chore: import upstream snapshot with attribution

examples/rag/db_schema_rag_example.py

@@ -0,0 +1,70 @@
+import os
+
+from dbgpt.configs.model_config import MODEL_PATH, PILOT_PATH
+from dbgpt.rag.embedding import DefaultEmbeddingFactory
+from dbgpt_ext.datasource.rdbms.conn_sqlite import SQLiteTempConnector
+from dbgpt_ext.rag.assembler import DBSchemaAssembler
+from dbgpt_ext.storage.vector_store.chroma_store import ChromaStore, ChromaVectorConfig
+
+"""DB struct rag example.
+    pre-requirements:
+    set your embedding model path in your example code.
+    ```
+    embedding_model_path = "{your_embedding_model_path}"
+    ```
+
+    Examples:
+        ..code-block:: shell
+            python examples/rag/db_schema_rag_example.py
+"""
+
+
+def _create_temporary_connection():
+    """Create a temporary database connection for testing."""
+    connect = SQLiteTempConnector.create_temporary_db()
+    connect.create_temp_tables(
+        {
+            "user": {
+                "columns": {
+                    "id": "INTEGER PRIMARY KEY",
+                    "name": "TEXT",
+                    "age": "INTEGER",
+                },
+                "data": [
+                    (1, "Tom", 10),
+                    (2, "Jerry", 16),
+                    (3, "Jack", 18),
+                    (4, "Alice", 20),
+                    (5, "Bob", 22),
+                ],
+            }
+        }
+    )
+    return connect
+
+
+def _create_vector_connector():
+    """Create vector connector."""
+    config = ChromaVectorConfig(
+        persist_path=PILOT_PATH,
+    )
+    return ChromaStore(
+        config,
+        name="embedding_rag_test",
+        embedding_fn=DefaultEmbeddingFactory(
+            default_model_name=os.path.join(MODEL_PATH, "text2vec-large-chinese"),
+        ).create(),
+    )
+
+
+if __name__ == "__main__":
+    connection = _create_temporary_connection()
+    vector_connector = _create_vector_connector()
+    assembler = DBSchemaAssembler.load_from_connection(
+        connector=connection, table_vector_store_connector=vector_connector
+    )
+    assembler.persist()
+    # get db schema retriever
+    retriever = assembler.as_retriever(top_k=1)
+    chunks = retriever.retrieve("show columns from user")
+    print(f"db schema rag example results:{[chunk.content for chunk in chunks]}")