feat(configs): add GEO AgentKit Server configuration
- llm_config.yaml: DeepSeek + OpenAI-compatible providers with env var substitution - skills/ (8 YAML): citation_detector, content_generator, deai_agent, geo_optimizer, monitor, schema_advisor, competitor_analyzer, trend_agent - Added intent fields for content_generator, competitor_analyzer, trend_agent - Added quality_gate fields for content_generator, deai_agent, geo_optimizer - Updated custom_handler paths to configs.geo_handlers - geo_tools.py: 14 FunctionTools calling GEO Backend via HTTP - geo_handlers.py: 3 custom handlers (citation/monitor/schema) calling /internal/ API - geo_server.py: FastAPI factory with LLM Gateway, Tool Registry, Skill Registry
This commit is contained in:
chiguyong
parent
47a848fbcb
commit
669ca604e5
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"""GEO AgentKit Server 配置包"""
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"""GEO 项目的 Custom Handler — 供 AgentKit Server 使用
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所有 Handler 通过 HTTP 回调 GEO Backend 的 /internal/ 端点，不直接访问 DB。
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"""
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import logging
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import os
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import httpx
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from agentkit.core.protocol import TaskMessage
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logger = logging.getLogger(__name__)
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GEO_BACKEND_URL = os.getenv("GEO_BACKEND_URL", "http://localhost:8000")
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INTERNAL_API_TOKEN = os.getenv("INTERNAL_API_TOKEN", "")
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def _internal_headers() -> dict:
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"""获取内部 API 请求头"""
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headers = {"Content-Type": "application/json"}
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if INTERNAL_API_TOKEN:
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headers["X-Internal-Token"] = INTERNAL_API_TOKEN
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return headers
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async def handle_citation_task(task: TaskMessage) -> dict:
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"""引用检测任务 — 通过 HTTP 回调 GEO Backend
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task_type 路由：
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- citation_detect: POST /internal/citation/detect
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- citation_detect_single: POST /internal/citation/detect-single
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"""
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if task.task_type == "citation_detect":
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return await _call_internal("/internal/citation/detect", task.input_data)
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elif task.task_type == "citation_detect_single":
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return await _call_internal("/internal/citation/detect-single", task.input_data)
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else:
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raise ValueError(f"Unsupported task type: {task.task_type}")
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async def handle_monitor_task(task: TaskMessage) -> dict:
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"""效果追踪任务 — 通过 HTTP 回调 GEO Backend
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task_type 路由：
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- monitor_track: POST /internal/monitor/track
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- monitor_check_single: POST /internal/monitor/check-single
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"""
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if task.task_type == "monitor_track":
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return await _call_internal("/internal/monitor/track", task.input_data)
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elif task.task_type == "monitor_check_single":
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return await _call_internal("/internal/monitor/check-single", task.input_data)
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else:
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raise ValueError(f"Unsupported task type: {task.task_type}")
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async def handle_schema_task(task: TaskMessage) -> dict:
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"""Schema 建议任务 — 通过 HTTP 回调 GEO Backend
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task_type 路由：
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- schema_advise: POST /internal/schema/advise
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"""
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if task.task_type == "schema_advise":
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return await _call_internal("/internal/schema/advise", task.input_data)
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else:
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raise ValueError(f"Unsupported task type: {task.task_type}")
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async def _call_internal(path: str, input_data: dict) -> dict:
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"""调用 GEO Backend 内部 API"""
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try:
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async with httpx.AsyncClient(timeout=300.0) as client:
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resp = await client.post(
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f"{GEO_BACKEND_URL}{path}",
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json=input_data,
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headers=_internal_headers(),
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)
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resp.raise_for_status()
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return resp.json()
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except httpx.HTTPStatusError as e:
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logger.error(f"HTTP error calling {path}: {e.response.status_code} {e.response.text[:500]}")
