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.

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.

RAG with langchain using Amazon Bedrock and Amazon OpenSearch

DadmaTools is a Persian NLP tools developed by Dadmatech Co.

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.

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

Upload personal docs and Chat with your PDF files with this GPT4-powered app. Built with LangChain, Pinecone Vector Database, deployed on Streamlit

Interactive chat application leveraging OpenAI's GPT-4 for real-time conversation simulations. Built with Flask, this project showcases streaming LLM responses in a user-friendly web interface.

A semantic search system for Airbnb listings in Stockholm, built with Superlinked and Qdrant. It leverages multi-attribute vector search and Retrieval-Augmented Generation (RAG) to enhance search accuracy, embedding different data types (e.g., price, description) with specialized models. Powered by FastAPI and Streamlit.

Vector Embedding Server in under 100 lines of code

AICUBE Embedding2Embedding - Unlock advanced embedding translation between distinct vector spaces with the AICUBE Embedding2Embedding. Seamlessly transform embeddings across various domains to enhance the flexibility and precision of your AI models, enabling smarter integrations.

Vector Index Benchmark for Embeddings

BestRAG: A library for hybrid RAG, combining dense, sparse, and late interaction methods for efficient document storage and search.

Improving Document Classification with Multi-Sense Embeddings Source Code (ECAI 2020)

Multilingual Semantic Search with Reranking on a prepared large vectorized dataset comprising 10 million Wikipedia documents. It supports dense retrieval, keyword search, and hybrid search.