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/nvidia_openai.py

@@ -0,0 +1,68 @@
+import sys
+import os
+
+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("openai"):
+    pm.install("openai")
+
+from openai import (
+    AsyncOpenAI,
+    APIConnectionError,
+    RateLimitError,
+    APITimeoutError,
+)
+from tenacity import (
+    retry,
+    stop_after_attempt,
+    wait_exponential,
+    retry_if_exception_type,
+)
+
+from lightrag.utils import (
+    wrap_embedding_func_with_attrs,
+)
+
+
+import numpy as np
+
+
+@wrap_embedding_func_with_attrs(
+    embedding_dim=2048, max_token_size=8192, model_name="nvidia_embedding_model"
+)
+@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 nvidia_openai_embed(
+    texts: list[str],
+    model: str = "nvidia/llama-3.2-nv-embedqa-1b-v1",
+    # refer to https://build.nvidia.com/nim?filters=usecase%3Ausecase_text_to_embedding
+    base_url: str = "https://integrate.api.nvidia.com/v1",
+    api_key: str = None,
+    input_type: str = "passage",  # query for retrieval, passage for embedding
+    trunc: str = "NONE",  # NONE or START or END
+    encode: str = "float",  # float or base64
+) -> np.ndarray:
+    if api_key:
+        os.environ["OPENAI_API_KEY"] = api_key
+
+    openai_async_client = (
+        AsyncOpenAI() if base_url is None else AsyncOpenAI(base_url=base_url)
+    )
+    response = await openai_async_client.embeddings.create(
+        model=model,
+        input=texts,
+        encoding_format=encode,
+        extra_body={"input_type": input_type, "truncate": trunc},
+    )
+    return np.array([dp.embedding for dp in response.data])