Search: “rag”

Thanks @juliafedorin for a great conversation! Some tidbits from the podcast on the AI space: - We've pivoted from building a RAG framework towards building the document infrastructure for agents - Do

3 years ago I gave a talk at the first @aiDotEngineer conference on 'Advanced RAG' techniques in order to work around the limitations of naive RAG. It's insane how much the world has changed since the

We're introducing Q2D-Web (Query2Doc-Web), a benchmark and public leaderboard for evaluating retrieval in agentic RAG systems. Q2D-Web tests how embedding models perform on large-scale web search usin

// When to Retrieve During Reasoning // Pay attention to this one, AI devs. (bookmark it) Most RAG systems retrieve once, before the model starts reasoning. Large reasoning models like o1 and R1 don't

We analyzed a year of agent research across 4 tasks: deep research, RAG indexing, agentic search and coding. Found that first asking ‘Is this task verifiable?’ exposed inefficiencies in the agent arch

Didn't expect this 🤯 We replaced embeddings with Jev in GPT Researcher's RAG pipeline and tested both on 28 research tasks from SimpleQA and open ended research. Jev beat embeddings on every quality m

A full tour through RAG, document context, and AI agents - from 2023 to 2026 🌎🤖 @hexapode gave a comprehensive 90-min workshop at @aiDotEngineer Singapore last week that comprehensively traces through

A beginner-friendly guide to building a simple RAG (RETRIEVAL-AUGMENTED GENERATION) AI Agent with Pinecone + n8n 🧵 If you’re learning AI automation and you’re confused about Pinecone, knowledge bases,

ROSE is the model engine. It reuses the same kernels for LLMs and embeddings. For embeddings, it skips the KV cache and uses ragged attention instead of paged attention. ROSE supports multiple attenti

Jörg Schad is the VP of Engineering at @pinecone. He joins @kbal11 to discuss moving beyond RAG with precomputed context, context artifacts, semantic layers, and AI retrieval. @joerg_schad https://sof

The latest RAG trend for the current agent harnesses (Codex, Cowork) is to do two passes of document processing to solve a knowledge work task over a data room of documents: 1️⃣ A fast and light pass,

Fine-tune your own embedding model for the price of a coffee. A great reranker can't surface a doc that was never retrieved. A RAG pipeline cannot cite a case it failed to retrieve. See how contrastiv

Happy Canada Day! 🇨🇦 AI, ML, RAG, and LLMs are a pretty niche space overall. But, I'm consistently impressed with the talent that Canada ships out, from colleagues to competitors. Gives me the patriot

We're excited to introduce the Retrieval Harness in LlamaParse - which is the 2026 version of RAG over documents Generalized agents need the right set of tools to scalably search and read through an a

Lots of advances coming from the open source harness movement (including Moltbook, RAG approaches back in the day, etc.) but they depend on the intelligence of the models created by a small handful of

One of the better agentic AI courses I've seen Nearly 10 hours of great content. Covers LangChain, LangGraph, RAG, deepagents, guardrails, and more Any other good Lang* resources out there for folks w

Agent filesystems are the new RAG It seems like this pattern is around to stay and will only get more robust over time. Agents need tools to not only read and search over documents, but an entire infr

Your AI chatbot is only as good as the data behind it. This n8n template from our friends at @apify shows you how to wire up a RAG pipeline using Apify + Pinecone + Gemini so your chatbot can answer q

PrivateGPT is back! In 2023, it became the first offline RAG implementation and a breakout open source project. Then we went quiet in 2024. Today, PrivateGPT 1.0 is here: a full application API layer

this is a fantastic example of a robust agentic memory system built on langgraph! there's 4 key parts 1. retrieval (rag) 2. storage, fragmented by memory type 3. reasoning* 4. learning (often called d

You can’t build an autonomous agent on a static RAG pipeline. Because goals mutate dynamically, agents require an advanced knowledge infrastructure, rather than forcing the model to hunt through a raw

We're hosting an agentic AI meetup in LA on May 28th — 5–7pm at Gulp in Playa Vista. Builders, founders, engineers. Drinks, no fluff. RAG systems, agentic workflows, or just starting out — all welcome

Context compression isn't new in RAG. Our contribution is making it query-aware, citation-preserving, and fast enough for orchestration. Read the full research blog: https://research.perplexity.ai/art

