fix(model): Fix remote embedding model error in some case

pilot/componet.py

@@ -152,6 +152,7 @@ def _build(self):
         @self.app.on_event("startup")
         async def startup_event():
             """ASGI app startup event handler."""
+            # TODO catch exception and shutdown if worker manager start failed
             asyncio.create_task(self.async_after_start())
             self.after_start()
 

pilot/embedding_engine/embedding_factory.py

@@ -18,9 +18,11 @@ def create(
 
 
 class DefaultEmbeddingFactory(EmbeddingFactory):
-    def __init__(self, system_app=None, model_name: str = None, **kwargs: Any) -> None:
+    def __init__(
+        self, system_app=None, default_model_name: str = None, **kwargs: Any
+    ) -> None:
         super().__init__(system_app=system_app)
-        self._default_model_name = model_name
+        self._default_model_name = default_model_name
         self.kwargs = kwargs
 
     def init_app(self, system_app):
@@ -31,9 +33,13 @@ def create(
     ) -> "Embeddings":
         if not model_name:
             model_name = self._default_model_name
+
+        new_kwargs = {k: v for k, v in self.kwargs.items()}
+        new_kwargs["model_name"] = model_name
+
         if embedding_cls:
-            return embedding_cls(model_name=model_name, **self.kwargs)
+            return embedding_cls(**new_kwargs)
         else:
             from langchain.embeddings import HuggingFaceEmbeddings
 
-            return HuggingFaceEmbeddings(model_name=model_name, **self.kwargs)
+            return HuggingFaceEmbeddings(**new_kwargs)

pilot/model/cluster/worker/embedding_worker.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Dict, List, Type
+from typing import Dict, List, Type, Optional
 
 from pilot.configs.model_config import get_device
 from pilot.model.loader import _get_model_real_path
@@ -45,21 +45,12 @@ def parse_parameters(
         self, command_args: List[str] = None
     ) -> EmbeddingModelParameters:
         param_cls = self.model_param_class()
-        model_args = EnvArgumentParser()
-        env_prefix = EnvArgumentParser.get_env_prefix(self.model_name)
-        model_params: EmbeddingModelParameters = model_args.parse_args_into_dataclass(
-            param_cls,
-            env_prefix=env_prefix,
-            command_args=command_args,
+        return _parse_embedding_params(
             model_name=self.model_name,
             model_path=self.model_path,
+            command_args=command_args,
+            param_cls=param_cls,
         )
-        if not model_params.device:
-            model_params.device = get_device()
-            logger.info(
-                f"[EmbeddingsModelWorker] Parameters of device is None, use {model_params.device}"
-            )
-        return model_params
 
     def start(
         self,
@@ -100,3 +91,26 @@ def embeddings(self, params: Dict) -> List[List[float]]:
         logger.info(f"Receive embeddings request, model: {model}")
         input: List[str] = params["input"]
         return self._embeddings_impl.embed_documents(input)
+
+
+def _parse_embedding_params(
+    model_name: str,
+    model_path: str,
+    command_args: List[str] = None,
+    param_cls: Optional[Type] = EmbeddingModelParameters,
+):
+    model_args = EnvArgumentParser()
+    env_prefix = EnvArgumentParser.get_env_prefix(model_name)
+    model_params: EmbeddingModelParameters = model_args.parse_args_into_dataclass(
+        param_cls,
+        env_prefix=env_prefix,
+        command_args=command_args,
+        model_name=model_name,
+        model_path=model_path,
+    )
+    if not model_params.device:
+        model_params.device = get_device()
+        logger.info(
+            f"[EmbeddingsModelWorker] Parameters of device is None, use {model_params.device}"
+        )
+    return model_params

pilot/model/cluster/worker/manager.py

@@ -635,6 +635,7 @@ def _setup_fastapi(worker_params: ModelWorkerParameters, app=None):
 
     @app.on_event("startup")
     async def startup_event():
+        # TODO catch exception and shutdown if worker manager start failed
         asyncio.create_task(worker_manager.start())
 
