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ramanujammv1988
On-device AI SDK for Flutter — LLM inference, vision, STT, TTS, image generation, embeddings, RAG, and function calling. Metal GPU on iOS/macOS.
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Results: 51
ramanujammv1988
On-device AI SDK for Flutter — LLM inference, vision, STT, TTS, image generation, embeddings, RAG, and function calling. Metal GPU on iOS/macOS.
pykeen
🤖 A Python library for learning and evaluating knowledge graph embeddings
awslabs
High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings.
Dicklesworthstone
A FastAPI service for semantic text search using precomputed embeddings and advanced similarity measures, with built-in support for various file types through textract.
Anush008
Rust library for generating vector embeddings, reranking locally!
curiosity-ai
🚀 Catalyst is a C# Natural Language Processing library built for speed. Inspired by spaCy's design, it brings pre-trained models, out-of-the box support for training wo…
apocas
RESTai is an AIaaS (AI as a Service) open-source platform. Supports many public and local LLM suported by Ollama/vLLM/etc. Precise embeddings usage, tuning, analytics et…
SeanLee97
Train and Infer Powerful Sentence Embeddings with AnglE | 🔥 SOTA on STS and MTEB Leaderboard
kelindar
Go library for embedded vector search and semantic embeddings using llama.cpp
hiDaDeng
cntext is a Python library for social science text analysis, offering word frequency, sentiment, word embeddings, and semantic projection to measure constructs like atti…
llm-tools
A NodeJS RAG framework to easily work with LLMs and embeddings
Muennighoff
SGPT: GPT Sentence Embeddings for Semantic Search
NeumTry
Neum AI is a best-in-class framework to manage the creation and synchronization of vector embeddings at large scale.
swarmauri
Modular Python SDK and monorepo for AI agents, LLM integrations, tools, parsers, embeddings, vector stores, and extensible application workflows.
rom1504
Easily compute clip embeddings and build a clip retrieval system with them
pguso
Demystify RAG by building it from scratch. Local LLMs, no black boxes - real understanding of embeddings, vector search, retrieval, and context-augmented generation.