Weaviate AI technology page Top Builders

Explore the top contributors showcasing the highest number of Weaviate AI technology page app submissions within our community.

Weaviate

Weaviate is an open-source vector database that enables you to store data objects and vector embeddings from your preferred ML models. Moreover, it offers smooth scalability for handling billions of data objects.

General
Author	Weaviate
Repository	https://github.com/weaviate
Type	Vector Database

Weaviate - Helpful Resources

Explore Weaviate resources to better understand and utilize Weaviate, the AI native vector database, effectively in your projects.

Weaviate Documentation Comprehensive documentation for Weaviate
Weaviate Quickstart In this quickstart guide, you'll discover how to build a vector database using Weaviate Cloud Services (WCS), import data, and conduct vector search.
WCS - Weaviate SaaS Connect to the Weaviate Cloud Services or to a local Weaviate (Keep in mind that Weaviate can also be utilized locally with Docker).

Weaviate Similarity search

Delve into Weaviate's similarity search to quickly and accurately identify closely related data within your vector database.

Weaviate Similarity / Vector search Documentation Explore the documentation on executing similarity-based searches with Weaviate.
Similarity Search Recipes Discover various similarity search recipes tailored for platforms like Cohere, OpenAI, HuggingFace, PaLM, and more.

GraphQL API

Weaviate GraphQL Documentation

Weaviate Generative Search

Generative Search - OpenAI Documentation
Generative Search - Cohere Documentation
Generative Search - PaLM Documentation
Generative Search Recipes Discover various generative search recipes tailored for platforms like Cohere, OpenAI, and PaLM.

Edit on Github

Weaviate AI technology page Hackathon projects

Discover innovative solutions crafted with Weaviate AI technology page, developed by our community members during our engaging hackathons.

GreenGo - AI Green Card Immigration Assistant

GreenGo - AI chatbot for immigrants to find answers and navigate the U.S. Green Card immigration process. We aim to provide accessible and comprehensible information spread across Reddit, USCIS, Social Media, Latest News and Group Chats. Our goal is to make immigration information accessible and comprehensible. Built during the Llama Impact Hackathon, this tool aims to make immigration information more accessible and reduce stress for immigrants going through various immigration processes, within the scope of Green Card Application for this hackathon. In the future, we aim to continue building to expand the information beyond Green Card to all immigrants, international students, your everyday people aspiring to step foot into US to experience everything it has to offer. Category Navigating Public Services 📌 Problem Statement: Government services can be complex & difficult to navigate, creating barriers for citizens. 🎯 Problem Statement Immigrants face significant challenges when: Navigating complex USCIS policies and procedures Understanding how to proceed with their applications Getting reliable information without access to legal counsel Self-filing applications without professional guidance Waiting for work authorization and other critical documents 💡 Solution GreenGo leverages AI to: Parse and understand USCIS policy manuals Provide accurate, source-backed answers to immigration questions Help users understand their options and next steps Make immigration information more accessible 🏗️ Architecture The application consists of three main components: Frontend: Next.js application providing the chat interface Backend: Flask API server handling chat interactions AI Engine: Weaviate vector database for storing USCIS policy data Llama 3.2 model for generating responses RAG (Retrieval Augmented Generation) for accurate information retrieval

VC Pitch Simulation and Dashboard

Angel Invesors, VCs, Startup Accelerators and Scouts Sift though large quantities of contexual data provided by startup seeking investment a very efficient process that can be optimized with GenAI. This solution is based on IBM's Prompt Lab structured input and output. The model is prompt with chuncks of texts from the provided pitch deck and instructed to extract specific data points based on given examples, the extracted data points are then integrated in the solution with the help of python to calculate the most important indicators for startup state such as Burn Rate, Customer Lifetime Value, Customer Acquisition Cost, Market Size. The model iterates across a given "startup profile" format to ensure completion of data and to prompte the user for any missing details before creating a dashboard showcasing the calculated variables with reference to the source extracted. In an advanced version, RAG implementation with Semantic Search could make this solution more powerful. This business productivity solution saves a lot of time and effort for a niche market that has to sift through raw startup data. Furthermore, the solution could be developed into providing the same "investor dashboard" generated to the startup that uploaded it, allowing an interactive pitch simulation session and utilizing a RAG of embedded "startup knowledge base" to exact the startup pitch.

