A cloud-native vector database, storage for next generation AI applications
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Updated
Jul 25, 2024 - Go
A cloud-native vector database, storage for next generation AI applications
A pure Python-implemented, lightweight, server-optional, multi-end compatible, vector database deployable locally or remotely.
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.
A curated list of awesome works related to high dimensional structure/vector search & database
VQLite - Simple and Lightweight Vector Search Engine based on Google ScaNN
The frontend of shotit, with full documentation.
Shotit is a screenshot-to-video search engine tailored for TV & Film, blazing-fast and compute-efficient.
The ultimate brain of Shotit, in charge of task coordination.
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 Python vector database you just need - no more, no less.
Vector Embedding Server in under 100 lines of code
Unsupervised Video Summarization via Successor Embeddings
Serverless, lightweight, and fast vector database on top of DynamoDB
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.
"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.
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