# RAGFlow CLI (Go Version)

This is the Go implementation of the RAGFlow command-line interface, compatible with the Python version's syntax.

## Features

- Interactive mode and single command execution
- Full compatibility with Python CLI syntax
- Recursive descent parser for SQL-like commands
- Virtual Filesystem for intuitive resource management
- Support for all major commands:
  - User management: LOGIN, REGISTER, CREATE USER, DROP USER, LIST USERS, etc.
  - Service management: LIST SERVICES, SHOW SERVICE, STARTUP/SHUTDOWN/RESTART SERVICE
  - Role management: CREATE ROLE, DROP ROLE, LIST ROLES, GRANT/REVOKE PERMISSION
  - Dataset management via Virtual Filesystem: `ls`, `search`, `mkdir`, `cat`, `rm`
  - Model management: SET/RESET DEFAULT LLM/VLM/EMBEDDING/etc.
  - And more...

## Usage

### Build and run

```bash
go build -o ragflow-cli ./cmd/ragflow-cli.go
./ragflow-cli
```

## Architecture

```
internal/cli/
├── cli.go              # Main CLI loop and interaction
├── client.go           # RAGFlowClient with Filesystem integration
├── http_client.go      # HTTP client for API communication
├── parser/             # Command parser package
│   ├── types.go        # Token and Command types
│   ├── lexer.go        # Lexical analyzer
│   └── parser.go       # Recursive descent parser
└── filesystem/         # Virtual Filesystem
    ├── engine.go       # Core engine: path resolution, command routing
    ├── types.go        # Node, Command, Result types
    ├── base.go         # Provider interface definition    
    ├── dataset.go      # Dataset provider implementation
    ├── file.go         # File manager provider implementation
    └── utils.go        # Helper functions
```

## Virtual Filesystem

The Virtual Filesystem provides a unified filesystem interface over RAGFlow's RESTful APIs.

### Design Principles

1. **No Server-Side Changes**: All logic implemented client-side using existing APIs
2. **Provider Pattern**: Modular providers for different resource types (datasets, files, etc.)
3. **Unified Interface**: Common `ls`, `search`, `mkdir` commands across all providers
4. **Path-Based Navigation**: Virtual paths like `/datasets`, `/datasets/{name}/files`

### Supported Paths

| Path | Description |
|------|-------------|
| `/datasets` | List all datasets |
| `/datasets/{name}` | List documents in dataset (default behavior) |
| `/datasets/{name}/{doc}` | Get document info |

### Commands

#### `ls [path] [options]` - List nodes at path

List contents of a path in the context filesystem.

**Arguments:**
- `[path]` - Path to list (default: "datasets")

**Options:**
- `-n, --limit <number>` - Maximum number of items to display (default: 10)
- `-h, --help` - Show ls help message

**Examples:**
```bash
ls                              # List all datasets (default 10)
ls -n 20                        # List 20 datasets
ls datasets/kb1                 # List files in kb1 dataset
ls datasets/kb1 -n 50           # List 50 files in kb1 dataset
```

#### `search [options]` - Search for content

Semantic search in datasets.

**Options:**
- `-n, --number` - Number of top results to return (default: 10)

**Output Formats:**
- Default: JSON format
- `--output plain` - Plain text format
- `--output table` - Table format with borders

**Examples:**
```bash
search "machine learning"                    # Search all datasets (JSON output)
search "neural networks" datasets/kb1        # Search in kb1
search "AI" datasets/kb1  --output plain     # Plain text output
search "RAG" -n 20                           # Return 20 results
SEARCH 'machine learning' ON DATASETS 'kb1' 'kb2'
SEARCH 'AI' ON DATASETS 'kb1' WITH top_k 1024 similarity_threshold 0.0 vector_similarity_weight 0.3 keyword true
SEARCH 'AI' ON DATASETS 'kb1' WITH cross_languages ['Chinese']
```

#### `cat <path>` - Display content

Display document content (if available).

**Examples:**
```bash
cat myskills/doc.md   # Show content of doc.md file
cat datasets/kb1/document.pdf   # Error: cannot display binary file content
```

## Command Examples

```sql
-- Authentication
LOGIN USER 'admin@example.com';

-- User management
REGISTER USER 'john' AS 'John Doe' PASSWORD 'secret';
CREATE USER 'jane' 'password123';
DROP USER 'jane';
LIST USERS;
SHOW USER 'john';

-- Service management
LIST SERVICES;
SHOW SERVICE 1;
STARTUP SERVICE 1;
SHUTDOWN SERVICE 1;
RESTART SERVICE 1;
PING;

-- Role management
CREATE ROLE admin DESCRIPTION 'Administrator role';
LIST ROLES;
GRANT read,write ON datasets TO ROLE admin;

-- Dataset management
CREATE DATASET 'my_dataset' WITH EMBEDDING 'text-embedding-ada-002' PARSER 'naive';
LIST DATASETS;
DROP DATASET 'my_dataset';

-- Model configuration
SET DEFAULT LLM 'gpt-4';
SET DEFAULT EMBEDDING 'text-embedding-ada-002';
RESET DEFAULT LLM;


## Parser Implementation

The parser uses a hand-written recursive descent approach instead of go-yacc for:
- Better control over error messages
- Easier to extend and maintain
- No code generation step required

The parser structure follows the grammar defined in the Python version, ensuring full syntax compatibility.
