semantic-search - Provide.ai

laravel, pgvector, postgresql, semantic-search, software-engineering

How I Added Semantic Search to a Production App Using pgvector: What the Tutorials Skip

Hafiq Iqmal / March 26, 2026

A real integration story from the database schema to the first query running in productionContinue reading on Towards AI »

AI & Machine Learning, approximate nearest neighbor, citation support, embeddings, faiss, hnsw, llm grounding, llmops, local llm, Natural Language Processing, ollama, python, RAG, retrieval augmented generation, semantic-search, sentence transformers, tutorial, Vector Databases, vector-search

Vector Search Using Ollama for Retrieval-Augmented Generation (RAG)

Vikram Singh / February 23, 2026

Table of Contents Vector Search Using Ollama for Retrieval-Augmented Generation (RAG) How Vector Search Powers Retrieval-Augmented Generation (RAG) From Search to Context The Flow of Meaning Putting It All Together What Is Retrieval-Augmented Generation (RAG)? The Retrieve-Read-Generate Architecture Explained Why…

The post Vector Search Using Ollama for Retrieval-Augmented Generation (RAG) appeared first on PyImageSearch.

ann, approximate nearest neighbor, cosine similarity, deep-learning, embeddings, faiss, flat index, hnsw, ivf, RAG, recall at k, retrieval augmented generation, semantic-search, tutorial, vector database, Vector Databases, vector-search

Vector Search with FAISS: Approximate Nearest Neighbor (ANN) Explained

Vikram Singh / February 16, 2026

Table of Contents Vector Search with FAISS: Approximate Nearest Neighbor (ANN) Explained From Exact to Approximate: Why Indexing Matters The Trouble with Brute-Force Search The Curse of Dimensionality Enter the Approximate Nearest Neighbor (ANN) Accuracy vs. Latency: The Core Trade-Off…

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TF-IDF vs. Embeddings: From Keywords to Semantic Search

Vikram Singh / February 9, 2026

Table of Contents TF-IDF vs. Embeddings: From Keywords to Semantic Search Series Preamble: From Text to RAG What You’ll Build Across the Series Project Structure Why Start with Embeddings The Problem with Keyword Search When “Different Words” Mean the Same…

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