feat:新增投顾agent和nl2sqlagent

nl2sql/embedding.py

@@ -0,0 +1,35 @@
+"""NL2SQL 向量化适配器，复用公共 LLM 客户端。"""
+from __future__ import annotations
+
+import logging
+
+from config.settings import settings
+from tool.llm import llm
+
+logger = logging.getLogger("nl2sql.embedding")
+EMBEDDING_BATCH_SIZE = 10
+
+
+class EmbeddingError(RuntimeError):
+    """向量服务返回无效数据时抛出的异常。"""
+
+
+async def embed_texts(texts: list[str], *, client=None) -> list[list[float]]:
+    if not texts:
+        return []
+    provider = client or llm
+    dimension = settings.llm.embed_dimensions
+    vectors: list[list[float]] = []
+    for start in range(0, len(texts), EMBEDDING_BATCH_SIZE):
+        batch = texts[start : start + EMBEDDING_BATCH_SIZE]
+        try:
+            batch_vectors = await provider.embed(batch)
+        except Exception as exc:  # noqa: BLE001 向量服务异常统一转换
+            logger.exception("NL2SQL embedding provider failed")
+            raise EmbeddingError("Embedding service unavailable") from exc
+        if len(batch_vectors) != len(batch) or any(
+            len(vector) != dimension for vector in batch_vectors
+        ):
+            raise EmbeddingError(f"Embedding dimension must be {dimension}")
+        vectors.extend(batch_vectors)
+    return vectors