特性：添加支持数据库查询的 AI 聊天功能
- 实现了一个新的 AI 聊天页面，用于自然语言查询，该页面会生成用于库存数据的只读 SQL 查询。 - 添加了本地内存存储，用于用户交互，允许 AI 记住最近的对话和笔记。 - 增强了聊天界面，增加了网络搜索和数据库查询执行选项。 - 更新了 README，包含了关于新 AI 聊天功能和其使用方法的详细信息。 - 引入了新的 CSS 样式以改善聊天界面的用户体验。 - 修改了现有模板以集成新的聊天功能，并提供从库存概览页面轻松访问。
This commit is contained in:
769
app.py
769
app.py
@@ -39,6 +39,7 @@ DB_DIR = os.path.join(BASE_DIR, "data")
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os.makedirs(DB_DIR, exist_ok=True)
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DB_PATH = os.path.join(DB_DIR, "inventory.db")
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APP_LOG_PATH = os.path.join(DB_DIR, "app.log")
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AI_CHAT_MEMORY_PATH = os.path.join(DB_DIR, "ai_chat_memory.json")
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# Flask 和 SQLAlchemy 基础初始化。
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app = Flask(__name__)
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@@ -100,6 +101,105 @@ def _read_log_lines(limit: int = 200) -> list[str]:
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return [line.rstrip("\n") for line in file_obj.readlines()[-limit:]]
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def _load_ai_chat_memory_store() -> dict:
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if not os.path.exists(AI_CHAT_MEMORY_PATH):
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return {}
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try:
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with open(AI_CHAT_MEMORY_PATH, "r", encoding="utf-8") as file_obj:
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payload = json.load(file_obj)
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return payload if isinstance(payload, dict) else {}
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except (OSError, json.JSONDecodeError):
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return {}
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def _save_ai_chat_memory_store(payload: dict) -> None:
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safe_payload = payload if isinstance(payload, dict) else {}
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with open(AI_CHAT_MEMORY_PATH, "w", encoding="utf-8") as file_obj:
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json.dump(safe_payload, file_obj, ensure_ascii=False, indent=2)
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def _normalize_memory_text(text: str, max_len: int = 220) -> str:
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cleaned = re.sub(r"\s+", " ", str(text or "")).strip()
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if len(cleaned) > max_len:
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return cleaned[: max_len - 3] + "..."
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return cleaned
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def _get_ai_chat_memory(username: str) -> dict:
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user_key = (username or "guest").strip() or "guest"
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store = _load_ai_chat_memory_store()
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block = store.get(user_key, {}) if isinstance(store, dict) else {}
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notes = block.get("notes", []) if isinstance(block, dict) else []
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turns = block.get("turns", []) if isinstance(block, dict) else []
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if not isinstance(notes, list):
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notes = []
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if not isinstance(turns, list):
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turns = []
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return {
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"user": user_key,
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"notes": [str(item) for item in notes[:30]],
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"turns": [item for item in turns[-20:] if isinstance(item, dict)],
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}
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def _memory_context_text(memory: dict) -> str:
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notes = memory.get("notes", []) if isinstance(memory, dict) else []
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turns = memory.get("turns", []) if isinstance(memory, dict) else []
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lines = []
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if notes:
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lines.append("用户长期记忆:")
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for note in notes[-12:]:
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lines.append(f"- {_normalize_memory_text(note, max_len=120)}")
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if turns:
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lines.append("最近本地记忆对话:")
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for turn in turns[-6:]:
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q = _normalize_memory_text(turn.get("q", ""), max_len=90)
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a = _normalize_memory_text(turn.get("a", ""), max_len=120)
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lines.append(f"Q: {q}")
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lines.append(f"A: {a}")
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return "\n".join(lines)
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def _append_ai_chat_memory(username: str, question: str, answer: str) -> None:
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user_key = (username or "guest").strip() or "guest"
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store = _load_ai_chat_memory_store()
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block = store.get(user_key)
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if not isinstance(block, dict):
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block = {"notes": [], "turns": []}
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notes = block.get("notes")
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turns = block.get("turns")
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if not isinstance(notes, list):
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notes = []
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if not isinstance(turns, list):
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turns = []
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q = _normalize_memory_text(question)
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a = _normalize_memory_text(answer)
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turns.append({"q": q, "a": a, "ts": datetime.utcnow().isoformat(timespec="seconds")})
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turns = turns[-30:]
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match = re.match(r"^(记住|记一下|请记住)[:：\s]*(.+)$", q)
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if match:
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note = _normalize_memory_text(match.group(2), max_len=140)
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if note and note not in notes:
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notes.append(note)
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notes = notes[-30:]
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block["notes"] = notes
