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120 lines
3.6 KiB
120 lines
3.6 KiB
import os
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import interpreter
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from swarms.agents.hf_agents import HFAgent
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from swarms.agents.omni_modal_agent import OmniModalAgent
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from swarms.models import OpenAIChat
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from swarms.tools.autogpt import tool
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from swarms.workers import Worker
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# Initialize API Key
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api_key = ""
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# Initialize the language model,
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# This model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC
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llm = OpenAIChat(
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openai_api_key=api_key,
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temperature=0.5,
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)
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# wrap a function with the tool decorator to make it a tool, then add docstrings for tool documentation
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@tool
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def hf_agent(task: str = None):
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"""
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An tool that uses an openai model to call and respond to a task by search for a model on huggingface
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It first downloads the model then uses it.
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Rules: Don't call this model for simple tasks like generating a summary, only call this tool for multi modal tasks like generating images, videos, speech, etc
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"""
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agent = HFAgent(model="text-davinci-003", api_key=api_key)
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response = agent.run(task, text="¡Este es un API muy agradable!")
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return response
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# wrap a function with the tool decorator to make it a tool
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@tool
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def omni_agent(task: str = None):
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"""
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An tool that uses an openai Model to utilize and call huggingface models and guide them to perform a task.
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Rules: Don't call this model for simple tasks like generating a summary, only call this tool for multi modal tasks like generating images, videos, speech
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The following tasks are what this tool should be used for:
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Tasks omni agent is good for:
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--------------
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document-question-answering
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image-captioning
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image-question-answering
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image-segmentation
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speech-to-text
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summarization
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text-classification
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text-question-answering
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translation
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huggingface-tools/text-to-image
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huggingface-tools/text-to-video
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text-to-speech
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huggingface-tools/text-download
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huggingface-tools/image-transformation
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"""
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agent = OmniModalAgent(llm)
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response = agent.run(task)
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return response
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# Code Interpreter
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@tool
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def compile(task: str):
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"""
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Open Interpreter lets LLMs run code (Python, Javascript, Shell, and more) locally.
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You can chat with Open Interpreter through a ChatGPT-like interface in your terminal
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by running $ interpreter after installing.
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This provides a natural-language interface to your computer's general-purpose capabilities:
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Create and edit photos, videos, PDFs, etc.
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Control a Chrome browser to perform research
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Plot, clean, and analyze large datasets
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...etc.
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⚠️ Note: You'll be asked to approve code before it's run.
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Rules: Only use when given to generate code or an application of some kind
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"""
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task = interpreter.chat(task, return_messages=True)
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interpreter.chat()
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interpreter.reset(task)
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os.environ["INTERPRETER_CLI_AUTO_RUN"] = True
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os.environ["INTERPRETER_CLI_FAST_MODE"] = True
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os.environ["INTERPRETER_CLI_DEBUG"] = True
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# Append tools to an list
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tools = [hf_agent, omni_agent, compile]
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# Initialize a single Worker node with previously defined tools in addition to it's
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# predefined tools
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node = Worker(
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llm=llm,
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ai_name="Optimus Prime",
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openai_api_key=api_key,
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ai_role="Worker in a swarm",
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external_tools=tools,
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human_in_the_loop=False,
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temperature=0.5,
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)
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# Specify task
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task = "What were the winning boston marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times."
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# Run the node on the task
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response = node.run(task)
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# Print the response
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print(response)
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