There are 5 repositories under vectorstore topic.
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://gpt-docs.h2o.ai/
An app to interact privately with your documents using the power of GPT, 100% privately, no data leaks
AIxplora is a open-source tool which let's you query all kind of files not limited to any length or format.
Using Langchain's ideas to build SpringBoot AI applications | 用langchain的**,构建SpringBoot AI应用
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.
Chat with your docs in PDF/PPTX/DOCX format, using LangChain and GPT4/ChatGPT from both Azure OpenAI Service and OpenAI
Using LlamaIndex, Redis, and OpenAI to chat with PDF documents. Supplementary material for blog post on Microsoft Developer Blog
An LLM GUI application; enables you to interact with your files, offering dynamic parameters that can modify response behavior during runtime.
📚 Local PDF-Integrated Chat Bot: Secure Conversations and Document Assistance with LLM-Powered Privacy
🧠 Quivr Chatbot extension - Instantly access Quivr, dump your files and chat with them using your Generative AI Second Brain using LLMs ( GPT 3.5/4, Private, Anthropic, VertexAI ) & Embeddings 🧠
Explore Multiple Vector Databases and chat with documents on Multiple LLM models, private LLM models
Chatbot for Indian Law using Llama-7B-chat using Langchain integration and Streamlit UI.
Ask question over your Notion Database! A naive Retrieval-Augmented Generation (RAG) pipeline backed by Langchain and Streamlit
AIGC 知识库问答系统快速搭建,便于企业级定制化,支持文档上传,向量存储,聊天式问答。
Using Hugging Face Hub Embeddings with Langchain document loaders to do some query answering
Integrated LLM-based document and data Q&A with knowledge graph visualization
This project is a reference implementation of the Hierarchical Navigable Small World graph paper by Malkov & Yashunin (2018) as a companion to the AWS presentation by Ben Duncan (Startup Solution Architect) "What you need to know about Vector Databases. From use-cases to a deep dive on the technology."
使用LLM大模型、langchain、fastapi、agent等技术实现ai和用户聊天,并且支持本地向量库、api接口工具,支持http sse流式输出
A practical sample of RAG pattern applied to a real-world use case: make finding samples using Azure SQL easier and more efficient!
Create LLM powered bots and port them into Discord & Slack
Autodoc project, aimed to autonomously generating documentation for any code repository and store info about it in vectorstore to further interactions
🧬🔍🗄️ 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.
NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering
RAG Architecture for Modern Chatbots
Sample of implementing a simple in-memory vector store
A practical guide to getting started with local 🤖 Large Language Models using Python, Ollama, & Streamlit. Includes basic chatbot setup, RAG-enabled PDF querying, & vectorstore visualization. Ideal for experimenting with LLMs on your own machine—no cloud req. 🦙
About A simple RAG (Retrieval-Augmented Generation) app built with Streamlit. Upload PDFs, ask questions, and get context-aware answers using Qdrant and Hugging Face Transformers.
Vector databases experimentation for the Knowledge Graphs and sentenceBERT embedding
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.