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block["turns"] = turns
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store[user_key] = block
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_save_ai_chat_memory_store(store)
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def _clear_ai_chat_memory(username: str) -> None:
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user_key = (username or "guest").strip() or "guest"
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store = _load_ai_chat_memory_store()
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if user_key in store:
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store.pop(user_key, None)
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_save_ai_chat_memory_store(store)
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_setup_app_logger()
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# 这里集中放全局常量，避免后面函数里散落硬编码。
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@@ -2875,6 +2975,675 @@ def _call_siliconflow_chat(
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raise RuntimeError("AI 返回格式无法解析") from exc
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DB_CHAT_ALLOWED_TABLES = {"boxes", "components", "inventory_events"}
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DB_CHAT_FORBIDDEN_KEYWORDS = {
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"insert",
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"update",
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"delete",
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"drop",
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"alter",
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"create",
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"replace",
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"truncate",
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"attach",
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"detach",
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"pragma",
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"vacuum",
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"reindex",
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"begin",
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"commit",
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"rollback",
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}
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def _extract_json_object_text(raw_text: str) -> str:
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text = (raw_text or "").strip()
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if not text:
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return ""
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if text.startswith("```"):
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text = re.sub(r"^```(?:json)?\s*", "", text)
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text = re.sub(r"\s*```$", "", text)
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first = text.find("{")
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last = text.rfind("}")
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if first >= 0 and last > first:
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return text[first : last + 1]
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return text
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def _build_db_chat_schema_hint() -> str:
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return (
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"可用表和字段:\n"
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"1) boxes(id, name, description, box_type, slot_capacity, slot_prefix, start_number)\n"
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"2) components(id, box_id, slot_index, part_no, name, specification, quantity, location, note, is_enabled)\n"
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"3) inventory_events(id, box_id, box_type, component_id, part_no, event_type, delta, created_at)\n"
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"禁止访问 users 表。"
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)
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def _is_safe_readonly_sql(raw_sql: str) -> tuple[bool, str, str]:
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sql = (raw_sql or "").strip()
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if not sql:
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return False, "SQL 为空", ""
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if sql.endswith(";"):
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sql = sql[:-1].strip()
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lowered = re.sub(r"\s+", " ", sql.lower())
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if ";" in sql:
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return False, "仅允许单条 SQL 查询", ""
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if not (lowered.startswith("select ") or lowered.startswith("with ")):
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return False, "仅允许 SELECT 只读查询", ""
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for keyword in DB_CHAT_FORBIDDEN_KEYWORDS:
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if re.search(rf"\b{re.escape(keyword)}\b", lowered):
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return False, f"SQL 包含危险关键字: {keyword}", ""
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if re.search(r"\busers\b", lowered):
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return False, "禁止访问 users 表", ""
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referenced_tables = re.findall(r"\b(?:from|join)\s+([a-zA-Z_][\w]*)", lowered)
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if not referenced_tables:
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return False, "SQL 未包含可识别的数据表", ""
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for table_name in referenced_tables:
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if table_name not in DB_CHAT_ALLOWED_TABLES:
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return False, f"不允许访问表: {table_name}", ""
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return True, "", sql
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def _ensure_query_limit(sql: str, row_limit: int = 80) -> str:
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if re.search(r"\blimit\s+\d+", sql, flags=re.IGNORECASE):
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return sql
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return f"{sql} LIMIT {row_limit}"
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def _serialize_sql_rows(rows, columns: list[str], *, max_cell_len: int = 120) -> list[dict]:
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serialized = []
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for row in rows:
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item = {}
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for idx, column in enumerate(columns):
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value = row[idx] if idx < len(row) else None
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if isinstance(value, datetime):
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text = value.strftime("%Y-%m-%d %H:%M:%S")
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else:
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text = "" if value is None else str(value)
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if len(text) > max_cell_len:
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text = text[: max_cell_len - 3] + "..."
