chore: add mode Qwen models, fix doc bug (#1178)

camel/types/enums.py

@@ -97,6 +97,10 @@ class ModelType(UnifiedModelType, Enum):
     QWEN_MATH_PLUS = "qwen-math-plus"
     QWEN_MATH_TURBO = "qwen-math-turbo"
     QWEN_CODER_TURBO = "qwen-coder-turbo"
+    QWEN_2_5_CODER_32B = "qwen2.5-coder-32b-instruct"
+    QWEN_2_5_72B = "qwen2.5-72b-instruct"
+    QWEN_2_5_32B = "qwen2.5-32b-instruct"
+    QWEN_2_5_14B = "qwen2.5-14b-instruct"
 
     # Yi models (01-ai)
     YI_LIGHTNING = "yi-lightning"
@@ -273,6 +277,10 @@ def is_qwen(self) -> bool:
             ModelType.QWEN_MATH_PLUS,
             ModelType.QWEN_MATH_TURBO,
             ModelType.QWEN_CODER_TURBO,
+            ModelType.QWEN_2_5_CODER_32B,
+            ModelType.QWEN_2_5_72B,
+            ModelType.QWEN_2_5_32B,
+            ModelType.QWEN_2_5_14B,
         }
 
     @property
@@ -344,6 +352,10 @@ def token_limit(self) -> int:
             ModelType.MISTRAL_PIXTRAL_12B,
             ModelType.MISTRAL_8B,
             ModelType.MISTRAL_3B,
+            ModelType.QWEN_2_5_CODER_32B,
+            ModelType.QWEN_2_5_72B,
+            ModelType.QWEN_2_5_32B,
+            ModelType.QWEN_2_5_14B,
         }:
             return 128_000
         elif self in {

docs/cookbooks/agents_with_rag.ipynb

@@ -320,17 +320,17 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "mHdEsIzSH_AD"
       },
       "source": [
         "## 2. Auto RAG\n",
-        "In this section we will run the `AutoRetriever` with default settings. It uses `OpenAIEmbedding` as default embedding model and `Milvus` as default vector storage.\n",
+        "In this section we will run the `AutoRetriever` with default settings. It uses `OpenAIEmbedding` as default embedding model and `Qdrant` as default vector storage.\n",
         "\n",
         "What you need to do is:\n",
         "- Set content input paths, which can be local paths or remote urls\n",
-        "- Set remote url and api key for Milvus\n",
         "- Give a query\n",
         "\n",
         "The Auto RAG pipeline would create collections for given content input paths, the collection name will be set automaticlly based on the content input path name, if the collection exists, it will do the retrieve directly."

docs/key_modules/models.md

@@ -59,6 +59,10 @@ The following table lists currently supported model platforms by CAMEL.
 | Qwen | qwen-math-plus | N |
 | Qwen | qwen-math-turbo | N |
 | Qwen | qwen-coder-turbo | N |
+| Qwen | qwen2.5-coder-32b-instruct | N |
+| Qwen | qwen2.5-72b-instruct | N |
+| Qwen | qwen2.5-32b-instruct | N |
+| Qwen | qwen2.5-14b-instruct | N |
 | ZhipuAI | glm-4v | Y |
 | ZhipuAI | glm-4 | N |
 | ZhipuAI | glm-3-turbo | N |

docs/key_modules/workforce.md

@@ -60,7 +60,7 @@ has been failed for a certain number of times (3 by default).
 
 Here is a diagram illustrating the workflow with a simple example.
 
-![Workforce Example](https://lh3.googleusercontent.com/pw/AP1GczMFbGi7pInBgiXoPbS8lOfIuGijWoo3EeRlz0OWPe7im1FWYXnD1xnbQpEbD_p4DVHtpWhQQHicGaEc1RaoyaEqg9396oGNPQYi4XQ8U3SBRxQV53KSrqzcE9RBMkGv7PgMMxXWVEiWA5rVe6oE9C8=w1315-h1324-s-no?authuser=0)
+![Workforce Example](https://i.postimg.cc/261dDM9W/work-force.png)
 
 ## Get Started
 

examples/models/qwen_model_example.py

@@ -19,8 +19,8 @@
 
 model = ModelFactory.create(
     model_platform=ModelPlatformType.QWEN,
-    model_type=ModelType.QWEN_MAX,
-    model_config_dict=QwenConfig(temperature=0.2, stream=False).as_dict(),
+    model_type=ModelType.QWEN_2_5_CODER_32B,
+    model_config_dict=QwenConfig(temperature=0.2).as_dict(),
 )
 
 # Define system message
@@ -29,17 +29,103 @@
 # Set agent
 camel_agent = ChatAgent(system_message=sys_msg, model=model)
 
-user_msg = """Say hi to CAMEL AI, one open-source community 
-    dedicated to the study of autonomous and communicative agents."""
+user_msg = """give me python code to develop a trading bot"""
 
