feat: optimize hf inference endpoint (#975)

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

-import decimal
-from functools import wraps
 from typing import List, Optional, Any
 
 from langchain import HuggingFaceHub
 from langchain.callbacks.manager import Callbacks
-from langchain.llms import HuggingFaceEndpoint
 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, MessageType
+from core.model_providers.models.entity.message import PromptMessage
 from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs
+from core.third_party.langchain.llms.huggingface_endpoint_llm import HuggingFaceEndpointLLM
 
 
 class HuggingfaceHubModel(BaseLLM):
@@ -19,12 +17,12 @@ class HuggingfaceHubModel(BaseLLM):
     def _init_client(self) -> Any:
         provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs)
         if self.credentials['huggingfacehub_api_type'] == 'inference_endpoints':
-            client = HuggingFaceEndpoint(
+            client = HuggingFaceEndpointLLM(
                 endpoint_url=self.credentials['huggingfacehub_endpoint_url'],
-                task='text2text-generation',
+                task=self.credentials['task_type'],
                 model_kwargs=provider_model_kwargs,
                 huggingfacehub_api_token=self.credentials['huggingfacehub_api_token'],
-                callbacks=self.callbacks,
+                callbacks=self.callbacks
             )
         else:
             client = HuggingFaceHub(

api/core/model_providers/providers/huggingface_hub_provider.py

@@ -2,7 +2,6 @@ import json
 from typing import Type
 
 from huggingface_hub import HfApi
-from langchain.llms import HuggingFaceEndpoint
 
 from core.helper import encrypter
 from core.model_providers.models.entity.model_params import KwargRule, ModelKwargsRules, ModelType
@@ -10,6 +9,7 @@ from core.model_providers.models.llm.huggingface_hub_model import HuggingfaceHub
 from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError
 
 from core.model_providers.models.base import BaseProviderModel
+from core.third_party.langchain.llms.huggingface_endpoint_llm import HuggingFaceEndpointLLM
 from models.provider import ProviderType
 
 
@@ -85,10 +85,16 @@ class HuggingfaceHubProvider(BaseModelProvider):
             if 'huggingfacehub_endpoint_url' not in credentials:
                 raise CredentialsValidateFailedError('Hugging Face Hub Endpoint URL must be provided.')
 
+            if 'task_type' not in credentials:
+                raise CredentialsValidateFailedError('Task Type must be provided.')
+
+            if credentials['task_type'] not in ("text2text-generation", "text-generation", "summarization"):
+                raise CredentialsValidateFailedError('Task Type must be one of text2text-generation, text-generation, summarization.')
+
             try:
-                llm = HuggingFaceEndpoint(
+                llm = HuggingFaceEndpointLLM(
                     endpoint_url=credentials['huggingfacehub_endpoint_url'],
-                    task="text2text-generation",
+                    task=credentials['task_type'],
                     model_kwargs={"temperature": 0.5, "max_new_tokens": 200},
                     huggingfacehub_api_token=credentials['huggingfacehub_api_token']
                 )
@@ -160,6 +166,10 @@ class HuggingfaceHubProvider(BaseModelProvider):
             }
 
         credentials = json.loads(provider_model.encrypted_config)
+
+        if 'task_type' not in credentials:
+            credentials['task_type'] = 'text-generation'
+
         if credentials['huggingfacehub_api_token']:
             credentials['huggingfacehub_api_token'] = encrypter.decrypt_token(
                 self.provider.tenant_id,

api/core/third_party/langchain/llms/huggingface_endpoint_llm.py

+from typing import Dict
+
+from langchain.llms import HuggingFaceEndpoint
+from pydantic import Extra, root_validator
+
+from langchain.utils import get_from_dict_or_env
+
+
+class HuggingFaceEndpointLLM(HuggingFaceEndpoint):
+    """HuggingFace Endpoint models.
+
+    To use, you should have the ``huggingface_hub`` python package installed, and the
+    environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass
+    it as a named parameter to the constructor.
+
+    Only supports `text-generation` and `text2text-generation` for now.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.llms import HuggingFaceEndpoint
+            endpoint_url = (
+                "https://abcdefghijklmnop.us-east-1.aws.endpoints.huggingface.cloud"
+            )
+            hf = HuggingFaceEndpoint(
+                endpoint_url=endpoint_url,
+                huggingfacehub_api_token="my-api-key"
+            )
+    """
+
+    @root_validator(allow_reuse=True)
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        huggingfacehub_api_token = get_from_dict_or_env(
+            values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
+        )
+
+        values["huggingfacehub_api_token"] = huggingfacehub_api_token
+        return values

api/tests/unit_tests/model_providers/test_huggingface_hub_provider.py

@@ -17,7 +17,8 @@ HOSTED_INFERENCE_API_VALIDATE_CREDENTIAL = {
 INFERENCE_ENDPOINTS_VALIDATE_CREDENTIAL = {
     'huggingfacehub_api_type': 'inference_endpoints',
     'huggingfacehub_api_token': 'valid_key',
-    'huggingfacehub_endpoint_url': 'valid_url'
+    'huggingfacehub_endpoint_url': 'valid_url',
+    'task_type': 'text-generation'
 }
 
 def encrypt_side_effect(tenant_id, encrypt_key):