Topic 5 of 16 in the learning path

Embeddings & Vector Databases

Embedding models, similarity search, vector stores and indexing.

Reels and posts (7)

• Liquid AI LFM 2.5 Encoder: a CPU-friendly encoder model · Melvin Vivas, X: Points out Liquid AI's LFM 2.5 Encoder, released in July 2026, which runs on CPU.
• How RAG Finds the Right Document Fast: Graph-Based Vector Search · Bashiri Smith, Facebook · 1:08: A skit about an interview question: how does a RAG app find the right document for a question?
• Sentence Transformers v6: ColBERT-Style Multi-Vector Models Fully Supported · Melvin Vivas, X: Sentence Transformers v6.0 has been released.
• Building Interactive AI Concept Demos with Claude Design (Embeddings Example) · Melvin Vivas, X · 0:19: In this 19-second X post with no narration, Melvin Vivas shows an interactive demo of how embeddings work that he made with Claude Design, Anthropic's tool for making visual design
• Gemini Embedding 2: One Vector Space for Five Modalities · Melvin Vivas, X: Gemini Embedding 2 is now generally available.
• Fine-Tuning MiniLM Embeddings with Synthetic Data, Running on CPU · Melvin Vivas, X: A practical case study in text matching.
• Fine-tuning all-MiniLM-L6-v2 to Beat OpenAI Embeddings · Melvin Vivas, X: The creator trained a custom embedding model starting from the small open-source all-MiniLM-L6-v2.

Watch, free (4)

• Word Embedding and Word2Vec, Clearly Explained!!! (StatQuest with Josh Starmer) · video · youtube.com · free
  StatQuest's visual explanation of word embeddings and how Word2Vec learns them.
  Mentioned in: 6 Free Videos to Move from Software Engineer to AI Engineer (Bashiri Smith on Facebook · notes), How to Relearn LLMs & RAG in 2026: A 7-Step Roadmap with Free Resources (Bashiri Smith on Facebook · notes)
• Understanding and Applying Text Embeddings (DeepLearning.AI) · course · deeplearning.ai · free
  A DeepLearning.AI course on embeddings and semantic search. Free with a DeepLearning.AI account during its platform beta; certificates are paid.
  Mentioned in: How to Relearn LLMs & RAG in 2026: A 7-Step Roadmap with Free Resources (Bashiri Smith on Facebook · notes)
• Vector Databases: from Embeddings to Applications (DeepLearning.AI) · course · deeplearning.ai · free
  A DeepLearning.AI short course on how vector databases work and how to build applications with them. Free with a DeepLearning.AI account during its platform beta; certificates are paid.
  Mentioned in: How to Relearn LLMs & RAG in 2026: A 7-Step Roadmap with Free Resources (Bashiri Smith on Facebook · notes)
• What is a Vector Database? Powering Semantic Search & AI Applications (IBM Technology) · video · youtube.com · free
  An IBM video introducing vector databases.
  Mentioned in: How to Relearn LLMs & RAG in 2026: A 7-Step Roadmap with Free Resources (Bashiri Smith on Facebook · notes)

Read and use (12)