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return {"error": f"HTTP {e.response.status_code}", "detail": e.response.text[:500]}
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except Exception as e:
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logger.error(f"Error calling {path}: {e}")
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return {"error": str(e)}
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"""GEO AgentKit Server 启动入口
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工厂函数 create_geo_app() 初始化 LLM Gateway、Tool Registry、Skill Registry，
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然后创建 FastAPI 应用。
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使用方式：
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uvicorn configs.geo_server:create_geo_app --factory --host 0.0.0.0 --port 8001
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"""
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import logging
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import os
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from agentkit.core.agent_pool import AgentPool
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from agentkit.llm.config import LLMConfig
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from agentkit.llm.gateway import LLMGateway
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from agentkit.llm.providers.openai import OpenAICompatibleProvider
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from agentkit.quality.gate import QualityGate
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from agentkit.quality.output import OutputStandardizer
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from agentkit.router.intent import IntentRouter
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from agentkit.server.app import create_app
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from agentkit.skills.loader import SkillLoader
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from agentkit.skills.registry import SkillRegistry
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from agentkit.tools.registry import ToolRegistry
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logger = logging.getLogger(__name__)
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# ─── 配置路径 ───
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CONFIGS_DIR = os.path.dirname(os.path.abspath(__file__))
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LLM_CONFIG_PATH = os.path.join(CONFIGS_DIR, "llm_config.yaml")
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SKILLS_DIR = os.path.join(CONFIGS_DIR, "skills")
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def _substitute_env_vars(config_path: str) -> dict:
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"""加载 YAML 配置并替换 ${VAR} 环境变量"""
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import yaml
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with open(config_path, encoding="utf-8") as f:
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raw = f.read()
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# 递归替换 ${VAR_NAME} 和 ${VAR_NAME:-default} 格式
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import re
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def _replace_env(match):
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var_expr = match.group(1)
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if ":-" in var_expr:
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var_name, default = var_expr.split(":-", 1)
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return os.getenv(var_name, default)
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return os.getenv(var_expr, match.group(0))
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resolved = re.sub(r"\$\{([^}]+)\}", _replace_env, raw)
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return yaml.safe_load(resolved)
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def _init_llm_gateway() -> LLMGateway:
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"""初始化 LLM Gateway 并注册 Provider"""
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config_data = _substitute_env_vars(LLM_CONFIG_PATH)
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config = LLMConfig.from_dict(config_data)
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gateway = LLMGateway(config)
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for provider_name, pconf in config.providers.items():
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if not pconf.api_key:
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logger.warning(f"Skipping provider '{provider_name}': no API key")
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continue
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models = list(pconf.models.keys()) if pconf.models else []
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default_model = models[0] if models else "gpt-4o-mini"
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provider = OpenAICompatibleProvider(
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api_key=pconf.api_key,
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base_url=pconf.base_url,
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default_model=default_model,
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)
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gateway.register_provider(provider_name, provider)
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logger.info(f"Provider '{provider_name}' registered with model '{default_model}'")
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return gateway
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def _init_tool_registry() -> ToolRegistry:
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"""初始化 Tool Registry 并注册 GEO Tools"""
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registry = ToolRegistry()
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from configs.geo_tools import register_geo_tools
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register_geo_tools(registry)
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return registry
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def _init_skill_registry(tool_registry: ToolRegistry) -> SkillRegistry:
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"""初始化 Skill Registry 并从 configs/skills/ 目录加载"""
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registry = SkillRegistry()
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loader = SkillLoader(registry, tool_registry)