Congrats on the launch! Filesystems are all you need (?) There wasn't a huge demand for 'managed RAG' services in 2023, but it's possible the infra and market was just not mature enough. Maybe filesys

The RAG era is ending for agentic AI — a new compilation-stage knowledge layer is what comes next http://venturebeat.com/ai/the-rag-era-is-ending-for-agentic-ai-a-new-compilation-stage-knowledge-layer

This is really well thought out. Filesystems are the new default abstraction for agents to interact with documents (the new RAG stack in 2026). The issue is actually figuring out how to productize thi

A million-token context window is not a strategy. 🛑 Our Head of DevRel, @RoieSchwabco , explains why dumping data is killing your RAG performance: 📍 One needle in a haystack? Easy. 📍 Multiple needles?

The Top AI Papers of the Week (April 19 - 26) - Skill-RAG - DeepSeek V4 - Autogenesis - Attention to Mamba - Stateless Decision Memory - Self-Evolving Logic Synthesis - Self-Generated World Knowledge

The 'bridge of authority' in RAG: ❌ LLMs can reason about 'blue skies' or 'purple skies' with the same authority. ✅ Vector DBs let you peer into the metadata to find the source/author. 'Do you trust a

LangChain Community Spotlight: Saving $1M in LLM Costs 💰 Gustaf, an AI Engineer, shows how he reduced a production RAG chatbot's costs by 90% and improved latency by 82% through caching, intelligent r

Lots of JevRAG variants showing up. Interesting to say the least. You can’t really make any conclusions with these scoped tests but it raises discussions and exciting directions to find optimization i

Obligatory plug for static embeddings. Are you using static embeddings in your projects? Would love to hear how people are leveraging them, whether for retrieval and beyond. Static embeddings offer un

It really isn't an exaggeration! LiteParse clocks in at an average of 3ms per page for a reason: it skips the heavy AI processing and cloud overhead entirely. Here is exactly how it pulls off that kin

Very cool idea to convert memory to skills. (bookmark it) Most agent memory systems retrieve past traces as passive context. MSCE turns them into executable skills instead. The training-free framework

My conversation with @jerryjliu0. He's building @llama_index: document infrastructure for AI agents, the layer underneath everything models need to work. Did you know he's the guy whose tool half the

Pinecone has introduced Nexus, a knowledge engine built specifically for AI agents. Instead of repeatedly searching documents with traditional RAG, Nexus compiles knowledge once from sources like data

if this takes off, then i was 4 years too early 😭 https://x.com/jerryjliu0/status/1590192512639332353 only OGs remember gpt tree index The entire RAG industry is about to get cooked. Researchers devel

Great paper from Google and colleagues. Trains Text-to-SQL agents using multi-agent RL. (bookmark it) This work proposes DualSQL, which splits Text-to-SQL into two agents, one that links the question

A database knows where your data is. A vector database understands what your data means. It finds information based on similarity and meaning, not just exact keywords, powering semantic search, RAG, r

There's one question you should ask before you build an agent. Most people skip it (and pay for it in compute). Over a year of agent research at @AI21Labs across deep research, RAG indexing, agentic s

Tool Attention Is All You Need // Tool Attention Is All You Need // New research proposes a practical fix for the hidden 'MCP tax.' The work introduces a dynamic tool gating mechanism built on an Inte

We're hosting the LA Agentic AI Meetup this Thursday, July 9, 5 to 7pm at Gulp in Playa Vista. Come talk to engineers, founders, and builders working across RAG, agentic workflows, and the broader AI

// Contextual Agentic Memory is a Memo, not True Memory // Most agent memory today isn't memory. They are more like memos. A new paper argues that vector stores, RAG buffers, and scratchpads implement

🔴 We're going live tomorrow for another Come Build with Pinecone session! Join us to build, break, and learn together. Each session, we pick up a real project — building RAG pipelines, wiring up agent

Pinecone now has a native extension for Gemini CLI. gemini extensions install https://github.com/pinecone-io/gemini-cli-extension → 9 MCP tools → 7 skills (quickstart, query, assistant, RAG and more)

Explore the top AI papers of the week with our brand-new MCP tools. Connect it to Codex, Claude Code, or Grok Bot. Introducing MCP tools for @dair_ai. Connect the MCP to your Codex/Claude/Grok Bot to