support all model

support all model

Author: jihe520

.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

@@ -26,13 +26,15 @@ def __init__(
         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
         self.chat_history: list[dict] = []  # 存储对话历史
         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 @@ async def run(self, prompt: str, system_prompt: str, sub_title: str) -> str:
             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 @@ async def run(self, prompt: str, subtask_title: str) -> CoderToWriter:
                         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}")
@@ -227,14 +238,24 @@ async def run(self, prompt: str, subtask_title: str) -> CoderToWriter:
 
                         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(
                             {"role": "user", "content": reflection_prompt}
                         )
                         # 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 @@ async def run(
             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 @@
 # 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