fix:统一llm请求函数,改动模型名称
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@@ -346,10 +346,10 @@ class Hippocampus:
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# 使用LLM提取关键词
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topic_num = min(5, max(1, int(len(text) * 0.1))) # 根据文本长度动态调整关键词数量
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# logger.info(f"提取关键词数量: {topic_num}")
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topics_response = await self.model_summary.generate_response(self.find_topic_llm(text, topic_num))
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topics_response, (reasoning_content, model_name) = await self.model_summary.generate_response_async(self.find_topic_llm(text, topic_num))
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# 提取关键词
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keywords = re.findall(r"<([^>]+)>", topics_response[0])
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keywords = re.findall(r"<([^>]+)>", topics_response)
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if not keywords:
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keywords = []
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else:
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@@ -701,10 +701,10 @@ class Hippocampus:
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# 使用LLM提取关键词
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topic_num = min(5, max(1, int(len(text) * 0.1))) # 根据文本长度动态调整关键词数量
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# logger.info(f"提取关键词数量: {topic_num}")
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topics_response = await self.model_summary.generate_response(self.find_topic_llm(text, topic_num))
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topics_response, (reasoning_content, model_name) = await self.model_summary.generate_response_async(self.find_topic_llm(text, topic_num))
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# 提取关键词
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keywords = re.findall(r"<([^>]+)>", topics_response[0])
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keywords = re.findall(r"<([^>]+)>", topics_response)
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if not keywords:
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keywords = []
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else:
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@@ -1248,12 +1248,12 @@ class ParahippocampalGyrus:
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# 2. 使用LLM提取关键主题
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topic_num = self.hippocampus.calculate_topic_num(input_text, compress_rate)
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topics_response = await self.hippocampus.model_summary.generate_response(
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topics_response, (reasoning_content, model_name) = await self.hippocampus.model_summary.generate_response_async(
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self.hippocampus.find_topic_llm(input_text, topic_num)
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)
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# 提取<>中的内容
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topics = re.findall(r"<([^>]+)>", topics_response[0])
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topics = re.findall(r"<([^>]+)>", topics_response)
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if not topics:
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topics = ["none"]
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