 # Get response information
 response = camel_agent.step(user_msg)
 print(response.msgs[0].content)
 
 '''
 ===============================================================================
-Hello CAMEL AI community! It's great to see such dedication to the study of 
-autonomous and communicative agents. Your work is truly inspiring and 
-contributes significantly to the field of AI. Keep up the fantastic work!
+Creating a trading bot involves several steps, including data acquisition, 
+strategy development, backtesting, and live trading. Below is a simplified 
+example of a trading bot using Python. This example will use the `ccxt` 
+library to interact with cryptocurrency exchanges and `pandas` for data 
+manipulation. The strategy used here is a simple moving average crossover 
+strategy.
+
+First, you need to install the required libraries:
+
+```bash
+pip install ccxt pandas
+```
+
+Here's a basic example of a trading bot:
+
+```python
+import ccxt
+import pandas as pd
+import time
+
+# Initialize the exchange
+exchange = ccxt.binance({
+    'apiKey': 'YOUR_API_KEY',
+    'secret': 'YOUR_SECRET_KEY',
+})
+
+# Define the trading parameters
+symbol = 'BTC/USDT'
+timeframe = '1h'
+short_window = 50
+long_window = 200
+amount_to_trade = 0.001  # Amount of BTC to trade
+
+def fetch_ohlcv(symbol, timeframe):
+    ohlcv = exchange.fetch_ohlcv(symbol, timeframe)
+    df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 
+    'close', 'volume'])
+    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
+    return df
+
+def calculate_moving_averages(df, short_window, long_window):
+    df['short_mavg'] = df['close'].rolling(window=short_window, min_periods=1).
+    mean()
+    df['long_mavg'] = df['close'].rolling(window=long_window, min_periods=1).
+    mean()
+    return df
+
+def get_signal(df):
+    if df['short_mavg'].iloc[-1] > df['long_mavg'].iloc[-1] and df
+    ['short_mavg'].iloc[-2] <= df['long_mavg'].iloc[-2]:
+        return 'buy'
+    elif df['short_mavg'].iloc[-1] < df['long_mavg'].iloc[-1] and df
+    ['short_mavg'].iloc[-2] >= df['long_mavg'].iloc[-2]:
+        return 'sell'
+    else:
+        return 'hold'
+
+def execute_trade(signal, symbol, amount):
+    if signal == 'buy':
+        order = exchange.create_market_buy_order(symbol, amount)
+        print(f"Executed BUY order: {order}")
+    elif signal == 'sell':
+        order = exchange.create_market_sell_order(symbol, amount)
+        print(f"Executed SELL order: {order}")
+
+def main():
+    while True:
+        try:
+            # Fetch OHLCV data
+            df = fetch_ohlcv(symbol, timeframe)
+
+            # Calculate moving averages
+            df = calculate_moving_averages(df, short_window, long_window)
+
+            # Get trading signal
+            signal = get_signal(df)
+
+            # Execute trade based on signal
+            execute_trade(signal, symbol, amount_to_trade)
+
+            # Wait for the next candle
+            time.sleep(60 * 60)  # Sleep for 1 hour
+
+        except Exception as e:
+            print(f"An error occurred: {e}")
+            time.sleep(60)  # Sleep for 1 minute before retrying
+
+if __name__ == "__main__":
+    main()
+```
 ===============================================================================
 '''

test/models/test_model_factory.py

@@ -31,7 +31,7 @@
     'model_platform, model_type',
     [
         (ModelPlatformType.OPENAI, ModelType.GPT_3_5_TURBO),
-        (ModelPlatformType.OPENAI, ModelType.GPT_4_TURBO),
+        (ModelPlatformType.OPENAI, ModelType.GPT_4O_MINI),
     ],
 )
 
@@ -50,11 +50,11 @@
         ),
         (
             ModelPlatformType.OPENAI,
-            ModelType.GPT_4,
+            ModelType.GPT_4O_MINI,
             ChatGPTConfig().as_dict(),
             None,
             OpenAITokenCounter,
-            ModelType.GPT_4,
+            ModelType.GPT_4O_MINI,
         ),
         # Test Stub model
         # Stub model uses StubTokenCounter as default
@@ -70,9 +70,9 @@
             ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
             ModelType.STUB,
             ChatGPTConfig().as_dict(),
-            OpenAITokenCounter(ModelType.GPT_4),
+            OpenAITokenCounter(ModelType.GPT_4O_MINI),
             OpenAITokenCounter,
-            ModelType.GPT_4,
+            ModelType.GPT_4O_MINI,
         ),
         # Test Anthropic model
         # Anthropic model uses AnthropicTokenCounter as default
@@ -97,9 +97,9 @@
             ModelPlatformType.GEMINI,
             ModelType.GEMINI_1_5_FLASH,
             GeminiConfig().as_dict(),
-            OpenAITokenCounter(ModelType.GPT_4),
+            OpenAITokenCounter(ModelType.GPT_4O_MINI),
             OpenAITokenCounter,
-            ModelType.GPT_4,
+            ModelType.GPT_4O_MINI,
         ),
         # Test Ollama model
         (
@@ -114,9 +114,9 @@
             ModelPlatformType.OLLAMA,
             "gpt-3.5-turbo",
             OllamaConfig().as_dict(),
-            OpenAITokenCounter(ModelType.GPT_4),
+            OpenAITokenCounter(ModelType.GPT_4O_MINI),
             OpenAITokenCounter,
-            ModelType.GPT_4,
+            ModelType.GPT_4O_MINI,
         ),
     ],
 )

test/models/test_qwen_model.py

@@ -34,6 +34,10 @@
         ModelType.QWEN_MATH_PLUS,
         ModelType.QWEN_MATH_TURBO,
         ModelType.QWEN_CODER_TURBO,
+        ModelType.QWEN_2_5_CODER_32B,
+        ModelType.QWEN_2_5_72B,
+        ModelType.QWEN_2_5_32B,
+        ModelType.QWEN_2_5_14B,
     ],
 )
 def test_qwen_model(model_type: ModelType):