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skills = loader.load_from_directory(SKILLS_DIR)
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logger.info(f"Loaded {len(skills)} skills from {SKILLS_DIR}")
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return registry
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def create_geo_app() -> "FastAPI":
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"""GEO AgentKit Server FastAPI 工厂函数"""
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llm_gateway = _init_llm_gateway()
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tool_registry = _init_tool_registry()
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skill_registry = _init_skill_registry(tool_registry)
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app = create_app(
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llm_gateway=llm_gateway,
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skill_registry=skill_registry,
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tool_registry=tool_registry,
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)
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app.title = "GEO AgentKit Server"
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logger.info(f"GEO AgentKit Server initialized: {len(skill_registry.list_skills())} skills, "
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f"{len(tool_registry.list_tools())} tools")
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return app
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"""GEO 项目的 Tool 注册 — 供 AgentKit Server 使用
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所有 Tool 通过 HTTP 调用 GEO Backend 的业务 API，不直接 import GEO 服务类。
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"""
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import logging
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import os
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from typing import Any
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import httpx
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from agentkit.tools.function_tool import FunctionTool
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from agentkit.tools.registry import ToolRegistry
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logger = logging.getLogger(__name__)
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GEO_BACKEND_URL = os.getenv("GEO_BACKEND_URL", "http://localhost:8000")
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INTERNAL_API_TOKEN = os.getenv("INTERNAL_API_TOKEN", "")
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def _internal_headers() -> dict:
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"""获取内部 API 请求头"""
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headers = {"Content-Type": "application/json"}
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if INTERNAL_API_TOKEN:
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headers["X-Internal-Token"] = INTERNAL_API_TOKEN
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return headers
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# ─── Citation Tools ───
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async def execute_single_platform(
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keyword: str,
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platform: str,
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target_brand: str,
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brand_aliases: list[str] | None = None,
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) -> dict:
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"""在单个 AI 平台执行引用检测"""
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try:
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async with httpx.AsyncClient(timeout=120.0) as client:
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resp = await client.post(
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f"{GEO_BACKEND_URL}/api/v1/ai-engines/execute-single-platform",
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json={
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"keyword": keyword,
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"platform": platform,
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"target_brand": target_brand,
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"brand_aliases": brand_aliases or [],
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},
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)
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resp.raise_for_status()
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return resp.json()
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except Exception as e:
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logger.error(f"execute_single_platform 失败: {e}")
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return {"error": str(e), "keyword": keyword, "platform": platform}
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async def get_or_create_task(query_id: str, platform: str) -> dict:
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"""获取或创建查询任务 — 通过内部 API"""
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try:
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async with httpx.AsyncClient(timeout=30.0) as client:
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resp = await client.post(
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f"{GEO_BACKEND_URL}/internal/citation/get-or-create-task",
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json={"query_id": query_id, "platform": platform},
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headers=_internal_headers(),
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)
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resp.raise_for_status()
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return resp.json()
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except Exception as e:
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logger.error(f"get_or_create_task 失败: {e}")
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return {"error": str(e), "query_id": query_id, "platform": platform}
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# ─── Content Tools ───
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async def retrieve_knowledge(
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knowledge_base_ids: list[str],
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query: str,
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top_k: int = 5,
