{"id":362329,"date":"2026-08-01T19:43:33","date_gmt":"2026-08-01T19:43:33","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/lancedb-vector-database-guide-features-anndpython-demo\/"},"modified":"2026-08-01T19:43:33","modified_gmt":"2026-08-01T19:43:33","slug":"lancedb-vector-database-guide-features-anndpython-demo","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=362329","title":{"rendered":"LanceDB Vector Database Guide: Features anndPython Demo"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div id=\"article-start\">\n<p>Large language models understand text well, but they become less effective when information is scattered across documents or mixed with images and other media. Modern AI systems rely on vector databases, which store embeddings and enable similarity search across collections.<\/p>\n<p>LanceDB is a vector database built for AI workloads, with native support for multimodal data and efficient retrieval.\u00a0In this article, we will examine how vector databases work, how they retrieve similar items, how multimodal search is implemented, and what makes LanceDB useful for modern AI applications.<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-what-is-a-vector-database\">What Is a Vector Database?<\/h2>\n<p>In simple terms, vector databases are databases that store high-dimensional numerical vectors (embeddings) of chunks (chunks are text-pieces of document(s)). A vector database stores the embeddings in an indexed manner, which means all similar embeddings sit close to each other in the database.<\/p>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1483\" height=\"975\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/07\/image-9-1.webp\" alt=\"What is a vector database?\" class=\"wp-image-256622\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/07\/image-9-1.webp 1483w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/07\/image-9-1-300x197.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/07\/image-9-1-768x505.webp 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/07\/image-9-1-150x99.webp 150w\" sizes=\"(max-width: 1483px) 100vw, 1483px\"\/><\/figure>\n<\/div>\n<p>We use a query and find the most similar items in the vector database. When we apply this approach and pass the most similar items to an LLM, then it becomes a RAG (Retrieval Augmented Generation).<\/p>\n<p>But how do we find similarities?  we use the embeddings or actual documents and take help of these approaches:<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>Distance metrics<\/strong><strong>:<\/strong> L2, cosine, dot product, hamming distance<\/li>\n<li><strong>Approximate Nearest Neighbor (ANN)<\/strong><strong>:<\/strong><strong> <\/strong>IVF, HNSW, PQ fast even at billions of rows, trading a small amount of recall for large speedups<\/li>\n<li><strong>Metadata filtering<\/strong><strong>:<\/strong><strong> <\/strong>Combining other approaches with filtering<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-lancedb-and-its-features\">LanceDB and Its Features<\/h2>\n<p>LanceDB is an open-source, vector database. It can be used locally, or you can use the enterprise version, or self-host and use LanceDB. <\/p>\n<h4 class=\"wp-block-heading\" id=\"h-key-features\">Key features:<\/h4>\n<ol class=\"wp-block-list\">\n<li><strong>Multimodal by design: <\/strong>text, vectors, images, audio, and video live as columns in the same table, not as separate data. But you can opt for a mulit-table approach if that works better for you.<\/li>\n<li><strong>Multiple index types:<\/strong> IVF \/ HNSW \/ PQ \/ RQ for vectors, BM25 for full text<\/li>\n<li><strong>Hybrid search: <\/strong>combine vector similarity and keyword (BM25) search and re-rankers can be used to rank the retrieved documents.<\/li>\n<li><strong>Versioning:<\/strong> every write creates a new version; you can checkout, restore, or tag any past version, like Git for your table.<\/li>\n<li><strong>Schema changes: <\/strong>add, rename, retype, or drop columns without rewriting the whole dataset, thanks to Lance\u2019s columnar storage.