refactor: 重构 config 目录结构，按领域分类静态配置

backend/src/core/config/initial/init_data.py

@@ -0,0 +1,130 @@
+from __future__ import annotations
+
+import uuid
+from pathlib import Path
+from typing import Any
+
+import yaml
+from pydantic import BaseModel, ValidationError
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from core.db.session import AsyncSessionLocal
+from core.logging import get_logger
+from models.llm import Llm
+from models.llm_factory import LlmFactory
+
+logger = get_logger("core.config.initial.init_data")
+
+
+class LlmFactorySeed(BaseModel):
+    name: str
+    request_url: str
+    avatar: str | None = None
+
+
+class LlmSeed(BaseModel):
+    model_code: str
+    factory_name: str
+
+
+class LlmCatalogSeed(BaseModel):
+    factories: list[LlmFactorySeed]
+    llms: list[LlmSeed]
+
+
+def _default_catalog_path() -> Path:
+    return (
+        Path(__file__).resolve().parents[1] / "static" / "database" / "llm_catalog.yaml"
+    )
+
+
+def load_llm_catalog(catalog_path: Path | None = None) -> dict[str, Any]:
+    path = catalog_path or _default_catalog_path()
+    with path.open("r", encoding="utf-8") as file:
+        loaded = yaml.safe_load(file) or {}
+    if not isinstance(loaded, dict):
+        raise ValueError(f"Invalid LLM catalog format: {path}")
+    raw_factories = loaded.get("factories", [])
+    raw_llms = loaded.get("llms", [])
+    if not isinstance(raw_factories, list) or not isinstance(raw_llms, list):
+        raise ValueError(f"Invalid LLM catalog sections: {path}")
+    try:
+        parsed = LlmCatalogSeed.model_validate(
+            {
+                "factories": list(raw_factories),
+                "llms": list(raw_llms),
+            }
+        )
+    except ValidationError as exc:
+        raise ValueError(f"Invalid LLM catalog data: {path}") from exc
+
+    return parsed.model_dump()
+
+
+async def _upsert_factory(
+    session: AsyncSession,
+    *,
+    name: str,
+    request_url: str,
+    avatar: str | None,
+) -> uuid.UUID:
+    result = await session.execute(select(LlmFactory).where(LlmFactory.name == name))
+    factory = result.scalar_one_or_none()
+
+    if factory is None:
+        factory = LlmFactory(name=name, request_url=request_url, avatar=avatar)
+        session.add(factory)
+        await session.flush()
+    else:
+        factory.request_url = request_url
+        factory.avatar = avatar
+
+    return factory.id
+
+
+async def _upsert_llm(
+    session: AsyncSession,
+    *,
+    model_code: str,
+    factory_id: uuid.UUID,
+) -> None:
+    result = await session.execute(select(Llm).where(Llm.model_code == model_code))
+    llm = result.scalar_one_or_none()
+    if llm is None:
+        session.add(Llm(model_code=model_code, factory_id=factory_id))
+        return
+    llm.factory_id = factory_id
+
+
+async def initialize_data() -> bool:
+    """Initialize bootstrap data."""
+    catalog = load_llm_catalog()
+
+    async with AsyncSessionLocal() as session:
+        async with session.begin():
+            factory_id_by_name: dict[str, uuid.UUID] = {}
+            for factory in catalog["factories"]:
+                factory_id = await _upsert_factory(
+                    session,
+                    name=factory["name"],
+                    request_url=factory["request_url"],
+                    avatar=factory.get("avatar"),
+                )
+                factory_id_by_name[factory["name"]] = factory_id
+
+            for llm in catalog["llms"]:
+                factory_name = llm["factory_name"]
+                resolved_factory_id = factory_id_by_name.get(factory_name)
+                if resolved_factory_id is None:
+                    raise RuntimeError(
+                        f"Factory '{factory_name}' not found for model '{llm['model_code']}'"
+                    )
+                await _upsert_llm(
+                    session,
+                    model_code=llm["model_code"],
+                    factory_id=resolved_factory_id,
+                )
+
+    logger.info("Initialized LLM factory/model seed data")
+    return True

backend/src/core/config/static/agent/crewai/agents.yaml

@@ -1,9 +0,0 @@
-intent:
-  role: Intent Agent
-  goal: Classify user intent and decide execution strategy
-execution:
-  role: Execution Agent
-  goal: Execute tasks with available tools
-organization:
-  role: Organization Agent
-  goal: Organize output for user-friendly response

backend/src/core/config/static/agent/crewai/prompts/execution.md

@@ -1,2 +0,0 @@
-你是任务执行代理。
-基于输入意图与上下文调用可用工具，并生成可验证执行结果。

backend/src/core/config/static/agent/crewai/prompts/intent.md

@@ -1,2 +0,0 @@
-你是意图识别代理。
-你的任务是识别用户输入的意图类型，并返回结构化意图标签。

backend/src/core/config/static/agent/crewai/prompts/organization.md

@@ -1,2 +0,0 @@
-你是结果整理代理。
-将执行结果组织为面向用户的清晰回复，保留关键信息与必要引用。

backend/src/core/config/static/agent/crewai/tasks.yaml

@@ -1,6 +0,0 @@
-intent:
-  description: Identify user intent and required capabilities
-execution:
-  description: Execute intent with tools and model calls
-organization:
-  description: Format final response and references

backend/src/core/config/static/crewai/agents.yaml

@@ -0,0 +1,22 @@
+intent:
+  role: Intent Agent
+  goal: Classify user intent and decide execution strategy
+  backstory: >
+    You are an expert intent classifier with deep understanding
+    of user query patterns and dialogue acts. Your role is to
+    analyze user input and determine the appropriate action.
+
+execution:
+  role: Execution Agent
+  goal: Execute tasks with available tools
+  backstory: >
+    You are a skilled task executor with expertise in tool calling,
+    API interactions, and result verification. You work systematically
+    to complete user requests.
+
+organization:
+  role: Organization Agent
+  goal: Organize output for user-friendly response
+  backstory: >
+    You specialize in presenting results in a clear, user-friendly manner.
+    You ensure responses are well-structured and actionable.

backend/src/core/config/static/crewai/tasks.yaml

@@ -0,0 +1,16 @@
+intent:
+  description: Identify user intent and required capabilities
+  expected_output: >
+    Structured intent classification with intent type, confidence score,
+    and recommended action plan
+
+execution:
+  description: Execute intent with tools and model calls
+  expected_output: >
+    Verified execution results with tool outputs, status, and any errors
+
+organization:
+  description: Format final response and references
+  expected_output: >
+    User-friendly response with structured output, citations, and
+    clear next steps if applicable

backend/src/core/config/static/database/user_agent_catalog.yaml

@@ -0,0 +1 @@
+agents: []