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) -> dict:
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"""从知识库检索相关内容 — 通过内部 API"""
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if not knowledge_base_ids or not query:
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return {"content": "暂无相关知识库内容", "sources": []}
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try:
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async with httpx.AsyncClient(timeout=30.0) as client:
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resp = await client.post(
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f"{GEO_BACKEND_URL}/internal/knowledge/search",
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json={"query": query, "knowledge_base_ids": knowledge_base_ids, "top_k": top_k},
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headers=_internal_headers(),
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)
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resp.raise_for_status()
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data = resp.json()
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results = data.get("results", [])
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if results:
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content_parts = []
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sources = []
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for r in results:
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title = r.get("document_title", "未知")
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content_parts.append(f"[来源: {title}]\n{r.get('content', '')}")
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sources.append(title)
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return {"content": "\n\n---\n\n".join(content_parts), "sources": sources}
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return {"content": "暂无相关知识库内容", "sources": []}
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except Exception as e:
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logger.warning(f"retrieve_knowledge 失败: {e}")
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return {"content": "暂无相关知识库内容", "sources": []}
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# ─── Monitor Tools ───
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async def monitor_check_and_compare(record_id: str) -> dict:
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"""检测并对比监测记录的变化 — 通过内部 API"""
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try:
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async with httpx.AsyncClient(timeout=60.0) as client:
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resp = await client.post(
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f"{GEO_BACKEND_URL}/internal/monitor/check",
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json={"record_id": record_id},
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headers=_internal_headers(),
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)
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resp.raise_for_status()
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return resp.json()
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except Exception as e:
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logger.error(f"monitor_check_and_compare 失败: {e}")
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return {"error": str(e), "record_id": record_id}
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async def monitor_generate_report(record_id: str) -> dict:
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"""生成监测变化报告 — 通过内部 API"""
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try:
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async with httpx.AsyncClient(timeout=60.0) as client:
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resp = await client.post(
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f"{GEO_BACKEND_URL}/internal/monitor/generate-report",
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json={"record_id": record_id},
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headers=_internal_headers(),
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)
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resp.raise_for_status()
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return resp.json()
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except Exception as e:
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logger.error(f"monitor_generate_report 失败: {e}")
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return {"error": str(e), "record_id": record_id}
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async def monitor_create_record(
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brand_id: str,
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query_keywords: str | None = None,
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platform: str | None = None,
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check_interval_hours: int = 24,
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) -> dict:
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"""创建监测记录 — 通过内部 API"""
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try:
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async with httpx.AsyncClient(timeout=30.0) as client:
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resp = await client.post(
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f"{GEO_BACKEND_URL}/internal/monitor/create-record",
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json={
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"brand_id": brand_id,
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"query_keywords": query_keywords,
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"platform": platform,
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"check_interval_hours": check_interval_hours,
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},
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headers=_internal_headers(),
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)
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resp.raise_for_status()
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return resp.json()
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except Exception as e:
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logger.error(f"monitor_create_record 失败: {e}")
