Best AI tools for< Semantic Search >

Biblos Semantic Bible Search & Summary is an AI-powered tool that offers a powerful Bible search experience. It provides semantic search capabilities and a powerful understanding model to enhance the user's exploration of the Bible. The tool aims to deliver lightning-fast searches and insightful summaries of both the Old Testament and New Testament.

ProductHunt AI 2.0 is a no-BS product finder that makes it super easy to find super-effective products and alternatives on the go with semantic searches. It is 100% free to use and backed by Supervised AI. With ProductHunt AI 2.0, you can build agents like this with no-code, use free open-source AI models, or deploy your own language model.

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.

Keytalk AI is a company that specializes in prompt engineering, which is the process of creating prompts that can be used to generate text, images, and other types of content using artificial intelligence (AI) models. Keytalk AI's mission is to make AI more accessible and user-friendly by providing tools and resources that make it easy for people to create and use AI-generated content. The company's flagship product is Keytalk Prompts, a library of pre-written prompts that can be used to generate content on a variety of topics. Keytalk AI also offers a range of other services, including consulting, training, and support.

SmarterFolder is an AI-powered tool designed for MacOS that enables users to perform semantic image searches on their local drive. By utilizing AI technology, users can find photos based on descriptions of the content within the images. The tool ensures full privacy as no images are shared or stored externally, providing a secure and efficient way to organize and retrieve photos.

Exa is a web API designed to provide AI applications with powerful access to the web by organizing and retrieving the best content using embeddings. It offers features like semantic search, similarity search, content scraping, and powerful filters to help developers and companies gather and process data for AI training and analysis. Exa is trusted by thousands of developers and companies for its speed, quality, and ability to provide up-to-date information from various sources on the web.

Clipmate AI is an AI-first Second Brain for managing bookmarks, screenshots, and various saved content effortlessly. It helps users combat information overload by organizing digital clutter, providing powerful features like automatic sync, semantic search, and auto-categorization. Users can add notes to bookmarks, chat with their bookmarks, and organize content into collections. Clipmate AI is designed for digital hoarders, designers, researchers, developers, marketers, and entrepreneurs to streamline their workflow and stay organized. The application offers multi-platform sync and integration with platforms like Twitter, Reddit, iOS Screenshots, and Spotify.

EnergeticAI is an open-source AI library that can be used in Node.js applications. It is optimized for serverless environments and provides fast cold-start, small module size, and pre-trained models. EnergeticAI can be used for a variety of tasks, including building recommendations, classifying text, and performing semantic search.

Lilac is an AI tool designed to enhance data quality and exploration for AI applications. It offers features such as data search, quantification, editing, clustering, semantic search, field comparison, and fuzzy-concept search. Lilac enables users to accelerate dataset computations and transformations, making it a valuable asset for data scientists and AI practitioners. The tool is trusted by Alignment Lab and is recommended for working with LLM datasets.

MiMi is a website intelligence tool that uses AI to enhance the user experience and drive sales. It offers a range of features including semantic search, chatbot, recommendations, virtual assistant, dynamic pricing, and automation. MiMi's AI engine can automatically learn and update knowledge from your site to provide an AI chatbot that can answer questions from visitors automatically. The machine learning algorithms can also learn from your site products and visitor behavior to bring recommender systems for your site. MiMi's AI algorithm serves as a virtual sales assistant, assisting websites in making flexible and tailored pricing decisions for each customer based on their behavior.

Trampoline is an AI-native proposal manager designed for sales teams to streamline the process of creating sales proposals. It leverages AI technology to help businesses excel at handling RFPs efficiently, saving valuable time and resources. Trampoline's innovative approach includes 'content upcycling' to quickly prepare necessary information, onboard new team members rapidly, and facilitate knowledge sharing within the organization. With features like semantic search, direct query forwarding, and expert contributions, Trampoline aims to revolutionize the way sales proposals are created and managed.

jamie is an AI Notetaker tool designed for meeting notes and automated action items. It provides human-quality meeting minutes across various meeting platforms in over 20 languages, both online and offline. With a privacy-first approach, jamie helps users save time by automatically generating summaries, transcripts, and action items from meeting audio. It works seamlessly with popular tools like Zoom and Teams, offering features such as task extraction, decision detection, semantic search, and custom note templates. Users can effortlessly retrieve information from meeting notes, ask questions, draft emails, and more, making jamie a valuable assistant for mastering busy-work and saving time.

Couture.ai is an AI-as-a-service platform that specializes in hyper-scale AI for tailored retail experiences. The platform assists global online retailers and fashion brands in personalizing customer experiences through prediction technology. Couture.ai offers cutting-edge solutions such as Virtual TryOn for visualizing products before purchase, Demand & Assortment Forecasting for inventory management, Live Search for semantic search solutions, and Obelisk Experience Engine for behavior insights-driven tailored experiences across various platforms. The platform aims to elevate customer experiences and optimize business outcomes through AI-driven solutions.

