mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-19 21:37:20 +08:00
b4c68ddd05
* 优化在线 API ，支持 completion 和 embedding，简化在线 API 开发方式 新功能 - 智谱AI、Minimax、千帆、千问 4 个在线模型支持 embeddings（不通过Fastchat，后续会单独提供相关api接口） - 在线模型自动检测传入参数，在传入非 messages 格式的 prompt 时，自动转换为 completion 形式，以支持 completion 接口 开发者： - 重构ApiModelWorker: - 所有在线 API 请求封装到 do_chat 方法：自动传入参数 ApiChatParams，简化参数与配置项的获取；自动处理与fastchat的接口 - 加强 API 请求错误处理，返回更有意义的信息 - 改用 qianfan sdk 重写 qianfan-api - 将所有在线模型的测试用例统一在一起，简化测试用例编写 * Delete requirements_langflow.txt
195 lines
7.1 KiB
Python
195 lines
7.1 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 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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# MODEL_VERSIONS = {
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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 = "bge-large-zh"
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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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import qianfan
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comp = qianfan.ChatCompletion(model=params.version,
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endpoint=params.version_url,
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ak=params.api_key,
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sk=params.secret_key,)
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text = ""
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for resp in comp.do(messages=params.messages,
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temperature=params.temperature,
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top_p=params.top_p,
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stream=True):
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if resp.code == 200:
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if chunk := resp.body.get("result"):
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text += chunk
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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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yield {
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"error_code": resp.code,
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"text": str(resp.body),
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}
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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],
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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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# 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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# yield {
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# "error_code": resp["error_code"],
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# "text": resp["error_msg"]
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# }
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def do_embeddings(self, params: ApiEmbeddingsParams) -> Dict:
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import qianfan
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params.load_config(self.model_names[0])
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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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# TODO: qianfan支持续写模型
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def get_embeddings(self, params):
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# TODO: 支持embeddings
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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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# TODO: 确认模板是否需要修改
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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) |