John Berryman’s Post

#Goodread Discover the power of hybrid search, combining vector and keyword search for precise results by understanding query semantics. 📚Dive deep into this article to master everything about #hybridsearch! https://lnkd.in/gr69gwRp #SemanticSearch

𝗘𝗹𝗲𝘃𝗮𝘁𝗶𝗻𝗴 𝗦𝗲𝗮𝗿𝗰𝗵 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 in RAG-based apps with SOTA reranking models. The mixedbread.ai team introduces a pioneering suite of SOTA rerank models, designed to enhance search results accuracy by integrating semantic understanding into existing keyword-based search infrastructures. Fully open-source under the Apache 2.0 license, these models are tailored for seamless integration, boosting the relevance of search outcomes without overhauling current systems. From the compact "mxbai-rerank-xsmall-v1" to the robust "mxbai-rerank-large-v1," each model is crafted to cater to varying needs, promising a notable improvement in search performance for complex queries. Quick Snapshots/Highlights: ◆ Fully open-source models with Apache 2.0 license. ◆ Models are designed for easy integration with existing search systems. ◆ Significant performance boost for domain-specific and complex queries. Key Features: ◆ Three Model Sizes: Small for efficiency, Base for balanced performance, and Large for maximum accuracy. ◆ Two-Stage Search Flow: Incorporates semantic relevance into the final search results. ◆ Easy to Use: Compatible with existing search stacks; offers offline and online usage options. ◆ Performance: Demonstrates superior accuracy and relevance in benchmarks against competitors. Additional Notes: The mixedbread rerank models stand out for their simplicity and effectiveness, enabling developers to leverage advanced semantic search capabilities with minimal effort. This release marks mixedbread.ai's commitment to enhancing search technologies, inviting feedback and community engagement for continuous improvement. A "must-have" for RAG development! https://lnkd.in/dRuYFQFk

How to Build Site Search with Astro, Qwik and Fuse.js In this post, I’ll explain how to build a site search using Astro’s content collections, static endpoints and Qwik’s Astro integration with Fuse.js. I’ve prepared a demo site and open source repo, which you’ll find at the following links: 🚀 https://lnkd.in/gjDDPYus ⚙️ https://lnkd.in/g5t37Q8h What Are Content Collections? Astro has a convenient way to “bulk” query or transform content of similar types. In the case of my demo, this would apply to blog posts that are all written in MDX. All blog posts share the same template or layout and schema. Here’s the schema for blog posts. // src/content/config.js import { z, defineCollection } from 'astro:content'; export const collections = { posts: defineCollection({ type: 'content', schema: z.object({ draft: z.boolean().optional(), audioFeedId: z.string().optional(), base: z.string(), title: z.string(), tags: z.array(z.string()).optional(), date: z.date(), author: z.string(), featuredImage: z.string(), }), }), }; You can see the src in the repo here: src/content/config.js. And for good measure, here’s the frontmatter for one of my blog posts (but all blog posts will use the same schema). // src/content/posts/2024/02/the-qwik-astro-audiofeed-experiment.mdx --- base: posts title: The Qwik, Astro, Audiofeed Experiment tags: [Qwik, Astro, Audiofeed, AI] date: 2024-02-06 author: Paul Scanlon featuredImage: https://lnkd.in/gghJ8P5Q --- You can see the src in the repo here: the-qwik-astro-audiofeed-experiment.mdx. How to Query Astro’s Content Collections To build site search functionality, I first need to query all the blog posts. I’ve achieved this using a static endpoint. I called it all-content.json.js and it lives in the src/pages directory. E.g.: // src/pages/all-content.json.js import { getCollection } from 'astro:content'; export const GET = async () => { const posts = await getCollection('posts'); const search = posts .filter((item) => item.data.draft !== true) .map((data) => { const { slug, data: { base, title, date }, } = data; return { date: date, title: title, base: base, path: `/${base}/${slug}`, }; }) .sort((a, b) => b.date - a.date); return new Response(JSON.stringify({ search })); }; Once I’ve queried all the blog posts using getCollection('posts'), I do a quick filter to remove any blog posts that might be in draft mode, then return just the fields from the frontmatter that will be helpful for the search, and then sort them by date. The result is stringified and returned as a standard Response. Here’s what the result looks like....

