@fengcch/n8n-nodes-302ai-chat/README.md

n8n community node for interacting with the 302.ai chat completion API

# @fengcch/n8n-nodes-302ai-chat

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This is an n8n community node for [302.AI](https://302.ai/) AI service integration.

## Prerequisites

You need to have a valid API key from [302.AI](https://302.ai/) to use this node.

## Installation

1.  Go to **Settings > Community Nodes** in your n8n instance.
2.  Select **Install** and enter `@fengcch/n8n-nodes-302ai-chat` in the search box.
3.  Click **Install** to add the node to your n8n instance.

## Configuration

1.  In your n8n workflow, add the "302.AI" node.
2.  In the "Credentials" section, click on **Create New**.
3.  Give your credential a name.
4.  Enter your API key from `302.AI` into the **API Key** field.
5.  Click **Save** to create the credential.

## Usage

### Chat Operation

1.  **Model Name or ID**: Select from available 302.ai models
2.  **System Prompt**: (Optional) Provide context for the AI
3.  **Message**: Your input message
4.  **Additional Fields**: Temperature, max tokens, etc.

### Multimodal Support
- Supports both text and image inputs in chat conversations
- Compatible with vision-capable models for image understanding
- **Image URL**: Optional field to include images in your conversation
- **Supported formats**: HTTP/HTTPS image URLs or base64 encoded images
- **Use cases**: Image analysis, visual question answering, content understanding from images

## Output

- **Standard response**: `json.response` contains the model's reply
- **Error handling**: `json.error` for any API issues

## Examples

### Basic Chat Example
```json
{
  "model": "gpt-3.5-turbo",
  "message": "Hello, how can AI help with automation?",
  "temperature": 0.7
}
```

### Pseudo-stream Mode

- Enable the **Pseudo-stream Mode** toggle (below Image URL) when the target model only supports streaming responses, for example Qwen3.
- The node consumes the full stream and still returns a single JSON result so you can read the reply from `json.response`.
- If the service emits `reasoning_content`, it is exposed as `json.reasoning` for easier debugging.

### Multimodal Example (Text + Image)
```json
{
  "model": "gpt-5",
  "message": "What do you see in this image?",
  "imageUrl": "https://example.com/image.jpg",
  "temperature": 0.5
}
```

### Response Example
```json
{
  "response": "AI can help with automation in many ways, including data processing, decision making, content generation, and workflow optimization..."
}
```

## License

[MIT](LICENSE)