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awslabs
High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings.
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Results: 35
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.
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 etβ¦
SeanLee97
Train and Infer Powerful Sentence Embeddings with AnglE | π₯ SOTA on STS and MTEB Leaderboard
kelindar
Go library for embedded vector search and semantic embeddings using llama.cpp
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.
ArcadeData
ArcadeDB Multi-Model Database, one DBMS that supports SQL, Cypher, Gremlin, HTTP/JSON, MongoDB and Redis. ArcadeDB is a conceptual fork of OrientDB, the first Multi-Modeβ¦
ClawBio
Semantic Similarity Index for disease research literature using PubMedBERT embeddings
superlinked
Deprecated historical repo. Superlinked now develops SIE, a self-hosted inference engine for embeddings, reranking, OCR, extraction, and document processing.
ZachNagengast
π SimilaritySearchKit is a Swift package providing on-device text embeddings and semantic search functionality for iOS and macOS applications.
AsyrafHussin
Laravel AI SDK for building AI-powered features. Use when creating agents, generating images or audio, working with embeddings, vector search, or testing AI features. Trβ¦
Aavache
A Web Crawler based on LLMs implemented with Ray and Huggingface. The embeddings are saved into a vector database for fast clustering and retrieval. Use it for your RAG.
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