• Pinecone · tool · pinecone.io · free
  A managed, production vector database (free tier).
  Mentioned in: Basic RAG Pipeline in 60 Seconds: From Documents to Grounded Answers (Bashiri Smith on Facebook · notes), Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), How RAG Works: Chunking, Embedding, Vector Storage, and Retrieval (Bashiri Smith on Facebook · notes), How RAG Works Under the Hood: Chunking, Embedding, Storage, Retrieval (Bashiri Smith on Facebook · notes) and 1 more
  In a shared PDF: The AI Pivot Field Guide - shared in this reel on Facebook, this reel on Facebook
• Weaviate · tool · weaviate.io · free
  An open-source vector database for storing embeddings and running semantic and hybrid search.
  Mentioned in: Basic RAG Pipeline in 60 Seconds: From Documents to Grounded Answers (Bashiri Smith on Facebook · notes), Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), How RAG Works: Chunking, Embedding, Vector Storage, and Retrieval (Bashiri Smith on Facebook · notes), How RAG Works Under the Hood: Chunking, Embedding, Storage, Retrieval (Bashiri Smith on Facebook · notes) and 1 more
• pgvector · tool · github.com · free
  An open-source Postgres extension for storing embeddings and running vector similarity search.
  Mentioned in: Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), How RAG Works: Chunking, Embedding, Vector Storage, and Retrieval (Bashiri Smith on Facebook · notes), 7 Habits to Become an AI Engineer: Books, Tooling, Research & Shipping (Bashiri Smith on Facebook · notes), How RAG Works Under the Hood: Chunking, Embedding, Storage, Retrieval (Bashiri Smith on Facebook · notes)
• all-MiniLM-L6-v2 · tool · huggingface.co · free
  A small open-source sentence-transformers embedding model that maps text to 384-dimensional vectors.
  Mentioned in: Fine-Tuning MiniLM Embeddings with Synthetic Data, Running on CPU (Melvin Vivas on X · notes), Fine-tuning all-MiniLM-L6-v2 to Beat OpenAI Embeddings (Melvin Vivas on X · notes)
• OpenAI text-embedding-3-small · tool · platform.openai.com · paid
  OpenAI's small paid embedding model, available through its API.
  Mentioned in: Fine-Tuning MiniLM Embeddings with Synthetic Data, Running on CPU (Melvin Vivas on X · notes), Fine-tuning all-MiniLM-L6-v2 to Beat OpenAI Embeddings (Melvin Vivas on X · notes)
• aibackends (Python package) · tool · pypi.org · free
  A Python package installed with pip and shown in the post's caption; it seems to be connected to the demo.
  Mentioned in: Building Interactive AI Concept Demos with Claude Design (Embeddings Example) (Melvin Vivas on X · notes)
• Chroma · tool · trychroma.com · free
  The easiest open-source vector database to start with.
  In a shared PDF: The AI Pivot Field Guide - shared in this reel on Facebook, this reel on Facebook
• FAISS · tool · faiss.ai · free
  Fast local similarity search from Meta.
  In a shared PDF: The AI Pivot Field Guide - shared in this reel on Facebook, this reel on Facebook
• Gemini Embedding 2 · tool · blog.google · check price · open in a browser to verify
  Google's multimodal embedding model, with one vector space for text, image, video, audio and PDF.
  Mentioned in: Gemini Embedding 2: One Vector Space for Five Modalities (Melvin Vivas on X · notes)
• LFM 2.5 Encoder · tool · liquid.ai · free
  Liquid AI's encoder model, released in July 2026, that can run on CPU.
  Mentioned in: Liquid AI LFM 2.5 Encoder: a CPU-friendly encoder model (Melvin Vivas on X · notes)
• OpenAI Ada embeddings · tool · platform.openai.com · paid
  OpenAI embedding model used by llama_index by default.
  Mentioned in: Q&A Over Your Own PDFs with LlamaIndex, OpenAI and Python (2023) (Melvin Vivas on melvinvivas.com · notes)
• Sentence Transformers · tool · sbert.net · free
  Open-source Python library for training and running embedding, sparse, reranker and (from v6) multi-vector models.
  Mentioned in: Sentence Transformers v6: ColBERT-Style Multi-Vector Models Fully Supported (Melvin Vivas on X · notes)

Build

• Compare dense, sparse and ColBERT-style multi-vector retrieval on your own RAG dataset. (from Sentence Transformers v6: ColBERT-Style Multi-Vector Models Fully Supported)
• Use Claude Design to build an interactive visual explainer of how embeddings work: text becomes vectors, and similar meanings sit close together. (from Building Interactive AI Concept Demos with Claude Design (Embeddings Example))
• Build a multimodal search/RAG system that searches text, images, audio and PDFs in one Gemini Embedding 2 index. (from Gemini Embedding 2: One Vector Space for Five Modalities)
• Fine-tune all-MiniLM-L6-v2 on synthetic pairs for a domain-specific text-matching task, publish it to Hugging Face, and serve it from a CPU-only Docker container (from Fine-Tuning MiniLM Embeddings with Synthetic Data, Running on CPU)
• Fine-tune all-MiniLM-L6-v2 on domain-specific pairs and compare retrieval quality against OpenAI text-embedding-3-small. (from Fine-tuning all-MiniLM-L6-v2 to Beat OpenAI Embeddings)