Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search

RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

Chat and Ask on your own data. Accelerator to quickly upload your own enterprise data and use OpenAI services to chat to that uploaded data and ask questions

cuVS - a library for vector search and clustering on the GPU

Harnessing the Memory Power of the Camelids

Semantic product search on Databricks

🗲 A high-performance on-disk dictionary.

⚡ Faster vector search with PDX: A vertical data layout for vectors

Sample Spring AI Application with several use cases

just testing langchain with llama cpp documents embeddings

The official Elasticsearch .NET Vector Store Connector for Microsoft Semantic Kernel.

Multi-User Chatbot with Langchain and Pinecone in Next.JS

A Question Generation Application leveraging RAG and Weaviate vector store to be able to retrieve relative contexts and generate a more useful answer-aware questions

The AI Assistant uses OpenAI's GPT models and Langchain for agent management and memory handling. With a Streamlit interface, it offers interactive responses and supports efficient document search with FAISS. Users can upload and search pdf, docx, and txt files, making it a versatile tool for answering questions and retrieving content.

AgriGenius: AI-Powered Agriculture Chatbot is a Python web application designed to empower farmers with information accessibility. AgriGenius leverages a Retrieval-Augmented Generation model to address farmer's agricultural queries with precise answers.

A command-line tool that ingests documents and generates instant answers to your questions about those documents using ChatGPT, giving you the Sheldon Cooper you never had at your fingertips.

A simple way to convert and manage files in vector storage.

In this we implements a Retrieval-Augmented Generation (RAG) based conversational AI agent designed for intelligent knowledge extraction from PDF documents. Leveraging LangChain and Google’s Gemini LLM

AI Data Assistant empowers data professionals by speaking both human language and machine langauges (SQL/Python)

This is a toy Web application written in Flask, featuring a Medical Assistant Chatbot powered by Large Language Models (LLMs) and Retrieval Augmented Generation (RAG).

minimem is a minimal implementation of in-memory vector-store using only numpy

CrateDB provider for LangChain.

In Development

Budget Buddy is a finance chatbot built using Chainlit and the LLaMA language model. It analyzes PDF documents, such as bank statements and budget reports, to provide personalized financial advice and insights. The chatbot is integrated with Hugging Face for model management, offering an interactive way to manage personal finances.

Q & A with multiple pdf App is a Python application that allows you to ask questions about the PDFs you upload using natural language model to generate accurate answers to your queries.

LLM powered ChatAI system. Added support for HF Embeddings and Models too

A powerful Retrieval Augmented Generation (RAG) application built with NVIDIA AI endpoints and Streamlit. This solution enables intelligent document analysis and question-answering using state-of-the-art language models, featuring multi-PDF processing, FAISS vector store integration, and advanced prompt engineering.

Orchestrating the interaction between users and Large Language Models