418 lines
16 KiB
Python
418 lines
16 KiB
Python
from dataclasses import field
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from email.policy import default
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from typing import List, Union, Any, Optional
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import time
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import threading
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from vanna.flask import Cache, MemoryCache
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import dmPython
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import orjson
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import pandas as pd
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from vanna.base import VannaBase
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from vanna.flask import MemoryCache
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from vanna.qdrant import Qdrant_VectorStore
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from qdrant_client import QdrantClient
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from openai import OpenAI
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import requests
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from decouple import config
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from util.utils import extract_nested_json, check_and_get_sql, get_chart_type_from_sql_answer
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import json
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from template.template import get_base_template
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from datetime import datetime
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import logging
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from util import train_ddl
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logger = logging.getLogger(__name__)
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import traceback
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class OpenAICompatibleLLM(VannaBase):
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def __init__(self, client=None, config_file=None):
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VannaBase.__init__(self, config=config_file)
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# default parameters - can be overrided using config
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self.temperature = 0.6
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self.max_tokens = 5000
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if "temperature" in config_file:
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self.temperature = config_file["temperature"]
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if "max_tokens" in config_file:
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self.max_tokens = config_file["max_tokens"]
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if "api_type" in config_file:
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raise Exception(
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"Passing api_type is now deprecated. Please pass an OpenAI client instead."
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)
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if "api_version" in config_file:
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raise Exception(
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"Passing api_version is now deprecated. Please pass an OpenAI client instead."
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)
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if client is not None:
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self.client = client
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return
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if "api_base" not in config_file:
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raise Exception("Please passing api_base")
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if "api_key" not in config_file:
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raise Exception("Please passing api_key")
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self.client = OpenAI(api_key=config_file["api_key"], base_url=config_file["api_base"])
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def system_message(self, message: str) -> any:
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return {"role": "system", "content": message}
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def connect_to_dameng(
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self,
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host: str = None,
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dbname: str = None,
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user: str = None,
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password: str = None,
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port: int = None,
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**kwargs
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):
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self.conn = None
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try:
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self.conn = dmPython.connect(user=user, password=password, server=host, port=port)
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except Exception as e:
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raise Exception(f"Failed to connect to dameng database: {e}")
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def run_sql_damengsql(sql: str) -> Union[pd.DataFrame, None]:
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logger.info(f"start to run_sql_damengsql")
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try:
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if not is_connection_alive(conn=self.conn):
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logger.info("connection is not alive, reconnecting..........")
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reconnect()
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# conn.ping(reconnect=True)
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cs = self.conn.cursor()
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cs.execute(sql)
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results = cs.fetchall()
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# Create a pandas dataframe from the results
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df = pd.DataFrame(
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results, columns=[desc[0] for desc in cs.description]
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)
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return df
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except Exception as e:
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self.conn.rollback()
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logger.error(f"Failed to execute sql query: {e}")
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raise e
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return None
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def reconnect():
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try:
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self.conn = dmPython.connect(user=user, password=password, server=host, port=port)
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except Exception as e:
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raise Exception(f"reconnect failed: {e}")
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def is_connection_alive(conn) -> bool:
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if conn is None:
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return False
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try:
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cursor = conn.cursor()
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cursor.execute("SELECT 1 FROM DUAL")
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cursor.close()
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return True
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except Exception as e:
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return False
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self.run_sql_is_set = True
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self.run_sql = run_sql_damengsql
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def user_message(self, message: str) -> any:
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return {"role": "user", "content": message}
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def assistant_message(self, message: str) -> any:
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return {"role": "assistant", "content": message}
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def submit_prompt(self, prompt, **kwargs) -> str:
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if prompt is None:
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raise Exception("Prompt is None")
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if len(prompt) == 0:
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raise Exception("Prompt is empty")
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print(prompt)
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num_tokens = 0
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for message in prompt:
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num_tokens += len(message["content"]) / 4
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if kwargs.get("model", None) is not None:
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model = kwargs.get("model", None)
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print(
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f"Using model {model} for {num_tokens} tokens (approx)"
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)
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response = self.client.chat.completions.create(
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model=model,
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messages=prompt,
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max_tokens=self.max_tokens,
