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.

Gurubase lets you add an "Ask AI" button to your technical docs, turning your content into an AI assistant. It uses web pages, PDFs, YouTube videos, and GitHub repos as sources to generate instant, accurate answers with references. Deploy it via Slack, Discord, GitHub or a web widget.

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

The Fast Vector Similarity Library is designed to provide efficient computation of various similarity measures between vectors.

Plugin that lets you ask questions about your documents including 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.

⚡ 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.

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.

RAG with langchain using Amazon Bedrock and Amazon OpenSearch

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.

DadmaTools is a Persian NLP tools developed by Dadmatech Co.

AI Native database for embedding vectors

langchain-chat is an AI-driven Q&A system that leverages OpenAI's GPT-4 model and FAISS for efficient document indexing. It loads and splits documents from websites or PDFs, remembers conversations, and provides accurate, context-aware answers based on the indexed data. Easy to set up and extend.

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

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

Sentiment analyzer for your tweets.

Ruby wrapper for the Weaviate vector search database API

Ruby wrapper for the Qdrant vector search database API

LLM Chatbot w/ Retrieval Augmented Generation using Llamaindex. It demonstrates how to impl. chunking, indexing, and source citation.