Add documentation for chat compeltion minmax

docs/my-website/docs/providers/minmax.md

@@ -163,6 +163,207 @@ for block in message.content:
     elif block.type == "text":
         print(f"Text:\n{block.text}\n")
 ```
+
+# MiniMax - v1/chat/completions
+
+## Usage with LiteLLM SDK
+
+You can use MiniMax's OpenAI-compatible API directly with LiteLLM:
+
+### Basic Chat Completion
+
+```python
+import litellm
+
+response = litellm.completion(
+    model="minimax/MiniMax-M2.1",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Hello, how are you?"}
+    ],
+    api_key="your-minimax-api-key",
+    api_base="https://api.minimax.io/v1"
+)
+
+print(response.choices[0].message.content)
+```
+
+### Using Environment Variables
+
+```bash
+export MINIMAX_API_KEY="your-minimax-api-key"
+export MINIMAX_API_BASE="https://api.minimax.io/v1"
+```
+
+```python
+import litellm
+
+response = litellm.completion(
+    model="minimax/MiniMax-M2.1",
+    messages=[{"role": "user", "content": "Hello!"}]
+)
+```
+
+### With Reasoning Split
+
+```python
+response = litellm.completion(
+    model="minimax/MiniMax-M2.1",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Solve: 2+2=?"}
+    ],
+    extra_body={"reasoning_split": True},
+    api_key="your-minimax-api-key",
+    api_base="https://api.minimax.io/v1"
+)
+
+# Access reasoning details if available
+if hasattr(response.choices[0].message, 'reasoning_details'):
+    print(f"Thinking: {response.choices[0].message.reasoning_details}")
+print(f"Response: {response.choices[0].message.content}")
+```
+
+### With Tool Calling
+
+```python
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_weather",
+            "description": "Get current weather",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {"type": "string"}
+                },
+                "required": ["location"]
+            }
+        }
+    }
+]
+
+response = litellm.completion(
+    model="minimax/MiniMax-M2.1",
+    messages=[{"role": "user", "content": "What's the weather in SF?"}],
+    tools=tools,
+    api_key="your-minimax-api-key",
+    api_base="https://api.minimax.io/v1"
+)
+```
+
+### Streaming
+
+```python
+response = litellm.completion(
+    model="minimax/MiniMax-M2.1",
+    messages=[{"role": "user", "content": "Tell me a story"}],
+    stream=True,
+    api_key="your-minimax-api-key",
+    api_base="https://api.minimax.io/v1"
+)
+
+for chunk in response:
+    if chunk.choices[0].delta.content:
+        print(chunk.choices[0].delta.content, end="")
+```
+
+
+## Usage with OpenAI SDK via LiteLLM Proxy
+
+You can also use MiniMax models with the OpenAI SDK by routing through LiteLLM Proxy:
+
+| Step | Description |
+|------|-------------|
+| **1. Start LiteLLM Proxy** | Configure proxy with MiniMax models in `config.yaml` |
+| **2. Set Environment Variables** | Point OpenAI SDK to proxy endpoint |
+| **3. Use OpenAI SDK** | Call MiniMax models using native OpenAI SDK |
+
+### Step 1: Configure LiteLLM Proxy
+
+Create a `config.yaml`:
+
+```yaml
+model_list:
+  - model_name: minimax/MiniMax-M2.1
+    litellm_params:
+      model: minimax/MiniMax-M2.1
+      api_key: os.environ/MINIMAX_API_KEY
+      api_base: https://api.minimax.io/v1
+```
+
+Start the proxy:
+
+```bash
+litellm --config config.yaml
+```
+
+### Step 2: Use with OpenAI SDK
+
+```python
+import os
+os.environ["OPENAI_BASE_URL"] = "http://localhost:4000"
+os.environ["OPENAI_API_KEY"] = "sk-1234"  # Your LiteLLM proxy key
+
+from openai import OpenAI
+
+client = OpenAI()
+
+response = client.chat.completions.create(
+    model="minimax/MiniMax-M2.1",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Hi, how are you?"},
+    ],
+    # Set reasoning_split=True to separate thinking content
+    extra_body={"reasoning_split": True},
+)
+
+# Access thinking and response
+if hasattr(response.choices[0].message, 'reasoning_details'):
+    print(f"Thinking:\n{response.choices[0].message.reasoning_details[0]['text']}\n")
+print(f"Text:\n{response.choices[0].message.content}\n")
+```
+
+### Streaming with OpenAI SDK
+
+```python
+from openai import OpenAI
+
+client = OpenAI()
+
+stream = client.chat.completions.create(
+    model="minimax/MiniMax-M2.1",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Tell me a story"},
+    ],
+    extra_body={"reasoning_split": True},
+    stream=True,
+)
+
+reasoning_buffer = ""
+text_buffer = ""
+
+for chunk in stream:
+    if hasattr(chunk.choices[0].delta, "reasoning_details") and chunk.choices[0].delta.reasoning_details:
+        for detail in chunk.choices[0].delta.reasoning_details:
+            if "text" in detail:
+                reasoning_text = detail["text"]
+                new_reasoning = reasoning_text[len(reasoning_buffer):]
+                if new_reasoning:
+                    print(new_reasoning, end="", flush=True)
+                    reasoning_buffer = reasoning_text
+
+    if chunk.choices[0].delta.content:
+        content_text = chunk.choices[0].delta.content
+        new_text = content_text[len(text_buffer):] if text_buffer else content_text
+        if new_text:
+            print(new_text, end="", flush=True)
+            text_buffer = content_text
+```
+
 ## Cost Calculation
 
 Cost calculation works automatically using the pricing information in `model_prices_and_context_window.json`.