[{"data":1,"prerenderedAt":146},["ShallowReactive",2],{"term-v\u002Fvector-database":3,"related-v\u002Fvector-database":132},{"id":4,"title":5,"acronym":6,"body":7,"category":113,"description":114,"difficulty":115,"extension":116,"letter":117,"meta":118,"navigation":119,"path":120,"related":121,"seo":126,"sitemap":127,"stem":130,"subcategory":6,"__hash__":131},"terms\u002Fterms\u002Fv\u002Fvector-database.md","Vector Database",null,{"type":8,"value":9,"toc":107},"minimark",[10,15,19,23,26,30,96,100,103],[11,12,14],"h2",{"id":13},"eli5-the-vibe-check","ELI5 — The Vibe Check",[16,17,18],"p",{},"A vector database is a special database built to store and search embeddings. Normal databases are bad at 'find me the 10 most similar items' — they'd have to check everything. Vector databases use clever indexing (like HNSW or IVF) to find similar vectors super fast. They're the backbone of AI-powered search and RAG systems.",[11,20,22],{"id":21},"real-talk","Real Talk",[16,24,25],{},"Vector databases store high-dimensional embedding vectors and provide efficient approximate nearest neighbor (ANN) search. They support similarity queries at scale using indexing algorithms like HNSW, IVF-PQ, and FAISS. Popular options include Pinecone, Weaviate, Qdrant, Chroma, and pgvector (Postgres extension).",[11,27,29],{"id":28},"show-me-the-code","Show Me The Code",[31,32,37],"pre",{"className":33,"code":34,"language":35,"meta":36,"style":36},"language-python shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","import chromadb\nclient = chromadb.Client()\ncollection = client.create_collection(\"docs\")\ncollection.add(\n    documents=[\"The cat sat on the mat\"],\n    embeddings=[[0.1, 0.2, 0.3]],\n    ids=[\"doc1\"]\n)\nresults = collection.query(query_embeddings=[[0.1, 0.2, 0.35]], n_results=3)\n","python","",[38,39,40,48,54,60,66,72,78,84,90],"code",{"__ignoreMap":36},[41,42,45],"span",{"class":43,"line":44},"line",1,[41,46,47],{},"import chromadb\n",[41,49,51],{"class":43,"line":50},2,[41,52,53],{},"client = chromadb.Client()\n",[41,55,57],{"class":43,"line":56},3,[41,58,59],{},"collection = client.create_collection(\"docs\")\n",[41,61,63],{"class":43,"line":62},4,[41,64,65],{},"collection.add(\n",[41,67,69],{"class":43,"line":68},5,[41,70,71],{},"    documents=[\"The cat sat on the mat\"],\n",[41,73,75],{"class":43,"line":74},6,[41,76,77],{},"    embeddings=[[0.1, 0.2, 0.3]],\n",[41,79,81],{"class":43,"line":80},7,[41,82,83],{},"    ids=[\"doc1\"]\n",[41,85,87],{"class":43,"line":86},8,[41,88,89],{},")\n",[41,91,93],{"class":43,"line":92},9,[41,94,95],{},"results = collection.query(query_embeddings=[[0.1, 0.2, 0.35]], n_results=3)\n",[11,97,99],{"id":98},"when-youll-hear-this","When You'll Hear This",[16,101,102],{},"\"Store all our support docs in a vector database for RAG.\" \u002F \"Query the vector database for the most relevant chunks.\"",[104,105,106],"style",{},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":36,"searchDepth":50,"depth":50,"links":108},[109,110,111,112],{"id":13,"depth":50,"text":14},{"id":21,"depth":50,"text":22},{"id":28,"depth":50,"text":29},{"id":98,"depth":50,"text":99},"ai","A vector database is a special database built to store and search embeddings.","intermediate","md","v",{},true,"\u002Fterms\u002Fv\u002Fvector-database",[122,123,124,125],"Vector","Embedding","RAG","Semantic Search",{"title":5,"description":114},{"changefreq":128,"priority":129},"weekly",0.7,"terms\u002Fv\u002Fvector-database","XPaPqABFlo9buldF5vxOJJduKAzeOVUpn8EOBLlV3VE",[133,136,140,143],{"title":123,"path":134,"acronym":6,"category":113,"difficulty":115,"description":135},"\u002Fterms\u002Fe\u002Fembedding","An embedding is turning words, sentences, or entire documents into lists of numbers (vectors) that capture their meaning.",{"title":124,"path":137,"acronym":138,"category":113,"difficulty":115,"description":139},"\u002Fterms\u002Fr\u002Frag","Retrieval Augmented Generation","RAG is how you give an AI access to your private documents without retraining it.",{"title":125,"path":141,"acronym":6,"category":113,"difficulty":115,"description":142},"\u002Fterms\u002Fs\u002Fsemantic-search","Semantic search finds results based on meaning, not just keyword matching.",{"title":122,"path":144,"acronym":6,"category":113,"difficulty":115,"description":145},"\u002Fterms\u002Fv\u002Fvector","In AI, a vector is just a list of numbers. But it's a list of numbers that means something — like [0.23, -0.91, 0.44, ...",1776518321890]