LLM local
ai-file-sorter
AI file organizer that sorts and renames files by understanding content.
★ 1,8 mil estrelasAGPL-3.0
Ver o guia →qdrant-loader é um projeto de RAG e conhecimento de código aberto de cbtw-apac (cbtw-apac/qdrant-loader no GitHub). Tem 55 estrelas no GitHub e 30 forks, é publicado sob a licença Apache-2.0 e foi atualizado pela última vez no GitHub em 1 de outubro de 2026.
Free rag & knowledge tool that enterprise-ready vector database toolkit for building s.
Fetch the source code onto your machine.
Set up the Python environment the project needs.
Copy the sample config or env file and adjust it.
Start the app from the source directory.
Built around cli tool for a focused workflow.
Built around confluence integration for a focused workflow.
Built around cursor ide for a focused workflow.
Built around developer tools for a focused workflow.
Inspect, fork, and modify every line of the code.
Run it on your own machine — no account lock-in.
Get the source on your machine.
git clone https://github.com/cbtw-apac/qdrant-loader.gitSet up Python dependencies.
cd qdrant-loader && pip install -r requirements.txtLaunch the application.
cd qdrant-loader && python main.pyIt enterprise-ready vector database toolkit for building searchable knowledge bases from multiple data sources. Supports multi-project management, automatic ingestion from Confluence/JIRA/Git, intelligent file conversion (PDF/Office/images), and semantic search. Includes MCP server for seamless AI assistant integration, giving you a working starting point you can run and adapt.
Yes — the code is open-source, so you can use and modify it without a license fee.
Clone the GitHub repository and follow the README; the typical flow is install dependencies, configure, and run.
It is primarily written in Python, so you will need that toolchain to build or extend it.
The GitHub repository is the hub: open issues for bugs and check the README and discussions for guidance.
AI file organizer that sorts and renames files by understanding content.
One-command installer for a full self-hosted AI stack on your server.
Terminal-first AI studio for creating content without leaving the CLI.
Ready-to-run templates for RAG, AI pipelines and enterprise search that stay in sync with live data
Graph RAG conversational knowledge base built on TiDB Serverless vector storage
Lightweight, lightning-fast in-process vector database from Alibaba that embeds in your app.
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Grátis e aberto: pegue o código-fonte, dê uma estrela no repositório e comece a criar.