support all model

support all model

.dockerignore

@@ -0,0 +1,17 @@
+# IDE settings
+.idea/
+.vscode/
+
+# Git
+.git/
+.gitignore
+
+# macOS
+.DS_Store
+
+# Other temp files or OS-specific files
+*.swp
+Thumbs.db
+
+docs/
+.cursor

.gitignore

@@ -6,3 +6,4 @@ backend/app/.DS_Store
 ._*
 .Spotlight-V100
 .Trashes
+.idea/

README.md

@@ -45,11 +45,16 @@
 - [x] codeinterpreter 接入云端 如 e2b 等供应商..
 - [ ] 多语言: R 语言, matlab
 - [ ] 绘图 napki,draw.io
+- [ ] 添加 benchmark
 
 ## 视频demo
 
 <video src="https://github.com/user-attachments/assets/954cb607-8e7e-45c6-8b15-f85e204a0c5d"></video>
 
+> [!CAUTION]
+> 项目处于实验探索迭代demo阶段，有许多需要改进优化改进地方，我(项目作者)很忙，有时间会优化更新
+> 欢迎贡献
+
 ## 📖 使用教程
 
 > 确保电脑中安装好 Python, Nodejs, **Redis** 环境
@@ -60,32 +65,21 @@
 
 1. 配置模型
 
-复制`/backend/.env.dev.example`到`/backend/.env.dev`(删除`.example` 后缀), 填写配置模型和 APIKEY
-[deepseek开发者平台](https://platform.deepseek.com/)
+复制`/backend/.env.dev.example`到`/backend/.env.dev`(删除`.example` 后缀)
+填写配置模型和 APIKEY
+推荐模型能力较强的、参数量大的模型。
 
 ```bash
-ENV=dev
-# 兼容 OpenAI 格式都行，具体看官方文档
-DEEPSEEK_API_KEY=
-DEEPSEEK_MODEL=
-DEEPSEEK_BASE_URL=
-# 模型最大问答次数
-MAX_CHAT_TURNS=60
-# 思考反思次数
-MAX_RETRIES=5
-
-LOG_LEVEL=DEBUG
-DEBUG=true
+# support all model, check out https://docs.litellm.ai/docs/ 
+API_KEY=
+# gpt-4.1,deepseek/deepseek-chat
+MODEL=
 # 确保安装 Redis
-REDIS_URL=redis://localhost:6379/0
-REDIS_MAX_CONNECTIONS=20
-CORS_ALLOW_ORIGINS=http://localhost:5173,http://localhost:3000
 ```
 
 复制`/fronted/.env.example`到`/fronted/.env`(删除`.example` 后缀)
 
 
-推荐模型能力较强的、参数量大的模型。
 
 2. 安装依赖
 

backend/.dockerignore

@@ -0,0 +1,22 @@
+# backend/.dockerignore
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+.Python
+env/
+venv/
+.venv/
+*.egg-info/
+.DS_Store
+.pytest_cache/
+.ruff_cache/
+.mypy_cache/
+# Git
+.git/
+.gitignore
+# IDE
+.idea/
+.vscode/
+# macOS
+.DS_Store

backend/.env.dev.example

@@ -1,15 +1,17 @@
 ENV=dev
-#兼容 OpenAI 格式都行，具体看官方文档
-DEEPSEEK_API_KEY=
-DEEPSEEK_MODEL=
-DEEPSEEK_BASE_URL=
+# support all model, check out https://docs.litellm.ai/docs/ 
+API_KEY=
+# gpt-4.1,deepseek/deepseek-chat
+MODEL=
+
 # 模型最大问答次数
 MAX_CHAT_TURNS=60
 # 思考反思次数
 MAX_RETRIES=5
 
-# 不需要填，默认调用本地 Python,建议不填，本地运行性能更好
+# 不需要填，默认调用本地 Python
 # E2B_API_KEY=
+SERVER_HOST=http://localhost:8000
 
 LOG_LEVEL=DEBUG
 DEBUG=true

backend/Dockerfile

@@ -22,4 +22,4 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 
 EXPOSE 8000
 
-CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000", "--ws-ping-interval", "60", "--ws-ping-timeout", "120"]
+CMD ["uv", "run", "uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000", "--ws-ping-interval", "60", "--ws-ping-timeout", "120"]

backend/app/config/setting.py

@@ -17,9 +17,9 @@ def parse_cors(value: str) -> list[str]:
 
 class Settings(BaseSettings):
     ENV: str
-    DEEPSEEK_API_KEY: str
-    DEEPSEEK_MODEL: str
-    DEEPSEEK_BASE_URL: str
+    API_KEY: str
+    MODEL: str
+    BASE_URL: Optional[str] = None
     MAX_CHAT_TURNS: int
     MAX_RETRIES: int
     E2B_API_KEY: Optional[str] = None
@@ -28,6 +28,7 @@ class Settings(BaseSettings):
     REDIS_URL: str
     REDIS_MAX_CONNECTIONS: int
     CORS_ALLOW_ORIGINS: Annotated[list[str] | str, BeforeValidator(parse_cors)]
+    SERVER_HOST: str = "http://localhost:8000"  # 默认值
 
     model_config = SettingsConfigDict(
         env_file=".env.dev",

backend/app/core/agents.py

@@ -26,6 +26,7 @@ class Agent:
         model: LLM,
         max_chat_turns: int = 30,  # 单个agent最大对话轮次
         user_output: UserOutput = None,
+        max_memory: int = 20,  # 最大记忆轮次
     ) -> None:
         self.task_id = task_id
         self.model = model
@@ -33,6 +34,7 @@ class Agent:
         self.max_chat_turns = max_chat_turns  # 最大对话轮次
         self.current_chat_turns = 0  # 当前对话轮次计数器
         self.user_output = user_output
+        self.max_memory = max_memory  # 最大记忆轮次
 
     async def run(self, prompt: str, system_prompt: str, sub_title: str) -> str:
         """
@@ -68,11 +70,20 @@ class Agent:
             return error_msg
 
     def append_chat_history(self, msg: dict) -> None:
+        self.clear_memory()
         self.chat_history.append(msg)
         # self.user_output.data_recorder.append_chat_history(
         # msg, agent_name=self.__class__.__name__
         # )
 
+    def clear_memory(self):
+        logger.info(f"{self.__class__.__name__}:清除记忆")
+        if len(self.chat_history) <= self.max_memory:
+            return
+
+        # 使用切片保留第一条和最后两条消息
+        self.chat_history = self.chat_history[:2] + self.chat_history[-5:]
+
 
 class ModelerAgent(Agent):  # 继承自Agent类而不是BaseModel
     def __init__(
@@ -207,18 +218,18 @@ class CoderAgent(Agent):  # 同样继承自Agent类
                         error_message,
                     ) = await self.code_interpreter.execute_code(code)
 
-                    # 记录执行结果
-
                     # 添加工具执行结果
-                    self.append_chat_history(
-                        {
-                            "role": "tool",
-                            "content": text_to_gpt,
-                            "tool_call_id": tool_id,
-                        }
-                    )
-                    # 代码执行错误
                     if error_occurred:
+                        # 即使发生错误也要添加tool响应
+                        self.append_chat_history(
+                            {
+                                "role": "tool",
+                                "content": error_message,
+                                "tool_call_id": tool_id,
+                                "name": "execute_code",
+                            }
+                        )
+
                         logger.warning(f"代码执行错误: {error_message}")
                         retry_count += 1
                         logger.info(f"当前尝试次:{retry_count} / {self.max_retries}")
@@ -227,7 +238,7 @@ class CoderAgent(Agent):  # 同样继承自Agent类
 
                         await redis_manager.publish_message(
                             self.task_id,
-                            SystemMessage(content="代码手反思错误", type="error"),
+                            SystemMessage(content="代码手反思纠正错误", type="error"),
                         )
 
                         self.append_chat_history(
@@ -235,6 +246,16 @@ class CoderAgent(Agent):  # 同样继承自Agent类
                         )
                         # 如果代码出错，返回重新开始
                         continue
+                    else:
+                        # 成功执行的tool响应
+                        self.append_chat_history(
+                            {
+                                "role": "tool",
+                                "content": text_to_gpt,
+                                "tool_call_id": tool_id,
+                                "name": "execute_code",
+                            }
+                        )
 
                     # 检查任务完成情况时也计入对话轮次
                     self.current_chat_turns += 1
@@ -326,7 +347,28 @@ class WriterAgent(Agent):  # 同样继承自Agent类
             image_prompt = f"\n可用的图片链接列表：\n{image_list}\n请在写作时适当引用这些图片链接。"
             prompt = prompt + image_prompt
 
