#Vector-embeddings
Showing 12 of 12 repositories tagged #vector-embeddings, ranked by stars
Enterprise-grade (40m+ LOC) codebase intelligence, zero-setup, local & private Plugin/Skill/Extension or MCP: hybrid semantic search, polyglot dependency graphs, symbol-level impact analysis & call-flow, interactive HTML viewer, cross-project & branch-aware search, DB/API/infra knowledge. 61% less tokens, 84% fewer calls, 37x faster. Cloud in beta.
local-first semantic code search engine
Single-file memory layer for AI agents, sub mili-second RAG on Apple Silicon. Metal Optimized On-Device. No Server. No API. One File. Pure Swift
🔎 SimilaritySearchKit is a Swift package providing on-device text embeddings and semantic search functionality for iOS and macOS applications.
Search and organise images and videos offline with on-device AI.
Browser-local AI code Q&A engine. Chat with your codebase, ensure privacy, and optimize LLM collaboration.
Memory for AI that works like yours—local, instant, persistent. 13x faster than Pinecone, 5x leaner than RAG. Finds what RAG misses. Zero cloud, zero cost.
Browse the top 10,000 packages on PyPI with the help of vector embeddings
Automatos AI: Open-source platform for advanced context engineering and multi-agent orchestration in enterprise automation. Built on frontier research in RAG, vector embeddings, cognitive tools, emergent symbols, and neural field theory—powered by FastAPI, Next.js, and PostgreSQL.
.NET library for local embedding generation using ONNX and Microsoft.Extensions.AI
rudradb-opin-examples is for example implementations of the pip install rudradb-opin
Match celebrity users with their respective tweets by making use of Semantic Textual Similarity on over 900+ celebrity users' 2.5 million+ crawled tweets utilizing SBERT, streamlit, tweepy and FastAPI