refine docs

docs/en/Components/Config.md

@@ -106,6 +106,24 @@ tools:
 
 For the complete list of supported tools and custom tools, please refer to [here](./Tools.md)
 
+## Skills Configuration
+
+> Optional, used when enabling Agent Skills
+
+```yaml
+skills:
+  # Path to skills directory or ModelScope repo ID
+  path: /path/to/skills
+  # Whether to auto-execute skills (default: True)
+  auto_execute: true
+  # Working directory for outputs
+  work_dir: /path/to/workspace
+  # Whether to use Docker sandbox for execution (default: True)
+  use_sandbox: false
+```
+
+For the complete skill module documentation (including architecture, directory structure, API reference, and security mechanisms), see [Agent Skills](./AgentSkills).
+
 ## Memory Compression Configuration
 
 > Optional, for context management in long conversations

docs/en/Components/MultimodalSupport.md

@@ -0,0 +1,299 @@
+---
+slug: MultimodalSupport
+title: Multimodal Support
+description: Ms-Agent multimodal conversation guide - image understanding and analysis configuration and usage.
+---
+
+# Multimodal Support
+
+This document describes how to use ms-agent for multimodal conversations, including image understanding and analysis capabilities.
+
+## Overview
+
+ms-agent supports multimodal models such as Alibaba Cloud's `qwen3.5-plus`. Multimodal models can:
+- Analyze image content
+- Recognize objects, scenes, and text in images
+- Engage in conversations based on image content
+
+## Prerequisites
+
+### 1. Install Dependencies
+
+Ensure the required packages are installed:
+
+```bash
+pip install openai
+```
+
+### 2. Configure API Key
+
+(Using qwen3.5-plus as an example) Obtain a DashScope API Key and set the environment variable:
+
+```bash
+export DASHSCOPE_API_KEY='your-dashscope-api-key'
+```
+
+Or set `dashscope_api_key` directly in the configuration file.
+
+## Configure Multimodal Models
+
+Multimodal functionality depends on two factors:
+1. **Choose a model that supports multimodal input** (e.g. `qwen3.5-plus`)
+2. **Use the correct message format** (containing `image_url` blocks)
+
+You can dynamically modify the model configuration in code on top of an existing config:
+
+```python
+from ms_agent.config import Config
+from ms_agent import LLMAgent
+import os
+
+# Use an existing configuration file (e.g. ms_agent/agent/agent.yaml)
+config = Config.from_task('ms_agent/agent/agent.yaml')
+
+# Override configuration for multimodal model
+config.llm.model = 'qwen3.5-plus'
+config.llm.service = 'dashscope'
+config.llm.dashscope_api_key = os.environ.get('DASHSCOPE_API_KEY', '')
+config.llm.modelscope_base_url = 'https://dashscope.aliyuncs.com/compatible-mode/v1'
+
+# Create LLMAgent
+agent = LLMAgent(config=config)
+```
+
+## Using LLMAgent for Multimodal Conversations
+
+Using `LLMAgent` for multimodal conversations is recommended, as it provides more complete features including memory management, tool calling, and callback support.
+
+### Basic Usage
+
+```python
+import asyncio
+import os
+from ms_agent import LLMAgent
+from ms_agent.config import Config
+from ms_agent.llm.utils import Message
+
+async def multimodal_chat():
+    # Create configuration
+    config = Config.from_task('ms_agent/agent/agent.yaml')
+    config.llm.model = 'qwen3.5-plus'
+    config.llm.service = 'dashscope'
+    config.llm.dashscope_api_key = os.environ.get('DASHSCOPE_API_KEY', '')
+    config.llm.modelscope_base_url = 'https://dashscope.aliyuncs.com/compatible-mode/v1'
+
+    # Create LLMAgent
+    agent = LLMAgent(config=config)
+
+    # Build multimodal message
+    multimodal_content = [
+        {"type": "text", "text": "Please describe this image."},
+        {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
+    ]
+
+    # Call the agent
+    response = await agent.run(messages=[Message(role="user", content=multimodal_content)])
+    print(response[-1].content)
+
+asyncio.run(multimodal_chat())
+```
+
+### Non-Stream Mode
+
+```python
+# Disable stream in configuration
+config.generation_config.stream = False
+
+agent = LLMAgent(config=config)
+
+multimodal_content = [
+    {"type": "text", "text": "Please describe this image."},
+    {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
+]
+
+# Non-stream mode: returns complete response directly
+response = await agent.run(messages=[Message(role="user", content=multimodal_content)])
+print(f"[Response] {response[-1].content}")
+print(f"[Token Usage] Input: {response[-1].prompt_tokens}, Output: {response[-1].completion_tokens}")
+```
+
+### Stream Mode
+
+```python
+# Enable stream in configuration
+config.generation_config.stream = True
+
+agent = LLMAgent(config=config)
+
+multimodal_content = [
+    {"type": "text", "text": "Please describe this image."},
