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11 repositorios

Awesome GitHub RepositoriesGenerative AI Integration Patterns

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.

Awesome Generative AI Integration Patterns GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • anthropics/claude-cookbooksAvatar de anthropics

    anthropics/claude-cookbooks

    45,835Ver en GitHub↗

    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.

    Jupyter Notebook
    Ver en GitHub↗45,835
  • googlecloudplatform/microservices-demoAvatar de GoogleCloudPlatform

    GoogleCloudPlatform/microservices-demo

    20,492Ver en GitHub↗

    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.

    Go
    Ver en GitHub↗20,492
  • google-gemini/cookbookAvatar de google-gemini

    google-gemini/cookbook

    17,418Ver en GitHub↗

    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.

    Jupyter Notebookgeminigemini-api
    Ver en GitHub↗17,418
  • dottxt-ai/outlinesAvatar de dottxt-ai

    dottxt-ai/outlines

    13,446Ver en GitHub↗

    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.

    Pythoncfggenerative-aijson
    Ver en GitHub↗13,446
  • ardanlabs/gotrainingAvatar de ardanlabs

    ardanlabs/gotraining

    12,212Ver en GitHub↗

    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.

    Go
    Ver en GitHub↗12,212
  • spring-projects/spring-aiAvatar de spring-projects

    spring-projects/spring-ai

    9,001Ver en GitHub↗

    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.

    Javaartificial-intelligencejavaspring-ai
    Ver en GitHub↗9,001
  • infrasys-ai/aiinfraAvatar de Infrasys-AI

    Infrasys-AI/AIInfra

    7,414Ver en GitHub↗

    Introduces AI agents, RAG, and other patterns for deploying large models in production.

    Jupyter Notebookaiinfraaisystem
    Ver en GitHub↗7,414
  • karminski/one-small-stepAvatar de karminski

    karminski/one-small-step

    6,699Ver en GitHub↗

    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.

    Ver en GitHub↗6,699
  • alibaba/alisqlAvatar de alibaba

    alibaba/AliSQL

    5,706Ver en GitHub↗

    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.

    C++alisqldatabaseduckdb
    Ver en GitHub↗5,706
  • ravendb/ravendbAvatar de ravendb

    ravendb/ravendb

    3,961Ver en GitHub↗

    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.

    C#csharpdatabasedocument-database
    Ver en GitHub↗3,961
  • daveebbelaar/ai-cookbookAvatar de daveebbelaar

    daveebbelaar/ai-cookbook

    3,772Ver en GitHub↗

    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.

    Pythonagentsaianthropic
    Ver en GitHub↗3,772
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  2. Artificial Intelligence & ML
  3. Generative AI Integration Patterns

Explorar subetiquetas

  • Advisor FrameworksWraps recurring patterns like memory, tool-calling, and RAG into reusable interceptors that transform data sent to and from language models. **Distinct from Generative AI Integration Patterns:** Distinct from Generative AI Integration Patterns: focuses on encapsulating patterns into reusable advisors, not general integration examples.
  • Application Pattern Explanations1 sub-etiquetaEducational content describing common architectural patterns for building AI applications. **Distinct from Generative AI Integration Patterns:** Distinct from Generative AI Integration Patterns: focuses on explaining patterns like RAG and agents rather than providing code-centric integration examples.
  • Reusable Advisor PatternsWraps recurring generative AI patterns like memory, tool-calling, and RAG into reusable interceptors. **Distinct from Generative AI Integration Patterns:** Distinct from Generative AI Integration Patterns: focuses on encapsulating patterns into reusable advisors, not just integration examples.