From bff5f85d603fd51c067db4b70a5993b9df1a9c43 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=99=88=E8=BE=85=E5=85=83?= <2401926342@qq.com> Date: Thu, 16 Apr 2026 13:48:44 +0800 Subject: [PATCH] Enhance SQL generation and streaming capabilities in the API server. Introduce optional parameters for streaming throttle and SQL stream granularity in `NLChatRequest`. Implement new functions for iterating SQL generation content pieces and adjusting streaming behavior based on user-defined settings. Update prompts for few-shot SQL adaptation and improve logging for SQL generation processes. Refactor orchestrator methods to support streaming responses and integrate few-shot SQL conditions. Update impact analysis documentation to reflect these changes. --- IMPACT_ANALYSIS.md | 80 ++++++++ __pycache__/api_server.cpython-312.pyc | Bin 50918 -> 53980 bytes api_server.py | 131 +++++++++--- .../__pycache__/orchestrator.cpython-312.pyc | Bin 33999 -> 39764 bytes backend/agents/orchestrator.py | 186 +++++++++++++++++- .../__pycache__/prompts.cpython-312.pyc | Bin 22348 -> 24519 bytes backend/config/prompts.py | 32 +++ .../deepseek_client.cpython-312.pyc | Bin 15512 -> 17476 bytes .../__pycache__/openai_client.cpython-312.pyc | Bin 14996 -> 17746 bytes backend/llm/deepseek_client.py | 39 +++- backend/llm/openai_client.py | 65 +++++- .../fewshot_chroma_store.cpython-312.pyc | Bin 11961 -> 11998 bytes .../fewshot_selector.cpython-312.pyc | Bin 27001 -> 28963 bytes backend/utils/fewshot_selector.py | 88 ++++++++- .../length.bin | Bin 4000 -> 4000 bytes logs/text2sql_api.log | 68 +------ 16 files changed, 586 insertions(+), 103 deletions(-) diff --git a/IMPACT_ANALYSIS.md b/IMPACT_ANALYSIS.md index 74c01bc..83b1027 100644 --- a/IMPACT_ANALYSIS.md +++ b/IMPACT_ANALYSIS.md @@ -592,3 +592,83 @@ ## 6. 配置变更 - 无新增环境变量；日志路径固定为仓库根下 `logs/text2sql_api.log`。 + +--- + +# Impact Analysis Report — Chroma Few-shot 黄金 SQL 条件适配（追加） + +## 1. 改动概览 + +- **背景与目标**：`data/embeddings/chroma_fewshot` 中存储的问答-SQL 视为已校验正确答案；当向量检索与当前问题足够相似时，不再仅作「风格示例」，而是以该 SQL 为骨架，仅让 LLM 调整 WHERE/HAVING 等与用户问题相关的**特殊条件**。 +- **涉及模块**：`backend/config/prompts.py`（`GOLDEN_SQL_ADAPT_*`）、`backend/utils/fewshot_selector.py`（`select_best_with_score`、`is_chroma_backend`）、`backend/agents/orchestrator.py`（`_generate_sql_golden_adapt`、`_generate_sql` 分支）、`api_server.py`（响应中可选 `fewshot_golden`）。 +- **改动类型**：生成策略增强（默认开启，可用环境变量关闭或调阈值）。 + +## 2. 方法级改动 + +| 位置 | 变更 | +|------|------| +| `FewShotSelector` | 新增 `_passes_filters`、`select_best_with_score`；`is_chroma_backend` 属性。 | +| `Text2SQLOrchestrator._generate_sql` | 在 Chroma 且满足阈值时调用 `_generate_sql_golden_adapt` 并设置 `_last_fewshot_golden`；否则沿用原 few-shot 注入 + 全量生成。 | +| `_generate_sql_golden_adapt` | 使用 `GOLDEN_SQL_ADAPT_SYSTEM/USER`，强调保留 JOIN/SELECT 结构、仅改条件。 | +| `generate` | `metadata` 增加 `fewshot_golden_reuse`、`fewshot_golden_qid`、`fewshot_golden_score`；失败路径亦回传（若曾走黄金分支）。 | +| `_nl_dict_from_generation` | 若存在黄金复用，增加 `fewshot_golden` 字段供前端展示/调试。 | + +## 3. 破坏性变更 + +- **否**。未命中阈值时行为与原先「多示例风格参考」一致。 + +## 4. 配置变更 + +| 环境变量 | 含义 | 默认 | +|----------|------|------| +| `FEWSHOT_GOLDEN_REUSE` | 是否启用黄金 SQL 条件适配 | `true` | +| `FEWSHOT_GOLDEN_ONLY_CHROMA` | 是否仅在 Chroma 后端启用（与 `chroma_fewshot` 策略一致） | `true` | +| `FEWSHOT_GOLDEN_MIN_SCORE` | 最低相似度（1-距离，约等于余弦相似度） | `0.88` | +| `FEWSHOT_GOLDEN_SQL_PROMPT_MAX` | 写入提示词的标准答案 SQL 最大字符数 | `16000` | + +## 5. 风险与回滚 + +- **风险**：阈值过低可能把不太相似的问题强行套在同一 SQL 上；过高则很少触发黄金分支。 +- **回滚**：设 `FEWSHOT_GOLDEN_REUSE=false` 或回退相关提交。 + +**回滚方式是否简单**：是。 + +--- + +# Impact Analysis Report — 流式 SSE sql_gen 分片与节流 + +## 1. 改动概览 + +- **背景与目标**：前端约定每条 SSE 为 `data: {"stage":"sql_gen","stream_kind":"content","content":"..."}`；需在服务端控制拆成「逐字符多条」或「与 LLM delta 一致」，并支持可选分片间隔。 +- **涉及模块**：`api_server.py`（`/g3sb/api/nl/chat/stream`、`_iter_sql_gen_content_pieces`、`_sse_stream_text_chunks`）。 +- **改动类型**：行为调整（默认分片粒度默认更贴近前端的 `char`；可配置回 `delta`）。 + +## 2. 方法级改动 + +| 位置 | 变更 | +|------|------| +| `NLChatRequest` | 新增可选 `sql_stream_granularity`（`sqlStreamGranularity`）；明确 `streaming_throttle` 为相邻 content 间隔毫秒。 | +| `_iter_sql_gen_content_pieces` | 支持传入 `mode`；未设置时 `SSE_SQL_GEN_SPLIT` 默认 `char`。 | +| `_chat_stream_events` | sql_gen 循环按粒度拆分后对每条 `data` 可选 `asyncio.sleep(throttle)`；寒暄分支 `_sse_stream_text_chunks` 支持相同节流。 | + +## 3. 调用方与影响范围 + +- **调用方**：仅 SSE 流式客户端；非流式 `/g3sb/api/nl/chat` 不变。 +- **破坏性变更**：否。未传新字段时：粒度由 `SSE_SQL_GEN_SPLIT` 决定（默认 `char`，事件条数多于旧版 `delta`）；若需旧行为可设 `SSE_SQL_GEN_SPLIT=delta` 或请求体 `sqlStreamGranularity: "delta"`。 + +## 4. 配置变更 + +| 环境变量 | 含义 | 默认 | +|----------|------|------| +| `SSE_SQL_GEN_SPLIT` | `char`：逐 Unicode 标量多条 SSE；`delta`：与 LLM 增量一致 | `char`（未设置 env 时由代码默认） | + +## 5. 风险与回滚 + +- **风险级别**：低。`char` 模式下 SSE 条数增加，带宽与前端拼接次数上升。 +- **回滚**：设 `SSE_SQL_GEN_SPLIT=delta` 或请求传 `sqlStreamGranularity: "delta"`。 + +**回滚方式是否简单**：是。 + +## 6. 验证与测试 + +- 已执行：`python -m py_compile api_server.py`。 diff --git a/__pycache__/api_server.cpython-312.pyc b/__pycache__/api_server.cpython-312.pyc index 9ad4c41f3d1e0fe53a7fc4bd446f4fbafa1cfef5..937744a2535672c8bf6dfcb3345baa4c5d1ef100 100644 GIT binary patch delta 13147 zcma)i3wTq<(db!8M^C>czhA;{+4zAmV2r^S%qtKGI1i8^Lih;qLl&%Lz>XZ7N3abh zKsEtFNFfO+P$1BVG!SSL@~62;liu2p)Iy+cjnl5daq( zwRW9ZXAd!l*h9^s_AqmpJ=`45>y(WV_DFLi$5o9{cD-3|H<%6fXmd2QkKuH6W2`;S z9LI4@W4t}VoM2BhC-SnkG0C26PPV6*Q?l%-=2W}UY~&TX#u4^3a~ct3f=y#fKT1$n zL8za_*4HJ>sei~{42I?`Tb2~ksuKirHZ;nyWd|DB6!i;&IsNz=LesE@STmbzzP9_5yQ(z0h1}FESU|N0~=)dU#{8eYANr z$0Hg`>|@MhI3C$J);`WW&R%LR<>jcx@%A!vS&4mud4j#%T+TaD)wlk;(zZ!~j?ERI ztg^lX?U7`zl%AIKn-u0LK%8=Pu$_6TZEAftFeFAN13k?)HPDy{wu<_#IObS`ZR$(H z%d+8`CIi%ML#IGG6s?7a`FgZP79*Wy;EezXzTwA3{z_ncJ 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zhfRTD@rcC@&gN!t8=gnSddfH0uw`oY;@LFSSA@NPHoM4x(*u8K>o*Y{KhwYMPL}(6 zK`eiQx*r8y2nN{v*MB50vH9nE$rzUTMrMF#yA7a_HX`6BcfN>rVU>R!J%rd%1o-`s zetkY}m`o_<4SE3qMsE5Ug1Zr5)T9{oC`LB=9RiF?A>HOJ z&IT{_gks$A676iFUc6>GJ?rU@DD!WWv96=C+$z`mHnUT2<`=^MH4}g=_`c8y0vxFc zxuj7O&38tDjQm6}d>|M<5KJEkDNnzZNUX7B8R@>^TLH^&N3oo@J5-m9>{oAR6 bytes: return f"data: {json.dumps(obj, ensure_ascii=False)}\n\n".encode("utf-8") +def _iter_sql_gen_content_pieces(text: str, mode: Optional[str] = None) -> List[str]: + """ + 将 LLM 流式片段再拆成前端期望的多条 SSE（与 chatStore onDelta 累加一致）。 + 每条均为：{"stage": "sql_gen", "stream_kind": "content", "content": "..."} + + mode / 环境变量 SSE_SQL_GEN_SPLIT： + - char（请求默认）：按 Unicode 标量逐字符发送（与「用户」「问题」逐条 data 一致） + - delta：与上游 LLM 每次 delta 一致（块更大、事件更少） + """ + if not text: + return [] + raw = (mode or os.getenv("SSE_SQL_GEN_SPLIT", "char") or "char").strip().lower() + if raw in ("delta", "none", "0", "false"): + return [text] + return list(text) + + # 与前端 chatStore onDelta 一致：多次 { stage, stream_kind: content, content } 累加 _DEFAULT_SSE_CHUNK_CHARS = int(os.getenv("SSE_STREAM_CHUNK_CHARS", "64")) @@ -256,16 +280,21 @@ async def _sse_stream_text_chunks( content: str, *, chunk_size: Optional[int] = None, + throttle_ms: Optional[int] = None, ) -> AsyncIterator[bytes]: """将长文本拆成多段 SSE，便于浏览器逐段渲染（流式）。""" if not content: return size = max(8, chunk_size or _DEFAULT_SSE_CHUNK_CHARS) + delay = (throttle_ms or 0) / 1000.0 if throttle_ms and throttle_ms > 0 else 0.0 for i in range(0, len(content), size): yield _sse_data( {"stage": stage, "stream_kind": "content", "content": content[i : i + size]} ) - await asyncio.sleep(0) + if delay: + await asyncio.sleep(delay) + else: + await asyncio.sleep(0) def _conversation_nl_dict(reply: str) -> Dict[str, Any]: @@ -584,29 +613,14 @@ def _nl_dict_from_generation(result: GenerationResult) -> Dict[str, Any]: qz = result.metadata.get("question_zh_normalized") if qo and qz: payload["query_normalization"] = {"original": qo, "zh": qz} + if result.metadata.get("fewshot_golden_reuse"): + payload["fewshot_golden"] = { + "qid": result.metadata.get("fewshot_golden_qid"), + "score": result.metadata.get("fewshot_golden_score"), + } return payload -def _sql_gen_stream_html(result: GenerationResult) -> str: - sql = (result.sql or "").strip() - inner = json.dumps({"sql": sql}, ensure_ascii=False) - parts = [f"{inner}"] - explain = "" - if result.metadata.get("sql_delivery_message"): - parts.append( - f'
{html_lib.escape(str(result.metadata["sql_delivery_message"]))}
' - ) - if result.metadata.get("db_empty_feedback"): - explain = str(result.metadata["db_empty_feedback"]) - elif result.warnings: - explain = str(result.warnings[0]) - elif result.errors: - explain = "; ".join(str(e) for e in result.errors[:5]) - if explain.strip(): - parts.append(f'
{html_lib.escape(explain)}
') - return "".join(parts) - - async def _run_generate( question: str, top_k: int = 20, @@ -684,7 +698,9 @@ async def _chat_stream_events(request: NLChatRequest) -> AsyncIterator[bytes]: reply[:500] + ("…" if len(reply) > 500 else ""), ) yield _sse_data({"stage": "orchestrator", "stream_kind": "content", "content": "CHAT"}) - async for pkt in _sse_stream_text_chunks("chat", reply): + async for pkt in _sse_stream_text_chunks( + "chat", reply, throttle_ms=request.streaming_throttle + ): yield pkt data_dict = _conversation_nl_dict(reply) yield _sse_data({"code": 200, "msg": "success", "data": data_dict}) @@ -692,8 +708,65 @@ async def _chat_stream_events(request: NLChatRequest) -> AsyncIterator[bytes]: return yield _sse_data({"stage": "orchestrator", "stream_kind": "content", "content": "DATA_QUERY"}) + dialect = resolve_sql_dialect(os.getenv("TEXT2SQL_DIALECT", "sqlserver")) + dc_ctx = (dialog_block or "").strip() or None + top_k = 20 + loop = asyncio.get_running_loop() + logger.info( + "[GEN/API/stream] dialect=%s top_k=%s