feat: pipeline 并行摘要 + 子进程 env 注入 + 循环导入修复

源码：
- runtime/__init__.py: resume_jobs/resume_service 改为懒加载，打破循环导入
- freshrss_pipeline_jobs.py / resume_jobs.py: 子进程注入 .env 环境变量
- freshrss_pipeline.py: LLM 摘要串行改并行 (ThreadPoolExecutor, max_workers=4)

配置：
- term_aliases: 19→149 条，大幅扩充别名映射
- term_stopwords: 19→132 条，增加过滤规则
- filter_context.personal.json: +7 个兴趣关键词

configs/filter_context.personal.json

@@ -33,8 +33,10 @@
     "Claude",
     "Claude Code",
     "CLAUDE.md",
+    "CLI",
     "Context Engineering",
     "Cursor",
+    "ChatGPT",
     "DeepSeek",
     "FastAPI",
     "Gin",
@@ -46,7 +48,9 @@
     "Kafka",
     "Kubernetes",
     "LLM",
+    "Loop Engineering",
     "MCP",
+    "MoE",
     "MySQL",
     "MySQL复制延迟",
     "OpenAI",
@@ -55,6 +59,7 @@
     "Prompt Engineering",
     "Python",
     "RAG",
+    "ReAct",
     "ReActAgent",
     "Redis",
     "Skill",
@@ -74,6 +79,8 @@
     "向量数据库",
     "多Agent协作",
     "大模型",
+    "子Agent",
+    "强化学习",
     "微服务",
     "渐进式披露",
     "知识库"