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stop=None,
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temperature=self.temperature,
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)
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elif kwargs.get("engine", None) is not None:
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engine = kwargs.get("engine", None)
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print(
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f"Using model {engine} for {num_tokens} tokens (approx)"
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)
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response = self.client.chat.completions.create(
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engine=engine,
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messages=prompt,
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max_tokens=self.max_tokens,
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stop=None,
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temperature=self.temperature,
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)
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elif self.config is not None and "engine" in self.config:
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print(
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f"Using engine {self.config['engine']} for {num_tokens} tokens (approx)"
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)
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response = self.client.chat.completions.create(
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engine=self.config["engine"],
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messages=prompt,
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max_tokens=self.max_tokens,
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stop=None,
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temperature=self.temperature,
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)
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elif self.config is not None and "model" in self.config:
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print(
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f"Using model {self.config['model']} for {num_tokens} tokens (approx)"
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)
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print(self.config)
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response = self.client.chat.completions.create(
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model=self.config["model"],
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messages=prompt,
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max_tokens=self.max_tokens,
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stop=None,
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temperature=self.temperature,
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)
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# data = {
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# "model": self.model,
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# "prompt": prompt,
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# "max_tokens": self.max_tokens,
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# "temperature": self.temperature,
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# }
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# response = requests.post(
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# url=f"{self.api_base}/completions",
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# headers=self.headers,
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# json=data
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# )
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else:
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if num_tokens > 3500:
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model = "kimi"
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else:
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model = "doubao"
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print(f"5.Using model {model} for {num_tokens} tokens (approx)")
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response = self.client.chat.completions.create(
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model=model,
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messages=prompt,
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max_tokens=self.max_tokens,
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stop=None,
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temperature=self.temperature,
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)
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for choice in response.choices:
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if "text" in choice:
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return choice.text
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return response.choices[0].message.content
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def generate_sql_2(self, question: str, cache=None,user_id=None, allow_llm_to_see_data=False, **kwargs) -> dict:
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try:
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logger.info("Start to generate_sql_2 in cus_vanna_srevice")
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question_sql_list = self.get_similar_question_sql(question, **kwargs)
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if question_sql_list and len(question_sql_list)>2:
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question_sql_list=question_sql_list[:2]
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ddl_list = self.get_related_ddl(question, **kwargs)
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#doc_list = self.get_related_documentation(question, **kwargs)
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template = get_base_template()
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sql_temp = template['template']['sql']
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char_temp = template['template']['chart']
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history = None
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if user_id and cache:
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history = cache.get(id=user_id, field="data")
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# --------基于提示词，生成sql以及图表类型
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sys_temp = sql_temp['system'].format(engine=config("DB_ENGINE", default='mysql'), lang='中文',
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schema=ddl_list, documentation=[train_ddl.train_document],
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history=history,retrieved_examples_data=question_sql_list,
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data_training=question_sql_list,)
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user_temp = sql_temp['user'].format(question=question,
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current_time=datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
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logger.info(f"user_temp:{user_temp}")
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logger.info(f"sys_temp:{sys_temp}")
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llm_response = self.submit_prompt(
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[{'role': 'system', 'content': sys_temp}, {'role': 'user', 'content': user_temp}], **kwargs)
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llm_response = str(llm_response.strip())
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logger.info(f"llm_response:{llm_response}")
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#优化中.......
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result = extract_nested_json(llm_response)
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logger.info(f"result:{result}")
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result = {"resp": orjson.loads(result)}
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logger.info(f"llm_response:{llm_response}")
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sql = check_and_get_sql(llm_response)
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logger.info(f"sql:{sql}")
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# ---------------生成图表
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char_type = get_chart_type_from_sql_answer(llm_response)
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logger.info(f"chart type:{char_type}")
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if char_type:
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sys_char_temp = char_temp['system'].format(engine=config("DB_ENGINE", default='mysql'),
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lang='中文', sql=sql, chart_type=char_type)
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user_char_temp = char_temp['user'].format(sql=sql, chart_type=char_type, question=question)
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llm_response2 = self.submit_prompt(
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[{'role': 'system', 'content': sys_char_temp}, {'role': 'user', 'content': user_char_temp}],
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**kwargs)
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print(llm_response2)
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result['chart'] = orjson.loads(extract_nested_json(llm_response2))
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logger.info(f"chart_response:{result}")
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logger.info("Finish to generate_sql_2 in cus_vanna_srevice")
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return result
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except Exception as e:
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logger.info("cus_vanna_srevice failed-------------------: ")
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traceback.print_exc()
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raise e