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item[column] = text
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serialized.append(item)
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return serialized
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def _build_db_chat_sql_plan(
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question: str,
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history: list[dict],
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memory_text: str,
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settings: dict,
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) -> tuple[str, str, str]:
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history_lines = []
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for row in history[-6:]:
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role = str(row.get("role", "user") or "user")
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content = str(row.get("content", "") or "").strip()
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if not content:
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continue
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history_lines.append(f"[{role}] {content[:200]}")
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system_prompt = (
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"你是库存数据库查询规划助手。"
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"请根据用户问题生成一条 SQLite 只读 SQL。"
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"只允许 SELECT/CTE，不要写入操作，不要访问 users 表。"
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"输出必须是 JSON: {\"sql\":string,\"reason\":string}，不要输出其他文字。"
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)
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user_prompt = (
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_build_db_chat_schema_hint()
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+ "\n\n对话历史:\n"
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+ "\n".join(history_lines or ["(无)"])
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+ "\n\n当前问题:\n"
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+ question
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)
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if memory_text:
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user_prompt += "\n\n本地记忆摘要:\n" + memory_text
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raw_text = _call_siliconflow_chat(
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system_prompt,
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user_prompt,
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api_url=settings["api_url"],
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model=settings["model"],
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api_key=settings["api_key"],
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timeout=settings["timeout"],
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)
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parsed = json.loads(_extract_json_object_text(raw_text))
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sql = str(parsed.get("sql", "") or "").strip()
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reason = str(parsed.get("reason", "") or "").strip() or "AI 未提供 SQL 说明"
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if not sql:
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raise RuntimeError("AI 未生成有效 SQL")
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return sql, reason, raw_text
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def _build_db_chat_web_context(question: str, rows: list[dict], timeout: int, max_queries: int = 2) -> list[dict]:
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"""为数据库聊天构建可选联网参考上下文。"""
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query_candidates = []
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base_query = (question or "").strip()
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if base_query:
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query_candidates.append(base_query)
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for row in rows[:3]:
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part_no = str(row.get("part_no", "") or "").strip()
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name = str(row.get("name", "") or "").strip()
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if part_no and name:
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query_candidates.append(f"{part_no} {name}")
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elif part_no:
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query_candidates.append(part_no)
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elif name:
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query_candidates.append(name)
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contexts = []
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used = set()
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for query in query_candidates:
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q = query.strip()
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if len(q) < 2 or q in used:
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continue
