新增了zeo++的部分

mcp/SearchPaperByEmbedding/demo.py

@@ -10,8 +10,8 @@ searcher.compute_embeddings()
 
 examples = [
     {
-        "title": "Improving Developer Emotion Classification via LLM-Based Augmentation",
+        "title": "Solid-State battery",
-        "abstract": "Detecting developer emotion in the informative data stream of technical commit messages..."
+        "abstract": "Solid-State battery"
     },
 ]
 

mcp/main.py

@@ -11,10 +11,15 @@ from starlette.routing import Mount
 from system_tools import create_system_mcp
 from materialproject_mcp import create_materials_mcp
 from softBV_remake import create_softbv_mcp
+from paper_search_mcp import create_paper_search_mcp
+from topological_analysis_models import create_topological_analysis_mcp
+
 # 创建 MCP 实例
 system_mcp = create_system_mcp()
 materials_mcp = create_materials_mcp()
 softbv_mcp = create_softbv_mcp()
+paper_search_mcp = create_paper_search_mcp()
+topological_analysis_mcp = create_topological_analysis_mcp()
 # 在 Starlette 的 lifespan 中启动 MCP 的 session manager
 @contextlib.asynccontextmanager
 async def lifespan(app: Starlette):
@@ -22,6 +27,8 @@ async def lifespan(app: Starlette):
         await stack.enter_async_context(system_mcp.session_manager.run())
         await stack.enter_async_context(materials_mcp.session_manager.run())
         await stack.enter_async_context(softbv_mcp.session_manager.run())
+        await stack.enter_async_context(paper_search_mcp.session_manager.run())
+        await stack.enter_async_context(topological_analysis_mcp.session_manager.run())
         yield  # 服务器运行期间
         # 退出时自动清理
 
@@ -32,6 +39,8 @@ app = Starlette(
         Mount("/system", app=system_mcp.streamable_http_app()),
         Mount("/materials", app=materials_mcp.streamable_http_app()),
         Mount("/softBV", app=softbv_mcp.streamable_http_app()),
+        Mount("/papersearch",app=paper_search_mcp.streamable_http_app()),
+        Mount("/topologicalAnalysis",app=topological_analysis_mcp.streamable_http_app()),
     ],
 )
 
@@ -41,4 +50,6 @@ app = Starlette(
 #   http://localhost:8000/system
 #   http://localhost:8000/materials
 #   http://localhost:8000/softBV
+#   http://localhost:8000/papersearch
+#   http://localhost:8000/topologicalAnalysis
 # 如果需要浏览器客户端访问（CORS 暴露 Mcp-Session-Id），请参考 README 中的 CORS 配置示例 [1]

