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Agentic AI refers to artificial intelligence systems that operate as autonomous agents, capable of perceiving their environment, setting goals, and executing actions to achieve those goals without continuous user intervention.

pgvector provides vector similarity search capabilities for PostgreSQL, and its functionality can be accessed and utilized in Python applications. It is an open source PostgreSQL extension that adds a new column type for storing and querying vectors, typically used in ML and AI applications.

A vector database in the context of Retrieval Augmented Generation (RAG) is a specialized database designed to efficiently store, index, and query high-dimensional vector embeddings.

Enterprise AI search software helps organizations find, retrieve, and analyze relevant information from massive stores of data.

Enterprise AI Search platforms use artificial intelligence to unify and search across all company data (docs, chats, systems) for fast, personalized answers, moving beyond keyword search to understand context, users, and intent, with top players like Instaclustr, Guru, Kore.ai, Moveworks, and Glean.

Graph RAG (Retrieval Augmented Generation with Graphs) is an advanced RAG technique that leverages knowledge graphs to provide LLMs with a more structured and contextually rich information for generating responses. It aims to overcome limitations of traditional RAG systems.

An open source vector database platform refers to a managed vector database solution built on top of one or more open source components, such as Instaclustr, Zilliz Cloud, or Qdrant Cloud. These solutions are based on general purpose open source databases such as PostgreSQL or specialized open source vector databases like Milvus.

Feature embedding is the process of transforming data, or its features, into continuous, lower-dimensional vector representations. These dense, compact vectors, called embeddings, capture meaningful relationships and abstract patterns within the data that might not be obvious in the raw features.