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swarms
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Update requirements.txt to include new dependencies

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pull/727/head
harshalmore31 3 days ago committed by GitHub
parent dfdd31a986
commit 6ae0b7c99f
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1 changed files with 170 additions and 136 deletions
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swarms/structs/ui/ui.py
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@ -14,6 +14,7 @@ from swarms.utils.litellm_wrapper import LiteLLM
from litellm import models_by_provider
from dotenv import set_key, find_dotenv
import logging # Import the logging module
import litellm # Import litellm exception
# Initialize logger
load_dotenv()
@ -21,7 +22,6 @@ load_dotenv()
# Initialize logger
logger = initialize_logger(log_folder="swarm_ui")
# Define the path to agent_prompts.json
PROMPT_JSON_PATH = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "agent_prompts.json"
@ -86,9 +86,9 @@ def load_prompts_from_json() -> Dict[str, str]:
"Agent-Onboarding_Agent": "You are an onboarding agent...",
}
AGENT_PROMPTS = load_prompts_from_json()
api_keys = {}
def initialize_agents(
dynamic_temp: float,
@ -103,49 +103,66 @@ def initialize_agents(
agents = []
seen_names = set()
try:
for agent_key in agent_keys:
if agent_key not in AGENT_PROMPTS:
raise ValueError(f"Invalid agent key: {agent_key}")
agent_prompt = AGENT_PROMPTS[agent_key]
agent_name = agent_key
# Ensure unique agent names
base_name = agent_name
counter = 1
while agent_name in seen_names:
agent_name = f"{base_name}_{counter}"
counter += 1
seen_names.add(agent_name)
llm = LiteLLM(
model_name=model_name,
system_prompt=agent_prompt,
temperature=temperature,
max_tokens=max_tokens,
)
agent = Agent(
agent_name=agent_name,
system_prompt=agent_prompt,
llm=llm,
max_loops=1,
autosave=True,
verbose=True,
dynamic_temperature_enabled=True,
saved_state_path=f"agent_{agent_name}.json",
user_name="pe_firm",
retry_attempts=1,
context_length=200000,
output_type="string", # here is the output type which is string
temperature=dynamic_temp,
)
print(
f"Agent created: {agent.agent_name}"
) # Debug: Print agent name
agents.append(agent)
logger.info(f"Agents initialized successfully: {[agent.agent_name for agent in agents]}")
return agents
for agent_key in agent_keys:
if agent_key not in AGENT_PROMPTS:
raise ValueError(f"Invalid agent key: {agent_key}")
agent_prompt = AGENT_PROMPTS[agent_key]
agent_name = agent_key
# Ensure unique agent names
base_name = agent_name
counter = 1
while agent_name in seen_names:
agent_name = f"{base_name}_{counter}"
counter += 1
seen_names.add(agent_name)
# Set API key using os.environ temporarily
if provider == "openai":
os.environ["OPENAI_API_KEY"] = api_key
elif provider == "anthropic":
os.environ["ANTHROPIC_API_KEY"] = api_key
elif provider == "cohere":
os.environ["COHERE_API_KEY"] = api_key
elif provider == "gemini":
os.environ["GEMINI_API_KEY"] = api_key
elif provider == "mistral":
os.environ["MISTRAL_API_KEY"] = api_key
elif provider == "groq":
os.environ["GROQ_API_KEY"] = api_key
elif provider == "perplexity":
os.environ["PERPLEXITY_API_KEY"] = api_key
# Add other providers and their environment variable names as needed
# Create LiteLLM instance (Now it will read from os.environ)
llm = LiteLLM(
model_name=model_name,
system_prompt=agent_prompt,
temperature=temperature,
max_tokens=max_tokens,
)
agent = Agent(
agent_name=agent_name,
system_prompt=agent_prompt,
llm=llm,
max_loops=1,
autosave=True,
verbose=True,
dynamic_temperature_enabled=True,
saved_state_path=f"agent_{agent_name}.json",
user_name="pe_firm",
retry_attempts=1,
context_length=200000,
output_type="string", # here is the output type which is string
temperature=dynamic_temp,
)
print(f"Agent created: {agent.agent_name}")
agents.append(agent)
logger.info(f"Agents initialized successfully: {[agent.agent_name for agent in agents]}")
return agents
except Exception as e:
logger.error(f"Error initializing agents: {e}", exc_info=True)
raise
@ -245,7 +262,6 @@ async def execute_task(
yield "Flow configuration is required for AgentRearrange", gr.update(visible=True), ""
return
# Generate unique agent names in the flow
flow_agents = []
used_agent_names = set()
@ -265,7 +281,6 @@ async def execute_task(
router_kwargs["flow"] = flow
router_kwargs["output_type"] = "string" # Changed output type here
if swarm_type == "MixtureOfAgents":
if len(agents) < 2:
logger.error("MixtureOfAgents requires at least 2 agents.")
