feat:ichat 修改接口 去掉langchain
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@ -14,4 +14,4 @@ class Message(BaseModel):
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"""
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conversation = models.ForeignKey(Conversation, on_delete=models.CASCADE, verbose_name='对话')
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content = models.TextField(verbose_name='消息内容')
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role = models.CharField("角色", max_length=10, help_text="system/user")
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role = models.CharField("角色", max_length=10, default='user', help_text="system/user")
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@ -1,4 +1,18 @@
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from rest_framework import serializers
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from .models import Conversation, Message
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from apps.utils.constants import EXCLUDE_FIELDS
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class CustomLLMrequestSerializer(serializers.Serializer):
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prompt = serializers.CharField()
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class MessageSerializer(serializers.ModelSerializer):
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class Meta:
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model = Message
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fields = ['id', 'conversation', 'mode', 'content', 'role']
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read_only_fields = EXCLUDE_FIELDS
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class ConversationSerializer(serializers.ModelSerializer):
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messages = MessageSerializer(many=True, read_only=True)
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class Meta:
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model = Conversation
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fields = ['id', 'title', 'messages']
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read_only_fields = EXCLUDE_FIELDS
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@ -1,8 +1,10 @@
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from django.urls import path
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from apps.ichat.views import QueryLLMview
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from apps.ichat.views import QueryLLMview, ConversationView
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API_BASE_URL = 'api/llm/ichat/'
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API_BASE_URL = 'api/ichat/'
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urlpatterns = [
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path(API_BASE_URL + 'query/', QueryLLMview.as_view(), name='llm_query'),
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path(API_BASE_URL + 'conversation/', ConversationView.as_view(), name='conversation')
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]
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@ -0,0 +1,87 @@
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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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mode: str = 'chat'
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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": self.build_message(prompt),
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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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print('content---', content)
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clean_sql = self.strip_sql_markdown(content) if self.mode == 'sql' else content.strip()
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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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else:
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return content.strip()
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def build_message(self, prompt: str) -> list:
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if self.mode == 'sql':
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system_prompt = (
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"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。\n"
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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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else:
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system_prompt = "你是一个聊天助手，请根据用户的问题，提供简洁明了的答案。"
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return [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": prompt},
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]
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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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mode = serializer.validated_data.get('mode', 'chat')
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llm = CustomLLM(model_url=model_url, mode=mode)
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print('prompt---', prompt, mode)
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if mode == 'sql':
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chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
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result = chain.invoke(prompt)
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else:
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result = llm._call(prompt)
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return Response({"result": result})
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@ -1,76 +1,173 @@
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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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import psycopg2
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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 apps.ichat.serializers import MessageSerializer, ConversationSerializer
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from rest_framework.response import Response
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from ichat.models import Conversation, Message
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from rest_framework.generics import get_object_or_404
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#本地部署模型
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# API_KEY = "sk-5644e2d6077b46b9a04a8a2b12d6b693"
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# API_BASE = "https://dashscope.aliyuncs.com/compatible-mode/v1"
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# MODEL = "qwen-plus"
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#本地部署的模式
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# API_KEY = "JJVAide0hw3eaugGmxecyYYFw45FX2LfhnYJtC+W2rw"
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# API_BASE = "http://106.0.4.200:9000/v1"
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# MODEL = "Qwen/Qwen2.5-14B-Instruct"
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# google gemini
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API_KEY = "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"
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API_BASE = "https://openrouter.ai/api/v1"
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MODEL="google/gemini-2.0-flash-exp:free"
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# deepseek v3
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# API_KEY = "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"
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# API_BASE = "https://openrouter.ai/api/v1"
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# MODEL="deepseek/deepseek-chat-v3-0324:free"
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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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TABLES = ["enm_mpoint", "enm_mpointstat", "enm_mplogx"] # 如果整个数据库全都给模型，准确率下降，所以只给模型部分表
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# 数据库连接
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def connect_db():
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from server.conf import DATABASES
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db_conf = DATABASES['default']
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conn = psycopg2.connect(
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host=db_conf['HOST'],
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port=db_conf['PORT'],
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user=db_conf['USER'],
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password=db_conf['PASSWORD'],
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database=db_conf['NAME']
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)
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return conn
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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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def get_schema_text(conn, table_names:list):