-        return await super().run(prompt, self.system_prompt, sub_title)
+        try:
+            logger.info(f"{self.__class__.__name__}:开始:执行对话")
+            self.current_chat_turns = 0  # 重置对话轮次计数器
+
+            # 更新对话历史
+            self.append_chat_history({"role": "system", "content": self.system_prompt})
+            self.append_chat_history({"role": "user", "content": prompt})
+
+            # 获取历史消息用于本次对话
+            response = await self.model.chat(
+                history=self.chat_history,
+                agent_name=self.__class__.__name__,
+                sub_title=sub_title,
+            )
+            response_content = response.choices[0].message.content
+            self.chat_history.append({"role": "assistant", "content": response_content})
+            logger.info(f"{self.__class__.__name__}:完成:执行对话")
+            return response_content
+        except Exception as e:
+            error_msg = f"执行过程中遇到错误: {str(e)}"
+            logger.error(f"Agent执行失败: {str(e)}")
+            return error_msg
 
     async def summarize(self) -> str:
         """

backend/app/core/functions.py

@@ -1,3 +1,6 @@
+from typing import List, Dict, Any
+from semanticscholar import SemanticScholar, PaginatedResults
+
 tools = [
     {
         "type": "function",
@@ -28,3 +31,21 @@ tools = [
 # TODO: read files
 
 # TODO: get_cites
+
+
+def search_papers(query: str) -> List[Dict[str, Any]]:
+    """Search for papers using a query string."""
+    sch = SemanticScholar()
+    results: PaginatedResults = sch.search_paper(query, limit=10)
+    return [
+        {
+            "title": paper.title,
+            "abstract": paper.abstract,
+            "authorsName": [author.name for author in paper.authors],
+            "citations": [citation.title for citation in paper.citations],
+        }
+        for paper in results
+    ]
+
+
+## writeragent tools

backend/app/core/llm.py

@@ -7,6 +7,7 @@ from app.schemas.response import AgentMessage, CoderMessage, WriterMessage
 from app.utils.enums import AgentType
 from app.utils.redis_manager import redis_manager
 import re
+from litellm import acompletion
 
 
 class LLM:
@@ -20,7 +21,6 @@ class LLM:
         self.api_key = api_key
         self.model = model
         self.base_url = base_url
-        self.client = OpenAI(api_key=api_key, base_url=base_url)
         self.chat_count = 0
         self.max_tokens: int | None = None  # 添加最大token数限制
         self.task_id = task_id
@@ -39,6 +39,7 @@ class LLM:
         logger.info(f"subtitle是:{sub_title}")
 
         kwargs = {
+            "api_key": self.api_key,
             "model": self.model,
             "messages": history,
             "stream": False,
@@ -52,16 +53,20 @@ class LLM:
         if self.max_tokens:
             kwargs["max_tokens"] = self.max_tokens
 
+        if self.base_url:
+            kwargs["base_url"] = self.base_url
+
         # TODO: stream 输出
         for attempt in range(max_retries):
             try:
-                completion = self.client.chat.completions.create(**kwargs)
-                logger.info(f"API返回: {completion}")
-                if not completion or not hasattr(completion, "choices"):
+                # completion = self.client.chat.completions.create(**kwargs)
+                response = await acompletion(**kwargs)
+                logger.info(f"API返回: {response}")
+                if not response or not hasattr(response, "choices"):
                     raise ValueError("无效的API响应")
                 self.chat_count += 1
-                await self.analyse_completion(completion, agent_name, sub_title)
-                return completion
+                await self.analyse_completion(response, agent_name, sub_title)
+                return response
             except json.JSONDecodeError:
                 logger.error(f"第{attempt + 1}次重试: API返回无效JSON")
                 if attempt < max_retries - 1:  # 如果不是最后一次尝试
@@ -106,13 +111,39 @@ class LLM:
         )
 
 
-class DeepSeekModel(LLM):
-    def __init__(
-        self,
-        api_key: str,
-        model: str,
-        base_url: str,
-        task_id: str,
-    ):
-        super().__init__(api_key, model, base_url, task_id)
-        self.client = OpenAI(api_key=self.api_key, base_url=self.base_url)
+# class DeepSeekModel(LLM):
+#     def __init__(
+#         self,
+#         api_key: str,
+#         model: str,
+#         base_url: str,
+#         task_id: str,
+#     ):
+#         super().__init__(api_key, model, base_url, task_id)
+# self.client = OpenAI(api_key=self.api_key, base_url=self.base_url)
+
+
+async def simple_chat(model: LLM, history: list) -> str:
+    """
+    Description of the function.
+
+    Args:
+        model (LLM): 模型
+        history (list): 构造好的历史记录（包含system_prompt,user_prompt）
+
+    Returns:
+        return_type: Description of the return value.
+    """
+    kwargs = {
+        "api_key": model.api_key,
+        "model": model.model,
+        "messages": history,
+        "stream": False,
+    }
+
+    if model.base_url:
+        kwargs["base_url"] = model.base_url
+
+    response = await acompletion(**kwargs)
+
+    return response.choices[0].message.content

backend/app/core/prompts.py

@@ -15,6 +15,8 @@ attention：不需要给出代码，只需要给出思路和模型
 
 CODER_PROMPT = """You are an AI code interpreter.
 Your goal is to help users do a variety of jobs by executing Python code.
+you are are skilled in python,numpy,pandas,matplotlib,seaborn,scikit-learn,xgboost,scipy and how to use their models, classes and functions.you can use them to do mathmodel and data analysis.
+
 
