Add new files and update existing files

backend/test1/chromedb.py

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+import chromadb
+
+# 创建 ChromaDB 客户端
+chroma_client = chromadb.PersistentClient(path="/home/bw/ctr/zjdataai-app/backend/storage_vector-1/")
+
+# 获取已存在的 "default" 集合
+collection = chroma_client.get_collection(name="default")
+
+# 获取集合中的所有数据
+results = collection.get(
+    include=['documents', 'metadatas', 'embeddings']  # 只包含允许的选项
+)
+
+# 将结果转换为字符串并保存到txt文件中
+with open('/home/bw/ctr/zjdataai-app/backend/test1/query_results-1.txt', 'w', encoding='utf-8') as file:
+    file.write(str(results))
+
+# 打印结果
+print("查询结果已保存到 query_results.txt 文件中。")

backend/test1/incorrect_answers_log.txt

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+

backend/test1/query_test.py

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+import re
+import os
+import sys
+from ctypes import cast
+
+from llama_index.core import VectorStoreIndex, SQLDatabase
+from llama_index.core.indices.struct_store import SQLTableRetrieverQueryEngine
+from llama_index.core.objects import SQLTableNodeMapping, ObjectIndex
+from llama_index.readers.database import DatabaseReader
+from sqlalchemy import create_engine
+
+from app.api.routers.chat import generate_filters
+from app.engine import get_index, makeDescriptionByEngine
+from app.engine.loaders.db import CustomDatabaseReader
+from app.engine.vectordb import get_vector_store
+from app.observability import init_observability
+from app.settings import init_settings
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+def read_questions_and_answers(file_path):
+    questions_and_answers = []
+    with open(file_path, 'r', encoding='utf-8') as file:
+        for line in file:
+            if "question" in line and "answer" in line:
+                question_part = line.split("：")[1].strip()  # 提取 question
+                answer_part = re.search(r"answer：.*?(\d+)", line)  # 使用正则提取 answer 中的数字
+                if answer_part:
+                    answer_value = answer_part.group(1)
+                    questions_and_answers.append((question_part, answer_value))
+    return questions_and_answers
+
+def save_results_to_file(question, result, correct_answer, file_path):
+    with open(file_path, 'a', encoding='utf-8') as file:
+        file.write(f"问题: {question}\n")
+        file.write(f"查询结果: {result}\n")
+        file.write(f"正确答案: {correct_answer}\n\n")
+
+def log_incorrect_answers(question, correct_answer, result, log_file_path):
+    with open(log_file_path, 'a', encoding='utf-8') as file:
+        file.write(f"错误问题: {question}\n")
+        file.write(f"正确答案: {correct_answer}\n")
+        file.write(f"查询结果: {result}\n\n")
+
+def main():
+    # 从命令行读取questions_file_path和查询类型
+    if len(sys.argv) < 3:
+        print("请提供questions.txt文件的路径和查询类型（vector 或 sql）")
+        sys.exit(1)
+    questions_file_path = sys.argv[1]
+    query_type = sys.argv[2].lower()  
+
+    # 获取脚本所在的目录
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    
+    # 设置结果文件和日志文件的路径
+    results_file_path = os.path.join(script_dir, "query_results.txt")
+    log_file_path = os.path.join(script_dir, "incorrect_answers_log.txt")
+
+    # 更新环境变量
+    os.environ['TOP_K'] = str(5)  # 向量的TOP_K值
+    os.environ['LLM_TEMPERATURE'] = str(0.1)  # 温度值
+    os.environ['similarity_top_k'] = str(5)  # SQL的TOP_K值
+
+    init_settings()
+    init_observability()
+
+    index = get_index()
+
+    top_k = int(os.getenv("TOP_K"))  # 向量的TOP_K值
+    temperature = float(os.getenv("LLM_TEMPERATURE"))  # 温度值
+    similarity_top_k = float(os.getenv("similarity_top_k"))  # SQL的TOP_K值
+    filters = generate_filters([])
+
+    engine = create_engine(os.getenv("SQL_DATABASE_URL", ""))
+    sql_database = SQLDatabase(engine)
+    table_schema_objs = makeDescriptionByEngine(sql_database)
+    table_node_mapping = SQLTableNodeMapping(sql_database)
+    # 创建SQL查询工具
+    sql_obj_index = ObjectIndex.from_objects(
+        table_schema_objs,
+        table_node_mapping,
+        index_cls=VectorStoreIndex,
+    )
+    sql_query_engine = SQLTableRetrieverQueryEngine(sql_database,
+                                                    sql_obj_index.as_retriever(similarity_top_k=similarity_top_k))
+
+    questions_and_answers = read_questions_and_answers(questions_file_path)
+
+    # 如果文件为空，则写入参数值
+    if os.path.getsize(results_file_path) == 0:
+        with open(results_file_path, 'w', encoding='utf-8') as file:
+            file.write(f"TOP_K: {top_k}\n")
+            file.write(f"LLM_TEMPERATURE: {temperature}\n")
+            file.write(f"similarity_top_k: {similarity_top_k}\n\n")
+
+    # 循环执行查询
+    for i, (question, correct_answer) in enumerate(questions_and_answers):
+        print(f"Executing query {i+1}: {question}")
+        
+        if query_type == "vector":
+            query_engine = index.as_query_engine(
+                similarity_top_k=top_k, filters=filters
+            )
+            query_result = query_engine.query(question)
+            print(f"向量查询结果: {query_result}\n")
+            save_results_to_file(question, f"向量查询结果: {query_result}", correct_answer, results_file_path)
+
+            # 提取向量查询结果中的数字进行匹配
+            query_result_number = re.search(r"(\d+)", str(query_result))
+        elif query_type == "sql":
+            sql_query_result = sql_query_engine.query(question)
+            print(f"SQL查询结果: {sql_query_result}\n")
+            save_results_to_file(question, f"SQL查询结果: {sql_query_result}", correct_answer, results_file_path)
+
+            # 提取SQL查询结果中的数字进行匹配
+            query_result_number = re.search(r"(\d+)", str(sql_query_result))
+        else:
+            print("无效的查询类型，请选择 'vector' 或 'sql'")
+            sys.exit(1)
+        
+        if query_result_number:
+            query_number = query_result_number.group(1)
+
+            # 判断查询结果是否与正确答案匹配
+            if query_number == correct_answer:
+                save_results_to_file(question, query_number, correct_answer, results_file_path)
+            else:
+                log_incorrect_answers(question, correct_answer, query_number, log_file_path)
+        else:
+            log_incorrect_answers(question, correct_answer, "未找到有效数字", log_file_path)
+
+if __name__ == "__main__":
+    from phoenix.trace import using_project
+
+    with using_project("ly_zjapp_test") as obj:
+        main()

