mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
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9c525b7fa5
新功能： - 优化 PDF 文件的 OCR，过滤无意义的小图片 by @liunux4odoo #2525 - 支持 Gemini 在线模型 by @yhfgyyf #2630 - 支持 GLM4 在线模型 by @zRzRzRzRzRzRzR - elasticsearch更新https连接 by @xldistance #2390 - 增强对PPT、DOC知识库文件的OCR识别 by @596192804 #2013 - 更新 Agent 对话功能 by @zRzRzRzRzRzRzR - 每次创建对象时从连接池获取连接，避免每次执行方法时都新建连接 by @Lijia0 #2480 - 实现 ChatOpenAI 判断token有没有超过模型的context上下文长度 by @glide-the - 更新运行数据库报错和项目里程碑 by @zRzRzRzRzRzRzR #2659 - 更新配置文件/文档/依赖 by @imClumsyPanda @zRzRzRzRzRzRzR - 添加日文版 readme by @eltociear #2787 修复： - langchain 更新后，PGVector 向量库连接错误 by @HALIndex #2591 - Minimax's model worker 错误 by @xyhshen - ES库无法向量检索.添加mappings创建向量索引 by MSZheng20 #2688
217 lines
8.2 KiB
Python
217 lines
8.2 KiB
Python
import sys
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from fastchat.conversation import Conversation
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from server.model_workers.base import *
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from server.utils import get_httpx_client
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from cachetools import cached, TTLCache
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import json
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from fastchat import conversation as conv
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import sys
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from server.model_workers.base import ApiEmbeddingsParams
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from typing import List, Literal, Dict
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from configs import logger, log_verbose
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MODEL_VERSIONS = {
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"ernie-bot-4": "completions_pro",
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"ernie-bot": "completions",
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"ernie-bot-turbo": "eb-instant",
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"bloomz-7b": "bloomz_7b1",
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"qianfan-bloomz-7b-c": "qianfan_bloomz_7b_compressed",
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"llama2-7b-chat": "llama_2_7b",
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"llama2-13b-chat": "llama_2_13b",
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"llama2-70b-chat": "llama_2_70b",
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"qianfan-llama2-ch-7b": "qianfan_chinese_llama_2_7b",
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"chatglm2-6b-32k": "chatglm2_6b_32k",
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"aquilachat-7b": "aquilachat_7b",
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# "linly-llama2-ch-7b": "", # 暂未发布
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# "linly-llama2-ch-13b": "", # 暂未发布
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# "chatglm2-6b": "", # 暂未发布
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# "chatglm2-6b-int4": "", # 暂未发布
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# "falcon-7b": "", # 暂未发布
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# "falcon-180b-chat": "", # 暂未发布
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# "falcon-40b": "", # 暂未发布
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# "rwkv4-world": "", # 暂未发布
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# "rwkv5-world": "", # 暂未发布
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# "rwkv4-pile-14b": "", # 暂未发布
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# "rwkv4-raven-14b": "", # 暂未发布
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# "open-llama-7b": "", # 暂未发布
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# "dolly-12b": "", # 暂未发布
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# "mpt-7b-instruct": "", # 暂未发布
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# "mpt-30b-instruct": "", # 暂未发布
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# "OA-Pythia-12B-SFT-4": "", # 暂未发布
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# "xverse-13b": "", # 暂未发布
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# # 以下为企业测试，需要单独申请
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# "flan-ul2": "",
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# "Cerebras-GPT-6.7B": ""
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# "Pythia-6.9B": ""
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}
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@cached(TTLCache(1, 1800)) # 经过测试，缓存的token可以使用，目前每30分钟刷新一次
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def get_baidu_access_token(api_key: str, secret_key: str) -> str:
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"""
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使用 AK，SK 生成鉴权签名（Access Token）
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:return: access_token，或是None(如果错误)
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"""
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url = "https://aip.baidubce.com/oauth/2.0/token"
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params = {"grant_type": "client_credentials", "client_id": api_key, "client_secret": secret_key}
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try:
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with get_httpx_client() as client:
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return client.get(url, params=params).json().get("access_token")
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except Exception as e:
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print(f"failed to get token from baidu: {e}")
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class QianFanWorker(ApiModelWorker):
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"""
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百度千帆
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"""
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DEFAULT_EMBED_MODEL = "embedding-v1"
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def __init__(
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self,
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*,
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version: Literal["ernie-bot", "ernie-bot-turbo"] = "ernie-bot",
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model_names: List[str] = ["qianfan-api"],
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controller_addr: str = None,
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worker_addr: str = None,
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**kwargs,
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):
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kwargs.update(model_names=model_names, controller_addr=controller_addr, worker_addr=worker_addr)
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kwargs.setdefault("context_len", 16384)
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super().__init__(**kwargs)
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self.version = version
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def do_chat(self, params: ApiChatParams) -> Dict:
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params.load_config(self.model_names[0])
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BASE_URL = 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat' \
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'/{model_version}?access_token={access_token}'
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access_token = get_baidu_access_token(params.api_key, params.secret_key)
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if not access_token:
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yield {
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"error_code": 403,
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"text": f"failed to get access token. have you set the correct api_key and secret key?",
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}
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url = BASE_URL.format(
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model_version=params.version_url or MODEL_VERSIONS[params.version.lower()],
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access_token=access_token,
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)
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payload = {
