Tag: semantic search
-
Implementing RAG Chunking in AI Models
RAG (Retrieval-Augmented Generation) Chunking is a sophisticated technique employed in AI systems to enhance their ability to retrieve and generate contextually relevant responses from large corpora of data. By combining retrieval mechanisms with generative capabilities, RAG models overcome the limitations of traditional language models that rely solely on internalized knowledge. Chunking further optimizes this process…
-
Implementing RAG Retrieval Process in AI Models
Retrieval-Augmented Generation (RAG) is an advanced technique in Natural Language Processing (NLP) that combines the capabilities of retrieval mechanisms with generative models. At its core, the retrieval process in RAG focuses on dynamically fetching relevant, context-specific information from external knowledge sources, such as document stores or databases, to enhance the contextual accuracy and factuality of…
-
Implementing RAG Vector Database in AI Models
Retrieval-Augmented Generation (RAG) leverages external knowledge to enhance AI models’ ability to generate accurate and contextually relevant outputs. A pivotal component of this architecture is the vector database, which enables the efficient retrieval of information by organizing and indexing knowledge in high-dimensional vector space. Vector databases serve as the backbone of RAG by storing embeddings…