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pull/518/head^2
Kye Gomez 7 months ago
parent 1ad7061d75
commit 9e32b81b89
6 changed files with 360 additions and 307 deletions
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192
api.py
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import asyncio
import os
from typing import List
import tiktoken
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from swarms import Agent, Anthropic, GPT4o, GPT4VisionAPI, OpenAIChat
from swarms.utils.loguru_logger import logger
from swarms_cloud.schema.cog_vlm_schemas import (
ChatCompletionResponse,
UsageInfo,
)
# Define the input model using Pydantic
class AgentInput(BaseModel):
agent_name: str = "Swarm Agent"
system_prompt: str = None
agent_description: str = None
model_name: str = "OpenAIChat"
max_loops: int = 1
autosave: bool = False
dynamic_temperature_enabled: bool = False
dashboard: bool = False
verbose: bool = False
streaming_on: bool = True
saved_state_path: str = None
sop: str = None
sop_list: List[str] = None
user_name: str = "User"
retry_attempts: int = 3
context_length: int = 8192
task: str = None
# Define the input model using Pydantic
class AgentOutput(BaseModel):
agent: AgentInput
completions: ChatCompletionResponse
async def count_tokens(
text: str,
):
try:
# Get the encoding for the specific model
encoding = tiktoken.get_encoding("gpt-4o")
# Encode the text
tokens = encoding.encode(text)
# Count the tokens
token_count = len(tokens)
return token_count
except Exception as e:
raise HTTPException(status_code=400, detail=str(e))
async def model_router(model_name: str):
"""
Function to switch to the specified model.
Parameters:
- model_name (str): The name of the model to switch to.
Returns:
- None
Raises:
- None
"""
# Logic to switch to the specified model
if model_name == "OpenAIChat":
# Switch to OpenAIChat model
llm = OpenAIChat()
elif model_name == "GPT4o":
# Switch to GPT4o model
llm = GPT4o(openai_api_key=os.getenv("OPENAI_API_KEY"))
elif model_name == "GPT4VisionAPI":
# Switch to GPT4VisionAPI model
llm = GPT4VisionAPI()
elif model_name == "Anthropic":
# Switch to Anthropic model
llm = Anthropic(anthropic_api_key=os.getenv("ANTHROPIC_API_KEY"))
else:
# Invalid model name
pass
return llm
# Create a FastAPI app
app = FastAPI(debug=True)
# Load the middleware to handle CORS
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# @app.get("/v1/models", response_model=ModelList)
# async def list_models():
# """
# An endpoint to list available models. It returns a list of model cards.
# This is useful for clients to query and understand what models are available for use.
# """
# model_card = ModelCard(
# id="cogvlm-chat-17b"
# ) # can be replaced by your model id like cogagent-chat-18b
# return ModelList(data=[model_card])
@app.post("v1/agent/completions", response_model=AgentOutput)
async def agent_completions(agent_input: AgentInput):
try:
logger.info(f"Received request: {agent_input}")
llm = model_router(agent_input.model_name)
agent = Agent(
agent_name=agent_input.agent_name,
system_prompt=agent_input.system_prompt,
agent_description=agent_input.agent_description,
llm=llm,
max_loops=agent_input.max_loops,
autosave=agent_input.autosave,
dynamic_temperature_enabled=agent_input.dynamic_temperature_enabled,
dashboard=agent_input.dashboard,
verbose=agent_input.verbose,
streaming_on=agent_input.streaming_on,
saved_state_path=agent_input.saved_state_path,
sop=agent_input.sop,
sop_list=agent_input.sop_list,
user_name=agent_input.user_name,
retry_attempts=agent_input.retry_attempts,
context_length=agent_input.context_length,
)
# Run the agent
logger.info(f"Running agent with task: {agent_input.task}")
completions = await agent.run(agent_input.task)
logger.info(f"Completions: {completions}")
all_input_tokens, output_tokens = await asyncio.gather(
count_tokens(agent.short_memory.return_history_as_string()),
count_tokens(completions),
)
logger.info(f"Token counts: {all_input_tokens}, {output_tokens}")
out = AgentOutput(
agent=agent_input,
completions=ChatCompletionResponse(
choices=[
{
"index": 0,
"message": {
"role": agent_input.agent_name,
"content": completions,
"name": None,
},
}
],
stream_choices=None,
usage_info=UsageInfo(
prompt_tokens=all_input_tokens,
completion_tokens=output_tokens,
total_tokens=all_input_tokens + output_tokens,
),
),
)
return out.json()
except Exception as e:
raise HTTPException(status_code=400, detail=str(e))
# if __name__ == "__main__":
# import uvicorn
# uvicorn.run(
# app, host="0.0.0.0", port=8000, use_colors=True, log_level="info"
# )

366
docs/swarms/structs/task.md
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- This is the class for the Task
