Merge pull request #82 from SuanmoSuanyangTechnology/feat/memory-perceptual
Feat/memory perceptual
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@@ -136,6 +136,9 @@ class MemoryPerceptualService:
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memory_items = []
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for memory in memories:
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meta_data = memory.meta_data or {}
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content = meta_data.get("content")
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content = Content(**content)
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memory_item = PerceptualMemoryItem(
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id=memory.id,
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perceptual_type=PerceptualType(memory.perceptual_type),
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@@ -143,6 +146,9 @@ class MemoryPerceptualService:
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file_name=memory.file_name,
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file_ext=memory.file_ext,
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summary=memory.summary,
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topic=content.topic,
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domain=content.domain,
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keywords=content.keywords,
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created_time=int(memory.created_time.timestamp()*1000),
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storage_type=FileStorageType(memory.storage_service),
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)
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@@ -35,7 +35,8 @@ You are a professional dialogue content summarizer, specializing in extracting c
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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. `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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4. `question`: The `question` field is a list of brief declarative statements describing objective pitfalls or problems the user actually encountered during the current conversation.
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Strict rules: Only include problems that clearly and directly affected task progress. Each item must be a short, factual, declarative statement. Only record issues that are explicitly observable from the conversation. Do NOT include assumptions, interpretations, or stylistic judgments.
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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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