Search
Glossary4
Open the full glossary- RAG (Retrieval-Augmented Generation)Look material up first, hand the retrieved passages to the model along with the question, and have it answer from them.
- RerankingThe second pass of retrieval: a cheap, fast method pulls a batch of candidates, then a smarter, pricier model reads the query together with each candidate to reorder them by true relevance.
- Semantic vs keyword searchThe core trade-off between two retrieval styles: keyword matches on whether wording is identical, semantic on whether meaning is close. The former is precise but rigid; the latter is flexible but can miss the point.
- Contextual RetrievalContextual Retrieval prepends each chunk with brief context from its source document before indexing.
Found 6 articles
Internal-Document Knowledge-Base Q&A System
Can't find company policies or process docs, and keep asking colleagues instead? This article shows how to build an intelligent Q&A system with AI + RAG, so employees ask questions right in their instant-messaging tool and get accurate answers with the source document attached. The focus is on designing for retrieval accuracy and a miss-tracking mechanism.
WeChat Open Source WeMM-Embedding Deep Dive: The Multimodal Embedding Model Topping MMEB-v2, Can It Run on Your Mac?
Breaking down WeMM-Embedding from the arXiv 2608.24053 technical report and source code: Qwen3.5 native multimodal backbone, <embedding> token pooling, two-stage training (Semantic-ID resampling + bidirectional KL distillation + model merging), full MMEB-v2/v3 leaderboard results, and local Mac Studio MPS benchmarks (47ms text / 321ms image / 6
Vector Memory Deep Dive: A Production-Grade Memory System That Ensures AI Agents Never Lose Memory Again
State amnesia is the #1 killer in production Agent environments. We built a four-layer vector memory system based on Qdrant + BGE-m3, with 9 retrieval modes, validated with 6,675 memories in real-world use, raising Chinese search accuracy from 0% to >78%. One line curl | bash, 100% local deployment, and data sovereignty stays in your hands.
The Engineering of AI Memory Retrieval: A Full-Matrix Field Report on Four Access Paths × Ten Scenarios
When an AI assistant needs to "remember everything", how does it retrieve memory? Direct file reads, Qdrant vector search, the MemoryHub capture pipeline, and agentmemory semantic indexing: a field comparison of four paths across ten real scenarios, revealing the boundaries and sweet spots of each path.
XSkill Deep Source Code Analysis: Technical Design, Risks, and Implications for MemoryHub from an ICML 2026 Paper
From the three-stage Skill lifecycle and two-layer Experience structure to cross-trajectory contrastive critique, cascading failures, embedding retrieval blind spots, and over-merging risks, this is a complete technical dissection of XSkill-Agent/XSkill, with an applicability assessment for the Agentic Research memory system.
RAG In-Depth Principles and Practice: A Complete Guide from Chunking to Rerank
The complete tech stack for Retrieval-Augmented Generation (RAG): Embedding selection, Chunking strategies, vector database comparison, Rerank optimization, with practical code examples.