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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
101 lines
3.6 KiB
Python
101 lines
3.6 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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import json
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from server.model_workers import SparkApi
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import websockets
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from server.utils import iter_over_async, asyncio
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from typing import List, Dict
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async def request(appid, api_key, api_secret, Spark_url, domain, question, temperature, max_token):
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wsParam = SparkApi.Ws_Param(appid, api_key, api_secret, Spark_url)
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wsUrl = wsParam.create_url()
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data = SparkApi.gen_params(appid, domain, question, temperature, max_token)
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print(data)
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async with websockets.connect(wsUrl) as ws:
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await ws.send(json.dumps(data, ensure_ascii=False))
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finish = False
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while not finish:
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chunk = await ws.recv()
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response = json.loads(chunk)
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if response.get("header", {}).get("status") == 2:
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finish = True
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if text := response.get("payload", {}).get("choices", {}).get("text"):
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yield text[0]["content"]
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class XingHuoWorker(ApiModelWorker):
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def __init__(
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self,
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*,
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model_names: List[str] = ["xinghuo-api"],
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controller_addr: str = None,
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worker_addr: str = None,
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version: 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", 8000)
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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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version_mapping = {
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"v1.5": {"domain": "general", "url": "ws://spark-api.xf-yun.com/v1.1/chat","max_tokens": 4000},
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"v2.0": {"domain": "generalv2", "url": "ws://spark-api.xf-yun.com/v2.1/chat","max_tokens": 8000},
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"v3.0": {"domain": "generalv3", "url": "ws://spark-api.xf-yun.com/v3.1/chat","max_tokens": 8000},
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}
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def get_version_details(version_key):
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return version_mapping.get(version_key, {"domain": None, "url": None})
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details = get_version_details(params.version)
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domain = details["domain"]
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Spark_url = details["url"]
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text = ""
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try:
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loop = asyncio.get_event_loop()
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except:
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loop = asyncio.new_event_loop()
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params.max_tokens = min(details["max_tokens"], params.max_tokens or 0)
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for chunk in iter_over_async(
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request(params.APPID, params.api_key, params.APISecret, Spark_url, domain, params.messages,
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params.temperature, params.max_tokens),
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loop=loop,
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):
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if chunk:
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text += chunk
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yield {"error_code": 0, "text": text}
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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 = XingHuoWorker(
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controller_addr="http://127.0.0.1:20001",
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worker_addr="http://127.0.0.1:21003",
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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=21003)
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