Merge branch 'main' into main

Author: ksolo

.env.sample

@@ -18,10 +18,16 @@ GOOGLE_REGION=
 GOOGLE_PROJECT_ID=
 
 # Hugging Face token 
-HUGGINGFACE_TOKEN=
+HF_TOKEN=
 
 # Fireworks
 FIREWORKS_API_KEY=
 
 # Together AI
 TOGETHER_API_KEY=
+
+# xAI
+XAI_API_KEY=
+
+# Sambanova
+SAMBANOVA_API_KEY=

.github/workflows/run_pytest.yml

@@ -18,7 +18,7 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           pip install poetry
-          poetry install
+          poetry install --with test
       - name: Test with pytest
         run: poetry run pytest
 

.gitignore

@@ -4,3 +4,9 @@ __pycache__/
 env/
 .env
 .google-adc
+
+# Testing
+.coverage
+
+# pyenv
+.python-version

README.md

@@ -1,13 +1,14 @@
 # aisuite
 
+[![PyPI](https://img.shields.io/pypi/v/aisuite)](https://pypi.org/project/aisuite/)
 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 
 Simple, unified interface to multiple Generative AI providers.
 
 `aisuite` makes it easy for developers to use multiple LLM through a standardized interface. Using an interface similar to OpenAI's, `aisuite` makes it easy to interact with the most popular LLMs and compare the results. It is a thin wrapper around python client libraries, and allows creators to seamlessly swap out and test responses from different LLM providers without changing their code. Today, the library is primarily focussed on chat completions. We will expand it cover more use cases in near future.
 
 Currently supported providers are -
-OpenAI, Anthropic, Azure, Google, AWS, Groq, Mistral, HuggingFace and Ollama.
+OpenAI, Anthropic, Azure, Google, AWS, Groq, Mistral, HuggingFace Ollama and Sambanova.
 To maximize stability, `aisuite` uses either the HTTP endpoint or the SDK for making calls to the provider.
 
 ## Installation
@@ -79,7 +80,7 @@ aisuite is released under the MIT License. You are free to use, modify, and dist
 
 ## Contributing
 
-If you would like to contribute, please read our [Contributing Guide](CONTRIBUTING.md) and join our [Discord](https://discord.gg/T6Nvn8ExSb) server!
+If you would like to contribute, please read our [Contributing Guide](https://github.com/andrewyng/aisuite/blob/main/CONTRIBUTING.md) and join our [Discord](https://discord.gg/T6Nvn8ExSb) server!
 
 ## Adding support for a provider
 We have made easy for a provider or volunteer to add support for a new platform.

aisuite/framework/message.py

@@ -1,4 +1,4 @@
-"""Interface to hold contents of api responses when they do not conform to the OpenAI style response"""
+"""Interface to hold contents of api responses when they do not confirm to the OpenAI style response"""
 
 
 class Message:

aisuite/providers/huggingface_provider.py

@@ -19,10 +19,10 @@ def __init__(self, **config):
         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")
+        self.token = config.get("token") or os.getenv("HF_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."
+                "Hugging Face token is missing. Please provide it in the config or set the HF_TOKEN environment variable."
             )
 
         # Optionally set a custom timeout (default to 30s)

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
+        )

aisuite/providers/xai_provider.py

@@ -0,0 +1,65 @@
+import os
+import httpx
+from aisuite.provider import Provider, LLMError
+from aisuite.framework import ChatCompletionResponse
+
+
+class XaiProvider(Provider):
+    """
+    xAI Provider using httpx for direct API calls.
+    """
+
+    BASE_URL = "https://api.x.ai/v1/chat/completions"
+
+    def __init__(self, **config):
+        """
+        Initialize the xAI 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("XAI_API_KEY"))
+        if not self.api_key:
+            raise ValueError(
+                "xAI API key is missing. Please provide it in the config or set the XAI_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 xAI 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 xAI 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"xAI 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

