Best AI tools for< Search Vectors >

123RF is a stock photo website that offers a variety of AI tools for photo editing. These tools include AI Image Generator, AI Image Upscaler, AI Generative Fill, AI Background Remix, AI Image Extender, and AI Writer. 123RF also offers a variety of other features, such as a photo editor, a video editor, and a music editor. 123RF's AI tools are designed to make photo editing easier and faster. With AI Image Generator, users can create unique visuals from scratch. AI Image Upscaler can be used to improve the quality of low-resolution images. AI Generative Fill can be used to remove or replace objects in images. AI Background Remix can be used to create professional backgrounds for products. AI Image Extender can be used to extend images to different ratios. AI Writer can be used to generate text for websites, social media, and other marketing materials. 123RF's AI tools are available to both free and paid users. Free users have access to a limited number of AI tools, while paid users have access to all of the AI tools. 123RF's AI tools are a valuable resource for anyone who needs to edit photos. These tools are easy to use and can save users a lot of time and effort.

Everypixel is a stock image search engine powered by AI that provides access to more than 50 best sources of free and premium images. Users can search for images by keywords or by uploading an image URL. The platform offers a wide range of images, including exclusive patterns, vectors, and microstock photos. Everypixel aims to simplify the process of finding high-quality images for personal and commercial use, with both free and paid options available.

Superlinked is a compute framework for your information retrieval and feature engineering systems, focused on turning complex data into vector embeddings. Vectors power most of what you already do online - hailing a cab, finding a funny video, getting a date, scrolling through a feed or paying with a tap. And yet, building production systems powered by vectors is still too hard! Our goal is to help enterprises put vectors at the center of their data & compute infrastructure, to build smarter and more reliable software.

Archade is an AI-powered marketplace that offers a wide range of creative assets for projects. It features an asset library with CAD blocks, vectors, cutouts, flat icons, and more. Users can also explore generative content through text-to-image generation. Archade aims to provide convenience and inspiration by offering a vast collection of assets for various projects, catering to both enterprise and startup solutions. The platform also provides updates, news, and technical support through its Discord channel.

Vectorize is a fast, accurate, and production-ready AI tool that helps users turn unstructured data into optimized vector search indexes. It leverages Large Language Models (LLMs) to create copilots and enhance customer experiences by extracting natural language from various sources. With built-in support for top AI platforms and a variety of embedding models and chunking strategies, Vectorize enables users to deploy real-time vector pipelines for accurate search results. The tool also offers out-of-the-box connectors to popular knowledge repositories and collaboration platforms, making it easy to transform knowledge into AI-generated content.

VecRank is an AI-powered Vector Search and Reranking API service that leverages cutting-edge GenAI technologies to enhance natural language understanding and contextual relevance. It offers a scalable, AI-driven search solution for software developers and business owners. With VecRank, users can revolutionize their search capabilities with the power of AI, enabling seamless integration and powerful tools that scale with their business needs. The service allows for bulk data upload, incremental data updates, and easy integration into various programming languages and platforms, all without the hassle of setting up infrastructure for embeddings and vector search databases.

Trieve is an AI-first infrastructure API that offers a modern solution for search, recommendations, and RAG (Retrieve and Generate) tasks. It combines language models with tools for fine-tuning ranking and relevance, providing production-ready capabilities for building search, discovery, and RAG experiences. Trieve supports semantic vector search, full-text search using BM25 & SPLADE models, custom embedding models, hybrid search, and sub-sentence highlighting. With features like merchandising, relevance tuning, and self-hostable options, Trieve empowers companies to enhance their search capabilities and user experiences.

Pinecone is a vector database designed to help power AI applications for various companies. It offers a serverless platform that enables users to build knowledgeable AI applications quickly and cost-effectively. With Pinecone, users can perform low-latency vector searches for tasks such as search, recommendation, detection, and more. The platform is scalable, secure, and cloud-native, making it suitable for a wide range of AI projects.