 When generating code:
 1. Use double quotes for strings containing Chinese characters
@@ -23,6 +25,7 @@ When generating code:
 4. The working directory is already set up, and any uploaded files are already in the current directory
 5. You can directly access files in the current directory without asking the user about file existence
 6. For data analysis tasks, if you see Excel files (.xlsx), use pandas to read them directly
+7. try to visualize the data , process and  results using seaborn and matplotlibs
 
 For example:
 # Correct:
@@ -43,6 +46,7 @@ You should:
    - Verify all required outputs are generated
    - Ensure data processing steps are completed
    - Confirm files are saved as requested
+   - Visualize the process and results
 5. If task is incomplete or error occurred:
    - Analyze the current state
    - Identify what's missing or wrong
@@ -56,7 +60,7 @@ You should:
 11. 保存的图片名称需要语义化，方便用户理解
 12. 在生成代码时，对于包含单引号的字符串，请使用双引号包裹，避免使用转义字符
 13. **你尽量在较少的对话轮次内完成任务。减少反复思考的次数**
-14. 在求解问题和建立模型过程中，适当的进行可视化
+14. 在求解问题和建立模型过程中，进行充分可视化
 
 
 Important:
@@ -142,5 +146,5 @@ Consider:
 7. If the task is complete, please provide a short summary of what was accomplished and don't call function tool.
 8. If the task is not complete, please rethink how to do and call function tool
 9. Don't ask user any thing about how to do and next to do,just do it by yourself
-
+10. have a good visualization?
 """

backend/app/core/workflow.py

@@ -1,14 +1,13 @@
 from app.core.agents import WriterAgent, CoderAgent
-from app.core.llm import LLM
+from app.core.llm import LLM, simple_chat
 from app.models.model import CoderToWriter
 from app.schemas.request import Problem
 from app.schemas.response import SystemMessage
 from app.utils.log_util import logger
-from app.utils.common_utils import create_work_dir, simple_chat, get_config_template
+from app.utils.common_utils import create_work_dir, get_config_template
 from app.models.user_output import UserOutput
 from app.config.setting import settings
 from app.tools.interpreter_factory import create_interpreter
-from app.core.llm import DeepSeekModel
 import json
 from app.utils.redis_manager import redis_manager
 from app.utils.notebook_serializer import NotebookSerializer
@@ -36,10 +35,10 @@ class MathModelWorkFlow(WorkFlow):
         self.work_dir = create_work_dir(self.task_id)
 
         # default choose deepseek model
-        deepseek_model = DeepSeekModel(
-            api_key=settings.DEEPSEEK_API_KEY,
-            model=settings.DEEPSEEK_MODEL,
-            base_url=settings.DEEPSEEK_BASE_URL,
+        llm_model = LLM(
+            api_key=settings.API_KEY,
+            model=settings.MODEL,
+            base_url=settings.BASE_URL,
             task_id=self.task_id,
         )
 
@@ -48,7 +47,7 @@ class MathModelWorkFlow(WorkFlow):
             SystemMessage(content="正在拆解问题问题"),
         )
 
-        self.format_questions(problem.ques_all, deepseek_model)
+        await self.format_questions(problem.ques_all, llm_model)
 
         user_output = UserOutput(work_dir=self.work_dir)
 
@@ -79,13 +78,21 @@ class MathModelWorkFlow(WorkFlow):
 
         coder_agent = CoderAgent(
             task_id=problem.task_id,
-            model=deepseek_model,
+            model=llm_model,
             work_dir=self.work_dir,
             max_chat_turns=settings.MAX_CHAT_TURNS,
             max_retries=settings.MAX_RETRIES,
             code_interpreter=code_interpreter,
         )
 
+        # TODO: 自定义 writer_agent mode llm
+        writer_agent = WriterAgent(
+            task_id=problem.task_id,
+            model=llm_model,
+            comp_template=problem.comp_template,
+            format_output=problem.format_output,
+        )
+
         ################################################ solution steps
         solution_steps = self.get_solution_steps()
 
