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98 lines
2.7 KiB
98 lines
2.7 KiB
import os
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from swarms import Agent, 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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# 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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api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
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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-General-11",
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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=False,
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dashboard=False,
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verbose=True,
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# interactive=True, # Set to False to disable interactive mode
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dynamic_temperature_enabled=True,
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saved_state_path="finance_agent.json",
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# tools=[#Add your functions here# ],
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# stopping_token="Stop!",
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# docs_folder="docs", # Enter your folder name
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# pdf_path="docs/finance_agent.pdf",
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# sop="Calculate the profit for a company.",
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# sop_list=["Calculate the profit for a company."],
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user_name="swarms_corp",
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# # docs="",
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retry_attempts=3,
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# context_length=1000,
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# tool_schema = dict
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context_length=200000,
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tool_system_prompt=None,
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)
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# # Convert the agent object to a dictionary
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print(agent.to_dict())
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print(agent.to_toml())
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print(agent.model_dump_json())
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print(agent.model_dump_yaml())
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# Ingest documents into the agent's knowledge base
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agent.ingest_docs("your_pdf_path.pdf")
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# Receive a message from a user and process it
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agent.receive_message(name="agent_name", message="message")
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# Send a message from the agent to a user
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agent.send_agent_message(agent_name="agent_name", message="message")
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# Ingest multiple documents into the agent's knowledge base
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agent.ingest_docs("your_pdf_path.pdf", "your_csv_path.csv")
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# Run the agent with a filtered system prompt
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agent.filtered_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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# Run the agent with multiple system prompts
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agent.bulk_run(
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[
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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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"Another system prompt",
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]
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)
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# Add a memory to the agent
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agent.add_memory("Add a memory to the agent")
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# Check the number of available tokens for the agent
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agent.check_available_tokens()
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# Perform token checks for the agent
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agent.tokens_checks()
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# Print the dashboard of the agent
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agent.print_dashboard()
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# Print the history and memory of the agent
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agent.print_history_and_memory()
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# Fetch all the documents from the doc folders
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agent.get_docs_from_doc_folders()
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# Activate agent ops
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agent.activate_agentops()
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agent.check_end_session_agentops()
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# Dump the model to a JSON file
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agent.model_dump_json()
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print(agent.to_toml())
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