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used.add(q)
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result = _fetch_open_search_context(q, timeout=timeout)
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sources = result.get("sources") or []
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if not sources:
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continue
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contexts.append(
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{
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"query": result.get("query", q),
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"sources": sources[:4],
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}
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)
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if len(contexts) >= max_queries:
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break
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return contexts
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def _extract_weather_location(question: str) -> str:
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text = (question or "").strip()
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if not text:
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return ""
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patterns = [
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r"查询(.{1,20}?)的?天气",
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r"(.{1,20}?)的?天气",
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]
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for pattern in patterns:
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match = re.search(pattern, text)
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if not match:
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continue
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candidate = re.sub(r"[\s，。！？?、]+", "", match.group(1) or "").strip()
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if candidate:
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return candidate
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return ""
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def _weather_code_to_text(code: int) -> str:
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mapping = {
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0: "晴",
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1: "大部晴",
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2: "多云",
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3: "阴",
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45: "雾",
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48: "雾凇",
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51: "小毛毛雨",
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53: "毛毛雨",
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55: "浓毛毛雨",
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61: "小雨",
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63: "中雨",
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65: "大雨",
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71: "小雪",
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73: "中雪",
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75: "大雪",
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80: "阵雨",
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81: "较强阵雨",
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82: "强阵雨",
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95: "雷阵雨",
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}
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return mapping.get(int(code), f"天气代码 {code}")
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def _fetch_weather_context(question: str, timeout: int) -> dict | None:
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"""天气问题联网兜底：使用 Open-Meteo 地理编码与实时天气接口。"""
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location = _extract_weather_location(question)
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if not location:
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location = "广州"
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try:
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geo_params = urllib.parse.urlencode(
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{
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"name": location,
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"count": "1",
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"language": "zh",
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"format": "json",
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}
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)
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geo_url = f"https://geocoding-api.open-meteo.com/v1/search?{geo_params}"
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geo_req = urllib.request.Request(
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geo_url,
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method="GET",
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headers={"User-Agent": "inventory-ai-chat/1.0"},
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)