     @app.on_event("shutdown")

pilot/server/base.py

@@ -102,6 +102,12 @@ class WebWerverParameters(BaseParameters):
             "help": "Whether to create a publicly shareable link for the interface. Creates an SSH tunnel to make your UI accessible from anywhere. "
         },
     )
+    remote_embedding: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "Whether to enable remote embedding models. If it is True, you need to start a embedding model through `dbgpt start worker --worker_type text2vec --model_name xxx --model_path xxx`"
+        },
+    )
     log_level: Optional[str] = field(
         default="INFO",
         metadata={

pilot/server/componet_configs.py

@@ -1,21 +1,65 @@
 from typing import Any, Type, TYPE_CHECKING
 
 from pilot.componet import SystemApp
-from pilot.embedding_engine.embedding_factory import EmbeddingFactory
+import logging
+from pilot.configs.model_config import get_device
+from pilot.embedding_engine.embedding_factory import (
+    EmbeddingFactory,
+    DefaultEmbeddingFactory,
+)
+from pilot.server.base import WebWerverParameters
+from pilot.utils.parameter_utils import EnvArgumentParser
 
 if TYPE_CHECKING:
     from langchain.embeddings.base import Embeddings
 
 
-def initialize_componets(system_app: SystemApp, embedding_model_name: str):
-    from pilot.model.cluster import worker_manager
+logger = logging.getLogger(__name__)
+
+
+def initialize_componets(
+    param: WebWerverParameters,
+    system_app: SystemApp,
+    embedding_model_name: str,
+    embedding_model_path: str,
+):
     from pilot.model.cluster.controller.controller import controller
 
-    system_app.register(
-        RemoteEmbeddingFactory, worker_manager, model_name=embedding_model_name
-    )
     system_app.register_instance(controller)
 
+    _initialize_embedding_model(
+        param, system_app, embedding_model_name, embedding_model_path
+    )
+
+
+def _initialize_embedding_model(
+    param: WebWerverParameters,
+    system_app: SystemApp,
+    embedding_model_name: str,
+    embedding_model_path: str,
+):
+    from pilot.model.cluster import worker_manager
+
+    if param.remote_embedding:
+        logger.info("Register remote RemoteEmbeddingFactory")
+        system_app.register(
+            RemoteEmbeddingFactory, worker_manager, model_name=embedding_model_name
+        )
+    else:
+        from pilot.model.parameter import EmbeddingModelParameters
+        from pilot.model.cluster.worker.embedding_worker import _parse_embedding_params
+
+        model_params: EmbeddingModelParameters = _parse_embedding_params(
+            model_name=embedding_model_name,
+            model_path=embedding_model_path,
+            param_cls=EmbeddingModelParameters,
+        )
+        kwargs = model_params.build_kwargs(model_name=embedding_model_path)
+        logger.info(f"Register local DefaultEmbeddingFactory with kwargs: {kwargs}")
+        system_app.register(
+            DefaultEmbeddingFactory, default_model_name=embedding_model_path, **kwargs
+        )
+
 
 class RemoteEmbeddingFactory(EmbeddingFactory):
     def __init__(

pilot/server/dbgpt_server.py

@@ -109,20 +109,27 @@ def initialize_app(param: WebWerverParameters = None, args: List[str] = None):
     # Before start
     system_app.before_start()
 
+    print(param)
+
+    embedding_model_name = CFG.EMBEDDING_MODEL
+    embedding_model_path = EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL]
+
     server_init(param, system_app)
     model_start_listener = _create_model_start_listener(system_app)
-    initialize_componets(system_app, CFG.EMBEDDING_MODEL)
+    initialize_componets(param, system_app, embedding_model_name, embedding_model_path)
 
     model_path = LLM_MODEL_CONFIG[CFG.LLM_MODEL]
     if not param.light:
         print("Model Unified Deployment Mode!")
+        if not param.remote_embedding:
+            embedding_model_name, embedding_model_path = None, None
         initialize_worker_manager_in_client(
             app=app,
             model_name=CFG.LLM_MODEL,
             model_path=model_path,
             local_port=param.port,
-            embedding_model_name=CFG.EMBEDDING_MODEL,
-            embedding_model_path=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL],
+            embedding_model_name=embedding_model_name,
+            embedding_model_path=embedding_model_path,
             start_listener=model_start_listener,
         )