<\/li>\n<li><strong>Object storage:<\/strong> the same API works against a local folder or an S3, GS or AZ path.<\/li>\n<li><strong>SDKs:<\/strong> <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2021\/05\/introduction-to-python-programming-beginners-guide\/\" target=\"_blank\" rel=\"noreferrer noopener\">Python<\/a>, TypeScript\/JavaScript, and Rust.<\/li>\n<\/ol>\n<h2 class=\"wp-block-heading\" id=\"h-demo\">Demo<\/h2>\n<p>In this section, let\u2019s look at Python examples to use LanceDB to store embeddings, search for similar items, to chunk and index a document, and look at how to store images in the vector tables. <\/p>\n<h3 class=\"wp-block-heading\" id=\"h-pre-requisites\">Pre-requisites<\/h3>\n<p>You will need an OpenAI key to create the <strong>embeddings, you can choose to use any alternatives as well. <\/strong><\/p>\n<h4 class=\"wp-block-heading\" id=\"h-installations\">Installations<\/h4>\n<pre class=\"wp-block-code\"><code>uv pip install lancedb pandas pyarrow pypdf pillow numpy openai open-clip-torch torch<\/code><\/pre>\n<p><strong>Note<\/strong>: uv is recommended for faster installation<\/p>\n<h4 class=\"wp-block-heading\" id=\"h-imports\">Imports<\/h4>\n<pre class=\"wp-block-code\"><code>import io\nfrom getpass import getpass\nfrom pathlib import Path\n\nimport lancedb\nimport numpy as np\nimport pandas as pd\nfrom pypdf import PdfReader\n\nOPENAI_API_KEY = getpass(\"Enter your OpenAI API key: \")<\/code><\/pre>\n<p><strong>Note:<\/strong> Enter the OpenAI key when prompted (If you are using OpenAI\u2019s embedding models)<\/p>\n<h4 class=\"wp-block-heading\" id=\"h-initialization\">Initialization<\/h4>\n<pre class=\"wp-block-code\"><code># Local, embedded LanceDB\ndb = lancedb.connect(\".\/lancedb_data\")\nprint(\"Connected to local LanceDB at .\/lancedb_data\")\nprint(\"Existing tables:\", db.table_names())<\/code><\/pre>\n<p>Intializing the database locally<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-basic-vector-search-example\">Basic vector search example<\/h3>\n<pre class=\"wp-block-code\"><code>data = [\n    {\n        \"id\": 1,\n        \"text\": \"A cat sleeping on a sofa\",\n        \"vector\": [0.1, 0.2, 0.3, 0.4],\n    },\n    {\n        \"id\": 2,\n        \"text\": \"A dog playing fetch in the park\",\n        \"vector\": [0.9, 0.8, 0.1, 0.2],\n    },\n    {\n        \"id\": 3,\n        \"text\": \"A kitten chasing a laser pointer\",\n        \"vector\": [0.15, 0.25, 0.35, 0.4],\n    },\n    {\n        \"id\": 4,\n        \"text\": \"A puppy running through a field\",\n        \"vector\": [0.85, 0.75, 0.15, 0.25],\n    },\n]\n\ntable = db.create_table(\"pets\", data=data, mode=\"overwrite\")\n\ntable.to_pandas()<\/code><\/pre>\n<p><strong>Note<\/strong>: The numerical vectors here are just example embeddings used to understand vector databases here. <\/p>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img decoding=\"async\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/07\/image-1-3niwhv.webp\" alt=\"Basic vector search example \u2013 illustration\"\/><\/figure>\n<\/div>\n<pre class=\"wp-block-code\"><code># Query vector close to the \"cat\" entries\nquery_vector = [0.12, 0.22, 0.32, 0.4]\n\nresults = (\n    table.search(query_vector)\n    .limit(2)\n    .select([\"id\", \"text\", \"_distance\"])\n    .to_pandas()\n)\n\nresults<\/code><\/pre>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img decoding=\"async\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/07\/image-2-3niwhv.webp\" alt=\"Basic vector search example\"\/><\/figure>\n<\/div>\n<p>The query is first converted to a vector and then the distance from all the other vectors is calculated, more the distance the less similar the query and text are.