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return {"error": str(e), "brand_id": brand_id}
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# ─── Schema Tools ───
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SCHEMA_TEMPLATES = {
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"Organization": {
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"@context": "https://schema.org", "@type": "Organization",
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"name": "", "description": "", "url": "", "logo": "", "sameAs": [],
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},
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"Product": {
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"@context": "https://schema.org", "@type": "Product",
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"name": "", "description": "",
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"brand": {"@type": "Brand", "name": ""},
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},
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"FAQPage": {
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"@context": "https://schema.org", "@type": "FAQPage",
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"mainEntity": [{"@type": "Question", "name": "", "acceptedAnswer": {"@type": "Answer", "text": ""}}],
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},
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"Article": {
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"@context": "https://schema.org", "@type": "Article",
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"headline": "", "description": "", "author": {"@type": "Organization", "name": ""},
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},
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"LocalBusiness": {
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"@context": "https://schema.org", "@type": "LocalBusiness",
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"name": "", "address": {"@type": "PostalAddress"},
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},
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}
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DIMENSION_SCHEMA_MAP = {
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"schema_marketing": ["Organization", "LocalBusiness"],
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"entity_clarity": ["Organization", "Product"],
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"citation_readiness": ["FAQPage", "Article"],
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"brand_visibility": ["Organization", "Product"],
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"local_seo": ["LocalBusiness"],
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}
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async def fill_schema_with_llm(
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schema_type: str,
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brand_info: dict | None = None,
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diagnosis_dimensions: dict | None = None,
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) -> dict:
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"""使用 LLM 填充 Schema JSON-LD 模板 — 通过 GEO Backend 内部 API"""
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try:
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async with httpx.AsyncClient(timeout=60.0) as client:
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resp = await client.post(
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f"{GEO_BACKEND_URL}/internal/schema/advise",
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json={
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"schema_type": schema_type,
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"brand_info": brand_info or {},
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"diagnosis_dimensions": diagnosis_dimensions or {},
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},
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headers=_internal_headers(),
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)
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resp.raise_for_status()
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return resp.json()
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except Exception as e:
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logger.error(f"fill_schema_with_llm 失败: {e}")
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return {"error": str(e), "schema_type": schema_type}
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async def identify_missing_dimensions(
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diagnosis_data: dict,
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focus_dimensions: list[str] | None = None,
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) -> dict:
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"""识别 Schema 缺失维度"""
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dimensions = []
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dimension_scores = diagnosis_data.get("dimensions", {})
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for dim_name, dim_info in dimension_scores.items():
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if dim_name not in DIMENSION_SCHEMA_MAP:
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continue
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if focus_dimensions and dim_name not in focus_dimensions:
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continue
|
||||
score = dim_info.get("score", 0) if isinstance(dim_info, dict) else dim_info
|
||||
max_score = dim_info.get("max_score", 100) if isinstance(dim_info, dict) else 100
|
||||
percentage = (score / max_score * 100) if max_score > 0 else 0
|
||||
if percentage < 80:
|
||||
dimensions.append({
|
||||
"dimension": dim_name,
|
||||
"current_score": round(score, 2),
|
||||
"max_score": max_score,
|
||||
"percentage": round(percentage, 2),
|
||||
})
|
||||
return {"missing_dimensions": dimensions}
|
||||
|
||||
|
||||
# ─── Competitor Tools ───
|
||||
|
||||
async def competitor_analyze(
|
||||
brand_id: str,
|
||||
analysis_types: list[str] | None = None,
|
||||
period_days: int = 30,
|
||||
) -> dict:
|
||||
"""执行竞品策略分析 — 通过 GEO Backend API"""
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=120.0) as client:
|
||||
resp = await client.post(
|
||||
f"{GEO_BACKEND_URL}/api/v1/competitor/analyze",
|
||||
json={
|
||||
"brand_id": brand_id,
|
||||
"analysis_types": analysis_types,
|
