From 474c7657fdbc6476309fd80a53189b340e83c0f3 Mon Sep 17 00:00:00 2001 From: Doiiars Date: Fri, 7 Nov 2025 15:18:20 +0800 Subject: [PATCH] =?UTF-8?q?1.=20LLM=E6=8E=A5=E5=8F=A3=E6=94=B9=E4=B8=BA?= =?UTF-8?q?=E5=AD=97=E8=8A=82=E7=BA=A7=E6=B5=81=E5=BC=8F=E6=8E=A5=E5=8F=A3?= =?UTF-8?q?=EF=BC=8C=E9=98=B2=E6=AD=A2=E8=B6=85=E6=97=B6=E9=94=99=E8=AF=AF?= =?UTF-8?q?=EF=BC=8C=E4=B9=9F=E9=81=BF=E5=85=8Dutf-8=E9=95=BF=E5=AD=97?= =?UTF-8?q?=E8=8A=82=E5=AD=97=E7=AC=A6=E6=8B=BC=E6=8E=A5=E9=94=99=E8=AF=AF?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- InsightEngine/llms/base.py | 73 ++++++++++++++++++- InsightEngine/nodes/formatting_node.py | 4 +- InsightEngine/nodes/report_structure_node.py | 4 +- InsightEngine/nodes/search_node.py | 8 +- InsightEngine/nodes/summary_node.py | 8 +- MediaEngine/llms/base.py | 73 ++++++++++++++++++- MediaEngine/nodes/formatting_node.py | 4 +- MediaEngine/nodes/report_structure_node.py | 2 +- MediaEngine/nodes/search_node.py | 4 +- MediaEngine/nodes/summary_node.py | 8 +- QueryEngine/llms/base.py | 73 ++++++++++++++++++- QueryEngine/nodes/formatting_node.py | 4 +- QueryEngine/nodes/report_structure_node.py | 2 +- QueryEngine/nodes/search_node.py | 4 +- QueryEngine/nodes/summary_node.py | 8 +- ReportEngine/llms/base.py | 67 ++++++++++++++++- ReportEngine/nodes/html_generation_node.py | 2 +- ReportEngine/nodes/template_selection_node.py | 2 +- utils/forum_reader.py | 5 +- 19 files changed, 315 insertions(+), 40 deletions(-) diff --git a/InsightEngine/llms/base.py b/InsightEngine/llms/base.py index d41bf58..48a5157 100644 --- a/InsightEngine/llms/base.py +++ b/InsightEngine/llms/base.py @@ -5,7 +5,8 @@ Unified OpenAI-compatible LLM client for the Insight Engine, with retry support. import os import sys from datetime import datetime -from typing import Any, Dict, Optional +from typing import Any, Dict, Optional, Iterator, Generator +from loguru import logger from openai import OpenAI @@ -82,6 +83,76 @@ class LLMClient: return self.validate_response(response.choices[0].message.content) return "" + @with_retry(LLM_RETRY_CONFIG) + def stream_invoke(self, system_prompt: str, user_prompt: str, **kwargs) -> Generator[str, None, None]: + """ + 流式调用LLM，逐步返回响应内容 + + Args: + system_prompt: 系统提示词 + user_prompt: 用户提示词 + **kwargs: 额外参数（temperature, top_p等） + + Yields: + 响应文本块（str） + """ + current_time = datetime.now().strftime("%Y年%m月%d日%H时%M分") + time_prefix = f"今天的实际时间是{current_time}" + if user_prompt: + user_prompt = f"{time_prefix}\n{user_prompt}" + else: + user_prompt = time_prefix + messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ] + + allowed_keys = {"temperature", "top_p", "presence_penalty", "frequency_penalty"} + extra_params = {key: value for key, value in kwargs.items() if key in allowed_keys and value is not None} + # 强制使用流式 + extra_params["stream"] = True + + timeout = kwargs.pop("timeout", self.timeout) + + try: + stream = self.client.chat.completions.create( + model=self.model_name, + messages=messages, + timeout=timeout, + **extra_params, + ) + + for chunk in stream: + if chunk.choices and len(chunk.choices) > 0: + delta = chunk.choices[0].delta + if delta and delta.content: + yield delta.content + except Exception as e: + logger.error(f"流式请求失败: {str(e)}") + raise e + + def stream_invoke_to_string(self, system_prompt: str, user_prompt: str, **kwargs) -> str: + """ + 流式调用LLM并安全地拼接为完整字符串（避免UTF-8多字节字符截断） + + Args: + system_prompt: 系统提示词 + user_prompt: 用户提示词 + **kwargs: 额外参数（temperature, top_p等） + + Returns: + 完整的响应字符串 + """ + # 以字节形式收集所有块 + byte_chunks = [] + for chunk in self.stream_invoke(system_prompt, user_prompt, **kwargs): + byte_chunks.append(chunk.encode('utf-8')) + + # 拼接所有字节，然后一次性解码 + if byte_chunks: + return b''.join(byte_chunks).decode('utf-8', errors='replace') + return "" + @staticmethod def validate_response(response: Optional[str]) -> str: if response is None: diff --git a/InsightEngine/nodes/formatting_node.py b/InsightEngine/nodes/formatting_node.py index 13af69d..6217b48 100644 --- a/InsightEngine/nodes/formatting_node.py +++ b/InsightEngine/nodes/formatting_node.py @@ -70,8 +70,8 @@ class ReportFormattingNode(BaseNode): logger.info("正在格式化最终报告") - # 调用LLM - response = self.llm_client.invoke( + # 调用LLM（流式，安全拼接UTF-8） + response = self.llm_client.stream_invoke_to_string( SYSTEM_PROMPT_REPORT_FORMATTING, message, ) diff --git a/InsightEngine/nodes/report_structure_node.py b/InsightEngine/nodes/report_structure_node.py index 8a84891..a5a04db 100644 --- a/InsightEngine/nodes/report_structure_node.py +++ b/InsightEngine/nodes/report_structure_node.py @@ -51,8 +51,8 @@ class ReportStructureNode(StateMutationNode): try: logger.info(f"正在为查询生成报告结构: {self.query}") - # 调用LLM - response = self.llm_client.invoke(SYSTEM_PROMPT_REPORT_STRUCTURE, self.query) + # 调用LLM（流式，安全拼接UTF-8） + response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_REPORT_STRUCTURE, self.query) # 处理响应 processed_response = self.process_output(response) diff --git a/InsightEngine/nodes/search_node.py b/InsightEngine/nodes/search_node.py index e81efba..3aa0462 100644 --- a/InsightEngine/nodes/search_node.py +++ b/InsightEngine/nodes/search_node.py @@ -65,8 +65,8 @@ class FirstSearchNode(BaseNode): logger.info("正在生成首次搜索查询") - # 调用LLM - response = self.llm_client.invoke(SYSTEM_PROMPT_FIRST_SEARCH, message) + # 调用LLM（流式，安全拼接UTF-8） + response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_FIRST_SEARCH, message) # 处理响应 processed_response = self.process_output(response) @@ -200,8 +200,8 @@ class ReflectionNode(BaseNode): logger.info("正在进行反思并生成新搜索查询") - # 调用LLM - response = self.llm_client.invoke(SYSTEM_PROMPT_REFLECTION, message) + # 调用LLM（流式，安全拼接UTF-8） + response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_REFLECTION, message) # 处理响应 processed_response = self.process_output(response) diff --git a/InsightEngine/nodes/summary_node.py b/InsightEngine/nodes/summary_node.py index 72f93e2..69b5bc8 100644 --- a/InsightEngine/nodes/summary_node.py +++ b/InsightEngine/nodes/summary_node.py @@ -99,8 +99,8 @@ class FirstSummaryNode(StateMutationNode): logger.info("正在生成首次段落总结") - # 调用LLM - response = self.llm_client.invoke(SYSTEM_PROMPT_FIRST_SUMMARY, message) + # 调用LLM（流式，安全拼接UTF-8） + response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_FIRST_SUMMARY, message) # 处理响应 processed_response = self.process_output(response) @@ -264,8 +264,8 @@ class ReflectionSummaryNode(StateMutationNode): logger.info("正在生成反思总结") - # 调用LLM - response = self.llm_client.invoke(SYSTEM_PROMPT_REFLECTION_SUMMARY, message) + # 调用LLM（流式，安全拼接UTF-8） + response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_REFLECTION_SUMMARY, message) # 处理响应 processed_response = self.process_output(response) diff --git a/MediaEngine/llms/base.py b/MediaEngine/llms/base.py index 5bedebb..4cbb3ca 