Best AI tools for< Search Vector Databases >

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.

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.

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.

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, tailored AI infrastructure patterns, and over 1M monthly downloads. The platform is known for its best-in-class hybrid search, integrations with major LLMs, and ease of deployment.

LangSearch is an AI tool that offers a free Web Search API and Rerank API, serving as the World Engine for AGI. It allows users to connect their LLM applications to access clean, accurate, high-quality context from billions of web documents, including news, images, videos, and more. The tool supports natural language search and provides enhanced search details for various content types.

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.

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.

Trieve is an AI-first infrastructure API that offers a comprehensive solution for search, recommendations, and RAG (retrieval-augmented generation). It combines advanced language models with tools for fine-tuning ranking and relevance, providing users with an all-in-one platform for enhancing search experiences across various categories. Trieve supports semantic vector search, full-text search using BM25 & SPLADE models, and hybrid search capabilities. The platform also enables users to tune and boost search results, manage ingestion and analytics effortlessly, and build unfair competitive advantages through search, discovery, and RAG experiences.

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.

Alt Cortex is an AI-powered news aggregation tool designed to help users curate, organize, summarize, and share content effortlessly. It leverages advanced technologies like vector search and OpenAI to provide users with relevant and concise insights. With features such as source control, automated updates, semantic categorization, intelligent summaries, and various sharing options, Alt Cortex aims to enhance user engagement and content clarity. The platform caters to a wide range of industries and purposes, offering solutions for content creators, educators, e-commerce sites, news outlets, corporate knowledge hubs, event organizers, nonprofits, health coaches, travel bloggers, real estate platforms, financial advisors, food bloggers, and more.

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.

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.

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.

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.

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.

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.

This repository contains examples, applications, starter code, & tutorials to help you kickstart your GenAI projects. * These are built using LanceDB, a free, open-source, serverless vectorDB that **requires no setup**. * It **integrates into python data ecosystem** so you can simply start using these in your existing data pipelines in pandas, arrow, pydantic etc. * LanceDB has **native Typescript SDK** using which you can **run vector search** in serverless functions! This repository is divided into 3 sections: - Examples - Get right into the code with minimal introduction, aimed at getting you from an idea to PoC within minutes! - Applications - Ready to use Python and web apps using applied LLMs, VectorDB and GenAI tools - Tutorials - A curated list of tutorials, blogs, Colabs and courses to get you started with GenAI in greater depth.

AI Samples for .NET is a repository containing various samples demonstrating how to use AI in .NET applications. It provides quickstarts using Semantic Kernel and Azure OpenAI SDK, covers LLM Core Concepts, End to End Examples, Local Models, Local Embedding Models, Tokenizers, Vector Databases, and Reference Examples. The repository showcases different AI-related projects and tools for developers to explore and learn from.

LLM Zoomcamp is a free online course focusing on real-life applications of Large Language Models (LLMs). Over 10 weeks, participants will learn to build an AI bot capable of answering questions based on a knowledge base. The course covers topics such as LLMs, RAG, open-source LLMs, vector databases, orchestration, monitoring, and advanced RAG systems. Pre-requisites include comfort with programming, Python, and the command line, with no prior exposure to AI or ML required. The course features a pre-course workshop and is led by instructors Alexey Grigorev and Magdalena Kuhn, with support from sponsors and partners.

Notes on AI state of the art, with a focus on generative and large language models. These are the "raw materials" for the https://lspace.swyx.io/ newsletter. This repo used to be called https://github.com/sw-yx/prompt-eng, but was renamed because Prompt Engineering is Overhyped. This is now an AI Engineering notes repo.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

The RAG ChatBot project combines Lama.cpp, Chroma, and Streamlit to build a Conversation-aware Chatbot and a Retrieval-augmented generation (RAG) ChatBot. The RAG Chatbot works by taking a collection of Markdown files as input and provides answers based on the context provided by those files. It utilizes a Memory Builder component to load Markdown pages, divide them into sections, calculate embeddings, and save them in an embedding database. The chatbot retrieves relevant sections from the database, rewrites questions for optimal retrieval, and generates answers using a local language model. It also remembers previous interactions for more accurate responses. Various strategies are implemented to deal with context overflows, including creating and refining context, hierarchical summarization, and async hierarchical summarization.

Txtai is an all-in-one embeddings database for semantic search, LLM orchestration, and language model workflows. It combines vector indexes, graph networks, and relational databases to enable vector search with SQL, topic modeling, retrieval augmented generation, and more. Txtai can stand alone or serve as a knowledge source for large language models (LLMs). Key features include vector search with SQL, object storage, topic modeling, graph analysis, multimodal indexing, embedding creation for various data types, pipelines powered by language models, workflows to connect pipelines, and support for Python, JavaScript, Java, Rust, and Go. Txtai is open-source under the Apache 2.0 license.

