embedding 模型 使用通用文本向量 text-embedding-v1 from dotenv import load_dotenv import os load_dotenv() os.environ['DASHSCOPE_API_KEY'] = os.getenv('DASHSCOPE_API_KEY') os.environ['DASHSCOPE_BASE_URL'] = os.getenv('DASHSCOPE_BASE_URL') import dashscope from http import HTTPStatus imp...
#"embed_model": "text-embedding-v1" # embedding 模型名称 }, # 百川 API,申请方式请参考 https://www.baichuan-ai.com/home#api-enter "baichuan-api": { "version": "Baichuan2-53B", "api_key": "", "secret_key": "", "provider": "BaiChuanWorker", }, # Azure API "azure-api": {...
定义app的路由:路由指向v1/chat/completions 定义app的处理函数:处理函数调用generate_text函数,传入request接收的兼容OpenAI的请求体模型。 文本和图像生成generate_text:提取query、image_url,构造query,传入qwen_vl.chat(),基于图片和文本生成response返回 API返回格式:拼接choices、message、content等构造兼容OpenAI API的...
AI代码解释 # vllm serve Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4INFO09-3006:47:30api_server.py:526]vLLMAPIserver version0.6.1.dev238+ge2c6e0a82INFO09-3006:47:30api_server.py:527]args:Namespace(model_tag='Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4',config='',host=None,port=8000,uvicorn_log_lev...
api_base = "http://localhost:8000/v1" openai.api_key = "none" # create a request activating streaming response for chunk in openai.ChatCompletion.create( model="Qwen", messages=[ {"role": "user", "content": "你好"} ], stream=True # Specifying stop words in streaming output format ...
sentence_embedding, model=self.model_id) except ImportError as e: raise ValueError( "Could not import some python packages." "Please install it with `pip install modelscope`." ) from e def _get_query_embedding(self, query: str) -> List[float]: text = query.replace("\n", " ") ...
"/root/autodl-tmp/bge-large-zh-v1.5", "piccolo-base-zh": "sensenova/piccolo-base-zh", "piccolo-large-zh": "sensenova/piccolo-large-zh", "nlp_gte_sentence-embedding_chinese-large": "damo/nlp_gte_sentence-embedding_chinese-large", "text-embedding-ada-002": "your OPENAI_API_KEY", }...
正是在这样的背景下,检索增强生成技术(Retrieval-Augmented Generation,RAG)应时而生,成为 AI 时代的一大趋势。RAG 通过在语言模型生成答案之前,先从广...
By using strong LLMs as judges and converting multimodal information into text. 2023.8.31 🌟🌟🌟 We release the Int4 quantized model for Qwen-VL-Chat, Qwen-VL-Chat-Int4, which requires low memory costs but achieves improved inference speed. Besides, there is no significant performance ...
, "吃海鲜是不可以吃柠檬的因为其中的维生素C会和海鲜中的矿物质形成砷" ] } result = pipeline_se(input=inputs) print (result) ''' {'text_embedding': array([[ 1.6415151e-04, 2.2334497e-02, -2.4202393e-02, ..., 2.7710509e-02, 2.5980933e-02, -3.1285528e-02], [-9.9107623e-03, ...