+    {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
+]
+
+# Stream mode: returns a generator
+generator = await agent.run(
+    messages=[Message(role="user", content=multimodal_content)],
+    stream=True
+)
+
+full_response = ""
+async for response_chunk in generator:
+    if response_chunk and len(response_chunk) > 0:
+        last_msg = response_chunk[-1]
+        if last_msg.content:
+            # Stream output of new content
+            print(last_msg.content[len(full_response):], end='', flush=True)
+            full_response = last_msg.content
+
+print(f"\n[Full Response] {full_response}")
+```
+
+### Multi-Turn Conversations
+
+LLMAgent supports multi-turn conversations, allowing you to mix images and text:
+
+```python
+agent = LLMAgent(config=config, tag="multimodal_conversation")
+
+# Turn 1: Send an image
+multimodal_content = [
+    {"type": "text", "text": "How many people are in this image?"},
+    {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
+]
+
+messages = [Message(role="user", content=multimodal_content)]
+response = await agent.run(messages=messages)
+print(f"[Turn 1 Response] {response[-1].content}")
+
+# Turn 2: Follow-up question (text only, preserving context)
+messages = response  # Use previous response as context
+messages.append(Message(role="user", content="What are they doing?"))
+response = await agent.run(messages=messages)
+print(f"[Turn 2 Response] {response[-1].content}")
+```
+
+## Multimodal Message Format
+
+ms-agent uses the OpenAI-compatible multimodal message format. Images can be provided in three ways:
+
+### 1. Image URL
+
+```python
+from ms_agent.llm.utils import Message
+
+multimodal_content = [
+    {"type": "text", "text": "Please describe this image."},
+    {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
+]
+
+messages = [
+    Message(role="user", content=multimodal_content)
+]
+
+response = llm.generate(messages=messages)
+```
+
+### 2. Base64 Encoding
+
+```python
+import base64
+
+# Read and encode the image
+with open('image.jpg', 'rb') as f:
+    image_data = base64.b64encode(f.read()).decode('utf-8')
+
+multimodal_content = [
+    {"type": "text", "text": "What is this?"},
+    {
+        "type": "image_url",
+        "image_url": {
+            "url": f"data:image/jpeg;base64,{image_data}"
+        }
+    }
+]
+
+messages = [Message(role="user", content=multimodal_content)]
+response = llm.generate(messages=messages)
+```
+
+### 3. Local File Path
+
+```python
+import base64
+import os
+
+image_path = 'path/to/image.png'
+
+# Get MIME type
+ext = os.path.splitext(image_path)[1].lower()
+mime_type = {
+    '.png': 'image/png',
+    '.jpg': 'image/jpeg',
+    '.jpeg': 'image/jpeg',
+    '.gif': 'image/gif',
+    '.webp': 'image/webp'
+}.get(ext, 'image/png')
+
+# Read and encode
+with open(image_path, 'rb') as f:
+    image_data = base64.b64encode(f.read()).decode('utf-8')
+
+multimodal_content = [
+    {"type": "text", "text": "Describe this image."},
+    {
+        "type": "image_url",
+        "image_url": {
+            "url": f"data:{mime_type};base64,{image_data}"
+        }
+    }
+]
+
+messages = [Message(role="user", content=multimodal_content)]
+response = llm.generate(messages=messages)
+```
+
+## Running Examples
+
+### Running the Agent Example
+
+```bash
+# Run the complete test suite (including stream and non-stream modes)
+python examples/agent/test_llm_agent_multimodal.py
+```
+
+## FAQ
+
+### Q: Are there image size limits?
+
+A: Yes, different models have different limits:
+- qwen3.5-plus: Recommended image size under 4MB
+- Recommended resolution not exceeding 2048x2048
+
+### Q: What image formats are supported?
+
+A: Commonly supported formats:
+- JPEG / JPG
+- PNG
+- GIF
+- WebP
+
+### Q: Can I send multiple images at once?
+
+A: Yes, you can add multiple `image_url` blocks in a single message:
+
+```python
+multimodal_content = [
+    {"type": "text", "text": "Compare these two images."},
+    {"type": "image_url", "image_url": {"url": "https://example.com/img1.jpg"}},
+    {"type": "image_url", "image_url": {"url": "https://example.com/img2.jpg"}}
+]
+```
+
+### Q: Is streaming output supported?
+
+A: Yes, multimodal conversations support streaming output. Set `stream: true`:
+
+```python
+config.generation_config.stream = True
+response = llm.generate(messages=messages, stream=True)
+```

docs/en/index.rst

@@ -20,14 +20,16 @@ MS-Agent DOCUMENTATION
    Components/LLMAgent
    Components/Workflow
    Components/SupportedModels
+   Components/MultimodalSupport
    Components/Tools
+   Components/AgentSkills
    Components/ContributorGuide
 
 .. toctree::
    :maxdepth: 2
    :caption: 📁 Projects
 
-   Projects/AgentSkills
+   Projects/CodeGenesis
    Projects/DeepResearch
    Projects/FinResearch
    Projects/VideoGeneration