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Lesson

Embeddings and Vector Databases

Embeddings convert text into numbers so you can search by meaning. The vector database stores those numbers along with metadata, permissions, and references to the original document.

  • Understand what an embedding contributes to RAG.
  • Choose Chroma, Qdrant, or FAISS based on your project.
  • Design collections with metadata and filters.

Practical choices

  • Chroma: ideal for quick local prototypes.
  • FAISS: fast and lightweight if you manage the metadata layer yourself.
  • Qdrant: a good option when you need filters, APIs, payloads, and orderly growth.
  • RAGFlow: useful when you want a full ingestion and chat interface with citations.
Terminal
collection: documentos_empresa
vectors:
  dense: embedding_semantico
payload:
  document_id
  page
  section
  owner
  visibility
  updated_at
OCR and Tables in Real-World PDFsHybrid search and reranking
Complete Aulafy mapSee how this lesson fits without leaving your path.

Complete Aulafy map

How all courses connect

This is not a checklist. Start with the foundation, choose an outcome, and go deeper only when your project needs more control.

  1. 1Understand
  2. 2Apply or build
  3. 3Operate with confidence
01

Choose an application

Turn the foundation into a visible outcome: a website, a business improvement, media, or an interactive experience.

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Continue into the technical branch when you need to maintain code, data, or infrastructure.

02

Build with code

Prepare your environment, work with coding agents, and run models while keeping control of your projects.

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This branch prepares you to design and operate reliable AI systems.

03

Take systems to production

Combine retrieval, agents, evaluation, security, deployment, and model adaptation when the problem requires it.

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You do not need every course: choose the component your system needs and return as it grows.

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