Feature/memory work (#68)
* feat(memory): add conversation title to conversation list response for frontend display * feat(memory): optimize conversation retrieval, enable working memory to return conversation question summaries * fix(memory): fix conversation re-generation logic * style(desc): improve description of get_conversation function
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@@ -33,18 +33,18 @@ You are a professional dialogue content summarizer, specializing in extracting c
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- Language: Please strictly output content in the language specified by the <Language> tag.
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- Structure: JSON object with five fields,:
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1. `theme`: A concise phrase describing the conversation’s core topic (e.g., "inquiry about delivery time rules");
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2. `theme_intro`: A brief explanation of the conversation’s core theme to clarify its specific scope and focus (e.g., "The conversation focuses on the user's inquiry about delivery time standards for regular and remote areas");
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3. `summary`: A single sentence including "user request + AI response + interaction logic" (≤100 words);
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4. `takeaways`: A list of brief bullet-point takeaways summarizing the key points from the conversation (e.g., ["User clarified delivery time differences between regular and remote areas"]).
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2. `summary`: A single sentence including "user request + AI response + interaction logic" (≤150 words);
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3. `takeaways`: A list of brief bullet-point takeaways summarizing the key points from the conversation (e.g., ["User clarified delivery time differences between regular and remote areas"]).
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4. `question`: A list of brief declarative statements summarizing the pitfalls the user encountered during the current conversation.Return an empty list if none are present.
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5. `info_score`: Numerical score (0–100) representing conversation information richness.
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- Language Style: Concise, objective, conversational (avoid overly formal terms).
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# Example JSON Output
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{
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"theme": string,
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"theme_intro": string,
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"summary": string,
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"takeaways": array[string],
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"question": array[string]
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"info_score": 85
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}
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{% endraw %}
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