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
265
.tmp/lightrag_inspect/lightrag_pkg/lightrag/llm/anthropic.py
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265
.tmp/lightrag_inspect/lightrag_pkg/lightrag/llm/anthropic.py
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from ..utils import verbose_debug, VERBOSE_DEBUG
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import sys
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import os
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import logging
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import warnings
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from typing import Any, Union, AsyncIterator
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import pipmaster as pm # Pipmaster for dynamic library install
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if sys.version_info < (3, 9):
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from typing import AsyncIterator
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else:
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from collections.abc import AsyncIterator
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# Install Anthropic SDK if not present
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if not pm.is_installed("anthropic"):
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pm.install("anthropic")
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from anthropic import (
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AsyncAnthropic,
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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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safe_unicode_decode,
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logger,
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)
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from lightrag.api import __api_version__
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# Custom exception for retry mechanism
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class InvalidResponseError(Exception):
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"""Custom exception class for triggering retry mechanism"""
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pass
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# Core Anthropic completion function with retry
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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, InvalidResponseError)
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),
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)
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async def anthropic_complete_if_cache(
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model: str,
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prompt: str,
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system_prompt: str | None = None,
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history_messages: list[dict[str, Any]] | None = None,
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enable_cot: bool = False,
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base_url: str | None = None,
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api_key: str | None = None,
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**kwargs: Any,
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) -> Union[str, AsyncIterator[str]]:
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if history_messages is None:
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history_messages = []
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if enable_cot:
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logger.debug(
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"enable_cot=True is not supported for the Anthropic API and will be ignored."
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)
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if not api_key:
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api_key = os.environ.get("ANTHROPIC_API_KEY")
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default_headers = {
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"User-Agent": f"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_8) LightRAG/{__api_version__}",
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"Content-Type": "application/json",
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}
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# Set logger level to INFO when VERBOSE_DEBUG is off
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if not VERBOSE_DEBUG and logger.level == logging.DEBUG:
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logging.getLogger("anthropic").setLevel(logging.INFO)
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kwargs.pop("hashing_kv", None)
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kwargs.pop("keyword_extraction", None)
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timeout = kwargs.pop("timeout", None)
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anthropic_async_client = (
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AsyncAnthropic(
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default_headers=default_headers, api_key=api_key, timeout=timeout
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)
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if base_url is None
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else AsyncAnthropic(
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base_url=base_url,
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default_headers=default_headers,
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api_key=api_key,
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timeout=timeout,
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)
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)
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messages: list[dict[str, Any]] = []
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messages.extend(history_messages)
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messages.append({"role": "user", "content": prompt})
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logger.debug("===== Sending Query to Anthropic LLM =====")
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logger.debug(f"Model: {model} Base URL: {base_url}")
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logger.debug(f"Additional kwargs: {kwargs}")
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verbose_debug(f"Query: {prompt}")
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verbose_debug(f"System prompt: {system_prompt}")
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try:
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create_params = {"model": model, "messages": messages, "stream": True, **kwargs}
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if system_prompt:
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create_params["system"] = system_prompt
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response = await anthropic_async_client.messages.create(**create_params)
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except APIConnectionError as e:
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logger.error(f"Anthropic API Connection Error: {e}")
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raise
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except RateLimitError as e:
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logger.error(f"Anthropic API Rate Limit Error: {e}")
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raise
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except APITimeoutError as e:
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logger.error(f"Anthropic API Timeout Error: {e}")
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raise
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except Exception as e:
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body = getattr(e, "body", None)
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request_id = getattr(e, "request_id", None)
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req = getattr(e, "request", None)
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extra_parts = []
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if body:
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extra_parts.append(f"Response body: {body}")
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if request_id:
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extra_parts.append(f"Request ID: {request_id}")
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if req is not None:
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extra_parts.append(f"Request URL: {req.url}")
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extra = ("\n" + "\n".join(extra_parts)) if extra_parts else ""
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logger.error(
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f"Anthropic API Call Failed,\nModel: {model},\nParams: {kwargs}, Got: {e}{extra}"
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)
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raise
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async def stream_response():
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try:
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async for event in response:
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content = (
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event.delta.text
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if hasattr(event, "delta")
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and hasattr(event.delta, "text")
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and event.delta.text
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else None
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)
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if content is None:
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continue
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if r"\u" in content:
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content = safe_unicode_decode(content.encode("utf-8"))
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yield content
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except Exception as e:
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logger.error(f"Error in stream response: {str(e)}")
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raise
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return stream_response()
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# Generic Anthropic completion function
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async def anthropic_complete(
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prompt: str,
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system_prompt: str | None = None,
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history_messages: list[dict[str, Any]] | None = None,
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enable_cot: bool = False,
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**kwargs: Any,
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) -> Union[str, AsyncIterator[str]]:
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if history_messages is None:
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history_messages = []
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model_name = kwargs["hashing_kv"].global_config["llm_model_name"]
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return await anthropic_complete_if_cache(
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model_name,
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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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# Claude 3 Opus specific completion
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async def claude_3_opus_complete(
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prompt: str,
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system_prompt: str | None = None,
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history_messages: list[dict[str, Any]] | None = None,
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enable_cot: bool = False,
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**kwargs: Any,
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) -> Union[str, AsyncIterator[str]]:
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if history_messages is None:
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history_messages = []
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return await anthropic_complete_if_cache(
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"claude-3-opus-20240229",
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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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# Claude 3 Sonnet specific completion
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async def claude_3_sonnet_complete(
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prompt: str,
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system_prompt: str | None = None,
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history_messages: list[dict[str, Any]] | None = None,
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enable_cot: bool = False,
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**kwargs: Any,
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) -> Union[str, AsyncIterator[str]]:
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if history_messages is None:
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history_messages = []
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return await anthropic_complete_if_cache(
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"claude-3-sonnet-20240229",
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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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# Claude 3 Haiku specific completion
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async def claude_3_haiku_complete(
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prompt: str,
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system_prompt: str | None = None,
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history_messages: list[dict[str, Any]] | None = None,
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enable_cot: bool = False,
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**kwargs: Any,
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) -> Union[str, AsyncIterator[str]]:
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if history_messages is None:
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history_messages = []
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return await anthropic_complete_if_cache(
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"claude-3-haiku-20240307",
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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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# Backward-compatibility shim: the previous embedding implementation lived in
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# this module under the (misleading) name ``anthropic_embed`` even though it
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# called Voyage AI under the hood. The real implementation now lives in
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# ``lightrag.llm.voyageai.voyageai_embed``. Keep the old name importable for one
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# release cycle so downstream users get a clear deprecation warning instead of
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# an ImportError. Remove in a future major version.
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def anthropic_embed(*args, **kwargs):
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"""Deprecated alias for :func:`lightrag.llm.voyageai.voyageai_embed`.
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This shim accepts the same arguments as the original ``anthropic_embed``
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function (which was always backed by VoyageAI) and forwards them to
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:func:`voyageai_embed`. It will be removed in a future release.
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"""
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warnings.warn(
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"lightrag.llm.anthropic.anthropic_embed is deprecated and will be "
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"removed in a future release. Import "
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"lightrag.llm.voyageai.voyageai_embed instead.",
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DeprecationWarning,
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stacklevel=2,
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)
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from lightrag.llm.voyageai import voyageai_embed
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return voyageai_embed.func(*args, **kwargs)
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