rag
6 notes tagged rag in the Applied LLMs topic. Putting language models to work: retrieval, prompting, evaluation and the behavior you only learn in production.
- Related notes by meaning: an embedding index won memo's Ask and lost the chat eval
Memo now picks the related notes Ask reads beside each article from a 522-note bge-m3 index instead of the folder a note sits in. The same index lost a blind 40-question…
- GraphRAG - building a knowledge graph for RAG system
In baseline Retrieval Augmented Generation (RAG), sometimes the result might not be accurate as expected since the query itself have multiple layers of reasoning or the…
- Evaluating caching in RAG systems
Caching is a vital technique that boosts performance by storing frequently accessed information. Let's see how it works with RAG. In this article, we will know how cache…
- Evaluating search engine in RAG systems
Hybrid search comes to solving problems in vector search and full-text search. Let's see how it works. In this article, we will know how hybrid search approach and how…
- Multimodal: in rag
In spite of having taken the world by storm, Large Language Models(LLM) still has some limitations such as limited context window and a knowledge cutoff date.…
- Building blocks of a RAG pipeline
How chunking, hierarchical tree summaries, re-ranking, query caching and vector-store chat memory fit together around a vector database in an LLM application.