76 lines
3.2 KiB
Python
76 lines
3.2 KiB
Python
import requests
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from langchain_core.language_models import LLM
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from langchain_core.outputs import LLMResult, Generation
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from langchain_experimental.sql import SQLDatabaseChain
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from langchain_community.utilities import SQLDatabase
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from server.conf import DATABASES
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from apps.ichat.serializers import CustomLLMrequestSerializer
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from rest_framework.views import APIView
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from urllib.parse import quote_plus
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from rest_framework.response import Response
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db_conf = DATABASES['default']
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# 密码需要 URL 编码（因为有特殊字符如 @）
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password_encodeed = quote_plus(db_conf['PASSWORD'])
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db = SQLDatabase.from_uri(f"postgresql+psycopg2://{db_conf['USER']}:{password_encodeed}@{db_conf['HOST']}/{db_conf['NAME']}", include_tables=["enm_mpoint", "enm_mpointstat"])
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# model_url = "http://14.22.88.72:11025/v1/chat/completions"
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model_url = "http://139.159.180.64:11434/v1/chat/completions"
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class CustomLLM(LLM):
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model_url: str
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def _call(self, prompt: str, stop: list = None) -> str:
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data = {
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"model": "glm4",
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"messages": [
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{
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"role": "system",
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"content": "你是一个 SQL 助手，严格遵循以下规则：\n"
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"1. 只返回 PostgreSQL 语法 SQL 语句。\n"
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"2. 严格禁止添加任何解释、注释、Markdown 代码块标记（包括 ```sql 和 ```）。\n"
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"3. 输出必须是纯 SQL，且可直接执行，无需任何额外处理。"
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"4. 在 SQL 中如有多个表，请始终使用表名前缀引用字段，避免字段歧义。"
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},
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{"role": "user", "content": prompt}
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],
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"stream": False
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}
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response = requests.post(self.model_url, json=data, timeout=600)
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response.raise_for_status()
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content = response.json()["choices"][0]["message"]["content"]
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clean_sql = self.strip_sql_markdown(content)
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return clean_sql
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def _generate(self, prompts: list, stop: list = None) -> LLMResult:
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generations = []
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for prompt in prompts:
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text = self._call(prompt, stop)
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generations.append([Generation(text=text)])
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return LLMResult(generations=generations)
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def strip_sql_markdown(self, content: str) -> str:
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import re
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# 去掉包裹在 ```sql 或 ``` 中的内容
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match = re.search(r"```sql\s*(.*?)```", content, re.DOTALL | re.IGNORECASE)
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if match:
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return match.group(1).strip()
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match = re.search(r"```\s*(.*?)```", content, re.DOTALL)
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if match:
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return match.group(1).strip()
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return content.strip()
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@property
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def _llm_type(self) -> str:
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return "custom_llm"
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class QueryLLMview(APIView):
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def post(self, request):
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serializer = CustomLLMrequestSerializer(data=request.data)
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serializer.is_valid(raise_exception=True)
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prompt = serializer.validated_data['prompt']
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llm = CustomLLM(model_url=model_url)
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chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
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result = chain.invoke(prompt)
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return Response({"result": result}) |