🛠️ What LLMs can I use?

import dotenv
from composio_claude import App, ComposioToolset
import anthropic

# Load environment variables from .env
dotenv.load_dotenv()

# Initialize the Composio Toolset
composio_toolset = ComposioToolset()

# Get GitHub tools that are pre-configured
tool = composio_toolset.get_tool(apps=[App.GITHUB])

# Initialize the Google AI Gemini model
client = anthropic.Anthropic()

my_task = "Star a repo composiohq/composio on GitHub"

# Create a chat completion request to decide on the action
response = client.beta.tools.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=1024,
    tools= actions,
    messages=[{"role": "user", "content": "Star me composiohq/composio repo in github."}],
)
print(response)

result = toolset.handle_tool_calls(response)
print(result)

import os

from composio_openai import App, ComposioToolSet
import groq

tool_set = ComposioToolSet()
tools = tool_set.get_tools(apps=[App.TAVILY])

groq_client = groq.Groq(api_key=os.environ.get("GROQ_API_KEY")) 
system_prompt = """You are a helpful assistant. 
whenever appropriate, use the Tavily search tool for the latest 
information from the internet to provide users with up-to-date information. 
Return the URL of the information.
"""

user_prompt = "Who was the gold medalist in Men's 100m sprint in paris olymics, 2024?"

messages=[
    {
        "role": "system",
        "content": system_prompt
    },
    {
        "role": "user",
        "content": user_prompt,
    }
]

response = groq_client.chat.completions.create(
    model="llama-3.1-70b-versatile",
    messages=messages,
    tools=tools,
    tool_choice="auto",
    max_tokens=4096
)
response_message = response.choices[0].message
tool_call_id = response_message.tool_calls[0].id
tool_name = response_message.tool_calls[0].function.name
# Execute function calls
response_after_tool_calls = tool_set.handle_tool_calls(response=response)

content = response_after_tool_calls[0]['data']['response_data']['results'][0]['content']
url = response_after_tool_calls[0]['data']['response_data']['results'][0]['url']

messages.append({
                   "tool_call_id": tool_call_id,
                   "role": "tool",
                   "name": tool_name,
                   "content": f"{content}+url:+{url}",
               })
second_response = groq_client.chat.completions.create(
  model="llama-3.1-70b-versatile",
  messages=messages
)
print(second_response.choices[0].message.content)

import os
import dotenv
from composio_llamaindex import Action, App, ComposioToolSet
from composio_llamaindex import App, ComposioToolSet, Action
from llama_index.core.agent import FunctionCallingAgentWorker
from llama_index.core.llms import ChatMessage
from llama_index.core.llms import Cerebras
from datetime import datetime
from llama_index.core import Settings

# Load environment variables from .env file
dotenv.load_dotenv()
Settings.llm = Cerebras(model="llama3.1-70b", api_key=os.environ["GROQ_API_KEY"])
llm = Cerebras(model="llama3.1-70b", api_key=os.environ["GROQ_API_KEY"])


# Initialize the ComposioToolSet
toolset = ComposioToolSet()

# Get the RAG tool from the Composio ToolSet
tools = toolset.get_tools(apps=[App.GOOGLECALENDAR])

# Retrieve the current date and time
date = datetime.today().strftime("%Y-%m-%d")
timezone = datetime.now().astimezone().tzinfo

# Setup Todo
todo = """
    1PM - 3PM -> Code,
    5PM - 7PM -> Meeting,
    9AM - 12AM -> Learn something,
    8PM - 10PM -> Game
"""

# Define the RAG Agent
prefix_messages = [
    ChatMessage(
        role="system",
        content=(
        """
        You are an AI agent responsible for taking actions on Google Calendar on users' behalf. 
        You need to take action on Calendar using Google Calendar APIs. Use correct tools to run APIs from the given tool-set.
        """
        ),
    )
]

# Initialize a FunctionCallingAgentWorker with the tools, LLM, and system messages
agent = FunctionCallingAgentWorker(
    tools=tools,  # Tools available for the agent to use
    llm=llm,  # Language model for processing requests
    prefix_messages=prefix_messages,  # Initial system messages for context
    max_function_calls=10,  # Maximum number of function calls allowed
    allow_parallel_tool_calls=False,  # Disallow parallel tool calls
    verbose=True,  # Enable verbose output
).as_agent()

response = agent.chat(
    """
Book slots according to {todo}. 
Properly Label them with the work provided to be done in that time period. 
Schedule it for today. Today's date is {date} (it's in YYYY-MM-DD format) 
and make the timezone be {timezone}.
    """
)
print(response)