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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.
LangChain is a software framework that helps facilitate the integration of large language models (LLMs) into applications. As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization , chatbots , and code analysis .
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 ...
In knowledge representation and reasoning, a knowledge graph is a knowledge base that uses a graph-structured data model or topology to represent and operate on data. Knowledge graphs are often used to store interlinked descriptions of entities – objects, events, situations or abstract concepts – while also encoding the free-form semantics ...
A chord diagram may refer to: . Chord diagram (music), a diagram showing the fingering of a chord on a guitar or other fretted musical instrument Chord diagram (information visualization), a diagram showing a many-to-many relationship between objects as curved arcs within a circle
A chord chart. Play ⓘ. A chord chart (or chart) is a form of musical notation that describes the basic harmonic and rhythmic information for a song or tune. It is the most common form of notation used by professional session musicians playing jazz or popular music.
In representation learning, knowledge graph embedding (KGE), also referred to as knowledge representation learning (KRL), or multi-relation learning, [1] is a machine learning task of learning a low-dimensional representation of a knowledge graph's entities and relations while preserving their semantic meaning.
NebulaGraph was developed in 2018 by Vesoft Inc. [3] In May 2019, NebulaGraph made free software on GitHub and its alpha version was released same year. [4]In June 2020, NebulaGraph raised $8M in a series pre-A funding round led by Redpoint China Ventures and Matrix Partners China.