configs/term_aliases.json

@@ -1,19 +1,149 @@
 {
-  "AI助手": "AI Agent",
+  "Agent": "Agent",
-  "图文RAG": "RAG",
+  "Agent框架": "Agent",
-  "Prompt架构": "Prompt Engineering",
+  "Agent能力": "Agent Skills",
-  "Agent Skill": "Agent Skills",
+  "智能体": "AI Agent",
-  "Binlog": "binlog",
+  "Agentic架构": "Agentic架构",
+  "多Agent协作": "多Agent协作",
+  "多智能体架构": "多Agent协作",
+  "Multi-Agent": "多Agent",
+  "Subagent": "子Agent",
+  "Sub Agents验证": "子Agent",
+  "子Agent": "子Agent",
+  "子智能体": "子Agent",
   "Coding Agent": "AI Coding Agent",
-  "Subagent": "SubAgent",
+  "AI编程": "AI Coding Agent",
-  "Subagents": "SubAgent",
+  "AI辅助编程": "AI Coding Agent",
-  "vibe coding": "Vibe Coding",
+  "代码生成": "AI代码生成",
+  "代码审查": "Code Review",
+  "Prompt": "Prompt Engineering",
+  "Prompt Caching": "提示缓存",
+  "RAG": "RAG",
+  "图文RAG": "RAG",
+  "向量检索": "向量检索",
+  "向量嵌入": "向量嵌入",
+  "Multi-Token Prediction": "多Token预测",
+  "Pair-In Pair-Out": "PIPO架构",
+  "PIPO": "PIPO架构",
+  "上下文管理": "上下文管理",
+  "上下文卸载": "上下文卸载",
+  "Self-GC": "上下文压缩",
+  "记忆管理": "上下文管理",
+  "会话管理": "上下文管理",
+  "Harness Engineering": "Harness工程化",
+  "Harness架构": "Harness工程化",
+  "Harness": "Harness工程化",
+  "Loop Engineering": "Loop Engineering",
+  "推理加速": "推理加速",
+  "推理深度": "推理深度",
+  "长链路推理": "长链路推理",
+  "RLVR": "RLVR",
+  "GRPO": "GRPO",
+  "强化学习": "强化学习",
+  "Multi-Agent RL": "多Agent强化学习",
+  "Viking AI搜索": "AI搜索",
+  "Viking AI Search": "AI搜索",
+  "智能搜索": "AI搜索",
+  "SearchCLI": "CLI搜索",
+  "视频生成": "AI视频生成",
+  "视频生成模型": "AI视频生成",
+  "LingBot-Video": "AI视频生成",
+  "视觉自回归模型": "AI视频生成",
+  "火山云数据库PostgreSQL Serverless版": "Serverless数据库",
+  "PostgreSQL": "PostgreSQL",
+  "MySQL": "MySQL",
+  "OceanBase": "OceanBase",
+  "StarRocks": "StarRocks",
+  "Milvus": "Milvus",
+  "Seal AI Zone": "AI安全",
+  "NEX沙箱": "沙箱隔离",
+  "MicroVM": "沙箱隔离",
+  "安全左移": "安全左移",
+  "安全中台": "AI安全",
+  "成本降低": "成本优化",
+  "成本杠杆": "成本优化",
+  "Scale-to-Zero": "弹性伸缩",
+  "Data as Git": "数据分支管理",
+  "Schema Diff": "Schema对比",
+  "Time Travel": "数据回溯",
+  "多端架构": "多端架构",
+  "契约化": "契约化架构",
+  "大仓": "大仓工程化",
+  "Vibe Coding": "Vibe Coding",
+  "LLM Judge": "LLM评估",
+  "SWE-Bench": "SWE-Bench",
+  "SWE Bench Pro": "SWE-Bench",
+  "SWE-Bench Pro": "SWE-Bench",
+  "Verification Agent": "验证Agent",
+  "CLI工具": "CLI",
+  "CLI": "CLI",
+  "漏桶算法": "限流架构",
+  "固定窗口限流": "限流架构",
+  "Suspend消费控制": "限流架构",
+  "RocketMQ LiteTopic": "消息队列",
+  "LLM Wiki": "LLM知识库",
+  "知识工程": "知识工程",
+  "语义资产": "语义资产管理",
+  "知识图谱": "知识图谱",
+  "知识库沉淀": "知识管理",
+  "Skill": "Skill",
+  "Skill Hub": "技能生态",
+  "具身智能": "具身智能",
+  "Open X-Embodiment": "具身智能",
+  "YOLO Classifier": "目标检测",
+  "MCP": "MCP",
+  "MCP连接器": "MCP",
+  "缓存击穿": "缓存优化",
+  "GPU算力调度": "算力调度",
+  "异构资源": "异构计算",
+  "XPU": "异构计算",
+  "弹性RDMA": "RDMA网络",
+  "国内主流GPU": "国产芯片",
+  "国产AI芯片": "国产芯片",
+  "Paxos协议": "分布式一致性",
+  "Token": "Token管理",
+  "Token效率": "Token管理",
+  "百万token上下文": "长上下文",
+  "MoE": "MoE架构",
+  "MoE架构": "MoE架构",
+  "思维链": "思维链",
+  "CoT Distillation": "思维链蒸馏",
+  "自然语言驱动": "自然语言交互",
+  "NL2SQL": "NL2SQL",
+  "AI对齐": "AI对齐",
+  "注意力机制": "注意力机制",
+  "多模态": "多模态",
+  "音视频工作台": "音视频处理",
+  "AI助手": "AI Agent",
   "Agent架构": "AI Agent",
   "Agent专业化": "AI Agent",
   "Agent Teams": "多Agent协作",
   "Agentic Engineering": "AI Agent",
-  "CLI工具": "CLI",
+  "AI智能体": "AI Agent",
-  "AI编程": "AI Coding Agent",
+  "LLM Agent": "AI Agent",
-  "记忆管理": "上下文管理",
+  "AI Harness": "Harness Engineering",
-  "会话管理": "上下文管理"
+  "AI代码生成": "AI Coding Agent",
+  "Memory管理": "上下文管理",
+  "Agent Skill": "Agent Skills",
+  "Binlog": "binlog",
+  "vibe coding": "Vibe Coding",
+  "Agent组织化协作平台": "Agent协作平台",
+  "Anthropic": "Anthropic",
+  "OpenClaw": "OpenClaw",
+  "WorkBuddy": "WorkBuddy",
+  "Claude": "Claude",
+  "ChatGPT": "ChatGPT",
+  "GPT": "GPT",
+  "Opus": "Opus",
+  "Sonnet": "Sonnet",
+  "Grok": "Grok",
+  "Qwen": "Qwen",
+  "GLM": "GLM",
+  "Claude Code": "Claude Code",
+  "Cursor": "Cursor",
+  "Codex": "Codex",
+  "Pi": "Pi",
+  "CoT": "CoT",
+  "SVG": "SVG",
+  "TTS": "TTS"
 }

