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/lollms.py

@@ -0,0 +1,177 @@
+import sys
+
+if sys.version_info < (3, 9):
+    from typing import AsyncIterator
+else:
+    from collections.abc import AsyncIterator
+import pipmaster as pm  # Pipmaster for dynamic library install
+
+if not pm.is_installed("aiohttp"):
+    pm.install("aiohttp")
+
+import aiohttp
+from tenacity import (
+    retry,
+    stop_after_attempt,
+    wait_exponential,
+    retry_if_exception_type,
+)
+
+from lightrag.exceptions import (
+    APIConnectionError,
+    RateLimitError,
+    APITimeoutError,
+)
+
+from typing import Union, List
+import numpy as np
+
+from lightrag.utils import (
+    wrap_embedding_func_with_attrs,
+)
+
+
+@retry(
+    stop=stop_after_attempt(3),
+    wait=wait_exponential(multiplier=1, min=4, max=10),
+    retry=retry_if_exception_type(
+        (RateLimitError, APIConnectionError, APITimeoutError)
+    ),
+)
+async def lollms_model_if_cache(
+    model,
+    prompt,
+    system_prompt=None,
+    history_messages=[],
+    enable_cot: bool = False,
+    base_url="http://localhost:9600",
+    **kwargs,
+) -> Union[str, AsyncIterator[str]]:
+    """Client implementation for lollms generation."""
+    if enable_cot:
+        from lightrag.utils import logger
+
+        logger.debug("enable_cot=True is not supported for lollms and will be ignored.")
+
+    stream = True if kwargs.get("stream") else False
+    api_key = kwargs.pop("api_key", None)
+    headers = (
+        {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"}
+        if api_key
+        else {"Content-Type": "application/json"}
+    )
+
+    # Extract lollms specific parameters
+    request_data = {
+        "prompt": prompt,
+        "model_name": model,
+        "personality": kwargs.get("personality", -1),
+        "n_predict": kwargs.get("n_predict", None),
+        "stream": stream,
+        "temperature": kwargs.get("temperature", 1.0),
+        "top_k": kwargs.get("top_k", 50),
+        "top_p": kwargs.get("top_p", 0.95),
+        "repeat_penalty": kwargs.get("repeat_penalty", 0.8),
+        "repeat_last_n": kwargs.get("repeat_last_n", 40),
+        "seed": kwargs.get("seed", None),
+        "n_threads": kwargs.get("n_threads", 8),
+    }
+
+    # Prepare the full prompt including history
+    full_prompt = ""
+    if system_prompt:
+        full_prompt += f"{system_prompt}\n"
+    for msg in history_messages:
+        full_prompt += f"{msg['role']}: {msg['content']}\n"
+    full_prompt += prompt
+
+    request_data["prompt"] = full_prompt
+    timeout = aiohttp.ClientTimeout(total=kwargs.get("timeout", None))
+
+    async with aiohttp.ClientSession(timeout=timeout, headers=headers) as session:
+        if stream:
+
+            async def inner():
+                async with session.post(
+                    f"{base_url}/lollms_generate", json=request_data
+                ) as response:
+                    async for line in response.content:
+                        yield line.decode().strip()
+
+            return inner()
+        else:
+            async with session.post(
+                f"{base_url}/lollms_generate", json=request_data
+            ) as response:
+                return await response.text()
+
+
+async def lollms_model_complete(
+    prompt,
+    system_prompt=None,
+    history_messages=[],
+    enable_cot: bool = False,
+    keyword_extraction=False,
+    **kwargs,
+) -> Union[str, AsyncIterator[str]]:
+    """Complete function for lollms model generation."""
+
+    # Extract and remove keyword_extraction from kwargs if present
+    keyword_extraction = kwargs.pop("keyword_extraction", None)
+
+    # Get model name from config
+    model_name = kwargs["hashing_kv"].global_config["llm_model_name"]
+
+    # If keyword extraction is needed, we might need to modify the prompt
+    # or add specific parameters for JSON output (if lollms supports it)
+    if keyword_extraction:
+        # Note: You might need to adjust this based on how lollms handles structured output
+        pass
+
+    return await lollms_model_if_cache(
+        model_name,
+        prompt,
+        system_prompt=system_prompt,
+        history_messages=history_messages,
+        enable_cot=enable_cot,
+        **kwargs,
+    )
+
+
+@wrap_embedding_func_with_attrs(
+    embedding_dim=1024, max_token_size=8192, model_name="lollms_embedding_model"
+)
+async def lollms_embed(
+    texts: List[str], embed_model=None, base_url="http://localhost:9600", **kwargs
+) -> np.ndarray:
+    """
+    Generate embeddings for a list of texts using lollms server.
+
+    Args:
+        texts: List of strings to embed
+        embed_model: Model name (not used directly as lollms uses configured vectorizer)
+        base_url: URL of the lollms server
+        **kwargs: Additional arguments passed to the request
+
+    Returns:
+        np.ndarray: Array of embeddings
+    """
+    api_key = kwargs.pop("api_key", None)
+    headers = (
+        {"Content-Type": "application/json", "Authorization": api_key}
+        if api_key
+        else {"Content-Type": "application/json"}
+    )
+    async with aiohttp.ClientSession(headers=headers) as session:
+        embeddings = []
+        for text in texts:
+            request_data = {"text": text}
+
+            async with session.post(
+                f"{base_url}/lollms_embed",
+                json=request_data,
+            ) as response:
+                result = await response.json()
+                embeddings.append(result["vector"])
+
+        return np.array(embeddings)