Private chat with local GPT with document, images, video, etc. 100% private, Apache 2.0. Supports oLLaMa, Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://codellama.h2o.ai/
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Jun 30, 2024 - Python
Private chat with local GPT with document, images, video, etc. 100% private, Apache 2.0. Supports oLLaMa, Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://codellama.h2o.ai/
Code for Embeddings, VectorStore, SemanticSearch, and RAG using Azure OpenAI
Using Hugging Face Hub Embeddings with Langchain document loaders to do some query answering
Demonstrates various AI functionalities using Spring AI, including chat responses, JSON data handling, image creation and description, real-time data loading, text-to-speech, and vector store usage.
Using Langchain's ideas to build SpringBoot AI applications | 用langchain的思想,构建SpringBoot AI应用
Creating a chat application that can handle multiple files using LlamaIndex, OpenAI, and Streamlit involves several steps
An experimental Simple RAG configurable to use any LLM model and any Embeddings model, using Langchain, Flask-SocketIO, and Llamacpp, ChromaDB, transformers
📚 Local PDF-Integrated Chat Bot: Secure Conversations and Document Assistance with LLM-Powered Privacy
RAG Architecture for Modern Chatbots
Discover and converse with advanced AI models like Mistral, LLAMA2, and GPT-3.5 from leading sources like OLLAMA, Hugging Face, and OpenAI. Easily extract insights from PDFs, web pages, and YouTube videos with our intuitive interface. Unlock the power of knowledge with seamless chat interactions.
AIxplora is a open-source tool which let's you query all kind of files not limited to any length or format.
🧬🔍🗄️ Unlock the power of vector indexing and search in your Go applications with the HNSW algorithm for approximate nearest neighbor search, seamlessly embedded within your application.
Nodejs a REST API is designed to provide users with an interactive chat interface where they can ask questions and receive responses generated by an AI model. The application utilizes OpenAI embeddings and Langchain to process the user's input and generate relevant responses based on the context of the conversation.
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