Embeddings and Vector Databases: worked example and design decisions | Complete Generative AI: From Foundations to Production
A self-paced 26-week learning path with 15 modules: AI foundations, Python, machine learning, transformers, LLMs, prompts, APIs, embeddings, RAG, frameworks, agents, customization, multimodal AI, Spring Boot with React, and production LLMOps. Includes 15 practical assignments and three portfolio projects.
Prerequisites: Begin with basic computer skills. Programming is introduced in Weeks 2–3. Use a local Python environment for coding labs; Java and React experience helps in Weeks 21–22. Model downloads, provider accounts and deployment resources are optional learner-managed dependencies. The 26-week schedule is suggested; reading estimates exclude independent project work.
Walk through an example
Hybrid retrieval combines lexical matching with semantic similarity, often by fusing ranked lists instead of directly adding scores on unrelated scales. For a job search, exact terms such as Java, required certifications and location filters may matter alongside semantic phrasing. Apply visibility constraints before documents enter the retrieval context. Evaluate top-k recall against manually labeled relevant jobs and inspect failures involving negation, abbreviations and hard requirements.
import math
def cosine(a, b):
if len(a) != len(b): raise ValueError("dimension mismatch")
denominator = math.sqrt(sum(x*x for x in a) * sum(y*y for y in b))
return sum(x*y for x, y in zip(a, b)) / denominator if denominator else 0
query = [1, 0] # Toy vectors, not trained embeddings
rows = [("python-api", [0.9, 0.1]), ("visual-design", [0.1, 0.9])]
print(sorted(((name, round(cosine(query, v), 3)) for name, v in rows),
key=lambda item: item[1], reverse=True))
Expected behavior
[('python-api', 0.994), ('visual-design', 0.11)] Read the example before adapting it. The code is displayed for study and local use; the academy does not execute it or contact an AI provider.
Review the boundary
List the assumptions in this example and identify which inputs come from an untrusted caller. Change one assumption at a time and write the expected behavior before running a local experiment. Preserve both successful and failed cases in your project notes. A demonstration that works once does not establish accuracy, security or production readiness.
pgvector project documentation
01. Introduction to Artificial Intelligence · Week 1
02. Python Programming for Gen AI · Weeks 2–3
03. Mathematics and Machine Learning Foundations · Week 4
04. Deep Learning and Transformers · Weeks 5–6
05. Large Language Models (LLMs) · Weeks 7–8
06. Prompt Engineering · Weeks 9–10
07. Generative AI APIs and Application Development · Week 11
08. Embeddings and Vector Databases · Weeks 12–13
09. Retrieval-Augmented Generation (RAG) · Weeks 14–15
10. LangChain, LlamaIndex and AI Frameworks · Week 16
11. AI Agents and Agentic AI · Weeks 17–18
12. Fine-Tuning and Model Customization · Week 19
13. Multimodal Generative AI · Week 20
14. Gen AI with Java Spring Boot and React · Weeks 21–22
15. Production Gen AI, LLMOps and Capstone · Weeks 23–26