Search Results for "python augmented reality" - Page 3
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Showing 194 open source projects for "python augmented reality"
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DeepWiki Open
AI-Powered Wiki Generator for GitHub/Gitlab/Bitbucket Repositories
DeepWiki Open is an open-source, AI-powered wiki generator that automatically creates fully navigable, richly structured wiki documentation for GitHub, GitLab, or Bitbucket repositories by combining code analysis, vector embeddings, retrieval-augmented generation (RAG), and visualization tools. Users can enter a repository URL and the system will clone the project, build semantic embeddings of its codebase, extract architecture and relationships, generate human-readable documentation, and... -
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Step-Audio 2
Multi-modal large language model designed for audio understanding
Step-Audio2 is an advanced, end-to-end multimodal large language model designed for high-fidelity audio understanding and natural speech conversation: unlike many pipelines that separate speech recognition, processing, and synthesis, Step-Audio2 processes raw audio, reasons about semantic and paralinguistic content (like emotion, speaker characteristics, non-verbal cues), and can generate contextually appropriate responses — including potentially generating or transforming audio output. It... -
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LocalGPT
Chat with your documents on your local device using GPT models
LocalGPT is a private, on-premises document intelligence platform for questioning, summarizing, and analyzing files with locally hosted language models. Its data remains on the user’s machine, making it suitable for confidential or offline workflows. The retrieval system combines semantic similarity, keyword matching, late chunking, contextual enrichment, and sentence-level pruning. A smart router chooses between retrieval-augmented generation and direct model responses for each query. An... -
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PixelRAG
The beginning of scalable pixel-native search
PixelRAG is a visual retrieval-augmented generation system that searches documents by how they look, not only by the text they contain. It renders web pages, PDFs, and images into screenshot tiles, then performs retrieval over those visual representations. This approach preserves layout, tables, charts, diagrams, infographics, and other visual structure that traditional HTML or text parsing can miss. The project includes tools for rendering, chunking, embedding, indexing, and serving visual... -
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Code-Graph-RAG
The ultimate RAG for your monorepo
Code-Graph-RAG is an advanced retrieval-augmented generation system designed specifically for understanding and interacting with large, multi-language codebases by transforming them into structured knowledge graphs. It uses Tree-sitter to parse source code into abstract syntax trees, extracting relationships between functions, classes, and modules to build a graph-based representation of the entire codebase. This structured approach enables more accurate and context-aware querying compared... -
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OpenViking
Context database designed specifically for AI Agents
OpenViking is an open-source context database engineered for efficient indexing and retrieval of large amounts of unstructured or semi-structured context data used by AI applications. It’s primarily designed to serve as a high-performance, scalable backend for storing app context, embeddings, conversational histories, and other textual artifacts that need rapid lookup and semantic search, which makes it especially useful for systems like chatbots or memory-augmented agents. The project is... -
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RAG Anything
RAG-Anything: All-in-One RAG Framework
RAG-Anything is an open-source unified framework that extends the Retrieval-Augmented Generation (RAG) paradigm to fully multimodal document and knowledge retrieval, enabling systems to ingest, parse, represent, and query rich content that includes text, images, tables, formulas, and other structured or visual elements. Traditional RAG systems are typically limited to text and cannot effectively work across heterogeneous document layouts, but RAG-Anything addresses this by modeling... -
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JUDI.jl
Julia Devito inversion
...The focus of the package lies on seismic modeling as well as PDE-constrained optimization such as full-waveform inversion (FWI) and imaging (LS-RTM). Wave equations in JUDI are solved with Devito, a Python domain-specific language for automated finite-difference (FD) computations. JUDI's modeling operators can also be used as layers in (convolutional) neural networks to implement physics-augmented deep learning algorithms thanks to its implementation of ChainRules's rrule for the linear operators representing the discre wave equation. -
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Text Embeddings Inference
High-performance inference server for text embeddings models API layer
Text Embeddings Inference is a high-performance server designed to serve text embedding models efficiently in production environments. It focuses on delivering fast and scalable embedding generation by leveraging optimized inference techniques and modern hardware acceleration. It is built to support transformer-based embedding models, making it suitable for tasks such as semantic search, clustering, and retrieval-augmented systems. It provides an API interface that allows developers to... -
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SimpleMem
SimpleMem: Efficient Lifelong Memory for LLM Agents
SimpleMem is a lightweight memory-augmented model framework that helps developers build AI applications that retain long-term context and recall relevant information without overloading model context windows. It provides easy-to-use APIs for storing structured memory entries, querying those memories using semantic search, and retrieving context to augment prompt inputs for downstream processing. Unlike monolithic systems where memory management is ad-hoc, SimpleMem formalizes a memory... -
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Hunyuan3D 2.0
High-Resolution 3D Assets Generation with Large Scale Diffusion Models
The Hunyuan3D-2 model, developed by Tencent, is designed for generating high-resolution 3D assets using large-scale diffusion models. This model offers advanced capabilities for creating detailed 3D models, including texture enhancements, multi-view shape generation, and rapid inference for real-time applications. It is particularly useful for industries requiring high-quality 3D content, such as gaming, film, and virtual reality. Hunyuan3D-2 supports various enhancements and is available... -
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LlamaIndex
Central interface to connect your LLM's with external data
LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. LlamaIndex is a simple, flexible interface between your external data and LLMs. It provides the following tools in an easy-to-use fashion. Provides indices over your unstructured and structured data for use with LLM's. These indices help to abstract away common boilerplate and pain points for in-context learning. Dealing with prompt limitations (e.g. 4096 tokens for Davinci) when... -
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model2Vec
Fast State-of-the-Art Static Embeddings
