semantic search Archives

Tech Optimizer

August 5, 2025

Building a job search engine with PostgreSQL’s advanced search features

Job search platforms connect employers and candidates through advanced search engines that analyze structured and unstructured data. These platforms require robust database technologies for executing complex queries, full-text and semantic searches, and geospatial functionalities. A modern job search engine consists of a data repository that stores job listings and candidate profiles, and a search engine that facilitates bidirectional searches. Key features of an effective job search engine include: - **Full-text search**: Provides lexical matching for job titles and skills, supporting exact phrase matching and typo-tolerant searches. - **Semantic search**: Uses vector-based similarity to understand context and relationships beyond literal terms. - **Geospatial search**: Incorporates geographic considerations to find opportunities within specific distances. PostgreSQL serves as both a data repository and search engine, supporting full-text search, semantic search via the pgvector extension, and geospatial queries using the PostGIS extension. The job search engine utilizes PostgreSQL to manage job listings and candidate profiles, enabling real-time searches across millions of entries. The data model for a job search engine includes tables for jobs and resumes, with columns for full-text search vectors, semantic vectors, and geographical locations. PostgreSQL's full-text search capabilities include tokenization, dictionaries for language-aware parsing, and ranking functions for relevance. Advanced features such as proximity search, simple and weighted ranking, and fuzzy matching enhance search accuracy. Vector embeddings represent text in high-dimensional space, allowing for semantic searches that recognize similar roles or skills. PostgreSQL supports vector similarity searches with specialized indexing methods like IVFFlat and HNSW for efficient querying. Geospatial search capabilities in PostgreSQL enable location-aware job searches, allowing candidates to find jobs within commuting distances. Combining different search techniques, such as full-text and semantic searches, provides more relevant results. Performance optimization features in PostgreSQL address challenges related to computational complexity, indexing overhead, and concurrent query loads. The architecture discussed is applicable to various applications beyond job search platforms, including e-commerce, real estate, content recommendation systems, travel, and healthcare provider matching.

Winsage

July 24, 2025

Microsoft is bringing powerful new AI tools to Windows 11

Microsoft has rolled out new features for Windows 11, transitioning tools from testing to general availability. Key additions include semantic and agentic search functionalities in the Settings menu, allowing users to express queries in natural language and request specific tasks. The Click-to-Do feature now integrates with Copilot and a new Reading Coach application for context-aware actions. Paint has been upgraded to allow AI-generated stickers and includes a new object select tool for easier image editing. The Photos app will introduce a Relight feature for dynamic lighting effects, limited to devices with Snapdragon X processors. The Snipping Tool has a new “perfect screenshot” feature that uses AI for improved selection, and a new Black Screen of Death and rapid recovery option have reduced crashes and restarts by 22%.

Winsage

July 12, 2025

A new Windows feature could allow your PC to fix itself

Microsoft is introducing a feature called "quick machine recovery" for Windows users, currently available in Windows 11 Build 26100.4762. This feature allows PCs to autonomously troubleshoot and fix issues after a crash, such as a Blue Screen of Death (BSOD), by booting into the Windows Recovery Environment (Windows RE). Quick machine recovery can connect to Microsoft’s servers to send crash data for analysis, eliminating the need for users to interpret error codes. It employs cloud remediation and auto remediation methods, though auto remediation is disabled by default on home PCs. The existing Startup Repair tool will still be available as a fallback option, and users can disable quick machine recovery if desired. Additionally, the build includes a refined "semantic search" capability in Settings, integration of Microsoft’s Reading Coach app, and the ability to export Recall snapshots to third-party applications in Europe.

Tech Optimizer

June 10, 2025

Why Instacart Moved to Postgres & pgvector to Boost Semantic Search

Instacart serves 14 million daily users and manages billions of products, necessitating advanced search capabilities that go beyond keyword matching to understand user intent. The search system must reflect real-time inventory changes, leading to significant workloads on the database. Instacart transitioned from Elasticsearch and FAISS to a hybrid architecture using Postgres and pgvector, improving search performance and reducing write workloads by tenfold. This normalization allowed for better storage of machine learning features and improved flexibility. Moving compute closer to storage with NVMe resulted in a twofold increase in search performance. Instacart's migration to pgvector eliminated data duplication and operational complexity, enhancing search quality and user satisfaction, evidenced by a 6% decrease in searches with zero results.

