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

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

dbgpt/model/proxy/llms/wenxin.py

@@ -0,0 +1,117 @@
+import requests
+import json
+from typing import List
+from dbgpt.model.proxy.llms.proxy_model import ProxyModel
+from dbgpt.core.interface.message import ModelMessage, ModelMessageRoleType
+from cachetools import cached, TTLCache
+
+
+@cached(TTLCache(1, 1800))
+def _build_access_token(api_key: str, secret_key: str) -> str:
+    """
+    Generate Access token according AK, SK
+    """
+
+    url = "https://aip.baidubce.com/oauth/2.0/token"
+    params = {
+        "grant_type": "client_credentials",
+        "client_id": api_key,
+        "client_secret": secret_key,
+    }
+
+    res = requests.get(url=url, params=params)
+
+    if res.status_code == 200:
+        return res.json().get("access_token")
+
+
+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 wenxin_generate_stream(
+    model: ProxyModel, tokenizer, params, device, context_len=2048
+):
+    MODEL_VERSION = {
+        "ERNIE-Bot": "completions",
+        "ERNIE-Bot-turbo": "eb-instant",
+    }
+
+    model_params = model.get_params()
+    model_name = model_params.proxyllm_backend
+    model_version = MODEL_VERSION.get(model_name)
+    if not model_version:
+        yield f"Unsupport model version {model_name}"
+
+    proxy_api_key = model_params.proxy_api_key
+    proxy_api_secret = model_params.proxy_api_secret
+    access_token = _build_access_token(proxy_api_key, proxy_api_secret)
+
+    headers = {"Content-Type": "application/json", "Accept": "application/json"}
+
+    proxy_server_url = f"https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/{model_version}?access_token={access_token}"
+
+    if not access_token:
+        yield "Failed to get access token. please set the correct api_key and secret key."
+
+    messages: List[ModelMessage] = params["messages"]
+
+    history, systems = __convert_2_wenxin_messages(messages)
+    system = ""
+    if systems and len(systems) > 0:
+        system = systems[0]
+    payload = {
+        "messages": history,
+        "system": system,
+        "temperature": params.get("temperature"),
+        "stream": True,
+    }
+
+    text = ""
+    res = requests.post(proxy_server_url, headers=headers, json=payload, stream=True)
+    print(f"Send request to {proxy_server_url} with real model {model_name}")
+    for line in res.iter_lines():
+        if line:
+            if not line.startswith(b"data: "):
+                error_message = line.decode("utf-8")
+                yield error_message
+            else:
+                json_data = line.split(b": ", 1)[1]
+                decoded_line = json_data.decode("utf-8")
+                if decoded_line.lower() != "[DONE]".lower():
+                    obj = json.loads(json_data)
+                    if obj["result"] is not None:
+                        content = obj["result"]
+                        text += content
+                yield text