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Retrieval-Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.
English: Diagram illustrating the two principle phases of GraphRAG with a knowledge graph with selectable access patterns for unstructured, structured and mixed data. Indexing Phase, knowledge graph construction: a) Documents (unstructured data) are transformed into a lexical graph with hiearchical levels of detail and cross-document topical ...
GraphRAG with a knowledge graph combining access patterns for unstructured, structured, and mixed data GraphRAG [ 40 ] (coined by Microsoft Research ) is a technique that extends RAG with the use of a knowledge graph (usually, LLM-generated) to allow the model to connect disparate pieces of information, synthesize insights, and holistically ...
Ragulator-Rag Complex, inactive. Ragulator-Rag Complex, active. The Ragulator-Rag complex is a regulator of lysosomal signalling and trafficking in eukaryotic cells, which plays an important role in regulating cell metabolism and growth in response to nutrient availability in the cell. [1]
Gremlin is an Apache2-licensed graph traversal language that can be used by graph system vendors. There are typically two types of graph system vendors: OLTP graph databases and OLAP graph processors.
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As with many enzymes, RAG proteins are fairly large. For example, mouse RAG-1 contains 1040 amino acids and mouse RAG-2 contains 527 amino acids. The enzymatic activity of the RAG proteins is concentrated largely in a core region; Residues 384–1008 of RAG-1 and residues 1–387 of RAG-2 retain most of the DNA cleavage activity.
Undirected hypergraphs are useful in modelling such things as satisfiability problems, [5] databases, [6] machine learning, [7] and Steiner tree problems. [8] They have been extensively used in machine learning tasks as the data model and classifier regularization (mathematics). [9]