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feat: implement LLM keyword extraction and fusion scoring for table matching - #1372
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ulleo
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Sep 14, 2026
ulleo
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- Add extract_keywords() to LLMService with conversation context support, using process_stream + start_log/end_log for logging consistency
- Add expand_with_terminology() to expand keywords with synonym matching
- Add fusion scoring in calc_table_embedding(): α * vec_score + (1-α) * keyword_score
- Add calc_keyword_score() with multi-level matching: table name exact (1.0), comment exact (0.9), comment substring (0.7), table name partial (0.25-0.5), field comment exact (0.5), field comment substring (0.4), field name partial (0.3)
- Scoring considers keyword coverage rate to differentiate tables with different numbers of matched keywords
- Extracted keywords persisted to ChatRecord for reuse across endpoints
- get_terminology_template() supports comma-separated keyword input with per-keyword matching for higher precision
- Add TABLE_EMBEDDING_ALPHA and TABLE_EMBEDDING_KEYWORD_ENABLED config
…atching - Add extract_keywords() to LLMService with conversation context support, using process_stream + start_log/end_log for logging consistency - Add expand_with_terminology() to expand keywords with synonym matching - Add fusion scoring in calc_table_embedding(): α * vec_score + (1-α) * keyword_score - Add calc_keyword_score() with multi-level matching: table name exact (1.0), comment exact (0.9), comment substring (0.7), table name partial (0.25-0.5), field comment exact (0.5), field comment substring (0.4), field name partial (0.3) - Scoring considers keyword coverage rate to differentiate tables with different numbers of matched keywords - Extracted keywords persisted to ChatRecord for reuse across endpoints - get_terminology_template() supports comma-separated keyword input with per-keyword matching for higher precision - Add TABLE_EMBEDDING_ALPHA and TABLE_EMBEDDING_KEYWORD_ENABLED config
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