Http Impl for Hugging Face.

aisuite/provider.py

@@ -24,22 +24,23 @@ class ProviderNames(str, Enum):
     AZURE = "azure"
     GROQ = "groq"
     GOOGLE = "google"
+    HUGGINGFACE = "huggingface"
 
 
 class ProviderFactory:
     """Factory to register and create provider instances based on keys."""
 
     _provider_info = {
         ProviderNames.OPENAI: (
-            "aisuite.providers.openai_http_provider",
+            "aisuite.providers.openai_provider",
             "OpenAIProvider",
         ),
         ProviderNames.AWS_BEDROCK: (
             "aisuite.providers.aws_bedrock_provider",
             "AWSBedrockProvider",
         ),
         ProviderNames.ANTHROPIC: (
-            "aisuite.providers.anthropic_http_provider",
+            "aisuite.providers.anthropic_provider",
             "AnthropicProvider",
         ),
         ProviderNames.AZURE: ("aisuite.providers.azure_provider", "AzureProvider"),
@@ -48,6 +49,10 @@ class ProviderFactory:
             "aisuite.providers.google_http_provider",
             "GoogleHttpProvider",
         ),
+        ProviderNames.HUGGINGFACE: (
+            "aisuite.providers.huggingface_provider",
+            "HuggingFaceProvider",
+        ),
     }
 
     @classmethod

aisuite/providers/huggingface_provider.py

@@ -0,0 +1,67 @@
+import os
+import httpx
+from aisuite.provider import Provider, LLMError
+from aisuite.framework import ChatCompletionResponse
+
+
+class HuggingFaceProvider(Provider):
+    """
+    HuggingFace Provider using httpx for direct API calls.
+    Currently, this provider support calls to HF serverless Inference Endpoints
+    which uses Text Generation Inference (TGI) as the backend.
+    TGI is OpenAI protocol compliant.
+    https://huggingface.co/inference-endpoints/
+    """
+
+    def __init__(self, **config):
+        """
+        Initialize the provider with the given configuration.
+        The token is fetched from the config or environment variables.
+        """
+        # Ensure API key is provided either in config or via environment variable
+        self.token = config.get("token") or os.getenv("HUGGINGFACE_TOKEN")
+        if not self.token:
+            raise ValueError(
+                "Hugging Face token is missing. Please provide it in the config or set the HUGGINGFACE_TOKEN environment variable."
+            )
+
+        # Optionally set a custom timeout (default to 30s)
+        self.timeout = config.get("timeout", 30)
+
+    def chat_completions_create(self, model, messages, **kwargs):
+        """
+        Makes a request to the Inference API endpoint using httpx.
+        """
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {self.token}",
+        }
+
+        data = {
+            "model": model,
+            "messages": messages,
+            **kwargs,  # Pass any additional arguments to the API
+        }
+
+        url = f"https://api-inference.huggingface.co/models/{model}/v1/chat/completions"
+        try:
+            # Make the request to Hugging Face endpoint.
+            response = httpx.post(url, json=data, headers=headers, timeout=self.timeout)
+            response.raise_for_status()
+        except httpx.HTTPStatusError as http_err:
+            raise LLMError(f"Hugging Face request failed: {http_err}")
+        except Exception as e:
+            raise LLMError(f"An error occurred: {e}")
+
+        # Return the normalized response
+        return self._normalize_response(response.json())
+
+    def _normalize_response(self, response_data):
+        """
+        Normalize the response to a common format (ChatCompletionResponse).
+        """
+        normalized_response = ChatCompletionResponse()
+        normalized_response.choices[0].message.content = response_data["choices"][0][
+            "message"
+        ]["content"]
+        return normalized_response