mcp/paper_search_mcp.py

@@ -0,0 +1,172 @@
+# paper_search_mcp.py (重构版)
+
+import os
+import asyncio
+from dataclasses import dataclass
+from typing import Any
+from collections.abc import AsyncIterator
+from contextlib import asynccontextmanager
+
+from pydantic import BaseModel, Field
+# 假设您的 search.py 提供了 PaperSearcher 类
+from SearchPaperByEmbedding.search import PaperSearcher
+
+from mcp.server.fastmcp import FastMCP, Context
+from mcp.server.session import ServerSession
+
+# ========= 1. 配置与常量 (修正版) =========
+
+# 获取当前脚本文件所在的目录的绝对路径
+_CURRENT_SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+
+# 基于项目根目录构造资源文件的绝对路径
+DATA_DIR = os.path.join(_CURRENT_SCRIPT_DIR,"SearchPaperByEmbedding")
+PAPERS_JSON_PATH = os.path.join(DATA_DIR, "iclr2026_papers.json")
+
+# 打印路径用于调试，确保它是正确的
+print(f"DEBUG: Trying to load papers from: {PAPERS_JSON_PATH}")
+
+MODEL_TYPE = os.getenv("EMBEDDING_MODEL_TYPE", "local")
+
+
+# ========= 2. 数据模型 =========
+
+class Paper(BaseModel):
+    """单篇论文的详细信息"""
+    title: str
+    authors: list[str]
+    abstract: str
+    pdf_url: str | None = None
+    similarity_score: float = Field(description="与查询的相似度分数，越高越相关")
+
+
+class SearchQuery(BaseModel):
+    """论文搜索的查询结构"""
+    title: str
+    abstract: str | None = Field(None, description="可选的摘要，以提供更丰富的上下文")
+
+
+# ========= 3. Lifespan (资源加载与管理) =========
+
+@dataclass
+class PaperSearchContext:
+    """在服务器生命周期内共享的、已初始化的 PaperSearcher 实例"""
+    searcher: PaperSearcher
+
+
+@asynccontextmanager
+async def paper_search_lifespan(_server: FastMCP) -> AsyncIterator[PaperSearchContext]:
+    """
+    FastMCP 生命周期：在服务器启动时初始化 PaperSearcher 并计算 Embeddings。
+    这确保了最耗时的部分只在启动时执行一次。
+    """
+    print(f"正在使用 '{MODEL_TYPE}' 模型初始化论文搜索引擎...")
+
+    # 使用 to_thread 运行同步的初始化代码
+    def _initialize_searcher():
+        # 1. 初始化
+        searcher = PaperSearcher(
+            PAPERS_JSON_PATH,
+            model_type=MODEL_TYPE
+        )
+        # 2. 计算/加载 Embeddings
+        print("正在计算或加载论文 Embeddings...")
+        searcher.compute_embeddings()
+        print("Embeddings 加载完成。")
+        return searcher
+
+    searcher = await asyncio.to_thread(_initialize_searcher)
+    print("论文搜索引擎已准备就绪！")
+
+    try:
+        # 使用 yield 将创建好的共享资源上下文传递给 MCP
+        yield PaperSearchContext(searcher=searcher)
+    finally:
+        print("论文搜索引擎服务关闭。")
+
+
+# ========= 4. MCP 服务器实例 =========
+
+mcp = FastMCP(
+    name="Paper Search",
+    instructions="通过 embedding 相似度检索 ICLR 2026 论文的工具。",
+    lifespan=paper_search_lifespan,
+    streamable_http_path="/",
+)
+
+
+# ========= 5. 工具实现 =========
+@mcp.tool()
+async def search_papers(
+    queries: list[SearchQuery],
+    top_k: int = 3,
+    ctx: Context[ServerSession, PaperSearchContext] | None = None,
+) -> list[Paper]:
+    """
+    根据给定的一个或多个查询（包含标题和可选的摘要），使用语义相似度搜索论文。
+    返回最相关的 top_k 个结果。
+    """
+    if ctx is None:
+        raise ValueError("Context is required for this operation.")
+    if not queries:
+        return []
+
+    app_ctx = ctx.request_context.lifespan_context
+    searcher = app_ctx.searcher
+
+    # 预处理查询，确保 title 和 abstract 至少有一个存在
+    examples_to_search = []
+    for q in queries:
+        query_dict = q.model_dump(exclude_none=True)
+        if "title" not in query_dict and "abstract" in query_dict:
+            query_dict["title"] = query_dict["abstract"]
+        elif "abstract" not in query_dict and "title" in query_dict:
+            query_dict["abstract"] = query_dict["title"]
+        examples_to_search.append(query_dict)
+
+    if not examples_to_search:
+        return []
+
+    query_display = queries[0].title or queries[0].abstract or "Empty Query"
+    await ctx.info(f"正在以查询 '{query_display}' 等内容搜索排名前 {top_k} 的论文...")
+
+    # 调用底层的 search 方法
+    results = await asyncio.to_thread(
+        searcher.search,
+        examples=examples_to_search,
+        top_k=top_k
+    )
+
+    # --- 关键修正：按照 search.py 返回的正确结构进行解析 ---
+    papers = []
+    for res in results:
+        paper_data = res.get('paper', {})  # 获取嵌套的论文信息字典
+        similarity = res.get('similarity', 0.0) # 获取相似度分数
+
+        papers.append(
+            Paper(
+                title=paper_data.get("title", "N/A"),
+                authors=paper_data.get("authors", []),
+                abstract=paper_data.get("abstract", ""),
+                # 注意：原始数据中 pdf url 的键是 'forum_url'
+                pdf_url=paper_data.get("forum_url"),
+                similarity_score=similarity  # 使用正确的相似度分数
+            )
+        )
+
+    await ctx.debug(f"找到 {len(papers)} 篇相关论文。")
+    return papers
+
+
+# ========= 6. 工厂函数 (用于 Starlette 集成) =========
+
+def create_paper_search_mcp() -> FastMCP:
+    """供 Starlette 挂载的工厂函数。"""
+    return mcp
+
+
+if __name__ == "__main__":
+    # 如果直接运行此文件，则启动一个独立的 MCP 服务器
+    mcp.run(transport="streamable-http")

