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maintenabilité logicielle

  • A Python library to implement webhooks for Dialogflow agents, handling user intents, contexts, and rich responses.
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    What is Dialogflow Fulfillment Python Library?
    The Dialogflow Fulfillment Python Library is an open-source framework that handles HTTP requests from Dialogflow, maps intents to Python handler functions, manages session and output contexts, and builds structured responses including text, cards, suggestion chips, and custom payloads. It abstracts the JSON structure of Dialogflow’s webhook API into convenient Python classes and methods, accelerating the creation of conversational backends and reducing boilerplate code when integrating with databases, CRM systems, or external APIs.
    Dialogflow Fulfillment Python Library Core Features
    • Intent-to-function mapping
    • Session and output context management
    • Rich response builders (text, card, quick replies)
    • Custom payload support
    • Event triggering
    • Session entity type handling
  • A TypeScript and JSON Schema library enabling developers to define and validate AI agent tool interfaces type-safely
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    What is Xemantic AI Tool Schema?
    Xemantic AI Tool Schema is a set of JSON Schema and TypeScript type definitions designed to standardize the way AI agent tools are described, validated, and invoked. Developers can define tool metadata such as name, description, and parameters, then validate instances against the schema or use generated TypeScript interfaces during development. The schema supports parameter types, nested structures, default values, and version control, ensuring robust validation and compatibility. By following a consistent schema, AI Agents can discover and call tools reliably at runtime, improving maintainability and reducing integration errors. The package integrates seamlessly with Xemantic AI Agents and can be extended for custom use cases.
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