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from fastchat.conversation import Conversation | |
from server.model_workers.base import * | |
from fastchat import conversation as conv | |
import sys | |
import json | |
from server.model_workers.base import ApiEmbeddingsParams | |
from server.utils import get_httpx_client | |
from typing import List, Dict | |
from configs import logger, log_verbose | |
class MiniMaxWorker(ApiModelWorker): | |
DEFAULT_EMBED_MODEL = "embo-01" | |
def __init__( | |
self, | |
*, | |
model_names: List[str] = ["minimax-api"], | |
controller_addr: str = None, | |
worker_addr: str = None, | |
version: str = "abab5.5-chat", | |
**kwargs, | |
): | |
kwargs.update(model_names=model_names, controller_addr=controller_addr, worker_addr=worker_addr) | |
kwargs.setdefault("context_len", 16384) | |
super().__init__(**kwargs) | |
self.version = version | |
def validate_messages(self, messages: List[Dict]) -> List[Dict]: | |
role_maps = { | |
"USER": self.user_role, | |
"assistant": self.ai_role, | |
"system": "system", | |
} | |
messages = [{"sender_type": role_maps[x["role"]], "text": x["content"]} for x in messages] | |
return messages | |
def do_chat(self, params: ApiChatParams) -> Dict: | |
# 按照官网推荐,直接调用abab 5.5模型 | |
params.load_config(self.model_names[0]) | |
url = 'https://api.minimax.chat/v1/text/chatcompletion{pro}?GroupId={group_id}' | |
pro = "_pro" if params.is_pro else "" | |
headers = { | |
"Authorization": f"Bearer {params.api_key}", | |
"Content-Type": "application/json", | |
} | |
messages = self.validate_messages(params.messages) | |
data = { | |
"model": params.version, | |
"stream": True, | |
"mask_sensitive_info": True, | |
"messages": messages, | |
"temperature": params.temperature, | |
"top_p": params.top_p, | |
"tokens_to_generate": params.max_tokens or 1024, | |
# 以下参数为minimax特有,传入空值会出错。 | |
# "prompt": params.system_message or self.conv.system_message, | |
# "bot_setting": [], | |
# "role_meta": params.role_meta, | |
} | |
if log_verbose: | |
logger.info(f'{self.__class__.__name__}:data: {data}') | |
logger.info(f'{self.__class__.__name__}:url: {url.format(pro=pro, group_id=params.group_id)}') | |
logger.info(f'{self.__class__.__name__}:headers: {headers}') | |
with get_httpx_client() as client: | |
response = client.stream("POST", | |
url.format(pro=pro, group_id=params.group_id), | |
headers=headers, | |
json=data) | |
with response as r: | |
text = "" | |
for e in r.iter_text(): | |
if not e.startswith("data: "): | |
data = { | |
"error_code": 500, | |
"text": f"minimax返回错误的结果:{e}", | |
"error": { | |
"message": f"minimax返回错误的结果:{e}", | |
"type": "invalid_request_error", | |
"param": None, | |
"code": None, | |
} | |
} | |
self.logger.error(f"请求 MiniMax API 时发生错误:{data}") | |
yield data | |
continue | |
data = json.loads(e[6:]) | |
if data.get("usage"): | |
break | |
if choices := data.get("choices"): | |
if chunk := choices[0].get("delta", ""): | |
text += chunk | |
yield {"error_code": 0, "text": text} | |
def do_embeddings(self, params: ApiEmbeddingsParams) -> Dict: | |
params.load_config(self.model_names[0]) | |
url = f"https://api.minimax.chat/v1/embeddings?GroupId={params.group_id}" | |
headers = { | |
"Authorization": f"Bearer {params.api_key}", | |
"Content-Type": "application/json", | |
} | |
data = { | |
"model": params.embed_model or self.DEFAULT_EMBED_MODEL, | |
"texts": [], | |
"type": "query" if params.to_query else "db", | |
} | |
if log_verbose: | |
logger.info(f'{self.__class__.__name__}:data: {data}') | |
logger.info(f'{self.__class__.__name__}:url: {url}') | |
logger.info(f'{self.__class__.__name__}:headers: {headers}') | |
with get_httpx_client() as client: | |
result = [] | |
i = 0 | |
batch_size = 10 | |
while i < len(params.texts): | |
texts = params.texts[i:i+batch_size] | |
data["texts"] = texts | |
r = client.post(url, headers=headers, json=data).json() | |
if embeddings := r.get("vectors"): | |
result += embeddings | |
elif error := r.get("base_resp"): | |
data = { | |
"code": error["status_code"], | |
"msg": error["status_msg"], | |
"error": { | |
"message": error["status_msg"], | |
"type": "invalid_request_error", | |
"param": None, | |
"code": None, | |
} | |
} | |
self.logger.error(f"请求 MiniMax API 时发生错误:{data}") | |
return data | |
i += batch_size | |
return {"code": 200, "data": result} | |
def get_embeddings(self, params): | |
print("embedding") | |
print(params) | |
def make_conv_template(self, conv_template: str = None, model_path: str = None) -> Conversation: | |
return conv.Conversation( | |
name=self.model_names[0], | |
system_message="你是MiniMax自主研发的大型语言模型,回答问题简洁有条理。", | |
messages=[], | |
roles=["USER", "BOT"], | |
sep="\n### ", | |
stop_str="###", | |
) | |
if __name__ == "__main__": | |
import uvicorn | |
from server.utils import MakeFastAPIOffline | |
from fastchat.serve.model_worker import app | |
worker = MiniMaxWorker( | |
controller_addr="http://127.0.0.1:20001", | |
worker_addr="http://127.0.0.1:21002", | |
) | |
sys.modules["fastchat.serve.model_worker"].worker = worker | |
MakeFastAPIOffline(app) | |
uvicorn.run(app, port=21002) | |