- For the constructor, it takes in the description, agent, args, kwargs, result, history, schedule_time, scheduler, trigger, action, condition, priority, and dependencies
- The `execute` method runs the task by calling the agent or model with the arguments and keyword arguments
- It sets a trigger, action, and condition for the task
- Task completion is checked with `is_completed` method
- `add_dependency` adds a task to the list of dependencies
- `set_priority` sets the priority of the task
```python
# Example 1: Creating and executing a Task
from swarms.models import OpenAIChat
from swarms.structs import Agent, Task
agent = Agent(llm=OpenAIChat(openai_api_key=""), max_loops=1, dashboard=False)
task = Task(agent=agent)
task.execute("What's the weather in miami")
print(task.result)
# Example 2: Adding a dependency and setting priority
task2 = Task(description="Task 2", agent=agent)
task.add_dependency(task2)
task.set_priority(1)
# Example 3: Executing a scheduled task
task3 = Task(description="Scheduled Task", agent=agent)
task3.schedule_time = datetime.datetime.now() + datetime.timedelta(minutes=30)
task3.handle_scheduled_task()
print(task3.is_completed())
# Task Class Documentation
The `Task` class is a pivotal component designed for managing tasks in a sequential workflow. This class allows for the execution of tasks using various agents, which can be callable objects or specific instances of the `Agent` class. It supports the scheduling of tasks, handling their dependencies, and setting conditions and actions that govern their execution.
Key features of the `Task` class include:
- Executing tasks with specified agents and handling their results.
- Scheduling tasks to run at specified times.
- Setting triggers, actions, and conditions for tasks.
- Managing task dependencies and priorities.
- Providing a history of task executions for tracking purposes.
## Class Definition
The `Task` class is defined as follows:
### Attributes
| Attribute | Type | Description |
|----------------|-----------------------------|---------------------------------------------------------------------------------------|
| `agent` | `Union[Callable, Agent]` | The agent or callable object to run the task. |
| `description` | `str` | Description of the task. |
| `result` | `Any` | Result of the task. |
| `history` | `List[Any]` | History of the task. |
| `schedule_time`| `datetime` | Time to schedule the task. |
| `scheduler` | `sched.scheduler` | Scheduler to schedule the task. |
| `trigger` | `Callable` | Trigger to run the task. |
| `action` | `Callable` | Action to run the task. |
| `condition` | `Callable` | Condition to run the task. |
| `priority` | `int` | Priority of the task. |
| `dependencies` | `List[Task]` | List of tasks that need to be completed before this task can be executed. |
| `args` | `List[Any]` | Arguments to pass to the agent or callable object. |
| `kwargs` | `Dict[str, Any]` | Keyword arguments to pass to the agent or callable object. |
## Methods
### `execute(self, *args, **kwargs)`
Executes the task by calling the agent or model with the specified arguments and keyword arguments. If a condition is set, the task will only execute if the condition returns `True`.
#### Parameters
- `args`: Arguments to pass to the agent or callable object.
- `kwargs`: Keyword arguments to pass to the agent or callable object.
#### Examples
```python
>>> from swarms.structs import Task, Agent
>>> from swarms.models import OpenAIChat
>>> agent = Agent(llm=OpenAIChat(openai_api_key=""), max_loops=1, dashboard=False)
>>> task = Task(description="What's the weather in Miami?", agent=agent)
>>> task.execute()
>>> task.result
```
### `handle_scheduled_task(self)`
Handles the execution of a scheduled task. If the schedule time is not set or has already passed, the task is executed immediately. Otherwise, the task is scheduled to be executed at the specified schedule time.
#### Examples
```python
>>> task.schedule_time = datetime.now() + timedelta(seconds=10)
>>> task.handle_scheduled_task()
```
### `set_trigger(self, trigger: Callable)`
Sets the trigger for the task.
#### Parameters
- `trigger` (`Callable`): The trigger to set.
#### Examples
```python
>>> def my_trigger():
>>> print("Trigger executed")
>>> task.set_trigger(my_trigger)
```
### `set_action(self, action: Callable)`
Sets the action for the task.
#### Parameters
- `action` (`Callable`): The action to set.
#### Examples
```python
>>> def my_action():
>>> print("Action executed")
>>> task.set_action(my_action)
```
### `set_condition(self, condition: Callable)`
Sets the condition for the task.
#### Parameters
- `condition` (`Callable`): The condition to set.