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with urllib.request.urlopen(geo_req, timeout=timeout) as resp:
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geo_raw = resp.read().decode("utf-8", errors="ignore")
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geo_data = json.loads(geo_raw)
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results = geo_data.get("results") or []
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if not results:
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return None
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first = results[0]
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latitude = first.get("latitude")
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longitude = first.get("longitude")
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if latitude is None or longitude is None:
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return None
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display_name = str(first.get("name") or location).strip() or location
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country = str(first.get("country") or "").strip()
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admin1 = str(first.get("admin1") or "").strip()
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place_name = " / ".join([v for v in [display_name, admin1, country] if v])
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weather_params = urllib.parse.urlencode(
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{
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"latitude": str(latitude),
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"longitude": str(longitude),
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"current": "temperature_2m,weather_code,wind_speed_10m",
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"timezone": "Asia/Shanghai",
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}
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)
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weather_url = f"https://api.open-meteo.com/v1/forecast?{weather_params}"
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weather_req = urllib.request.Request(
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weather_url,
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method="GET",
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headers={"User-Agent": "inventory-ai-chat/1.0"},
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)
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with urllib.request.urlopen(weather_req, timeout=timeout) as resp:
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weather_raw = resp.read().decode("utf-8", errors="ignore")
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weather_data = json.loads(weather_raw)
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current = weather_data.get("current") or {}
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if not current:
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return None
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temp = current.get("temperature_2m")
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code = int(current.get("weather_code", 0) or 0)
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wind = current.get("wind_speed_10m")
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time_text = str(current.get("time") or "").strip()
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weather_text = _weather_code_to_text(code)
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snippet = f"{place_name} 当前{weather_text}，气温约 {temp}°C，风速约 {wind} km/h，时间 {time_text}。"
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source = {
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"title": f"Open-Meteo 实时天气（{place_name}）",
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"snippet": snippet,
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"url": "https://open-meteo.com/",
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"reliability_level": "high",
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"reliability_label": "高可信",
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"reliability_reason": "公开气象 API 实时数据",
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"domain": "open-meteo.com",
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}
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return {
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"query": f"{location} 天气",
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"sources": [source],
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}
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except Exception:
|
||||
return None
|
||||
|
||||
|
||||
def _build_db_chat_answer(
|
||||
question: str,
|
||||
sql: str,
|
||||
sql_reason: str,
|
||||
rows: list[dict],
|
||||
web_context: list[dict],
|
||||
settings: dict,
|
||||
) -> str:
|
||||
if not rows:
|
||||
return "查询已执行，但没有匹配数据。你可以换个条件，例如指定料号、时间范围或盒型。"
|
||||
|
||||
system_prompt = (
|
||||
"你是库存分析助手。"
|
||||
"请仅根据提供的 SQL 结果回答，禁止虚构不存在的数据。"
|
||||
"回答用简明中文，优先给结论，再给关键证据。"
|
||||
"若提供了联网参考，请明确标注为参考信息，不得当作数据库事实。"
|
||||
)
|
||||
user_prompt = (
|
||||
"用户问题:\n"
|
||||
+ question
|
||||
+ "\n\n执行SQL:\n"
|
||||
+ sql
|
||||