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def generate_rewritten_question(self, last_question: str, new_question: str, **kwargs) -> str:
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logger.info(f"generate_rewritten_question---------------{new_question}")
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if last_question is None:
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return new_question
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prompt = [
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self.system_message(
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"Your goal is to combine a sequence of questions into a singular question if they are related. If the second question does not relate to the first question and is fully self-contained, return the second question. Return just the new combined question with no additional explanations. The question should theoretically be answerable with a single SQL statement."),
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self.user_message(new_question),
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]
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return self.submit_prompt(prompt=prompt, **kwargs)
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class CustomQdrant_VectorStore(Qdrant_VectorStore):
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def __init__(
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self,
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config_file={}
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):
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self.embedding_model_name = config('EMBEDDING_MODEL_NAME', default='')
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self.embedding_api_base = config('EMBEDDING_MODEL_BASE_URL', default='')
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self.embedding_api_key = config('EMBEDDING_MODEL_API_KEY', default='')
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super().__init__(config_file)
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def generate_embedding(self, data: str, **kwargs) -> List[float]:
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def _get_error_string(response: requests.Response) -> str:
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try:
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if response.content:
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return response.json()["detail"]
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except Exception:
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pass
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try:
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response.raise_for_status()
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except requests.HTTPError as e:
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return str(e)
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return "Unknown error"
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request_body = {
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"model": self.embedding_model_name,
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"sentences": [data],
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}
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request_body.update(kwargs)
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response = requests.post(
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url=f"{self.embedding_api_base}",
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json=request_body,
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headers={"Authorization": f"Bearer {self.embedding_api_key}", 'Content-Type': 'application/json'},
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)
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if response.status_code != 200:
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raise RuntimeError(
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f"Failed to create the embeddings, detail: {_get_error_string(response)}"
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)
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result = response.json()
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embeddings = result['embeddings']
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return embeddings[0]
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class CustomVanna(CustomQdrant_VectorStore, OpenAICompatibleLLM):
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def __init__(self, llm_config=None, vector_store_config=None):
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CustomQdrant_VectorStore.__init__(self, config_file=vector_store_config)
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OpenAICompatibleLLM.__init__(self, config_file=llm_config)
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class TTLCacheWrapper:
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"为MemoryCache()添加带ttl的包装器，防治内存泄漏"
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def __init__(self, cache: Optional[Cache] = None, ttl: int = 3600):
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self.cache = cache or MemoryCache()
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self.ttl = ttl
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self._expiry_times = {}
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self._cleanup_thread = None
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self._start_cleanup()
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def _start_cleanup(self):
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"""启动后台清理线程"""
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def cleanup():
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while True:
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current_time = time.time()
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expired_keys = []
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# 找出所有过期的key
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for key_info, expiry in self._expiry_times.items():
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if expiry <= current_time:
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expired_keys.append(key_info)
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# 清理过期数据
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for key_info in expired_keys:
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id, field = key_info
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if hasattr(self.cache, 'delete'):
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self.cache.delete(id=id)
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del self._expiry_times[key_info]
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time.sleep(180) # 每3分钟清理一次
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self._cleanup_thread = threading.Thread(target=cleanup, daemon=True)
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self._cleanup_thread.start()
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def set(self, id: str, field: str, value: Any, ttl: Optional[int] = None):
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"""设置缓存值，支持TTL"""
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# 使用提供的TTL或默认TTL
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actual_ttl = ttl if ttl is not None else self.ttl
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# 调用原始cache的set方法
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self.cache.set(id=id, field=field, value=value)
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# 记录过期时间
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key_info = (id, field)
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self._expiry_times[key_info] = time.time() + actual_ttl
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def get(self, id: str, field: str) -> Any:
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"""获取缓存值，自动处理过期"""
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key_info = (id, field)
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# 检查是否过期
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if key_info in self._expiry_times:
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if time.time() > self._expiry_times[key_info]:
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# 已过期，删除并返回None
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if hasattr(self.cache, 'delete'):
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self.cache.delete(id=id)
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del self._expiry_times[key_info]
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return None
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# 返回缓存值
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return self.cache.get(id=id, field=field)
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def delete(self, id: str, field: str):
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"""删除缓存值"""
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key_info = (id, field)
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if hasattr(self.cache, 'delete'):
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self.cache.delete(id=id)
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if key_info in self._expiry_times:
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del self._expiry_times[key_info]
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def exists(self, id: str, field: str) -> bool:
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"""检查缓存是否存在且未过期"""
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key_info = (id, field)
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if key_info in self._expiry_times:
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if time.time() > self._expiry_times[key_info]:
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# 已过期，清理并返回False
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self.delete(id=id, field=field)
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return False
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return self.get(id=id, field=field) is not None
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# 代理其他方法到原始cache
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def __getattr__(self, name):
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return getattr(self.cache, name) |