> For the complete documentation index, see [llms.txt](https://docs-conversational-ai.syntphony.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs-conversational-ai.syntphony.com/_eva-3.4.1_1/using-eva/experience.md). # Experience eva's platform is based on a microservices architecture and every component has its Rest APIs. A basic understanding of this architecture helps understand the APIs and the content on the next chapters. The Broker is the orchestrator of the solution. It is responsible for receiving the information needed to execute a conversation. Its API is the one called when a channel receives a message from the user. Also, it is responsible for calling NLPs dynamically, depending on which NLP was selected by the user in the Cockpit, and for calling external services when these are registered in the Cockpit through the webhook fields. The main database, which is used by the Broker, is a MySQL database that contains all configuration made through the Cockpit as well as data generated through conversations by the Broker. Another important component is the Dialog Manager, where you can build and manage the virtual agent conversation flows. This module has its own NoSQL database, for a better performance of the virtual agent. All conversation flows created in the Cockpit are stored in this MongoDB. The extraction details explained in the following chapters will detail only the MySQL database, since it contains the conversation logs. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs-conversational-ai.syntphony.com/_eva-3.4.1_1/using-eva/experience.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.