Moogle is a semantic search tool that provides users with the ability to find theorems quickly and efficiently. It offers a streamlined search experience over the mathlib4 database, enabling users to access relevant mathematical information with ease. Moogle is designed to enhance research productivity and facilitate the exploration of mathematical concepts in a user-friendly manner.

Hotseat AI is a legal research assistant that allows users to search through a collection of legal documents to find expert-level quotes matching their queries in seconds. It supports searching over public documents like GDPR, ECJ rulings, and European guidelines, and also offers features like metadata extraction, fast and pro search options, and the ability to upload and search over private documents. The tool is currently in private beta and focuses on EU regulations related to tech, fintech, banking, and financial services.

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.

Law.co is an advanced AI platform designed specifically for lawyers and law firms to streamline legal operations and enhance efficiency. The platform offers a semantic database search with access to over 1 million historical legal cases and 40,000 legal contracts, enabling users to perform detailed legal research, contract drafting, document review, and more. Law.co leverages custom-trained artificial intelligence and semantic search tools to deliver measurable results, revolutionizing legal research and document preparation processes for legal professionals.

Alan AI is an advanced conversational AI platform that offers a wide range of AI solutions for various industries. It simplifies tasks, enhances business operations, and empowers sales strategies through AI technology. The platform provides features like question answering, semantic search, reporting, private data sources, and context awareness. With a focus on actionable AI, Alan AI aims to redefine learning and streamline decision-making processes. It offers a comprehensive suite of tools for developers, including technology architecture overview, integration, deployment, and analytics. Alan AI stands out for its innovative approach to AI reasoning, transparency, and control, making it a valuable asset for organizations seeking to leverage AI capabilities.

ContextClue is an AI text analysis tool that offers enhanced document insights through features like text summarization, report generation, and LLM-driven semantic search. It helps users summarize multi-format content, automate document creation, and enhance research by understanding context and intent. ContextClue empowers users to efficiently analyze documents, extract insights, and generate content with unparalleled accuracy. The tool can be customized and integrated into existing workflows, making it suitable for various industries and tasks.

Emdash is an AI-powered tool designed to help users organize their book highlights effectively. By utilizing AI technology, Emdash can analyze and categorize text snippets, making it easier for users to remember and learn from their readings. The tool offers features such as conceptual cousins, instant semantic search, tagging, rating, note-taking, and reflection capabilities. Users can also export their organized data back to epub format for review on e-readers. Emdash is free, open-source, and aims to provide a seamless reading experience for book enthusiasts.

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.

AI-Powered Search provides code examples for the book 'AI-Powered Search' by Trey Grainger, Doug Turnbull, and Max Irwin. The book teaches modern machine learning techniques for building search engines that continuously learn from users and content to deliver more intelligent and domain-aware search experiences. It covers semantic search, retrieval augmented generation, question answering, summarization, fine-tuning transformer-based models, personalized search, machine-learned ranking, click models, and more. The code examples are in Python, leveraging PySpark for data processing and Apache Solr as the default search engine. The repository is open source under the Apache License, Version 2.0.

Local-GenAI Search is a local generative search engine powered by the Llama3 model, allowing users to ask questions about their local files and receive concise answers with relevant document references. It utilizes MS MARCO embeddings for semantic search and can run locally on a 32GB laptop or computer. The tool can be used to index local documents, search for information, and provide generative search services through a user interface.

searchGPT is an open-source project that aims to build a search engine based on Large Language Model (LLM) technology to provide natural language answers. It supports web search with real-time results, file content search, and semantic search from sources like the Internet. The tool integrates LLM technologies such as OpenAI and GooseAI, and offers an easy-to-use frontend user interface. The project is designed to provide grounded answers by referencing real-time factual information, addressing the limitations of LLM's training data. Contributions, especially from frontend developers, are welcome under the MIT License.

SemanticFinder is a frontend-only live semantic search tool that calculates embeddings and cosine similarity client-side using transformers.js and SOTA embedding models from Huggingface. It allows users to search through large texts like books with pre-indexed examples, customize search parameters, and offers data privacy by keeping input text in the browser. The tool can be used for basic search tasks, analyzing texts for recurring themes, and has potential integrations with various applications like wikis, chat apps, and personal history search. It also provides options for building browser extensions and future ideas for further enhancements and integrations.

Cosdata is a cutting-edge AI data platform designed to power the next generation search pipelines. It features immutability, version control, and excels in semantic search, structured knowledge graphs, hybrid search capabilities, real-time search at scale, and ML pipeline integration. The platform is customizable, scalable, efficient, enterprise-grade, easy to use, and can manage multi-modal data. It offers high performance, indexing, low latency, and high requests per second. Cosdata is designed to meet the demands of modern search applications, empowering businesses to harness the full potential of their data.

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.

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.

