The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text
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Updated
Jul 22, 2025 - C++
The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text
The universal tool suite for vector database management. Manage Pinecone, Chroma, Qdrant, Weaviate and more vector databases with ease.
Notebooks & Example Apps for Search & AI Applications with Elasticsearch
Hello everyone this repo will contain my journey of machine learning and DeepLearning with some exciting projects
TinyVecDB is an ultra fast embedded vector database.
⚡️Lightning fast in-memory VectorDB written in rust🦀
⚡️ Transform AI/ML operations: Transparency, Control and Cost Optimization. ⚡️
Example application querying data in different ways
Elysium Knowledge Repository is an open source initiative to embed all of Humanity's multi-modal knowledge and wisdom.
With ME_GPT, you can store your cherished memories in a local vector database and leverage them to give Chat GPT the extraordinary ability to clone your unique personality and knowledge.
This Repository collects my material about my course (Mastering Vector Databases for AI applicstions) by Eng/ Mohammed Agoor
Two approaches to generating optimized embeddings in the Retrieval-Augmented Generation (RAG) Pattern
I just wanna build my own LLM with RAG
VectorCV is a React app w/ a Flask Back-end that allows you (recruiters) to enter your candidates' CV's and find out who are the best candidates for the job. This is all done through embeddings, vector databases and queries by GPT-3.5
The simple client of NautilusDB, a Clound-Native Vector Search Service
RAG pipeline framework for Localized LLM users
A Quick Note taking application, which runs locally within the browser.
The project uses Memory Based- RAG for healthcare queries, searching FAISS vector database for relevant answers. If no results are found, an AI fallback mechanism steps in. The AI Agent employs Selenium headless drivers, automation, web scraping, etc techniques to enhance search efficiency, ensuring accurate, real-time responses.
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