Pinned Repositories
.github
AgenticFleet
An Adaptative Multi-Agent System
APICenter-Portal-Starter
Reference implementation of API Center portal for a web-based API discovery and consumption experience
FleetUI
FlowiseAILandingPage
GraphFleet
An open-source integration of GraphRAG for Agentic System with NoCode
openai-node
The official Node.js / Typescript library for the OpenAI API
QredenceOS
Trueblock
Our project enhances Trulens analytics through two key initiatives: developing an interactive visual node for integration in Jupyter notebooks, and creating a comprehensive RAG framework for Trulens documentation. These efforts aim to simplify and enrich the user experience with Trulens, making advanced data analysis more accessible and intuitive.
visualblocks
Visual Blocks for ML is a Google visual programming framework that lets you create ML pipelines in a no-code graph editor. You – and your users – can quickly prototype workflows by connecting drag-and-drop ML components, including models, user inputs, processors, and visualizations.
Qredence's Repositories
Qredence/GraphFleet
An open-source integration of GraphRAG for Agentic System with NoCode
Qredence/Trueblock
Our project enhances Trulens analytics through two key initiatives: developing an interactive visual node for integration in Jupyter notebooks, and creating a comprehensive RAG framework for Trulens documentation. These efforts aim to simplify and enrich the user experience with Trulens, making advanced data analysis more accessible and intuitive.
Qredence/.github
Qredence/AgenticFleet
An Adaptative Multi-Agent System
Qredence/APICenter-Portal-Starter
Reference implementation of API Center portal for a web-based API discovery and consumption experience
Qredence/FleetUI
Qredence/FlowiseAILandingPage
Qredence/openai-node
The official Node.js / Typescript library for the OpenAI API
Qredence/QredenceOS
Qredence/visualblocks
Visual Blocks for ML is a Google visual programming framework that lets you create ML pipelines in a no-code graph editor. You – and your users – can quickly prototype workflows by connecting drag-and-drop ML components, including models, user inputs, processors, and visualizations.
Qredence/documentation
Documentation for Qredence AI Intelligent solutions
Qredence/python-sample-vscode-flask-tutorial
Sample code for the Flask tutorial in the VS Code documentation