configs/term_change_log.json

@@ -536,6 +536,60 @@
       "value": "上下文管理",
       "suggestion_date": "2026-05-14",
       "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-07-15T02:17:50.155586Z",
+      "action": "add_interest_keyword",
+      "term": "强化学习",
+      "reason": "top 0.8% by frequency，growth=100%，not yet covered by interest keywords or stopwords. Evidence: total_count=10, days_seen=10, recent_count=10.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-07-15.json",
+      "suggestion_date": "2026-07-15",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-07-15T02:17:50.155586Z",
+      "action": "add_interest_keyword",
+      "term": "ReAct",
+      "reason": "top 1.1% by frequency，growth=100%，not yet covered by interest keywords or stopwords. Evidence: total_count=8, days_seen=8, recent_count=8.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-07-15.json",
+      "suggestion_date": "2026-07-15",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-07-15T02:17:50.155586Z",
+      "action": "add_interest_keyword",
+      "term": "CLI",
+      "reason": "top 1.1% by frequency，growth=100%，not yet covered by interest keywords or stopwords. Evidence: total_count=8, days_seen=7, recent_count=8.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-07-15.json",
+      "suggestion_date": "2026-07-15",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-07-15T02:17:50.155586Z",
+      "action": "add_interest_keyword",
+      "term": "子Agent",
+      "reason": "top 1.3% by frequency，growth=100%，not yet covered by interest keywords or stopwords. Evidence: total_count=7, days_seen=6, recent_count=7.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-07-15.json",
+      "suggestion_date": "2026-07-15",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-07-15T02:17:50.155586Z",
+      "action": "add_interest_keyword",
+      "term": "Loop Engineering",
+      "reason": "top 1.5% by frequency，growth=100%，not yet covered by interest keywords or stopwords. Evidence: total_count=6, days_seen=6, recent_count=6.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-07-15.json",
+      "suggestion_date": "2026-07-15",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-07-15T02:17:50.155586Z",
+      "action": "add_interest_keyword",
+      "term": "MoE",
+      "reason": "top 2.0% by frequency，growth=100%，not yet covered by interest keywords or stopwords. Evidence: total_count=5, days_seen=4, recent_count=5.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-07-15.json",
+      "suggestion_date": "2026-07-15",
+      "based_on_days": 365
     }
   ]
 }