@ -403,6 +418,14 @@ async def execute_task(
logger.error(f"Task execution timed out after {timeout} seconds", exc_info=True)
yield f"Task execution timed out after {timeout} seconds", gr.update(visible=True), ""
return
except litellm.exceptions.APIError as e: # Catch litellm APIError
logger.error(f"LiteLLM API Error: {e}", exc_info=True)
yield f"LiteLLM API Error: {e}", gr.update(visible=True), ""
return
except litellm.exceptions.AuthenticationError as e: # Catch litellm AuthenticationError
logger.error(f"LiteLLM Authentication Error: {e}", exc_info=True)
yield f"LiteLLM Authentication Error: {e}", gr.update(visible=True), ""
return
except Exception as e:
logger.error(f"Error executing task: {e}", exc_info=True)
yield f"Error executing task: {e}", gr.update(visible=True), ""
@ -419,7 +442,6 @@ async def execute_task(
finally:
logger.info(f"Task execution finished for: {task} with swarm type: {swarm_type}")
def format_output(data:Optional[str], swarm_type:str, error_display=None) -> str:
if data is None:
return "Error : No output from the swarm."
@ -433,7 +455,7 @@ def format_output(data:Optional[str], swarm_type:str, error_display=None) -> str
if os.path.exists(data):
return parse_spreadsheet_swarm_output(data, error_display)
else:
return parse_json_output(data, error_display)
return data # Directly return JSON response
elif swarm_type == "auto":
return parse_auto_swarm_output(data, error_display)
else:
@ -552,65 +574,68 @@ def parse_auto_swarm_output(data: Optional[str], error_display=None) -> str:
output += f"Agent Flow: `{agent_flow}`\n\n---\n"
output += f"Agent Task Execution\n\n"
# Handle nested MixtureOfAgents data
# Handle nested MixtureOfAgents data or other swarm type data
if (
"outputs" in parsed_data
and isinstance(parsed_data["outputs"], list)
and parsed_data["outputs"]
and isinstance(parsed_data["outputs"][0], dict)
and parsed_data["outputs"][0].get("agent_name") == "auto"
):
mixture_data = parsed_data["outputs"][0].get("steps", [])
if mixture_data and isinstance(mixture_data[0], dict) and "content" in mixture_data[0]:
try:
mixture_content = json.loads(mixture_data[0]["content"])
output += parse_mixture_of_agents_data(mixture_content)
except json.JSONDecodeError as e:
logger.error(f"Error decoding nested MixtureOfAgents data: {e}", exc_info=True)
return f"Error decoding nested MixtureOfAgents data: {e}"
else :
for i, agent_output in enumerate(parsed_data["outputs"], start=3):
if not isinstance(agent_output, dict):
errors.append(f"Error: Agent output at index {i} is not a dictionary")
continue
if "agent_name" not in agent_output:
errors.append(f"Error: 'agent_name' key is missing at index {i}")
continue
if "steps" not in agent_output:
errors.append(f"Error: 'steps' key is missing at index {i}")
continue
if agent_output["steps"] is None:
errors.append(f"Error: 'steps' data is None at index {i}")
continue
if not isinstance(agent_output["steps"], list):
errors.append(f"Error: 'steps' data is not a list at index {i}")
continue
if parsed_data["outputs"][0].get("agent_name") == "auto":
mixture_data = parsed_data["outputs"][0].get("steps", [])
if mixture_data and isinstance(mixture_data[0], dict) and "content" in mixture_data[0]:
try:
mixture_content = json.loads(mixture_data[0]["content"])
output += parse_mixture_of_agents_data(mixture_content)
except json.JSONDecodeError as e:
logger.error(f"Error decoding nested MixtureOfAgents data: {e}", exc_info=True)
return f"Error decoding nested MixtureOfAgents data: {e}"
else:
for i, agent_output in enumerate(parsed_data["outputs"], start=3):
if not isinstance(agent_output, dict):
errors.append(f"Error: Agent output at index {i} is not a dictionary")
continue
if "agent_name" not in agent_output:
errors.append(f"Error: 'agent_name' key is missing at index {i}")
continue
if "steps" not in agent_output:
errors.append(f"Error: 'steps' key is missing at index {i}")
continue
if agent_output["steps"] is None:
errors.append(f"Error: 'steps' data is None at index {i}")
continue
if not isinstance(agent_output["steps"], list):
errors.append(f"Error: 'steps' data is not a list at index {i}")
continue
agent_name = agent_output["agent_name"]
output += f"Run {(3-i)} (Agent: `{agent_name}`)\n\n"
agent_name = agent_output["agent_name"]
output += f"Run {(3-i)} (Agent: `{agent_name}`)\n\n"
# Iterate over steps
for j, step in enumerate(agent_output["steps"], start=3):
if not isinstance(step, dict):
errors.append(f"Error: step at index {j} is not a dictionary at {i} agent output.")
continue
if step is None:
errors.append(f"Error: step at index {j} is None at {i} agent output")
continue
# Iterate over steps
for j, step in enumerate(agent_output["steps"], start=3):
if not isinstance(step, dict):
errors.append(f"Error: step at index {j} is not a dictionary at {i} agent output.")
continue
if step is None:
errors.append(f"Error: step at index {j} is None at {i} agent output")
continue
if "role" not in step:
errors.append(f"Error: 'role' key missing at step {j} at {i} agent output.")
continue
if "role" not in step:
errors.append(f"Error: 'role' key missing at step {j} at {i} agent output.")
continue
if "content" not in step:
errors.append(f"Error: 'content' key missing at step {j} at {i} agent output.")
continue
if "content" not in step:
errors.append(f"Error: 'content' key missing at step {j} at {i} agent output.")
continue
if step["role"].strip() != "System:": # Filter out system prompts
content = step["content"]
output += f"Step {(3-j)}:\n"
output += f"Response : {content}\n\n"
if step["role"].strip() != "System:": # Filter out system prompts
content = step["content"]
output += f"Step {(3-j)}:\n"
output += f"Response : {content}\n\n"
else:
logger.error("Error: 'outputs' data is not in the expected format.")
return "Error: 'outputs' data is not in the expected format."
output += f"Overall Completion Time: `{overall_time}`"
@ -805,7 +830,6 @@ def parse_agent_rearrange_output(data: Optional[str], error_display=None) -> str
logger.error(f"Error during parsing AgentRearrange output: {e}", exc_info=True)
return f"Error during parsing: {str(e)}"
def parse_mixture_of_agents_output(data: Optional[str], error_display=None) -> str:
"""Parses the MixtureOfAgents output string and formats it for display."""
logger.info("Parsing MixtureOfAgents output...")
@ -922,7 +946,6 @@ def parse_mixture_of_agents_output(data: Optional[str], error_display=None) -> s
logger.error(f"Error during parsing MixtureOfAgents output: {e}", exc_info=True)
return f"Error during parsing: {str(e)}"
def parse_sequential_workflow_output(data: Optional[str], error_display=None) -> str:
"""Parses the SequentialWorkflow output string and formats it for display."""
logger.info("Parsing SequentialWorkflow output...")