#### Examples
```python
>>> def my_condition():
>>> print("Condition checked")
>>> return True
>>> task.set_condition(my_condition)
```
### `is_completed(self)`
Checks whether the task has been completed.
#### Returns
- `bool`: `True` if the task has been completed, `False` otherwise.
#### Examples
```python
>>> task.is_completed()
```
### `add_dependency(self, task)`
Adds a task to the list of dependencies.
#### Parameters
- `task` (`Task`): The task to add as a dependency.
#### Examples
```python
>>> dependent_task = Task(description="Dependent Task")
>>> task.add_dependency(dependent_task)
```
### `set_priority(self, priority: int)`
Sets the priority of the task.
#### Parameters
- `priority` (`int`): The priority to set.
#### Examples
```python
>>> task.set_priority(5)
```
### `check_dependency_completion(self)`
Checks whether all the dependencies have been completed.
#### Returns
- `bool`: `True` if all the dependencies have been completed, `False` otherwise.
#### Examples
```python
>>> task.check_dependency_completion()
```
### `context(self, task: "Task" = None, context: List["Task"] = None, *args, **kwargs)`
Sets the context for the task. For a sequential workflow, it sequentially adds the context of the previous task in the list.
#### Parameters
- `task` (`Task`, optional): The task whose context is to be set.
- `context` (`List[Task]`, optional): The list of tasks to set the context.
#### Examples
```python
>>> task1 = Task(description="Task 1")
>>> task2 = Task(description="Task 2")
>>> task2.context(context=[task1])
```
## Usage Examples
### Basic Usage
```python
import os
from dotenv import load_dotenv
from swarms import Agent, OpenAIChat, Task
# Load the environment variables
load_dotenv()
# Define a function to be used as the action
def my_action():
print("Action executed")
# Define a function to be used as the condition
def my_condition():
print("Condition checked")
return True
# Create an agent
agent = Agent(
llm=OpenAIChat(openai_api_key=os.environ["OPENAI_API_KEY"]),
max_loops=1,
dashboard=False,
)
# Create a task
task = Task(
description="Generate a report on the top 3 biggest expenses for small businesses and how businesses can save 20%",
agent=agent,
)
# Set the action and condition
task.set_action(my_action)
task.set_condition(my_condition)
# Execute the task
print("Executing task...")
task.run()
# Check if the task is completed
if task.is_completed():
print("Task completed")
else:
print("Task not completed")
# Output the result of the task
print(f"Task result: {task.result}")
```
### Scheduled Task Execution
```python
from datetime import datetime, timedelta
import os
from dotenv import load_dotenv
from swarms import Agent, OpenAIChat, Task
# Load the environment variables
load_dotenv()
# Create an agent
agent = Agent(
llm=OpenAIChat(openai_api_key=os.environ["OPENAI_API_KEY"]),
max_loops=1,
dashboard=False,
)
# Create a task
task = Task(
description="Scheduled task example",
agent=agent,
schedule_time=datetime.now() + timedelta(seconds=10)
)
# Handle scheduled task
task.handle_scheduled_task()
```
### Task with Dependencies
```python
import os
from dotenv import load_dotenv
from swarms import Agent, OpenAIChat, Task
# Load the environment variables
load_dotenv()
# Create agents
agent1 = Agent(
llm=OpenAIChat(openai_api_key=os.environ["OPENAI_API_KEY"]),
max_loops=1,
dashboard=False,
)
agent2 = Agent(
llm=OpenAIChat(openai_api_key=os.environ["OPENAI_API_KEY"]),
max_loops=1,
dashboard=False,
)
# Create tasks
task1 = Task(description="First task", agent=agent1)
task2 = Task(description="Second task", agent=agent2)
# Add dependency
task2.add_dependency(task1)
# Execute tasks
print("Executing first task...")
task1.run()
print("Executing second task...")
task2.run()
# Check if tasks are completed
print(f"Task 1 completed: {task1.is_completed()}")
print(f"Task 2 completed: {task2.is_completed()}")
```
### Task Context
```python
import os
from dotenv import load_dotenv
from swarms import Agent, OpenAIChat, Task
# Load the environment variables
load_dotenv()
# Create an agent
agent = Agent(
llm=OpenAIChat(openai_api_key=os.environ["OPENAI_API_KEY"]),
max_loops
=1,
dashboard=False,
)
# Create tasks
task1 = Task(description="First task", agent=agent)
task2 = Task(description="Second task", agent=agent)
# Set context for the second task
task2.context(context=[task1])
# Execute tasks
print("Executing first task...")
