diff --git a/Retrieval_new/README.md b/Retrieval_new/README.md
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diff --git a/Retrieval_new/embedding.py b/Retrieval_new/embedding.py
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+++ b/Retrieval_new/embedding.py
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+import os
+import json
+from flask import Flask, request, make_response
+from logzero import logger
+from functools import lru_cache
+
+from utils import *
+from local_encoder import EmbeddingModel
+
+# current_path = os.path.dirname(os.path.abspath(__file__)) # for local
+current_path = os.getcwd()  # for docker
+logger.info(f"{current_path}")
+
+app = Flask(__name__)
+os.environ["CUDA_DEVICE_ORDER"] = 'PCI_BUS_ID'
+os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
+
+
+path = "/home/zhangxj/models/bge-large-zh-v1.5"
+model = EmbeddingModel(path)
+
+@lru_cache()
+def process_and_embed(sentence):
+    # 判断是否为中文
+    if has_no_chinese(sentence):
+        # 英文处理
+        clean_text = preprocess_eng(sentence)
+        processed_text = get_noun_en(clean_text)
+    else:
+        # 中文处理
+        clean_text = preprocess_zh(sentence)
+        processed_text = get_noun_zh(clean_text)
+    
+    # 如果处理后为空，使用原始文本
+    if not processed_text.strip():
+        processed_text = sentence
+        
+    # 获取向量编码
+    embeddings = model.get_embeddings(processed_text)
+    return embeddings
+
+@app.route('/embedding/', methods=["POST"])
+def run_cls():
+    resp_info = dict()
+    if request.method == "POST":
+        sentences = request.json.get('sentences')
+        if sentences is not None and len(sentences) != 0:
+            logger.info(sentences)
+            resp_info["code"] = 200
+            resp_info["data"] = process_and_embed(sentences)
+        else:
+            resp_info["msg"] = "Input is None, please check !"
+            resp_info["code"] = 406
+    resp = make_response(json.dumps(resp_info))
+    resp.status_code = 200
+    return resp
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5163, debug=False)
+    # res = process_and_embed("土豆")
+    # print(res)
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diff --git a/Retrieval_new/local_encoder.py b/Retrieval_new/local_encoder.py
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+++ b/Retrieval_new/local_encoder.py
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+from transformers import AutoTokenizer, AutoModel
+import torch
+import os
+import logging
+from typing import List, Union, Optional
+
+# 配置日志
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+class EmbeddingModel:
+    """
+    文本向量编码模型类
+    封装了加载模型和生成文本向量的功能
+    """
+    def __init__(self, model_path: str):
+        """
+        初始化向量编码模型
+        
+        参数:
+            model_path (str): 预训练模型的路径
+        """
+        self.model_path = model_path
+        try:
+            logger.info(f"正在从{model_path}加载模型")
+            self.tokenizer = AutoTokenizer.from_pretrained(model_path)
+            self.model = AutoModel.from_pretrained(model_path)
+            self.model.eval()  # 设置模型为评估模式
+            logger.info("模型加载成功")
+        except Exception as e:
+            logger.error(f"模型加载失败: {str(e)}")
+            raise
+
+    def get_embeddings(self, sentences: Union[str, List[str]]) -> List[List[float]]:
+        """
+        为输入文本生成向量编码
+        
+        参数:
+            sentences (Union[str, List[str]]): 单个句子或句子列表
+            
+        返回:
+            List[List[float]]: 归一化后的向量编码列表
+        """
+        # 将单个字符串转换为列表以统一处理
+        if isinstance(sentences, str):
+            sentences = [sentences]
+            
+        try:
+            # 对输入进行分词
+            encoded_input = self.tokenizer(
+                sentences, 
+                padding=True,  # 填充
+                truncation=True,  # 截断
+                return_tensors='pt'  # 返回PyTorch张量
+            )
+            
+            # 如果有GPU则使用GPU
+            device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+            self.model.to(device)
+            encoded_input = {k: v.to(device) for k, v in encoded_input.items()}
+            
+            # 生成向量编码
+            with torch.no_grad():  # 不计算梯度
+                model_output = self.model(**encoded_input)
+                # 获取[CLS]标记的向量表示
+                embeddings = model_output[0][:, 0]
+                
+            # 对向量进行归一化
+            embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
+                
+            # 转换为numpy列表并返回
+            return embeddings.cpu().numpy().tolist()
+            
+        except Exception as e:
+            logger.error(f"生成向量编码时出错: {str(e)}")
