RAG / 向量检索排行

This RAG (Retrieval-Augmented Generation) project is implemented using pure Java. This approach makes it easier to adapt to enterprise-level environments and is more conducive to secondary development.

Real-time on-device text-to-image and image-to-image Semantic Search with video stream camera capture using USearch & UForm AI Swift SDKs for Apple devices 🍏

ChatIndex: Tree indexing and retrieval for long conversational memory

Memory for AI that works like yours—local, instant, persistent. 13x faster than Pinecone, 5x leaner than RAG. Finds what RAG misses. Zero cloud, zero cost.

learning resource of langgraph for dummy

Search Claude Code, Codex, Pi, OpenCode, Github Copilot & Cursor transcripts. Resume sessions. Track tokens.

Forge is an open-source, self-hosted alternative for visually building and shipping AI agents and workflows without giving up control of your infrastructure.

MIT-licensed PowerShell document automation for Word, Excel, PowerPoint, PDF, email, PST/OST, OneNote, Visio, OpenDocument, and mixed-format Reader workflows.

Agentic AI research papers, benchmarks, frameworks, and tools curated across 24 domains.

An agentic AI application that allows you to chat with your papers and gather also information from papers on ArXiv and on PubMed

Read books the way Feynman did — along with a continuously evolving network of agent-simulated great minds

Cerul — video memory infrastructure for AI products. SDKs, CLI, MCP, and developer integrations.

The latest graphrag interface is used, using the local ollama to provide the LLM interface.Support for using the pip installation

A Java backend engineer learning AI full-stack in public — Python · FastAPI · RAG · pgvector · Next.js

(ACL 2025 Main) A Comprehensive Benchmark for Code Information Retrieval.

Explore sample applications and tutorials demonstrating the prowess of Amazon Bedrock with Python. Learn to integrate Bedrock with databases, use RAG techniques, and showcase experiments with langchain and streamlit.

CrawlAI RAG is an AI-powered website intelligence platform that allows users to crawl entire websites, index their content, and ask natural-language questions using Retrieval-Augmented Generation (RAG). It transforms static websites into queryable knowledge bases.

[EMNLP 2024 (Oral)] Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA

GEO（AI 搜索引擎优化）智能内容优化平台 — 帮助品牌在 ChatGPT、Perplexity 等 AI 回答中被优先引用。支持关键词策略、多平台内容生成、E-E-A-T 优化、多模型验证和数据分析。

This open-source repository offers reference code for integrating workplace datastores with Cohere's LLMs, enabling developers and businesses to perform seamless retrieval-augmented generation (RAG) on their own data.