100644 --- a/MediaEngine/llms/base.py +++ b/MediaEngine/llms/base.py @@ -5,7 +5,8 @@ Unified OpenAI-compatible LLM client for the Media Engine, with retry support. import os import sys from datetime import datetime -from typing import Any, Dict, Optional +from typing import Any, Dict, Optional, Generator +from loguru import logger from openai import OpenAI @@ -85,6 +86,76 @@ class LLMClient: return self.validate_response(response.choices[0].message.content) return "" + @with_retry(LLM_RETRY_CONFIG) + def stream_invoke(self, system_prompt: str, user_prompt: str, **kwargs) -> Generator[str, None, None]: + """ + 流式调用LLM，逐步返回响应内容 + + Args: + system_prompt: 系统提示词 + user_prompt: 用户提示词 + **kwargs: 额外参数（temperature, top_p等） + + Yields: + 响应文本块（str） + """ + current_time = datetime.now().strftime("%Y年%m月%d日%H时%M分") + time_prefix = f"今天的实际时间是{current_time}" + if user_prompt: + user_prompt = f"{time_prefix}\n{user_prompt}" + else: + user_prompt = time_prefix + messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ] + + allowed_keys = {"temperature", "top_p", "presence_penalty", "frequency_penalty"} + extra_params = {key: value for key, value in kwargs.items() if key in allowed_keys and value is not None} + # 强制使用流式 + extra_params["stream"] = True + + timeout = kwargs.pop("timeout", self.timeout) + + try: + stream = self.client.chat.completions.create( + model=self.model_name, + messages=messages, + timeout=timeout, + **extra_params, + ) + + for chunk in stream: + if chunk.choices and len(chunk.choices) > 0: + delta = chunk.choices[0].delta + if delta and delta.content: + yield delta.content + except Exception as e: + logger.error(f"流式请求失败: {str(e)}") + raise e + + def stream_invoke_to_string(self, system_prompt: str, user_prompt: str, **kwargs) -> str: + """ + 流式调用LLM并安全地拼接为完整字符串（避免UTF-8多字节字符截断） + + Args: + system_prompt: 系统提示词 + user_prompt: 用户提示词 + **kwargs: 额外参数（temperature, top_p等） + + Returns: + 完整的响应字符串 + """ + # 以字节形式收集所有块 + byte_chunks = [] + for chunk in self.stream_invoke(system_prompt, user_prompt, **kwargs): + byte_chunks.append(chunk.encode('utf-8')) + + # 拼接所有字节，然后一次性解码 + if byte_chunks: + return b''.join(byte_chunks).decode('utf-8', errors='replace') + return "" + @staticmethod def validate_response(response: Optional[str]) -> str: if response is None: diff --git a/MediaEngine/nodes/formatting_node.py b/MediaEngine/nodes/formatting_node.py index a0ef03d..c605da3 100644 --- a/MediaEngine/nodes/formatting_node.py +++ b/MediaEngine/nodes/formatting_node.py @@ -68,8 +68,8 @@ class ReportFormattingNode(BaseNode): logger.info("正在格式化最终报告") - # 调用LLM生成Markdown格式 - response = self.llm_client.invoke( + # 调用LLM生成Markdown格式（流式，安全拼接UTF-8） + response = self.llm_client.stream_invoke_to_string( SYSTEM_PROMPT_REPORT_FORMATTING, message, ) diff --git a/MediaEngine/nodes/report_structure_node.py b/MediaEngine/nodes/report_structure_node.py index c1e7214..353b97e 100644 --- a/MediaEngine/nodes/report_structure_node.py +++ b/MediaEngine/nodes/report_structure_node.py @@ -52,7 +52,7 @@ class ReportStructureNode(StateMutationNode): logger.info(f"正在为查询生成报告结构: {self.query}") # 调用LLM - response = self.llm_client.invoke(SYSTEM_PROMPT_REPORT_STRUCTURE, self.query) + response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_REPORT_STRUCTURE, self.query) # 处理响应 processed_response = self.process_output(response) diff --git a/MediaEngine/nodes/search_node.py