Pinecone is a vector database designed to build knowledgeable AI applications. It offers a serverless platform with high capacity and low cost, enabling users to perform low-latency vector search for various AI tasks. Pinecone is easy to start and scale, allowing users to create an account, upload vector embeddings, and retrieve relevant data quickly. The platform combines vector search with metadata filters and keyword boosting for better application performance. Pinecone is secure, reliable, and cloud-native, making it suitable for powering mission-critical AI applications.

Pongo is an AI-powered tool that helps reduce hallucinations in Large Language Models (LLMs) by up to 80%. It utilizes multiple state-of-the-art semantic similarity models and a proprietary ranking algorithm to ensure accurate and relevant search results. Pongo integrates seamlessly with existing pipelines, whether using a vector database or Elasticsearch, and processes top search results to deliver refined and reliable information. Its distributed architecture ensures consistent latency, handling a wide range of requests without compromising speed. Pongo prioritizes data security, operating at runtime with zero data retention and no data leaving its secure AWS VPC.

Vellum AI is an AI platform that supports using Microsoft Azure hosted OpenAI models. It offers tools for prompt engineering, semantic search, prompt chaining, evaluations, and monitoring. Vellum enables users to build AI systems with features like workflow automation, document analysis, fine-tuning, Q&A over documents, intent classification, summarization, vector search, chatbots, blog generation, sentiment analysis, and more. The platform is backed by top VCs and founders of well-known companies, providing a complete solution for building LLM-powered applications.

deepset is an AI platform that offers enterprise-level products and solutions for AI teams. It provides deepset Cloud, a platform built with Haystack, enabling fast and accurate prototyping, building, and launching of advanced AI applications. The platform streamlines the AI application development lifecycle, offering processes, tools, and expertise to move from prototype to production efficiently. With deepset Cloud, users can optimize solution accuracy, performance, and cost, and deploy AI applications at any scale with one click. The platform also allows users to explore new models and configurations without limits, extending their team with access to world-class AI engineers for guidance and support.

SvectorDB is a vector database built from the ground up for serverless applications. It is designed to be highly scalable, performant, and easy to use. SvectorDB can be used for a variety of applications, including recommendation engines, document search, and image search.

Web Transpose is an AI-powered web scraping and web crawling API that allows users to transform any website into structured data. By utilizing artificial intelligence, Web Transpose can instantly build web scrapers for any website, enabling users to extract valuable information efficiently and accurately. The tool is designed for production use, offering low latency and effective proxy handling. Web Transpose learns the structure of the target website, reducing latency and preventing hallucinations commonly associated with traditional web scraping methods. Users can query any website like an API and build products quickly using the scraped data.

Stockphotos.com is a user-friendly stock agency offering millions of images for commercial use. The website provides unlimited downloads, AI-powered creative tools, and a variety of media resources. Users can access stock images, illustrations, footage, icons, fonts, and smart tools to enhance their creativity. Stockphotos.com also offers competitive pricing, helpful customer support, and a fair usage policy. With features like Magic AI Edits, AI Search, Background Remover, AI Upscaler, and Every Generator, users can easily enhance and manipulate images. The website caters to individuals, families, businesses, and creative professionals looking for high-quality, affordable stock media.

Pinecone is a vector database that helps power AI for the world's best companies. It is a serverless database that lets you deliver remarkable GenAI applications faster, at up to 50x lower cost. Pinecone is easy to use and can be integrated with your favorite cloud provider, data sources, models, frameworks, and more.

Weaviate is an AI-native database designed to bring intuitive AI-native applications to life with less hallucination, data leakage, and vendor lock-in. It offers features like Hybrid Search, Retrieval-Augmented Generation, Generative Feedback Loops, and Cost-performance optimization. Weaviate empowers developers to build AI-native applications with flexible, reliable, open-source foundations, including a vector database and surrounding services. With over 1M monthly downloads, Weaviate is a core piece of the AI-native stack for developers and enterprises, providing model inference and AI infrastructure tailored to specific use cases.