Senu the startup copilot

The development in GenAI, NLP, Sentiments Analysis allows for disrupting the startup building journey, creating unprecedented opportunities for startup and the their supporting ecosystem to grow. Senu utilizes GenAI, NLP, Sentiment analysis to produce a Startup Pitch Simulator and a Startup Copilot, providing real-time assessment for startup pitches and producing a clear roadmap for startup to take their business to the next level, furthermore, this rich real-time data becomes the bloodline of new asset classes built around milestone-based commitments. The system also offers a convenient interface for startup program managers and investors to assess, support and monitor the performance of their portfolios

INSTArget

INSTArget is an AI-based app designed to help Instagram businesses and content creators analyze competitors' data and gain insights to enhance their own profiles. The app works by analyzing the user's Instagram profile information and fetching similar profiles within the same category and niche. Users can also narrow down searches by city and/or country. The app retrieves data from the latest five posts of these similar profiles using the APIFY API. This data is then analyzed by the Falcon 180B LLM to provide insights on why those posts are engaging and how users can apply similar strategies to their own content. Additionally, the app features a RAG-based (Retrieval Augmented Generation) chatbot, using Falcon LLM for generation. This chatbot allows users to delve deeper into specific profiles. In this mode, the app fetches and stores data on the last 50 posts of a profile in the Weaviate vector database, enabling users to ask detailed questions about the profile and receive cited answers with links to the relevant posts. The app also includes a content creation chatbot that assists users in generating content ideas tailored to their account type, including posts, reels, and stories. The chatbot automatically generates ideas and allows for interactive follow-up questions. INSTArget also provides access to collections of vectorized data from various Instagram categories like Fashion, Beauty, Travel, Photography, Technology, and more. These collections, each containing data from an average of 300 posts, are used by the "Ask Me" RAG-based chatbot to answer user queries with cited answers linked to the source posts. Users can also specify a particular profile and have its last 50 posts vectorized for detailed analysis. One of the best features of INSTArget is that any user can access the "Ask Me" chatbot without needing to fill out any form details. Users can ask questions related to specific profile categories or chat about a particular profile of their choice.

Senu Startup Copilot

Startups often struggle with getting timely, specific, and accurate expert assessments, which leaves them unsure of their next steps and negatively impacts their productivity and motivation. Investors, on the other hand, face challenges in sifting through startups that lack critical information, which leads to high man-hour costs and uncertainty in tracking the progress of promising ventures. Introducing Senu: Startup Copilot, an AI-driven companion named after the eagle in Assassin’s Creed, designed to guide startups step-by-step through their building journey. Senu starts with an expert assessment, helps develop a clear roadmap, and recommends the best actions and resources to increase the likelihood of success. For investors, Senu offers clarity on which ventures are making rapid progress, ensuring no opportunity is overlooked. Senu saves startups time and money by providing instant expert feedback and eliminating the need for costly consultants and startup programs. It also enhances their chances of being matched with the right funding, services, or action steps. Senu is the future of startup building, providing comprehensive support for both entrepreneurs and investors.

EdugenAI

The Learning Management System (LMS) employs state-of-the-art Generative AI to transform the exam creation process. This sophisticated system automatically produces and customizes exams by analyzing and interpreting content from uploaded documents. Utilizing Llama Parse, the AI can extract images and tables that are often missed by standard parsers. The LMS enhances the relevance and accuracy of assessments by employing AI agents Empowered by Falcon Amazing 180B LLM that could expand the context limit and improved generation to easily generate and validate questions based on the uploaded material using Retrieval-Augmented Generation (RAG). Built with a production-ready, cloud-based serverless architecture, this system streamlines the exam creation process, minimizes manual effort, and equips educators with a robust tool to generate high-quality, personalized exams tailored to their students' needs.