dialog_context_chars=%s question_len=%s preview=%r", + dialect, + top_k, + len(dc_ctx) if dc_ctx else 0, + len(text), + text[:300] + ("…" if len(text) > 300 else ""), + ) try: - result = await _run_generate(text, dialog_context=dialog_block or None, request=request) + async with _maybe_override_orch_llm(orch, request) as o: + chunk_queue: asyncio.Queue[Optional[str]] = asyncio.Queue() + holder: Dict[str, Any] = {} + + def _run_generate_sync() -> None: + try: + holder["result"] = o.generate( + question=text.strip(), + dialect=dialect, + top_k_candidates=top_k, + dialog_context=dc_ctx, + sql_stream_callback=lambda c: loop.call_soon_threadsafe( + chunk_queue.put_nowait, c + ), + ) + except Exception as e: + holder["error"] = e + finally: + loop.call_soon_threadsafe(chunk_queue.put_nowait, None) + + gen_task = asyncio.create_task(asyncio.to_thread(_run_generate_sync)) + throttle_ms = request.streaming_throttle or 0 + sql_chunk_delay = throttle_ms / 1000.0 if throttle_ms > 0 else 0.0 + gran = ( + (request.sql_stream_granularity or "").strip() + or os.getenv("SSE_SQL_GEN_SPLIT", "char") + or "char" + ).lower() + while True: + piece = await chunk_queue.get() + if piece is None: + break + for frag in _iter_sql_gen_content_pieces(piece, mode=gran): + yield _sse_data( + { + "stage": "sql_gen", + "stream_kind": "content", + "content": frag, + } + ) + if sql_chunk_delay: + await asyncio.sleep(sql_chunk_delay) + await gen_task + if holder.get("error"): + raise holder["error"] + result = holder["result"] except Exception as e: logger.error(f"[API/stream] 生成异常: {e}", exc_info=True) yield _sse_data({"code": 500, "msg": str(e), "data": None}) @@ -727,9 +800,6 @@ async def _chat_stream_events(request: NLChatRequest) -> AsyncIterator[bytes]: except Exception: pass - html = _sql_gen_stream_html(result) - async for pkt in _sse_stream_text_chunks("sql_gen", html): - yield pkt data_dict = _nl_dict_from_generation(result) msg = "success" if result.valid else "partial" yield _sse_data({"code": 200, "msg": msg, "data": data_dict}) @@ -856,7 +926,10 @@ async def nl_chat(request: NLChatRequest): async def nl_chat_stream(request: NLChatRequest): """ 自然语言对话流式接口（SSE）。 - 事件体为 JSON：分片 delta `{stage, stream_kind, content}` 或结束包 `{code, msg, data}`。 + 事件体为 JSON：分片 `data: {"stage","stream_kind","content"}`（例：sql_gen 时 + `stream_kind` 为 `content`）或结束包 `{code, msg, data}`。 + 可选：`sql_stream_granularity` / `sqlStreamGranularity`（delta|char，未传则 `SSE_SQL_GEN_SPLIT`，默认 char）、 + `streaming_throttle`（相邻 content 分片间隔毫秒）。 """ return StreamingResponse( _chat_stream_events(request), diff --git a/backend/agents/__pycache__/orchestrator.cpython-312.pyc b/backend/agents/__pycache__/orchestrator.cpython-312.pyc index 40bd50dc7a0ad07f818ccd0473b2216e9568ced5..081ed23046fd65c9d447e1103eb1d3079b1925a4 100644 GIT binary patch delta 12907 zcmb7r33yb;mF~T__kCZ~YDujHNiDRnV+>dpLPB6RBLOx>$dYcO1uby9WtQBwz`>@m 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zLb!C=%0Zg|SUU$D5VuEr9^W$U-a>Te+7K#i;S!9SBU+wJIK>V!(g+Le&lUq>%zH@Q!H(<9gC9uOR z&kV^UX=Mdn&ZeG-2HV~h+I}lEPYKQM3(db3R=h7PW~EQg_oiLM@n->BKKTCtj*9U* diff --git a/backend/agents/orchestrator.py b/backend/agents/orchestrator.py index 935dd0e..99eef6d 100644 --- a/backend/agents/orchestrator.py +++ b/backend/agents/orchestrator.py @@ -5,13 +5,13 @@ Text2SQL 多智能体编排器 import logging import os # 新增 -from typing import Dict, List, Optional, Tuple +from typing import Callable, Dict, List, Optional, Tuple from dataclasses import dataclass, field from schema.manager import SchemaManager from schema.indexer import SchemaIndexer from llm.deepseek_client import DeepSeekClient, DeepSeekConfig -from utils.fewshot_selector import FewShotSelector # 新增 +from utils.fewshot_selector import ExperienceSample, FewShotSelector # 新增 logger = logging.getLogger(__name__) @@ -121,6 +121,9 @@ class Text2SQLOrchestrator: logger.warning(f"Few-shot加载失败: {e}，将使用标准生成") self.fewshot_enabled = False + # 若本轮走「Chroma 黄金 SQL 条件适配」，在 metadata 中回传 qid/分数 + self._last_fewshot_golden: Optional[Tuple[str, float]] = None + logger.info( f"[OK] Text2SQLOrchestrator初始化完成: " f"max_retry={max_retry}, use_vector_search={use_vector_search}" @@ -335,6 +338,118 @@ class Text2SQLOrchestrator: return expanded + def _sql_chat_completion_text( + self, + messages: List[Dict[str, str]], + *, + sql_stream_callback: Optional[Callable[[str], None]] = None, + ) -> str: + """ + SQL 生成相关的一次 LLM 调用：可选流式回调（将原始 completion 文本分片回传）。 + 无回调或非流式失败时与非流式 chat 行为一致。 + """ + ds = self.deepseek + if sql_stream_callback is not None and hasattr(ds, "chat_stream"): + try: + parts: List[str] = [] + for piece in ds.chat_stream(messages, temperature=0.0, top_p=1.0): + if piece: + parts.append(piece) + sql_stream_callback(piece) + return "".join(parts).strip() + except Exception as e: + logger.warning("[GEN] chat_stream 失败，回退非流式: %s", e) + msg = ds.chat(messages, temperature=0.0, top_p=1.0) + return (msg.content or "").strip() + + def _generate_sql_golden_adapt( + self, + question: str, + schema_str: str, + dialect: str, + golden: ExperienceSample, + golden_score: float, + validation_feedback: Optional[str], + dialog_context: Optional[str], + sql_stream_callback: Optional[Callable[[str], None]] = None, + ) -> str: + """ + Chroma few-shot 库内 SQL 视为正确答案：在高分相似命中下，仅让模型调整条件/字面量以匹配当前问题。 + """ + from config.prompts import GOLDEN_SQL_ADAPT_SYSTEM, GOLDEN_SQL_ADAPT_USER + from utils.sql_parser import normalize_sql_for_dialect + + gsql = (golden.sql or "").strip() + max_sql = int(os.getenv("FEWSHOT_GOLDEN_SQL_PROMPT_MAX", "16000")) + if len(gsql) > max_sql: + gsql = gsql[:max_sql] + "\n-- …(标准答案过长，已截断)" + + dialect_label = dialect + if dialect == "tsql": + dialect_label = "Microsoft SQL Server (T-SQL)" + + dc = (dialog_context or "").strip() + prefix = "" + if dc: + prefix = ( + "【对话上文】（用于理解指代与续问条件；请结合「当前用户问题」调整 WHERE 等。）\n" + f"{dc}\n\n" + ) + + user_content = prefix + GOLDEN_SQL_ADAPT_USER.format( + golden_score=golden_score, + golden_question=(golden.question_zh or "").strip(), + golden_sql=gsql, + schema=schema_str, + question=question, + dialect=dialect_label, + ) + if dialect == "tsql": + user_content += ( + "\n\n【硬性要求】目标库为 SQL Server（T-SQL）：禁止使用 MySQL 反引号 `；" + "标识符如需引用请使用方括号，例如 [TableName]、[ColumnName]。" + "字符串连接使用 `+`（与系统提示中的标准版式范例一致）。" + "「今日」「当天」等与日期列比较时，使用 `CAST(GETDATE() AS DATE)`，" + "**禁止** `CURDATE()`、`NOW()`、`CURRENT_DATE`（MySQL）。" + "条件请使用 T-SQL 惯用写法（例如 IS NOT NULL）。" + "排版仍须遵守：关键字大写、SELECT 每列一行缩进、WHERE 续行以 AND 开头、PascalCase 英文别名。" + "\n**禁止**在单引号字符串字面量或 `N'…'` 中出现任何中日韩文字；" + "业务中文须映射为 Schema 注释中的代码或通过维表 JOIN，勿写 `= '过户费'` 这类比对。" + ) + + if validation_feedback: + user_content += ( + "\n\n【上次校验未通过】请在保留标准答案主干的前提下修正 SQL；" + "表名、列名必须与「当前 Schema 片段」中完全一致。\n" + f"{validation_feedback}" + ) + + messages = [ + {"role": "system", "content": GOLDEN_SQL_ADAPT_SYSTEM}, + {"role": "user", "content": user_content}, + ] + + logger.info( + "[GEN] 黄金 few-shot 条件适配: qid=%s score=%.4f", + golden.qid, + golden_score, + ) + sql = self._sql_chat_completion_text( + messages, sql_stream_callback=sql_stream_callback + ) + + if "```sql" in sql: + sql = sql[sql.find("```sql") + 6 : sql.find("```", sql.find("```sql") + 6)].strip() + elif "```" in sql: + sql = sql[sql.find("```") + 3 : sql.find("```", sql.find("```") + 3)].strip() + + sql = normalize_sql_for_dialect(sql, dialect) + + lim = 12000 + body = sql if len(sql) <= lim else sql[:lim] + "\n…(日志已截断)" + logger.info("生成的SQL（黄金适配，chars=%s）:\n%s", len(sql), body) + return sql + def _generate_sql( self, question: str, @@ -342,6 +457,7 @@ class Text2SQLOrchestrator: dialect: str = "tsql", validation_feedback: Optional[str] = None, dialog_context: Optional[str] = None, + sql_stream_callback: Optional[Callable[[str], None]] = None, ) -> str: """ SQL生成（SQL Generator Agent） @@ -353,6 +469,7 @@ class Text2SQLOrchestrator: validation_feedback: 非空时附加到用户提示（重试时传入上次校验错误） dialog_context: 前几轮对话摘要；与 ``question`` 一并供指代消解与续问。 + sql_stream_callback: 若提供且 LLM 支持 chat_stream，则在 SQL 主生成/黄金适配时流式回传原始文本分片。 Returns: SQL语句 @@ -360,11 +477,57 @@ class Text2SQLOrchestrator: from config.prompts import SQL_GENERATOR_SYSTEM, SQL_GENERATOR_USER from utils.sql_parser import normalize_sql_for_dialect + self._last_fewshot_golden = None dc = (dialog_context or "").strip() fewshot_question = question if dc: fewshot_question = f"{dc}\n\n【当前问】{question}" + golden_reuse = os.getenv("FEWSHOT_GOLDEN_REUSE", "true").lower() in ( + "1", + "true", + "yes", + ) + only_chroma = os.getenv("FEWSHOT_GOLDEN_ONLY_CHROMA", "true").lower() in ( + "1", + "true", + "yes", + ) + golden_min = float(os.getenv("FEWSHOT_GOLDEN_MIN_SCORE", "0.88")) + chroma_ok = bool( + self.fewshot_selector and self.fewshot_selector.is_chroma_backend + ) + if only_chroma and not chroma_ok: + golden_reuse = False + + if ( + golden_reuse + and self.fewshot_enabled + and self.fewshot_selector + ): + best = self.fewshot_selector.select_best_with_score( + fewshot_question, + min_rating=self.fewshot_min_rating, + ) + if ( + best + and best[1] >= golden_min + and (best[0].sql or "").strip() + ): + ex, sc = best + sql_out = self._generate_sql_golden_adapt( + question=question, + schema_str=schema_str, + dialect=dialect, + golden=ex, + golden_score=sc, + validation_feedback=validation_feedback, + dialog_context=dialog_context, + sql_stream_callback=sql_stream_callback, + ) + self._last_fewshot_golden = (ex.qid, sc) + return sql_out + # Few-shot 增强 if self.fewshot_enabled and self.fewshot_selector: try: @@ -431,8 +594,9 @@ class Text2SQLOrchestrator: ] # 