rasa : 创建镜像text2vec_env

virtual-patient-rasa/Dockerfile

@@ -1,6 +1,9 @@
 # 设置基础镜像
 FROM rasa_dev:1.0.0
 
+
+COPY ./docs/docker-entrypoint.sh /usr/local/bin/docker-entrypoint.sh
+
 # 设置工作目录
 WORKDIR /home/app
 
@@ -11,6 +14,5 @@ COPY docs/rasa /rasa
 
 # 暴漏服务端口
 EXPOSE 8890
-
 # 设置启动命令
-ENTRYPOINT ["java","-jar","-Duser.timezone=Asia/Shanghai","/home/app/virtual-patient-rasa-1.0-SNAPSHOT.jar"]
+ENTRYPOINT ["/usr/local/bin/docker-entrypoint.sh"]

virtual-patient-rasa/README.md

@@ -0,0 +1,6 @@
+#### rasa镜像版本说明
+| 镜像名称     | 镜像版本  |镜像id| 说明                      |
+|----------|-------| --- |-------------------------|
+| rasa_dev | 1.0.0 | 365fe9f00bac | rasa镜像基础版本，只包含必要的rasa服务 |
+| rasa_dev | 1.1.0 |  | 在1.0.0的基础上添加text2vec服务  |
+| rasa_dev | 2.0.0 | 22313f228098 | 添加rasa-java服务   |

virtual-patient-rasa/docker_1_1_0/Dockerfile

@@ -0,0 +1,14 @@
+# 设置基础镜像
+FROM rasa_dev:1.0.0
+
+COPY ./bert_chinese /usr/local/text2vec/bert_chinese
+COPY ./app.py /usr/local/text2vec/
+
+RUN /root/anaconda3/condabin/conda  create --name text2vec_env python=3.9 -y && \
+    /root/anaconda3/condabin/conda run --no-capture-output --name text2vec_env   pip install torch && \
+    /root/anaconda3/condabin/conda run --no-capture-output --name text2vec_env pip install flask && \
+    /root/anaconda3/condabin/conda run --no-capture-output --name text2vec_env pip install text2vec -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+expose 5000
+#CMD [ "/root/anaconda3/condabin/conda","run","--no-capture-output","--name","text2vec_env", "python", "/usr/local/text2vec/app.py"]
+

virtual-patient-rasa/docker_1_1_0/app.py

@@ -0,0 +1,101 @@
+from flask import Flask, request, jsonify
+from text2vec import SentenceModel
+import numpy as np
+import traceback
+import json
+import os
+
+app = Flask(__name__)
+
+# 获取当前脚本所在的目录
+current_dir = os.path.dirname(os.path.abspath(__file__))
+
+# BERT模型路径
+model_path = os.path.join(current_dir, 'bert_chinese')
+model = None
+questions_data = []  # 用于存储问题数据
+sentence_embeddings = []
+default_threshold = 0.7
+# 数据集文件路径
+dataset_file_path = os.path.join(current_dir, 'question.json')
+
+# 初始化函数，用于加载模型和数据集
+def initialize_app():
+    global model, questions_data, sentence_embeddings
+    model = SentenceModel(model_path)
+    load_dataset()
+
+# 加载数据集
+def load_dataset():
+    global questions_data, sentence_embeddings, sentences
+    try:
+        with open(dataset_file_path, 'r', encoding='utf-8') as file:
+            questions_data = json.load(file)
+            sentences = [item["question"] for item in questions_data]
+            # 重新编码句子
+            sentence_embeddings = [model.encode(sent) / np.linalg.norm(model.encode(sent)) for sent in sentences]
+
+    except Exception as e:
+        traceback.print_exc()
+        print(f"Error loading dataset: {str(e)}")
+
+# 错误处理程序
+@app.errorhandler(Exception)
+def handle_error(e):
+    traceback.print_exc()
+    return jsonify({'error': str(e)}), 500
+
+# 初始化应用
+initialize_app()
+
+# 替换数据集的接口
+@app.route('/update_dataset', methods=['POST'])
+def update_dataset():
+    global questions_data, sentence_embeddings
+    new_dataset = request.json or []
+
+    # 更新数据集
+    try:
+        with open(dataset_file_path, 'w', encoding='utf-8') as file:
+            json.dump(new_dataset, file, ensure_ascii=False, indent=2)
+        load_dataset()
+        return jsonify({'status': 'success', 'message': '数据集更新成功'})
+    except Exception as e:
+        traceback.print_exc()
+        return jsonify({'error': f'更新数据集错误: {str(e)}'}), 500
+
+# 获取匹配的接口
+@app.route('/matches', methods=['POST'])
+def get_matches():
+    query_sentence = request.json.get('query_sentence', '')
+    query_embedding = model.encode([query_sentence])[0]
+    # 对向量进行单位化
+    query_embedding = query_embedding / np.linalg.norm(query_embedding)
+    # 获取阈值参数，如果请求中没有提供阈值，则使用默认阈值
+    threshold = request.json.get('threshold', default_threshold)
+    # 计算相似度
+    similarities = [embedding.dot(query_embedding) for embedding in sentence_embeddings]
+
+    # 获取所有相似度高于阈值的匹配项
+    matches = [{'id': questions_data[i]["id"], 'sentence': sentences[i], 'similarity': float(similarity)}
+               for i, similarity in enumerate(similarities) if similarity >= threshold]
+
+    return jsonify({'status': 'success', 'matches': matches} if matches else {'status': 'success', 'message': '未找到匹配项'})
+
+# 获取所有相似度的接口
+@app.route('/get_all_similarities', methods=['POST'])
+def get_all_similarities():
+    query_sentence = request.json.get('query_sentence', '')
+    query_embedding = model.encode([query_sentence])[0]
+    # 对向量进行单位化
+    query_embedding = query_embedding / np.linalg.norm(query_embedding)
+
+    # 计算所有数据的相似度和对应的文本
+    results = [{'id': questions_data[i]["id"], 'sentence': sentences[i], 'similarity': float(embedding.dot(query_embedding))}
+               for i, embedding in enumerate(sentence_embeddings)]
+
+    # 返回所有相似度和对应文本
+    return jsonify({'status': 'success', 'results': results})
+
+if __name__ == '__main__':
+    app.run(debug=True, host='0.0.0.0')

virtual-patient-rasa/docker_1_1_0/bert_chinese/config.json

@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "hfl/chinese-macbert-base",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.12.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 21128
+}

virtual-patient-rasa/docs/docker-entrypoint.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+
+# 启动text2vec-server
+/root/anaconda3/condabin/conda run --no-capture-output --name text2vec_env python  /usr/local/text2vec/app.py
+pyton
+# 启动jar包
+java -jar -Duser.timezone=Asia/Shanghai /data/vp/virtual-patient-rasa-1.0-SNAPSHOT.jar "$@"