Refactor example code (#183)

Signed-off-by: lvliang-intel <liang1.lv@intel.com>
Signed-off-by: Yue, Wenjiao <wenjiao.yue@intel.com>
Signed-off-by: chensuyue <suyue.chen@intel.com>

DocSum/docsum.py

@@ -0,0 +1,57 @@
+# Copyright (c) 2024 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import asyncio
+import os
+
+from comps import DocSumGateway, MicroService, ServiceOrchestrator, ServiceType
+
+MEGA_SERVICE_HOST_IP = os.getenv("MEGA_SERVICE_HOST_IP", "0.0.0.0")
+MEGA_SERVICE_PORT = os.getenv("MEGA_SERVICE_PORT", 8888)
+LLM_SERVICE_HOST_IP = os.getenv("LLM_SERVICE_HOST_IP", "0.0.0.0")
+LLM_SERVICE_PORT = os.getenv("LLM_SERVICE_PORT", 9000)
+
+
+class DocSumService:
+    def __init__(self, host="0.0.0.0", port=8000):
+        self.host = host
+        self.port = port
+        self.megaservice = ServiceOrchestrator()
+
+    def add_remote_service(self):
+        llm = MicroService(
+            name="llm",
+            host=LLM_SERVICE_HOST_IP,
+            port=LLM_SERVICE_PORT,
+            endpoint="/v1/chat/docsum",
+            use_remote_service=True,
+            service_type=ServiceType.LLM,
+        )
+        self.megaservice.add(llm)
+        self.gateway = DocSumGateway(megaservice=self.megaservice, host="0.0.0.0", port=self.port)
+
+    async def schedule(self):
+        await self.megaservice.schedule(
+            initial_inputs={
+                "text": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."
+            }
+        )
+        result_dict = self.megaservice.result_dict
+        print(result_dict)
+
+
+if __name__ == "__main__":
+    docsum = DocSumService(host=MEGA_SERVICE_HOST_IP, port=MEGA_SERVICE_PORT)
+    docsum.add_remote_service()
+    asyncio.run(docsum.schedule())