This guide walks you through creating a fully local, zero‑cloud RAG knowledge base using DeepSeek V4, AnythingLLM, and the BGE‑M3 embedding model, covering component choices, step‑by‑step installation, advanced tuning, troubleshooting, use‑case scenarios, and cost estimation.
This guide walks through setting up a Ubuntu VM, installing Docker, pulling the AnythingLLM image, configuring storage, launching the container, and using it to ingest and query local documents with a DeepSeek‑R1 model.
This guide walks through setting up a local personal knowledge base using Ollama, DeepSeek‑R1, and AnythingLLM, and demonstrates how to integrate the Continue AI code assistant into VSCode, covering installation, configuration, and usage tips for efficient, secure development.
This guide walks you through setting up a powerful local AI knowledge base using Ollama, DeepSeek‑R1, and AnythingLLM, and shows how to integrate the Continue extension into VSCode for seamless, secure, and efficient development workflows.
This guide explains how to locally deploy the open‑source DeepSeek model using Ollama, enhance interaction with Chatbox and Page Assist, and connect a local knowledge base via AnythingLLM's RAG architecture, providing step‑by‑step instructions, hardware requirements, and API examples for a self‑hosted AI system.
This guide walks you through preparing a JD Cloud GPU instance, installing NVIDIA drivers, deploying Ollama, running the DeepSeek LLM (including model download and execution), configuring the Chatbox graphical client for interactive queries, and optionally feeding local documents into AnythingLLM for a private knowledge base.
Learn step‑by‑step how to prepare a JD Cloud GPU instance, install GPU drivers, deploy Ollama, run DeepSeek‑R1 models, configure graphical clients like Chatbox on Windows and macOS, and optionally feed local data using AnythingLLM to build an offline knowledge base.
This guide compares Ollama and Hugging Face for running large language models locally, detailing API and local execution methods, installation steps, model selection, resource requirements, integration with AnythingLLM, container deployment, embedding and vector store setup, and practical observations on performance and limitations.
