swarms/DOCS/agents

Agents

Agents are individual building blocks in a swarm, they each have a driving force in our case a language model, with long term memory and the capacity to use tools in other words an agent is:

LLM => Long Term Memory => Tools

That's it.

That's as simple as it can get.

Our Agent classes have to be as simple as humanely possible, they should be plug in and play with any of language model classes, vectorstores, and tools.

File structure

* memory
* models
* tools 
* utils
* mem

Swarms Documentation

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Language Models

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Language models are the driving force of our agents. They are responsible for generating text based on a given prompt. We currently support two types of language models: Anthropic and HuggingFace.

Anthropic

The Anthropic class is a wrapper for the Anthropic large language models.

Initialization

Anthropic(model="claude-2", max_tokens_to_sample=256, temperature=None, top_k=None, top_p=None, streaming=False, default_request_timeout=None)

Parameters

• model (str, optional): The name of the model to use. Default is "claude-2".

• max_tokens_to_sample (int, optional): The maximum number of tokens to sample. Default is 256.

• temperature (float, optional): The temperature to use for the generation. Higher values result in more random outputs.

• top_k (int, optional): The number of top tokens to consider for the generation.

• top_p (float, optional): The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling.

• streaming (bool, optional): Whether to use streaming mode. Default is False.

• default_request_timeout (int, optional): The default request timeout in seconds. Default is 600.

Example

anthropic = Anthropic(model="claude-2", max_tokens_to_sample=100, temperature=0.8)

Generation

anthropic.generate(prompt, stop=None)

Parameters

• prompt (str): The prompt to use for the generation.

• stop (list, optional): A list of stop sequences. The generation will stop if one of these sequences is encountered.

Returns

• str: The generated text.

Example

prompt = "Once upon a time"

stop = ["The end"]

print(anthropic.generate(prompt, stop))

HuggingFaceLLM

The HuggingFaceLLM class is a wrapper for the HuggingFace language models.

Initialization

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

Parameters

• model_id (str): The ID of the model to use.

• device (str, optional): The device to use for the generation. Default is "cuda" if available, otherwise "cpu".

• max_length (int, optional): The maximum length of the generated text. Default is 20.

• quantize (bool, optional): Whether to quantize the model. Default is False.

• quantization_config (dict, optional): The configuration for the quantization.

Example

huggingface = HuggingFaceLLM(model_id="gpt2", device="cpu", max_length=50)

Generation

huggingface.generate(prompt_text: str, max_length: int = None)

Parameters

• prompt_text (str): The prompt to use for the generation.

• max_length (int, optional): The maximum length of the generated text. If not provided, the default value specified during initialization is used.

Returns

• str: The generated text.

Example

prompt = "Once upon a time"

print(huggingface.generate(prompt))

Full Examples

# Import the necessary classes

from swarms import Anthropic, HuggingFaceLLM

# Create an instance of the Anthropic class

anthropic = Anthropic(model="claude-2", max_tokens_to_sample=100, temperature=0.8)

# Use the Anthropic instance to generate text

prompt = "Once upon a time"

stop = ["The end"]

print("Anthropic output:")

print(anthropic.generate(prompt, stop))

# Create an instance of the HuggingFaceLLM class

huggingface = HuggingFaceLLM(model_id="gpt2", device="cpu", max_length=50)

# Use the HuggingFaceLLM instance to generate text

prompt = "Once upon a time"

print("\nHuggingFaceLLM output:")

print(huggingface.generate(prompt))