summarize_all_communities

Generate summaries for academic communities in bulk or parallel processing. Specify community levels, control concurrency, and optionally target specific communities for automated literature review generation.

Instructions

批量/并行生成社区摘要

Args: level: 社区层级，"macro" 或 "micro"（或整数 1/2） concurrency: 并发数，默认 5 comm_ids: 指定社区 ID 列表 force: 是否强制重新生成

Input Schema

TableJSON Schema

Name	Required	Description	Default
`level`	No
`concurrency`	No
`comm_ids`	No
`force`	No

Implementation Reference

src/paperlib_mcp/tools/graph_summarize.py:427-488 (handler)

Core implementation of the summarize_all_communities tool. Normalizes input level, queries target community IDs (filtering by level and whether already summarized unless force), limits concurrency with semaphore, processes each community by calling summarize_community_v1_run in parallel using asyncio.gather, and returns summary statistics.

async def summarize_all_communities_run(
    level: str | int | None = None,
    concurrency: int = 5,
    comm_ids: list[int] | None = None,
    force: bool = False,
) -> dict[str, Any]:
    """批量/并行生成社区摘要 (Core Implementation)"""
    # Normalize level to string "macro" or "micro" to match DB column type (TEXT)
    level_str: str | None = None
    if level is not None:
        l_lower = str(level).lower()
        if l_lower in ["macro", "1"]:
            level_str = "macro"
        elif l_lower in ["micro", "2"]:
            level_str = "micro"
        else:
            level_str = l_lower
    
    # 1. 确定要处理的 communities
    if comm_ids:
        target_ids = comm_ids
    else:
        query = "SELECT comm_id FROM communities WHERE 1=1"
        params = []
        if level_str is not None:
            query += " AND level = %s"
            params.append(level_str)
        
        if not force:
            query += " AND NOT EXISTS (SELECT 1 FROM community_summaries s WHERE s.comm_id = communities.comm_id)"
            
        rows = query_all(query, tuple(params))
        target_ids = [r["comm_id"] for r in rows]
        
    if not target_ids:
        return {"message": "No communities to summarize", "count": 0}
        
    # 2. 并行执行
    sem = asyncio.Semaphore(concurrency)
    
    async def process_one(cid):
        async with sem:
            res = await summarize_community_v1_run(comm_id=cid)
            return {
                "comm_id": cid, 
                "success": not res.get("error"), 
                "error": res.get("error")
            }
            
    tasks = [process_one(cid) for cid in target_ids]
    results = await asyncio.gather(*tasks)
    
    success_count = sum(1 for r in results if r["success"])
    failed_count = len(results) - success_count
    
    return {
        "total": len(target_ids),
        "success": success_count,
        "failed": failed_count,
        "details": results[:20]  # 返回前 20 个结果
    }

src/paperlib_mcp/tools/graph_summarize.py:510-530 (registration)

MCP tool registration via @mcp.tool() decorator. Defines the tool interface with type-hinted parameters and docstring, acting as a thin wrapper that delegates execution to summarize_all_communities_run.

@mcp.tool()
async def summarize_all_communities(
    level: str | None = None,
    concurrency: int = 5,
    comm_ids: list[int] | None = None,
    force: bool = False,
) -> dict[str, Any]:
    """批量/并行生成社区摘要
    
    Args:
        level: 社区层级，"macro" 或 "micro"（或整数 1/2）
        concurrency: 并发数，默认 5
        comm_ids: 指定社区 ID 列表
        force: 是否强制重新生成
    """
    return await summarize_all_communities_run(
        level=level,
        concurrency=concurrency,
        comm_ids=comm_ids,
        force=force
    )

src/paperlib_mcp/server.py:43-43 (registration)
Top-level registration call in the main MCP server setup that invokes the module's register function to add the summarize_all_communities tool (among others) to the FastMCP instance.
```
register_graph_summarize_tools(mcp)
```

Paperlib MCP