49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
from dotenv import load_dotenv
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load_dotenv()
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import time
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from openai import OpenAI
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from datetime import datetime
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import config, debug, prompt, json, uuid
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from utils.logger import logger
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"""
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备注：
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目前使用ollama部署的qwen2.5:32b-instruct-q5_K_M,
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如果输入字符串过长(大致超过1000个字符或1500token)会因上下文窗口过短导致提示词被遗忘，
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进而输出不合规定的结果！
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"""
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llm = OpenAI(base_url=config.LLM_BASE_URL, api_key=config.API_KEY)
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class entityExtractionProcess:
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def entity_extract(input_text):
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messages = [
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{"role": "system", "content": prompt.ENTITY_EXTRACT},
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{"role": "system", "content": f"今天的日期是：{str(datetime.today())}"},
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{"role": "user", "content": f"{input_text}"},
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]
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timenow = time.time()
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response = (
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llm.chat.completions.create(
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model=config.MODEL, messages=messages, temperature=0, max_tokens=128_000
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)
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.choices[0]
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.message.content
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)
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# 补全其他信息
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response["sys_id"] = uuid.uuid4()
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response["entity_code"] = uuid.uuid4()
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response["sys_version"] = 1
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response["sys_create_time"] = datetime.datetime.now()
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response["sys_update_time"] = None
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logger.warning(f"本次输出花费时间：{time.time() - timenow} 秒")
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return json.loads(response)
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