+ "\n\nSQL说明:\n"
|
||||
+ sql_reason
|
||||
+ "\n\n查询结果(JSON):\n"
|
||||
+ json.dumps(rows, ensure_ascii=False)
|
||||
)
|
||||
if web_context:
|
||||
user_prompt += "\n\n联网参考(JSON):\n" + json.dumps(web_context, ensure_ascii=False)
|
||||
|
||||
return _call_siliconflow_chat(
|
||||
system_prompt,
|
||||
user_prompt,
|
||||
api_url=settings["api_url"],
|
||||
model=settings["model"],
|
||||
api_key=settings["api_key"],
|
||||
timeout=settings["timeout"],
|
||||
)
|
||||
|
||||
|
||||
def _is_general_chat_question(question: str) -> bool:
|
||||
text = (question or "").strip().lower()
|
||||
if not text:
|
||||
return False
|
||||
|
||||
general_hints = [
|
||||
"你是什么",
|
||||
"你是谁",
|
||||
"你能做什么",
|
||||
"聊天",
|
||||
"模型",
|
||||
"介绍",
|
||||
"hello",
|
||||
"hi",
|
||||
"你好",
|
||||
"谢谢",
|
||||
]
|
||||
data_hints = [
|
||||
"库存",
|
||||
"器件",
|
||||
"料号",
|
||||
"盒",
|
||||
"袋",
|
||||
"出库",
|
||||
"入库",
|
||||
"数量",
|
||||
"part_no",
|
||||
"component",
|
||||
"box",
|
||||
]
|
||||
|
||||
has_general_hint = any(hint in text for hint in general_hints)
|
||||
has_data_hint = any(hint in text for hint in data_hints)
|
||||
return has_general_hint and not has_data_hint
|
||||
|
||||
|
||||
def _build_general_chat_answer(
|
||||
question: str,
|
||||
history: list[dict],
|
||||
web_context: list[dict],
|
||||
memory_text: str,
|
||||
settings: dict,
|
||||
) -> str:
|
||||
q = (question or "").strip()
|
||||
q_lower = q.lower()
|
||||
if "模型" in q or "model" in q_lower:
|
||||
model_name = str(settings.get("model", "") or "未配置")
|
||||
return f"当前系统配置的 AI 模型是：{model_name}。"
|
||||
|
||||
history_lines = []
|
||||
for row in history[-8:]:
|
||||
role = str(row.get("role", "user") or "user")
|
||||
content = str(row.get("content", "") or "").strip()
|
||||
if not content:
|
||||
continue
|
||||
history_lines.append(f"[{role}] {content[:220]}")
|
||||
|
||||
system_prompt = (
|
||||
"你是系统内置通用助手。"
|
||||
"请直接回答用户问题，不要把自己限制为仅库存问题。"
|
||||
"对于时效性问题（如天气、新闻、实时价格），若提供了联网参考就优先依据参考；"
|
||||
"若没有联网参考，需明确说明可能不够实时。"
|
||||
"回答保持简洁中文。"
|
||||
)
|
||||
user_prompt = (
|
||||
"对话历史:\n"
|
||||
+ "\n".join(history_lines or ["(无)"])
|
||||
+ "\n\n用户当前问题:\n"
|
||||
+ question
|
||||
)
|
||||
if memory_text:
|
||||
user_prompt += "\n\n本地记忆摘要:\n" + memory_text
|
||||
if web_context:
|
||||
user_prompt += "\n\n联网参考(JSON):\n" + json.dumps(web_context, ensure_ascii=False)
|
||||
|
||||
return _call_siliconflow_chat(
|
||||
system_prompt,
|
||||
user_prompt,
|
||||
api_url=settings["api_url"],
|
||||
model=settings["model"],
|
||||
api_key=settings["api_key"],
|
||||
timeout=settings["timeout"],
|
||||
)
|
||||
|
||||
|
||||
@app.route("/ai/chat")
|
||||
def ai_chat_page():
|
||||
return render_template("ai_chat.html")
|
||||
|
||||
|
||||
@app.route("/ai/chat/memory/clear", methods=["POST"])
|
||||
def ai_chat_memory_clear():
|
||||
username = (session.get("username") or "guest").strip() or "guest"
|
||||
_clear_ai_chat_memory(username)
|
||||
_log_event(logging.INFO, "ai_chat_memory_cleared", user=username)
|
||||
return {"ok": True, "message": "本地记忆已清空"}
|
||||
|
||||
|
||||
@app.route("/ai/chat/query", methods=["POST"])
|
||||
def ai_chat_query():
|
||||
payload = request.get_json(silent=True) or {}
|
||||
question = str(payload.get("question", "") or "").strip()
|
||||
history = payload.get("history", [])
|
||||
allow_web_search = _is_truthy_form_value(str(payload.get("allow_web_search", "")))
|
||||
allow_db_query = _is_truthy_form_value(str(payload.get("allow_db_query", "")))
|
||||
if not isinstance(history, list):
|
||||
history = []
|
||||
|
||||
if not question:
|
||||
return {"ok": False, "message": "请输入问题"}, 400
|
||||
|
||||
username = (session.get("username") or "guest").strip() or "guest"
|
||||
memory_payload = _get_ai_chat_memory(username)
|
||||
memory_text = _memory_context_text(memory_payload)
|
||||
|
||||
settings = _get_ai_settings()
|
||||
if not settings.get("api_key") or not settings.get("api_url") or not settings.get("model"):
|
||||
return {"ok": False, "message": "AI 参数不完整，请先到参数页配置"}, 400
|
||||
|
||||
if not allow_db_query:
|
||||
web_context = []
|
||||
if allow_web_search:
|
||||
web_context = _build_db_chat_web_context(question, [], timeout=settings.get("timeout", 30), max_queries=1)
|
||||
if not web_context and ("天气" in question or "weather" in question.lower()):
|
||||
weather_ctx = _fetch_weather_context(question, timeout=settings.get("timeout", 30))
|
||||
if weather_ctx:
|
||||
web_context = [weather_ctx]
|
||||
answer = _build_general_chat_answer(question, history, web_context, memory_text, settings)
|
||||
_append_ai_chat_memory(username, question, answer)
|
||||
_log_event(
|
||||
logging.INFO,
|
||||
"ai_chat_general_success",
|
||||
question=question,
|
||||
allow_web_search=allow_web_search,
|
||||
allow_db_query=allow_db_query,
|
||||
web_sources=sum(len(item.get("sources", [])) for item in web_context),
|
||||
memory_notes=len(memory_payload.get("notes", [])),
|
||||
)
|
||||
return {
|
||||
"ok": True,
|
||||
"answer": answer,
|
||||
"sql": "",
|
||||
"planner_reason": "未启用数据库查询，按通用问答模式处理",
|
||||
"planner_raw": "",
|
||||
"row_count": 0,
|
||||
"rows_preview": [],
|
||||
"web_context": web_context,
|
||||
"allow_web_search": allow_web_search,
|
||||
"allow_db_query": allow_db_query,
|