||||
"period_days": period_days,
|
||||
},
|
||||
)
|
||||
resp.raise_for_status()
|
||||
return resp.json()
|
||||
except Exception as e:
|
||||
logger.error(f"competitor_analyze 失败: {e}")
|
||||
return {"error": str(e), "brand_id": brand_id}
|
||||
|
||||
|
||||
async def competitor_gap_analysis(
|
||||
brand_id: str,
|
||||
period_days: int = 30,
|
||||
) -> dict:
|
||||
"""执行竞品差距分析 — 通过 GEO Backend API"""
|
||||
return await competitor_analyze(
|
||||
brand_id=brand_id,
|
||||
analysis_types=["citation_gap", "platform_coverage", "query_overlap"],
|
||||
period_days=period_days,
|
||||
)
|
||||
|
||||
|
||||
# ─── Trend Tools ───
|
||||
|
||||
async def trend_insight(
|
||||
brand_id: str,
|
||||
days: int = 30,
|
||||
platforms: list[str] | None = None,
|
||||
keywords: list[str] | None = None,
|
||||
) -> dict:
|
||||
"""执行趋势洞察分析 — 通过 GEO Backend API"""
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=120.0) as client:
|
||||
resp = await client.post(
|
||||
f"{GEO_BACKEND_URL}/api/v1/trends/insight",
|
||||
json={
|
||||
"brand_id": brand_id,
|
||||
"days": days,
|
||||
"platforms": platforms,
|
||||
"keywords": keywords,
|
||||
},
|
||||
)
|
||||
resp.raise_for_status()
|
||||
return resp.json()
|
||||
except Exception as e:
|
||||
logger.error(f"trend_insight 失败: {e}")
|
||||
return {"error": str(e), "brand_id": brand_id}
|
||||
|
||||
|
||||
async def trend_hotspot(
|
||||
brand_id: str,
|
||||
days: int = 30,
|
||||
) -> dict:
|
||||
"""检测引用量突增的热点话题 — 通过 GEO Backend API"""
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=120.0) as client:
|
||||
resp = await client.post(
|
||||
f"{GEO_BACKEND_URL}/api/v1/trends/hotspot",
|
||||
json={"brand_id": brand_id, "days": days},
|
||||
)
|
||||
resp.raise_for_status()
|
||||
return resp.json()
|
||||
except Exception as e:
|
||||
logger.error(f"trend_hotspot 失败: {e}")
|
||||
return {"error": str(e), "brand_id": brand_id}
|
||||
|
||||
|
||||
# ─── Knowledge Tools ───
|
||||
|
||||
async def search_knowledge(
|
||||
query: str,
|
||||
knowledge_base_ids: list[str],
|
||||
top_k: int = 5,
|
||||
) -> dict:
|
||||
"""从知识库检索相关内容 — 通过内部 API"""
|
||||
return await retrieve_knowledge(
|
||||
knowledge_base_ids=knowledge_base_ids,
|
||||
query=query,
|
||||
top_k=top_k,
|
||||
)
|
||||
|
||||
|
||||
async def detect_ai_patterns(content: str, platform_id: str) -> dict:
|
||||
"""检测内容中的 AI 生成模式 — 通过 GEO Backend API"""
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=30.0) as client:
|
||||
resp = await client.post(
|
||||
f"{GEO_BACKEND_URL}/api/v1/ai-engines/detect-ai-patterns",
|
||||
json={"content": content, "platform_id": platform_id},
|
||||
)
|
||||
resp.raise_for_status()
|
||||
return resp.json()
|
||||
except Exception as e:
|
||||
logger.error(f"detect_ai_patterns 失败: {e}")
|
||||
return {"error": str(e), "patterns": [], "count": 0}
|
||||
|
||||
|
||||
# ─── Registration ───
|
||||
|
||||
def register_geo_tools(registry: ToolRegistry) -> None:
|
||||
"""注册 GEO 项目的所有 Tool"""
|
||||
|
||||
# Citation
|
||||
registry.register(FunctionTool(
|
||||
name="execute_single_platform",
|
||||
description="在单个AI平台执行引用检测",
|
||||
func=execute_single_platform,
|
||||
tags=["citation", "detection"],
|
||||
))
|
||||
registry.register(FunctionTool(
|
||||
name="get_or_create_task",
|
||||
description="获取或创建引用检测的查询任务",
|
||||
func=get_or_create_task,
|
||||
tags=["citation", "task"],
|
||||
))
|
||||
|
||||
# Content
|
||||
registry.register(FunctionTool(
|
||||
name="retrieve_knowledge",
|
||||
description="从知识库检索相关内容",
|
||||
func=retrieve_knowledge,
|
||||
tags=["content", "rag", "knowledge"],
|
||||
))
|
||||
|
||||
# Monitor
|
||||
registry.register(FunctionTool(
|
||||
name="monitor_check_and_compare",
|
||||
description="检测并对比监测记录的变化",
|
||||
func=monitor_check_and_compare,
|
||||
tags=["monitor", "tracking"],
|
||||
))
|
||||
registry.register(FunctionTool(
|
||||
name="monitor_generate_report",
|
||||
description="生成监测变化报告",
|
||||
func=monitor_generate_report,
|
||||
tags=["monitor", "report"],
|
||||
))
|
||||
registry.register(FunctionTool(
|
||||
name="monitor_create_record",
|
||||
description="创建新的监测记录",
|
||||
func=monitor_create_record,
|
||||
tags=["monitor", "record"],
|
||||
))
|
||||
|
||||
# Schema
|
||||
registry.register(FunctionTool(
|
||||
name="fill_schema_with_llm",
|
||||
description="使用LLM填充Schema JSON-LD模板",
|
||||
func=fill_schema_with_llm,
|
||||
tags=["schema", "llm"],
|
||||
))
|
||||
registry.register(FunctionTool(
|
||||
name="identify_missing_dimensions",
|
||||
description="识别Schema缺失维度",
|
||||
func=identify_missing_dimensions,
|
||||
tags=["schema", "diagnosis"],
|
||||
))
|
||||
|
||||
# Competitor
|
||||
registry.register(FunctionTool(
|
||||
name="competitor_analyze",
|
||||
description="执行竞品策略分析",
|
||||
func=competitor_analyze,
|
||||
tags=["competitor", "analysis"],
|
||||
))
|
||||
registry.register(FunctionTool(
|
||||
name="competitor_gap_analysis",
|
||||
description="执行竞品差距分析",
|
||||
func=competitor_gap_analysis,
|
||||
tags=["competitor", "gap"],
|
||||
))
|
||||
|
||||
# Trend
|
||||
registry.register(FunctionTool(
|
||||
name="trend_insight",
|
||||
description="分析品牌引用趋势",
|
||||
func=trend_insight,
|
||||
tags=["trend", "insight"],
|
||||
))
|
||||
registry.register(FunctionTool(
|
||||
name="trend_hotspot",
|
||||
description="检测引用量突增的热点话题",
|
||||
func=trend_hotspot,
|
||||
tags=["trend", "hotspot"],
|
||||
))
|
||||
|
||||
# Knowledge
|
||||
registry.register(FunctionTool(
|
||||
name="search_knowledge",
|
||||
description="从知识库检索相关内容",
|
||||
func=search_knowledge,
|
||||
tags=["knowledge", "rag"],
|
||||
))
|
||||
registry.register(FunctionTool(
|
||||
name="detect_ai_patterns",
|
||||
description="检测内容中的AI生成模式",
|
||||
func=detect_ai_patterns,
|
||||
tags=["knowledge", "deai"],
|
||||
))
|
||||
|
||||
logger.info(f"GEO tools registered: {len(registry.list_all_tools())} tools")
|
||||
|
|
@ -0,0 +1,30 @@
|
|||
# LLM Provider 配置 — AgentKit Server 使用
|
||||
# 环境变量替换：${VAR_NAME} 在启动时由 LLMConfig.from_yaml() 处理
|
||||
|
||||
providers:
|
||||
deepseek:
|
||||
api_key: "${DEEPSEEK_API_KEY}"
|
||||
base_url: "https://api.deepseek.com/v1"
|
||||
models:
|
||||
deepseek-chat:
|
||||
max_tokens: 64000
|
||||
cost_per_1k_input: 0.00014
|
||||
cost_per_1k_output: 0.00028
|
||||
|
||||
openai:
|
||||
api_key: "${OPENAI_API_KEY}"
|
||||
base_url: "${OPENAI_BASE_URL:-https://coding.dashscope.aliyuncs.com/v1}"
|
||||
models:
|
||||
qwen3-coder-plus:
|
||||
max_tokens: 64000
|
||||
cost_per_1k_input: 0.00014
|
||||
cost_per_1k_output: 0.00028
|
||||
|
||||