backend/test1/question.py

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+import os
+import random
+from sqlalchemy import create_engine, MetaData, Table, select, func
+from sqlalchemy.orm import sessionmaker
+from dotenv import load_dotenv
+
+load_dotenv()
+
+def generate_questions(file_path, num_questions_per_table=10):
+    engine = create_engine(os.getenv("SQL_DATABASE_URL", ""))
+    metadata = MetaData()
+    metadata.reflect(bind=engine)
+
+    # 定义表名及其对应的列索引和问题模板
+    tables_info = {
+        "ProjectProperties": (0, "Attribute_Value", "{name_value}的属性值是多少?"),
+        "OtherFee": (0, "Amount", "{name_value}的金额是多少?"),
+        "FeeCollectionTable": (0, "Rate", "{name_value}的费率是多少?"),
+        "ProjectDivision": (0, "Total_Price", "{name_value}的合价是多少?"),
+        "ProjectDivisions_CostPreview": (0, "Direct_Fee", "{name_value}的直接费是多少?"),
+        "TotalCalculateTable": (0, "Amount", "{name_value}的金额是多少?"),
+        "ProjectQuantities": (0, "Code", "{name_value}的编码是多少?")
+    }
+
+    questions = []
+
+    for table_name, (name_index, value_column, question_template) in tables_info.items():
+        # 加载这张表
+        table = Table(table_name, metadata, autoload_with=engine)
+
+        # 创建会话
+        Session = sessionmaker(bind=engine)
+        session = Session()
+
+        # 获取列名
+        name_column = table.columns.keys()[name_index]
+
+        # 对于每个表生成num_questions_per_table个问题
+        for _ in range(num_questions_per_table):
+            # 查询表中的随机一行，并获取名称列的值
+            row = session.query(table).order_by(func.random()).first()
+            name_value = getattr(row, name_column)
+
+            # 构造问题
+            question = question_template.format(name_value=name_value)
+            questions.append(question)
+
+    # 写入文件
+    with open(file_path, 'w', encoding='utf-8') as file:
+        for question in questions:
+            file.write(question + '\n')
+
+
+if __name__ == "__main__":
+    questions_file_path = "/home/bw/ctr/zjdataai-app/backend/test1/questions.txt"
+    generate_questions(questions_file_path)

