11 مستودعات
Code-centric examples for embedding generative models into existing software applications.
Distinguishing note: Focuses on the integration aspect of generative AI, distinct from general AI architecture.
Explore 11 awesome GitHub repositories matching artificial intelligence & ml · Generative AI Integration Patterns. Refine with filters or upvote what's useful.
This repository serves as a comprehensive library of architectural blueprints and code examples for integrating large language models into software applications. It functions as a developer learning resource, providing structured tutorials and implementation patterns that demonstrate how to build intelligent features using advanced prompting and data processing techniques. The collection distinguishes itself by focusing on complex reasoning and data-grounding workflows. It provides practical guidance on implementing retrieval-augmented generation pipelines, which connect language models to pr
Provides a curated collection of code examples for integrating large language models into applications.
This project is a Kubernetes microservices reference architecture implemented as a functional cloud-native e-commerce demo. It serves as a distributed systems testbed designed to demonstrate cloud-native deployment patterns, orchestration, and the interaction of independent services. The system showcases a polyglot service implementation using high-performance communication via gRPC and REST. It integrates generative AI for image analysis and product recommendations, and implements a service mesh to manage traffic, security, and observability between services. The application covers e-commer
Demonstrates the integration of generative AI for image analysis and product recommendations within a microservices architecture.
The Gemini Cookbook is a comprehensive collection of implementation patterns, code samples, and development guides designed for building applications with Google Gemini models. It serves as a central resource for developers to integrate multimodal generative artificial intelligence into their software, providing the necessary frameworks to manage model interactions, stateful workflows, and structured data extraction. The repository distinguishes itself by offering specialized toolkits for autonomous agent orchestration, enabling the construction of agents that can execute code, browse the web
Provides a collection of implementation patterns and code samples for integrating generative AI models.
Outlines is a library designed to ensure machine-readable output from generative models by applying programmatic constraints during the token sampling process. It functions as a toolkit for forcing large language models to generate text that strictly adheres to JSON schemas, regular expressions, and formal grammars, enabling the integration of model responses into existing software systems. The library distinguishes itself by integrating formal language rules directly into the sampling loop. It achieves this by converting regular expressions into deterministic finite automata and utilizing lo
Provides a library for building reliable applications by validating and enforcing schema compliance in model responses.
This repository provides curated learning paths, structured courseware, and technical materials for mastering Go programming, container orchestration, and software architecture. It serves as a comprehensive educational resource for systems programming, focusing on language mechanics, memory safety, and high-performance backend design. The project distinguishes itself through a multi-modal instructional design that combines instructor-led workshops, project-based curricula, and competency-based certifications. It offers specialized guidance on building production-grade AI infrastructure, inclu
Provides instruction and patterns for integrating generative AI, including RAG pipelines and tool-based scheduling.
Spring AI is an application framework for Java that provides a portable, fluent API for integrating AI models, tools, and vector stores into applications. It wraps multiple AI providers behind a common interface, allowing developers to switch between chat, embedding, image, and speech models without changing application code. The framework includes a chainable chat client API similar to WebClient or RestClient, supports both synchronous and streaming interactions, and offers structured output conversion that transforms unstructured AI responses into strongly-typed Java objects. The framework
Wraps recurring patterns like memory, tool-calling, and RAG into reusable advisors that transform data flow.
Introduces AI agents, RAG, and other patterns for deploying large models in production.
One Small Step is an educational resource that explains core AI and large language model concepts through short, accessible articles designed to be read in under five minutes. It covers the structure and function of key LLM components like attention mechanisms and tokenization, as well as foundational machine learning mathematics such as matrix rank and overfitting. The project also serves as a guide to the GGUF file format, which packages all model parameters and metadata into a single compact binary file for cross-platform deployment without external dependencies. It explains how this forma
Explains common AI application patterns like retrieval-augmented generation and AI agents.
AliSQL is a fork of MySQL by Alibaba that extends the relational database management system with enhancements for high performance, scalability, and enterprise-grade availability. It retains the core MySQL identity as a SQL-based database for storing, organizing, and retrieving structured data, while adding optimizations for large-scale transactional and analytical workloads. The project differentiates itself through a set of Alibaba-specific improvements, including a columnar engine for accelerating analytical queries directly on MySQL tables, and a distributed, shared-nothing NDB Cluster en
Integrates generative AI capabilities for data analysis and processing directly within the database.
RavenDB is a multi-model NoSQL document database designed for high-performance, ACID-compliant data storage. It persists structured information as schema-flexible JSON documents and utilizes a unit-of-work session pattern to track entity changes and batch modifications into atomic transactions. The platform is built on a distributed architecture that supports horizontal scaling through sharding and ensures high availability via multi-node, master-to-master cluster replication. The database distinguishes itself through a self-optimizing query engine that automatically creates and maintains ind
Embeds intelligent AI agents and vector search capabilities directly into database workflows to automate analysis and content generation.
This project is an AI engineering cookbook and tutorial suite providing step-by-step patterns for building production-ready artificial intelligence systems. It serves as an implementation guide and framework for integrating large language models into software applications. The repository functions as a generative AI pattern library, offering curated code snippets and modular scripts to connect models to external data and tools. It provides a collection of practical examples and reusable implementation patterns designed to accelerate the development of AI features and prototypes. The codebase
Provides code-centric examples and implementation patterns for embedding generative models into software applications.