Beginner

PostgreSQL for AI — Beginner

Foundational five-day path: Postgres as an AI datastore, installing pgvector, similarity search in SQL, vector indexing, and a first Postgres-backed RAG system.

5 lessons ~280 min total
  1. 1

    Postgres as an AI Datastore

    Why PostgreSQL is a strong backbone for AI and RAG: one ACID system for embeddings, JSONB metadata, and relational data, plus a SQL refresher (SELECT/WHERE/ORDER BY/LIMIT/JOIN) for AI engineers.

    45 minPostgres BasicsJSONBData Types
  2. 2

    Installing pgvector & Your First Embeddings

    Enable the pgvector extension, declare a vector(n) column sized to your embedding model, and insert your first embeddings — with a preview of halfvec and sparsevec.

    50 minpgvectorVector ColumnEmbeddings
  3. 3

    Similarity Search in SQL

    pgvector's distance operators (<-> L2, <#> inner product, <=> cosine), choosing the metric that matches your model, KNN with ORDER BY ... LIMIT, and combining similarity with WHERE filters.

    55 minDistance OperatorsKNN QueriesCosine/L2
  4. 4

    Indexing Vectors: HNSW & IVFFlat

    Why a sequential scan gets slow, the two pgvector index types (HNSW and IVFFlat) and their parameters, matching the index to the distance operator, and the recall/speed/build-time tradeoff.

    55 minHNSWIVFFlatIndex Tuning
  5. 5

    Your First Postgres RAG

    The beginner capstone — build a complete RAG pipeline on Postgres: documents/chunks schema, ingest and embed, KNN retrieval with a metadata filter and an HNSW index, context assembly, and generation.

    75 minRAGCapstonepgvector Pipeline