chentianrui
110 lines
4.3 KiB
Python
110 lines
4.3 KiB
Python
import os
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import json
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import sys
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from llama_index.core import VectorStoreIndex, SQLDatabase
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from llama_index.core.indices.struct_store import SQLTableRetrieverQueryEngine
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from llama_index.core.objects import SQLTableNodeMapping, ObjectIndex
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from sqlalchemy import create_engine
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from app.api.routers.chat import generate_filters
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from app.engine import get_index, makeDescriptionByEngine
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from app.engine.vectordb import get_vector_store
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from app.observability import init_observability
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from app.settings import init_settings
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from dotenv import load_dotenv
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load_dotenv()
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def read_questions(file_path):
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with open(file_path, 'r', encoding='utf-8') as file:
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data = json.load(file)
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questions = [item["question"] for item in data]
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return questions
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def save_results_to_file(question, result, file_path):
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result_data = {
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"question": question,
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"result": result
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}
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with open(file_path, 'a', encoding='utf-8') as file:
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json.dump(result_data, file, ensure_ascii=False)
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file.write('\n')
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def main(questions_file, query_type):
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# 更新环境变量
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os.environ['TOP_K'] = str(5) # 向量的TOP_K值
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os.environ['similarity_top_k'] = str(1) # SQL的TOP_K值固定为1
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init_settings()
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init_observability()
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index = get_index()
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top_k = int(os.getenv("TOP_K")) # 向量的TOP_K值
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similarity_top_k = int(os.getenv("similarity_top_k")) # SQL的TOP_K值
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filters = generate_filters([])
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engine = create_engine(os.getenv("SQL_DATABASE_URL", ""))
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sql_database = SQLDatabase(engine)
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table_schema_objs = makeDescriptionByEngine(sql_database)
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table_node_mapping = SQLTableNodeMapping(sql_database)
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# 创建SQL查询工具
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sql_obj_index = ObjectIndex.from_objects(
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table_schema_objs,
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table_node_mapping,
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index_cls=VectorStoreIndex,
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)
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sql_query_engine = SQLTableRetrieverQueryEngine(sql_database,
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sql_obj_index.as_retriever(similarity_top_k=similarity_top_k))
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script_dir = os.path.dirname(os.path.abspath(__file__))
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questions_file_path = os.path.join(script_dir, questions_file)
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results_file_path = os.path.join(script_dir, "parameters_results.json")
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questions = read_questions(questions_file_path)
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# # 如果文件为空，则写入参数值
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# if not os.path.isfile(results_file_path):
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# with open(results_file_path, 'w', encoding='utf-8') as file:
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# json.dump({
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# "TOP_K": top_k,
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# "similarity_top_k": similarity_top_k
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# }, file, ensure_ascii=False)
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# file.write('\n')
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# 循环执行查询
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for i, question in enumerate(questions):
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print(f"Executing query {i+1}: {question}")
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# 对于每个问题，测试不同的温度值
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for temperature in range(1, 11): # 从1到10
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temperature_value = temperature / 10.0 # 从0.1到1.0
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os.environ['LLM_TEMPERATURE'] = str(temperature_value)
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if query_type == "vector":
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query_engine = index.as_query_engine(
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similarity_top_k=top_k, filters=filters
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)
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query_result = query_engine.query(question)
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print(f"Vector Query Result: {query_result}\n")
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save_results_to_file(question, f"Current parameters: TOP_K={top_k}, similarity_top_k={similarity_top_k}, Temperature: {temperature_value:.1f}, Vector Query Result: {query_result}", results_file_path)
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elif query_type == "sql":
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sql_query_result = sql_query_engine.query(question)
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print(f"SQL Query Result: {sql_query_result}\n")
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save_results_to_file(question, f"Current parameters: TOP_K={top_k}, similarity_top_k={similarity_top_k}, Temperature: {temperature_value:.1f}, SQL Query Result: {sql_query_result}", results_file_path)
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else:
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print("无效的查询类型，请选择 'vector' 或 'sql'")
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sys.exit(1)
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if __name__ == "__main__":
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if len(sys.argv) < 3:
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print("请提供questions.json文件的路径和查询类型（vector 或 sql）")
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sys.exit(1)
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questions_file = sys.argv[1]
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query_type = sys.argv[2].lower()
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from phoenix.trace import using_project
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with using_project(questions_file) as obj:
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main(questions_file, query_type) |