||||
"chat_mode": "general",
|
||||
}
|
||||
|
||||
try:
|
||||
sql_raw, planner_reason, planner_text = _build_db_chat_sql_plan(
|
||||
question,
|
||||
history,
|
||||
memory_text,
|
||||
settings,
|
||||
)
|
||||
except Exception as exc:
|
||||
_log_event(
|
||||
logging.WARNING,
|
||||
"ai_chat_plan_error",
|
||||
error=str(exc),
|
||||
question=question,
|
||||
model=settings.get("model", ""),
|
||||
)
|
||||
if not _is_general_chat_question(question):
|
||||
return {"ok": False, "message": f"SQL 规划失败: {exc}"}, 400
|
||||
|
||||
web_context = []
|
||||
if allow_web_search:
|
||||
web_context = _build_db_chat_web_context(question, [], timeout=settings.get("timeout", 30), max_queries=1)
|
||||
if not web_context and ("天气" in question or "weather" in question.lower()):
|
||||
weather_ctx = _fetch_weather_context(question, timeout=settings.get("timeout", 30))
|
||||
if weather_ctx:
|
||||
web_context = [weather_ctx]
|
||||
answer = _build_general_chat_answer(question, history, web_context, memory_text, settings)
|
||||
_append_ai_chat_memory(username, question, answer)
|
||||
return {
|
||||
"ok": True,
|
||||
"answer": answer,
|
||||
"sql": "",
|
||||
"planner_reason": "已切换到通用对话模式",
|
||||
"planner_raw": "",
|
||||
"row_count": 0,
|
||||
"rows_preview": [],
|
||||
"web_context": web_context,
|
||||
"allow_web_search": allow_web_search,
|
||||
"allow_db_query": allow_db_query,
|
||||
"chat_mode": "general",
|
||||
}
|
||||
|
||||
safe, reject_reason, safe_sql = _is_safe_readonly_sql(sql_raw)
|
||||
if not safe:
|
||||
_log_event(
|
||||
logging.WARNING,
|
||||
"ai_chat_sql_rejected",
|
||||
reason=reject_reason,
|
||||
sql=sql_raw,
|
||||
question=question,
|
||||
)
|
||||
can_fallback_general = (
|
||||
"未包含可识别的数据表" in reject_reason
|
||||
or _is_general_chat_question(question)
|
||||
)
|
||||
if not can_fallback_general:
|
||||
return {"ok": False, "message": f"SQL 被安全策略拒绝: {reject_reason}"}, 400
|
||||
|
||||
web_context = []
|
||||
if allow_web_search:
|
||||
web_context = _build_db_chat_web_context(question, [], timeout=settings.get("timeout", 30), max_queries=1)
|
||||
if not web_context and ("天气" in question or "weather" in question.lower()):
|
||||
weather_ctx = _fetch_weather_context(question, timeout=settings.get("timeout", 30))
|
||||
if weather_ctx:
|
||||
web_context = [weather_ctx]
|
||||
answer = _build_general_chat_answer(question, history, web_context, memory_text, settings)
|
||||
_append_ai_chat_memory(username, question, answer)
|
||||
return {
|
||||
"ok": True,
|
||||
"answer": answer,
|
||||
"sql": "",
|
||||
"planner_reason": "已切换到通用对话模式",
|
||||
"planner_raw": planner_text,
|
||||
"row_count": 0,
|
||||
"rows_preview": [],
|
||||
"web_context": web_context,
|
||||
"allow_web_search": allow_web_search,
|
||||
"allow_db_query": allow_db_query,
|
||||
"chat_mode": "general",
|
||||
}
|
||||
|
||||
final_sql = _ensure_query_limit(safe_sql, row_limit=80)
|
||||
try:
|
||||
query_result = db.session.execute(db.text(final_sql))
|
||||
columns = list(query_result.keys())
|
||||
rows = query_result.fetchall()
|
||||
except Exception as exc:
|
||||
_log_event(
|
||||
logging.ERROR,
|
||||
"ai_chat_query_execute_error",
|
||||
error=str(exc),
|
||||
sql=final_sql,
|
||||
question=question,
|
||||
traceback=traceback.format_exc(),
|
||||
)
|
||||
return {"ok": False, "message": "SQL 执行失败，请调整问题后重试"}, 400
|
||||
|
||||
serialized_rows = _serialize_sql_rows(rows, columns)
|
||||
web_context = []
|
||||
if allow_web_search:
|
||||
try:
|
||||
web_context = _build_db_chat_web_context(
|
||||
question,
|
||||
serialized_rows,
|
||||
timeout=settings.get("timeout", 30),
|
||||
)
|
||||
except Exception as exc:
|
||||
_log_event(
|
||||
logging.WARNING,
|
||||
"ai_chat_web_context_error",
|
||||
error=str(exc),
|
||||
question=question,
|
||||
)
|
||||
web_context = []
|
||||
|
||||
try:
|
||||
answer = _build_db_chat_answer(
|
||||
question,
|
||||
final_sql,
|
||||
planner_reason,
|
||||
serialized_rows,
|
||||
web_context,
|
||||
settings,
|
||||
)
|
||||
except Exception as exc:
|
||||
_log_event(
|
||||
logging.WARNING,
|
||||
"ai_chat_answer_error",
|
||||
error=str(exc),
|
||||
sql=final_sql,
|
||||
row_count=len(serialized_rows),
|
||||
)
|
||||
answer = (
|
||||
"查询已完成，但 AI 总结超时/失败。"
|
||||
f"共返回 {len(serialized_rows)} 行数据，你可以查看本次 SQL 并缩小问题范围后重试。"
|
||||
)
|
||||
|
||||
_append_ai_chat_memory(username, question, answer)
|
||||
|
||||
_log_event(
|
||||
logging.INFO,
|
||||
"ai_chat_query_success",
|
||||
question=question,
|
||||
sql=final_sql,
|
||||
row_count=len(serialized_rows),
|
||||
web_sources=sum(len(item.get("sources", [])) for item in web_context),
|
||||
memory_notes=len(memory_payload.get("notes", [])),
|
||||
)
|
||||
return {
|
||||
"ok": True,
|
||||
"answer": answer,
|
||||
"sql": final_sql,
|
||||
"planner_reason": planner_reason,
|
||||
"planner_raw": planner_text,
|
||||
"row_count": len(serialized_rows),
|
||||
"rows_preview": serialized_rows[:8],
|
||||
"web_context": web_context,
|
||||
"allow_web_search": allow_web_search,
|
||||
"allow_db_query": allow_db_query,
|
||||
"chat_mode": "db",
|
||||
}
|
||||
|
||||
|
||||
def _is_safe_next_path(path: str) -> bool:
|
||||
candidate = (path or "").strip()
|
||||
if not candidate:
|
||||
|
||||
Reference in New Issue
Block a user