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Vector Search

14 items tagged with "vector-search"

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Best Practices1

Best Practice

Retrieval-Augmented Generation (RAG) Best Practices

RAG grounds a large language model in external documents retrieved at query time, reducing hallucination and letting answers reflect current, private data without retraining the model.

Tutorials3

Tutorial

How to set up vector similarity search with pgvector

Install pgvector in PostgreSQL, store embeddings, build an ANN index, and run nearest-neighbor queries for semantic search.

Tutorial

How to build a RAG pipeline for question answering

Build a retrieval-augmented generation pipeline that grounds an LLM's answers in your own documents using chunking, embeddings, and a vector store.

Tutorial

How to add semantic search with embeddings and a vector database

Add meaning-based search to an app by generating text embeddings and querying a vector database for nearest neighbors.

Reference Architectures1

Reference Architecture

Vector Search Platform for RAG

A vector search platform on Azure powering retrieval-augmented generation with embeddings, a vector database, and hybrid search.

Stacks1

Stack

RAG Stack (LangChain + pgvector + LLM)

Retrieval-augmented generation stack: LangChain orchestrates retrieval over pgvector embeddings in Postgres and grounds an LLM's answers in your own data.

Comparisons2

Comparison

Elasticsearch vs OpenSearch

The original search and analytics engine versus its Apache-licensed open-source fork led by AWS.

Comparison

pgvector vs Pinecone

A Postgres extension that adds vector search to your existing database versus a fully managed, purpose-built vector database.

Benchmarks2

Benchmark

ANN-Benchmarks

The standard open benchmark for approximate nearest neighbor search, plotting recall against queries-per-second across vector index libraries and databases.

Benchmark

BigANN Benchmark

A billion-scale approximate nearest neighbor benchmark testing vector search algorithms on large data sets with constraints on memory, throughput, and recall.

FAQs1

FAQ

What are embeddings in machine learning?

Embeddings are dense numerical vectors that represent text, images, or other data in a continuous space where semantic similarity corresponds to geome...

Glossaries3

Glossary

Embedding

An embedding is a dense numeric vector that represents the meaning of text, an image, or other data so that similar items sit close together in vector space.

Glossary

Vector Search

Vector search finds items whose embeddings are closest to a query embedding, enabling semantic retrieval by meaning rather than exact keyword match.

Glossary

Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation is a technique that retrieves relevant documents at query time and supplies them to an LLM as context so its answers are grounded in external data.