Browsing: Retrieval

Semantic search, or embedding-based retrieval, has been a key component within many AI applications. Yet, a surprising number of applications I’ve seen still don’t do reranking, despite…

In the world of voice AI, the difference between a helpful assistant and an awkward interaction is measured in milliseconds. While text-based Retrieval-Augmented Generation (RAG) systems…

In the current AI landscape, the ‘context window’ has become a blunt instrument. We’ve been told that if we simply expand the memory of a frontier…

fails in predictable ways. Retrieval returns bad chunks; the model hallucinates. You fix your chunking and move on. The debugging surface is small because the architecture…

OpenViking is an open-source Context Database for AI Agents from Volcengine. The project is built around a simple architectural concept: agent systems should not treat context…

In my latest post, I how hybrid search can be utilised to significantly improve the effectiveness of a RAG pipeline. RAG, in its basic version, using…

class EverMemAgentOS: def __init__( self, workdir: str = “/content/evermem_agent_os”, db_name: str = “evermem.sqlite”, embedding_model: str = “sentence-transformers/all-MiniLM-L6-v2”, gen_model: str = “google/flan-t5-small”, stm_max_turns: int = 10, ltm_topk:…

In industrial recommendation systems, the shift toward Generative Retrieval (GR) is replacing traditional embedding-based nearest neighbor search with Large Language Models (LLMs). These models represent items…

Perplexity has released pplx-embed, a collection of multilingual embedding models optimized for large-scale retrieval tasks. These models are designed to handle the noise and complexity of…

Large context windows have dramatically increased how much information modern language models can process in a single prompt. With models capable of handling hundreds of thousands—or…