Embeddings are a good starting point for the AI curious app developer

Running embeddings locally and in databases

• Multiple options cited for local embedding generation: Rust libraries, Candle-based wrappers, SentenceTransformers, HuggingFace models, CLIP models inside Postgres extensions, Ollama/BERT-based models, and Supabase’s built‑in embedding API.
• Embeddings can be stored in “boring” databases as arrays of floats; vector DBs or extensions (pgvector, sqlite‑vss, Redis vector search) mainly help with fast similarity search.
• Some prefer keeping DBs “dumb” (no HTTP calls/cron logic), others like DB‑integrated pipelines that auto‑update embeddings.
• Performance experiences vary: pgvector works fine for many up to low‑millions of vectors; others argue specialized systems (Pinecone, FAISS, hnswlib, etc.) matter at larger scales.

Do you really need a vector database?

• Several commenters argue vector DBs are overkill for learning and small apps: brute‑force cosine similarity with NumPy/Pandas or in‑memory arrays is often fast enough.
• Others emphasize convenience and ecosystem support, especially outside Python, as reasons to adopt vector stores early.
• Cost and operational overhead of hosted vector DBs are raised as concerns for hobby projects.

What embeddings are and how to understand them

• Debate over pedagogy: some advocate starting with bag‑of‑words / TF‑IDF + cosine similarity to build intuition; others call that a “dead end” that misleads about modern embeddings.
• Clarifications that simple word‑count vectors are document embeddings, not word embeddings; useful but high‑dimensional and crude.
• Discussion connects older IR methods (BoW, co‑occurrence matrices, PMI, SVD) to modern word embeddings, with disagreement on how strong that conceptual link is.
• Several readers want deeper explanations of how embeddings are trained and why they exhibit properties like analogies; others argue you don’t need that depth to be productive.

Limitations: homonyms, style, meaning

• Homonyms cause amusing but problematic results (e.g., “king” → “ruler” tool icons, “rodent” → computer mice); context and attention mechanisms are seen as necessary to disambiguate.
• Hybrid search (keyword + semantic), richer descriptions, rerankers, user feedback, and contextual queries are suggested mitigations.
• Questions arise about using embeddings for style rather than semantics; some think it’s possible but may require classifiers on top, others see current embeddings as too crude.

Use cases and experimentation

• People report success using embeddings for semantic search (docs, icons, images, resumes, news clustering, deduplication), recommendation, and SEO/“topical authority” understanding.
• Many emphasize embeddings as a low‑friction, deterministic entry point into AI for app developers, even if the underlying models remain “black boxes.”