Your Name
4 months ago
parent
80d66db4a5
commit
bd187f98ac
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import os
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from swarm_models import OpenAIChat
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from swarms import Agent, GroupChat
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# Example usage:
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api_key = os.getenv("OPENAI_API_KEY")
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# Create individual agents with the OpenAIChat model
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model1 = OpenAIChat(
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openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
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)
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model2 = OpenAIChat(
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openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
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)
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model3 = OpenAIChat(
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openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
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)
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agent1 = Agent(
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agent_name="Agent1",
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llm=model1,
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max_loops=1,
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autosave=True,
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dashboard=False,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="agent1_state.json",
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user_name="swarms_corp",
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retry_attempts=1,
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context_length=200000,
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return_step_meta=False,
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)
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agent2 = Agent(
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agent_name="Agent2",
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llm=model2,
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max_loops=1,
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autosave=True,
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dashboard=False,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="agent2_state.json",
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user_name="swarms_corp",
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retry_attempts=1,
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context_length=200000,
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return_step_meta=False,
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)
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agent3 = Agent(
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agent_name="Agent3",
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llm=model3,
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max_loops=1,
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autosave=True,
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dashboard=False,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="agent3_state.json",
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user_name="swarms_corp",
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retry_attempts=1,
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context_length=200000,
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return_step_meta=False,
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)
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aggregator_agent = Agent(
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agent_name="AggregatorAgent",
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llm=model1,
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max_loops=1,
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autosave=True,
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dashboard=False,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="aggregator_agent_state.json",
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user_name="swarms_corp",
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retry_attempts=1,
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context_length=200000,
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return_step_meta=False,
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)
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# Create the Mixture of Agents class
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moa = GroupChat(
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agents=[agent1, agent2, agent3],
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max_rounds=1,
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group_objective="Establish a ROTH IRA",
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selector_agent=aggregator_agent,
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)
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out = moa.run(
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"How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria?"
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)
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print(out)
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import os
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from swarms import Agent
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from swarm_models import OpenAIChat
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from swarms.prompts.finance_agent_sys_prompt import (
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FINANCIAL_AGENT_SYS_PROMPT,
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)
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from dotenv import load_dotenv
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load_dotenv()
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# Get the OpenAI API key from the environment variable
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api_key = os.getenv("OPENAI_API_KEY")
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# Create an instance of the OpenAIChat class
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model = OpenAIChat(
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openai_api_key=api_key,
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model_name="gpt-4o-mini",
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temperature=0.1,
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max_tokens=2000,
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)
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# Initialize the agent
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agent = Agent(
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agent_name="Financial-Analysis-Agent",
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system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
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llm=model,
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max_loops=1,
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autosave=True,
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dashboard=False,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="finance_agent.json",
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user_name="swarms_corp",
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retry_attempts=1,
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context_length=200000,
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return_step_meta=False,
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# output_type="json",
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output_type=str,
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)
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out = agent.run(
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"How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria"
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)
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print(out)
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def log_agent_data(data: dict):
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import requests
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data_dict = {
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"data": data,
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}
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url = "https://swarms.world/api/get-agents/log-agents"
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headers = {
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"Content-Type": "application/json",
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"Authorization": "Bearer sk-f24a13ed139f757d99cdd9cdcae710fccead92681606a97086d9711f69d44869",
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}
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response = requests.post(url, json=data_dict, headers=headers)
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return response.json()
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out = log_agent_data(agent.to_dict())
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print(out)
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import os
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from swarm_models import OpenAIChat
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from swarms import Agent, MixtureOfAgents
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# Example usage:
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api_key = os.getenv("OPENAI_API_KEY")
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# Create individual agents with the OpenAIChat model
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model1 = OpenAIChat(
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openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
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)
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model2 = OpenAIChat(
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openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
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)
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model3 = OpenAIChat(
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openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
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)
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agent1 = Agent(
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agent_name="Agent1",
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llm=model1,
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max_loops=1,
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autosave=True,
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dashboard=False,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="agent1_state.json",
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user_name="swarms_corp",
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retry_attempts=1,
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context_length=200000,
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return_step_meta=False,
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)
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agent2 = Agent(
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agent_name="Agent2",
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llm=model2,
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max_loops=1,
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autosave=True,
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dashboard=False,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="agent2_state.json",
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user_name="swarms_corp",
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retry_attempts=1,
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context_length=200000,
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return_step_meta=False,
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)
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agent3 = Agent(
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agent_name="Agent3",
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llm=model3,
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max_loops=1,
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autosave=True,
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dashboard=False,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="agent3_state.json",
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user_name="swarms_corp",
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retry_attempts=1,
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context_length=200000,
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return_step_meta=False,
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)
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aggregator_agent = Agent(
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agent_name="AggregatorAgent",
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llm=model1,
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max_loops=1,
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autosave=True,
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dashboard=False,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="aggregator_agent_state.json",
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user_name="swarms_corp",
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retry_attempts=1,
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context_length=200000,
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return_step_meta=False,
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)
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# Create the Mixture of Agents class
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moa = MixtureOfAgents(
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reference_agents=[agent1, agent2, agent3],
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aggregator_agent=aggregator_agent,
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aggregator_system_prompt="""You have been provided with a set of responses from various agents.
