feat: implement backend logic

.gitignore

@@ -6,6 +6,7 @@ dist/
 wheels/
 *.egg-info
 .coverage
+.coverage.*
 agent_history.gif
 static/browser_history/*.gif
 

pyproject.toml

@@ -32,6 +32,7 @@ dependencies = [
     "arxiv>=2.2.0",
     "mcp>=1.6.0",
     "langchain-mcp-adapters>=0.0.9",
+    "langchain-deepseek>=0.1.3",
 ]
 
 [project.optional-dependencies]

src/config/configuration.py

@@ -23,6 +23,7 @@ class Configuration:
     max_search_results: int = 3  # Maximum number of search results
     mcp_settings: dict = None  # MCP settings, including dynamic loaded tools
     report_style: str = ReportStyle.ACADEMIC.value  # Report style
+    enable_deep_thinking: bool = False  # Whether to enable deep thinking
 
     @classmethod
     def from_runnable_config(

src/graph/nodes.py

@@ -101,7 +101,10 @@ def planner_node(
             }
         ]
 
-    if AGENT_LLM_MAP["planner"] == "basic":
+    if (
+        AGENT_LLM_MAP["planner"] == "basic"
+        and not configurable.enable_deep_thinking
+    ):
         llm = get_llm_by_type(AGENT_LLM_MAP["planner"]).with_structured_output(
             Plan,
             method="json_mode",
@@ -114,7 +117,10 @@ def planner_node(
         return Command(goto="reporter")
 
     full_response = ""
-    if AGENT_LLM_MAP["planner"] == "basic":
+    if (
+        AGENT_LLM_MAP["planner"] == "basic"
+        and not configurable.enable_deep_thinking
+    ):
         response = llm.invoke(messages)
         full_response = response.model_dump_json(indent=4, exclude_none=True)
     else:

src/llms/llm.py

@@ -6,6 +6,7 @@ from typing import Any, Dict
 import os
 
 from langchain_openai import ChatOpenAI
+from langchain_deepseek import ChatDeepSeek
 
 from src.config import load_yaml_config
 from src.config.agents import LLMType
@@ -29,7 +30,9 @@ def _get_env_llm_conf(llm_type: str) -> Dict[str, Any]:
     return conf
 
 
-def _create_llm_use_conf(llm_type: LLMType, conf: Dict[str, Any]) -> ChatOpenAI:
+def _create_llm_use_conf(
+    llm_type: LLMType, conf: Dict[str, Any]
+) -> ChatOpenAI | ChatDeepSeek:
     llm_type_map = {
         "reasoning": conf.get("REASONING_MODEL", {}),
         "basic": conf.get("BASIC_MODEL", {}),
@@ -47,7 +50,14 @@ def _create_llm_use_conf(llm_type: LLMType, conf: Dict[str, Any]) -> ChatOpenAI:
     if not merged_conf:
         raise ValueError(f"Unknown LLM Conf: {llm_type}")
 
-    return ChatOpenAI(**merged_conf)
+    if llm_type == "reasoning":
+        merged_conf["api_base"] = merged_conf.pop("base_url")
+
+    return (
+        ChatOpenAI(**merged_conf)
+        if llm_type != "reasoning"
+        else ChatDeepSeek(**merged_conf)
+    )
 
 
 def get_llm_by_type(

src/server/app.py

@@ -24,7 +24,6 @@ from src.prompt_enhancer.graph.builder import build_graph as build_prompt_enhanc
 from src.rag.builder import build_retriever
 from src.rag.retriever import Resource
 from src.server.chat_request import (
-    ChatMessage,
     ChatRequest,
     EnhancePromptRequest,
     GeneratePodcastRequest,
@@ -81,6 +80,7 @@ async def chat_stream(request: ChatRequest):
             request.mcp_settings,
             request.enable_background_investigation,
             request.report_style,
+            request.enable_deep_thinking,
         ),
         media_type="text/event-stream",
     )
@@ -98,6 +98,7 @@ async def _astream_workflow_generator(
     mcp_settings: dict,
     enable_background_investigation: bool,
     report_style: ReportStyle,
+    enable_deep_thinking: bool,
 ):
     input_ = {
         "messages": messages,
@@ -125,6 +126,7 @@ async def _astream_workflow_generator(
             "max_search_results": max_search_results,
             "mcp_settings": mcp_settings,
             "report_style": report_style.value,
+            "enable_deep_thinking": enable_deep_thinking,
         },
         stream_mode=["messages", "updates"],
         subgraphs=True,
@@ -156,6 +158,10 @@ async def _astream_workflow_generator(
             "role": "assistant",
             "content": message_chunk.content,
         }
+        if message_chunk.additional_kwargs.get("reasoning_content"):
+            event_stream_message["reasoning_content"] = message_chunk.additional_kwargs[
+                "reasoning_content"
+            ]
         if message_chunk.response_metadata.get("finish_reason"):
             event_stream_message["finish_reason"] = message_chunk.response_metadata.get(
                 "finish_reason"

src/server/chat_request.py

@@ -62,6 +62,9 @@ class ChatRequest(BaseModel):
     report_style: Optional[ReportStyle] = Field(
         ReportStyle.ACADEMIC, description="The style of the report"
     )
+    enable_deep_thinking: Optional[bool] = Field(
+        False, description="Whether to enable deep thinking"
+    )
 
 
 class TTSRequest(BaseModel):