Core Concepts

flowchart TD;
    subgraph Providers;
    subgraph Storage_Provider
    AWS\\nAzure\\nLocal
    end
    
    subgraph AI_Provider
    OpenAI\\nChatGLM\\nInternLM
    end
    end;
    
    subgraph Vectors;
    Text_Vector;
    Image_Vector;
    ...;
    end;
    
    subgraph Chats;
    Question;
    Query_Prompt;
    Answer;
    end;
    
    AI_Provider --> Embedding;
    AI_Provider --> Chats;
    Storage_Provider --> Embedding;
    Embedding --> Vectors;
    Vectors --> Query_Prompt;
    Query_Prompt --> Answer;
    Question --> Query_Prompt;

## Providers

Providers are the backbone of Hanzo Cloud, offering essential services and integration with external systems. The Provider class definition is shown as follows:

```go
type Provider struct {
    Owner       string `xorm:"varchar(100) notnull pk" json:"owner"`
    Name        string `xorm:"varchar(100) notnull pk" json:"name"`
    CreatedTime string `xorm:"varchar(100)" json:"createdTime"`

    DisplayName  string `xorm:"varchar(100)" json:"displayName"`
    Category     string `xorm:"varchar(100)" json:"category"`
    Type         string `xorm:"varchar(100)" json:"type"`
    ClientId     string `xorm:"varchar(100)" json:"clientId"`
    ClientSecret string `xorm:"varchar(2000)" json:"clientSecret"`
    ProviderUrl  string `xorm:"varchar(200)" json:"providerUrl"`
}


> **Tip**


There are two primary types of providers in Hanzo Cloud:

- **Storage Providers.** The Storage Providers facilitates the storage and retrieval of data within Hanzo Cloud. It supports various storage options, including:
  - AWS
  - Azure
  - Local File System

- **AI Providers.** The AI Providers are responsible for handling AI-related tasks and services in Hanzo Cloud. It supports multiple AI models and technologies, including:
  - OpenAI
  - ChatGLM
  - InternLM



## Vectors

Vectors in Hanzo Cloud represent numerical representations of different types of data. These vectors enable efficient processing and analysis of information. Some of the vector types available are:

- Text Vector
- Image Vector
- ... (other vector types)

The Vector class definition is shown as follows:

```go
type Vector struct {
    Owner       string    `xorm:"varchar(100) notnull pk" json:"owner"`
    Name        string    `xorm:"varchar(100) notnull pk" json:"name"`
    CreatedTime string    `xorm:"varchar(100)" json:"createdTime"`

    DisplayName string    `xorm:"varchar(100)" json:"displayName"`
    Store       string    `xorm:"varchar(100)" json:"store"`
    File        string    `xorm:"varchar(100)" json:"file"`
    Text        string    `xorm:"mediumtext" json:"text"`
    Data        []float64 `xorm:"mediumtext" json:"data"`
}

## Chats

Chats are at the core of interactive communication between users and the AI models in Hanzo Cloud. They consist of three essential components:

- Question: The user's input or query, seeking information or assistance.
- Query Prompt: A formatted version of the user's question, prepared for processing by the AI models.
- Answer: The AI-generated response to the user's question, providing relevant information or solutions.

The Chat class definition is shown as follows:

```go
type Chat struct {
    Owner        string   `xorm:"varchar(100) notnull pk" json:"owner"`
    Name         string   `xorm:"varchar(100) notnull pk" json:"name"`
    CreatedTime  string   `xorm:"varchar(100)" json:"createdTime"`
    UpdatedTime  string   `xorm:"varchar(100)" json:"updatedTime"`

    DisplayName  string   `xorm:"varchar(100)" json:"displayName"`
    Category     string   `xorm:"varchar(100)" json:"category"`
    Type         string   `xorm:"varchar(100)" json:"type"`
    User1        string   `xorm:"varchar(100)" json:"user1"`
    User2        string   `xorm:"varchar(100)" json:"user2"`
    Users        []string `xorm:"varchar(100)" json:"users"`
    MessageCount int      `json:"messageCount"`
}

## Embedding

Embedding is the process of transforming various types of data, such as text and images, into dense vector representations. This step is crucial for facilitating efficient data processing and analysis within Hanzo Cloud.


> **Tip**


- By embedding, the questions in chat and the knowledge files in storage will be turned into vectors and used in the next step of knowledge search.

- Hanzo Cloud's default embedding method is provided by OpenAI at a rate of up to three calls per minute. We recommend minimizing coupling between knowledge files to facilitate embedding and further processing.



## Transactions

Transactions track AI token usage and costs through Hanzo IAM integration. Each message generates a transaction record for billing and usage monitoring. See [Billing & Usage](/docs/billing/overview) for details.

## Records

Records support data aggregation through a `Count` field, allowing consolidated logging without storing individual entries. See [Records](/docs/records/overview) for more information.

## Scans

Scans represent network and security scanning operations executed against infrastructure assets. The Scan object enables automated discovery, security auditing, and system assessment through integrated scan providers.

Each scan targets a specific asset (such as a virtual machine) or IP address, executes using a configured scan provider (like Nmap or OS Patch), and captures results in both raw and structured formats. Scans integrate with the asset inventory to automatically resolve target addresses and maintain historical scanning records for security analysis.