Building a Semantic Search Engine with PartyKit's Vector Database in 160 Lines of Code
PartyKit now includes a vector database and access to an embedding model. Here’s a guide on how to use them to build a search engine.
Read the full articleYou might also wanna read
Semantic and Hybrid Search Now Possible Directly in the Browser
A new technical approach enables semantic and hybrid search to run entirely within the browser, without relying on a server backend. The met
Hybrid Search Implementation Guide: Combining Vector and Keyword Search for RAG
Master hybrid search for RAG systems. Learn to combine semantic vector search with keyword matching for superior retrieval quality using pra
Embeddings and Semantic Search, Explained
Embeddings turn text into vectors so you can search by meaning. Learn cosine similarity and build semantic search in Python, with a live exp
Lessons from Building a Document Q&A Bot: The Hidden Complexity of Embeddings
I spent a weekend building a Q&A bot for my team's internal docs. It sounded easy: dump PDFs into...
dev.to·12d agoBuilding Intelligent Search: A Tutorial on Aiven for OpenSearch and Vertex AI
Build a smarter semantic search engine with Aiven for OpenSearch and Vertex AI. Vertex AI generates vector embeddings for OpenSearch to find
Client Side Semantic Search with BGE Embeddings in JavaScript
How I built client side semantic search with BGE embeddings in JavaScript that runs entirely in the browser using transformers.js, IndexedDB

Comments
Sign in to join the conversation.
No comments yet. Be the first.