MyScaleDB is a SQL vector database optimized for AI applications, enabling developers to manage and process massive volumes of data efficiently. It offers fast and powerful vector search, filtered search, and SQL-vector join queries, making it fully SQL-compatible. MyScaleDB provides unmatched performance and scalability by leveraging cutting-edge OLAP database architecture and advanced vector algorithms. It is production-ready for AI applications, supporting structured data, text, vector, JSON, geospatial, and time-series data. MyScale Cloud offers fully-managed MyScaleDB with premium features on billion-scale data, making it cost-effective and simpler to use compared to specialized vector databases. Built on top of ClickHouse, MyScaleDB combines structured and vector search efficiently, ensuring high accuracy and performance in filtered search operations.

Denser Retriever is an enterprise-grade AI retriever designed to streamline AI integration into applications, combining keyword-based searches, vector databases, and machine learning rerankers using xgboost. It provides state-of-the-art accuracy on MTEB Retrieval benchmarking and supports various heterogeneous retrievers for end-to-end applications like chatbots and semantic search.

VectorETL is a lightweight ETL framework designed to assist Data & AI engineers in processing data for AI applications quickly. It streamlines the conversion of diverse data sources into vector embeddings and storage in various vector databases. The framework supports multiple data sources, embedding models, and vector database targets, simplifying the creation and management of vector search systems for semantic search, recommendation systems, and other vector-based operations.

SuperDuperDB is a Python framework for integrating AI models, APIs, and vector search engines directly with your existing databases, including hosting of your own models, streaming inference and scalable model training/fine-tuning. Build, deploy and manage any AI application without the need for complex pipelines, infrastructure as well as specialized vector databases, and moving our data there, by integrating AI at your data's source: - Generative AI, LLMs, RAG, vector search - Standard machine learning use-cases (classification, segmentation, regression, forecasting recommendation etc.) - Custom AI use-cases involving specialized models - Even the most complex applications/workflows in which different models work together SuperDuperDB is **not** a database. Think `db = superduper(db)`: SuperDuperDB transforms your databases into an intelligent platform that allows you to leverage the full AI and Python ecosystem. A single development and deployment environment for all your AI applications in one place, fully scalable and easy to manage.

A curated repository of code recipes, demos, and resources for basic and advanced Redis use cases in the AI ecosystem. It includes demos for ArxivChatGuru, Redis VSS, Vertex AI & Redis, Agentic RAG, ArXiv Search, and Product Search. Recipes cover topics like Getting started with RAG, Semantic Cache, Advanced RAG, and Recommendation systems. The repository also provides integrations/tools like RedisVL, AWS Bedrock, LangChain Python, LangChain JS, LlamaIndex, Semantic Kernel, RelevanceAI, and DocArray. Additional content includes blog posts, talks, reviews, and documentation related to Vector Similarity Search, AI-Powered Document Search, Vector Databases, Real-Time Product Recommendations, and more. Benchmarks compare Redis against other Vector Databases and ANN benchmarks. Documentation includes QuickStart guides, official literature for Vector Similarity Search, Redis-py client library docs, Redis Stack documentation, and Redis client list.

Azure Functions OpenAI Extension is a project that adds support for OpenAI LLM (GPT-3.5-turbo, GPT-4) bindings in Azure Functions. It provides NuGet packages for various functionalities like text completions, chat completions, assistants, embeddings generators, and semantic search. The project requires .NET 6 SDK or greater, Azure Functions Core Tools v4.x, and specific settings in Azure Function or local settings for development. It offers features like text completions, chat completion, assistants with custom skills, embeddings generators for text relatedness, and semantic search using vector databases. The project also includes examples in C# and Python for different functionalities.

LLMInterviewQuestions is a repository containing over 100+ interview questions for Large Language Models (LLM) used by top companies like Google, NVIDIA, Meta, Microsoft, and Fortune 500 companies. The questions cover various topics related to LLMs, including prompt engineering, retrieval augmented generation, chunking, embedding models, internal working of vector databases, advanced search algorithms, language models internal working, supervised fine-tuning of LLM, preference alignment, evaluation of LLM system, hallucination control techniques, deployment of LLM, agent-based system, prompt hacking, and miscellaneous topics. The questions are organized into 15 categories to facilitate learning and preparation.