@@ -116,14 +123,6 @@ class MathModelWorkFlow(WorkFlow):
                 SystemMessage(content=f"论文手开始写{key}部分"),
             )
 
-            # TODO: 自定义 writer_agent mode llm
-            writer_agent = WriterAgent(
-                task_id=problem.task_id,
-                model=deepseek_model,
-                comp_template=problem.comp_template,
-                format_output=problem.format_output,
-            )
-
             ## TODO: 图片引用错误
             writer_response = await writer_agent.run(
                 writer_prompt,
@@ -155,7 +154,7 @@ class MathModelWorkFlow(WorkFlow):
             # TODO: writer_agent 是否不需要初始化
             writer_agent = WriterAgent(
                 task_id=problem.task_id,
-                model=deepseek_model,
+                model=llm_model,
                 comp_template=problem.comp_template,
                 format_output=problem.format_output,
             )
@@ -166,7 +165,7 @@ class MathModelWorkFlow(WorkFlow):
 
         user_output.save_result(ques_count=self.ques_count)
 
-    def format_questions(self, ques_all: str, model: LLM) -> None:
+    async def format_questions(self, ques_all: str, model: LLM) -> None:
         """用户输入问题 使用LLM 格式化 questions"""
         # TODO:  "note": <补充说明,如果没有补充说明，请填 null>,
         from app.core.prompts import FORMAT_QUESTIONS_PROMPT
@@ -178,7 +177,7 @@ class MathModelWorkFlow(WorkFlow):
             },
             {"role": "user", "content": ques_all},
         ]
-        json_str = simple_chat(model, history)
+        json_str = await simple_chat(model, history)
         json_str = json_str.replace("```json", "").replace("```", "").strip()
 
         if not json_str:

backend/app/routers/modeling.py

@@ -19,6 +19,7 @@ from app.utils.common_utils import md_2_docx, get_work_dir
 import subprocess
 from icecream import ic
 from pydantic import BaseModel
+from app.utils.track import track_cost_callback
 
 router = APIRouter()
 
@@ -190,3 +191,10 @@ async def run_modeling_task_async(
     )
     # 转换md为docx
     md_2_docx(task_id)
+
+
+@router.get("/track")
+async def track(task_id: str):
+    # 获取任务的token使用情况
+
+    pass

backend/app/tools/local_interpreter.py

@@ -43,10 +43,15 @@ class LocalCodeInterpreter(BaseCodeInterpreter):
             f"os.chdir(work_dir)\n"
             f"print('当前工作目录:', os.getcwd())\n"
             f"import matplotlib.pyplot as plt\n"
+            f"import matplotlib as mpl\n"
             # 更完整的中文字体配置
-            f"plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans', 'Arial Unicode MS', 'sans-serif']\n"
+            f"plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'Microsoft YaHei', 'WenQuanYi Micro Hei', 'PingFang SC', 'Hiragino Sans GB', 'Heiti SC', 'DejaVu Sans', 'sans-serif']\n"
             f"plt.rcParams['axes.unicode_minus'] = False\n"
             f"plt.rcParams['font.family'] = 'sans-serif'\n"
+            f"mpl.rcParams['font.size'] = 12\n"
+            f"mpl.rcParams['axes.labelsize'] = 12\n"
+            f"mpl.rcParams['xtick.labelsize'] = 10\n"
+            f"mpl.rcParams['ytick.labelsize'] = 10\n"
             # 设置DPI以获得更清晰的显示
         )
         self.execute_code_(init_code)
@@ -186,7 +191,8 @@ class LocalCodeInterpreter(BaseCodeInterpreter):
                 # TODO: 正确返回格式
                 if "traceback" in iopub_msg["content"]:
                     output = "\n".join(iopub_msg["content"]["traceback"])
-                    all_output.append(("error", output))
+                    cleaned_output = self.delete_color_control_char(output)
+                    all_output.append(("error", cleaned_output))
         return all_output
 
     async def get_created_images(self, section: str) -> list[str]:

