feat: 重构知识库系统，移除Hermes集成，增强RAG和同步功能

主要变更:
- 移除Hermes智能体及相关回调服务
- 新增知识库RAG、同步、调度、规范化和索引任务服务
- 重构orchestrator服务，增强运行时聊天功能
- 更新前端聊天、政策制度、设置等页面样式和逻辑
- 更新expense_claims和document_intelligence服务
- 删除llm_wiki相关服务和测试文件
- 更新docker-compose配置和启动脚本

.tmp/lightrag_inspect/lightrag_pkg/lightrag/llm/deprecated/siliconcloud.py

@@ -0,0 +1,69 @@
+import sys
+
+if sys.version_info < (3, 9):
+    pass
+else:
+    pass
+import pipmaster as pm  # Pipmaster for dynamic library install
+
+# install specific modules
+if not pm.is_installed("lmdeploy"):
+    pm.install("lmdeploy")
+
+from openai import (
+    APIConnectionError,
+    RateLimitError,
+    APITimeoutError,
+)
+from tenacity import (
+    retry,
+    stop_after_attempt,
+    wait_exponential,
+    retry_if_exception_type,
+)
+
+
+import numpy as np
+import aiohttp
+import base64
+import struct
+
+
+@retry(
+    stop=stop_after_attempt(3),
+    wait=wait_exponential(multiplier=1, min=4, max=60),
+    retry=retry_if_exception_type(
+        (RateLimitError, APIConnectionError, APITimeoutError)
+    ),
+)
+async def siliconcloud_embedding(
+    texts: list[str],
+    model: str = "netease-youdao/bce-embedding-base_v1",
+    base_url: str = "https://api.siliconflow.cn/v1/embeddings",
+    max_token_size: int = 8192,
+    api_key: str = None,
+) -> np.ndarray:
+    if api_key and not api_key.startswith("Bearer "):
+        api_key = "Bearer " + api_key
+
+    headers = {"Authorization": api_key, "Content-Type": "application/json"}
+
+    truncate_texts = [text[0:max_token_size] for text in texts]
+
+    payload = {"model": model, "input": truncate_texts, "encoding_format": "base64"}
+
+    base64_strings = []
+    async with aiohttp.ClientSession() as session:
+        async with session.post(base_url, headers=headers, json=payload) as response:
+            content = await response.json()
+            if "code" in content:
+                raise ValueError(content)
+            base64_strings = [item["embedding"] for item in content["data"]]
+
+    embeddings = []
+    for string in base64_strings:
+        decode_bytes = base64.b64decode(string)
+        n = len(decode_bytes) // 4
+        float_array = struct.unpack("<" + "f" * n, decode_bytes)
+        embeddings.append(float_array)
+    return np.array(embeddings)