This course has 18 lessons. Each lesson covers its own topic so start wherever you like! Lessons are labeled either "Learn" lessons explaining a Generative AI concept or "Build" lessons that explain a concept and code examples in both **Python** and **TypeScript** when possible. Each lesson also includes a "Keep Learning" section with additional learning tools. **What You Need** * Access to the Azure OpenAI Service **OR** OpenAI API - _Only required to complete coding lessons_ * Basic knowledge of Python or Typescript is helpful - *For absolute beginners check out these Python and TypeScript courses. * A Github account to fork this entire repo to your own GitHub account We have created a **Course Setup** lesson to help you with setting up your development environment. Don't forget to star (🌟) this repo to find it easier later. ## 🧠 Ready to Deploy? If you are looking for more advanced code samples, check out our collection of Generative AI Code Samples in both **Python** and **TypeScript**. ## 🗣️ Meet Other Learners, Get Support Join our official AI Discord server to meet and network with other learners taking this course and get support. ## 🚀 Building a Startup? Sign up for Microsoft for Startups Founders Hub to receive **free OpenAI credits** and up to **$150k towards Azure credits to access OpenAI models through Azure OpenAI Services**. ## 🙏 Want to help? Do you have suggestions or found spelling or code errors? Raise an issue or Create a pull request ## 📂 Each lesson includes: * A short video introduction to the topic * A written lesson located in the README * Python and TypeScript code samples supporting Azure OpenAI and OpenAI API * Links to extra resources to continue your learning ## 🗃️ Lessons | | Lesson Link | Description | Additional Learning | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | | 00 | Course Setup | **Learn:** How to Setup Your Development Environment | Learn More | | 01 | Introduction to Generative AI and LLMs | **Learn:** Understanding what Generative AI is and how Large Language Models (LLMs) work. | Learn More | | 02 | Exploring and comparing different LLMs | **Learn:** How to select the right model for your use case | Learn More | | 03 | Using Generative AI Responsibly | **Learn:** How to build Generative AI Applications responsibly | Learn More | | 04 | Understanding Prompt Engineering Fundamentals | **Learn:** Hands-on Prompt Engineering Best Practices | Learn More | | 05 | Creating Advanced Prompts | **Learn:** How to apply prompt engineering techniques that improve the outcome of your prompts. | Learn More | | 06 | Building Text Generation Applications | **Build:** A text generation app using Azure OpenAI | Learn More | | 07 | Building Chat Applications | **Build:** Techniques for efficiently building and integrating chat applications. | Learn More | | 08 | Building Search Apps Vector Databases | **Build:** A search application that uses Embeddings to search for data. | Learn More | | 09 | Building Image Generation Applications | **Build:** A image generation application | Learn More | | 10 | Building Low Code AI Applications | **Build:** A Generative AI application using Low Code tools | Learn More | | 11 | Integrating External Applications with Function Calling | **Build:** What is function calling and its use cases for applications | Learn More | | 12 | Designing UX for AI Applications | **Learn:** How to apply UX design principles when developing Generative AI Applications | Learn More | | 13 | Securing Your Generative AI Applications | **Learn:** The threats and risks to AI systems and methods to secure these systems. | Learn More | | 14 | The Generative AI Application Lifecycle | **Learn:** The tools and metrics to manage the LLM Lifecycle and LLMOps | Learn More | | 15 | Retrieval Augmented Generation (RAG) and Vector Databases | **Build:** An application using a RAG Framework to retrieve embeddings from a Vector Databases | Learn More | | 16 | Open Source Models and Hugging Face | **Build:** An application using open source models available on Hugging Face | Learn More | | 17 | AI Agents | **Build:** An application using an AI Agent Framework | Learn More | | 18 | Fine-Tuning LLMs | **Learn:** The what, why and how of fine-tuning LLMs | Learn More |

Redis Vector Library (RedisVL) is a Python client library for building AI applications on top of Redis. It provides a high-level interface for managing vector indexes, performing vector search, and integrating with popular embedding models and providers. RedisVL is designed to make it easy for developers to build and deploy AI applications that leverage the speed, flexibility, and reliability of Redis.

Marqo is more than a vector database, it's an end-to-end vector search engine for both text and images. Vector generation, storage and retrieval are handled out of the box through a single API. No need to bring your own embeddings.

MyScaleDB is a SQL vector database designed for scalable AI applications, enabling developers to efficiently manage and process massive volumes of data using familiar SQL. It offers fast and efficient vector search, filtered search, and SQL-vector join queries. MyScaleDB is fully SQL-compatible and production-ready for AI applications, providing unmatched performance and scalability through cutting-edge OLAP architecture and advanced vector algorithms. Built on top of ClickHouse, it combines structured and vectorized data management for high accuracy and speed in filtered searches.

Quick YouTube, US equity data, and web page summarization with vector transcript search -- no logins needed.

AI Search Engine - About us & for Advertising at DigitalMediaMarketing.Agency

I conjure advanced Google search queries.

Analyzes GSC data and highlights the impact of search updates.

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

Search data on the impact of AI on jobs, productivity and operations published by Synthetic Work (https://synthetic.work)

Analyse content through the official Google Search Quality Rater Guidelines.

Refines search queries with specific terms and includes Google links

Your go-to expert for web searches.

Proustian Chatbot with Full French & English Volumes

GPT search. Click ["] to start working.

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

Optimized with advanced search operators for refined results. Specializing in finding and linking top custom GPTs from builders around the world. Version 0.3.0

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

Expert en Islam offrant des conseils détaillés sur la base du Saint Coran et des Hadiths

This GPT answers all the questions related to Google Search Console

OnlyFans Search Engine, provided by FansIQ.com

Expert in tailored accelerator search for startups.

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

Summarize about the topic based on scientifically sound information