@@ -122,7 +122,7 @@
    "source": [
     "# IMP NOTE: Azure expects model endpoint to be passed in the format of \"azure:<model_name>\".\n",
     "# 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",
+    "# In the example 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",
     "client.configure({\"azure\" : {\n",
     "  \"api_key\": os.environ[\"AZURE_API_KEY\"],\n",
@@ -142,7 +142,7 @@
    "source": [
     "# HuggingFace expects the model to be passed in the format of \"huggingface:<model_name>\".\n",
     "# 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",
+    "# In the example below, the model is \"mistralai/Mistral-7B-Instruct-v0.3\".\n",
     "# The model is deployed as serverless inference endpoint in HuggingFace.\n",
     "hf_model = \"huggingface:mistralai/Mistral-7B-Instruct-v0.3\"\n",
     "response = client.chat.completions.create(model=hf_model, messages=messages)\n",
@@ -159,7 +159,7 @@
     "\n",
     "# Groq expects the model to be passed in the format of \"groq:<model_name>\".\n",
     "# The model name is the full name of the model in Groq.\n",
-    "# In the exmaple below, the model is \"llama3-8b-8192\".\n",
+    "# In the example below, the model is \"llama3-8b-8192\".\n",
     "groq_llama3_8b = \"groq:llama3-8b-8192\"\n",
     "# groq_llama3_70b = \"groq:llama3-70b-8192\"\n",
     "response = client.chat.completions.create(model=groq_llama3_8b, messages=messages)\n",

guides/README.md

@@ -9,6 +9,8 @@ Here're the instructions for:
 - [Google](google.md)
 - [Hugging Face](huggingface.md)
 - [OpenAI](openai.md)
+- [SambaNova](sambanova.md)
+- [xAI](xai.md)
 
 Unless otherwise stated, these guides have not been endorsed by the providers. 
 

guides/google.md

@@ -22,7 +22,7 @@ Set the `GOOGLE_PROJECT_ID` environment variable to the ID of your project. You
 
 ### Set your preferred region in an environment variable.
 
-Set the `GOOGLE_REGION` environment variable to the ID of your project. You can find the Project ID by visiting the project dashboard in the "Project Info" section toward the top of the page.
+Set the `GOOGLE_REGION` environment variable. You can find the region by going to Project Dashboard under VertexAI side navigation menu, and then scrolling to the bottom of the page.
 
 ## Create a Service Account For API Access
 
@@ -89,4 +89,4 @@ response = client.chat.completions.create(
 print(response.choices[0].message.content)
 ```
 
-Happy coding! If you would like to contribute, please read our [Contributing Guide](../CONTRIBUTING.md).
+Happy coding! If you would like to contribute, please read our [Contributing Guide](../CONTRIBUTING.md).

guides/groq.md

@@ -0,0 +1,39 @@
+# Groq
+
+To use Groq with `aisuite`, you’ll need a free [Groq account](https://console.groq.com/). After logging in, go to the [API Keys](https://console.groq.com/keys) section in your account settings and generate a new Groq API key. Once you have your key, add it to your environment as follows:
+
+```shell
+export GROQ_API_KEY="your-groq-api-key"
+```
+
+## Create a Python Chat Completion
+
+1. First, install the `groq` Python client library:
+
+```shell
+pip install groq
+```
+
+2. Now you can simply create your first chat completion with the following example code or customize by swapoping out the `model_id` with any of the other available [models powered by Groq](https://console.groq.com/docs/models) and `messages` array with whatever you'd like:
+```python
+import aisuite as ai
+client = ai.Client()
+
+provider = "groq"
+model_id = "llama-3.2-3b-preview"
+
+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).

guides/huggingface.md

@@ -18,7 +18,7 @@ After setting up your model, you'll need to gather the following information:
 Set the following environment variables to make authentication and requests easy:
 
 ```shell
-export HUGGINGFACE_TOKEN="your-api-token"
+export HF_TOKEN="your-api-token"
 ```
 
 ## Create a Chat Completion

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).

guides/xai.md

@@ -0,0 +1,33 @@
+# xAI
+
+To use xAI with `aisuite`, you’ll need an [API key](https://console.x.ai/). Generate a new key and once you have your key, add it to your environment as follows:
+
+```shell
+export XAI_API_KEY="your-xai-api-key"
+```
+
+## Create a Chat Completion
+
+Sample code:
+```python
+import aisuite as ai
+client = ai.Client()
+
+models = ["xai:grok-beta"]
+
+messages = [
+    {"role": "system", "content": "Respond in Pirate English."},
+    {"role": "user", "content": "Tell me a joke."},
+]
+
+for model in models:
+    response = client.chat.completions.create(
+        model=model,
+        messages=messages,
+        temperature=0.75
+    )
+    print(response.choices[0].message.content)
+
+```
+
+Happy coding! If you’d like to contribute, please read our [Contributing Guide](CONTRIBUTING.md).