embedding-vectors

Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.

A FastAPI service for semantic text search using precomputed embeddings and advanced similarity measures, with built-in support for various file types through textract.

High-performance vector similarity library in Rust with Python bindings: Spearman, Kendall, distance correlation, Jensen-Shannon, Hoeffding's D, and bootstrapped confidence intervals

Score documents using embedding-vectors dot-product or cosine-similarity with ES Lucene engine

Program that lets you ask questions about your documents, audio, and video files.

Extract knowledge from all information sources using gpt and other language models. Index and make Q&A session with information sources.

Vector Storage is a vector database that enables semantic similarity searches on text documents in the browser's local storage. It uses OpenAI embeddings to convert documents into vectors and allows searching for similar documents based on cosine similarity.

A client side vector search library that can embed, store, search, and cache vectors. Works on the browser and node. It outperforms OpenAI's text-embedding-ada-002 and is way faster than Pinecone and other VectorDBs.

RAG with langchain using Amazon Bedrock and Amazon OpenSearch

⚡ GUI for editing LLM vector embeddings. No more blind chunking. Upload content in any file extension, join and split chunks, edit metadata and embedding tokens + remove stop-words and punctuation with one click, add images, and download in .veml to share it with your team.

DadmaTools is a Persian NLP tools developed by Dadmatech Co.

The Next-Gen Database for AI—an infrastructure designed for data and AI. As the MySQL of the AI era.

AI Native database for embedding vectors

[𝗜𝗖𝗠𝗟 𝟮𝟬𝟮𝟲] Dispersion loss counteracts embedding condensation and improves generalization in small language models

A Python CLI to test, benchmark, and find the best RAG chunking strategy for your Markdown documents.

A monolingual and cross-lingual meta-embedding generation and evaluation framework

Vectory provides a collection of tools to track and compare embedding versions.

Vector Index Benchmark for Embeddings (VIBE) is an extensible benchmark for approximate nearest neighbor search methods, or vector indexes, using modern embedding datasets.

Sentiment analyzer for your tweets.

Ruby wrapper for the Qdrant vector search database API