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cur = conn.cursor()
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query = """
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SELECT
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table_name, column_name, data_type
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FROM
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information_schema.columns
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WHERE
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table_schema = 'public'
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and table_name in %s;
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"""
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cur.execute(query, (tuple(table_names), ))
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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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schema = {}
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for table_name, column_name, data_type in cur.fetchall():
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if table_name not in schema:
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schema[table_name] = []
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schema[table_name].append(f"{column_name} ({data_type})")
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cur.close()
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schema_text = ""
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for table_name, columns in schema.items():
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schema_text += f"表{table_name} 包含列：{', '.join(columns)}\n"
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return schema_text
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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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# 调用大模型生成sql
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def call_llm_api(prompt, api_key=API_KEY, api_base=API_BASE, model=MODEL):
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url = f"{api_base}/chat/completions"
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {api_key}"
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}
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payload = {
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"model": model,
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"messages": [{"role": "user", "content": prompt}],
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"temperature": 0,
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}
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response = requests.post(url, headers=headers, json=payload)
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response.raise_for_status()
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print("\n大模型返回：\n", response.json())
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return response.json()["choices"][0]["message"]["content"]
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def execute_sql(conn, sql_query):
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cur = conn.cursor()
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cur.execute(sql_query)
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try:
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rows = cur.fetchall()
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columns = [desc[0] for desc in cur.description]
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result = [dict(zip(columns, row)) for row in rows]
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except psycopg2.ProgrammingError:
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result = cur.statusmessage
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cur.close()
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return result
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def strip_sql_markdown(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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else:
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return None
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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 = MessageSerializer(data=request.data)
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serializer.is_valid(raise_exception=True)
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serializer.save()
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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})
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conn = connect_db()
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# 数据库表结构
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schema_text = get_schema_text(conn, TABLES)
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user_prompt = f"""你是可能是一个专业的数据库工程师，根据以下数据库结构：
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{schema_text}
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请根据我的需求生成一条标准的PostgreSQL SQL语句，直接返回SQL，不要额外解释。
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需求是：{prompt}
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"""
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llm_data = call_llm_api(user_prompt)
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# 判断是否生成的是sql 如果不是直接返回message
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generated_sql = strip_sql_markdown(llm_data)
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if generated_sql:
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try:
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result = execute_sql(conn, generated_sql)
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return Response({"result": result})
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except Exception as e:
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print("\n第一次执行SQL报错了，错误信息：", str(e))
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# 如果第一次执行SQL报错，则重新生成SQL
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fix_prompt = f"""刚才你生成的SQL出现了错误，错误信息是：{str(e)}
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请根据这个错误修正你的SQL，返回新的正确的SQL，直接给出SQL，不要解释。
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数据库结构如下：
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{schema_text}
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用户需求是：{prompt}
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"""
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fixed_sql = call_llm_api(fix_prompt)
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fixed_sql = strip_sql_markdown(fixed_sql)
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try:
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results = execute_sql(conn, fixed_sql)
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print("\n修正后的查询结果：")
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print(results)
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return Response({"result": results})
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except Exception as e2:
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print("\n修正后的SQL仍然报错，错误信息：", str(e2))
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return Response({"error": "SQL执行失败", "detail": str(e2)}, status=400)
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finally:
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conn.close()
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else:
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return Response({"result": llm_data})
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# 先新建对话 生成对话session_id
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class ConversationView(APIView):
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def get(self, request):
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conversation = Conversation.objects.all()
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serializer = ConversationSerializer(conversation, many=True)
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return Response(serializer.data)
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def post(self, request):
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serializer = ConversationSerializer(data=request.data)
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serializer.is_valid(raise_exception=True)
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serializer.save()
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return Response(serializer.data)
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def put(self, request, pk):
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conversation = get_object_or_404(Conversation, pk=pk)
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serializer = ConversationSerializer(conversation, data=request.data)
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serializer.is_valid(raise_exception=True)
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serializer.save()
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return Response(serializer.data)
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