Episodic memory records what happened. Semantic memory stores what the agent has learned. This part builds a production semantic memory layer using Qdrant and Python, with fact extraction, importance-weighted upserts, and similarity retrieval that lets agents build genuine knowledge about users and domains over time.
Tag: embeddings
Vector Databases Part 5: Advanced Optimization and Reranking Strategies
The gap between acceptable and exceptional RAG performance often comes down to optimization decisions made after basic implementation. Production systems require careful tuning of reranking
Vector Databases: From Hype to Production Reality – Part 1: Understanding Vector Databases
The artificial intelligence landscape has undergone a seismic shift in recent years, and at the center of this transformation lies a technology that most developers
Build Your First LangChain Application – Part 5: Building Vector Stores and Semantic Search
Learn how to build vector stores using FAISS and implement semantic search with Azure OpenAI embeddings. Enable powerful document search based on meaning, not just keywords.