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.

pyLLMSearch is an advanced RAG system that offers a convenient question-answering system with a simple YAML-based configuration. It enables interaction with multiple collections of local documents, with improvements in document parsing, hybrid search, chat history, deep linking, re-ranking, customizable embeddings, and more. The package is designed to work with custom Large Language Models (LLMs) from OpenAI or installed locally. It supports various document formats, incremental embedding updates, dense and sparse embeddings, multiple embedding models, 'Retrieve and Re-rank' strategy, HyDE (Hypothetical Document Embeddings), multi-querying, chat history, and interaction with embedded documents using different models. It also offers simple CLI and web interfaces, deep linking, offline response saving, and an experimental API.

yt-fts is a command line program that uses yt-dlp to scrape all of a YouTube channels subtitles and load them into a sqlite database for full text search. It allows users to query a channel for specific keywords or phrases and generates time stamped YouTube URLs to the videos containing the keyword. Additionally, it supports semantic search via the OpenAI embeddings API using chromadb.

Trieve is an advanced relevance API for hybrid search, recommendations, and RAG. It offers a range of features including self-hosting, semantic dense vector search, typo tolerant full-text/neural search, sub-sentence highlighting, recommendations, convenient RAG API routes, the ability to bring your own models, hybrid search with cross-encoder re-ranking, recency biasing, tunable popularity-based ranking, filtering, duplicate detection, and grouping. Trieve is designed to be flexible and customizable, allowing users to tailor it to their specific needs. It is also easy to use, with a simple API and well-documented features.

Vectara Answer is a sample app for Vectara-powered Summarized Semantic Search (or question-answering) with advanced configuration options. For examples of what you can build with Vectara Answer, check out Ask News, LegalAid, or any of the other demo applications.

Vespa is an open-source search and AI engine that provides a unified platform for building and deploying search and AI applications. Vespa sample applications showcase various use cases and features of Vespa, including basic search, recommendation, semantic search, image search, text ranking, e-commerce search, question answering, search-as-you-type, and ML inference serving.

Chaindesk is a no-code platform that allows users to easily set up a semantic search system for personal data without technical knowledge. It supports loading data from various sources such as raw text, web pages, files (Word, Excel, PowerPoint, PDF, Markdown, Plain Text), and upcoming support for web sites, Notion, and Airtable. The platform offers a user-friendly interface for managing datastores, querying data via a secure API endpoint, and auto-generating ChatGPT Plugins for each datastore. Chaindesk utilizes a Vector Database (Qdrant), Openai's text-embedding-ada-002 for embeddings, and has a chunk size of 1024 tokens. The technology stack includes Next.js, Joy UI, LangchainJS, PostgreSQL, Prisma, and Qdrant, inspired by the ChatGPT Retrieval Plugin.

This repository contains a Rest-Backend for a Conversational Agent that allows embedding documents, semantic search, QA based on documents, and document processing with Large Language Models. It uses Aleph Alpha and OpenAI Large Language Models to generate responses to user queries, includes a vector database, and provides a REST API built with FastAPI. The project also features semantic search, secret management for API keys, installation instructions, and development guidelines for both backend and frontend components.

The AI Workshop repository provides a comprehensive guide to utilizing OpenAI's APIs, including Chat Completion, Embedding, and Assistant APIs. It offers hands-on demonstrations and code examples to help users understand the capabilities of these APIs. The workshop covers topics such as creating interactive chatbots, performing semantic search using text embeddings, and building custom assistants with specific data and context. Users can enhance their understanding of AI applications in education, research, and other domains through practical examples and usage notes.

PolyMind is a multimodal, function calling powered LLM webui designed for various tasks such as internet searching, image generation, port scanning, Wolfram Alpha integration, Python interpretation, and semantic search. It offers a plugin system for adding extra functions and supports different models and endpoints. The tool allows users to interact via function calling and provides features like image input, image generation, and text file search. The application's configuration is stored in a `config.json` file with options for backend selection, compatibility mode, IP address settings, API key, and enabled features.

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access a GPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval. The repo includes sample data so it's ready to try end to end. In this sample application we use a fictitious company called Contoso Electronics, and the experience allows its employees to ask questions about the benefits, internal policies, as well as job descriptions and roles.

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access the ChatGPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval.

Expert in schema.org, guiding precise use of 'additionalType'.

Analyse & visualise semantic networks entities and attributes for content creation.

Guiding on Semantic SEO, from understanding core concepts to applying advanced strategies.

BIAN v12.0

Typescript-based expert in NextJS, Semantic-ui

Aids in SPARQL queries for Semantic-Art's Gist Ontology

Talk to Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning (LFG)

Helps you build logic security into your GPTs custom instructions. Documentation: https://github.com/infotrix/SSLLMs---Semantic-Secuirty-for-LLM-GPTs

Experto en lógica semántica SEO y resolución de problemas, formado por Pau Segui.

Esperto di SEO in analisi semantica, keyword research e messy middle funnel che interagisce con docs e sheets

A linguistic explorer that delves into the depths of words and phrases, revealing their richest meanings and most resonant synonyms, closely aligned with their original intent.