284 lines
9.2 KiB
Plaintext
284 lines
9.2 KiB
Plaintext
def _escape(value):
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if value is None:
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return None
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return str(value).replace("'", "''")
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def _parse_bool(value, default=True):
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if value is None or value == '':
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return default
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if isinstance(value, bool):
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return value
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return str(value).lower() in ('1', 'true', 'yes', 'on')
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def _parse_messages(ns):
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"""解析历史消息：支持 list 或 JSON 字符串"""
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raw = ns.get('messages')
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if not raw:
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return []
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if isinstance(raw, list):
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return raw
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if isinstance(raw, str):
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import json
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try:
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return json.loads(raw)
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except Exception:
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return []
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return []
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def build_user_content(ns):
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"""
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构建单条 user 消息的 content，支持文本 / 图片 / 文档链接。
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参数（可组合）：
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message / text 文本
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image_url 图片 URL
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image_base64 图片 base64（不含 data: 前缀）
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document_url 文档 URL（以 file 类型传给兼容接口）
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document_text 文档纯文本（拼入 text）
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"""
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text_parts = []
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if ns.get('message'):
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text_parts.append(str(ns.get('message')))
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if ns.get('text'):
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text_parts.append(str(ns.get('text')))
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if ns.get('document_text'):
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text_parts.append(str(ns.get('document_text')))
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parts = []
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merged_text = '\n'.join([p for p in text_parts if p]).strip()
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if merged_text:
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parts.append({'type': 'text', 'text': merged_text})
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if ns.get('image_url'):
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parts.append({
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'type': 'image_url',
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'image_url': {'url': ns.get('image_url')},
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})
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if ns.get('image_base64'):
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mime = ns.get('image_mime') or 'image/jpeg'
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b64 = ns.get('image_base64')
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if not str(b64).startswith('data:'):
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b64 = 'data:%s;base64,%s' % (mime, b64)
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parts.append({
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'type': 'image_url',
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'image_url': {'url': b64},
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})
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if ns.get('document_url'):
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parts.append({
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'type': 'file',
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'file': {'file_url': ns.get('document_url')},
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})
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if not parts:
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return ''
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if len(parts) == 1 and parts[0]['type'] == 'text':
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return parts[0]['text']
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return parts
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async def _resolve_chat_config(ns, sor):
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"""解析 API 地址与 Bearer Token"""
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api_url = 'https://api.deepseek.com/chat/completions'
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api_key = 'sk-c22d6573e85a4d3fa8ab932386cf2909'
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# api_url = ns.get('api_url')
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# api_key = ns.get('api_key')
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if not api_url and ns.get('model_id'):
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doc_rows = await sor.sqlExe(
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"SELECT api_url FROM model_api_doc WHERE model_id = '%s' LIMIT 1;"
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% _escape(ns.get('model_id')),
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{},
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)
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if doc_rows and doc_rows[0].get('api_url'):
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api_url = doc_rows[0]['api_url']
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if not str(api_url).endswith('/chat/completions'):
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api_url = str(api_url).rstrip('/') + '/chat/completions'
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if not api_url:
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param_rows = await sor.R('params', {'pname': 'cntoai_llm_chat_url'})
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if param_rows:
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api_url = param_rows[0]['pvalue']
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else:
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domain_rows = await sor.R('params', {'pname': 'cntoai_domain'})
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if domain_rows:
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api_url = domain_rows[0]['pvalue'].rstrip('/') + '/llmage/v1/chat/completions'
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else:
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api_url = 'https://ai.atvoe.com/llmage/v1/chat/completions'
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if not api_key:
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userid = ns.get('userid') or await get_user()
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if userid:
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action = ns.get('apikey_action') or 'user_self_create'
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keys = await sor.R('user_api_keys', {'userid': userid, 'action': action})
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if not keys:
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keys = await sor.R('user_api_keys', {'userid': userid, 'action': 'sync'})
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if keys:
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api_key = keys[0].get('opc_apikey')
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if not api_key:
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key_rows = await sor.R('params', {'pname': 'cntoai_llm_api_key'})
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if key_rows:
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api_key = key_rows[0]['pvalue']
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return api_url, api_key
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def _extract_stream_piece(payload):
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"""从 SSE chunk 中提取文本（兼容 OpenAI / Qwen 等格式）"""
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choice = (payload.get('choices') or [{}])[0]
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delta = choice.get('delta') or {}
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message = choice.get('message') or {}
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piece = (
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delta.get('content')
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or delta.get('reasoning_content')
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or message.get('content')
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or choice.get('text')
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or payload.get('content')