model_aliases:
|
||||
default: "deepseek/deepseek-chat"
|
||||
fast: "deepseek/deepseek-chat"
|
||||
powerful: "deepseek/deepseek-chat"
|
||||
|
||||
fallbacks:
|
||||
deepseek/deepseek-chat:
|
||||
- "openai/qwen3-coder-plus"
|
||||
|
|
@ -0,0 +1,56 @@
|
|||
name: citation_detector
|
||||
agent_type: citation_detection
|
||||
version: "1.0.0"
|
||||
description: "AI平台引用检测Agent：检测目标品牌在各AI平台回答中的引用情况"
|
||||
task_mode: custom
|
||||
supported_tasks:
|
||||
- citation_detect
|
||||
- citation_detect_single
|
||||
max_concurrency: 3
|
||||
custom_handler: "configs.geo_handlers.handle_citation_task"
|
||||
|
||||
input_schema:
|
||||
type: object
|
||||
properties:
|
||||
query_id:
|
||||
type: string
|
||||
description: 查询ID（citation_detect模式）
|
||||
keyword:
|
||||
type: string
|
||||
description: 关键词（citation_detect_single模式）
|
||||
platform:
|
||||
type: string
|
||||
description: 平台名称（citation_detect_single模式）
|
||||
target_brand:
|
||||
type: string
|
||||
description: 目标品牌（citation_detect_single模式）
|
||||
brand_aliases:
|
||||
type: array
|
||||
items:
|
||||
type: string
|
||||
description: 品牌别名列表
|
||||
|
||||
output_schema:
|
||||
type: object
|
||||
properties:
|
||||
query_id:
|
||||
type: string
|
||||
keyword:
|
||||
type: string
|
||||
total_records:
|
||||
type: integer
|
||||
cited_count:
|
||||
type: integer
|
||||
records:
|
||||
type: array
|
||||
|
||||
tools:
|
||||
- execute_single_platform
|
||||
- get_or_create_task
|
||||
|
||||
memory:
|
||||
working:
|
||||
enabled: true
|
||||
episodic:
|
||||
enabled: true
|
||||
track_success: true
|
||||
|
|
@ -0,0 +1,56 @@
|
|||
name: competitor_analyzer
|
||||
agent_type: competitor_analysis
|
||||
version: "1.0.0"
|
||||
description: "竞品策略分析Agent：对比品牌与竞品的引用数据，识别差距领域，发现机会点，生成策略建议"
|
||||
task_mode: tool_call
|
||||
supported_tasks:
|
||||
- competitor_analyze
|
||||
- competitor_gap_analysis
|
||||
max_concurrency: 2
|
||||
|
||||
intent:
|
||||
keywords: ["竞品", "对比", "竞争", "competitor", "gap", "分析"]
|
||||
description: "用户需要分析竞品策略、对比品牌差距或发现竞争机会"
|
||||
examples:
|
||||
- "分析我的竞品策略"
|
||||
- "对比我和竞品的差距"
|
||||
- "竞品分析"
|
||||
|
||||
input_schema:
|
||||
type: object
|
||||
required:
|
||||
- brand_id
|
||||
properties:
|
||||
brand_id:
|
||||
type: string
|
||||
description: 品牌ID
|
||||
analysis_types:
|
||||
type: array
|
||||
items:
|
||||
type: string
|
||||
description: 分析类型列表
|
||||
period_days:
|
||||
type: integer
|
||||
description: 分析周期（天）
|
||||
default: 30
|
||||
|
||||
output_schema:
|
||||
type: object
|
||||
properties:
|
||||
brand_id:
|
||||
type: string
|
||||
analysis:
|
||||
type: object
|
||||
recommendations:
|
||||
type: array
|
||||
|
||||
tools:
|
||||
- competitor_analyze
|
||||
- competitor_gap_analysis
|
||||
|
||||
memory:
|
||||
working:
|
||||
enabled: true
|
||||
episodic:
|
||||
enabled: true
|
||||
track_success: true
|
||||
|
|
@ -0,0 +1,110 @@
|
|||
name: content_generator
|
||||
agent_type: content_generation
|
||||
version: "1.0.0"
|
||||
description: "AI内容生成Agent：支持选题推荐和文章生成，可结合知识库RAG检索"
|
||||
task_mode: llm_generate
|
||||
supported_tasks:
|
||||
- generate_topics
|
||||
- generate_article
|
||||
max_concurrency: 2
|
||||
|
||||
intent:
|
||||
keywords: ["生成内容", "写文章", "选题", "generate", "content", "创作"]
|
||||
description: "用户需要生成SEO/GEO优化内容、推荐选题或撰写文章"
|
||||
examples:
|
||||
- "帮我写一篇关于AI的文章"
|
||||
- "推荐一些选题"
|
||||
- "生成关于品牌的内容"
|
||||
|
||||
input_schema:
|
||||
type: object
|
||||
required:
|
||||
- target_keyword
|
||||
properties:
|
||||
target_keyword:
|
||||
type: string
|
||||
description: 目标关键词
|
||||
brand_name:
|
||||
type: string
|
||||
description: 品牌名称
|
||||
brand_description:
|
||||
type: string
|
||||
description: 品牌描述
|
||||
target_platform:
|
||||
type: string
|
||||
description: 目标平台
|
||||
default: "通用"
|
||||
knowledge_base_ids:
|
||||
type: array
|
||||
items:
|
||||
type: string
|
||||
description: 知识库ID列表，用于RAG检索
|
||||
topic_title:
|
||||
type: string
|
||||
description: 选题标题（generate_article时使用）
|
||||
word_count:
|
||||
type: integer
|
||||
description: 目标字数
|
||||
default: 2000
|
||||
content_style:
|
||||
type: string
|
||||
description: 内容风格
|
||||
default: "专业严谨"
|
||||
content_angle:
|
||||
type: string
|
||||
description: 内容角度
|
||||
model:
|
||||
type: string
|
||||
description: 指定LLM模型
|
||||
|
||||
output_schema:
|
||||
type: object
|
||||
properties:
|
||||
topics:
|
||||
type: array
|
||||
description: 选题列表
|
||||
content:
|
||||
type: string
|
||||
description: 生成的文章内容
|
||||
word_count:
|
||||
type: integer
|
||||
usage:
|
||||
type: object
|
||||
|
||||
prompt:
|
||||
identity: "你是一个专业的内容生成助手，擅长为品牌创作高质量的SEO/GEO优化内容"
|
||||
context: "品牌需要通过优质内容提升在AI搜索引擎中的可见性和引用率"
|
||||
instructions: |
|
||||
根据用户提供的关键词、品牌信息和知识库内容，生成符合要求的内容。
|
||||
- generate_topics: 生成选题列表，每个选题包含 title、reason、keywords 字段
|
||||
- generate_article: 生成完整文章，确保内容专业、结构清晰、关键词自然融入
|
||||
constraints: |
|
||||
- 内容必须原创，避免抄袭
|
||||
- 关键词密度适中，不要堆砌
|
||||
- 文章结构清晰，段落分明
|
||||
- 数据和引用需标注来源
|
||||
output_format: "以 JSON 格式输出，generate_topics 返回 {topics: [{title, reason, keywords}]}，generate_article 返回 {content, word_count}"
|
||||
examples: ""
|
||||
|
||||
llm:
|
||||
model: "deepseek"
|
||||
temperature: 0.7
|
||||
max_tokens: 4000
|
||||
|
||||
tools:
|
||||
- retrieve_knowledge
|
||||
|
||||
quality_gate:
|
||||
required_fields: ["content"]
|
||||
min_word_count: 500
|
||||
max_retries: 1
|
||||
|
||||
memory:
|
||||
working:
|
||||
enabled: true
|
||||
episodic:
|
||||
enabled: true
|
||||
track_success: true
|
||||
semantic:
|
||||
enabled: true
|
||||
knowledge_base_ids_field: "knowledge_base_ids"
|
||||
|
|
@ -0,0 +1,81 @@
|
|||
name: deai_agent
|
||||
agent_type: deai_processing
|
||||
version: "1.1.0"
|
||||
description: "内容去AI化Agent：消除AI生成特征，使文章更自然流畅"
|
||||
task_mode: llm_generate
|
||||
supported_tasks:
|
||||
- deai_process
|
||||
max_concurrency: 2
|
||||
|
||||
input_schema:
|
||||
type: object
|
||||
required:
|
||||
- content
|
||||
properties:
|
||||
content:
|
||||
type: string
|
||||
description: 待处理的文章内容
|
||||
platform:
|
||||
type: string
|
||||
description: 目标平台ID（如 zhihu, wechat）
|
||||
style:
|
||||
type: string
|
||||
description: 目标风格
|
||||
default: "自然流畅"
|
||||
preserve_structure:
|
||||
type: boolean
|
||||
description: 是否保留原有结构
|
||||
default: true
|
||||
|
||||
output_schema:
|
||||
type: object
|
||||
properties:
|
||||
content:
|
||||
type: string
|
||||
description: 处理后的内容
|
||||
original_word_count:
|
||||
type: integer
|
||||
processed_word_count:
|
||||
type: integer
|
||||
usage:
|
||||
type: object
|
||||
detected_ai_patterns:
