feat(rag): Support rag retriever evaluation (#1291)

dbgpt/core/interface/embeddings.py

@@ -0,0 +1,32 @@
+"""Interface for embedding models."""
+import asyncio
+from abc import ABC, abstractmethod
+from typing import List
+
+
+class Embeddings(ABC):
+    """Interface for embedding models.
+
+    Refer to `Langchain Embeddings <https://github.com/langchain-ai/langchain/tree/
+    master/libs/langchain/langchain/embeddings>`_.
+    """
+
+    @abstractmethod
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed search docs."""
+
+    @abstractmethod
+    def embed_query(self, text: str) -> List[float]:
+        """Embed query text."""
+
+    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Asynchronous Embed search docs."""
+        return await asyncio.get_running_loop().run_in_executor(
+            None, self.embed_documents, texts
+        )
+
+    async def aembed_query(self, text: str) -> List[float]:
+        """Asynchronous Embed query text."""
+        return await asyncio.get_running_loop().run_in_executor(
+            None, self.embed_query, text
+        )