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ai-g3sb-backman2.0/backend/utils/__pycache__/dialog_classifier.cpython-312.pyc
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0.1.1 暂存
2026-04-14 10:28:22 +08:00
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Enhance impact analysis and configuration for vector persistence and API integration. Update main.spec to clarify data handling for ChromaDB, ensuring vector directories are excluded from packaging. Modify Text2SQLOrchestrator to unify question normalization for consistent SQL generation across languages. Introduce SchemaIndexer improvements for persistent vector storage and optimize embedding retrieval processes. Update documentation and comments for clarity on configuration changes and behavior adjustments.
2026-04-15 11:25:19 +08:00
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0.1.1 暂存
2026-04-14 10:28:22 +08:00
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Enhance dialog context handling for Text2SQL queries by integrating session history. Introduce new methods for summarizing previous assistant messages and determining if the last interaction was a data query. Update environment configuration for embedding options and improve error handling in SQL generation. Add user-facing delivery messages for successful SQL execution. This update supports more coherent follow-up questions and improves user experience in conversational interactions.
2026-04-14 18:02:12 +08:00
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0.1.1 暂存
2026-04-14 10:28:22 +08:00
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Enhance dialog context handling for Text2SQL queries by integrating session history. Introduce new methods for summarizing previous assistant messages and determining if the last interaction was a data query. Update environment configuration for embedding options and improve error handling in SQL generation. Add user-facing delivery messages for successful SQL execution. This update supports more coherent follow-up questions and improves user experience in conversational interactions.
2026-04-14 18:02:12 +08:00
用户输入�图分类：区分「自然语言查数 / Text2SQL�与「寒暄�致谢�元问题�等�适�直接生� SQL 的对�。
支�环境�� ``DIALOG_INTENT_CLASSIFIER``：
- ``rules``：仅用关键�与短语规则（无 LLM 调用）。
- ``hybrid``（默认）：明显查数�/寒暄/元问题走规则；其余交 LLM 判断（需调用方传入 ``llm_client``）。
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Optional[str]Úreply_suggestion)rrrÚ__annotations__rrrrrrs…Ø ÓØ[Ø&*Ð�mÔ*rruØ您好，我是业务库 Text2SQL 助手。
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0.1.1 暂存
2026-04-14 10:28:22 +08:00
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Enhance dialog context handling for Text2SQL queries by integrating session history. Introduce new methods for summarizing previous assistant messages and determining if the last interaction was a data query. Update environment configuration for embedding options and improve error handling in SQL generation. Add user-facing delivery messages for successful SQL execution. This update supports more coherent follow-up questions and improves user experience in conversational interactions.
2026-04-14 18:02:12 +08:00
规则分类（与历�行为一致）：优先查数关键�；寒暄/元问题为 conversation；
若上一轮为数�查询且�寒暄/元问题，则倾� text2sql。
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0.1.1 暂存
2026-04-14 10:28:22 +08:00
t¬«St j|«r4tjd«ttj
Enhance dialog context handling for Text2SQL queries by integrating session history. Introduce new methods for summarizing previous assistant messages and determining if the last interaction was a data query. Update environment configuration for embedding options and improve error handling in SQL generation. Add user-facing delivery messages for successful SQL execution. This update supports more coherent follow-up questions and improves user experience in conversational interactions.
2026-04-14 18:02:12 +08:00
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对用户一轮输入�分类。
``DIALOG_INTENT_CLASSIFIER``：
- ``rules``：仅规则。
- ``hybrid``：规则快速命中查数�/寒暄/元问题�返回；�则在有 ``llm_client`` 时用 LLM，
失败或无客户端时回退规则。
Args:
user_text: 用户输入。
last_turn_was_data_query: 上一轮助手是�为数�查询（供规则与 LLM �考）。
dialog_context: 会�摘�，供 LLM �考（�选）。
llm_client: DeepSeek 客户端；hybrid 下 LLM 分支需�。
0.1.1 暂存
2026-04-14 10:28:22 +08:00
rTr]r`z;[dialog] intent=text2sql (query/business hint, hybrid fast)Nz;[dialog] intent=conversation (chitchat phrase, hybrid fast)z9[dialog] intent=conversation (meta question, hybrid fast))r^r„z.[dialog] LLM intent failed, fallback rules: %s)r\rjrOrr rrarbrcrYrdrrQr@rerfrgrŠÚ ExceptionrZ)rhr^r„r‹ÚmoderNriÚes rÚclassify_dialogr�ÝsT€ô* #Ó $€Dà ˆw‚Ü%Ø Ð0Hô
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Enhance dialog context handling for Text2SQL queries by integrating session history. Introduce new methods for summarizing previous assistant messages and determining if the last interaction was a data query. Update environment configuration for embedding options and improve error handling in SQL generation. Add user-facing delivery messages for successful SQL execution. This update supports more coherent follow-up questions and improves user experience in conversational interactions.
2026-04-14 18:02:12 +08:00
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