backend/app/tools/openalex_scholar.py

@@ -0,0 +1,201 @@
+import requests
+import json
+from typing import List, Dict, Any
+
+
+class OpenAlexScholar:
+    def __init__(self, email: str = None):
+        """Initialize OpenAlex client.
+
+        Args:
+            email: Optional email for better API service
+        """
+        self.base_url = "https://api.openalex.org"
+        self.email = email
+
+    def _get_request_url(self, endpoint: str) -> str:
+        """Construct request URL with email parameter if provided."""
+        if endpoint.startswith("/"):
+            endpoint = endpoint[1:]
+        return f"{self.base_url}/{endpoint}"
+
+    def _get_abstract_from_index(self, abstract_inverted_index: Dict) -> str:
+        """从abstract_inverted_index中重建摘要文本
+
+        Args:
+            abstract_inverted_index: OpenAlex API返回的倒排索引
+
+        Returns:
+            重建的摘要文本
+        """
+        if not abstract_inverted_index:
+            return ""
+
+        # 创建一个足够大的空列表来存放所有单词
+        max_position = 0
+        for positions in abstract_inverted_index.values():
+            if positions and max(positions) > max_position:
+                max_position = max(positions)
+
+        words = [""] * (max_position + 1)
+
+        # 在正确的位置填入单词
+        for word, positions in abstract_inverted_index.items():
+            for position in positions:
+                words[position] = word
+
+        # 拼接单词形成文本
+        return " ".join(words).strip()
+
+    def search_papers(self, query: str, limit: int = 10) -> List[Dict[str, Any]]:
+        """Search for papers using OpenAlex API.
+
+        Args:
+            query: Search query string
+            limit: Maximum number of results to return
+
+        Returns:
+            List of papers with their details
+        """
+        # 构建基础 URL
+        base_url = self._get_request_url("works")
+
+        # 设置请求参数，根据API支持的字段进行选择
+        params = {
+            "search": query,
+            "per_page": limit,
+            "select": "id,title,display_name,authorships,cited_by_count,doi,publication_year,biblio,abstract_inverted_index",
+        }
+
+        # 添加邮箱参数到请求URL
+        if self.email:
+            params["mailto"] = self.email
+
+        # 设置请求头，包含User-Agent和邮箱信息
+        headers = {
+            "User-Agent": f"OpenAlexScholar/1.0 (mailto:{self.email})"
+            if self.email
+            else "OpenAlexScholar/1.0"
+        }
+
+        # 让 requests 处理参数编码和 URL 构建
+        try:
+            print(f"请求 URL: {base_url} 参数: {params}")
+            response = requests.get(base_url, params=params, headers=headers)
+            print(f"响应状态: {response.status_code}")
+
+            response.raise_for_status()
+            results = response.json()
+        except requests.exceptions.HTTPError as e:
+            print(f"HTTP 错误: {e}")
+            if response.status_code == 403:
+                print(
+                    "提示: 403错误通常意味着您需要提供有效的邮箱地址或者遵循礼貌池（polite pool）规则"
+                )
+            if hasattr(response, "text"):
+                print(f"响应内容: {response.text}")
+            raise
+        except Exception as e:
+            print(f"请求出错: {e}")
+            raise
+
+        papers = []
+        for work in results.get("results", []):
+            # 从倒排索引中获取摘要
+            abstract = self._get_abstract_from_index(
+                work.get("abstract_inverted_index", {})
+            )
+
+            # 获取作者信息
+            authors = []
+            for authorship in work.get("authorships", []):
+                author = authorship.get("author", {})
+                if author:
+                    author_info = {
+                        "name": author.get("display_name"),
+                        "position": authorship.get("author_position"),
+                        "institution": authorship.get("institutions", [{}])[0].get(
+                            "display_name"
+                        )
+                        if authorship.get("institutions")
+                        else None,
+                    }
+                    authors.append(author_info)
+
+            # 获取引用格式信息
+            biblio = work.get("biblio", {})
+            citation = {
+                "volume": biblio.get("volume"),
+                "issue": biblio.get("issue"),
+                "first_page": biblio.get("first_page"),
+                "last_page": biblio.get("last_page"),
+            }
+
+            paper = {
+                "title": work.get("display_name") or work.get("title", ""),
+                "abstract": abstract,
+                "authors": authors,
+                "citations_count": work.get("cited_by_count"),
+                "doi": work.get("doi"),
+                "publication_year": work.get("publication_year"),
+                "citation_info": citation,
+                # 构建引用格式
+                "citation_format": self._format_citation(work),
+            }
+            papers.append(paper)
+
+        return papers
+
+    def _format_citation(self, work: Dict[str, Any]) -> str:
+        """Format citation in a readable format."""
+        # 获取所有作者
+        authors = [
+            authorship.get("author", {}).get("display_name")
+            for authorship in work.get("authorships", [])
+            if authorship.get("author")
+        ]
+
+        # 格式化作者列表
+        if len(authors) > 3:
+            authors_str = f"{authors[0]} et al."
+        else:
+            authors_str = ", ".join(authors)
+
+        # 获取标题
+        title = work.get("display_name") or work.get("title", "")
+
+        # 获取年份
+        year = work.get("publication_year", "")
+
+        # 获取DOI
+        doi = work.get("doi", "")
+
+        # 构建引用格式
+        citation = f"{authors_str} ({year}). {title}."
+        if doi:
+            citation += f" DOI: {doi}"
+
+        return citation
+
+
+if __name__ == "__main__":
+    # Example usage
+    scholar = OpenAlexScholar(email="jihencn@foxmail.com")  # 请替换为您的真实邮箱
+    try:
+        papers = scholar.search_papers("machine learning")
+        for paper in papers:
+            print("\n" + "=" * 80)
+            print(f"标题: {paper['title']}")
+            print(f"\n摘要: {paper['abstract']}")
+            print("\n作者:")
+            for author in paper["authors"]:
+                print(f"- {author['name']}")
+                if author["institution"]:
+                    print(f"  所属机构: {author['institution']}")
+            print(f"\n引用次数: {paper['citations_count']}")
+            print(f"发表年份: {paper['publication_year']}")
+            print(f"\n引用格式:\n{paper['citation_format']}")
+            print("=" * 80)
+    except Exception as e:
+        print(f"发生错误: {e}")
+        print("请检查您的网络连接或API访问权限。")

