Merge branch 'rc/new-plugin' into refactor/new-plugin-system

pkg/provider/modelmgr/modelmgr.py

@@ -20,13 +20,16 @@ class ModelManager:
 
     llm_models: list[requester.RuntimeLLMModel]
 
+    embedding_models: list[requester.RuntimeEmbeddingModel]
+
     requester_components: list[engine.Component]
 
-    requester_dict: dict[str, type[requester.LLMAPIRequester]]  # cache
+    requester_dict: dict[str, type[requester.ProviderAPIRequester]]  # cache
 
     def __init__(self, ap: app.Application):
         self.ap = ap
         self.llm_models = []
+        self.embedding_models = []
         self.requester_components = []
         self.requester_dict = {}
 
@@ -34,7 +37,7 @@ class ModelManager:
         self.requester_components = self.ap.discover.get_components_by_kind('LLMAPIRequester')
 
         # forge requester class dict
-        requester_dict: dict[str, type[requester.LLMAPIRequester]] = {}
+        requester_dict: dict[str, type[requester.ProviderAPIRequester]] = {}
         for component in self.requester_components:
             requester_dict[component.metadata.name] = component.get_python_component_class()
 
@@ -47,13 +50,11 @@ class ModelManager:
         self.ap.logger.info('Loading models from db...')
 
         self.llm_models = []
+        self.embedding_models = []
 
         # llm models
         result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.LLMModel))
-
         llm_models = result.all()
-
-        # load models
         for llm_model in llm_models:
             try:
                 await self.load_llm_model(llm_model)
@@ -62,11 +63,17 @@ class ModelManager:
             except Exception as e:
                 self.ap.logger.error(f'Failed to load model {llm_model.uuid}: {e}\n{traceback.format_exc()}')
 
+        # embedding models
+        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.EmbeddingModel))
+        embedding_models = result.all()
+        for embedding_model in embedding_models:
+            await self.load_embedding_model(embedding_model)
+
     async def init_runtime_llm_model(
         self,
         model_info: persistence_model.LLMModel | sqlalchemy.Row[persistence_model.LLMModel] | dict,
     ):
-        """初始化运行时模型"""
+        """初始化运行时 LLM 模型"""
         if isinstance(model_info, sqlalchemy.Row):
             model_info = persistence_model.LLMModel(**model_info._mapping)
         elif isinstance(model_info, dict):
@@ -90,31 +97,85 @@ class ModelManager:
 
         return runtime_llm_model
 
+    async def init_runtime_embedding_model(
+        self,
+        model_info: persistence_model.EmbeddingModel | sqlalchemy.Row[persistence_model.EmbeddingModel] | dict,
+    ):
+        """初始化运行时 Embedding 模型"""
+        if isinstance(model_info, sqlalchemy.Row):
+            model_info = persistence_model.EmbeddingModel(**model_info._mapping)
+        elif isinstance(model_info, dict):
+            model_info = persistence_model.EmbeddingModel(**model_info)
+
+        requester_inst = self.requester_dict[model_info.requester](ap=self.ap, config=model_info.requester_config)
+
+        await requester_inst.initialize()
+
+        runtime_embedding_model = requester.RuntimeEmbeddingModel(
+            model_entity=model_info,
+            token_mgr=token.TokenManager(
+                name=model_info.uuid,
+                tokens=model_info.api_keys,
+            ),
+            requester=requester_inst,
+        )
+
+        return runtime_embedding_model
+
     async def load_llm_model(
         self,
         model_info: persistence_model.LLMModel | sqlalchemy.Row[persistence_model.LLMModel] | dict,
     ):
-        """加载模型"""
+        """加载 LLM 模型"""
         runtime_llm_model = await self.init_runtime_llm_model(model_info)
         self.llm_models.append(runtime_llm_model)
 
+    async def load_embedding_model(
+        self,
+        model_info: persistence_model.EmbeddingModel | sqlalchemy.Row[persistence_model.EmbeddingModel] | dict,
+    ):
+        """加载 Embedding 模型"""
+        runtime_embedding_model = await self.init_runtime_embedding_model(model_info)
+        self.embedding_models.append(runtime_embedding_model)
+
     async def get_model_by_uuid(self, uuid: str) -> requester.RuntimeLLMModel:
-        """通过uuid获取模型"""
+        """通过uuid获取 LLM 模型"""
         for model in self.llm_models:
             if model.model_entity.uuid == uuid:
                 return model
-        raise ValueError(f'model {uuid} not found')
+        raise ValueError(f'LLM model {uuid} not found')
+
+    async def get_embedding_model_by_uuid(self, uuid: str) -> requester.RuntimeEmbeddingModel:
+        """通过uuid获取 Embedding 模型"""
+        for model in self.embedding_models:
+            if model.model_entity.uuid == uuid:
+                return model
+        raise ValueError(f'Embedding model {uuid} not found')
 
     async def remove_llm_model(self, model_uuid: str):
-        """移除模型"""
+        """移除 LLM 模型"""
         for model in self.llm_models:
             if model.model_entity.uuid == model_uuid:
                 self.llm_models.remove(model)
                 return
 
-    def get_available_requesters_info(self) -> list[dict]:
+    async def remove_embedding_model(self, model_uuid: str):
+        """移除 Embedding 模型"""
+        for model in self.embedding_models:
+            if model.model_entity.uuid == model_uuid:
+                self.embedding_models.remove(model)
+                return
+
+    def get_available_requesters_info(self, model_type: str) -> list[dict]:
         """获取所有可用的请求器"""
-        return [component.to_plain_dict() for component in self.requester_components]
+        if model_type != '':
+            return [
+                component.to_plain_dict()
+                for component in self.requester_components
+                if model_type in component.spec['support_type']
+            ]
+        else:
+            return [component.to_plain_dict() for component in self.requester_components]
 
     def get_available_requester_info_by_name(self, name: str) -> dict | None:
         """通过名称获取请求器信息"""