|
||||
type: array
|
||||
|
||||
prompt:
|
||||
identity: "你是一个专业的内容改写专家，擅长将AI生成的文本改写为自然、人类化的表达"
|
||||
context: "平台对AI生成内容的检测越来越严格，需要将内容改写为更自然的风格"
|
||||
instructions: |
|
||||
对提供的文章内容进行去AI化处理：
|
||||
1. 替换AI常用表达（如"总之"、"综上所述"、"首先其次最后"等）
|
||||
2. 增加口语化表达和个人观点
|
||||
3. 调整句式结构，避免过于工整的排比
|
||||
4. 保留核心信息和数据
|
||||
5. 如有平台特定要求，遵循平台规则
|
||||
constraints: |
|
||||
- 保留原文的核心信息和数据
|
||||
- 不要改变文章的主题和立场
|
||||
- 保持专业性的同时增加自然感
|
||||
- 如指定平台，需符合该平台的内容规范
|
||||
output_format: "返回处理后的完整文章内容"
|
||||
examples: ""
|
||||
|
||||
llm:
|
||||
model: "deepseek"
|
||||
temperature: 0.9
|
||||
max_tokens: 8000
|
||||
|
||||
tools:
|
||||
- detect_ai_patterns
|
||||
|
||||
quality_gate:
|
||||
required_fields: ["content"]
|
||||
min_word_count: 200
|
||||
max_retries: 1
|
||||
|
||||
memory:
|
||||
working:
|
||||
enabled: true
|
||||
episodic:
|
||||
enabled: true
|
||||
track_success: true
|
||||
|
|
@ -0,0 +1,83 @@
|
|||
name: geo_optimizer
|
||||
agent_type: geo_optimization
|
||||
version: "1.0.0"
|
||||
description: "GEO/SEO内容优化Agent：提升内容在AI搜索引擎中的可见性和引用率"
|
||||
task_mode: llm_generate
|
||||
supported_tasks:
|
||||
- geo_optimize
|
||||
max_concurrency: 2
|
||||
|
||||
input_schema:
|
||||
type: object
|
||||
required:
|
||||
- content
|
||||
- target_keywords
|
||||
properties:
|
||||
content:
|
||||
type: string
|
||||
description: 待优化文章
|
||||
target_keywords:
|
||||
type: array
|
||||
items:
|
||||
type: string
|
||||
description: 目标关键词列表
|
||||
target_platform:
|
||||
type: string
|
||||
description: 目标平台
|
||||
default: "通用"
|
||||
optimization_level:
|
||||
type: string
|
||||
enum: [light, moderate, aggressive]
|
||||
description: 优化级别
|
||||
default: "moderate"
|
||||
|
||||
output_schema:
|
||||
type: object
|
||||
properties:
|
||||
optimized_content:
|
||||
type: string
|
||||
seo_score:
|
||||
type: number
|
||||
changes:
|
||||
type: array
|
||||
items:
|
||||
type: string
|
||||
usage:
|
||||
type: object
|
||||
|
||||
prompt:
|
||||
identity: "你是一个GEO/SEO优化专家，擅长优化内容以提升在AI搜索引擎中的可见性"
|
||||
context: "品牌需要通过内容优化提升在AI搜索结果中的引用率和排名"
|
||||
instructions: |
|
||||
对提供的文章进行GEO/SEO优化：
|
||||
1. 自然融入目标关键词
|
||||
2. 优化标题和段落结构
|
||||
3. 增加结构化数据标记建议
|
||||
4. 提升内容的权威性和引用价值
|
||||
5. 根据optimization_level调整优化力度
|
||||
constraints: |
|
||||
- 优化后的内容必须保持原意
|
||||
- 关键词融入要自然，避免堆砌
|
||||
- 保持文章可读性
|
||||
- 不要添加虚假信息
|
||||
output_format: "以 JSON 格式输出: {optimized_content: string, seo_score: number, changes: [string]}"
|
||||
examples: ""
|
||||
|
||||
llm:
|
||||
model: "deepseek"
|
||||
temperature: 0.5
|
||||
max_tokens: 8000
|
||||
|
||||
tools: []
|
||||
|
||||
quality_gate:
|
||||
required_fields: ["optimized_content"]
|
||||
min_word_count: 200
|
||||
max_retries: 1
|
||||
|
||||
memory:
|
||||
working:
|
||||
enabled: true
|
||||
episodic:
|
||||
enabled: true
|
||||
track_success: true
|
||||
|
|
@ -0,0 +1,55 @@
|
|||
name: monitor
|
||||
agent_type: performance_tracker
|
||||
version: "1.0.0"
|
||||
description: "效果追踪Agent：监测品牌引用量、情感、排名变化，生成变化报告"
|
||||
task_mode: custom
|
||||
supported_tasks:
|
||||
- monitor_track
|
||||
- monitor_check_single
|
||||
max_concurrency: 3
|
||||
custom_handler: "configs.geo_handlers.handle_monitor_task"
|
||||
|
||||
input_schema:
|
||||
type: object
|
||||
required:
|
||||
- brand_id
|
||||
properties:
|
||||
brand_id:
|
||||
type: string
|
||||
description: 品牌ID
|
||||
keyword:
|
||||
type: string
|
||||
description: 关键词（monitor_check_single模式）
|
||||
platform:
|
||||
type: string
|
||||
description: 平台名称（monitor_check_single模式）
|
||||
check_interval_hours:
|
||||
type: integer
|
||||
description: 检测间隔小时数
|
||||
default: 24
|
||||
|
||||
output_schema:
|
||||
type: object
|
||||
properties:
|
||||
brand_id:
|
||||
type: string
|
||||
brand_name:
|
||||
type: string
|
||||
total_queries:
|
||||
type: integer
|
||||
checked_records:
|
||||
type: integer
|
||||
reports:
|
||||
type: array
|
||||
|
||||
tools:
|
||||
- monitor_check_and_compare
|
||||
- monitor_generate_report
|
||||
- monitor_create_record
|
||||
|
||||
memory:
|
||||
working:
|
||||
enabled: true
|
||||
episodic:
|
||||
enabled: true
|
||||
track_success: true
|
||||
|
|
@ -0,0 +1,49 @@
|
|||
name: schema_advisor
|
||||
agent_type: schema_advisor
|
||||
version: "1.0.0"
|
||||
description: "Schema优化建议Agent：识别Schema缺失维度，生成JSON-LD结构化数据建议"
|
||||
task_mode: custom
|
||||
supported_tasks:
|
||||
- schema_advise
|
||||
max_concurrency: 2
|
||||
custom_handler: "configs.geo_handlers.handle_schema_task"
|
||||
|
||||
input_schema:
|
||||
type: object
|
||||
required:
|
||||
- brand_id
|
||||
properties:
|
||||
brand_id:
|
||||
type: string
|
||||
description: 品牌ID
|
||||
diagnosis_data:
|
||||
type: object
|
||||
description: 诊断数据
|
||||
brand_info:
|
||||
type: object
|
||||
description: 品牌信息
|
||||
focus_dimensions:
|
||||
type: array
|
||||
items:
|
||||
type: string
|
||||
description: 重点关注维度
|
||||
|
||||
output_schema:
|
||||
type: object
|
||||
properties:
|
||||
brand_id:
|
||||
type: string
|
||||
suggestions:
|
||||
type: array
|
||||
total:
|
||||
type: integer
|
||||
|
||||
tools:
|
||||
- fill_schema_with_llm
|
||||
|
||||
memory:
|
||||
working:
|
||||
enabled: true
|
||||
episodic:
|
||||
enabled: true
|
||||
track_success: true
|
||||
|
|
@ -0,0 +1,61 @@
|
|||
name: trend_agent
|
||||
agent_type: trend_analysis
|
||||
version: "1.0.0"
|
||||
description: "趋势洞察Agent：分析品牌引用趋势、识别热点话题、推断变化原因并生成建议"
|
||||
task_mode: tool_call
|
||||
supported_tasks:
|
||||
- trend_insight
|
||||
- trend_hotspot
|
||||
max_concurrency: 2
|
||||
|
||||
intent:
|
||||
keywords: ["趋势", "热点", "洞察", "trend", "hotspot", "insight"]
|
||||
description: "用户需要分析品牌趋势、识别热点话题或获取行业洞察"
|
||||
examples:
|
||||
- "分析品牌趋势"
|
||||
- "最近的热点话题是什么"
|
||||
- "趋势洞察"
|
||||
|
||||
input_schema:
|
||||
type: object
|
||||
required:
|
||||
- brand_id
|
||||
properties:
|
||||
brand_id:
|
||||
type: string
|
||||
description: 品牌ID
|
||||
days:
|
||||
type: integer
|
||||
description: 分析天数
|
||||
default: 30
|
||||
platforms:
|
||||
type: array
|
||||
items:
|
||||
type: string
|
||||
description: 平台列表
|
||||
keywords:
|
||||
type: array
|
||||
items:
|
||||
type: string
|
||||
description: 关键词列表
|
||||
|
||||
output_schema:
|
||||
type: object
|
||||
properties:
|
||||
brand_id:
|
||||
type: string
|
||||
trends:
|
||||
type: array
|
||||
hotspots:
|
||||
type: array
|
||||
|
||||
tools:
|
||||
- trend_insight
|
||||
- trend_hotspot
|
||||
|
||||
memory:
|
||||
working:
|
||||
enabled: true
|
||||
episodic:
|
||||
enabled: true
|
||||
track_success: true
|
||||
Loading…
Reference in New Issue