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
107 lines
3.5 KiB
Python
107 lines
3.5 KiB
Python
from fastchat.conversation import Conversation
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from server.model_workers.base import *
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from fastchat import conversation as conv
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import sys
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from typing import List, Dict, Iterator, Literal
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from configs import logger, log_verbose
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import requests
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import jwt
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import time
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import json
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def generate_token(apikey: str, exp_seconds: int):
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try:
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id, secret = apikey.split(".")
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except Exception as e:
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raise Exception("invalid apikey", e)
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payload = {
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"api_key": id,
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"exp": int(round(time.time() * 1000)) + exp_seconds * 1000,
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"timestamp": int(round(time.time() * 1000)),
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}
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return jwt.encode(
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payload,
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secret,
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algorithm="HS256",
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headers={"alg": "HS256", "sign_type": "SIGN"},
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)
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class ChatGLMWorker(ApiModelWorker):
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def __init__(
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self,
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*,
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model_names: List[str] = ["zhipu-api"],
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controller_addr: str = None,
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worker_addr: str = None,
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version: Literal["chatglm_turbo"] = "chatglm_turbo",
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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", 4096)
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super().__init__(**kwargs)
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self.version = version
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def do_chat(self, params: ApiChatParams) -> Iterator[Dict]:
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params.load_config(self.model_names[0])
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token = generate_token(params.api_key, 60)
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {token}"
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}
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data = {
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"model": params.version,
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"messages": params.messages,
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"max_tokens": params.max_tokens,
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"temperature": params.temperature,
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"stream": False
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}
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url = "https://open.bigmodel.cn/api/paas/v4/chat/completions"
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response = requests.post(url, headers=headers, json=data)
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# for chunk in response.iter_lines():
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# if chunk:
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# chunk_str = chunk.decode('utf-8')
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# json_start_pos = chunk_str.find('{"id"')
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# if json_start_pos != -1:
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# json_str = chunk_str[json_start_pos:]
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# json_data = json.loads(json_str)
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# for choice in json_data.get('choices', []):
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# delta = choice.get('delta', {})
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# content = delta.get('content', '')
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# yield {"error_code": 0, "text": content}
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ans = response.json()
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content = ans["choices"][0]["message"]["content"]
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yield {"error_code": 0, "text": content}
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def get_embeddings(self, params):
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# 临时解决方案，不支持embedding
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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="你是智谱AI小助手，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", "system"],
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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 = ChatGLMWorker(
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controller_addr="http://127.0.0.1:20001",
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worker_addr="http://127.0.0.1:21001",
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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=21001)
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