mcp/topological_analysis_models.py

@@ -0,0 +1,185 @@
+# topological_analysis_tools.py
+
+import posixpath
+import re
+from typing import Any, Literal
+from contextlib import asynccontextmanager
+from collections.abc import AsyncIterator
+
+import asyncssh
+from pydantic import BaseModel, Field
+
+# 从 MCP SDK 导入核心组件 [1]
+from mcp.server.fastmcp import FastMCP, Context
+from mcp.server.session import ServerSession
+
+# 从 lifespan.py 导入共享的配置和数据类 [3]
+# 注意: 我们不再需要导入 system_lifespan
+from lifespan import (
+    SharedAppContext,
+    REMOTE_HOST,
+    REMOTE_USER,
+    PRIVATE_KEY_PATH,
+    INITIAL_WORKING_DIRECTORY,
+)
+
+# ==============================================================================
+# 1. 辅助函数 (为实现独立性而复制)
+# ==============================================================================
+
+def shell_quote(arg: str) -> str:
+    """安全地将字符串转义为单个 shell 参数 [5]。"""
+    return "'" + arg.replace("'", "'\"'\"'") + "'"
+
+def _safe_join(sandbox_root: str, relative_path: str) -> str:
+    """安全地将用户提供的相对路径连接到沙箱根目录 [5]。"""
+    rel = (relative_path or ".").strip()
+    if rel.startswith("/"):
+        rel = rel.lstrip("/")
+    combined = posixpath.normpath(posixpath.join(sandbox_root, rel))
+    root_norm = sandbox_root.rstrip("/")
+    if combined != root_norm and not combined.startswith(root_norm + "/"):
+        raise ValueError("路径越界: 仅允许访问沙箱目录内部")
+    if ".." in combined.split("/"):
+        raise ValueError("非法路径: 不允许使用 '..' 跨目录")
+    return combined
+
+# ==============================================================================
+# 2. 独立的生命周期管理器
+# ==============================================================================
+
+@asynccontextmanager
+async def analysis_lifespan(_server: FastMCP) -> AsyncIterator[SharedAppContext]:
+    """
+    为拓扑分析工具独立管理 SSH 连接的生命周期。
+    """
+    conn: asyncssh.SSHClientConnection | None = None
+    print("分析工具: 正在建立独立的 SSH 连接...")
+    try:
+        conn = await asyncssh.connect(
+            REMOTE_HOST,
+            username=REMOTE_USER,
+            client_keys=[PRIVATE_KEY_PATH],
+        )
+        print(f"分析工具: 独立的 SSH 连接到 {REMOTE_HOST} 成功!")
+        yield SharedAppContext(ssh_connection=conn, sandbox_path=INITIAL_WORKING_DIRECTORY)
+    finally:
+        if conn:
+            conn.close()
+            await conn.wait_closed()
+            print("分析工具: 独立的 SSH 连接已关闭。")
+
+
+# ==============================================================================
+# 3. 数据模型与解析函数 (与之前相同)
+# ==============================================================================
+# (为简洁起见，此处省略了 Pydantic 模型和 _parse_analysis_output 函数的定义，
+#  请将上一版本中的这部分代码粘贴到这里)
+VALID_CONFIGS = Literal["O.yaml", "S.yaml", "Cl.yaml", "Br.yaml"]
+class SubmitTopologicalAnalysisParams(BaseModel):
+    cif_file_path: str = Field(description="远程服务器上 CIF 文件的相对路径。")
+    config_files: list[VALID_CONFIGS] = Field(description="选择一个或多个用于分析的 YAML 配置文件。")
+    output_file_path: str = Field(description="用于保存计算结果的输出文件相对路径。")
+class AnalysisResult(BaseModel):
+    percolation_diameter_a: float | None = Field(None, description="渗透直径 (Percolation diameter), 单位 Å。")
+    connectivity_distance: float | None = Field(None, description="连通距离 (the minium of d), 单位 Å。")
+    max_node_length_a: float | None = Field(None, description="最大节点长度 (Maximum node length), 单位 Å。")
+    min_cation_distance_a: float | None = Field(None, description="到阳离子的最小距离, 单位 Å。")
+    total_time_s: float | None = Field(None, description="总计算耗时, 单位秒。")
+    long_node_count: int | None = Field(None, description="长节点数量 (Long node number)。")
+    short_node_count: int | None = Field(None, description="短节点数量 (Short node number)。")
+    warnings: list[str] = Field(default_factory=list, description="解析过程中遇到的警告或缺失的关键值信息。")
+def _parse_analysis_output(log_content: str) -> AnalysisResult:
+    def find_float(pattern: str) -> float | None:
+        match = re.search(pattern, log_content)
+        return float(match.group(1)) if match else None
+    def find_int(pattern: str) -> int | None:
+        match = re.search(pattern, log_content)
+        return int(match.group(1)) if match else None