configs/term_stopwords.json

@@ -1,19 +1,126 @@
 [
+  "1688",
   "AGI",
   "AIHOT",
   "AI日报",
   "All In Code",
+  "Andrej Karpathy",
+  "Anthropic",
   "auto-twitter-campaign",
+  "Boundaries",
   "ChangeSet",
   "Channels",
+  "Claude Fable 5",
+  "Claude Mythos",
+  "Cohere",
+  "Confidence Head",
+  "Cosmos 3",
+  "Databricks",
+  "DINOv2",
+  "domain-mapping",
+  "Dropbox",
+  "EchoGen",
+  "FLUX.1-dev VAE",
+  "GB300 GPU",
+  "GLM 5.2",
+  "GLM5.0",
+  "GPT-5.5",
+  "GPT-Live",
+  "GPT5.5",
+  "Grok 4.5",
+  "GrowBrain",
+  "iMedImage",
+  "iMedLoop",
+  "iMedMaaS",
+  "iMedStudio",
+  "J-space",
+  "JLens",
+  "John Jumper",
+  "J空间",
+  "KAIROS",
+  "KubeRay",
+  "LibTV Agent",
+  "LingBot-Video",
   "Lumina",
-  "Memory",
+  "Markdown",
+  "Marvis",
+  "MDASH",
+  "Meta Superintelligence Labs",
+  "MTS",
+  "Muse Image",
+  "Muse Video",
+  "N-gram Embedding",
+  "OCP China",
+  "OCP China 2026",
+  "On-Policy Distillation",
+  "OPC训练营",
+  "OpenAI",
+  "OpenBMC",
+  "OpenClaw",
   "OpenViking",
-  "Prompt",
+  "Opus 4.8",
+  "Qwen3",
+  "Qwen3-30B-A3B",
+  "RAS API",
+  "Redfish",
+  "ScMoE",
+  "Seal AI Zone",
+  "SealRouter",
   "Seedance 2.0",
+  "Sonnet 5",
+  "Spec模式",
+  "STE固件团队",
+  "Three.js",
+  "Unity AI Gateway",
+  "Vant Weapp",
+  "WeTV",
+  "WorkBuddy",
+  "wpc",
+  "YOLO Classifier",
+  "一人公司",
+  "中国科学技术大学",
+  "五大扶持体系",
+  "出门问问",
+  "分镜",
+  "剧本",
   "奋斗文化",
+  "字节跳动",
   "小银",
+  "得力",
+  "德适科技",
+  "成都天府长岛",
+  "扣子",
+  "星云平台",
+  "火山引擎",
+  "百度百舸",
+  "百炼网关",
+  "科大讯飞",
+  "腾讯云开发者社区",
+  "腾讯混元Hy3",
+  "蚂蚁灵波",
+  "贝尔实验室",
   "质量门禁",
+  "配乐",
+  "配音",
   "银行客户经理",
-  "飞盘物理"
+  "飞书妙搭",
+  "飞盘物理",
+  "自动化",
+  "定时任务",
+  "开源模型",
+  "陌生化",
+  "AlphaFold",
+  "Brand Kit",
+  "DataWorks",
+  "Enhance-Nanocodec",
+  "IRIS Codec",
+  "Lovart",
+  "MiniMax M3",
+  "Gemini 3.5 Flash",
+  "Codex",
+  "CodeBuddy",
+  "Claude Cowork",
+  "AGENTS.md",
+  "Claude",
+  "RLVR"
 ]

configs/term_watchlist.json

@@ -1,6 +1,6 @@
 {
   "schema_version": "v1",
-  "updated_at": "2026-05-14T09:12:50.749877Z",
+  "updated_at": "2026-07-15T02:17:50.155586Z",
   "terms": [
     {
       "term": "A2A",

src/summary_mcp/runtime/__init__.py

@@ -6,8 +6,30 @@ from .freshrss_pipeline_jobs import (
     start_freshrss_pipeline_job,
 )
 from .query_service import get_delivery_payload, get_run_report, get_run_status, list_run_artifacts, list_runs
-from .resume_jobs import get_resume_job_result, get_resume_job_status, start_resume_job
+
-from .resume_service import inspect_resume_plan, resume_run
+# NOTE: resume_jobs and resume_service are NOT eagerly imported here to avoid
+# a circular import chain:
+#   workflows/freshrss_pipeline.py -> runtime -> resume_jobs -> resume_service
+#   -> workflows/freshrss_pipeline.py (circular!)
+# They are lazy-loaded via __getattr__ when accessed as summary_mcp.runtime.*
+
+
+def __getattr__(name):
+    import importlib
+
+    _LAZY = {
+        "get_resume_job_result": ("resume_jobs", "get_resume_job_result"),
+        "get_resume_job_status": ("resume_jobs", "get_resume_job_status"),
+        "start_resume_job": ("resume_jobs", "start_resume_job"),
+        "inspect_resume_plan": ("resume_service", "inspect_resume_plan"),
+        "resume_run": ("resume_service", "resume_run"),
+    }
+    if name in _LAZY:
+        mod_name, attr_name = _LAZY[name]
+        mod = importlib.import_module(f".{mod_name}", __package__)
+        return getattr(mod, attr_name)
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
 
