feat: optimize memory extraction for concise output and precise retrieval

[lishixiang0705]

Problem

In production, extracted memories average 500-2000 chars per item, causing:

1. Embedding vector dilution — any query fuzzy-matches long content, scores cluster in 0.18-0.21 range
2. Poor retrieval discrimination — relevant and irrelevant items score similarly
3. Context bloat — 5 injected memories can exceed 5000 chars (~3000 tokens) per turn

Solution

1. Prompt optimization (memory_extraction.yaml)

• Add explicit length targets: abstract ~50-80 chars, content 2-4 sentences
• Add good/bad examples showing concise vs verbose patterns
• Guide LLM to split multi-topic memories into separate atomic items
• Emphasize fact-dense "sticky note" style over narrative expansion

2. Vectorization improvement (memory_extractor.py)

• Use abstract instead of content for embedding generation
• Shorter text → more focused vectors → better cosine similarity discrimination
• Fallback: abstract or content ensures no empty embeddings

Expected Impact

• Memory size: 500-2000 chars → 100-300 chars per item
• Injection cost: ~3000 tokens/turn → ~600 tokens/turn (80% reduction)
• Retrieval precision: score spread from 0.03 → 0.15+ between relevant/irrelevant

Files Changed

• openviking/prompts/templates/compression/memory_extraction.yaml — prompt template
• openviking/session/memory_extractor.py — 2 lines: set_vectorize text source

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