backend/app/utils/common_utils.py

@@ -6,6 +6,7 @@ from app.utils.enums import CompTemplate
 from app.utils.log_util import logger
 import re
 import pypandoc
+from app.config.setting import settings
 
 
 def create_task_id() -> str:
@@ -74,33 +75,11 @@ def get_current_files(folder_path: str, type: str = "all") -> list[str]:
         ]
 
 
-def simple_chat(model, history: list) -> str:
-    """
-    Description of the function.
-
-    Args:
-        model (BaseModel): 模型
-        history (list): 构造好的历史记录（包含system_prompt,user_prompt）
-
-    Returns:
-        return_type: Description of the return value.
-    """
-    kwargs = {
-        "model": model.model,
-        "messages": history,
-        "stream": False,
-    }
-
-    completion = model.client.chat.completions.create(**kwargs)
-
-    return completion.choices[0].message.content
-
-
 # 判断content是否包含图片 xx.png,对其处理为    ![filename](http://localhost:8000/static/20250428-200915-ebc154d4/filename.jpg)
 def transform_link(task_id: str, content: str):
     content = re.sub(
         r"!\[(.*?)\]\((.*?\.(?:png|jpg|jpeg|gif|bmp|webp))\)",
-        lambda match: f"![{match.group(1)}](http://localhost:8000/static/{task_id}/{match.group(2)})",
+        lambda match: f"![{match.group(1)}]({settings.SERVER_HOST}/static/{task_id}/{match.group(2)})",
         content,
     )
     return content

backend/app/utils/track.py

@@ -0,0 +1,28 @@
+import litellm
+from app.utils.redis_manager import redis_manager
+
+
+# track_cost_callback
+def track_cost_callback(
+    kwargs,  # kwargs to completion
+    completion_response,  # response from completion
+    start_time,
+    end_time,  # start/end time
+):
+    try:
+        response_cost = kwargs.get("response_cost", 0)
+        total_tokens = kwargs.get("total_tokens", 0)
+
+        # redis
+        print("streaming response_cost", response_cost)
+        print("streaming total_tokens", total_tokens)
+
+        # 更新redis
+        return
+    except Exception as e:
+        print(e)
+        pass
+
+
+# set callback
+litellm.success_callback = [track_cost_callback]  # set custom callback function

backend/pyproject.toml

@@ -15,6 +15,7 @@ dependencies = [
     "icecream>=2.1.4",
     "ipykernel>=6.29.5",
     "jupyter-client>=8.6.3",
+    "litellm>=1.69.0",
     "loguru>=0.7.3",
     "matplotlib>=3.10.1",
     "nbformat>=5.10.4",
@@ -29,6 +30,8 @@ dependencies = [
     "scikit-learn>=1.6.1",
     "scipy>=1.15.2",
     "seaborn>=0.13.2",
+    "semanticscholar>=0.10.0",
+    "statsmodels>=0.14.4",
     "uvicorn[standard]>=0.34.0",
     "websocket>=0.2.1",
     "xgboost>=3.0.0",

backend/uv.lock

@@ -14,6 +14,64 @@ wheels = [
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 ]
 