task1.run()
print("Executing second task...")
task2.run()
# Output the context of the second task
print(f"Task 2 context: {task2.history}")
```

0
swarm_workers_agents.py → playground/demos/plant_biologist_swarm/agricultural_swarm.py
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0
task_example.py → playground/structs/task_example.py
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23
json_log_cleanup.py → scripts/cleanup/json_log_cleanup.py
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import os
import shutil
from loguru import logger
def cleanup_json_logs(name: str = None):
# Define the root directory and the target directory
@ -26,8 +26,27 @@ def cleanup_json_logs(name: str = None):
# Move the file to the target directory
shutil.move(file_path, target_path)
logger.info(f"Moved file {file_path} to {target_path}")
# Delete Chroma and Ruff cache
chroma_cache = os.path.join(root_dir, ".chroma_cache")
ruff_cache = os.path.join(root_dir, ".ruff_cache")
if os.path.exists(chroma_cache):
shutil.rmtree(chroma_cache)
logger.info(f"Deleted Chroma cache at {chroma_cache}")
if os.path.exists(ruff_cache):
shutil.rmtree(ruff_cache)
logger.info(f"Deleted Ruff cache at {ruff_cache}")
# Delete the "chroma" folder
chroma_folder = os.path.join(root_dir, "chroma")
if os.path.exists(chroma_folder):
shutil.rmtree(chroma_folder)
logger.info(f"Deleted Chroma folder at {chroma_folder}")
print(f"All JSON, LOG and TXT files have been moved to {target_dir}")
logger.info(f"All JSON, LOG and TXT files have been moved to {target_dir}")
# Call the function

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sky_serve.yaml
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envs:
# MODEL_NAME: meta-llama/Meta-Llama-3-70B-Instruct
MODEL_NAME: meta-llama/Meta-Llama-3-8B
HF_TOKEN: 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 # Change to your own huggingface token, or use --env to pass.
HF_HUB_ENABLE_HF_TRANSFER: True
# Service configuration
service:
readiness_probe:
path: /v1/chat/completions # Path for the readiness probe
post_data:
model: $MODEL_NAME # Specify the model name
messages:
- role: user
content: Hello! What is your name? # Specify the initial message
max_tokens: 1 # Maximum number of tokens
readiness_probe: /v1/health # Additional readiness probe
# Replica Policy
replica_policy:
min_replicas: 1 # Minimum number of replicas
max_replicas: 10 # Maximum number of replicas
target_qps_per_replica: 2.5 # Target queries per second per replica
upscale_delay_seconds: 200 # Delay before upscaling replicas
downscale_delay_seconds: 1200 # Delay before downscaling replicas
resources:
# accelerators: {L4:8, A10g:8, A10:8, A100:4, A100:8, A100-80GB:2, A100-80GB:4, A100-80GB:8}
accelerators: {A10g, A10, L40, A40} # We can use cheaper accelerators for 8B model.
# cpus: 32+
use_spot: True
disk_size: 100 # Ensure model checkpoints can fit.
# disk_tier: best
ports: 8081 # Expose to internet traffic.
setup: |
#Install vllm
conda activate vllm
if [ $? -ne 0 ]; then
conda create -n vllm python=3.10 -y
conda activate vllm
fi
pip install vllm==0.4.0.post1
# Install Gradio for web UI.
pip install gradio openai
pip install flash-attn==2.5.7
pip install hf_transfer
run: |
# Serve VLM
conda activate vllm
echo 'Starting vllm api server...'
# https://github.com/vllm-project/vllm/issues/3098
export PATH=$PATH:/sbin
# NOTE: --gpu-memory-utilization 0.95 needed for 4-GPU nodes.
python3 -u -m vllm.entrypoints.openai.api_server \
--port 8090 \
--model meta-llama/Meta-Llama-3-8B \
--trust-remote-code --tensor-parallel-size 4 \
--gpu-memory-utilization 0.95 \
--max-num-seqs 64 \
# Serve Gradio
# echo 'Starting gradio server...'
# git clone https://github.com/vllm-project/vllm.git || true
# python vllm/examples/gradio_openai_chatbot_webserver.py \
# -m $MODEL_NAME \
# --port 8811 \
# --model-url http://localhost:8081/v1 \
# --stop-token-ids 128009,128001
# --share
echo 'Starting gradio server...'
git clone https://github.com/vllm-project/vllm.git || true
python3 vllm/examples/gradio_openai_chatbot_webserver.py \
-m meta-llama/Meta-Llama-3-8B\
--port 8811 \
--model-url http://localhost:8081/v1 \
--stop-token-ids 128009,128001
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