 __all__ = [
     "ArtifactRecord",

src/summary_mcp/runtime/freshrss_pipeline_jobs.py

@@ -9,6 +9,8 @@ from pathlib import Path
 from typing import Any
 from uuid import uuid4
 
+from dotenv import dotenv_values
+
 from .run_store import RunStore
 from .query_service import _resolve_run_record
 
@@ -29,6 +31,25 @@ DEFAULT_STAGES = [
 ]
 MIN_JOB_STALE_SECONDS = 30 * 60
 MAX_JOB_STALE_SECONDS = 6 * 60 * 60
+DEFAULT_DOTENV_PATH = REPO_ROOT / ".env"
+
+
+def _build_subprocess_env() -> dict[str, str]:
+    """Build an env dict for subprocess, merging parent env with .env values.
+
+    The subprocess inherits the Hermes MCP server's environment, but .env values
+    may not be in os.environ at the time the subprocess is spawned. This function
+    loads them from .env and merges them in so the child process sees all needed
+    variables (LLM_API_KEY, LLM_MODEL, LLM_API_URL, FRESHRSS_*, etc.) directly
+    in os.environ, avoiding any dotenv-loading timing issues inside the subprocess.
+    """
+    env = os.environ.copy()
+    if DEFAULT_DOTENV_PATH.exists():
+        for key, value in dotenv_values(DEFAULT_DOTENV_PATH).items():
+            if isinstance(key, str) and isinstance(value, str) and value:
+                # Only set if not already present in parent env
+                env.setdefault(key, value)
+    return env
 
 
 def _now() -> datetime:
@@ -218,6 +239,7 @@ def start_freshrss_pipeline_job(
             stdout=subprocess.DEVNULL,
             stderr=subprocess.DEVNULL,
             start_new_session=True,
+            env=_build_subprocess_env(),
         )
     except Exception as exc:
         report_file = _write_job_report(

src/summary_mcp/runtime/resume_jobs.py

@@ -9,6 +9,8 @@ from pathlib import Path
 from typing import Any
 from uuid import uuid4
 
+from dotenv import dotenv_values
+
 from .query_service import _resolve_run_record
 from .resume_service import (
     SUPPORTED_RESUME_STAGES,
@@ -38,6 +40,22 @@ DEFAULT_STAGES = [
 ]
 MIN_JOB_STALE_SECONDS = 30 * 60
 MAX_JOB_STALE_SECONDS = 6 * 60 * 60
+DEFAULT_DOTENV_PATH = REPO_ROOT / ".env"
+
+
+def _build_subprocess_env() -> dict[str, str]:
+    """Build an env dict for subprocess, merging parent env with .env values.
+
+    Ensures the subprocess sees all needed variables (LLM_API_KEY, LLM_MODEL,
+    LLM_API_URL, FRESHRSS_*, etc.) directly in os.environ, avoiding dotenv
+    timing issues in the child process.
+    """
+    env = os.environ.copy()
+    if DEFAULT_DOTENV_PATH.exists():
+        for key, value in dotenv_values(DEFAULT_DOTENV_PATH).items():
+            if isinstance(key, str) and isinstance(value, str) and value:
+                env.setdefault(key, value)
+    return env
 
 
 def _now() -> datetime:
@@ -276,6 +294,7 @@ def start_resume_job(*, run_id: str) -> dict[str, Any]:
             stdout=subprocess.DEVNULL,
             stderr=subprocess.DEVNULL,
             start_new_session=True,
+            env=_build_subprocess_env(),
         )
     except Exception as exc:
         report_file = _write_job_report(

src/summary_mcp/workflows/freshrss_pipeline.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import json
 import os
+from concurrent.futures import ThreadPoolExecutor, as_completed
 from datetime import date, datetime, timezone
 from pathlib import Path
 from typing import Any
@@ -192,7 +193,7 @@ def _build_candidate_batch_payload(*, run_id: str, item_contexts: list[dict[str,
     }
 