SVGStud.io is an AI-based tool for searching and generating Scalable Vector Graphics (SVGs). SVG (Scalable Vector Graphics) is an XML-based format for describing two-dimensional vector graphics. SVGStud.io offers functionalities such as free SVG bundles, semantic SVG search, AI-based SVG generator, and the ability to convert SVGs to other formats like DXF and EPS. It is a valuable tool for graphic designers looking to create high-quality, scalable graphics for web design and high-resolution displays.

SingleStore is a real-time data platform designed for apps, analytics, and gen AI. It offers faster hybrid vector + full-text search, fast-scaling integrations, and a free tier. SingleStore can read, write, and reason on petabyte-scale data in milliseconds. It supports streaming ingestion, high concurrency, first-class vector support, record lookups, and more.

Resume Matcher is a free, open-source Applicant Tracking System (ATS) tool that uses Machine Learning and Natural Language Processing to match resumes with job descriptions. It empowers users to tailor their resumes for each job application by providing insights on similarities and differences between the resume and job requirements. The platform offers data visualizations, text similarity analysis, and plans to incorporate advanced features like Vector Similarity. With a user-friendly interface and Python-based technology, Resume Matcher aims to simplify the job search process for developers.

LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrieval, filtering, and management of embeddings. The key features of LanceDB include: Production-scale vector search with no servers to manage. Store, query, and filter vectors, metadata, and multi-modal data (text, images, videos, point clouds, and more). Support for vector similarity search, full-text search, and SQL. Native Python and Javascript/Typescript support. Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure. GPU support in building vector index(*). Ecosystem integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB, and more on the way. LanceDB's core is written in Rust 🦀 and is built using Lance, an open-source columnar format designed for performant ML workloads.

pgvecto.rs is a Postgres extension written in Rust that provides vector similarity search functions. It offers ultra-low-latency, high-precision vector search capabilities, including sparse vector search and full-text search. With complete SQL support, async indexing, and easy data management, it simplifies data handling. The extension supports various data types like FP16/INT8, binary vectors, and Matryoshka embeddings. It ensures system performance with production-ready features, high availability, and resource efficiency. Security and permissions are managed through easy access control. The tool allows users to create tables with vector columns, insert vector data, and calculate distances between vectors using different operators. It also supports half-precision floating-point numbers for better performance and memory usage optimization.

Deep Lake is a Database for AI powered by a storage format optimized for deep-learning applications. Deep Lake can be used for: 1. Storing data and vectors while building LLM applications 2. Managing datasets while training deep learning models Deep Lake simplifies the deployment of enterprise-grade LLM-based products by offering storage for all data types (embeddings, audio, text, videos, images, pdfs, annotations, etc.), querying and vector search, data streaming while training models at scale, data versioning and lineage, and integrations with popular tools such as LangChain, LlamaIndex, Weights & Biases, and many more. Deep Lake works with data of any size, it is serverless, and it enables you to store all of your data in your own cloud and in one place. Deep Lake is used by Intel, Bayer Radiology, Matterport, ZERO Systems, Red Cross, Yale, & Oxford.

NucliaDB is a robust database that allows storing and searching on unstructured data. It is an out of the box hybrid search database, utilizing vector, full text and graph indexes. NucliaDB is written in Rust and Python. We designed it to index large datasets and provide multi-teanant support. When utilizing NucliaDB with Nuclia cloud, you are able to the power of an NLP database without the hassle of data extraction, enrichment and inference. We do all the hard work for you.

LLPhant is a comprehensive PHP Generative AI Framework that provides a simple and powerful way to build apps. It supports Symfony and Laravel and offers a wide range of features, including text generation, chatbots, text summarization, and more. LLPhant is compatible with OpenAI and Ollama and can be used to perform a variety of tasks, including creating semantic search, chatbots, personalized content, and text summarization.