+            raise
+
+def main():
+    """
+    主函数，用于演示模型的使用
+    """
+    # 从环境变量获取模型路径或使用默认路径
+    model_path = os.environ.get(
+        "MODEL_PATH", 
+        "/home/zhangxj/models/bge-large-zh-v1.5"
+    )
+    
+    try:
+        # 初始化模型
+        embedding_model = EmbeddingModel(model_path)
+        
+        # 示例1: 单个句子向量编码
+        single_sentence = "金刚烷"
+        embeddings = embedding_model.get_embeddings(single_sentence)
+        logger.info(f"单个句子向量维度: {len(embeddings[0])}")
+        
+        # 示例2: 多个句子向量编码
+        sentences = [
+            "The weather is so nice!",
+            "It's so sunny outside!",
+            "He drove to the stadium.",
+        ]
+        batch_embeddings = embedding_model.get_embeddings(sentences)
+        logger.info(f"多个句子向量形状: ({len(batch_embeddings)}, {len(batch_embeddings[0])})")
+        
+    except Exception as e:
+        logger.error(f"主函数执行出错: {str(e)}")
+        
+if __name__ == '__main__':
+    main()
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diff --git a/Retrieval_new/main.py b/Retrieval_new/main.py
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+++ b/Retrieval_new/main.py
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+import requests
+import json
+
+# API 地址
+API_URL = "http://0.0.0.0:5163/embedding/"
+
+# 要发送的数据（JSON 格式）
+data = {
+    "sentences": "土豆"  # 单句示例，可改为 ["土豆", "西红柿"] 处理多句
+}
+
+# 发送 POST 请求
+try:
+    # 设置请求头，明确指定内容类型为 JSON
+    headers = {"Content-Type": "application/json"}
+    
+    # 使用 POST 方法发送请求，data 需要序列化为 JSON 字符串
+    response = requests.post(
+        url=API_URL,
+        data=json.dumps(data),  # 将字典转换为 JSON 字符串
+        headers=headers
+    )
+    
+    # 检查响应状态码
+    if response.status_code == 200:
+        # 解析返回的 JSON 数据
+        result = response.json()
+        print("API Response:", result)
+        
+        # 根据返回的 code 判断结果
+        if result.get("code") == 200:
+            embeddings = result.get("data")
+            print("Embeddings:", embeddings)
+        else:
+            print("Error from API:", result.get("msg"))
+    else:
+        print(f"Request failed with status code: {response.status_code}")
+        print("Response text:", response.text)
+
+except requests.exceptions.RequestException as e:
+    print(f"Error connecting to API: {e}")
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diff --git a/Retrieval_new/utils.py b/Retrieval_new/utils.py
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+++ b/Retrieval_new/utils.py
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+import nltk
+from nltk.tokenize import word_tokenize
+from nltk import pos_tag
+import jieba.posseg as pseg
+
+# 下载相关数据
+nltk.download('punkt')
+nltk.download('averaged_perceptron_tagger')
+
+
+from nltk.stem import WordNetLemmatizer
+import string
+import re
+
+def preprocess_eng(text):
+    '''
+    英文文本预处理：小写化，去除标点（待定），去除特殊符号，只保留单词
+    拼写是否正确：是，因为是从ecoinvent导入的，没有拼写错误；
+    词干提取(stemming)和词形还原(lemmatization)：可以处理一下，有的提取不准确，不做此操作
+    '''
+    # 去除标点
+    text = text.translate(str.maketrans('', '', string.punctuation))
+    # 去除数字
+    text = re.sub(r'\d+', ' ', text)
+    # 去除多余字符
+    text = re.sub(r'[^A-Za-z0-9\s]', '', text)
+     # 去除多余空格
+    text = re.sub(r'\s+', ' ', text)
+    return text
+
+def preprocess_zh(text):
+    '''
+    中文文本预处理:只保留中文内容，去除英文、数字和标点
+    '''
+    text = str(text)
+    # 去除英文
+    text = re.sub(r'[a-zA-Z]',' ',text)
+    text = re.sub(r'\d', ' ', text)
+    # 去除中文标点符号
+    text = re.sub(r'[，。！？、；：“”（）《》【】-]', ' ', text)
+    # 去除英文标点符号
+    text = re.sub(r'[.,!?;:"\'\(\)\[\]{}]', ' ', text)
+    # 去除空格
+    text = re.sub(r'\s+','',text)
+
+    return text
+
+# 英文名词处理
+def get_noun_en(text):
+    # 分词
+    words = word_tokenize(text)
+    # 词性标注
+    tagged = pos_tag(words)
+
+    # 提取名词
+    nouns = [word for word, tag in tagged if tag.startswith('NN')]
+    noun = ' '.join(nouns)
+    return noun
+
+# 中文名词提取
+def get_noun_zh(text):
+    x = str(text)
+    if x=='nan':
+        return ''
+    words = pseg.cut(text)
+    nouns = [word for word, flag in words if flag.startswith('n')]
+    noun = ' '.join(nouns)
+    return noun
+
+def has_no_chinese(text):
+    """
+    判断一个文本是否不包含中文字符
+    
+    参数:
+        text (str): 需要检查的文本
+        
+    返回:
+        bool: 如果文本中没有中文字符返回True，否则返回False
+    """
+    for char in text:
+        if '\u4e00' <= char <= '\u9fff' or \
+           '\u3400' <= char <= '\u4dbf' or \
+           '\u2f00' <= char <= '\u2fdf' or \
+           '\u3100' <= char <= '\u312f' or \
+           '\u31a0' <= char <= '\u31bf':
+            return False
+    return True
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