<\/p>\n<h4 class=\"wp-block-heading\" id=\"h-filtering-the-table\">Filtering the table<\/h4>\n<pre class=\"wp-block-code\"><code># Indexed search combined with a metadata filter\nfiltered_results = (\n    table.search(query_vector)\n    .where(\"id != 2\")\n    .limit(2)\n    .select([\"id\", \"text\", \"_distance\"])\n    .to_pandas()\n)\n\nfiltered_results<\/code><\/pre>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img decoding=\"async\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/07\/image-3-3niwhv.webp\" alt=\"Filtering the table \u2013 illustration\"\/><\/figure>\n<\/div>\n<p>Filtering can be perfomed to exclude or select categories or IDs. You can see the \u201cwhere\u201d condition in the code.<\/p>\n<h4 class=\"wp-block-heading\" id=\"h-creating-vector-index\">Creating Vector Index<\/h4>\n<pre class=\"wp-block-code\"><code>table.create_index(\n    metric=\"cosine\",\n    vector_column_name=\"vector\",\n    index_type=\"IVF_FLAT\",\n)<\/code><\/pre>\n<p>This syntax can be used to create a vector index of type IVF (inverted file index) and cosine similarity to group similar items together. You can change these parameters according to your needs.<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-pdf-ingestion-and-retrieval\">PDF ingestion and retrieval<\/h3>\n<pre class=\"wp-block-code\"><code>from pathlib import Path\n\npdf_path = Path(\"assets\/exploring-ann-algorithms.pdf\")\n\nassert pdf_path.exists(), f\"Expected a PDF at {pdf_path}\"\n\nprint(\n    f\"Using {pdf_path} ({pdf_path.stat().st_size} bytes)\"\n)<\/code><\/pre>\n<p>You can use any PDF, or you can download articles (PDF) from Analytics Vidhya for ingestion.<\/p>\n<pre class=\"wp-block-code\"><code>reader = PdfReader(str(pdf_path))\n\nchunks = []\n\nfor page_num, page in enumerate(reader.pages):\n    text = (page.extract_text() or \"\").strip()\n\n    if text:\n        chunks.append({\n            \"page\": page_num,\n            \"text\": text,\n        })\n\nprint(f\"Extracted text from {len(chunks)} pages\")<\/code><\/pre>\n<p>Extracted text from 14 pages<\/p>\n<pre class=\"wp-block-code\"><code>from openai import OpenAI\n\n\ndef embed_text(text: str) -&gt; list[float]:\n    client = OpenAI(api_key=OPENAI_API_KEY)\n    response = client.embeddings.create(\n        model=\"text-embedding-3-small\",\n        input=text,\n    )\n    return response.data[0].embedding\n\n\npdf_rows = [\n    {\n        \"id\": f\"ann-pdf-p{chunk['page']}\",\n        \"source\": pdf_path.name,\n        \"page\": chunk[\"page\"],\n        \"text\": chunk[\"text\"],\n        \"vector\": embed_text(chunk[\"text\"]),\n    }\n    for chunk in chunks\n]\n\npdf_table = db.create_table(\n    \"ann_pdf_pages\",\n    data=pdf_rows,\n    mode=\"overwrite\",\n)\n\npdf_table.to_pandas()[[\"id\", \"page\", \"source\"]]<\/code><\/pre>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img decoding=\"async\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/07\/image-4-3niwhv.webp\" alt=\"PDF ingestion and insertion\"\/><\/figure>\n<\/div>\n<p>Let\u2019s use an actual embedding model to create the embeddings (text-embedding-3-small from OpenAI). We have chunked the PDF into 14 parts (each page is a chunk) and then embedded them into the vector table. <\/p>\n<h4 class=\"wp-block-heading\" id=\"h-example-query\">Example Query<\/h4>\n<pre class=\"wp-block-code\"><code># Semantic search over the PDF's pages\nquery = \"How does the HNSW algorithm find nearest neighbors?\"\nquery_vec = embed_text(query)\n\nmatches = (\n    pdf_table.search(query_vec)\n    .limit(3)\n    .select([\"id\", \"page\", \"text\", \"_distance\"])\n    .to_pandas()\n)\n\nmatches<\/code><\/pre>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img decoding=\"async\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/07\/image-5-3niwhv.webp\" alt=\"Example Query\"\/><\/figure>\n<\/div>\n<pre class=\"wp-block-code\"><code>print(matches[\"text\"][0])<\/code><\/pre>\n<p><strong>Output:<\/strong><\/p>\n<p><em>How HNSW Works 1. As shown in the above image, each vertex in the graph <\/em><em>represents<\/em><em> a data point. 2. Connect each vertex with a configurable number of nearest vertices in a greedy manner.