Unified framework for building enterprise RAG pipelines with small, specialized models
ChatGPT, embedding search, and retrieval-augmented generation for Squeak/Smalltalk
Question Answering Generative AI application with Large Language Models (LLMs), Amazon Bedrock, and Amazon DocumentDB (with MongoDB Compatibility)
MovieGPT: A RAG, Gen AI application for Movie Recommendations
Unstract's interface to LLMs, Embeddings and VectorDBs.
Open-source framework to make AI agents' team collaboration as effective as human collaboration.
Automation of Prioritization and Categorization of Support Tickets Using LLMs and Vector DBs
A web site crawler for semantic search.
GeniusAI: Personalized AI companions powered by Llama 2 13B model. Engage in diverse conversations, explore personas, and revolutionize learning interactively.
YouTubeGPT • AI Chat with 100+ videos ft. YouTuber Marques Brownlee (@ MKBHD) ⚡️🔴🤖💬
Benchmark study on LanceDB, an embedded vector DB, for full-text search and vector search
Slides for "Retrieval Augmented Generation" video
Vector Storage is a vector database that enables semantic similarity searches on text documents in the browser's local storage. It uses OpenAI embeddings to convert documents into vectors and allows searching for similar documents based on cosine similarity.
Columbus is a cloud-based search platform for searching hosted cloud apps on your personal Kubernetes.
This project generates question over a given corpus of information. It uses a LLM and the FAISS vector DB to acomplish the above mentioned objectives.
Swift Vector Database. On-device, local vector database for building the next-generation of user experiences
A proof-of-concept of retrieval-augmented generation, using Google's PaLM API.
GGNN: State of the Art Graph-based GPU Nearest Neighbor Search
Add a description, image, and links to the vector-db topic page so that developers can more easily learn about it.
To associate your repository with the vector-db topic, visit your repo's landing page and select "manage topics."