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
85 lines
2.8 KiB
Python
85 lines
2.8 KiB
Python
import json
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import time
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import hashlib
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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 fastchat import conversation as conv
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import json
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from typing import List, Literal, Dict
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import requests
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class TianGongWorker(ApiModelWorker):
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def __init__(
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self,
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*,
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controller_addr: str = None,
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worker_addr: str = None,
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model_names: List[str] = ["tiangong-api"],
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version: Literal["SkyChat-MegaVerse"] = "SkyChat-MegaVerse",
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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", 32768)
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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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url = 'https://sky-api.singularity-ai.com/saas/api/v4/generate'
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data = {
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"messages": params.messages,
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"model": "SkyChat-MegaVerse"
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}
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timestamp = str(int(time.time()))
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sign_content = params.api_key + params.secret_key + timestamp
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sign_result = hashlib.md5(sign_content.encode('utf-8')).hexdigest()
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headers = {
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"app_key": params.api_key,
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"timestamp": timestamp,
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"sign": sign_result,
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"Content-Type": "application/json",
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"stream": "true" # or change to "false" 不处理流式返回内容
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}
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# 发起请求并获取响应
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response = requests.post(url, headers=headers, json=data, stream=True)
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text = ""
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# 处理响应流
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for line in response.iter_lines(chunk_size=None, decode_unicode=True):
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if line:
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# 处理接收到的数据
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# print(line.decode('utf-8'))
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resp = json.loads(line)
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if resp["code"] == 200:
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text += resp['resp_data']['reply']
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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["code"],
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"text": resp["code_msg"]
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}
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self.logger.error(f"请求天工 API 时出错：{data}")
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yield data
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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="",
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messages=[],
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roles=["user", "system"],
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sep="\n### ",
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stop_str="###",
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)
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