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.6.1"
+source = { registry = "https://pypi.org/simple" }
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+wheels = [
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+]
+
+[[package]]
+name = "aiohttp"
+version = "3.11.18"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "aiohappyeyeballs" },
+    { name = "aiosignal" },
+    { name = "attrs" },
+    { name = "frozenlist" },
+    { name = "multidict" },
+    { name = "propcache" },
+    { name = "yarl" },
+]
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@@ -136,6 +207,8 @@ dependencies = [
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     { name = "scipy" },
     { name = "seaborn" },
+    { name = "semanticscholar" },
+    { name = "statsmodels" },
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     { name = "websocket" },
     { name = "xgboost" },
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+version = "3.21.0"
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+
 [[package]]
 name = "zope-event"
 version = "5.0"

docker-compose.yml

@@ -1,26 +1,52 @@
-version: '3.8'
+# docker-compose.yml
+
 services:
-  backend:
-    build: ./backend
-    container_name: mathmodel-backend
-    ports:
-      - "8000:8000"
-    env_file:
-      - ./backend/.env.prod
-    restart: always
-    depends_on:
-      - redis
-  frontend:
-    build: ./frontend
-    container_name: mathmodel-frontend
-    ports:
-      - "80:80"
-    restart: always
-    depends_on:
-      - backend
   redis:
-    image: redis:7
-    container_name: mathmodel-redis
+    image: redis:alpine
+    container_name: mathmodelagent_redis
     ports:
-      - "6379:6379"
-    restart: always
+      - "6379:6379" # 将 Redis 容器的 6379 端口映射到主机的 6379 端口
+    volumes:
+      - redis_data:/data # Redis 数据的持久化存储卷
+
+  backend:
+    build:
+      context: ./backend # Dockerfile 的上下文路径
+      dockerfile: Dockerfile # Dockerfile 文件名
+    container_name: mathmodelagent_backend
+    ports:
+      - "8000:8000" # 将后端容器的 8000 端口映射到主机的 8000 端口
+    env_file:
+      - ./backend/.env.dev #从此文件加载环境变量
+    environment:
+      - ENV=DEV # 为后端显式设置 ENV
+      # .env.dev 文件中的 REDIS_URL 应为 redis://redis:6379/0
+    volumes:
+      - ./backend:/app # 挂载后端代码以实现热重载 (开发环境)
+      - ./backend/project/work_dir:/app/project/work_dir # 持久化生成的文件
+      - backend_venv:/app/.venv # 可选：持久化 venv 以在依赖不变时加快后续构建速度
+    depends_on:
+      - redis # 确保 Redis 在后端启动前启动
+    stdin_open: true # 保持标准输入打开
+    tty: true      # 分配一个伪终端
+
+  frontend:
+    build:
+      context: ./frontend
+      dockerfile: Dockerfile
+    container_name: mathmodelagent_frontend
+    ports:
+      - "5173:5173" # 将前端容器的 5173 端口 (Vite 默认) 映射到主机的 5173 端口
+    env_file:
+      - ./frontend/.env.development # 加载前端的环境变量
+    volumes:
+      - ./frontend:/app # 挂载前端代码以实现热重载 (开发环境)
+      - /app/node_modules # 匿名卷，防止主机的 node_modules 覆盖容器内的
+    depends_on:
+      - backend # 确保后端可用 (尽管前端通常只需要其 URL)
+    stdin_open: true
+    tty: true
+
+volumes:
+  redis_data: # 定义用于 Redis 持久化的命名卷
+  backend_venv: # 定义用于后端 venv 的命名卷

frontend/.dockerignore

@@ -0,0 +1,40 @@
+# frontend/.dockerignore
+
+# Node modules
+node_modules/
+
+# Build output
+dist/
+build/
+.next/
+.output/
+.nuxt/
+# (根据你的前端框架和构建工具，可能还有其他构建输出目录)
+
+# Logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+
+# Environment files (通常通过 docker-compose volumes 或 env_file 管理)
+.env
+.env*.local
+.env*.production
+# (除非你有特定的 .env 文件是用于构建过程且不含敏感信息)
+
+# IDE settings
+.idea/
+.vscode/
+
+# Git
+.git/
+.gitignore
+
+# macOS
+.DS_Store
+
+# Other temp files or OS-specific files
+*.swp
+Thumbs.db

frontend/Dockerfile

@@ -6,10 +6,10 @@ COPY package.json pnpm-lock.yaml ./
 RUN npm install -g pnpm
 RUN pnpm install
 COPY . .
-RUN pnpm build
 
-# 部署阶段
-FROM nginx:alpine
-COPY --from=build /app/dist /usr/share/nginx/html
-EXPOSE 80
-CMD ["nginx", "-g", "daemon off;"]
+# 应用将运行的端口 (Vite 开发环境通常是 5173)
+EXPOSE 5173
+
+# 运行开发服务器的命令
+# --host 确保可以从容器外部访问
+CMD ["pnpm", "run", "dev", "--host"]