 
-def _persist_summary_batch_artifact(*, run_store: RunStore, run_dir: Path, item_contexts: list[dict[str, Any]]) -> Path:
+def _persist_summary_batch_artifact(*, run_store, run_dir: Path, item_contexts: list[dict[str, Any]]) -> Path:
     output_path = _summary_batch_output(run_dir)
     _save_json(
         output_path,
@@ -202,7 +203,7 @@ def _persist_summary_batch_artifact(*, run_store: RunStore, run_dir: Path, item_
     return output_path
 
 
-def _persist_candidate_batch_artifact(*, run_store: RunStore, run_dir: Path, item_contexts: list[dict[str, Any]]) -> Path:
+def _persist_candidate_batch_artifact(*, run_store, run_dir: Path, item_contexts: list[dict[str, Any]]) -> Path:
     output_path = _candidate_batch_output(run_dir)
     _save_json(
         output_path,
@@ -461,37 +462,50 @@ def run_freshrss_pipeline(
         summary_success_count = 0
         summary_failed_count = 0
         summary_candidates = [ctx for ctx in item_contexts if ctx["extraction"] is not None and ctx["extraction"].success]
-        for item_context in summary_candidates:
+        # Parallelize LLM summaries — I/O bound calls, independent per article
-            item_report = item_context["item_report"]
+        with ThreadPoolExecutor(max_workers=min(len(summary_candidates) or 1, 4)) as pool:
-            summary_exit_code, summary_payload, summary_report = run_loop_payload(
+            fut_map = {}
-                extracted_payload=item_context["extracted_payload"],
+            for item_context in summary_candidates:
-                prompt_path=resolved_prompt_path,
+                fut = pool.submit(
-                output_path=item_context["summary_output"],
+                    run_loop_payload,
-                max_retries=max_retries,
+                    extracted_payload=item_context["extracted_payload"],
-                timeout_seconds=timeout_seconds,
+                    prompt_path=resolved_prompt_path,
-                api_key=resolved_llm_api_key,
+                    output_path=item_context["summary_output"],
-                model=resolved_llm_model,
+                    max_retries=max_retries,
-                api_url=resolved_llm_api_url,
+                    timeout_seconds=timeout_seconds,
-            )
+                    api_key=resolved_llm_api_key,
-            if summary_exit_code != 0 or summary_payload is None:
+                    model=resolved_llm_model,
-                item_report["status"] = "summary_failed"
+                    api_url=resolved_llm_api_url,
-                if summary_report is not None:
+                )
-                    item_report["summary_errors"] = summary_report.errors
+                fut_map[fut] = item_context
-                summary_failed_count += 1
-            else:
-                item_context["summary_payload"] = summary_payload
-                item_report["status"] = "summarized"
-                summary_success_count += 1
 
-            run_store.update_stage(
+            for fut in as_completed(fut_map):
-                SUMMARY_STAGE,
+                item_context = fut_map[fut]
-                outputs={
+                item_report = item_context["item_report"]
-                    "expected_items": extracted_success_count,
+                try:
-                    "completed_items": summary_success_count + summary_failed_count,
+                    summary_exit_code, summary_payload, summary_report = fut.result()
-                    "success_count": summary_success_count,
+                except Exception as exc:
-                    "failed_count": summary_failed_count,
+                    summary_exit_code, summary_payload, summary_report = 1, None, None
-                },
+
-            )
+                if summary_exit_code != 0 or summary_payload is None:
+                    item_report["status"] = "summary_failed"
+                    if summary_report is not None:
+                        item_report["summary_errors"] = summary_report.errors
+                    summary_failed_count += 1
+                else:
+                    item_context["summary_payload"] = summary_payload
+                    item_report["status"] = "summarized"
+                    summary_success_count += 1
+
+                run_store.update_stage(
+                    SUMMARY_STAGE,
+                    outputs={
+                        "expected_items": extracted_success_count,
+                        "completed_items": summary_success_count + summary_failed_count,
+                        "success_count": summary_success_count,
+                        "failed_count": summary_failed_count,
+                    },
+                )
 
         summary_batch_output = _persist_summary_batch_artifact(
             run_store=run_store,