<\/em><\/p>\n<p><strong>Note:<\/strong><strong> <\/strong>You can change the chunking strategy, do it on chunk size (number of chunks) instead of splitting it into varied sized chunks.<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-multimodal-embedding\">Multimodal embedding<\/h2>\n<pre class=\"wp-block-code\"><code>from pathlib import Path\nfrom PIL import Image\n\nimage_files = {\n    \"cat\": \"assets\/cat.jpg\",\n    \"dog\": \"assets\/dog.jpg\",\n    \"horse\": \"assets\/horse.jpg\",\n    \"peacock\": \"assets\/peacock.jpg\",\n}\n\nimages_bytes = {}\n\nfor label, path in image_files.items():\n    p = Path(path)\n    assert p.exists(), f\"Expected an image at {p}\"\n    images_bytes[label] = p.read_bytes()\n    print(f\"Loaded {label}: {path} ({len(images_bytes[label])} bytes)\")<\/code><\/pre>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img decoding=\"async\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/07\/image-6-3niwhv.webp\" alt=\"Multimodal embedding \"\/><\/figure>\n<\/div>\n<pre class=\"wp-block-code\"><code>from lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\n# Registers LanceDB's built-in OpenCLIP embedding function\nclip = get_registry().get(\"open-clip\").create()\n\n\nclass ImageDoc(LanceModel):\n    id: str\n    image_bytes: bytes = clip.SourceField()\n    vector: Vector(clip.ndims()) = clip.VectorField()\n\n\nimages_table = db.create_table(\n    \"images\",\n    schema=ImageDoc,\n    mode=\"overwrite\",\n)\n\n# LanceDB computes vector from image_bytes\nimages_table.add([\n    {\"id\": label, \"image_bytes\": data}\n    for label, data in images_bytes.items()\n])\n\nimages_table.to_pandas().drop(columns=[\"image_bytes\"])<\/code><\/pre>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img decoding=\"async\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/07\/image-7-3niwhv.webp\" alt=\"Multimodal embedding \"\/><\/figure>\n<\/div>\n<pre class=\"wp-block-code\"><code>query = \"a colorful bird with a fanned tail\"\n\nranked = (\n    images_table.search(query)\n    .select([\"id\", \"_distance\"])\n    .to_pandas()\n)\n\nprint(ranked)\n\ntop = ranked.iloc[0]\nprint(f\"\\nTop match: {top['id']} (distance={top['_distance']:.4f})\")\n\nimg = Image.open(io.BytesIO(images_bytes[top[\"id\"]]))<\/code><\/pre>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img decoding=\"async\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/07\/image-8-3niwhv.webp\" alt=\"Multimodal embedding \"\/><\/figure>\n<\/div>\n<p>We\u2019re using CLIP via LanceDB to embed the image data into the table. And as you can see it works, the query returns the peacock image as the most similar item.<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-potential-applications\"> Potential Applications<\/h2>\n<p>Here are some of the use-cases of LanceDB:<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>Retrieval-Augmented Generation (RAG):<\/strong> store document chunks and their embeddings, then pass the relevant context into an LLM.<\/li>\n<li><strong>Semantic and hybrid search:<\/strong> keyword search or keyword search plus meaning-based search together.<\/li>\n<li><strong>Multimodal search: <\/strong>search images, matching similar audio clips, pulling frames from video, all alongside structured metadata.<\/li>\n<li><strong>Training and feature stores:<\/strong> Supports datasets for training and evaluation, with schema evolution when you need to add derived features later.<\/li>\n<li><strong>A<\/strong><strong>nomaly detection: <\/strong>spot duplicate (or almost duplicate) records or outliers using distance-based search.<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-conclusion\">Conclusion<\/h2>\n<p>LanceDB serves well as vector database and more. It combines vectors, metadata, and media in a single versioned, embedded table, with ANN indexing, hybrid search, and object storage support built in. That cuts out a lot of work while building a RAG and search apps. The examples here are just a starting point; things get interesting once you experiment and explore things your own way.