ketangangal

docker

Docker best practices

graphrag

A modular graph-based Retrieval-Augmented Generation (RAG) system

LongRAG

Official repo for "LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs".

krakend-ce

KrakenD Community Edition: High-performance, stateless, declarative, API Gateway written in Go.

awesome-system-design-resources

Learn System Design concepts and prepare for interviews using free resources.

MM-REACT

Official repo for MM-REACT

awesome-generative-ai

A curated list of modern Generative Artificial Intelligence projects and services

lunary

The production toolkit for LLMs. Observability, prompt management and evaluations.

zero-to-lightning

zero-to-lightning

Flowise

Drag & drop UI to build your customized LLM flow

qdrant-client

Python client for Qdrant vector search engine

mteb

MTEB: Massive Text Embedding Benchmark

ceph

Ceph is a distributed object, block, and file storage platform

minio

The Object Store for AI Data Infrastructure

chainlit

Build Conversational AI in minutes ⚡️

mojo

The Mojo Programming Language

uv

An extremely fast Python package installer and resolver, written in Rust.

devika

Devika is an Agentic AI Software Engineer that can understand high-level human instructions, break them down into steps, research relevant information, and write code to achieve the given objective. Devika aims to be a competitive open-source alternative to Devin by Cognition AI.

dspy

DSPy: The framework for programming—not prompting—foundation models

Functions-Tools-and-Agents-with-LangChain

You’ll explore new advancements like ChatGPT’s function calling capability, and build a conversational agent using a new syntax called LangChain Expression Language (LCEL) for tasks like tagging, extraction, tool selection, and routing.

llm-applications

A comprehensive guide to building RAG-based LLM applications for production.

bao

Chat Bot with LLM and Fact Reference. RAG(Retrieval Augmented Generation) and LangChain backed

ray-llm

RayLLM - LLMs on Ray

amazon-sagemaker-examples

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

amazon-sagemaker-asynchronous-inference-computer-vision

sagemaker-pytorch-inference-toolkit

Toolkit for allowing inference and serving with PyTorch on SageMaker. Dockerfiles used for building SageMaker Pytorch Containers are at https://github.com/aws/deep-learning-containers.

s5cmd

Parallel S3 and local filesystem execution tool.

sagemaker-inference-toolkit

Serve machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.

gen-ai-sagemaker

deep-learning-containers

AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.