Tagged articles

bge-m3

3 articles · Page 1 of 1

Sep 21, 2026 · Databases

Easysearch 2.4.0 Vector Search: Verified Guide to Indexing, Querying & Hybrid Search Pitfalls

This article provides a step-by-step verified guide to implementing vector search in Easysearch 2.4.0, covering index creation with dense_vector fields, data ingestion with embeddings, k-NN query pitfalls including the mandatory 'k' parameter, and the critical distinction between compound queries and RRF-based hybrid search for combining keyword and semantic search.

EasysearchEmbeddingHNSW

0 likes · 10 min read

Easysearch 2.4.0 Vector Search: Verified Guide to Indexing, Querying & Hybrid Search Pitfalls

Lao Guo's Learning Space

Apr 27, 2026 · Artificial Intelligence

Build a Private Knowledge Base from Scratch with DeepSeek V4 and AnythingLLM

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.

DeepSeek-V4RAGanythingllm

0 likes · 18 min read

Build a Private Knowledge Base from Scratch with DeepSeek V4 and AnythingLLM

Frontend AI Walk

Mar 11, 2026 · Artificial Intelligence

OpenClaw Full‑Domestic Model Stack: 6 Role‑Based Selections and Strategies

This guide outlines a role‑based selection strategy for building a fully domestic OpenClaw model stack, explains common pitfalls when replacing foreign models, details why specific Chinese models fit each role, presents three balanced configurations, and offers a step‑by‑step migration plan.

DeepSeekGLM-5Kimi K2.5

0 likes · 15 min read

OpenClaw Full‑Domestic Model Stack: 6 Role‑Based Selections and Strategies