Articles tagged "RAG"

EmbeddingGemma 2 runs in 191MB RAM (text only) and about 1.5GB BF16 for all modalities, no GPU. Memory table, setup, local RAG. October 2026.

Hindsight is an open-source (MIT) agent memory system by Vectorize built to create agents that learn over time, not just recall chat history — it hits state-of-the-art scores on the LongMemEval benchmark. Updated Sep 2026.

WeKnora is an open-source LLM knowledge platform built by Tencent's WeChat team. It turns documents into RAG search, a ReAct agent, and a self-maintaining wiki under an MIT license you can self-host. This guide covers setup, features, and how it compares to alternatives.

OpenViking is an open-source context database from Volcano Engine unifying AI agent memory, knowledge, and skills as a filesystem for traceable retrieval.

In August 2026, Firecrawl open-sourced AnyDoc and pdf-inspector, two Rust-based document parsing libraries. This article explains how AnyDoc converts 14 formats to Markdown and how pdf-inspector classifies PDF pages to skip unnecessary OCR, with benchmarks and usage examples.

Turso — built on libSQL, the open-source fork of SQLite — packages edge distribution, embedded replicas, and native vector search into one product. This 2026 guide explains how Turso differs from other serverless databases, the May 2026 pricing, real production patterns (multi-tenant SaaS, RAG, mobile AI), strengths and trade-offs, competitive positioning, and sustainability — all from publicly available information and a practitioner's perspective.

Complete guide to Amazon S3 Vectors (GA since December 2025). Covers up to 90% cost reduction vs dedicated vector DBs, 2-billion vectors per index, RAG with Bedrock Knowledge Bases, and Python code examples.

This article explains how to build a RAG (Retrieval-Augmented Generation) customer support system by combining Ollama's embedding models with OpenClaw agents. Through vector database integration, you can generate accurate answers from FAQ documents and deploy AI support across multiple channels like LINE and Slack.

Learn how to build a RAG-enabled internal knowledge base AI using Qwen3.5-9B and OpenClaw. This guide covers document ingestion from PDFs, Word files, and internal wikis, vector database integration, and best practices for achieving accurate information retrieval with natural dialogue. We provide AI agent implementation support for companies in Shinagawa-ku, Minato-ku, Shibuya-ku, Setagaya-ku, and Ota-ku to enhance operational efficiency.

A comprehensive guide to building an internal knowledge search system with Qwen3.5-9B and RAG. Covers document ingestion, Japanese-optimized embeddings, vector database selection, chunking strategies, 262K context utilization, citation tracking, and accuracy evaluation methodology.

Learn how to build a high-accuracy internal knowledge search system using OpenClaw and RAG (Retrieval-Augmented Generation). This guide covers local vector database setup with ChromaDB, Qdrant, and Weaviate, document indexing strategies, and practical deployment for searching across PDFs, Word documents, and internal wikis.