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"messages": params.messages,
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"temperature": params.temperature,
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"stream": True
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}
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headers = {
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'Content-Type': 'application/json',
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'Accept': 'application/json',
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}
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text = ""
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if log_verbose:
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logger.info(f'{self.__class__.__name__}:data: {payload}')
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logger.info(f'{self.__class__.__name__}:url: {url}')
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logger.info(f'{self.__class__.__name__}:headers: {headers}')
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with get_httpx_client() as client:
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with client.stream("POST", url, headers=headers, json=payload) as response:
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for line in response.iter_lines():
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if not line.strip():
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continue
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if line.startswith("data: "):
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line = line[6:]
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resp = json.loads(line)
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if "result" in resp.keys():
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text += resp["result"]
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yield {
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"error_code": 0,
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"text": text
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}
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else:
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data = {
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"error_code": resp["error_code"],
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"text": resp["error_msg"],
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"error": {
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"message": resp["error_msg"],
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"type": "invalid_request_error",
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"param": None,
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"code": None,
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}
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}
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self.logger.error(f"请求千帆 API 时发生错误：{data}")
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yield data
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def do_embeddings(self, params: ApiEmbeddingsParams) -> Dict:
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params.load_config(self.model_names[0])
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# import qianfan
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# embed = qianfan.Embedding(ak=params.api_key, sk=params.secret_key)
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# resp = embed.do(texts = params.texts, model=params.embed_model or self.DEFAULT_EMBED_MODEL)
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# if resp.code == 200:
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# embeddings = [x.embedding for x in resp.body.get("data", [])]
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# return {"code": 200, "embeddings": embeddings}
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# else:
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# return {"code": resp.code, "msg": str(resp.body)}
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embed_model = params.embed_model or self.DEFAULT_EMBED_MODEL
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access_token = get_baidu_access_token(params.api_key, params.secret_key)
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url = f"https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/embeddings/{embed_model}?access_token={access_token}"
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if log_verbose:
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logger.info(f'{self.__class__.__name__}:url: {url}')
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with get_httpx_client() as client:
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result = []
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i = 0
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batch_size = 10
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while i < len(params.texts):
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texts = params.texts[i:i + batch_size]
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resp = client.post(url, json={"input": texts}).json()
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if "error_code" in resp:
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data = {
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"code": resp["error_code"],
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"msg": resp["error_msg"],
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"error": {
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"message": resp["error_msg"],
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"type": "invalid_request_error",
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"param": None,
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"code": None,
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}
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}
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self.logger.error(f"请求千帆 API 时发生错误：{data}")
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return data
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else:
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embeddings = [x["embedding"] for x in resp.get("data", [])]
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result += embeddings
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i += batch_size
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return {"code": 200, "data": result}
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def get_embeddings(self, params):
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print("embedding")
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print(params)
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def make_conv_template(self, conv_template: str = None, model_path: str = None) -> Conversation:
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return conv.Conversation(
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name=self.model_names[0],
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system_message="你是一个聪明的助手，请根据用户的提示来完成任务",
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messages=[],
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roles=["user", "assistant"],
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sep="\n### ",
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stop_str="###",
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)
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if __name__ == "__main__":
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import uvicorn
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from server.utils import MakeFastAPIOffline
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from fastchat.serve.model_worker import app
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worker = QianFanWorker(
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controller_addr="http://127.0.0.1:20001",
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worker_addr="http://127.0.0.1:21004"
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)
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sys.modules["fastchat.serve.model_worker"].worker = worker
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MakeFastAPIOffline(app)
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uvicorn.run(app, port=21004)
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