## swarms Package Documentation

Welcome to the documentation for the "swarms" package, designed to facilitate seamless integration with various AI language models and APIs. This package empowers developers, end-users, and system administrators to interact with AI models from different providers, such as OpenAI, Hugging Face, Google PaLM, and Anthropic.

### Table of Contents
1. [OpenAI](#openai)
2. [HuggingFace](#huggingface)
3. [Google PaLM](#google-palm)
4. [Anthropic](#anthropic)

### 1. OpenAI (swarms.OpenAI)

The OpenAI class provides an interface to interact with OpenAI's language models. It allows both synchronous and asynchronous interactions.

**Constructor:**
```python
OpenAI(api_key: str, system: str = None, console: bool = True, model: str = None, params: dict = None, save_messages: bool = True)
```

**Attributes:**
- `api_key` (str): Your OpenAI API key.
- `system` (str, optional): A system message to be used in conversations.
- `console` (bool, default=True): Display console logs.
- `model` (str, optional): Name of the language model to use.
- `params` (dict, optional): Additional parameters for model interactions.
- `save_messages` (bool, default=True): Save conversation messages.

**Methods:**
- `generate(message: str, **kwargs) -> str`: Generate a response using the OpenAI model.
- `generate_async(message: str, **kwargs) -> str`: Generate a response asynchronously.
- `ask_multiple(ids: List[str], question_template: str) -> List[str]`: Query multiple IDs simultaneously.
- `stream_multiple(ids: List[str], question_template: str) -> List[str]`: Stream multiple responses.

**Usage Example:**
```python
from swarms import OpenAI
import asyncio

chat = OpenAI(api_key="YOUR_OPENAI_API_KEY")

response = chat.generate("Hello, how can I assist you?")
print(response)

ids = ["id1", "id2", "id3"]
async_responses = asyncio.run(chat.ask_multiple(ids, "How is {id}?"))
print(async_responses)
```

### 2. HuggingFace (swarms.HuggingFaceLLM)

The HuggingFaceLLM class allows interaction with language models from Hugging Face.

**Constructor:**
```python
HuggingFaceLLM(model_id: str, device: str = None, max_length: int = 20, quantize: bool = False, quantization_config: dict = None)
```

**Attributes:**
- `model_id` (str): ID or name of the Hugging Face model.
- `device` (str, optional): Device to run the model on (e.g., 'cuda', 'cpu').
- `max_length` (int, default=20): Maximum length of generated text.
- `quantize` (bool, default=False): Apply model quantization.
- `quantization_config` (dict, optional): Configuration for quantization.

**Methods:**
- `generate(prompt_text: str, max_length: int = None) -> str`: Generate text based on a prompt.

**Usage Example:**
```python
from swarms import HuggingFaceLLM

model_id = "gpt2"
hugging_face_model = HuggingFaceLLM(model_id=model_id)

prompt = "Once upon a time"
generated_text = hugging_face_model.generate(prompt)
print(generated_text)
```

### 3. Google PaLM (swarms.GooglePalm)

The GooglePalm class provides an interface for Google's PaLM Chat API.

**Constructor:**
```python
GooglePalm(model_name: str = "models/chat-bison-001", google_api_key: str = None, temperature: float = None, top_p: float = None, top_k: int = None, n: int = 1)
```

**Attributes:**
- `model_name` (str): Name of the Google PaLM model.
- `google_api_key` (str, optional): Google API key.
- `temperature` (float, optional): Temperature for text generation.
- `top_p` (float, optional): Top-p sampling value.
- `top_k` (int, optional): Top-k sampling value.
- `n` (int, default=1): Number of candidate completions.

**Methods:**
- `generate(messages: List[Dict[str, Any]], stop: List[str] = None, **kwargs) -> Dict[str, Any]`: Generate text based on a list of messages.
- `__call__(messages: List[Dict[str, Any]], stop: List[str] = None, **kwargs) -> Dict[str, Any]`: Generate text using the call syntax.

**Usage Example:**
```python
from swarms import GooglePalm

google_palm = GooglePalm()
messages = [{"role": "system", "content": "You are a helpful assistant"}, {"role": "user", "content": "Tell me a joke"}]

response = google_palm.generate(messages)
print(response["choices"][0]["text"])
```

### 4. Anthropic (swarms.Anthropic)

The Anthropic class enables interaction with Anthropic's large language models.

**Constructor:**
```python
Anthropic(model: str = "claude-2", max_tokens_to_sample: int = 256, temperature: float = None, top_k: int = None, top_p: float = None, streaming: bool = False, default_request_timeout: int = None)
```

**Attributes:**
- `model` (str): Name of the Anthropic model.
- `max_tokens_to_sample` (int, default=256): Maximum tokens to sample.
- `temperature` (float, optional): Temperature for text generation.
- `top_k` (int, optional): Top-k sampling value.
- `top_p` (float, optional): Top-p sampling value.
- `streaming` (bool, default=False): Enable streaming mode.
- `default_request_timeout` (int, optional): Default request timeout.

**Methods:**
- `generate(prompt: str, stop: List[str] = None) -> str`: Generate text based on a prompt.

**Usage Example:**
```python
from swarms import Anthropic

anthropic = Anthropic()
prompt = "Once upon a time"
generated_text = anthropic.generate(prompt)
print(generated_text)
```

This concludes the documentation for the "swarms" package, providing you with tools to seamlessly integrate with various language models and APIs. Happy coding!