与选表一致：生成阶段默认贪心解码，减少同一中文问题多次 SQL 不一致 - response = self.deepseek.chat(messages, temperature=0.0, top_p=1.0) - sql = response.content.strip() + sql = self._sql_chat_completion_text( + messages, sql_stream_callback=sql_stream_callback + ) # 清理可能的markdown代码块 if "```sql" in sql: @@ -609,6 +773,7 @@ class Text2SQLOrchestrator: top_k_candidates: int = 20, include_schema_in_result: bool = False, dialog_context: Optional[str] = None, + sql_stream_callback: Optional[Callable[[str], None]] = None, ) -> GenerationResult: """ 主生成流程 @@ -619,10 +784,12 @@ class Text2SQLOrchestrator: top_k_candidates: 粗筛候选表数量 include_schema_in_result: 结果中是否包含使用的Schema字符串 dialog_context: 前几轮对话可读摘要；选表、向量粗筛、SQL 生成与无数据说明会参考 + sql_stream_callback: 可选；SQL 主生成 LLM 输出分片回调（用于 API SSE） Returns: GenerationResult对象 """ + self._last_fewshot_golden = None original_question = (question or "").strip() translation_meta: Dict = {} work_question = original_question @@ -726,6 +893,7 @@ class Text2SQLOrchestrator: dialect, validation_feedback=feedback, dialog_context=dc_raw or None, + sql_stream_callback=sql_stream_callback, ) last_sql = sql except Exception as e: @@ -770,6 +938,11 @@ class Text2SQLOrchestrator: meta["sql_delivery_message"] = None if dc_raw: meta["dialog_context_chars"] = len(dc_raw) + if self._last_fewshot_golden: + gq, gsc = self._last_fewshot_golden + meta["fewshot_golden_reuse"] = True + meta["fewshot_golden_qid"] = gq + meta["fewshot_golden_score"] = gsc result = GenerationResult( sql=sql, @@ -792,6 +965,11 @@ class Text2SQLOrchestrator: fail_meta["db_execution_status"] = last_db_execution_status if dc_raw: fail_meta["dialog_context_chars"] = len(dc_raw) + if self._last_fewshot_golden: + gq, gsc = self._last_fewshot_golden + fail_meta["fewshot_golden_reuse"] = True + fail_meta["fewshot_golden_qid"] = gq + fail_meta["fewshot_golden_score"] = gsc return GenerationResult( sql=last_sql or "", valid=False, diff --git a/backend/config/__pycache__/prompts.cpython-312.pyc b/backend/config/__pycache__/prompts.cpython-312.pyc index 8a4418c01d64703011be1b7a7bcf6b511eefb5f3..4eae5899154c5ec51cbc3b10b9bddb411ce44cde 100644 GIT binary patch delta 2291 zcmZuzO-vg{6t>f-srZ|XvVZR}|8?s9eAxXreF(ZIGiyMJ_cv#0Iv!rd8GO?dFw5)NlkF{Vf{ zlwI2v`WePzQDhknrnG?&p2%4&JEepo4dHu*Z6Snb{K)T9#ZjCJkdWI#7*EY>{)Z?! zqWj$uBa&#bp!H=ulhPlCAPH^DZ?V8$ij*KxLj%g@&`v3&$}xN&CWn!40EJKQ0_Tu=O9?x$J 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DeepSeekClient: logger.error(f"DeepSeek API调用失败: {e}") raise + def chat_stream( + self, + messages: List[Dict[str, str]], + **kwargs: Any, + ) -> Iterator[str]: + """ + 流式聊天：按 completion 增量产出文本片段（与 chat 同参，固定 stream=True）。 + """ + params: Dict[str, Any] = { + "model": self.config.model_name, + "messages": messages, + "temperature": kwargs.get("temperature", self.config.temperature), + "max_tokens": kwargs.get("max_tokens", self.config.max_tokens), + "top_p": kwargs.get("top_p", self.config.top_p), + "frequency_penalty": kwargs.get( + "frequency_penalty", self.config.frequency_penalty + ), + "presence_penalty": kwargs.get( + "presence_penalty", self.config.presence_penalty + ), + "stream": True, + "timeout": kwargs.get("timeout", self.config.timeout), + } + if self.config.extra_headers: + params["extra_headers"] = self.config.extra_headers + try: + stream = self.client.chat.completions.create(**params) + for chunk in stream: + if not chunk.choices: + continue + delta = chunk.choices[0].delta + if delta and getattr(delta, "content", None): + yield delta.content + except Exception as e: + logger.error(f"DeepSeek API流式调用失败: {e}") + raise + def chat_with_json( self, messages: List[Dict[str, str]], diff --git a/backend/llm/openai_client.py b/backend/llm/openai_client.py index a2a94dd..62004e3 100644 --- a/backend/llm/openai_client.py +++ b/backend/llm/openai_client.py @@ -10,7 +10,7 @@ import json import logging import os from dataclasses import dataclass -from typing import Any, Dict, List, Optional +from typing import Any, Dict, Iterator, List, Optional from openai import AsyncOpenAI, OpenAI # type: ignore[import-not-found] from openai.types.chat import ( # type: ignore[import-not-found] @@ -119,6 +119,69 @@ class OpenAIClient: logger.error("OpenAI API调用失败: %s", e) raise + def chat_stream( + self, + messages: List[Dict[str, str]], + **kwargs: Any, + ) -> Iterator[str]: + """流式聊天：按 completion 增量产出文本片段（与 chat 同参，固定 stream=True）。""" + max_tokens = kwargs.get("max_tokens", self.config.max_tokens) + max_completion_tokens = kwargs.get("max_completion_tokens", None) + + params: Dict[str, Any] = { + "model": self.config.model_name, + "messages": messages, + "temperature": kwargs.get("temperature", self.config.temperature), + "top_p": kwargs.get("top_p", self.config.top_p), + "frequency_penalty": kwargs.get( + "frequency_penalty", self.config.frequency_penalty + ), + "presence_penalty": kwargs.get( + "presence_penalty", self.config.presence_penalty + ), + "stream": True, + "timeout": kwargs.get("timeout", self.config.timeout), + } + if max_completion_tokens is not None: + params["max_completion_tokens"] = max_completion_tokens + else: + params["max_tokens"] = max_tokens + + if self.config.extra_headers: + params["extra_headers"] = self.config.extra_headers + + try: + stream = self.client.chat.completions.create(**params) + for chunk in stream: + if not chunk.choices: + continue + delta = chunk.choices[0].delta + if delta and getattr(delta, "content", None): + yield delta.content + except Exception as e: + msg = str(e) + if ( + "Unsupported parameter" in msg + and "max_tokens" in msg + and "max_completion_tokens" in msg + and "max_completion_tokens" not in params + ): + params.pop("max_tokens", None) + params["max_completion_tokens"] = max_tokens + try: + stream = self.client.chat.completions.create(**params) + for chunk in stream: + if not chunk.choices: + continue + delta = chunk.choices[0].delta + if delta and getattr(delta, "content", None): + yield delta.content + return + except Exception: + pass + logger.error("OpenAI API流式调用失败: %s", e) + raise + def chat_with_json( self, messages: List[Dict[str, str]], **kwargs ) -> Dict[str, Any]: diff --git a/backend/utils/__pycache__/fewshot_chroma_store.cpython-312.pyc b/backend/utils/__pycache__/fewshot_chroma_store.cpython-312.pyc index 018318322f4de52a08cfbd9a050b24c9fe9809be..bfe91373590fb0a3a7728cbbc937278faf47a3c3 100644 GIT binary patch delta 67 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logger.info("[OK] Few-shot 使用 Chroma（%s 条）", self._chroma_store.count()) + @property + def is_chroma_backend(self) -> bool: + """是否使用 Chroma 持久化库（`data/embeddings/chroma_fewshot` 等）。""" + return self._chroma_store is not None + + def _passes_filters( + self, + sample: "ExperienceSample", + min_rating: Optional[int], + required_tags: Optional[List[str]], + max_difficulty: str, + exclude_qids: Optional[List[str]], + ) -> bool: + if exclude_qids and sample.qid in exclude_qids: + return False + if min_rating and sample.rating is not None and sample.rating < min_rating: + return False + if max_difficulty == "easy" and sample.difficulty != "easy": + return False + if max_difficulty == "medium" and sample.difficulty == "hard": + return False + if required_tags and not all(tag in sample.tags for tag in required_tags): + return False + return True + + def select_best_with_score( + self, + question: str, + min_rating: Optional[int] = None, + required_tags: Optional[List[str]] = None, + max_difficulty: str = "hard", + exclude_qids: Optional[List[str]] = None, + ) -> Optional[Tuple[ExperienceSample, float]]: + """ + 返回通过筛选的**相似度最高**一条样本及分数 ``[0,1]``（与向量余弦一致：1 - distance）。 + 无命中时返回 ``None``。 + """ + if self._chroma_store is not None: + from utils.fewshot_chroma_store import sample_from_chroma_metadata + + over_fetch = 96 + rows = self._chroma_store.search_raw(question, top_k=over_fetch) + for score, meta, doc in rows: + s = sample_from_chroma_metadata(meta, doc) + if not self._passes_filters( + s, min_rating, required_tags, max_difficulty, exclude_qids + ): + continue + logger.info( + "[Few-shot] best_with_score: qid=%s score=%.4f preview=%r", + s.qid, + float(score), + (s.question_zh or "")[:100], + ) + return (s, float(score)) + return None + + if self._embedder is None or self.embeddings is None or len(self.samples) == 0: + return None + + q_emb = self._embedder.encode( + [question], + batch_size=1, + normalize=True, + show_progress=False, + )[0] + scores = np.dot(self.embeddings, q_emb) + best: Optional[Tuple[float, ExperienceSample]] = None + for idx, (score, sample) in enumerate(zip(scores, self.samples)): + if not self._passes_filters( + sample, min_rating, required_tags, max_difficulty, exclude_qids + ): + continue + s = float(score) + if best is None or s > best[0]: + best = (s, sample) + if best is None: + return None + logger.info( + "[Few-shot] best_with_score(numpy): qid=%s score=%.4f preview=%r", + best[1].qid, + best[0], + (best[1].question_zh or "")[:100], + ) + return (best[1], best[0]) + def _load_samples(self): """从 JSONL 加载样本到内存（非 Chroma 模式必需；Chroma 空库时用于首次灌库）。""" if self.samples_path is None: diff --git a/data/embeddings/chroma_fewshot/9756e6ea-6d51-4e24-a490-4a58f0a585b9/length.bin b/data/embeddings/chroma_fewshot/9756e6ea-6d51-4e24-a490-4a58f0a585b9/length.bin index d33cd2cde2cc90752d8fce7e08ae86ec0ccc3530..824aa08f2a6716a3d9cd6da2b7e9c619c1e0e006 100644 GIT binary patch literal 4000 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[__main__] api_server.py:1047 () | 启动服务: http://0.0.0.0:8041 -2026-04-16 10:32:17 INFO [__main__] api_server.py:1048 () | API文档: http://0.0.0.0:8041/docs -2026-04-16 10:32:17 INFO [uvicorn.error] server.py:92 _serve() | Started server process [6728] -2026-04-16 10:32:17 INFO [uvicorn.error] on.py:48 startup() | Waiting for application startup. -2026-04-16 10:32:17 INFO [__main__] api_server.py:741 lifespan() | ============================================================ -2026-04-16 10:32:17 INFO [__main__] api_server.py:742 lifespan() | Text2SQL API Server 启动中... -2026-04-16 10:32:17 INFO [__main__] api_server.py:743 lifespan() | ============================================================ -2026-04-16 10:32:17 INFO [main] main.py:126 setup_environment() | [OK] 环境检查通过 -2026-04-16 10:32:17 INFO [main] main.py:127 setup_environment() | - Schema: data\schemas\G3SB_MCDataDictionary_table_structure.json -2026-04-16 10:32:17 INFO [main] main.py:128 setup_environment() | - LLM: openai -2026-04-16 10:32:17 INFO [main] main.py:154 load_schema() | 加载Schema: ./data/schemas/G3SB_MCDataDictionary_table_structure.json -2026-04-16 10:32:17 INFO [main] main.py:156 load_schema() | 表注释(meta): ./data/schemas/G3SB_MCDataDictionary_table_meta.json -2026-04-16 10:32:18 INFO [schema.loader] loader.py:107 load_from_json() | [OK] 加载Schema完成(G3SB schemas): G3SB_MCDataDictionary_table_structure, 共2516张表 -2026-04-16 10:32:18 INFO [main] main.py:163 load_schema() | [OK] Schema加载完成: G3SB_MCDataDictionary_table_structure, 共2516张表, 59196个字段 -2026-04-16 10:32:21 INFO [llm.openai_client] openai_client.py:55 __init__() | [OK] OpenAIClient初始化: model=gpt-5.4, base_url=http://113.192.49.54:9080/v1 -2026-04-16 10:32:23 INFO [utils.embedding] embedding.py:144 __init__() | 使用 OpenAI Embedding API：model=text-embedding-ada-002，base_url=http://113.192.49.54:9080/v1，max_batch=100 -2026-04-16 10:32:25 INFO [utils.fewshot_chroma_store] fewshot_chroma_store.py:86 __init__() | [OK] FewShotChromaStore: 磁盘 data\embeddings\chroma_fewshot collection=fewshot_samples count=50 -2026-04-16 10:32:25 INFO [utils.fewshot_selector] fewshot_selector.py:128 _init_chroma_mode() | [Few-shot] 已从向量库加载（Chroma 50 条，data\embeddings\chroma_fewshot） -2026-04-16 10:32:25 INFO [utils.fewshot_selector] fewshot_selector.py:159 _init_chroma_mode() | [OK] Few-shot 使用 Chroma（50 条） -2026-04-16 10:32:25 INFO [agents.orchestrator] orchestrator.py:116 __init__() | Few-shot已启用: top_k=3, min_rating=7 -2026-04-16 10:32:25 INFO [agents.orchestrator] orchestrator.py:124 __init__() | [OK] Text2SQLOrchestrator初始化完成: max_retry=2, use_vector_search=True, fewshot=on, nl→zh_norm=on -2026-04-16 10:32:25 INFO [__main__] api_server.py:133 get_orchestrator() | [OK] Orchestrator 初始化完成 -2026-04-16 10:32:25 INFO [__main__] api_server.py:747 lifespan() | [OK] 服务已就绪 -2026-04-16 10:32:25 INFO [uvicorn.error] on.py:62 startup() | Application startup complete. -2026-04-16 10:32:25 INFO [uvicorn.error] server.py:224 _log_started_message() | Uvicorn running on http://0.0.0.0:8041 (Press CTRL+C to quit) -2026-04-16 10:32:42 INFO [uvicorn.access] httptools_impl.py:483 send() | 127.0.0.1:56978 - "POST /g3sb/api/nl/chat/stream HTTP/1.1" 200 -2026-04-16 10:32:42 INFO [__main__] api_server.py:654 _chat_stream_events() | [API/stream] 开始: user_id='anonymous' visitor_biz_id=None session_id=None service_code=None model='gpt-4o-mini' lang_code='auto' msg_chars=15 preview='所有客户账户之间的股票转移记录' dialog_context_chars=0 last_turn_was_data_query=False -2026-04-16 10:32:45 INFO [llm.openai_client] openai_client.py:55 __init__() | [OK] OpenAIClient初始化: model=gpt-4o-mini, base_url=http://113.192.49.54:9080/v1 -2026-04-16 10:32:45 INFO [utils.dialog_classifier] dialog_classifier.py:261 classify_dialog() | [dialog] intent=text2sql (hybrid fast: query hint) preview='所有客户账户之间的股票转移记录' -2026-04-16 10:32:45 INFO [__main__] api_server.py:620 _run_generate() | [GEN/API] dialect=tsql top_k=20 dialog_context_chars=0 question_len=15 preview='所有客户账户之间的股票转移记录' -2026-04-16 10:32:48 INFO [llm.openai_client] openai_client.py:55 __init__() | [OK] OpenAIClient初始化: model=gpt-4o-mini, base_url=http://113.192.49.54:9080/v1 -2026-04-16 10:32:51 INFO [agents.orchestrator] orchestrator.py:636 generate() | [GEN] 问句已归一中文：所有客户账户的股票转移记录 -2026-04-16 10:32:51 INFO [agents.orchestrator] orchestrator.py:649 generate() | [GEN] 开始生成SQL: question_chars=13 preview='所有客户账户的股票转移记录' dialog_context_chars=0 -2026-04-16 10:32:51 INFO [agents.orchestrator] orchestrator.py:664 generate() | 尝试 #1 -2026-04-16 10:32:51 INFO [schema.indexer] indexer.py:82 __init__() | [OK] 初始化SchemaIndexer(Chroma磁盘 path=data\embeddings\chroma): collection=schema_tables, count=2516 -2026-04-16 10:32:51 INFO [schema.indexer] indexer.py:106 ensure_index_for_schema() | Schema 向量索引已就绪（2516 张表），跳过向量化 -2026-04-16 10:32:51 INFO [agents.orchestrator] orchestrator.py:185 _coarse_filter() | [Orchestrator] 开始向量检索: query_chars=13 query_preview='所有客户账户的股票转移记录' -2026-04-16 10:32:53 INFO [utils.embedding] embedding.py:163 _set_dim_from_vector() | [OK] Embedding 向量维度：1536 -2026-04-16 10:32:53 INFO [schema.indexer] indexer.py:244 search() | Schema 向量检索: query_chars=13 命中=20（阈值=0.1）top=[('VSBHKRpt0430', 0.8264), ('VSBHKRpt0431', 0.8188), ('TSBTransferInstruction', 0.8147), ('VSBTransferInstruction', 0.8134), ('VSBHKRpt0397', 0.8126), ('VSBHKRpt0672', 0.8117), ('VSBHKRpt0090C', 0.81), ('VSBHKRpt0570', 0.8084), ('VSBHKRpt1030A', 0.8077), ('VSBRpt0999E', 0.8065), ('VSBRpt0999C', 0.8064), ('TSBAccountEntitlementRelease', 0.8063), ('VSBRpt0999A', 0.8059), ('TSBAccountInstrumentMovement', 0.8038), ('VSBTransferInstructionGenerationByAccountContract', 0.8033)] -2026-04-16 10:32:53 INFO [agents.orchestrator] orchestrator.py:201 _coarse_filter() | [Orchestrator] 向量粗筛: 命中=20 张（阈值内），表名+分: [('VSBHKRpt0430', 0.8264), ('VSBHKRpt0431', 0.8188), ('TSBTransferInstruction', 0.8147), ('VSBTransferInstruction', 0.8134), ('VSBHKRpt0397', 0.8126), ('VSBHKRpt0672', 0.8117), ('VSBHKRpt0090C', 0.81), ('VSBHKRpt0570', 0.8084), ('VSBHKRpt1030A', 0.8077), ('VSBRpt0999E', 0.8065), ('VSBRpt0999C', 0.8064), ('TSBAccountEntitlementRelease', 0.8063), ('VSBRpt0999A', 0.8059), ('TSBAccountInstrumentMovement', 0.8038), ('VSBTransferInstructionGenerationByAccountContract', 0.8033), ('VSBRpt0060', 0.8021), ('XCGatewayStockReconciliationReport', 0.802), ('TSBAccountEntitlementHold', 0.8013), ('WSBBatchLocationTransferDetail', 0.8012), ('VCAccountCashMovement', 0.8012)] -2026-04-16 10:32:57 INFO [agents.orchestrator] orchestrator.py:255 _llm_select_tables() | LLM精筛选中表：['VSBHKRpt0430', 'VSBHKRpt0431', 'TSBTransferInstruction', 'VSBTransferInstruction', 'TSBAccountInstrumentMovement'] | reasoning_chars=175 reasoning_preview='问题涉及客户账户的股票转移记录，VSBHKRpt0430和VSBHKRpt0431提供了客户股票转移的日报信息，TSBTransferInstruction和VSBTransferInstruction记录账户转移指令，TSBAccountInstrumentMovement则详细记录账户的股票移动交易。这些表共同涵盖了客户账户的股票转移相关信息。' -2026-04-16 10:32:57 INFO [agents.orchestrator] orchestrator.py:692 generate() | 选中表：['VSBHKRpt0430', 'VSBHKRpt0431', 'TSBTransferInstruction', 'VSBTransferInstruction', 'TSBAccountInstrumentMovement']，扩展后：['VSBHKRpt0430', 'VSBHKRpt0431', 'TSBTransferInstruction', 'TSBAccountInstrumentMovement', 'VSBTransferInstruction'] -2026-04-16 10:32:58 INFO [utils.fewshot_selector] fewshot_selector.py:271 _select_chroma() | Few-shot(Chroma)选择: 问题='所有客户账户的股票转移记录...' → 选中3个示例 (top_k=3, min_rating=7) -2026-04-16 10:32:58 INFO [agents.orchestrator] orchestrator.py:382 _generate_sql() | 已注入 3 个 few-shot 示例: qid=['Q2', 'Q40', 'Q22'] question_zh_preview=['列出今日所有客户账户之间的股票转移记录。', '列出今日执行的所有股票转移/移动。', '列出今日所有公司账户与客户账户之间的现金划转。'] -2026-04-16 10:33:07 INFO [agents.orchestrator] orchestrator.py:447 _generate_sql() | 生成的SQL（chars=476）: -SELECT - m.MovementID, - m.AccountID AS FromAccountID, - m.TransferToAccountID, - m.InstrumentID, - i.Name AS InstrumentSymbol, - m.MovementType, - m.Quantity AS TransferQuantity, - m.ValueDate AS TransferDate -FROM TSBAccountInstrumentMovement m -LEFT JOIN MCInstrument i ON m.InstrumentID = i.InstrumentID -WHERE m.MovementType = 'T' - AND m.ValueDate >= CAST(GETDATE() AS DATE) - AND m.ValueDate < DATEADD(DAY, 1, CAST(GETDATE() AS DATE)) -ORDER BY m.ValueDate; -2026-04-16 10:33:07 INFO [db.engine] engine.py:45 get_engine() | SQLAlchemy engine initialized from database_url -2026-04-16 10:33:07 INFO [db.dbhub_tools] dbhub_tools.py:627 _execute_sql() | _execute_sql 执行语句数=1 readonly=True max_rows=1 -2026-04-16 10:33:10 INFO [agents.orchestrator] orchestrator.py:590 _validate_sql() | [validate] 程序+探针+LLM 汇总: valid=True err_count=0 warn_count=0 db_execution_status=0 sql_chars=476 -2026-04-16 10:33:10 INFO [agents.orchestrator] orchestrator.py:753 generate() | [OK] SQL生成与验证通过（1次尝试） -2026-04-16 10:33:10 INFO [__main__] api_server.py:703 _chat_stream_events() | [API/stream] Text2SQL 完成: valid=True attempts=1 tables_used=['VSBHKRpt0430', 'VSBHKRpt0431', 'TSBTransferInstruction', 'TSBAccountInstrumentMovement', 'VSBTransferInstruction'] sql_chars=476 sql_head="SELECT\n m.MovementID,\n m.AccountID AS FromAccountID,\n m.TransferToAccountID,\n m.InstrumentID,\n i.Name AS InstrumentSymbol,\n m.MovementType,\n m.Quantity AS TransferQuantity,\n m.ValueDate AS TransferDate\nFROM TSBAccountInstrumentMovement m\nLEFT JOIN MCInstrument i ON m.InstrumentID = i.InstrumentID\nWHERE m.MovementType = 'T'\n AND m.ValueDate >= CAST(GETDATE() AS DATE)\n AND m.ValueDate < DATEADD(DAY, 1, CAST(GETDATE() AS DATE))\nORDER BY m.ValueDate;" +2026-04-16 13:35:24 INFO [utils.repo_logging] repo_logging.py:79 configure_text2sql_api_logging() | 日志文件: C:\Users\24019\Desktop\backman-camel\logs\text2sql_api.log