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
This commit is contained in:
caoxiaozhu
248
.tmp/lightrag_inspect/lightrag_pkg/lightrag/llm/zhipu.py
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248
.tmp/lightrag_inspect/lightrag_pkg/lightrag/llm/zhipu.py
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import sys
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import re
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import json
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from ..utils import verbose_debug
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if sys.version_info < (3, 9):
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pass
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else:
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pass
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import pipmaster as pm # Pipmaster for dynamic library install
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# install specific modules
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if not pm.is_installed("zhipuai"):
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pm.install("zhipuai")
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from openai import (
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APIConnectionError,
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RateLimitError,
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APITimeoutError,
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)
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from tenacity import (
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retry,
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stop_after_attempt,
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wait_exponential,
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retry_if_exception_type,
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)
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from lightrag.utils import (
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wrap_embedding_func_with_attrs,
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logger,
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)
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from lightrag.types import GPTKeywordExtractionFormat
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import numpy as np
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from typing import Union, List, Optional, Dict
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@retry(
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stop=stop_after_attempt(3),
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wait=wait_exponential(multiplier=1, min=4, max=10),
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retry=retry_if_exception_type(
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(RateLimitError, APIConnectionError, APITimeoutError)
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),
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)
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async def zhipu_complete_if_cache(
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prompt: Union[str, List[Dict[str, str]]],
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model: str = "glm-4-flashx", # The most cost/performance balance model in glm-4 series
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api_key: Optional[str] = None,
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system_prompt: Optional[str] = None,
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history_messages: List[Dict[str, str]] = [],
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enable_cot: bool = False, # LightRAG output switch: include reasoning_content as <think>...</think>
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thinking: Optional[
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Dict[str, object]
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] = None, # Zhipu request param: use {"type": "enabled"} to enable thinking
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**kwargs,
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) -> str:
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"""Call Zhipu chat completions with optional official thinking support.
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Parameter roles:
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- `thinking`: forwarded to the Zhipu API as-is. To enable thinking output,
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pass a config such as `{"type": "enabled"}`.
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- `enable_cot`: LightRAG-only formatting switch. When True and the API
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returns `reasoning_content`, it is preserved in the final string as
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`<think>...</think>`.
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"""
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# dynamically load ZhipuAI
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try:
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from zhipuai import ZhipuAI
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except ImportError:
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raise ImportError("Please install zhipuai before initialize zhipuai backend.")
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if api_key:
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client = ZhipuAI(api_key=api_key)
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else:
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# please set ZHIPUAI_API_KEY in your environment
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# os.environ["ZHIPUAI_API_KEY"]
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client = ZhipuAI()
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messages = []
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if not system_prompt:
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system_prompt = "You are a helpful assistant. Note that sensitive words in the content should be replaced with ***"
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# Add system prompt if provided
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.extend(history_messages)
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messages.append({"role": "user", "content": prompt})
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# Add debug logging
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logger.debug("===== Query Input to LLM =====")
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logger.debug(f"Query: {prompt}")
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verbose_debug(f"System prompt: {system_prompt}")
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# Remove unsupported kwargs
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kwargs = {
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k: v for k, v in kwargs.items() if k not in ["hashing_kv", "keyword_extraction"]
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}
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# `thinking` is an official Zhipu request field. Example:
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# {"type": "enabled"} enables reasoning output on supported models.
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if thinking is not None:
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kwargs["thinking"] = thinking
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response = client.chat.completions.create(model=model, messages=messages, **kwargs)
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message = response.choices[0].message
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content = message.content or ""
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reasoning_content = getattr(message, "reasoning_content", "") or ""
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if enable_cot and reasoning_content.strip():
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if content:
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return f"<think>{reasoning_content}</think>{content}"
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return f"<think>{reasoning_content}</think>"
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return content
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async def zhipu_complete(
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prompt,
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system_prompt=None,
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history_messages=[],
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keyword_extraction=False,
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enable_cot: bool = False,
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**kwargs,
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):
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# Pop keyword_extraction from kwargs to avoid passing it to zhipu_complete_if_cache
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keyword_extraction = kwargs.pop("keyword_extraction", keyword_extraction)
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if keyword_extraction:
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# Add a system prompt to guide the model to return JSON format
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extraction_prompt = """You are a helpful assistant that extracts keywords from text.
