Advanced escalabilidade de aplicativos Tools for Professionals

Discover cutting-edge escalabilidade de aplicativos tools built for intricate workflows. Perfect for experienced users and complex projects.

escalabilidade de aplicativos

  • Appomate develops custom-built web and mobile apps.
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    What is Appomate?
    Appomate helps entrepreneurs and enterprises create, launch, and scale high-quality web and mobile apps. With expertise in UI/UX design, marketing, and technology innovation, Appomate provides comprehensive solutions to accelerate growth. Their skilled team offers a full suite of product development services to transform your ideas into functional applications, fostering business communication and collaboration.
  • Deploy cloud applications securely and efficiently with Defang's AI-driven solutions.
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    What is Defang?
    Defang is an AI-enabled cloud deployment tool that allows developers to easily and securely deploy applications to their cloud of choice using a single command. It transforms any Docker Compose-compatible project into a live deployment instantly, provides AI-guided debugging, and supports any programming language or framework. Whether you use AWS, GCP, or DigitalOcean, Defang ensures your deployments are secure, scalable, and cost-efficient. The platform supports various environments like development, staging, and production, making it ideal for projects of any scale.
  • Graph_RAG enables RAG-powered knowledge graph creation, integrating document retrieval, entity/relation extraction, and graph database queries for precise answers.
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    What is Graph_RAG?
    Graph_RAG is a Python-based framework designed to build and query knowledge graphs for retrieval-augmented generation (RAG). It supports ingestion of unstructured documents, automated extraction of entities and relationships using LLMs or NLP tools, and storage in graph databases such as Neo4j. With Graph_RAG, developers can construct connected knowledge graphs, execute semantic graph queries to identify relevant nodes and paths, and feed the retrieved context into LLM prompts. The framework provides modular pipelines, configurable components, and integration examples to facilitate end-to-end RAG applications, improving answer accuracy and interpretability through structured knowledge representation.
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