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feat: add DeepSeek official API support and capture reasoning content

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- Add DeepSeek provider routing: deepseek-chat/deepseek-reasoner use
  api.deepseek.com, other models use SiliconFlow
- Add stream_with_reasoning() using raw OpenAI SDK to capture
  reasoning_content (langchain_openai strips this field)
- RAG chain and conversation chain both use stream_with_reasoning
  for proper reasoning display in thinking models
- Frontend model selector: grouped by provider (DeepSeek official +
  SiliconFlow), default changed to deepseek-chat
- Regenerate message converted to streaming with reasoning capture
- Minor UI: globals.css additions, chat store refactoring

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
xiaopeng
2026-05-27 12:37:31 +08:00
parent 51d6fd22c0
commit ecd5c7ff31
9 changed files with 385 additions and 215 deletions
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backend/src/llm/siliconflow.py
+93 -35
View File
@@ -1,44 +1,63 @@
"""
硅基流动大模型API集成
大模型API集成 — 支持 SiliconFlow 和 DeepSeek 官方
"""
import os
from typing import List, Dict, Any, Optional, AsyncGenerator
from typing import List, Dict, Any, Optional, AsyncGenerator, Tuple
from langchain_openai import ChatOpenAI
from langchain_core.messages import BaseMessage, HumanMessage, AIMessage, SystemMessage
from langchain_core.callbacks.base import BaseCallbackHandler
from langchain_core.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
import openai
from ..core.config import get_settings
settings = get_settings()
# DeepSeek 官方模型 ID 前缀（用于自动路由）
DEEPSEEK_OFFICIAL_MODELS = {
"deepseek-chat",
"deepseek-reasoner",
}
# 模型 ID → (api_key, base_url) 的路由映射
def _resolve_provider(model: str) -> tuple[str, str]:
"""根据模型 ID 选择 API provider，返回 (api_key, base_url)"""
# DeepSeek 官方模型（不带 siliconflow 前缀的纯 deepseek-xxx）
if model in DEEPSEEK_OFFICIAL_MODELS or model.startswith("deepseek/"):
if not settings.deepseek_api_key:
raise ValueError(
f"模型 {model} 需要 DeepSeek 官方 API Key，"
"请在 .env 中配置 DEEPSEEK_API_KEY"
)
actual_model = model.replace("deepseek/", "")
return settings.deepseek_api_key, settings.deepseek_base_url, actual_model
# 默认走 SiliconFlow
return settings.siliconflow_api_key, settings.siliconflow_base_url, model
class SiliconFlowLLM:
"""硅基流动大模型客户端"""
"""大模型客户端 — 自动路由 SiliconFlow / DeepSeek 官方"""
def __init__(self, model: Optional[str] = None):
"""初始化LLM客户端"""
# 设置环境变量
os.environ["OPENAI_API_KEY"] = settings.siliconflow_api_key
os.environ["OPENAI_API_BASE"] = settings.siliconflow_base_url
# 使用传入的模型或默认模型
self.model_name = model or settings.siliconflow_model
print(f"[DEBUG-LLM] 初始化LLM客户端，使用模型: {self.model_name} (传入参数: {model}, 默认配置: {settings.siliconflow_model})")
# 创建LLM实例
raw_model = model or settings.siliconflow_model
api_key, base_url, resolved_model = _resolve_provider(raw_model)
self.model_name = resolved_model
print(f"[LLM] 模型: {resolved_model}, API: {base_url}")
self.llm = ChatOpenAI(
model=self.model_name,
api_key=settings.siliconflow_api_key,
base_url=settings.siliconflow_base_url,
model=resolved_model,
api_key=api_key,
base_url=base_url,
temperature=0.7,
max_tokens=2000,
streaming=True
)
# 检查实际使用的模型名称
actual_model = getattr(self.llm, 'model_name', None) or getattr(self.llm, 'model', None) or str(self.llm)
print(f"[DEBUG-LLM] ChatOpenAI实例创建完成，实际模型: {actual_model}")
self._api_key = api_key
self._base_url = base_url
def chat(self, messages: List[BaseMessage], **kwargs) -> str:
"""同步聊天"""
try:
@@ -46,7 +65,7 @@ class SiliconFlowLLM:
return response.content
except Exception as e:
raise Exception(f"LLM调用失败: {str(e)}")
async def achat(self, messages: List[BaseMessage], **kwargs) -> str:
"""异步聊天"""
try:
@@ -54,7 +73,7 @@ class SiliconFlowLLM:
return response.content
except Exception as e:
raise Exception(f"LLM异步调用失败: {str(e)}")
async def stream_chat(self, messages: List[BaseMessage], **kwargs) -> AsyncGenerator[str, None]:
"""流式聊天"""
try:
@@ -63,31 +82,71 @@ class SiliconFlowLLM:
yield chunk.content
except Exception as e:
raise Exception(f"LLM流式调用失败: {str(e)}")
async def stream_with_reasoning(
self, messages: List[BaseMessage], **kwargs
) -> AsyncGenerator[Tuple[str, str], None]:
"""流式调用（直接使用 OpenAI SDK，捕获推理内容）
Yields:
(type, content) — type 为 "reasoning" 或 "content"
"""
client = openai.AsyncOpenAI(
api_key=self._api_key,
base_url=self._base_url
)
openai_messages = []
for msg in messages:
if isinstance(msg, SystemMessage):
openai_messages.append({"role": "system", "content": msg.content})
elif isinstance(msg, HumanMessage):
openai_messages.append({"role": "user", "content": msg.content})
elif isinstance(msg, AIMessage):
openai_messages.append({"role": "assistant", "content": msg.content})
else:
openai_messages.append({"role": "user", "content": str(msg.content)})
stream = await client.chat.completions.create(
model=self.model_name,
messages=openai_messages,
stream=True,
**kwargs
)
async for chunk in stream:
if not chunk.choices:
continue
delta = chunk.choices[0].delta
rc = getattr(delta, 'reasoning_content', None)
if rc:
yield ("reasoning", rc)
if delta.content:
yield ("content", delta.content)
def create_messages(
self,
user_message: str,
self,
user_message: str,
system_prompt: Optional[str] = None,
chat_history: Optional[List[Dict[str, str]]] = None
) -> List[BaseMessage]:
"""创建消息列表"""
messages = []
# 添加系统提示
if system_prompt:
messages.append(SystemMessage(content=system_prompt))
# 添加聊天历史
if chat_history:
for msg in chat_history:
if msg["role"] == "user":
messages.append(HumanMessage(content=msg["content"]))
elif msg["role"] == "assistant":
messages.append(AIMessage(content=msg["content"]))
# 添加当前用户消息
messages.append(HumanMessage(content=user_message))
return messages
@@ -97,12 +156,11 @@ llm_client = SiliconFlowLLM()
def get_llm_client(model: Optional[str] = None) -> SiliconFlowLLM:
"""获取LLM客户端实例
Args:
model: 可选的模型名称，如果提供则创建新的实例，否则返回默认实例
"""
if model is None:
return llm_client
else:
# 为指定模型创建新实例
return SiliconFlowLLM(model=model)