Tech Optimizer

June 1, 2025

Supercharging vector search performance and relevance with pgvector 0.8.0 on Amazon Aurora PostgreSQL

The Amazon Aurora PostgreSQL-Compatible Edition has integrated support for pgvector 0.8.0, enhancing vector search capabilities significantly. This version achieves up to 9x faster query processing and delivers 100x more relevant search results compared to previous iterations. Key improvements in pgvector 0.8.0 include: - Up to 5.7x improvement in query performance for specific patterns. - Introduction of the iterative_scan feature, enhancing recall for filter queries requiring approximate nearest neighbor (ANN) index searches. - Improved cost estimation for more efficient query planning, allowing the selection of traditional indexes for complex filtered searches. - Flexible performance tuning with two modes for iterative_scan: relaxed_order and strict_order. Previous versions faced challenges with overfiltering, where combining vector similarity search with traditional SQL filters resulted in fewer results than expected. The HNSW indexing algorithm accelerates vector similarity searches by creating a multi-layered graph structure for efficient navigation. Iterative index scans in pgvector 0.8.0 improve query reliability and performance by applying filters during the scanning process, reducing false negatives and enhancing performance. The configuration options for iterative scanning include off, strict_order, and relaxed_order, with relaxed_order providing the best balance between performance and accuracy. In performance tests, pgvector 0.8.0 demonstrated substantial improvements across various query patterns, with significant enhancements in result completeness and recall. The database can now efficiently handle enterprise-scale applications, such as e-commerce and recommendation systems, with high performance and result quality even as product catalogs expand. Best practices for deploying pgvector 0.8.0 include using HNSW with recommended parameters, creating additional indexes on commonly filtered metadata columns, and tuning query parameters based on specific use cases. Operational best practices involve using Graviton4-based instances for optimal performance, monitoring and tuning slow vector queries, and considering partitioning for very large tables.

Tech Optimizer

May 24, 2025

Connect Amazon Bedrock Agents with Amazon Aurora PostgreSQL using Amazon RDS Data API

Generative AI applications are being integrated with relational databases, allowing organizations to utilize structured data for training AI models. This integration involves using the RDS Data API with Amazon Aurora PostgreSQL-Compatible Edition and Amazon Bedrock for AI model access and automation. The solution enables natural language queries to be converted into SQL statements, executed against the database, and returns results in a user-friendly format. The architecture includes several steps: invoking the Amazon Bedrock agent with natural language input, generating SQL queries using large language models (LLMs), executing those queries via the Data API, and returning formatted results. Security measures are in place to restrict operations to read-only, preventing modifications that could compromise data integrity. To implement this solution, prerequisites include deploying an Aurora PostgreSQL cluster using AWS CDK and setting up the necessary Lambda functions and IAM roles. The agent is designed to convert natural language prompts into SQL queries and execute them securely. Testing can be conducted through the Amazon Bedrock console or the InvokeAgent API, with options for tracing the agent's steps. Key considerations for this integration include limiting it to read-only workloads, implementing parameter validation to prevent SQL injection, and ensuring comprehensive logging and auditing. For multi-tenant applications, appropriate isolation controls should be established. To avoid future charges, all resources created through CDK should be deleted after use.

Tech Optimizer

May 21, 2025

Achieve up to 1.7 times higher write throughput and 1.38 times better price performance with Amazon Aurora PostgreSQL on AWS Graviton4-based R8g instances

