A cloud-native vector database, storage for next generation AI applications
-
Updated
May 31, 2024 - Go
A cloud-native vector database, storage for next generation AI applications
A FastAPI service for semantic text search using precomputed embeddings and advanced similarity measures, with built-in support for various file types through textract.
A curated list of awesome works related to high dimensional structure/vector search & database
Cottontail DB is a column store vector database aimed at multimedia retrieval. It allows for classical boolean as well as vector-space retrieval (nearest neighbour search) used in similarity search using a unified data and query model.
Shotit is a screenshot-to-video search engine tailored for TV & Film, blazing-fast and compute-efficient.
A Python vector database you just need - no more, no less.
Vector Embedding Server in under 100 lines of code
The frontend of shotit, with full documentation.
Unsupervised Video Summarization via Successor Embeddings
Serverless, lightweight, and fast vector database on top of DynamoDB
The ultimate brain of Shotit, in charge of task coordination.
Media broker for serving video preview for shotit
langchain-chat is an AI-driven Q&A system that leverages OpenAI's GPT-4 model and FAISS for efficient document indexing. It loads and splits documents from websites or PDFs, remembers conversations, and provides accurate, context-aware answers based on the indexed data. Easy to set up and extend.
Four core workers of shotit: watcher, hasher, loader and searcher.
VQLite - Simple and Lightweight Vector Search Engine based on Google ScaNN
"if-then-else" over topics made up of free-form sentences. Build conversations, not LLM chains!
The README profile of Shotit.
The ChatGPT Long Term Memory package is a powerful tool designed to empower your projects with the ability to handle a large number of simultaneous users and external sources.
Search for code by what it does in natural language, using machine learning embeddings.
Sort the search results of Shotit to increase the correctness of Top1 result by using Keras and Faiss.
Add a description, image, and links to the embedding-similarity topic page so that developers can more easily learn about it.
To associate your repository with the embedding-similarity topic, visit your repo's landing page and select "manage topics."