fix: MySQL Database not support DDL init and upgrade. (#1133)

Co-authored-by: csunny <cfqsunny@163.com>

examples/rag/simple_rag_embedding_example.py

@@ -0,0 +1,102 @@
+import os
+from typing import Dict, List
+
+from pydantic import BaseModel, Field
+
+from dbgpt._private.config import Config
+from dbgpt.configs.model_config import EMBEDDING_MODEL_CONFIG, MODEL_PATH, PILOT_PATH
+from dbgpt.core.awel import DAG, HttpTrigger, MapOperator
+from dbgpt.rag.embedding.embedding_factory import DefaultEmbeddingFactory
+from dbgpt.rag.knowledge.base import KnowledgeType
+from dbgpt.rag.operators.knowledge import KnowledgeOperator
+from dbgpt.serve.rag.operators.embedding import EmbeddingAssemblerOperator
+from dbgpt.storage.vector_store.chroma_store import ChromaVectorConfig
+from dbgpt.storage.vector_store.connector import VectorStoreConnector
+
+"""AWEL: Simple rag embedding operator example
+    
+    Examples:
+    pre-requirements:
+        python examples/awel/simple_rag_embedding_example.py 
+        ..code-block:: shell
+            curl --location --request POST 'http://127.0.0.1:5555/api/v1/awel/trigger/examples/rag/embedding' \
+            --header 'Content-Type: application/json' \
+            --data-raw '{
+              "url": "https://docs.dbgpt.site/docs/awel"
+            }'
+"""
+
+CFG = Config()
+
+
+def _create_vector_connector() -> VectorStoreConnector:
+    """Create vector connector."""
+    return VectorStoreConnector.from_default(
+        "Chroma",
+        vector_store_config=ChromaVectorConfig(
+            name="vector_name",
+            persist_path=os.path.join(PILOT_PATH, "data"),
+        ),
+        embedding_fn=DefaultEmbeddingFactory(
+            default_model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL],
+        ).create(),
+    )
+
+
+class TriggerReqBody(BaseModel):
+    url: str = Field(..., description="url")
+
+
+class RequestHandleOperator(MapOperator[TriggerReqBody, Dict]):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    async def map(self, input_value: TriggerReqBody) -> Dict:
+        params = {
+            "url": input_value.url,
+        }
+        print(f"Receive input value: {input_value}")
+        return params
+
+
+class ResultOperator(MapOperator):
+    """The Result Operator."""
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    async def map(self, chunks: List) -> str:
+        result = f"embedding success, there are {len(chunks)} chunks."
+        print(result)
+        return result
+
+
+with DAG("simple_sdk_rag_embedding_example") as dag:
+    trigger = HttpTrigger(
+        "/examples/rag/embedding", methods="POST", request_body=TriggerReqBody
+    )
+    request_handle_task = RequestHandleOperator()
+    knowledge_operator = KnowledgeOperator(knowledge_type=KnowledgeType.URL)
+    vector_connector = _create_vector_connector()
+    url_parser_operator = MapOperator(map_function=lambda x: x["url"])
+    embedding_operator = EmbeddingAssemblerOperator(
+        vector_store_connector=vector_connector,
+    )
+    output_task = ResultOperator()
+    (
+        trigger
+        >> request_handle_task
+        >> url_parser_operator
+        >> knowledge_operator
+        >> embedding_operator
+        >> output_task
+    )
+
+if __name__ == "__main__":
+    if dag.leaf_nodes[0].dev_mode:
+        # Development mode, you can run the dag locally for debugging.
+        from dbgpt.core.awel import setup_dev_environment
+
+        setup_dev_environment([dag], port=5555)
+    else:
+        pass