feat: server xinference support (#927)

api/core/model_providers/model_provider_factory.py

@@ -57,6 +57,9 @@ class ModelProviderFactory:
         elif provider_name == 'huggingface_hub':
             from core.model_providers.providers.huggingface_hub_provider import HuggingfaceHubProvider
             return HuggingfaceHubProvider
+        elif provider_name == 'xinference':
+            from core.model_providers.providers.xinference_provider import XinferenceProvider
+            return XinferenceProvider
         else:
             raise NotImplementedError
 

api/core/model_providers/models/llm/xinference_model.py

@@ -0,0 +1,69 @@
+from typing import List, Optional, Any
+
+from langchain.callbacks.manager import Callbacks
+from langchain.llms import Xinference
+from langchain.schema import LLMResult
+
+from core.model_providers.error import LLMBadRequestError
+from core.model_providers.models.llm.base import BaseLLM
+from core.model_providers.models.entity.message import PromptMessage
+from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs
+
+
+class XinferenceModel(BaseLLM):
+    model_mode: ModelMode = ModelMode.COMPLETION
+
+    def _init_client(self) -> Any:
+        self.provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs)
+
+        client = Xinference(
+            **self.credentials,
+        )
+
+        client.callbacks = self.callbacks
+
+        return client
+
+    def _run(self, messages: List[PromptMessage],
+             stop: Optional[List[str]] = None,
+             callbacks: Callbacks = None,
+             **kwargs) -> LLMResult:
+        """
+        run predict by prompt messages and stop words.
+
+        :param messages:
+        :param stop:
+        :param callbacks:
+        :return:
+        """
+        prompts = self._get_prompt_from_messages(messages)
+        return self._client.generate(
+            [prompts],
+            stop,
+            callbacks,
+            generate_config={
+                "stop": stop,
+                "stream": self.streaming,
+                **self.provider_model_kwargs,
+            }
+        )
+
+    def get_num_tokens(self, messages: List[PromptMessage]) -> int:
+        """
+        get num tokens of prompt messages.
+
+        :param messages:
+        :return:
+        """
+        prompts = self._get_prompt_from_messages(messages)
+        return max(self._client.get_num_tokens(prompts), 0)
+
+    def _set_model_kwargs(self, model_kwargs: ModelKwargs):
+        pass
+
+    def handle_exceptions(self, ex: Exception) -> Exception:
+        return LLMBadRequestError(f"Xinference: {str(ex)}")
+
+    @classmethod
+    def support_streaming(cls):
+        return True

api/core/model_providers/providers/xinference_provider.py

@@ -0,0 +1,141 @@
+import json
+from typing import Type
+
+from langchain.llms import Xinference
+
+from core.helper import encrypter
+from core.model_providers.models.entity.model_params import KwargRule, ModelKwargsRules, ModelType
+from core.model_providers.models.llm.xinference_model import XinferenceModel
+from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError
+
+from core.model_providers.models.base import BaseProviderModel
+from models.provider import ProviderType
+
+
+class XinferenceProvider(BaseModelProvider):
+    @property
+    def provider_name(self):
+        """
+        Returns the name of a provider.
+        """
+        return 'xinference'
+
+    def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]:
+        return []
+
+    def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]:
+        """
+        Returns the model class.
+
+        :param model_type:
+        :return:
+        """
+        if model_type == ModelType.TEXT_GENERATION:
+            model_class = XinferenceModel
+        else:
+            raise NotImplementedError
+
+        return model_class
+
+    def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules:
+        """
+        get model parameter rules.
+
+        :param model_name:
+        :param model_type:
+        :return:
+        """
+        return ModelKwargsRules(
+            temperature=KwargRule[float](min=0, max=2, default=1),
+            top_p=KwargRule[float](min=0, max=1, default=0.7),
+            presence_penalty=KwargRule[float](min=-2, max=2, default=0),
+            frequency_penalty=KwargRule[float](min=-2, max=2, default=0),
+            max_tokens=KwargRule[int](alias='max_token', min=10, max=4000, default=256),
+        )
+
+    @classmethod
+    def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict):
+        """
+        check model credentials valid.
+
+        :param model_name:
+        :param model_type:
+        :param credentials:
+        """
+        if 'server_url' not in credentials:
+            raise CredentialsValidateFailedError('Xinference Server URL must be provided.')
+
+        if 'model_uid' not in credentials:
+            raise CredentialsValidateFailedError('Xinference Model UID must be provided.')
+
+        try:
+            credential_kwargs = {
+                'server_url': credentials['server_url'],
+                'model_uid': credentials['model_uid'],
+            }
+
+            llm = Xinference(
+                **credential_kwargs
+            )
+
+            llm("ping", generate_config={'max_tokens': 10})
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    @classmethod
+    def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType,
+                                  credentials: dict) -> dict:
+        """
+        encrypt model credentials for save.
+
+        :param tenant_id:
+        :param model_name:
+        :param model_type:
+        :param credentials:
+        :return:
+        """
+        credentials['server_url'] = encrypter.encrypt_token(tenant_id, credentials['server_url'])
+        return credentials
+
+    def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict:
+        """
+        get credentials for llm use.
+
+        :param model_name:
+        :param model_type:
+        :param obfuscated:
+        :return:
+        """
+        if self.provider.provider_type != ProviderType.CUSTOM.value:
+            raise NotImplementedError
+
+        provider_model = self._get_provider_model(model_name, model_type)
+
+        if not provider_model.encrypted_config:
+            return {
+                'server_url': None,
+                'model_uid': None,
+            }
+
+        credentials = json.loads(provider_model.encrypted_config)
+        if credentials['server_url']:
+            credentials['server_url'] = encrypter.decrypt_token(
+                self.provider.tenant_id,
+                credentials['server_url']
+            )
+
+            if obfuscated:
+                credentials['server_url'] = encrypter.obfuscated_token(credentials['server_url'])
+
+        return credentials
+
+    @classmethod
+    def is_provider_credentials_valid_or_raise(cls, credentials: dict):
+        return
+
+    @classmethod
+    def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict:
+        return {}
+
+    def get_provider_credentials(self, obfuscated: bool = False) -> dict:
+        return {}

api/core/model_providers/rules/_providers.json

@@ -8,5 +8,6 @@
   "wenxin",
   "chatglm",
   "replicate",
-  "huggingface_hub"
+  "huggingface_hub",
+  "xinference"
 ]

api/core/model_providers/rules/xinference.json

@@ -0,0 +1,7 @@
+{
+    "support_provider_types": [
+        "custom"
+    ],
+    "system_config": null,
+    "model_flexibility": "configurable"
+}