b/MediaEngine/nodes/search_node.py index e81efba..e44ee72 100644 --- a/MediaEngine/nodes/search_node.py +++ b/MediaEngine/nodes/search_node.py @@ -66,7 +66,7 @@ class FirstSearchNode(BaseNode): logger.info("正在生成首次搜索查询") # 调用LLM - response = self.llm_client.invoke(SYSTEM_PROMPT_FIRST_SEARCH, message) + response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_FIRST_SEARCH, message) # 处理响应 processed_response = self.process_output(response) @@ -201,7 +201,7 @@ class ReflectionNode(BaseNode): logger.info("正在进行反思并生成新搜索查询") # 调用LLM - response = self.llm_client.invoke(SYSTEM_PROMPT_REFLECTION, message) + response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_REFLECTION, message) # 处理响应 processed_response = self.process_output(response) diff --git a/MediaEngine/nodes/summary_node.py b/MediaEngine/nodes/summary_node.py index 748fbb9..2980a76 100644 --- a/MediaEngine/nodes/summary_node.py +++ b/MediaEngine/nodes/summary_node.py @@ -99,8 +99,8 @@ class FirstSummaryNode(StateMutationNode): logger.info("正在生成首次段落总结") - # 调用LLM生成总结 - response = self.llm_client.invoke( + # 调用LLM生成总结（流式，安全拼接UTF-8） + response = self.llm_client.stream_invoke_to_string( SYSTEM_PROMPT_FIRST_SUMMARY, message, ) @@ -267,8 +267,8 @@ class ReflectionSummaryNode(StateMutationNode): logger.info("正在生成反思总结") - # 调用LLM生成总结 - response = self.llm_client.invoke( + # 调用LLM生成总结（流式，安全拼接UTF-8） + response = self.llm_client.stream_invoke_to_string( SYSTEM_PROMPT_REFLECTION_SUMMARY, message, ) diff --git a/QueryEngine/llms/base.py b/QueryEngine/llms/base.py index e75e2b1..399ab65 100644 --- a/QueryEngine/llms/base.py +++ b/QueryEngine/llms/base.py @@ -5,7 +5,8 @@ Unified OpenAI-compatible LLM client for the Query Engine, with retry support. import os import sys from datetime import datetime -from typing import Any, Dict, Optional +from typing import Any, Dict, Optional, Generator +from loguru import logger from openai import OpenAI @@ -82,6 +83,76 @@ class LLMClient: return self.validate_response(response.choices[0].message.content) return "" + @with_retry(LLM_RETRY_CONFIG) + def stream_invoke(self, system_prompt: str, user_prompt: str, **kwargs) -> Generator[str, None, None]: + """ + 流式调用LLM，逐步返回响应内容 + + Args: + system_prompt: 系统提示词 + user_prompt: 用户提示词 + **kwargs: 额外参数（temperature, top_p等） + + Yields: + 响应文本块（str） + """ + current_time = datetime.now().strftime("%Y年%m月%d日%H时%M分") + time_prefix = f"今天的实际时间是{current_time}" + if user_prompt: + user_prompt = f"{time_prefix}\n{user_prompt}" + else: + user_prompt = time_prefix + messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ] + + allowed_keys = {"temperature", "top_p", "presence_penalty", "frequency_penalty"} + extra_params = {key: value for key, value in kwargs.items() if key in allowed_keys and value is not None} + # 强制使用流式 + extra_params["stream"] = True + + timeout = kwargs.pop("timeout", self.timeout) + + try: + stream = self.client.chat.completions.create( + model=self.model_name, + messages=messages, + timeout=timeout, + **extra_params, + ) + + for chunk in stream: + if chunk.choices and len(chunk.choices) > 0: + delta = chunk.choices[0].delta + if delta and delta.content: + yield delta.content + except Exception as e: + logger.error(f"流式请求失败: {str(e)}") + raise e + + def stream_invoke_to_string(self, system_prompt: str, user_prompt: str, **kwargs) -> str: + """ + 流式调用LLM并安全地拼接为完整字符串（避免UTF-8多字节字符截断） + + Args: + system_prompt: 系统提示词 + user_prompt: 用户提示词 + **kwargs: 额外参数（temperature, top_p等） + + Returns: + 完整的响应字符串 + """ + # 以字节形式收集所有块 + byte_chunks = [] + for chunk in self.stream_invoke(system_prompt, user_prompt, **kwargs): + byte_chunks.append(chunk.encode('utf-8')) + + # 拼接所有字节，然后一次性解码 + if byte_chunks: + return b''.join(byte_chunks).decode('utf-8', errors='replace') + return "" + @staticmethod def validate_response(response: Optional[str]) -> str: if response is None: diff --git a/QueryEngine/nodes/formatting_node.py b/QueryEngine/nodes/formatting_node.py index a522380..9880519 100644 --- a/QueryEngine/nodes/formatting_node.py +++ b/QueryEngine/nodes/formatting_node.py @@ -68,8 +68,8 @@ class ReportFormattingNode(BaseNode): logger.info("正在格式化最终报告") - # 调用LLM生成Markdown格式 - response = self.llm_client.invoke( + # 调用LLM生成Markdown格式（流式，安全拼接UTF-8） + response = self.llm_client.stream_invoke_to_string( SYSTEM_PROMPT_REPORT_FORMATTING, message, ) diff --git a/QueryEngine/nodes/report_structure_node.py b/QueryEngine/nodes/report_structure_node.py index c1e7214..353b97e 100644 --- a/QueryEngine/nodes/report_structure_node.py +++ b/QueryEngine/nodes/report_structure_node.py @@ -52,7 +52,7 @@ class ReportStructureNode(StateMutationNode): logger.info(f"正在为查询生成报告结构: {self.query}") # 调用LLM - response = self.llm_client.invoke(SYSTEM_PROMPT_REPORT_STRUCTURE, self.query) + response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_REPORT_STRUCTURE, self.query) # 处理响应 processed_response = self.process_output(response) diff --git a/QueryEngine/nodes/search_node.py b/QueryEngine/nodes/search_node.py index e81efba..e44ee72 100644 --- a/QueryEngine/nodes/search_node.py +++ b/QueryEngine/nodes/search_node.py @@ -66,7 +66,7 @@ class FirstSearchNode(BaseNode): logger.info("正在生成首次搜索查询") # 调用LLM - response = self.llm_client.invoke(SYSTEM_PROMPT_FIRST_SEARCH, message) + response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_FIRST_SEARCH, message) # 处理响应 processed_response = self.process_output(response) @@ -201,7 +201,7 @@ class ReflectionNode(BaseNode): logger.info("正在进行反思并生成新搜索查询") # 调用LLM - response = self.llm_client.invoke(SYSTEM_PROMPT_REFLECTION, message) + response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_REFLECTION, message) # 处理响应 processed_response = self.process_output(response) diff --git a/QueryEngine/nodes/summary_node.py b/QueryEngine/nodes/summary_node.py index 217db54..2c5942c 100644 --- a/QueryEngine/nodes/summary_node.py +++ b/QueryEngine/nodes/summary_node.py @@ -99,8 +99,8 @@ class FirstSummaryNode(StateMutationNode): logger.info("正在生成首次段落总结") - # 调用LLM生成总结 - response = self.llm_client.invoke( + # 调用LLM生成总结（流式，安全拼接UTF-8） + response = self.llm_client.stream_invoke_to_string( SYSTEM_PROMPT_FIRST_SUMMARY, message, ) @@ -267,8 +267,8 @@ class ReflectionSummaryNode(StateMutationNode): logger.info("正在生成反思总结") - # 调用LLM生成总结 - response = self.llm_client.invoke( + # 调用LLM生成总结（流式，安全拼接UTF-8） + response = self.llm_client.stream_invoke_to_string( SYSTEM_PROMPT_REFLECTION_SUMMARY, message, ) diff --git a/ReportEngine/llms/base.py b/ReportEngine/llms/base.py index 7ab9d34..64e34ac 100644 --- a/ReportEngine/llms/base.py +++ b/ReportEngine/llms/base.py @@ -4,7 +4,8 @@ Unified OpenAI-compatible LLM client for the Report Engine, with retry support. import os import sys -from typing import Any, Dict, Optional +from typing import