215 lines
9.0 KiB
Python
215 lines
9.0 KiB
Python
import requests
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import json
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import faiss
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import numpy as np
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from rest_framework.views import APIView
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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 apps.ichat.models import Conversation, Message
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from apps.ichat.utils import connect_db, extract_sql_code, execute_sql, get_schema_text, is_safe_sql, save_message_thread_safe
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from django.http import StreamingHttpResponse, JsonResponse
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from rest_framework.decorators import action
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from apps.utils.viewsets import CustomGenericViewSet, CustomModelViewSet
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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 = "qwen14b"
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# 文本向量化模型
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EM_MODEL = "m3e-base"
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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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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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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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# 表结构向量化
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def embed_text(texts: list[str]) -> list[list[float]]:
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url = f"{API_BASE}/embeddings"
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_payload = {
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"model": EM_MODEL,
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"input": texts
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}
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try:
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response = requests.post(url, headers=HEADERS, json=_payload)
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except requests.exceptions.RequestException as e:
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return JsonResponse({"error":f"Embedding API调用失败: {e}"}, status=500)
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print("embeddings", response["data"])
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return [e['embedding'] for e in response['data']]
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# 创建Faiss索引
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def create_index(embeddings: list[list[float]], texts: list[str], table_names: list[str]):
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index = faiss.IndexFlatL2(len(embeddings[0]))
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index.add(np.array(embeddings)).astype("float32")
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index_table_map = {i: {"table": table_names[i], "text": texts[i]} for i in range(len(table_names))}
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return index, index_table_map
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# 查询
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def search_similar_tables(query:str, index, index_table_map, k:int=5):
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query_embedding = embed_text([query])[0]
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distances, indices = index.search(np.array([query_embedding]).astype("float32"), k)
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return [index_table_map[i] for i in indices[0]]
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def get_tables(conn) -> list[str]:
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with conn.cursor() as cur:
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cur.execute("""
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SELECT tablename
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FROM pg_tables
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WHERE schemaname = 'public'
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AND tableowner = 'postgres';
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""")
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return [row[0] for row in cur.fetchall()]
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# 主函数：提取表结构、嵌入向量并存储到 FAISS
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def get_relation_table(query):
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conn = connect_db()
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table_names = get_tables(conn)
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schemas = get_schema_text(conn, table_names)
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texts = [s["text"] for s in schemas]
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# table_names = [s["table"] for s in schemas]
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embeddings = embed_text(texts)
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index, index_table_map = create_index(embeddings, texts, table_names)
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results = search_similar_tables(query, index, index_table_map)
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for result in results:
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print(f"表名: {result['table']}\n结构: {result['text']}")
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if len(results) == 0:
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return "没有找到相关表结构"
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return results
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class QueryLLMviewSet(CustomModelViewSet):
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queryset = Message.objects.all()
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serializer_class = MessageSerializer
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ordering = ['create_time']
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perms_map = {'get':'*', 'post':'*', 'put':'*'}
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@action(methods=['post'], detail=False, perms_map={'post':'*'} ,serializer_class=MessageSerializer)
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def completion(self, request):
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serializer = self.get_serializer(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['content']
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conversation = serializer.validated_data['conversation']
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if not prompt or not conversation:
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return JsonResponse({"error": "缺少 prompt 或 conversation"}, status=400)
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save_message_thread_safe(content=prompt, conversation=conversation, role="user")
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url = f"{API_BASE}/chat/completions"
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user_prompt = f"""
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我提问的问题是:{prompt}请判断我的问题是否与数据库查询或操作相关。如果是，回答"database"；如果不是，回答"general"。
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注意：
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只需回答"database"或"general"即可，不要有其他内容。
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"""
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_payload = {
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"model": MODEL,
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"messages": [{"role": "user", "content": user_prompt}],
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"temperature": 0,
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"max_tokens": 10
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}
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try:
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class_response = requests.post(url, headers=HEADERS, json=_payload)
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class_response.raise_for_status()
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class_result = class_response.json()
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question_type = class_result.get('choices', [{}])[0].get('message', {}).get('content', '').strip().lower()
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print("question_type", question_type)
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if question_type == "database":
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schema_text = get_relation_table(prompt)
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print("schema_text----------------------", schema_text)
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user_prompt = f"""你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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else:
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user_prompt = f"""
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回答以下问题，不需要涉及数据库查询：
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问题: {prompt}
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请直接回答问题，不要提及数据库或SQL。
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"""
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# TODO 是否应该拿到conservastion的id，然后根据id去数据库查询所以的messages, 然后赋值给messages
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# history = Message.objects.filter(conversation=conversation).order_by('create_time')
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# chat_history = [{"role": msg.role, "content": msg.content} for msg in history]
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# chat_history.append({"role": "user", "content": prompt})
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chat_history = [{"role":"user", "content":user_prompt}]
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print("chat_history", chat_history)
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payload = {
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"model": MODEL,
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"messages": chat_history,
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"temperature": 0,
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"stream": True
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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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except requests.exceptions.RequestException as e:
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return JsonResponse({"error":f"LLM API调用失败: {e}"}, status=500)
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def stream_generator():
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accumulated_content = ""
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for line in response.iter_lines():
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if line:
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decoded_line = line.decode('utf-8')
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if decoded_line.startswith('data:'):
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if decoded_line.strip() == "data: [DONE]":
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break # OpenAI-style标志结束
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try:
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data = json.loads(decoded_line[6:])
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content = data.get('choices', [{}])[0].get('delta', {}).get('content', '')
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if content:
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accumulated_content += content
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yield f"data: {content}\n\n"
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except Exception as e:
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yield f"data: [解析失败]: {str(e)}\n\n"
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print("accumulated_content", accumulated_content)
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save_message_thread_safe(content=accumulated_content, conversation=conversation, role="system")
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if question_type == "database":
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sql = extract_sql_code(accumulated_content)
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if sql:
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try:
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conn = connect_db()
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if is_safe_sql(sql):
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result = execute_sql(conn, sql)
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save_message_thread_safe(content=f"SQL结果: {result}", conversation=conversation, role="system")
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yield f"data: SQL执行结果: {result}\n\n"
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else:
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yield f"data: 拒绝执行非查询类 SQL：{sql}\n\n"
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except Exception as e:
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yield f"data: SQL执行失败: {str(e)}\n\n"
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finally:
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if conn:
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conn.close()
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else:
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yield "data: \\n[文本结束]\n\n"
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return StreamingHttpResponse(stream_generator(), content_type='text/event-stream')
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# 先新建对话 生成对话session_id
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class ConversationViewSet(CustomModelViewSet):
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queryset = Conversation.objects.all()
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serializer_class = ConversationSerializer
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ordering = ['create_time']
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perms_map = {'get':'*', 'post':'*', 'put':'*'}
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