INTROBLOCK · 01
VEC · 7 MIN PREVIEW
Vector DBs & embedding pipelines
From raw text to ANN search at scale. Pick a store, version your embeddings, ship retrieval that survives reality.
CONCEPTBLOCK · 02
What an embedding pipeline actually is
An embedding pipeline is a data pipeline whose output is a vector index, not a table. You take heterogeneous source text, normalise it, split it into chunks small enough to embed but big enough to mean something, push each chunk through an embedding model, and write the resulting fixed-size float arrays into a vector store with the original metadata. The store gives you ANN search — approximate nearest neighbours — at sub-second latency over millions of vectors. Everything else (re-ranking, hybrid, filters) is a layer on top.
TIPThe pipeline is the product. The model is replaceable; the chunking and metadata strategy are what make retrieval feel smart.
WATCH OUTIf your embedding model changes, every vector in your index is now technically wrong. Plan for re-embedding from day one.
DIAGRAMBLOCK · 03
Source -> chunk -> embed -> index -> query
Same model on both sides. Versioned together. Always.
CODEBLOCK · 04
12-line embedding pipeline (real)
PYTHON1import os, psycopg, openai
2from openai import OpenAI
3
4client = OpenAI()
5conn = psycopg.connect(os.environ["DATABASE_URL"])
6
7def embed_chunks(chunks, source):
8 rows = []
9 for c in chunks:
10 v = client.embeddings.create(model="text-embedding-3-small", input=c).data[0].embedding
11 rows.append((source, c, v))
12 with conn.cursor() as cur:
13 cur.executemany("INSERT INTO docs (source, chunk, embedding) VALUES (%s, %s, %s)", rows)
14 conn.commit()
pgvector with the embedding column typed as vector(1536). One model. One table. Production-ready in ~12 lines.
CHEATSHEETBLOCK · 05
Five things to remember
01Pick chunk size by the question shape, not by the doc length.
02Always store the source URI + offsets alongside the vector. You will need them for citation and debugging.
03Re-embedding is a migration. Version your embeddings like a schema.
04ANN recall != accuracy. Tune ef_search / nprobe for your latency budget.
05Hybrid (BM25 + vectors) almost always beats pure vector at top-k.
MINIGAME · RAPIDFIRETFBLOCK · 06
True or false: 6 seconds each
An embedding model's output dimension is fixed.
CLAIM 1/5 · READY · scroll into view
LESSON COMPLETEBLOCK · 07
Pipeline mental model: locked.
NEXTEmbedding pipeline architecture
WHAT YOU'LL WALK AWAY WITH
Real skills, real career delta.
Skills you'll gain
08- Architect an embedding pipelineWorking
Outcome from completing the course: architect an embedding pipeline.
- Compare pgvector / Weaviate / PineconeWorking
Outcome from completing the course: compare pgvector / weaviate / pinecone.
- Version embeddings without breaking searchWorking
Outcome from completing the course: version embeddings without breaking search.
- Pipeline architectureWorking
Covered in lesson sequence — drop-in ready.
- Index types comparedWorking
Covered in lesson sequence — drop-in ready.
- Chunking + embedding co-designWorking
Covered in lesson sequence — drop-in ready.
- Versioning embeddingsWorking
Covered in lesson sequence — drop-in ready.
- Cost at scaleWorking
Covered in lesson sequence — drop-in ready.
Career & income delta
Career moves
- Lead a Vector DBs & embedding pipelines initiative on your team — most orgs have it on the roadmap and few have shipped it.
- Consulting work at $150-300/hr — 'VEC shipped to production' is a sought-after specialty in 2026.
- Move from generic IC to platform/AI-platform team where Vector DBs & embedding pipelines expertise is the entry ticket.
Income impact
- $15-40K bump for senior ICs adding Vector DBs & embedding pipelines to their resume.
- Freelance / consulting demand for the same skill: $150-300/hr in 2026.
- Closing enterprise deals often hinges on demonstrating the production patterns from this course.
Market resilience
- Vector DBs & embedding pipelines is a durable skill across model and framework consolidations.
- Production guardrails (cost caps, observability, audit, evals) carry forward to whatever the 2027 stack is.
- Core patterns transfer to cloud, on-prem, and hybrid deployments.