backend/test1/questions.txt

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+question：线路参数_转角次数的属性值是多少?     answer：线路参数_转角次数的属性值是64
+question：接地土石方量的属性值是多少?      answer：接地土石方量的属性值是16
+question：工程监理费的金额是多少?      answer：工程监理费的金额是131009.92
+question：矿产压覆评估费用的金额是多少?      answer：矿产压覆评估费用的金额是0
+question：线路取费表（余物清理）的费率是多少?      answer：线路取费表（余物清理）的费率是100
+question：线路取费表（拆除）的费率是多少?      answer：线路取费表（拆除）的费率是100
+question：一般线路本体工程的合价是多少?      answer：一般线路本体工程的合价是55105688268.5176
+question：基础工程的合价是多少?      answer：基础工程的合价是49051649642.9667
+question：线路取费表(调试工程)aa的直接费是多少?      answer：线路取费表(调试工程)aa的直接费是22411207942.4858
+question：线路取费表的直接费是多少?       answer：线路取费表的直接费是7314300665.34141
+question：一般线路本体工程的金额是多少?       answer：一般线路本体工程的金额是55105688268.5176
+question：架空输电线路本体工程的金额是多少?      answer：架空输电线路本体工程的金额是55105688268.5176
+question：截止阀的编码是多少?      answer：截止阀的编码是F01010101
+question：自定义主材的编码是多少?      answer：自定义主材的编码是asd

backend/test1/test_parameters.py

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+import os
+from ctypes import cast
+import sys
+
+from llama_index.core import VectorStoreIndex, SQLDatabase
+from llama_index.core.indices.struct_store import SQLTableRetrieverQueryEngine
+from llama_index.core.objects import SQLTableNodeMapping, ObjectIndex
+from llama_index.readers.database import DatabaseReader
+from sqlalchemy import create_engine
+
+from app.api.routers.chat import generate_filters
+from app.engine import get_index, makeDescriptionByEngine
+from app.engine.loaders.db import CustomDatabaseReader
+from app.engine.vectordb import get_vector_store
+from app.observability import init_observability
+from app.settings import init_settings
+from dotenv import load_dotenv
+
+load_dotenv()
+
+def read_questions(file_path):
+    questions = []
+    with open(file_path, 'r', encoding='utf-8') as file:
+        for line in file:
+            if "question" in line:
+                question_part = line.split("：")[1].strip()  # 提取 "question" 后的内容
+                questions.append(question_part)
+    return questions
+
+def save_results_to_file(question, result, file_path):
+    with open(file_path, 'a', encoding='utf-8') as file:
+        file.write(f"问题: {question}\n")
+        file.write(f"结果: {result}\n\n")
+
+def main():
+    # 从命令行读取questions_file_path
+    if len(sys.argv) < 2:
+        print("请提供questions.txt文件的路径")
+        sys.exit(1)
+    questions_file_path = sys.argv[1]
+    # 更新环境变量
+    os.environ['TOP_K'] = str(5)  # 向量的TOP_K值
+    os.environ['LLM_TEMPERATURE'] = str(0.1)  # 温度值
+    os.environ['similarity_top_k'] = str(5)  # SQL的TOP_K值
+
+    init_settings()
+    init_observability()
+
+    index = get_index()
+
+    top_k = int(os.getenv("TOP_K"))  # 向量的TOP_K值
+    temperature = float(os.getenv("LLM_TEMPERATURE"))  # 温度值
+    similarity_top_k = float(os.getenv("similarity_top_k"))  # SQL的TOP_K值
+    filters = generate_filters([])
+
+    engine = create_engine(os.getenv("SQL_DATABASE_URL", ""))
+    sql_database = SQLDatabase(engine)
+    table_schema_objs = makeDescriptionByEngine(sql_database)
+    table_node_mapping = SQLTableNodeMapping(sql_database)
+    # 创建SQL查询工具
+    sql_obj_index = ObjectIndex.from_objects(
+        table_schema_objs,
+        table_node_mapping,
+        index_cls=VectorStoreIndex,
+    )
+    sql_query_engine = SQLTableRetrieverQueryEngine(sql_database,
+                                                    sql_obj_index.as_retriever(similarity_top_k=similarity_top_k))
+
+    questions = read_questions(questions_file_path)
+
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+
+    results_file_path = os.path.join(script_dir, "query_results.txt")
+    
+    # 如果文件为空，则写入参数值
+    if os.path.getsize(results_file_path) == 0:
+        with open(results_file_path, 'w', encoding='utf-8') as file:
+            file.write(f"TOP_K: {top_k}\n")
+            file.write(f"LLM_TEMPERATURE: {temperature}\n")
+            file.write(f"similarity_top_k: {similarity_top_k}\n\n")
+
+    # 循环执行查询
+    for i, question in enumerate(questions):
+        print(f"Executing query {i+1}: {question}")
+        # query_engine = index.as_query_engine(
+        #     similarity_top_k=top_k, filters=filters
+        # )
+        # query_result = query_engine.query(question)
+
+        # print(f"向量查询结果: {query_result}\n")
+        # save_results_to_file(question, f"向量查询结果: {query_result}", results_file_path)
+
+        sql_query_result = sql_query_engine.query(question)
+        print(f"SQL查询结果: {sql_query_result}\n")
+        save_results_to_file(question, f"SQL查询结果: {sql_query_result}", results_file_path)
+
+if __name__ == "__main__":
+    from phoenix.trace import using_project
+
+    with using_project("ly_zjapp_test") as obj:
+        main()