parent
090d731dab
commit
de76a3ad39
@ -1,57 +1 @@
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from swarms import Agent
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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 swarms.structs.majority_voting import MajorityVoting
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from dotenv import load_dotenv
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def streaming_callback(agent_name: str, chunk: str, is_final: bool):
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# Chunk buffer static per call (reset each session)
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if not hasattr(streaming_callback, "_buffer"):
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streaming_callback._buffer = ""
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streaming_callback._buffer_size = 0
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min_chunk_size = 512 # or any large chunk size you want
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if chunk:
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streaming_callback._buffer += chunk
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streaming_callback._buffer_size += len(chunk)
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if streaming_callback._buffer_size >= min_chunk_size or is_final:
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if streaming_callback._buffer:
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print(streaming_callback._buffer, end="", flush=True)
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streaming_callback._buffer = ""
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streaming_callback._buffer_size = 0
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if is_final:
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print()
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load_dotenv()
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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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agent_description="Personal finance advisor agent",
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system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
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max_loops=1,
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model_name="gpt-4.1",
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dynamic_temperature_enabled=True,
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user_name="swarms_corp",
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retry_attempts=3,
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context_length=8192,
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return_step_meta=False,
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output_type="str", # "json", "dict", "csv" OR "string" "yaml" and
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auto_generate_prompt=False, # Auto generate prompt for the agent based on name, description, and system prompt, task
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max_tokens=4000, # max output tokens
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saved_state_path="agent_00.json",
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interactive=False,
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streaming_on=True, #if concurrent agents want to be streamed
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)
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swarm = MajorityVoting(agents=[agent, agent, agent])
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swarm.run(
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"Create a table of super high growth opportunities for AI. I have $40k to invest in ETFs, index funds, and more. Please create a table in markdown.",
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streaming_callback=streaming_callback,
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)
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