model2vec is an innovative embedding framework that converts large sentence transformer models into compact, high-speed static embedding models while preserving much of their semantic performance. The project focuses on dramatically reducing the computational cost of generating embeddings, achieving significant improvements in speed and model size without requiring large datasets for retraining. By using a distillation-based approach, it can produce lightweight models that run efficiently on... -
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BeeAI Framework
Build production-ready AI agents in both Python and Typescript
BeeAI Framework is an open-source, production-grade toolkit designed for building intelligent AI agents and complex multi-agent systems that can reason, act, and collaborate to solve real-world problems at scale. It goes beyond simple prompt-based interactions by introducing rule-based governance and constraint enforcement, enabling developers to create agents with predictable and controllable behavior while still preserving advanced reasoning capabilities. The framework supports both Python... -
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Watchdog
Python library and shell utilities to monitor filesystem events
...Tricks are actually event handlers that subclass watchdog.tricks.Trick and are written by plugin authors. Trick classes are augmented with a few additional features that regular event handlers don't need. The directory containing the tricks.yaml file will be monitored. Each trick class is initialized with its corresponding keys in the tricks.yaml file as arguments and events are fed to an instance of this class as they arrive. -
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Depth Anything 3
Recovering the Visual Space from Any Views
Depth Anything 3 is a research-driven project that brings accurate and dense depth estimation to any input image or video, enabling foundational understanding of 3D structure from 2D visual content. Designed to work across diverse scenes, lighting conditions, and image types, it uses advanced neural networks trained on large, heterogeneous datasets, producing depth maps that reveal scene depth relationships and object surfaces with strong fidelity. The model can be applied to photography,... -
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NeMo Retriever Library
Document content and metadata extraction microservice
NeMo Retriever Library is a scalable microservice framework designed for extracting, structuring, and enriching content from documents to support downstream generative AI applications. It processes various document types by splitting them into components such as text, tables, charts, and images, and then applies OCR and contextual analysis to convert them into structured data formats. The system is built on NVIDIA NIM microservices, enabling high-performance parallel processing and efficient... -
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VideoRAG
"VideoRAG: Chat with Your Videos
VideoRAG is a retrieval-augmented generation (RAG) framework tailored for video content that enables AI systems to answer questions, summarize, and reason over long videos by combining visual embeddings with contextual search. The system works by first breaking video into clips, extracting visual and audio-textual features, and indexing them into embeddings, then using an LLM with a retriever to pull relevant segments on demand. When a user query is received, VideoRAG locates semantically... -
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MiniRAG
Making RAG Simpler with Small and Open-Sourced Language Models
MiniRAG is a lightweight retrieval-augmented generation tool designed to bring the benefits of RAG workflows to smaller datasets, edge environments, and constrained compute settings by simplifying embedding, indexing, and retrieval. It extracts text from documents, codes, or other structured inputs and converts them into embeddings using efficient models, then stores these vectors for fast nearest-neighbor search without requiring huge databases or separate vector servers. When a query is... -
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Engram
A New Axis of Sparsity for Large Language Models
Engram is a high-performance embedding and similarity search library focused on making retrieval-augmented workflows efficient, scalable, and easy to adopt by developers building search, recommendation, or semantic matching systems. It provides utilities to generate embeddings from text or other structured data, index them using efficient approximate nearest neighbor algorithms, and perform real-time similarity queries even on large corpora. Engineered with speed and memory efficiency in... -
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WorldGen
Generate Any 3D Scene in Seconds
WorldGen is an AI model and library that can generate full 3D scenes in a matter of seconds from either text prompts or reference images. It is designed to create interactive environments suitable for games, simulations, robotics research, and virtual reality, rather than just static 3D assets. The core idea is that you describe a world in natural language and WorldGen produces a navigable 3D scene that you can freely explore in 360 degrees, with loop closure so that the space remains... -
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LMCache
Supercharge Your LLM with the Fastest KV Cache Layer
LMCache is an extension layer for LLM serving engines that accelerates inference, especially with long contexts, by storing and reusing key-value (KV) attention caches across requests. Instead of rebuilding KV states for repeated or shared text segments, LMCache persists and retrieves them from multiple tiers—GPU memory, CPU DRAM, and local disk—then injects them into subsequent requests to reduce TTFT and increase throughput. Its design supports reuse beyond strict prefix matching and... -
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Ciphey
Decrypt encryptions without knowing the key or cipher
Fully automated decryption/decoding/cracking tool using natural language processing & artificial intelligence, along with some common sense. You don't know, you just know it's possibly encrypted. Ciphey will figure it out for you. Ciphey can solve most things in 3 seconds or less. Ciphey aims to be a tool to automate a lot of decryptions & decodings such as multiple base encodings, classical ciphers, hashes or more advanced cryptography. If you don't know much about cryptography, or you want... -
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Vibe Coding CN
The ultimate workstation for turning ideas into reality
vibe-coding-cn is an open-source guide and resource collection for AI-assisted software development. It presents a workflow for turning ideas into working projects through structured AI pair programming. The methodology emphasizes planning, modular architecture, clear goals, strong context, and human review instead of uncontrolled AI generation. It covers project conception, technology selection, implementation, debugging, testing, and expansion. The repository also organizes prompts,... -
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JamAI Base
The collaborative spreadsheet for AI
JamAI Base is an open-source backend platform designed to simplify the development of retrieval-augmented generation systems and AI-driven applications. The platform integrates both a relational database and a vector database into a single embedded architecture, allowing developers to store structured data alongside semantic embeddings. It includes built-in orchestration for large language models, vector search, and reranking pipelines so that AI applications can retrieve relevant...