This repository contains samples to help design AI applications using data from an Azure SQL Database. It showcases technical concepts and workflows integrating Azure SQL data with popular AI components both within and outside Azure. The samples cover various AI features such as Azure Cognitive Services, Promptflow, OpenAI, Vanna.AI, Content Moderation, LangChain, and more. Additionally, there are end-to-end samples like Similar Content Finder, Session Conference Assistant, Chatbots, Vectorization, SQL Server Database Development, Redis Vector Search, and Similarity Search with FAISS.

R2R (RAG to Riches) is a fast and efficient framework for serving high-quality Retrieval-Augmented Generation (RAG) to end users. The framework is designed with customizable pipelines and a feature-rich FastAPI implementation, enabling developers to quickly deploy and scale RAG-based applications. R2R was conceived to bridge the gap between local LLM experimentation and scalable production solutions. **R2R is to LangChain/LlamaIndex what NextJS is to React**. A JavaScript client for R2R deployments can be found here. ### Key Features * **🚀 Deploy** : Instantly launch production-ready RAG pipelines with streaming capabilities. * **🧩 Customize** : Tailor your pipeline with intuitive configuration files. * **🔌 Extend** : Enhance your pipeline with custom code integrations. * **⚖️ Autoscale** : Scale your pipeline effortlessly in the cloud using SciPhi. * **🤖 OSS** : Benefit from a framework developed by the open-source community, designed to simplify RAG deployment.

Vearch is a cloud-native distributed vector database designed for efficient similarity search of embedding vectors in AI applications. It supports hybrid search with vector search and scalar filtering, offers fast vector retrieval from millions of objects in milliseconds, and ensures scalability and reliability through replication and elastic scaling out. Users can deploy Vearch cluster on Kubernetes, add charts from the repository or locally, start with Docker-compose, or compile from source code. The tool includes components like Master for schema management, Router for RESTful API, and PartitionServer for hosting document partitions with raft-based replication. Vearch can be used for building visual search systems for indexing images and offers a Python SDK for easy installation and usage. The tool is suitable for AI developers and researchers looking for efficient vector search capabilities in their applications.

NekoImageGallery is an online AI image search engine that utilizes the Clip model and Qdrant vector database. It supports keyword search and similar image search. The tool generates 768-dimensional vectors for each image using the Clip model, supports OCR text search using PaddleOCR, and efficiently searches vectors using the Qdrant vector database. Users can deploy the tool locally or via Docker, with options for metadata storage using Qdrant database or local file storage. The tool provides API documentation through FastAPI's built-in Swagger UI and can be used for tasks like image search, text extraction, and vector search.

Qdrant is a vector similarity search engine and vector database. It is written in Rust, which makes it fast and reliable even under high load. Qdrant can be used for a variety of applications, including: * Semantic search * Image search * Product recommendations * Chatbots * Anomaly detection Qdrant offers a variety of features, including: * Payload storage and filtering * Hybrid search with sparse vectors * Vector quantization and on-disk storage * Distributed deployment * Highlighted features such as query planning, payload indexes, SIMD hardware acceleration, async I/O, and write-ahead logging Qdrant is available as a fully managed cloud service or as an open-source software that can be deployed on-premises.

The Clarifai Python SDK offers a comprehensive set of tools to integrate Clarifai's AI platform to leverage computer vision capabilities like classification , detection ,segementation and natural language capabilities like classification , summarisation , generation , Q&A ,etc into your applications. With just a few lines of code, you can leverage cutting-edge artificial intelligence to unlock valuable insights from visual and textual content.

This repository showcases a project that indexes millions of Wikipedia articles using Upstash Vector. It includes a semantic search engine and a RAG chatbot SDK. The project involves preparing and embedding Wikipedia articles, indexing vectors, building a semantic search engine, and implementing a RAG chatbot. Key features include indexing over 144 million vectors, multilingual support, cross-lingual semantic search, and a RAG chatbot. Technologies used include Upstash Vector, Upstash Redis, Upstash RAG Chat SDK, SentenceTransformers, and Meta-Llama-3-8B-Instruct for LLM provider.