Upgrading to Graviton4-based R8g instances with Aurora PostgreSQL-Compatible 17.4 in an Aurora I/O-Optimized cluster configuration results in significant performance improvements. The new instances provide up to 1.7 times higher write throughput, 1.38 times better price-performance, and reduce commit latency by up to 46% on r8g.16xlarge instances and 38% on r8g.2xlarge instances compared to Graviton2-based R6g instances. The Amazon Aurora PostgreSQL-Compatible Edition now supports AWS Graviton4-based R8g instances and PostgreSQL 17.4, which introduces performance enhancements for I/O-Optimized configurations, optimizing write operations and batch processing. R8g instances offer up to 192 vCPUs and 1.5 TB of memory, supporting larger configurations and providing up to 50 Gbps of network bandwidth. PostgreSQL 17 includes vacuum improvements, eliminates the need to drop logical replication slots during upgrades, and expands SQL/JSON standards. Aurora PostgreSQL-Compatible separates compute from storage, enabling independent scaling and maintaining six-way replication for durability, while processing changes as log records to reduce I/O operations. Performance benchmarks using HammerDB show improvements in throughput and commit latency across various workloads. For small workloads on 2xlarge instance size, throughput increased by 50.25% and commit latency improved by 33.87%. For medium workloads on 16xlarge instance size, throughput increased by 30% and commit latency improved by 17.44%. The most significant performance benefits arise from combining hardware upgrades from Graviton2 to Graviton4 with database engine upgrades from PostgreSQL 15.10 to 17.4. For small workloads, throughput increased by 70% and commit latency improved by 38.71%. For medium workloads, throughput increased by 70% and commit latency improved by 46.67%. Cost efficiency is also enhanced, with a 38% improvement in price performance and a 61.26% improvement in price-performance ratio when comparing Graviton2 and Graviton4 instances. Reserved Instances for Graviton4-based R8g instances offer additional cost-optimization opportunities.

Winsage

May 20, 2025

Securing the Model Context Protocol: Building a safer agentic future on Windows

The Model Context Protocol (MCP) is a lightweight, open protocol functioning as JSON-RPC over HTTP, facilitating standardized discovery and invocation of tools. MCP defines three roles: MCP Hosts (applications accessing capabilities), MCP Clients (initiators of requests), and MCP Servers (services exposing functionalities). Windows 11 will incorporate MCP to enable developers to create intelligent applications leveraging generative AI. An early preview of MCP capabilities will be available for developer feedback. MCP introduces security risks, including cross-prompt injection, authentication gaps, credential leakage, tool poisoning, lack of containment, limited security review, registry risks, and command injection. To address these, Windows 11's MCP Security Architecture will establish security requirements for MCP servers, ensuring user safety and transparency, enforcing least privilege, and implementing security controls like proxy-mediated communication, tool-level authorization, a central server registry, and runtime isolation. MCP servers must comply with security requirements, including mandatory code signing, unchanged tool definitions at runtime, security testing, mandatory package identity, and declared privileges. An early private preview of MCP server capability will be offered to developers post-Microsoft Build for feedback, with a secure-by-default enforcement strategy planned for broader availability. Microsoft aims to enhance defenses continuously and collaborate with partners to bolster MCP's security framework.

Winsage

April 12, 2025

Announcing Windows 11 Insider Preview Build 26200.5551 (Dev Channel)

Windows 11 Insider Preview Build 26200.5551 (KB5055617) has been released to the Dev Channel. The Dev Channel has advanced to the 26200 series builds, and the option to switch to the Beta Channel is no longer available. This build is based on Windows 11, version 24H2, and shares features with the 26120 series in the Beta Channel. New features include enhanced Windows Search on Copilot+ PCs, allowing users to search for settings using phrases. Narrator now provides detailed image descriptions powered by AI for improved accessibility on Copilot+ PCs. Improvements are being rolled out for widgets, including the ability to read content directly from the MSN feed. Fixes include resolving crashes in Settings, File Explorer, and issues with app icons in the taskbar. Known issues include problems with Windows Recovery Environment and certain apps failing to launch post-update. The Dev Channel updates are based on Windows 11, version 24H2, and features may evolve or be removed based on feedback. Users can enable a toggle to receive updates more quickly.

Winsage

April 11, 2025

The new Windows 11 Beta update makes widgets better than ever before

Microsoft's latest update for Windows 11 Beta introduces the ability to read MSN articles directly from the Widgets feed, allowing users to access full articles, slideshows, and videos without using a browser. Users can customize the visibility of the MSN feed on their Widgets board. The update also enhances Copilot+ PCs with an AI-powered semantic search bar for natural language queries and improves the Narrator feature, which now analyzes screen content to provide descriptions of images and visual elements for visually impaired users.