Search Results for "machine learning python"

turbovec is a Rust-based vector index with Python bindings for fast similarity search. It is built around TurboQuant, a quantization approach designed to reduce vector storage while preserving useful distance information. The project targets workloads where embedding search needs to be compact, efficient, and practical to integrate into Python applications. It avoids a separate training phase for the quantizer, which can simplify setup compared with systems that require codebook learning. ...

Elasticsearch is a distributed, RESTful search and analytics engine that lets you store, search and analyze with ease at scale. It lets you perform and combine many types of searches; it scales seamlessly, and offers answers incredibly fast with search results you can rank based on a variety of factors. Elasticsearch can be used for a wide variety of use cases, from maps and metrics to site search and workplace search, and with all data types.

FreeAskInternet is a self-hosted search and answer application that combines web results with large language models. A local SearXNG instance sends queries to multiple search engines and returns a broader collection of sources. The backend crawls selected pages, supplies their content to a configured model, and streams a synthesized response to the chat interface. It supports several hosted model services as well as custom local backends such as Ollama and llama.cpp. The web interface is...

ENAS in PyTorch is a PyTorch implementation of Efficient Neural Architecture Search (ENAS), a method that automates the design of neural network architectures through reinforcement learning and parameter sharing. The repository demonstrates how a controller network can explore a large search space and discover high-performing architectures while dramatically reducing the computational cost traditionally associated with neural architecture search. It is primarily intended as a research and...

ScenConnect shows scenarios as networks of situation and event tag sets, for fast comparisons. It links scenarios to tags, scores, and other metadata, creating situationals suitable for search, mining, machine learning, and planning.