Merge branch 'main' into rp_ConvBasedProviderLoad

aisuite/providers/__init__.py

@@ -1,6 +0,0 @@
-"""Provides the individual provider interfaces for each FM provider."""
-
-from .fireworks_interface import FireworksInterface
-from .octo_interface import OctoInterface
-from .replicate_interface import ReplicateInterface
-from .together_interface import TogetherInterface

aisuite/providers/fireworks_provider.py

@@ -0,0 +1,65 @@
+import os
+import httpx
+from aisuite.provider import Provider, LLMError
+from aisuite.framework import ChatCompletionResponse
+
+
+class FireworksProvider(Provider):
+    """
+    Fireworks AI Provider using httpx for direct API calls.
+    """
+
+    BASE_URL = "https://api.fireworks.ai/inference/v1/chat/completions"
+
+    def __init__(self, **config):
+        """
+        Initialize the Fireworks provider with the given configuration.
+        The API key is fetched from the config or environment variables.
+        """
+        self.api_key = config.get("api_key", os.getenv("FIREWORKS_API_KEY"))
+        if not self.api_key:
+            raise ValueError(
+                "Fireworks API key is missing. Please provide it in the config or set the FIREWORKS_API_KEY 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 Fireworks AI chat completions endpoint using httpx.
+        """
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+        }
+
+        data = {
+            "model": model,
+            "messages": messages,
+            **kwargs,  # Pass any additional arguments to the API
+        }
+
+        try:
+            # Make the request to Fireworks AI endpoint.
+            response = httpx.post(
+                self.BASE_URL, json=data, headers=headers, timeout=self.timeout
+            )
+            response.raise_for_status()
+        except httpx.HTTPStatusError as http_err:
+            raise LLMError(f"Fireworks AI 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

aisuite/providers/together_provider.py

@@ -0,0 +1,65 @@
+import os
+import httpx
+from aisuite.provider import Provider, LLMError
+from aisuite.framework import ChatCompletionResponse
+
+
+class TogetherProvider(Provider):
+    """
+    Together AI Provider using httpx for direct API calls.
+    """
+
+    BASE_URL = "https://api.together.xyz/v1/chat/completions"
+
+    def __init__(self, **config):
+        """
+        Initialize the Fireworks provider with the given configuration.
+        The API key is fetched from the config or environment variables.
+        """
+        self.api_key = config.get("api_key", os.getenv("TOGETHER_API_KEY"))
+        if not self.api_key:
+            raise ValueError(
+                "Together API key is missing. Please provide it in the config or set the TOGETHER_API_KEY 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 Fireworks AI chat completions endpoint using httpx.
+        """
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+        }
+
+        data = {
+            "model": model,
+            "messages": messages,
+            **kwargs,  # Pass any additional arguments to the API
+        }
+
+        try:
+            # Make the request to Fireworks AI endpoint.
+            response = httpx.post(
+                self.BASE_URL, json=data, headers=headers, timeout=self.timeout
+            )
+            response.raise_for_status()
+        except httpx.HTTPStatusError as http_err:
+            raise LLMError(f"Together AI 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

examples/client.ipynb

@@ -108,7 +108,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "aws_bedrock_llama3_8b = \"aws-bedrock:meta.llama3-1-8b-instruct-v1:0\"\n",
+    "aws_bedrock_llama3_8b = \"aws:meta.llama3-1-8b-instruct-v1:0\"\n",
     "response = client.chat.completions.create(model=aws_bedrock_llama3_8b, messages=messages)\n",
     "print(response.choices[0].message.content)"
    ]
@@ -124,13 +124,12 @@
     "# The model name is the deployment name in Project/Deployments.\n",
     "# In the exmaple below, the model is \"mistral-large-2407\", but the name given to the\n",
     "# deployment is \"aisuite-mistral-large-2407\" under the deployments section in Azure.\n",
-    "client2 = ai.Client()\n",
-    "client2.configure({\"azure\" : {\n",
+    "client.configure({\"azure\" : {\n",
     "  \"api_key\": os.environ[\"AZURE_API_KEY\"],\n",
     "  \"base_url\": \"https://aisuite-mistral-large-2407.westus3.models.ai.azure.com/v1/\",\n",
     "}});\n",
     "azure_model = \"azure:aisuite-mistral-large-2407\"\n",
-    "response = client2.chat.completions.create(model=azure_model, messages=messages)\n",
+    "response = client.chat.completions.create(model=azure_model, messages=messages)\n",
     "print(response.choices[0].message.content)"
    ]
   },
@@ -145,9 +144,8 @@
     "# The model name is the full name of the model in HuggingFace.\n",
     "# In the exmaple below, the model is \"mistralai/Mistral-7B-Instruct-v0.3\".\n",
     "# The model is deployed as serverless inference endpoint in HuggingFace.\n",
-    "client3 = ai.Client()\n",
     "hf_model = \"huggingface:mistralai/Mistral-7B-Instruct-v0.3\"\n",
-    "response = client3.chat.completions.create(model=hf_model, messages=messages)\n",
+    "response = client.chat.completions.create(model=hf_model, messages=messages)\n",
     "print(response.choices[0].message.content)"
    ]
   },
@@ -164,7 +162,6 @@
     "# In the exmaple below, the model is \"llama3-8b-8192\".\n",
     "groq_llama3_8b = \"groq:llama3-8b-8192\"\n",
     "# groq_llama3_70b = \"groq:llama3-70b-8192\"\n",
-    "\n",
     "response = client.chat.completions.create(model=groq_llama3_8b, messages=messages)\n",
     "print(response.choices[0].message.content)"
    ]
@@ -210,6 +207,30 @@
     "response = client.chat.completions.create(model=openai_gpt35, messages=messages, temperature=0.75)\n",
     "print(response.choices[0].message.content)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "321783ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fireworks_model = \"fireworks:accounts/fireworks/models/llama-v3p2-3b-instruct\"\n",
+    "response = client.chat.completions.create(model=fireworks_model, messages=messages, temperature=0.75, presence_penalty=0.5, frequency_penalty=0.5)\n",
+    "print(response.choices[0].message.content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e30e5ae0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "togetherai_model = \"together:meta-llama/Llama-3.2-3B-Instruct-Turbo\"\n",
+    "response = client.chat.completions.create(model=togetherai_model, messages=messages, temperature=0.75, top_p=0.7, top_k=50)\n",
+    "print(response.choices[0].message.content)"
+   ]
   }
  ],
  "metadata": {