An advertisement system based on Java spring cloud microservices and C++ FAISS embedding search
-
Updated
May 22, 2022 - Java
An advertisement system based on Java spring cloud microservices and C++ FAISS embedding search
Final project for the master's degree in Computer Science course "Cloud Computing" at the University of Rome "La Sapienza" (A.Y. 2022-2023)
I have implemented the common operations in NLP domain (实现NLP中各种常规操作,如分词、句法、命名实体识别、语义话题模型、爬虫、ElasticSearch和Faiss向量检索,huggingface-transformers完成各种任务,2023)
llama-2 based chat system that helps people get answer from their own pdf with the help of langchain. • Developed the system in local system using llama.cpp, langchain, FAISS vector db, Huggingface embeddings.
Application built with Langchain and Streamlit Using OpenAI and Google Palm in which user can query questions related upto 3 new articles including extractive question answering and summarization and also user can add a youtube video link and on basis of that user will get the desired video summarization.
Simple and Efficient DiskANN implementation
Facebook's Faiss CPU example with Dockerfile ready and tested for Deepnote so you don't have to try and fail like I did 😎
Connexion is a social media platform in its infancy. Users are matched based on personality type and common interests/hobbies.
Welcome to BluBasilico-ai, an intelligent chatbot designed to aid you in all your culinary adventures. With a vast database of over 6000 recipes, BluBasilico-ai helps you decide what to cook based on your current pantry or fridge ingredients. You can also create new recipes with guidance from our AI.
python-fastapi-vector-search
a real-time search system that handles cryptocurrency data with the CoinCap API
Flask-based web application designed for similarity searches on news articles, which can be generalized to any text corpus. Input a paragraph or url and returns the most similar news articles from the database.
Python full-stack application that leverages technologies such as Python, PyPDF2, Langchain, Firebase, Lottie, Faiss, Hugginface embedding models, and Streamlit to facilitate multi-PDF analysis through natural language processing, providing users with a seamless and intuitive experience for processing PDFs and obtaining content-related insights
Add a description, image, and links to the faiss topic page so that developers can more easily learn about it.
To associate your repository with the faiss topic, visit your repo's landing page and select "manage topics."