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or ''
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)
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if piece is None:
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return ''
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return str(piece)
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async def _read_stream_response(response):
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"""解析 SSE 流式响应；若上游未按 SSE 返回则回退解析整段 JSON"""
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import json
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chunks = []
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buffer = ''
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async for raw in response.content:
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buffer += raw.decode('utf-8', errors='ignore')
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while '\n' in buffer:
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line, buffer = buffer.split('\n', 1)
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line = line.strip()
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if not line or line.startswith(':'):
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continue
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if not line.startswith('data:'):
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continue
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data = line[5:].strip()
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if data == '[DONE]':
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return ''.join(chunks), chunks
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try:
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payload = json.loads(data)
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piece = _extract_stream_piece(payload)
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if piece:
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chunks.append(piece)
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except Exception:
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continue
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reply = ''.join(chunks)
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if reply:
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return reply, chunks
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# 上游可能忽略 stream=true，直接返回完整 JSON
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tail = buffer.strip()
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if tail:
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try:
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body = json.loads(tail)
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choice = (body.get('choices') or [{}])[0]
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msg = choice.get('message') or {}
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reply = msg.get('content') or choice.get('text') or ''
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if reply:
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return str(reply), [str(reply)]
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except Exception:
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pass
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return reply, chunks
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async def llm_chat_completions(ns={}):
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"""
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OpenAI 兼容 chat/completions（aiohttp）。
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参数：
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model (str) 模型名，必填
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message / text 当前用户文本
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messages 历史消息 JSON 数组或 list，多轮对话
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stream (bool) 是否流式，默认 True
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image_url / image_base64 图片
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document_url / document_text 文档
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api_url / api_key 可覆盖默认配置
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model_id 从 model_api_doc 读取 api_url
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userid 用于查 user_api_keys
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"""
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import aiohttp
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import json
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import traceback
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model = ns.get('model')
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if not model:
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return {'status': False, 'msg': 'model is required'}
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stream = _parse_bool(ns.get('stream'), True)
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history = _parse_messages(ns)
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user_content = build_user_content(ns)
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if not user_content and not history:
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return {'status': False, 'msg': 'message is required'}
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messages = list(history)
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if user_content:
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messages.append({'role': 'user', 'content': user_content})
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payload = {
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'model': model,
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'stream': stream,
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'messages': messages,
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}
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db = DBPools()
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async with db.sqlorContext('kboss') as sor:
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try:
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api_url, api_key = await _resolve_chat_config(ns, sor)
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if not api_key:
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return {'status': False, 'msg': '未找到 API Key，请先创建或配置 cntoai_llm_api_key'}
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headers = {
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'Content-Type': 'application/json',
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'Authorization': 'Bearer %s' % api_key,
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}
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async with aiohttp.ClientSession(
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timeout=aiohttp.ClientTimeout(total=600),
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) as session:
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async with session.post(api_url, headers=headers, json=payload) as response:
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if response.status != 200:
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err_text = await response.text()
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return {
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'status': False,
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'msg': '模型请求失败 HTTP %s: %s' % (response.status, err_text[:500]),
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}
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stream_chunks = []
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if stream:
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reply, stream_chunks = await _read_stream_response(response)
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usage = {}
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else:
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body = await response.json()
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choice = (body.get('choices') or [{}])[0]
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msg = choice.get('message') or {}
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reply = msg.get('content') or ''
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usage = body.get('usage') or {}
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return {
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'status': True,
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'msg': 'chat success',
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'data': {
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'model': model,
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'reply': reply,
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'messages': messages + [{'role': 'assistant', 'content': reply}],
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'usage': usage,
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'stream': stream,
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'chunk_count': len(stream_chunks),
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'chunks': stream_chunks if ns.get('with_chunks') else None,
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},
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}
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except Exception:
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return {'status': False, 'msg': 'chat failed, %s' % traceback.format_exc()}
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ret = await llm_chat_completions(params_kw)
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return ret
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