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OpenAI
- San Francisco Bay Area
- https://scholar.google.com/citations?user=KvAyakQAAAAJ
Stars
Fast lexical search implementing BM25 in Python using Numpy, Numba and Scipy
Perplexica is an AI-powered answering engine. It is an Open source alternative to Perplexity AI
Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai
build your own vector database -- the littlest hnsw
Command line tool and async library to perform basic file operations on local paths, Google Cloud Storage paths and Azure Blob Storage paths.
π°οΈ An approximate nearest-neighbor search library for Python and Java with a focus on ease of use, simplicity, and deployability.
utilities for decoding deep representations (like sentence embeddings) back to text
Semantic cache for LLMs. Fully integrated with LangChain and llama_index.
JVector: the most advanced embedded vector search engine
A minimal implementation of diffusion models for text generation
The implementation of "TabR: Unlocking the Power of Retrieval-Augmented Tabular Deep Learning"
Image captioning from scratch (or pre-trained vision/language models) using transformers
π efficient approximate nearest neighbor search algorithm collections library written in Rust π¦ .
Hierarchical Navigable Small World (HNSW) algorithm for vector similarity search in PostgreSQL
Fast Open-Source Search & Clustering engine Γ for Vectors & Arbitrary Objects Γ in C++, C, Python, JavaScript, Rust, Java, Objective-C, Swift, C#, GoLang, and Wolfram π
800,000 step-level correctness labels on LLM solutions to MATH problems
Official PyTorch implementation of ODISE: Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models [CVPR 2023 Highlight]
hnswlib-wasm attempts to create a browser friendly version of hnswlib
hnswlib-node provides Node.js bindings for Hnswlib
A joint community effort to create one central leaderboard for LLMs.
ImageBind One Embedding Space to Bind Them All
Making large AI models cheaper, faster and more accessible
Universal LLM Deployment Engine with ML Compilation