# Core Concepts

> Master the FastGPT system topology, entity hierarchy, and domain definitions.

Source: https://doc.fastgpt.io/en/guide/getting-started/concepts

FastGPT is built upon a clean domain model. Understanding how applications, knowledge assets, external extensions, and access boundaries interact helps you build resilient, maintainable enterprise AI systems.

## System Topology Overview

FastGPT organizes operational entities into configuration, runtime, and persistent storage layers:

| Layer         | Primary Responsibility                                  | Included Entities                                                                                          |
| ------------- | ------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- |
| Configuration | Business assets authored and governed by builders       | Application drafts, collections, skill packages, tool schemas, environment variables                       |
| Runtime       | Scheduling engine managing executions and transitions   | Conversation turns, execution snapshots, sandbox containers, MCP connections, worker queues                |
| Storage       | Infrastructure providing durability and search indexing | MongoDB operational store, vector databases, full-text indexes, S3 object storage, Redis caches and queues |

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## Application Entity Hierarchy

In FastGPT, an **Application** is the primary unit delivering a complete conversational or procedural experience to end users and external systems.

### Application Types

* **Chat Agent**: A conversational application configured around a single chat model, answering queries through system instructions, context history, and attached knowledge bases.
* **Chat Agent V2**: An advanced agent equipped with dynamic planning and tool invocation capabilities, using a ReAct loop to evaluate goals, select tools, and perform multi-step reasoning.
* **Workflow**: A deterministic execution pipeline orchestrated with nodes and directed edges on a visual canvas, governing data transformation, conditional branching, and batch execution.

### Lifecycle and Snapshots

* **Draft Version**: An active workspace configuration where builders edit and test changes. The platform supports automatic draft updates and explicit manual checkpoints.
* **Published Version**: A validated baseline frozen for production use. Publishing synchronizes the configuration to the team and updates the active application version across all publishing channels.
* **Publishing Channel**: An external ingress point exposing application functionality, including the Login-Free Window, embedded chat widgets, and OpenAPI keys. MCP services are configured separately from the "MCP Services" entry in the Studio sidebar.

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## Knowledge Asset System

A **Knowledge Base** is a structured repository of source material prepared through an automated ingestion pipeline, providing verifiable ground truth for Retrieval-Augmented Generation (RAG).

### Three-Tier Organization

1. **Knowledge Base**: The top-level administrative boundary defining global embedding models, text search settings, and collaborator access control.
2. **Collection**: The organizational unit within a Dataset, displayed in the interface as **Files (Collections)** and **Folders**. Files store ingested documents, web pages, or blank collections; Folders organize nested items.
3. **Data Chunk**: The granular, independently maintainable unit of information produced by segmentation. Each chunk links to one or more searchable indexes.

### Dual-Engine Indexing

FastGPT pairs semantic vector retrieval with keyword-based full-text indexing:

* **Embedding Index**: Encodes semantic features into dense vector representations, excelling at semantic similarity and synonymous expressions.
* **Full-Text Index**: Employs tokenization and BM25 ranking for exact keyword matches, ensuring high recall for proper nouns, serial numbers, and specialized codes.
* **Reranker**: Evaluates multi-channel candidates through a unified relevance scoring pass, delivering the most accurate contextual chunks to the model.

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## Tool and Extension Ecosystem

FastGPT interfaces with external platforms and custom code through modular extension standards:

* **Tool**: A callable operation with explicit input and output schemas. Related operations are organized under a **Tool Set** with shared configuration and connection credentials.
* **Workflow Tool**: A standalone workflow with declared inputs and outputs that can run independently or be mounted onto an Agent as a sub-routine.
* **Skill**: A versioned bundle containing instructions, templates, and script assets, loaded into execution sandboxes to supply specialized procedural guidance to agents.
* **MCP Tool**: An external tool exposed to FastGPT through the standardized Model Context Protocol.
* **MCP Service**: A published FastGPT gateway that presents internal applications and knowledge bases as callable tools to external clients.

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## Security and Permission Context

The system enforces strict access control across organizations and resources:

* **Team Management**: Establishes tenant boundaries. Team members are Owner, Administrator, or Common members.
* **Resource Collaborator Permissions (ACL)**: Controls granular access levels (Read permission, Write permission, Administrator) on individual applications and knowledge bases; for applications the read level is shown as "Chat only", and "View chat logs" can be granted separately.
* **Platform Administration**: Managed by platform root administrators, governing global model catalogs, tenant resource quotas, and system infrastructure.
* **Identity Proxy**: An authorization mode where an external integration executes requests on behalf of a designated active team member, resolving access rules using that member's credentials.

## Next Steps

* Selection guide: Read [Choosing Application Types](/en/guide/getting-started/application-types).
* Hands-on tutorial: Start building in [Quick Start Guide](/en/guide/getting-started/quick-start).
* Knowledge management: Explore [Knowledge Base Lifecycle](/en/guide/dataset/lifecycle).