<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-frequently-asked-questions\">Frequently Asked Questions<\/h2>\n<div class=\"schema-faq wp-block-yoast-faq-block\">\n<div class=\"schema-faq-section\" id=\"faq-question-1785218001133\"><strong class=\"schema-faq-question\">Q1. Do I need to run a separate server for LanceDB?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. No. LanceDB runs embedded inside your application for local or object storage use.<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1785218001270\"><strong class=\"schema-faq-question\">Q2. Which distance metric should I use?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. Use whatever your embedding model was trained on. Cosine or dot product are the usual picks for text and image embeddings, L2 is a fine otherwise.<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1785218001407\"><strong class=\"schema-faq-question\">Q3. Can I filter results by metadata, not just vector similarity?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. Yes. Chain where(\u201csql_expression\u201d) onto any search query to filter and search.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"border-top py-3 author-info my-4\">\n<div class=\"author-card d-flex align-items-center\">\n<div class=\"flex-shrink-0 overflow-hidden\">\n                                    <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/author\/mounish12439\/\" class=\"text-decoration-none active-avatar\"><br \/>\n                                                                       <img decoding=\"async\" src=\"https:\/\/av-eks-lekhak.s3.amazonaws.com\/media\/lekhak-profile-images\/converted_image_ZFxQ96b.webp\" width=\"48\" height=\"48\" alt=\"Mounish V\" loading=\"lazy\" class=\"rounded-circle\"\/><br \/>\n                                                                <\/a>\n                                <\/div>\n<\/p><\/div>\n<p>Passionate about technology and innovation, a graduate of Vellore Institute of Technology. Currently working as a Data Science Trainee, focusing on Data Science. Deeply interested in Deep Learning and Generative AI, eager to explore cutting-edge techniques to solve complex problems and create impactful solutions.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<p><h4 class=\"fs-24 text-dark\">Login to continue reading and enjoy expert-curated content.<\/h4>\n<p>                        <button class=\"btn btn-primary mx-auto d-table\" data-bs-toggle=\"modal\" data-bs-target=\"#loginModal\" id=\"readMoreBtn\">Keep Reading for Free<\/button>\n                    <\/p>\n\n","protected":false},"excerpt":{"rendered":"<p>Large language models understand text well, but they become less effective when information is scattered across documents or mixed with images and other media. Modern AI systems rely on vector databases, which store embeddings and enable similarity search across collections. LanceDB is a vector database built for AI workloads, with native support for multimodal data [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":362330,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12033],"tags":[183569,16120,16597,6284,2059,183568,26624],"dealstore":[],"offerexpiration":[],"class_list":["post-362329","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-anndpython","tag-database","tag-demo","tag-features","tag-guide","tag-lancedb","tag-vector"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>LanceDB Vector Database Guide: Features anndPython Demo - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fivemor.com\/?p=362329\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"LanceDB Vector Database Guide: Features anndPython Demo - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"Large language models understand text well, but they become less effective when information is scattered across documents or mixed with images and other media. 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