🚀 Cohere's ReRank: Elevating Search Quality with AI-Powered Semantic Relevance 🔍 Cohere's ReRank endpoint, as detailed in their recent blog post, is a transformative tool in the realm of search technology. This AI-powered endpoint acts as a final stage in a search flow, providing a ranking of relevant documents per a user's query. It's a game-changer for companies looking to integrate semantic relevance into their existing keyword-based search systems without overhauling their infrastructure. Key Features of Cohere's ReRank - Enhanced Search Results: ReRank improves search quality, especially for complex and domain-specific queries, by reordering initial results based on semantic relevance. - Easy Integration: It can be added to existing search workflows with just a single line of code, making it a low-complexity method to introduce semantic search technology. - Multilingual Support: The model works for 100+ languages, ensuring great search quality across different languages. Transforming Search Experiences - Boosting Relevance in Traditional Searches: For businesses using keyword-based search engines like Elasticsearch, OpenSearch, or Solr, ReRank offers a significant enhancement in search relevance. - Practical and Efficient: The endpoint augments existing search systems, providing state-of-the-art performance in search quality without the need for complex migrations. - Versatile Applications: From e-commerce to knowledge bases, ReRank can dramatically improve the user experience by ensuring that search results are more aligned with user intent. The Future of Search Technology - AI-Driven Search Innovation: Cohere's ReRank represents a significant step forward in search technology, enabling more sophisticated and user-friendly search applications. - Empowering Businesses with AI: With its ease of integration and performance, ReRank empowers businesses to leverage AI for better search results without extensive technical overhead. - Fostering Continuous Improvement: As an evolving tool, ReRank invites feedback from users to enhance and refine its capabilities, aligning with the dynamic nature of AI technology. Embracing Advanced Search Solutions Cohere's ReRank is more than just a search tool; it's a testament to the power of AI in enhancing digital experiences. It's about leveraging technology to make information discovery more intuitive, relevant, and efficient. 💬 Your Thoughts? How do you see AI-powered tools like Cohere's ReRank impacting search experiences in your industry? What strategies should be adopted to maximize the benefits of this technology? 🔗 https://lnkd.in/deJgKcux #AI #SearchTechnology #SemanticSearch #TechInnovation #Cohere

In the realm of search engines, Meilisearch stands out as a rising star. Built on the foundation of inverted indexes - the backbone of modern search technology - it's making waves with its simple HTTP API and hybrid search system. But as with any rapidly growing technology, it's hitting growing pains. At its core, Meilisearch uses inverted indexes: data structures that map content (like words) to their locations (like document IDs). Imagine a book's index, but for every word in a vast collection of documents. This allows for blazingly fast keyword searches, but it's the scaling of this system that's causing headaches. The current indexer, while impressive in its ability to process 250 million documents in about 20 hours, is struggling with datasets in the hundreds of millions that grow by tens of millions weekly. It's built on LMDB (Lightning Memory-Mapped Database), offering excellent read scalability and atomic transactions. But it's hampered by single-writer limitations and visible fragmentation. A chunk-based approach was introduced to combat out-of-memory issues, but it's led to excessive disk usage and filesystem pressure. The system splits document updates into 4MiB temporary files, processed by various extractors in parallel. While this solved the immediate memory problems, it's created a new bottleneck in I/O operations. The proposed solution? A ground-up rewrite. This isn't just tweaking parameters; it's rethinking core assumptions. Key ideas include: 1. Enabling parallel reading during writes, a technique that could dramatically speed up indexing. 2. Better memory utilisation to reduce reliance on slow disk operations. 3. Smarter batching of operations to reduce write amplification. 4. Switching from Whatlang to Whichlang for language detection, promising a 10x speedup. These changes could transform Meilisearch's ability to handle massive, rapidly-growing datasets. But they come with risks: delayed features, potential new bugs, and uncertain timelines. Is this the right move? Meilisearch has a track record of successful rewrites, including overhauling their index-scheduler and porting Spotify's Annoy library. But in an industry often criticised for "reinventing the wheel", is this constant churn of rewrites a necessary evil or a sign of poor initial design? https://lnkd.in/e7TqXGtf #DatabaseOptimisation #SearchEngines #SoftwareArchitecture

🚀 Design A Web Crawler The blog post thoroughly explains the design and working of a Web Crawler, which is used by search engines to discover new or updated content on the web. It discusses its scale, features, step-by-step workflow, and key components like URL Frontier, HTML Downloader, and Content Parser. The post also addresses challenges in developing a scalable web crawler and offers solutions for performance optimization, robustness, and extensibility. Read the full blog post by Aryan Agarwal at https://lnkd.in/e5yKUbuq #WebCrawler #SearchEngine #Algorithm #DataMining #WebDesign #Codedash

Unlock the power of semantic search for your WordPress site with Convoworks, Pinecone, and OpenAI GPT. This in-depth guide takes you through the process of transforming words into vector databases, enabling nuanced, context-aware search results that go beyond traditional keyword matching. With a step-by-step tutorial, learn how to set up your Pinecone account, integrate Convoworks, and leverage OpenAI GPT to create embeddings that understand the intent behind search queries. Discover a modern approach to enhancing your website's search capabilities today! #SemanticSearch #WordPress #Convoworks #OpenAIGPT #Pinecone #VectorDatabases #NoCode https://lnkd.in/dDjZFZkU

In the past, we’ve measured the value of Schema Markup purely through the lens of rich results. However, we’ve seen a lot of changes in rich results and the overall search experience this past year. The rise of generative AI-powered search engines, accompanied by volatility in rich results, has prompted our team to dive deeper into the semantic value of Schema Markup & entity linking as it pertains to search today. Read our latest article to learn the value of entity linking, the tools enabling you to do it at scale, and the results we’ve seen from implementing entity linking with our Enterprise clients ⬇️ https://bit.ly/3SYGx35 #EntityLinking #Entities #SchemaMarkup #SemanticSearch

Big news! SEOPress 7.7 is here with GPT-4 Turbo integration, offering blazing speed and efficiency for your SEO needs. Check it out: https://lnkd.in/dHGecQnH #SEOPress #GPT4 #SEO

Super insightful article to read. Structured data is much more than rich results and heres what you need to know!