feat(awel): New MessageConverter and more AWEL operators (#1039)

dbgpt/model/proxy/llms/bard.py

@@ -13,6 +13,8 @@ def bard_generate_stream(
     proxy_api_key = model_params.proxy_api_key
     proxy_server_url = model_params.proxy_server_url
 
+    convert_to_compatible_format = params.get("convert_to_compatible_format", False)
+
     history = []
     messages: List[ModelMessage] = params["messages"]
     for message in messages:
@@ -25,14 +27,15 @@ def bard_generate_stream(
         else:
             pass
 
-    last_user_input_index = None
-    for i in range(len(history) - 1, -1, -1):
-        if history[i]["role"] == "user":
-            last_user_input_index = i
-            break
-    if last_user_input_index:
-        last_user_input = history.pop(last_user_input_index)
-        history.append(last_user_input)
+    if convert_to_compatible_format:
+        last_user_input_index = None
+        for i in range(len(history) - 1, -1, -1):
+            if history[i]["role"] == "user":
+                last_user_input_index = i
+                break
+        if last_user_input_index:
+            last_user_input = history.pop(last_user_input_index)
+            history.append(last_user_input)
 
     msgs = []
     for msg in history:

dbgpt/model/proxy/llms/chatgpt.py

@@ -128,7 +128,10 @@ def _build_request(model: ProxyModel, params):
     messages: List[ModelMessage] = params["messages"]
 
     # history = __convert_2_gpt_messages(messages)
-    history = ModelMessage.to_openai_messages(messages)
+    convert_to_compatible_format = params.get("convert_to_compatible_format", False)
+    history = ModelMessage.to_openai_messages(
+        messages, convert_to_compatible_format=convert_to_compatible_format
+    )
     payloads = {
         "temperature": params.get("temperature"),
         "max_tokens": params.get("max_new_tokens"),

dbgpt/model/proxy/llms/gemini.py

@@ -12,7 +12,6 @@ def gemini_generate_stream(
     """Zhipu ai, see: https://open.bigmodel.cn/dev/api#overview"""
     model_params = model.get_params()
     print(f"Model: {model}, model_params: {model_params}")
-    global history
 
     # TODO proxy model use unified config?
     proxy_api_key = model_params.proxy_api_key

dbgpt/model/proxy/llms/spark.py

@@ -56,6 +56,9 @@ def spark_generate_stream(
             del messages[index]
             break
 
+    # TODO: Support convert_to_compatible_format config
+    convert_to_compatible_format = params.get("convert_to_compatible_format", False)
+
     history = []
     # Add history conversation
     for message in messages:

dbgpt/model/proxy/llms/tongyi.py

@@ -53,8 +53,12 @@ def tongyi_generate_stream(
         proxyllm_backend = Generation.Models.qwen_turbo  # By Default qwen_turbo
 
     messages: List[ModelMessage] = params["messages"]
+    convert_to_compatible_format = params.get("convert_to_compatible_format", False)
 
-    history = __convert_2_tongyi_messages(messages)
+    if convert_to_compatible_format:
+        history = __convert_2_tongyi_messages(messages)
+    else:
+        history = ModelMessage.to_openai_messages(messages)
     gen = Generation()
     res = gen.call(
         proxyllm_backend,

dbgpt/model/proxy/llms/wenxin.py

@@ -25,8 +25,29 @@ def _build_access_token(api_key: str, secret_key: str) -> str:
         return res.json().get("access_token")
 
 
+def _to_wenxin_messages(messages: List[ModelMessage]):
+    """Convert messages to wenxin compatible format
+
+    See https://cloud.baidu.com/doc/WENXINWORKSHOP/s/jlil56u11
+    """
+    wenxin_messages = []
+    system_messages = []
+    for message in messages:
+        if message.role == ModelMessageRoleType.HUMAN:
+            wenxin_messages.append({"role": "user", "content": message.content})
+        elif message.role == ModelMessageRoleType.SYSTEM:
+            system_messages.append(message.content)
+        elif message.role == ModelMessageRoleType.AI:
+            wenxin_messages.append({"role": "assistant", "content": message.content})
+        else:
+            pass
+    if len(system_messages) > 1:
+        raise ValueError("Wenxin only support one system message")
+    str_system_message = system_messages[0] if len(system_messages) > 0 else ""
+    return wenxin_messages, str_system_message
+
+
 def __convert_2_wenxin_messages(messages: List[ModelMessage]):
-    chat_round = 0
     wenxin_messages = []
 
     last_usr_message = ""
@@ -57,7 +78,8 @@ def __convert_2_wenxin_messages(messages: List[ModelMessage]):
         last_message = messages[-1]
         end_message = last_message.content
     wenxin_messages.append({"role": "user", "content": end_message})
-    return wenxin_messages, system_messages
+    str_system_message = system_messages[0] if len(system_messages) > 0 else ""
+    return wenxin_messages, str_system_message
 
 
 def wenxin_generate_stream(
@@ -87,13 +109,14 @@ def wenxin_generate_stream(
 
     messages: List[ModelMessage] = params["messages"]
 
-    history, systems = __convert_2_wenxin_messages(messages)
-    system = ""
-    if systems and len(systems) > 0:
-        system = systems[0]
+    convert_to_compatible_format = params.get("convert_to_compatible_format", False)
+    if convert_to_compatible_format:
+        history, system_message = __convert_2_wenxin_messages(messages)
+    else:
+        history, system_message = _to_wenxin_messages(messages)
     payload = {
         "messages": history,
-        "system": system,
+        "system": system_message,
         "temperature": params.get("temperature"),
         "stream": True,
     }

dbgpt/model/proxy/llms/zhipu.py

@@ -57,6 +57,10 @@ def zhipu_generate_stream(
     zhipuai.api_key = proxy_api_key
 
     messages: List[ModelMessage] = params["messages"]
+
+    # TODO: Support convert_to_compatible_format config, zhipu not support system message
+    convert_to_compatible_format = params.get("convert_to_compatible_format", False)
+
     history, systems = __convert_2_zhipu_messages(messages)
     res = zhipuai.model_api.sse_invoke(
         model=proxyllm_backend,