indexing

Current AI has launched the Open Source AI Gap Map v0.1, an index of 421 open source AI products including software, models, datasets, and hardware, with underlying data released under an MIT license.

The author criticizes the RAG and vector database approach, proposing that the correct methods include using memory correctly, chunking and indexing, summarizing, providing search tools for agents, and using SRAM-only inference services such as Groq and Cerebras.

Xiaomi's smart factory replaced Loki with GreptimeDB for log storage, handling billions of rows per month with tailored indexing: Bloom skipping for high-cardinality trace_id, inverted index for low-cardinality fields, and fulltext search on message bodies.

RL-Index proposes a reinforcement learning-based agentic indexing framework that shifts reasoning from query time to the indexing stage by augmenting documents with LLM-generated rationales, improving retrieval effectiveness and reducing online latency.

A thread explaining the four essential layers for building production-grade RAG systems beyond simple chunk-embed-retrieve-generate: intelligent query routing, advanced indexing, multi-type retrieval, and continuous evaluation.

The release of PyLate introduces MaxSim kernels for GPU-accelerated training with lower memory requirements and TACHIOM for fast multi-vector indexing and search on CPU.

Educational article explaining FAISS, a library for billion-scale similarity search, covering vector embeddings, nearest neighbor search, and techniques like IVF and Product Quantization for efficient retrieval.

This video provides an in-depth introduction to the file organization of IBM System/360, covering sequential and random data processing, indexing mechanisms, device independence, and cost advantages. It is a historical technical review.

A developer built a Model Context Protocol (MCP) index containing 3 million arXiv papers to help LLMs retrieve accurate research citations and reduce hallucinations, and is now seeking testers for feedback.

CocoIndex is a tool that provides continuously fresh context for AI agents by incrementally updating embeddings, summaries, and knowledge graphs from codebases, docs, PDFs, Slack, meeting notes, and videos.

A tutorial on optimizing MongoDB query performance using indexes, demonstrating how to identify slow queries, apply compound indexes, and manage them visually using the VisuaLeaf tool. The workflow covers query profiling, index recommendations, and common indexing mistakes.