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Your task is to synthesize these responses into a single, high-quality response.""",
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layers=3,
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)
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out = moa.run(
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"How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria?"
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)
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print(out)
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def log_agent_data(data: dict):
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import requests
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data_dict = {
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"data": data,
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}
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url = "https://swarms.world/api/get-agents/log-agents"
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headers = {
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"Content-Type": "application/json",
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"Authorization": "Bearer sk-f24a13ed139f757d99cdd9cdcae710fccead92681606a97086d9711f69d44869",
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}
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response = requests.post(url, json=data_dict, headers=headers)
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return response.json()
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import subprocess
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import sys
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from loguru import logger
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from typing import Tuple, Union, List
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from e2b_code_interpreter import CodeInterpreter
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from dotenv import load_dotenv
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load_dotenv()
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# Helper function to lazily install the package if not found
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def lazy_install(package: str) -> None:
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try:
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__import__(package)
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except ImportError:
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logger.warning(f"{package} not found. Installing now...")
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subprocess.check_call(
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[sys.executable, "-m", "pip", "install", package]
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)
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# Ensure e2b_code_interpreter is installed lazily
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lazy_install("e2b_code_interpreter")
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def code_interpret(
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code_interpreter: CodeInterpreter, code: str
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) -> Union[Tuple[List[str], List[str]], None]:
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"""
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Runs AI-generated code using the provided CodeInterpreter and logs the process.
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Args:
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code_interpreter (CodeInterpreter): An instance of the CodeInterpreter class.
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code (str): The code string to be executed.
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Returns:
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Union[Tuple[List[str], List[str]], None]: A tuple of (results, logs) if successful,
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or None if an error occurred.
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Raises:
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ValueError: If the code or code_interpreter is invalid.
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"""
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if not isinstance(code_interpreter, CodeInterpreter):
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logger.error("Invalid CodeInterpreter instance provided.")
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raise ValueError(
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"code_interpreter must be an instance of CodeInterpreter."
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)
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if not isinstance(code, str) or not code.strip():
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logger.error("Invalid code provided.")
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raise ValueError("code must be a non-empty string.")
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logger.info(
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f"\n{'='*50}\n> Running the following AI-generated code:\n{code}\n{'='*50}"
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)
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try:
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exec_result = code_interpreter.notebook.exec_cell(
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code,
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# on_stderr=lambda stderr: logger.error(f"[Code Interpreter stderr] {stderr}"),
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# on_stdout=lambda stdout: logger.info(f"[Code Interpreter stdout] {stdout}")
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)
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if exec_result.error:
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logger.error(
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f"[Code Interpreter error] {exec_result.error}"
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)
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return None
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else:
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logger.success("Code executed successfully.")
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# return exec_result.results, exec_result.logs
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# return exec_result.results
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prompt = f"{exec_result.results}: {exec_result.logs}"
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return prompt
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except Exception:
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logger.exception(
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"An error occurred during code interpretation."
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)
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return None
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# # from e2b_code_interpreter import CodeInterpreter
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# interpreter = CodeInterpreter()
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# code = "print('Hello, World!')"
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# result = code_interpret(interpreter, code)
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# if result:
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# results = result
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# print("Execution Results:", results)
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# # print("Execution Logs:", logs)
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@ -1,97 +0,0 @@
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import json
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import re
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from typing import Type, TypeVar
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from pydantic import BaseModel, ValidationError
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T = TypeVar("T", bound=BaseModel)
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class JsonParsingException(Exception):
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"""Custom exception for errors in JSON parsing."""
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class JsonOutputParser:
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"""Parse JSON output using a Pydantic model.
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This parser is designed to extract JSON formatted data from a given string
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and parse it using a specified Pydantic model for validation.
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Attributes:
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pydantic_object: A Pydantic model class for parsing and validation.
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pattern: A regex pattern to match JSON code blocks.
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Examples:
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>>> from pydantic import BaseModel
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>>> from swarms.utils.json_output_parser import JsonOutputParser
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>>> class MyModel(BaseModel):
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... name: str
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... age: int
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...
