#Embedding-vectors
Showing 14 of 14 repositories tagged #embedding-vectors, ranked by stars
Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
High-performance vector similarity library in Rust with Python bindings: Spearman, Kendall, distance correlation, Jensen-Shannon, Hoeffding's D, and bootstrapped confidence intervals
Program that lets you ask questions about your documents, audio, and video files.
RAG with langchain using Amazon Bedrock and Amazon OpenSearch
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
A Python CLI to test, benchmark, and find the best RAG chunking strategy for your Markdown documents.
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
The highest-scoring AI memory system ever benchmarked that isn't reliant on LLM reranking. And it's free & burns less tokens.
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.
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.
🛡️ Automate web app pentesting with AI to find real exploits before attackers do, enhancing your app’s security proactively.
An end-to-end RAG chatbot using FastAPI, LangChain, ChromaDB & Streamlit with Multi-LLM support to answer questions from uploaded PDFs.