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Qdrant

8 articles · Page 1 of 1
AI Engineer Programming
AI Engineer Programming
Jul 5, 2026 · Artificial Intelligence

Predicting Weak Retrieval Without an LLM: Low‑Cost Signals and Gateways

Most retrieval systems apply the same pipeline to every query, but this one‑size‑fits‑all approach fails on hard queries and wastes compute on easy ones; the article defines cheap, no‑LLM signals to predict weak retrieval, evaluates them across three corpora, and proposes a gating method to upgrade only the weak cases.

Qdrantcheap signalsdense variance
0 likes · 15 min read
Predicting Weak Retrieval Without an LLM: Low‑Cost Signals and Gateways
AI Architect Hub
AI Architect Hub
May 3, 2026 · Artificial Intelligence

Choosing the Right Vector Database: Milvus, Chroma, Weaviate, Qdrant, FAISS Compared

This article compares five popular vector databases—Chroma, Milvus, Weaviate, Qdrant, and FAISS—detailing their positions, strengths, weaknesses, suitable scenarios, a selection‑dimension matrix, common pitfalls, code implementations for a unified RAG pipeline, best‑practice recommendations, and thought questions to guide engineers in choosing and migrating vector stores.

ChromaFAISSMilvus
0 likes · 23 min read
Choosing the Right Vector Database: Milvus, Chroma, Weaviate, Qdrant, FAISS Compared
Programmer XiaoFu
Programmer XiaoFu
Apr 20, 2026 · Artificial Intelligence

How Java + LangChain4j Can Eliminate Messy Chunking for High‑Quality RAG Document Splitting

The article explains why fixed‑size chunking harms RAG recall, demonstrates three semantic‑chunking strategies—including recursive punctuation splitting, overlapping windows, and parent‑child document mapping—and provides complete Java/LangChain4j code that integrates tokenizers, Redis, and Qdrant to boost retrieval performance.

EmbeddingJavaLangChain4j
0 likes · 10 min read
How Java + LangChain4j Can Eliminate Messy Chunking for High‑Quality RAG Document Splitting
Hailey Says
Hailey Says
Aug 10, 2025 · Artificial Intelligence

Building a MAS‑Powered RAG System for Blog Search and Q&A

This article walks through constructing an agentic RAG pipeline that combines LangChain, LangGraph, Google Gemini embeddings, and Qdrant vector storage to enable automatic query rewriting, relevance grading, and concise answers to blog‑post questions via a Streamlit UI.

Google GeminiLangChainLangGraph
0 likes · 9 min read
Building a MAS‑Powered RAG System for Blog Search and Q&A
JavaEdge
JavaEdge
Oct 15, 2024 · Artificial Intelligence

Build a Real‑Time Search & Bazi AI Agent with LangChain & FastAPI

This tutorial walks through creating a LangChain tool‑calling agent that combines a real‑time web search tool, a Qdrant vector store for local knowledge retrieval, and a custom Bazi fortune‑telling service, all wrapped in a FastAPI application for interactive use.

AI AgentFastAPILangChain
0 likes · 15 min read
Build a Real‑Time Search & Bazi AI Agent with LangChain & FastAPI
JD Tech Talk
JD Tech Talk
Oct 8, 2024 · Artificial Intelligence

Building a Retrieval‑Augmented Generation (RAG) System with Rust and Qdrant

This article explains how to construct a Retrieval‑Augmented Generation pipeline in Rust, covering knowledge‑base creation with Qdrant, model loading and embedding using the candle library, data ingestion, and integration of a Rust‑based inference service based on mistral.rs, while also discussing resource usage and common pitfalls.

AIEmbeddingLLM
0 likes · 16 min read
Building a Retrieval‑Augmented Generation (RAG) System with Rust and Qdrant