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KNOWLEDGE

Vector Databases

Specialized databases that store numeric embeddings of content and support fast similarity search, the retrieval backbone of RAG systems.

  • Category Knowledge
  • Used In 3 Cortex Services

Technology Overview

What Is Vector Databases?

A vector database stores high-dimensional numeric representations ("embeddings") of text, images or other content, and is optimized for finding the items whose embeddings are most similar to a given query embedding, rather than exact keyword matches.

At a Glance

Key Capabilities

  • Vector databases support fast approximate-nearest-neighbor search across large document collections, metadata filtering alongside similarity search, and incremental indexing as new content is added, enabling semantic search that understands meaning rather than only literal keyword overlap.
How Cortex Uses It
Cortex Texas AI uses vector databases as the retrieval layer beneath RAG (retrieval-augmented generation) systems: a client's documents are chunked, converted to embeddings and indexed, so that at query time the most relevant passages can be retrieved and passed to a language model to ground its answer in the client's own content.
  • Accuracy
  • Latency
  • Cost
  • Privacy
  • Integration
  • Multimodal Needs
  • Reasoning Depth

Connectivity

Integration Scenarios

  • 01

    Common integration scenarios include enterprise knowledge assistants that search internal documentation, customer-support assistants grounded in help-center content, and document-intelligence pipelines that need to find related prior documents.

Model Selection

The Right Model Depends on the Workload

Several vector database products and libraries are available with broadly similar capabilities; Cortex Texas AI selects the specific technology per project based on scale, hosting requirements and the client's existing infrastructure.

In Practice

Used In These Services

01

AI Strategy & Consulting

Independent assessment and roadmapping to identify where AI can realistically create value in your business, and how to implement it responsibly.

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02

AI Agents & Agentic Systems

AI agents that take multi-step action, calling your tools and systems to complete tasks rather than just answering questions.

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03

RAG & Enterprise Knowledge Systems

Retrieval-augmented AI systems that answer questions grounded in your own documents, with accurate, source-linked responses.

Explore service

Related Insights

View all insights

Sep 1, 2026

Vector Databases Explained

A vector database stores numeric representations of content and finds the closest matches by meaning, not exact keywords, making it the retrieval backbone behind most RAG and enterprise knowledge systems.

Read article

Aug 25, 2026

RAG vs. Fine-Tuning: Choosing the Right Approach

RAG and fine-tuning are two different ways to make a general AI model useful for your specific data and needs. Most enterprise use cases are better served by RAG; fine-tuning solves a narrower, different problem.

Read article

Aug 18, 2026

What Is RAG (Retrieval-Augmented Generation)?

RAG (retrieval-augmented generation) grounds an AI model's answers in passages retrieved from your own documents at query time, rather than relying solely on what the model learned during training.

Read article

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