmented Generation (RAG) techniques of 2025, including Traditional RAG, Long RAG, Self-RAG, and more.

TL;DR Deep research is the hottest AI feature right now. Every LLM provider is adding to...

Streamline the process of working with legal contracts by implementing an end-to-end solution using agentic GraphRAG.

Vector search represents a revolution in information retrieval. Vector embedding - by taking account of context and semantic meaning - empowers vector search to return more relevant and accurate results. In this article we compare two common approaches to multi-attribute vector search.

Here we demonstrate how to pass multimodal input directly to models.

Learn when and how to use GraphRAG and how it can improve on some search tasks

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

Colab notebook

Here’s how to build an AI agent that auto-generates a company risk report over dozens of public filings 📈📉 Batch analyzing a ton of documents and writing up a memo would take 20+ hours of work. Agents have the potential to automate this but they completely fall apart without

Learn how to use multi-vector embeddings in Weaviate.

Docs, Snippets, Guides. Contribute to cohere-ai/cohere-developer-experience development by creating an account on GitHub.

Docs, Snippets, Guides. Contribute to cohere-ai/cohere-developer-experience development by creating an account on GitHub.

Relerance feedback: from ancient history to LLMs Why relevance feedback techniques are good on paper but not popular in neural search and what we can do about it