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Please analyze the content and extract two types of keywords:
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1. High-level keywords: Important concepts and main themes
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2. Low-level keywords: Specific details and supporting elements
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Return your response in this exact JSON format:
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{
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"high_level_keywords": ["keyword1", "keyword2"],
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"low_level_keywords": ["keyword1", "keyword2", "keyword3"]
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}
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Only return the JSON, no other text."""
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# Combine with existing system prompt if any
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if system_prompt:
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system_prompt = f"{system_prompt}\n\n{extraction_prompt}"
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else:
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system_prompt = extraction_prompt
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try:
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response = await zhipu_complete_if_cache(
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prompt=prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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enable_cot=enable_cot,
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**kwargs,
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)
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# Try to parse as JSON
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try:
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data = json.loads(response)
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return GPTKeywordExtractionFormat(
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high_level_keywords=data.get("high_level_keywords", []),
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low_level_keywords=data.get("low_level_keywords", []),
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)
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except json.JSONDecodeError:
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# If direct JSON parsing fails, try to extract JSON from text
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match = re.search(r"\{[\s\S]*\}", response)
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if match:
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try:
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data = json.loads(match.group())
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return GPTKeywordExtractionFormat(
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high_level_keywords=data.get("high_level_keywords", []),
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low_level_keywords=data.get("low_level_keywords", []),
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)
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except json.JSONDecodeError:
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pass
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# If all parsing fails, log warning and return empty format
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logger.warning(
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f"Failed to parse keyword extraction response: {response}"
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)
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return GPTKeywordExtractionFormat(
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high_level_keywords=[], low_level_keywords=[]
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)
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except Exception as e:
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logger.error(f"Error during keyword extraction: {str(e)}")
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return GPTKeywordExtractionFormat(
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high_level_keywords=[], low_level_keywords=[]
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)
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else:
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# For non-keyword-extraction, just return the raw response string
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return await zhipu_complete_if_cache(
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prompt=prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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enable_cot=enable_cot,
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**kwargs,
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)
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@wrap_embedding_func_with_attrs(
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embedding_dim=1024, max_token_size=8192, model_name="embedding-3"
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)
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@retry(
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stop=stop_after_attempt(3),
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wait=wait_exponential(multiplier=1, min=4, max=60),
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retry=retry_if_exception_type(
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(RateLimitError, APIConnectionError, APITimeoutError)
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),
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)
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async def zhipu_embedding(
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texts: list[str],
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model: str = "embedding-3",
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api_key: str = None,
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embedding_dim: int | None = None,
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**kwargs,
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) -> np.ndarray:
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# dynamically load ZhipuAI
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try:
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from zhipuai import ZhipuAI
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except ImportError:
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raise ImportError("Please install zhipuai before initialize zhipuai backend.")
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if api_key:
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client = ZhipuAI(api_key=api_key)
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else:
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# please set ZHIPUAI_API_KEY in your environment
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# os.environ["ZHIPUAI_API_KEY"]
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client = ZhipuAI()
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# Convert single text to list if needed
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if isinstance(texts, str):
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texts = [texts]
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embeddings = []
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for text in texts:
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try:
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request_kwargs = dict(kwargs)
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if embedding_dim is not None:
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request_kwargs["dimensions"] = embedding_dim
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response = client.embeddings.create(
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model=model, input=[text], **request_kwargs
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)
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embeddings.append(response.data[0].embedding)
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except Exception as e:
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raise Exception(f"Error calling ChatGLM Embedding API: {str(e)}")
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return np.array(embeddings)
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