+    data = { "percolation_diameter_a": find_float(r"Percolation diameter \(A\):\s*([\d\.]+)"), "max_node_length_a": find_float(r"Maximum node length detected:\s*([\d\.]+)\s*A"), "min_cation_distance_a": find_float(r"minimum distance to cations is\s*([\d\.]+)\s*A"), "total_time_s": find_float(r"Total used time:\s*([\d\.]+)"), "long_node_count": find_int(r"Long node number:\s*(\d+)"), "short_node_count": find_int(r"Short node number:\s*(\d+)"), }
+    conn_dist_match = re.search(r"the minium of d\s*([\d\.]+)", log_content, re.MULTILINE)
+    data["connectivity_distance"] = float(conn_dist_match.group(1)) if conn_dist_match else None
+    warnings = [k + " 未找到" for k, v in data.items() if v is None and k not in ["min_cation_distance_a", "long_node_count", "short_node_count"]]
+    return AnalysisResult(**data, warnings=warnings)
+
+
+# ==============================================================================
+# 4. MCP 服务器工厂函数
+# ==============================================================================
+
+def create_topological_analysis_mcp() -> FastMCP:
+    """
+    供 Starlette 挂载的工厂函数。
+    这个函数现在创建并使用自己独立的 lifespan 管理器。
+    """
+    analysis_mcp = FastMCP(
+        name="Topological Analysis Tools",
+        instructions="用于在远程服务器上提交和分析拓扑计算任务的工具集。",
+        streamable_http_path="/",
+        lifespan=analysis_lifespan, # 关键: 使用本文件内定义的独立 lifespan
+    )
+
+    @analysis_mcp.tool()
+    async def submit_topological_analysis(
+        params: SubmitTopologicalAnalysisParams,
+        ctx: Context[ServerSession, SharedAppContext],
+    ) -> dict[str, Any]:
+        """【步骤1/2】异步提交拓扑分析任务。"""
+        # ... (工具的实现逻辑与之前完全相同)
+        try:
+            app_ctx = ctx.request_context.lifespan_context
+            conn = app_ctx.ssh_connection
+            sandbox_root = app_ctx.sandbox_path
+            home_dir = f"/cluster/home/{REMOTE_USER}"
+            tool_dir = f"{home_dir}/tool/Topological_analyse"
+            cif_abs_path = _safe_join(sandbox_root, params.cif_file_path)
+            output_abs_path = _safe_join(sandbox_root, params.output_file_path)
+            config_args = " ".join([shell_quote(f"{tool_dir}/{cfg}") for cfg in params.config_files])
+            config_flags = f"-i {config_args}"
+            command = f"""
+            nohup sh -c '
+                source {shell_quote(f"{home_dir}/.bashrc")} && \\
+                conda activate {shell_quote(f"{home_dir}/anaconda3/envs/zeo")} && \\
+                python {shell_quote(f"{tool_dir}/analyze_voronoi_nodes.py")} \\
+                       {shell_quote(cif_abs_path)} \\
+                       {config_flags}
+            ' > {shell_quote(output_abs_path)} 2>&1 &
+            """.strip()
+            await ctx.info("提交后台拓扑分析任务...")
+            await conn.run(command, check=True)
+            return {"success": True, "message": "拓扑分析任务已成功提交。", "output_file": params.output_file_path}
+        except Exception as e:
+            msg = f"提交后台任务失败: {e}"
+            await ctx.error(msg)
+            return {"success": False, "error": msg}
+
+    @analysis_mcp.tool()
+    async def analyze_topological_results(
+        output_file_path: str,
+        ctx: Context[ServerSession, SharedAppContext],
+    ) -> AnalysisResult:
+        """【步骤2/2】读取并分析拓扑分析任务的输出文件。"""
+        # ... (工具的实现逻辑与之前完全相同)
+        try:
+            await ctx.info(f"开始分析结果文件: {output_file_path}")
+            app_ctx = ctx.request_context.lifespan_context
+            conn = app_ctx.ssh_connection
+            sandbox_root = app_ctx.sandbox_path
+            target_path = _safe_join(sandbox_root, output_file_path)
+            async with conn.start_sftp_client() as sftp:
+                async with sftp.open(target_path, "r") as f:
+                    log_content = await f.read()
+            if not log_content:
+                raise ValueError("结果文件为空。")
+            analysis_data = _parse_analysis_output(log_content)
+            return analysis_data
+        except FileNotFoundError:
+            msg = f"分析失败: 结果文件 '{output_file_path}' 不存在。"
+            await ctx.error(msg)
+            return AnalysisResult(warnings=[msg])
+        except Exception as e:
+            msg = f"分析结果时发生错误: {e}"
+            await ctx.error(msg)
+            return AnalysisResult(warnings=[msg])
+
+    return analysis_mcp