refactor: The first refactored version for sdk release (#907)

Co-authored-by: chengfangyin2 <chengfangyin3@jd.com>

dbgpt/model/proxy/llms/zhipu.py

@@ -0,0 +1,101 @@
+from typing import List
+
+from dbgpt.model.proxy.llms.proxy_model import ProxyModel
+from dbgpt.core.interface.message import ModelMessage, ModelMessageRoleType
+
+CHATGLM_DEFAULT_MODEL = "chatglm_pro"
+
+
+def __convert_2_wenxin_messages(messages: List[ModelMessage]):
+    chat_round = 0
+    wenxin_messages = []
+
+    last_usr_message = ""
+    system_messages = []
+
+    for message in messages:
+        if message.role == ModelMessageRoleType.HUMAN:
+            last_usr_message = message.content
+        elif message.role == ModelMessageRoleType.SYSTEM:
+            system_messages.append(message.content)
+        elif message.role == ModelMessageRoleType.AI:
+            last_ai_message = message.content
+            wenxin_messages.append({"role": "user", "content": last_usr_message})
+            wenxin_messages.append({"role": "assistant", "content": last_ai_message})
+
+    # build last user messge
+
+    if len(system_messages) > 0:
+        if len(system_messages) > 1:
+            end_message = system_messages[-1]
+        else:
+            last_message = messages[-1]
+            if last_message.role == ModelMessageRoleType.HUMAN:
+                end_message = system_messages[-1] + "\n" + last_message.content
+            else:
+                end_message = system_messages[-1]
+    else:
+        last_message = messages[-1]
+        end_message = last_message.content
+    wenxin_messages.append({"role": "user", "content": end_message})
+    return wenxin_messages, system_messages
+
+
+def zhipu_generate_stream(
+    model: ProxyModel, tokenizer, params, device, context_len=2048
+):
+    """Zhipu ai, see: https://open.bigmodel.cn/dev/api#overview"""
+    model_params = model.get_params()
+    print(f"Model: {model}, model_params: {model_params}")
+
+    # TODO proxy model use unified config?
+    proxy_api_key = model_params.proxy_api_key
+    proxyllm_backend = CHATGLM_DEFAULT_MODEL or model_params.proxyllm_backend
+
+    import zhipuai
+
+    zhipuai.api_key = proxy_api_key
+
+    messages: List[ModelMessage] = params["messages"]
+    # Add history conversation
+    # system = ""
+    # if len(messages) > 1 and messages[0].role == ModelMessageRoleType.SYSTEM:
+    #     role_define = messages.pop(0)
+    #     system = role_define.content
+    # else:
+    #     message = messages.pop(0)
+    #     if message.role == ModelMessageRoleType.HUMAN:
+    #         history.append({"role": "user", "content": message.content})
+    # for message in messages:
+    #     if message.role == ModelMessageRoleType.SYSTEM:
+    #         history.append({"role": "user", "content": message.content})
+    #     # elif message.role == ModelMessageRoleType.HUMAN:
+    #     #     history.append({"role": "user", "content": message.content})
+    #     elif message.role == ModelMessageRoleType.AI:
+    #         history.append({"role": "assistant", "content": message.content})
+    #     else:
+    #         pass
+    #
+    # # temp_his = history[::-1]
+    # temp_his = history
+    # last_user_input = None
+    # for m in temp_his:
+    #     if m["role"] == "user":
+    #         last_user_input = m
+    #         break
+    #
+    # if last_user_input:
+    #     history.remove(last_user_input)
+    #     history.append(last_user_input)
+
+    history, systems = __convert_2_wenxin_messages(messages)
+    res = zhipuai.model_api.sse_invoke(
+        model=proxyllm_backend,
+        prompt=history,
+        temperature=params.get("temperature"),
+        top_p=params.get("top_p"),
+        incremental=False,
+    )
+    for r in res.events():
+        if r.event == "add":
+            yield r.data