Rag
Research papers, repositories, and articles about rag
Showing 22 of 22 items
opendatalab/MinerU
Pipeline that converts messy PDFs and Office docs into clean markdown or JSON tuned for LLM and agent workflows. It's quickly becoming a standard pre-processing tool. Plug it in if you're serious about document-heavy RAG. ([github.com](https://github.com/trending?since=daily))
mvanhorn/last30days-skill
An AI agent skill that scrapes Reddit, X, YouTube, Hacker News, Polymarket, and the web for the last 30 days, then synthesizes a grounded summary. Use it to replace generic web search with a behavior-driven snapshot of what real people and real money care about.
chopratejas/headroom
Headroom compresses tool outputs, logs, and RAG chunks before they ever hit the model, often cutting tokens by 60–95%. It acts as a library, proxy, and MCP server so you can slash running costs without sacrificing answer quality.
Chaining the Evidence: Robust Reinforcement Learning for Deep Search Agents with Citation-Aware Rubric Rewards
The authors design a reward scheme that scores agents on how well they build evidence chains with proper citations, not just final answers. Their new training method reduces shortcut tricks and hallucinated claims, so deep research agents behave more like careful analysts.
Over-Searching in Search-Augmented Large Language Models
This work shows that search‑augmented models often call tools even when search hurts answers or wastes tokens. It introduces a cost‑aware metric and mitigation tricks, so teams can dial back needless retrieval instead of just adding more context.
LogicPoison: Logical Attacks on Graph Retrieval-Augmented Generation
Shows how to poison graph-structured knowledge used by retrieval-augmented systems. Focuses on attacks that subtly flip logical conclusions, not just surface facts.
ruvnet/ruflo
Agent orchestration platform tuned for Claude-based systems. Focuses on multi-agent swarms, enterprise deployments, and built-in RAG and code workflows. If you’re standardizing on Claude for serious products, study this before rolling your own orchestrator. ([github.com](https://github.com/trending?since=daily))
GoogleCloudPlatform/generative-ai
Large collection of Gemini on Vertex AI notebooks and sample apps. Great starting point if you want to build production-style systems on Google Cloud fast.
openai/openai-cookbook
The OpenAI cookbook is a large set of worked examples for building with OpenAI’s API. Treat it as a pattern library for chat apps, agents, RAG systems, and fine-grained evaluations.
Improving Multi-step RAG with Hypergraph-based Memory for Long-Context Complex Relational Modeling
HGMem turns the “scratchpad” of a multi-step retrieval system into a hypergraph that connects many related facts at once. This richer memory structure helps language models keep global context straight over long tasks, boosting performance on challenging reasoning and long-document benchmarks.
ObjectGraph: From Document Injection to Knowledge Traversal — A Native File Format for the Agentic Era
Proposes a new file format that treats documents as typed graphs instead of long strings dumped into context windows. Agents query and traverse nodes, cutting tokens used by up to ~95% while keeping task accuracy. If your agents still paste whole PDFs into prompts, this hints at a cleaner architecture layer. ([arxiv.org](https://arxiv.org/abs/2604.27820))
yichuan-w/LEANN
LEANN is a compact retrieval system for "RAG on everything" with big storage savings. It compresses document representations while keeping accuracy high, making private, on-device retrieval far cheaper.
RyanCodrai/turbovec
Turbovec is a vector index built on TurboQuant with Rust internals and Python bindings. It targets high-speed similarity search for embeddings. Drop it into your stack if your current vector store is the bottleneck.
Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning
Uses a language model’s own feedback as a training signal for retrieval rerankers in RAG pipelines. Aims to pick more useful documents for question answering.
LIT-RAGBench: Benchmarking Generator Capabilities of Large Language Models in Retrieval-Augmented Generation
Defines a benchmark that scores how models actually write answers when given retrieved documents. Helps teams compare RAG setups on answer quality, not just retrieval hit rates.
lfnovo/open-notebook
open-notebook recreates NotebookLM as an open-source app with more control and features. It lets you spin up your own AI notebook that reasons over your documents without being locked into a single provider.
WeKnora
Tencent’s LLM-powered framework for deep document understanding, semantic retrieval, and context-aware answering with a RAG paradigm. Essentially a production-grade answer engine stack rather than a toy demo. ([github.com](https://github.com/trending?since=daily))
Neuro-RIT: Neuron-Guided Instruction Tuning for Robust Retrieval-Augmented Language Model
Introduces Neuro-RIT, which looks at individual neurons while customizing language models for retrieval-heavy tasks. The aim is steadier answers when retrieved documents shift or are noisy.
refactoringhq/tolaria
Tolaria is a desktop app for managing markdown knowledge bases, often paired with local LLMs. It makes it easier to turn notes into an AI-ready memory store. Try it if your personal or team knowledge is scattered across files and you want AI on top.
langchain-ai/rag-from-scratch
Step-by-step notebooks for building retrieval-augmented generation systems without heavy frameworks. Walks through indexing, retrieval, and response patterns. If your team keeps misusing generic RAG libraries, force everyone to work through this once. ([github.com](https://github.com/trending/jupyter-notebook?since=daily))
pandas-ai
pandas-ai turns DataFrames and SQL/CSV/Parquet sources into a conversational interface, translating natural-language questions into code or SQL, running them in a (configurable) sandbox, and optionally using RAG and semantic schemas to answer more complex queries. It’s attractive for quickly giving analysts or business users an LLM front-end on top of existing data, though you do need to pay attention to security configurations given its history of prompt-injection/RCE issues that were later mitigated with new settings. ([github.com](https://github.com/sinaptik-ai/pandas-ai?utm_source=openai))
Naiad: Novel Agentic Intelligent Autonomous System for Inland Water Monitoring
Naiad chains an AI agent with weather data, satellite imagery, and domain tools to monitor lakes and rivers end to end. It lets non-experts ask plain-language questions and get tailored environmental reports, showing how agent stacks can tackle real infrastructure problems.