@ -1259,6 +1282,9 @@ class UI:
- MixtureOfAgents: Combines multiple agents with an aggregator
- SpreadSheetSwarm: Specialized for spreadsheet operations
- Auto: Automatically determines optimal workflow
**Note:**
Spreasheet swarm saves data in csv, will work in local setup !
"""
)
return gr.Column()
@ -1596,6 +1622,8 @@ def create_app():
except Exception as e:
return f"Error saving agent prompt {str(e)}"
# In the run_task_wrapper function, modify the API key handling
async def run_task_wrapper(
task,
max_loops,
@ -1614,7 +1642,6 @@ def create_app():
# Update status
yield "Processing...", "Running task...", "", gr.update(visible=False), gr.update(visible=False)
# Prepare flow for AgentRearrange
flow = None
if swarm_type == "AgentRearrange":
@ -1634,47 +1661,15 @@ def create_app():
f"Flow string: {flow}"
) # Debug: Print flow string
# Save API key to .env
env_path = find_dotenv()
if not env_path:
env_path = os.path.join(os.getcwd(), ".env")
with open(env_path, "w") as f:
f.write("")
if provider == "openai":
set_key(env_path, "OPENAI_API_KEY", api_key)
elif provider == "anthropic":
set_key(
env_path, "ANTHROPIC_API_KEY", api_key
)
elif provider == "cohere":
set_key(env_path, "COHERE_API_KEY", api_key)
elif provider == "gemini":
set_key(env_path, "GEMINI_API_KEY", api_key)
elif provider == "mistral":
set_key(env_path, "MISTRAL_API_KEY", api_key)
elif provider == "groq":
set_key(env_path, "GROQ_API_KEY", api_key)
elif provider == "perplexity":
set_key(
env_path, "PERPLEXITY_API_KEY", api_key
)
else:
yield (
f"Error: {provider} this provider is not"
" present",
f"Error: {provider} not supported",
"",
gr.update(visible=True),
gr.update(visible=False)
)
return
# save api keys in memory
api_keys[provider] = api_key
agents = initialize_agents(
dynamic_temp,
agent_prompt_selector,
model_name,
provider,
api_key,
api_keys.get(provider), # Access API key from the dictionary
temperature,
max_tokens,
)
@ -1698,7 +1693,7 @@ def create_app():
flow=flow,
model_name=model_name,
provider=provider,
api_key=api_key,
api_key=api_keys.get(provider), # Pass the api key from memory
temperature=temperature,
max_tokens=max_tokens,
agents=agents_dict, # Changed here
@ -1716,6 +1711,45 @@ def create_app():
yield f"Error: {str(e)}", f"Error: {str(e)}", "", gr.update(visible=True), gr.update(visible=True)
return
# Save API key to .env
env_path = find_dotenv()
if not env_path:
env_path = os.path.join(os.getcwd(), ".env")
with open(env_path, "w") as f:
f.write("")
if not env_path:
env_path = os.path.join(os.getcwd(), ".env")
with open(env_path, "w") as f:
f.write("")
if provider == "openai":
set_key(env_path, "OPENAI_API_KEY", api_key)
elif provider == "anthropic":
set_key(
env_path, "ANTHROPIC_API_KEY", api_key
)
elif provider == "cohere":
set_key(env_path, "COHERE_API_KEY", api_key)
elif provider == "gemini":
set_key(env_path, "GEMINI_API_KEY", api_key)
elif provider == "mistral":
set_key(env_path, "MISTRAL_API_KEY", api_key)
elif provider == "groq":
set_key(env_path, "GROQ_API_KEY", api_key)
elif provider == "perplexity":
set_key(
env_path, "PERPLEXITY_API_KEY", api_key
)
else:
yield (
f"Error: {provider} this provider is not"
" present",
f"Error: {provider} not supported",
"",
gr.update(visible=True),
gr.update(visible=False)
)
return
# Connect the update functions
agent_selector.change(
fn=update_ui_for_swarm_type,

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