AwaDB is an AI native database designed for embedding vectors. It simplifies database usage by eliminating the need for schema definition and manual indexing. The system ensures real-time search capabilities with millisecond-level latency. Built on 5 years of production experience with Vearch, AwaDB incorporates best practices from the community to offer stability and efficiency. Users can easily add and search for embedded sentences using the provided client libraries or RESTful API.

Infinity is an AI-native database designed for LLM applications, providing incredibly fast full-text and vector search capabilities. It supports a wide range of data types, including vectors, full-text, and structured data, and offers a fused search feature that combines multiple embeddings and full text. Infinity is easy to use, with an intuitive Python API and a single-binary architecture that simplifies deployment. It achieves high performance, with 0.1 milliseconds query latency on million-scale vector datasets and up to 15K QPS.

vecs is a Python client for managing and querying vector stores in PostgreSQL with the pgvector extension. It allows users to create collections of vectors with associated metadata, index the collections for fast search performance, and query the collections based on specified filters. The tool simplifies the process of working with vector data in a PostgreSQL database, making it easier to store, retrieve, and analyze vector information.

This repository provides code samples in Python, C#, REST, and JavaScript for vector support in Azure AI Search. It includes demos for various languages showcasing vectorization of data, creating indexes, and querying vector data. Additionally, it offers tools like Azure AI Search Lab for experimenting with AI-enabled search scenarios in Azure and templates for deploying custom chat-with-your-data solutions. The repository also features documentation on vector search, hybrid search, creating and querying vector indexes, and REST API references for Azure AI Search and Azure OpenAI Service.

Superlinked is a compute framework for information retrieval and feature engineering systems, focusing on converting complex data into vector embeddings for RAG, Search, RecSys, and Analytics stack integration. It enables custom model performance in machine learning with pre-trained model convenience. The tool allows users to build multimodal vectors, define weights at query time, and avoid postprocessing & rerank requirements. Users can explore the computational model through simple scripts and python notebooks, with a future release planned for production usage with built-in data infra and vector database integrations.

Boxcars is a Ruby gem that enables users to create new systems with AI composability, incorporating concepts such as LLMs, Search, SQL, Rails Active Record, Vector Search, and more. It allows users to work with Boxcars, Trains, Prompts, Engines, and VectorStores to solve problems and generate text results. The gem is designed to be user-friendly for beginners and can be extended with custom concepts. Boxcars is actively seeking ways to enhance security measures to prevent malicious actions. Users can use Boxcars for tasks like running calculations, performing searches, generating Ruby code for math operations, and interacting with APIs like OpenAI, Anthropic, and Google SERP.

ChatWeb is a tool that can crawl web pages, extract text from PDF, DOCX, TXT files, and generate an embedded summary. It can answer questions based on text content using chatAPI and embeddingAPI based on GPT3.5. The tool calculates similarity scores between text vectors to generate summaries, performs nearest neighbor searches, and designs prompts to answer user questions. It aims to extract relevant content from text and provide accurate search results based on keywords. ChatWeb supports various modes, languages, and settings, including temperature control and PostgreSQL integration.

The serverless-pdf-chat repository contains a sample application that allows users to ask natural language questions of any PDF document they upload. It leverages serverless services like Amazon Bedrock, AWS Lambda, and Amazon DynamoDB to provide text generation and analysis capabilities. The application architecture involves uploading a PDF document to an S3 bucket, extracting metadata, converting text to vectors, and using a LangChain to search for information related to user prompts. The application is not intended for production use and serves as a demonstration and educational tool.

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Creates Google Ads headlines in bulk based on direct response copy principles.

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Analyse content through the official Google Search Quality Rater Guidelines.

Refines search queries with specific terms and includes Google links

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Browse the web for the search term and summarize the results from sources

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Create the most effective database or search engine queries using keywords, truncation, and Boolean operators!

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This GPT answers all the questions related to Google Search Console

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I'm Quora Search, specialized in finding Quora answers and insights.

Summarize about the topic based on scientifically sound information