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>>> parser = JsonOutputParser(MyModel)
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>>> text = "```json\n{\"name\": \"John\", \"age\": 42}\n```"
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>>> model = parser.parse(text)
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>>> model.name
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"""
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def __init__(self, pydantic_object: Type[T]):
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self.pydantic_object = pydantic_object
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self.pattern = re.compile(
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r"^```(?:json)?(?P<json>[^`]*)", re.MULTILINE | re.DOTALL
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)
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def parse(self, text: str) -> T:
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"""Parse the provided text to extract and validate JSON data.
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Args:
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text: A string containing potential JSON data.
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Returns:
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An instance of the specified Pydantic model with parsed data.
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Raises:
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JsonParsingException: If parsing or validation fails.
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"""
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try:
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match = re.search(self.pattern, text.strip())
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json_str = match.group("json") if match else text
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json_object = json.loads(json_str)
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return self.pydantic_object.parse_obj(json_object)
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except (json.JSONDecodeError, ValidationError) as e:
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name = self.pydantic_object.__name__
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msg = (
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f"Failed to parse {name} from text '{text}'."
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f" Error: {e}"
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)
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raise JsonParsingException(msg) from e
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def get_format_instructions(self) -> str:
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"""Generate formatting instructions based on the Pydantic model schema.
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Returns:
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A string containing formatting instructions.
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"""
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schema = self.pydantic_object.schema()
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reduced_schema = {
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k: v
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for k, v in schema.items()
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if k not in ["title", "type"]
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}
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schema_str = json.dumps(reduced_schema, indent=4)
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format_instructions = (
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f"JSON Formatting Instructions:\n{schema_str}"
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)
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return format_instructions
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# # Example usage
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# class ExampleModel(BaseModel):
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# field1: int
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# field2: str
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# parser = JsonOutputParser(ExampleModel)
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# # Use parser.parse(text) to parse JSON data
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@ -1,90 +0,0 @@
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import json
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import re
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from typing import Type, TypeVar
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import yaml
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from pydantic import BaseModel, ValidationError
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T = TypeVar("T", bound=BaseModel)
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class YamlParsingException(Exception):
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"""Custom exception for errors in YAML parsing."""
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class YamlOutputParser:
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"""Parse YAML output using a Pydantic model.
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This parser is designed to extract YAML formatted data from a given string
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and parse it using a specified Pydantic model for validation.
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Attributes:
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pydantic_object: A Pydantic model class for parsing and validation.
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pattern: A regex pattern to match YAML code blocks.
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Examples:
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>>> from pydantic import BaseModel
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>>> from swarms.utils.yaml_output_parser import YamlOutputParser
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>>> class MyModel(BaseModel):
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... name: str
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... age: int
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...
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>>> parser = YamlOutputParser(MyModel)
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>>> text = "```yaml\nname: John\nage: 42\n```"
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>>> model = parser.parse(text)
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>>> model.name
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"""
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def __init__(self, pydantic_object: Type[T]):
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self.pydantic_object = pydantic_object
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self.pattern = re.compile(
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r"^```(?:ya?ml)?(?P<yaml>[^`]*)", re.MULTILINE | re.DOTALL
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)
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def parse(self, text: str) -> T:
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"""Parse the provided text to extract and validate YAML data.
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Args:
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text: A string containing potential YAML data.
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|
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Returns:
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An instance of the specified Pydantic model with parsed data.
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Raises:
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YamlParsingException: If parsing or validation fails.
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"""
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try:
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match = re.search(self.pattern, text.strip())
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yaml_str = match.group("yaml") if match else text
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|
||||
json_object = yaml.safe_load(yaml_str)
|
||||
return self.pydantic_object.parse_obj(json_object)
|
||||
|
||||
except (yaml.YAMLError, ValidationError) as e:
|
||||
name = self.pydantic_object.__name__
|
||||
msg = (
|
||||
f"Failed to parse {name} from text '{text}'."
|
||||
f" Error: {e}"
|
||||
)
|
||||
raise YamlParsingException(msg) from e
|
||||
|
||||
def get_format_instructions(self) -> str:
|
||||
"""Generate formatting instructions based on the Pydantic model schema.
|
||||
|
||||
Returns:
|
||||
A string containing formatting instructions.
|
||||
"""
|
||||
schema = self.pydantic_object.schema()
|
||||
reduced_schema = {
|
||||
k: v
|
||||
for k, v in schema.items()
|
||||
if k not in ["title", "type"]
|
||||
}
|
||||
schema_str = json.dumps(reduced_schema, indent=4)
|
||||
|
||||
format_instructions = (
|
||||
f"YAML Formatting Instructions:\n{schema_str}"
|
||||
)
|
||||
return format_instructions
|
||||
Loading…
Reference in new issue