Add SambaNova Provider (#54)

* add sambanova provider

* add documentation

* update comments in sambanova provider for review

* fix client constructor to pass in the entire config

* fix linting error

.env.sample

@@ -25,3 +25,6 @@ FIREWORKS_API_KEY=
 
 # Together AI
 TOGETHER_API_KEY=
+
+# Sambanova
+SAMBANOVA_API_KEY=

aisuite/providers/sambanova_provider.py

@@ -0,0 +1,30 @@
+import os
+from aisuite.provider import Provider
+from openai import OpenAI
+
+
+class SambanovaProvider(Provider):
+    def __init__(self, **config):
+        """
+        Initialize the SambaNova provider with the given configuration.
+        Pass the entire configuration dictionary to the OpenAI client constructor.
+        """
+        # Ensure API key is provided either in config or via environment variable
+        config.setdefault("api_key", os.getenv("SAMBANOVA_API_KEY"))
+        if not config["api_key"]:
+            raise ValueError(
+                "Sambanova API key is missing. Please provide it in the config or set the SAMBANOVA_API_KEY environment variable."
+            )
+
+        config["base_url"] = "https://api.sambanova.ai/v1/"
+        # Pass the entire config to the OpenAI client constructor
+        self.client = OpenAI(**config)
+
+    def chat_completions_create(self, model, messages, **kwargs):
+        # Any exception raised by Sambanova will be returned to the caller.
+        # Maybe we should catch them and raise a custom LLMError.
+        return self.client.chat.completions.create(
+            model=model,
+            messages=messages,
+            **kwargs  # Pass any additional arguments to the Sambanova API
+        )

guides/README.md

@@ -9,6 +9,7 @@ Here're the instructions for:
 - [Google](google.md)
 - [Hugging Face](huggingface.md)
 - [OpenAI](openai.md)
+- [SambaNova](sambanova.md)
 
 Unless otherwise stated, these guides have not been endorsed by the providers. 
 

guides/sambanova.md

@@ -0,0 +1,44 @@
+# Sambanova
+
+To use Sambanova with `aisuite`, you’ll need a [Sambanova Cloud](https://cloud.sambanova.ai/) account. After logging in, go to the [API](https://cloud.sambanova.ai/apis) section and generate a new key. Once you have your key, add it to your environment as follows:
+
+```shell
+export SAMBANOVA_API_KEY="your-sambanova-api-key"
+```
+
+## Create a Chat Completion
+
+Install the `openai` Python client:
+
+Example with pip:
+```shell
+pip install openai
+```
+
+Example with poetry:
+```shell
+poetry add openai
+```
+
+In your code:
+```python
+import aisuite as ai
+client = ai.Client()
+
+provider = "sambanova"
+model_id = "Meta-Llama-3.1-405B-Instruct"
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "What’s the weather like in San Francisco?"},
+]
+
+response = client.chat.completions.create(
+    model=f"{provider}:{model_id}",
+    messages=messages,
+)
+
+print(response.choices[0].message.content)
+```
+
+Happy coding! If you’d like to contribute, please read our [Contributing Guide](CONTRIBUTING.md).

tests/providers/test_sambanova_provider.py

@@ -0,0 +1,46 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from aisuite.providers.sambanova_provider import SambanovaProvider
+
+
+@pytest.fixture(autouse=True)
+def set_api_key_env_var(monkeypatch):
+    """Fixture to set environment variables for tests."""
+    monkeypatch.setenv("SAMBANOVA_API_KEY", "test-api-key")
+
+
+def test_sambanova_provider():
+    """High-level test that the provider is initialized and chat completions are requested successfully."""
+
+    user_greeting = "Hello!"
+    message_history = [{"role": "user", "content": user_greeting}]
+    selected_model = "our-favorite-model"
+    chosen_temperature = 0.75
+    response_text_content = "mocked-text-response-from-model"
+
+    provider = SambanovaProvider()
+    mock_response = MagicMock()
+    mock_response.choices = [MagicMock()]
+    mock_response.choices[0].message = MagicMock()
+    mock_response.choices[0].message.content = response_text_content
+
+    with patch.object(
+        provider.client.chat.completions,
+        "create",
+        return_value=mock_response,
+    ) as mock_create:
+        response = provider.chat_completions_create(
+            messages=message_history,
+            model=selected_model,
+            temperature=chosen_temperature,
+        )
+
+        mock_create.assert_called_with(
+            messages=message_history,
+            model=selected_model,
+            temperature=chosen_temperature,
+        )
+
+        assert response.choices[0].message.content == response_text_content