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feat；优化了记忆检索的速度和token消耗（将question提出交给planenr)

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SengokuCola
2025-12-24 18:43:32 +08:00
parent 490589b0ad
commit 0852af49f9
15 changed files with 448 additions and 152 deletions
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4
src/bw_learner/expression_selector.py
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@@ -407,8 +407,8 @@ class ExpressionSelector:
# 4. 调用LLM
content, (reasoning_content, model_name, _) = await self.llm_model.generate_response_async(prompt=prompt)
print(prompt)
print(content)
# print(prompt)
# print(content)
if not content:
logger.warning("LLM返回空结果")

7
src/bw_learner/jargon_explainer.py
View File

@@ -341,7 +341,7 @@ async def retrieve_concepts_with_jargon(concepts: List[str], chat_id: str) -> st
meaning = result.get("meaning", "").strip()
if found_content and meaning:
output_parts.append(f"找到 '{found_content}' 的含义为：{meaning}")
results.append("，".join(output_parts))
results.append("\n".join(output_parts)) # 换行分隔每个jargon解释
logger.info(f"在jargon库中找到匹配（模糊搜索）: {concept}，找到{len(jargon_results)}条结果")
else:
# 精确匹配
@@ -350,7 +350,8 @@ async def retrieve_concepts_with_jargon(concepts: List[str], chat_id: str) -> st
meaning = result.get("meaning", "").strip()
if meaning:
output_parts.append(f"'{concept}' 为黑话或者网络简写，含义为：{meaning}")
results.append("；".join(output_parts) if len(output_parts) > 1 else output_parts[0])
# 换行分隔每个jargon解释
results.append("\n".join(output_parts) if len(output_parts) > 1 else output_parts[0])
exact_matches.append(concept) # 收集精确匹配的概念，稍后统一打印
else:
# 未找到，不返回占位信息，只记录日志
@@ -361,5 +362,5 @@ async def retrieve_concepts_with_jargon(concepts: List[str], chat_id: str) -> st
logger.info(f"找到黑话: {', '.join(exact_matches)}，共找到{len(exact_matches)}条结果")
if results:
return "【概念检索结果】\n" + "\n".join(results) + "\n"
return "你了解以下词语可能的含义：\n" + "\n".join(results) + "\n"
return ""