Any, Dict, Optional, Generator +from loguru import logger from openai import OpenAI @@ -75,6 +76,70 @@ class LLMClient: return self.validate_response(response.choices[0].message.content) return "" + @with_retry(LLM_RETRY_CONFIG) + def stream_invoke(self, system_prompt: str, user_prompt: str, **kwargs) -> Generator[str, None, None]: + """ + 流式调用LLM，逐步返回响应内容 + + Args: + system_prompt: 系统提示词 + user_prompt: 用户提示词 + **kwargs: 额外参数（temperature, top_p等） + + Yields: + 响应文本块（str） + """ + messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ] + + allowed_keys = {"temperature", "top_p", "presence_penalty", "frequency_penalty"} + extra_params = {key: value for key, value in kwargs.items() if key in allowed_keys and value is not None} + # 强制使用流式 + extra_params["stream"] = True + + timeout = kwargs.pop("timeout", self.timeout) + + try: + stream = self.client.chat.completions.create( + model=self.model_name, + messages=messages, + timeout=timeout, + **extra_params, + ) + + for chunk in stream: + if chunk.choices and len(chunk.choices) > 0: + delta = chunk.choices[0].delta + if delta and delta.content: + yield delta.content + except Exception as e: + logger.error(f"流式请求失败: {str(e)}") + raise e + + def stream_invoke_to_string(self, system_prompt: str, user_prompt: str, **kwargs) -> str: + """ + 流式调用LLM并安全地拼接为完整字符串（避免UTF-8多字节字符截断） + + Args: + system_prompt: 系统提示词 + user_prompt: 用户提示词 + **kwargs: 额外参数（temperature, top_p等） + + Returns: + 完整的响应字符串 + """ + # 以字节形式收集所有块 + byte_chunks = [] + for chunk in self.stream_invoke(system_prompt, user_prompt, **kwargs): + byte_chunks.append(chunk.encode('utf-8')) + + # 拼接所有字节，然后一次性解码 + if byte_chunks: + return b''.join(byte_chunks).decode('utf-8', errors='replace') + return "" + @staticmethod def validate_response(response: Optional[str]) -> str: if response is None: diff --git a/ReportEngine/nodes/html_generation_node.py b/ReportEngine/nodes/html_generation_node.py index 85a6b53..2086528 100644 --- a/ReportEngine/nodes/html_generation_node.py +++ b/ReportEngine/nodes/html_generation_node.py @@ -60,7 +60,7 @@ class HTMLGenerationNode(StateMutationNode): message = json.dumps(llm_input, ensure_ascii=False, indent=2) # 调用LLM生成HTML - response = self.llm_client.invoke(SYSTEM_PROMPT_HTML_GENERATION, message) + response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_HTML_GENERATION, message) # 处理响应（简化版） processed_response = self.process_output(response) diff --git a/ReportEngine/nodes/template_selection_node.py b/ReportEngine/nodes/template_selection_node.py index d21c96e..832d08e 100644 --- a/ReportEngine/nodes/template_selection_node.py +++ b/ReportEngine/nodes/template_selection_node.py @@ -115,7 +115,7 @@ class TemplateSelectionNode(BaseNode): 请根据查询内容、报告内容和论坛日志的具体情况，选择最合适的模板。""" # 调用LLM - response = self.llm_client.invoke(SYSTEM_PROMPT_TEMPLATE_SELECTION, user_message) + response = self.llm_client.stream_invoke_to_string(SYSTEM_PROMPT_TEMPLATE_SELECTION, user_message) # 检查响应是否为空 if not response or not response.strip(): diff --git a/utils/forum_reader.py b/utils/forum_reader.py index 8943988..93f000e 100644 --- a/utils/forum_reader.py +++ b/utils/forum_reader.py @@ -6,10 +6,7 @@ Forum日志读取工具 import re from pathlib import Path from typing import Optional, List, Dict -import logging - -logger = logging.getLogger(__name__) - +from loguru import logger def get_latest_host_speech(log_dir: str = "logs") -> Optional[str]: """