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[FEAT][SpreadSheetSwarm][Docs]

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- AutoSwarm: "swarms/structs/auto_swarm.md"
- GroupChat: "swarms/structs/group_chat.md"
- AgentRegistry: "swarms/structs/agent_registry.md"
- SpreadSheetSwarm: "swarms/structs/spreadsheet_swarm.md"
# - Workflows: "swarms/structs/workflows.md"
# - Workflows:
# - BaseWorkflow: "swarms/structs/base_workflow.md"

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# SpreadSheetSwarm Documentation
---
## Class Definition
```python
class SpreadSheetSwarm:
```
## Full Path
```python
from swarms.structs.spreadsheet_swarm import SpreadSheetSwarm
```
### Attributes
The `SpreadSheetSwarm` class contains several attributes that define its behavior and configuration. These attributes are initialized in the constructor (`__init__` method) and are used throughout the class to manage the swarm's operations.
| Attribute | Type | Description |
|--------------------|-----------------------------------|---------------------------------------------------------------------------------------------|
| `name` | `str` | The name of the swarm. |
| `description` | `str` | A description of the swarm's purpose. |
| `agents` | `Union[Agent, List[Agent]]` | The agents participating in the swarm. Can be a single agent or a list of agents. |
| `autosave_on` | `bool` | Flag indicating whether autosave is enabled. |
| `save_file_path` | `str` | The file path where the swarm data will be saved. |
| `task_queue` | `queue.Queue` | The queue that stores tasks to be processed by the agents. |
| `lock` | `threading.Lock` | A lock used for thread synchronization to prevent race conditions. |
| `metadata` | `SwarmRunMetadata` | Metadata for the swarm run, including start time, end time, tasks completed, and outputs. |
| `run_all_agents` | `bool` | Flag indicating whether to run all agents or just one. |
| `repeat_count` | `int` | The number of times to repeat the task. |
| `workspace_dir` | `str` | The directory where the workspace is located, retrieved from environment variables. |
### Parameters
- **`name`** (`str`, optional): The name of the swarm. Default is `"Spreadsheet-Swarm"`.
- **`description`** (`str`, optional): A brief description of the swarm. Default is `"A swarm that processes tasks from a queue using multiple agents on different threads."`.
- **`agents`** (`Union[Agent, List[Agent]]`, optional): The agents participating in the swarm. Default is an empty list.
- **`autosave_on`** (`bool`, optional): A flag to indicate if autosave is enabled. Default is `True`.
- **`save_file_path`** (`str`, optional): The file path where swarm data will be saved. Default is `"spreedsheet_swarm.csv"`.
- **`run_all_agents`** (`bool`, optional): Flag to determine if all agents should run. Default is `True`.
- **`repeat_count`** (`int`, optional): The number of times to repeat the task. Default is `1`.
- **`workspace_dir`** (`str`, optional): The directory where the workspace is located. Default is retrieved from environment variable `WORKSPACE_DIR`.
### Constructor (`__init__`)
The constructor initializes the `SpreadSheetSwarm` with the provided parameters. It sets up the task queue, locks for thread synchronization, and initializes the metadata.
---
## Methods
### `reliability_check`
```python
def reliability_check(self):
```
#### Description
The `reliability_check` method performs a series of checks to ensure that the swarm is properly configured before it begins processing tasks. It verifies that there are agents available and that a valid file path is provided for saving the swarm's data. If any of these checks fail, an exception is raised.
#### Raises
- **`ValueError`**: Raised if no agents are provided or if no save file path is specified.
#### Example
```python
swarm = SpreadSheetSwarm(agents=[agent1, agent2])
swarm.reliability_check()
```
---
### `run`
```python
def run(self, task: str, *args, **kwargs):
```
#### Description
The `run` method starts the task processing using the swarm. Depending on the configuration, it can either run all agents or a specific subset of them. The method tracks the start and end times of the task, executes the task multiple times if specified, and logs the results.
#### Parameters
- **`task`** (`str`): The task to be executed by the swarm.
- **`*args`**: Additional positional arguments to pass to the agents.
- **`**kwargs`**: Additional keyword arguments to pass to the agents.
#### Example
```python
swarm = SpreadSheetSwarm(agents=[agent1, agent2])
swarm.run("Process Data")
```
---
### `export_to_json`
```python
def export_to_json(self):
```
#### Description
The `export_to_json` method generates a JSON representation of the swarm's metadata. This can be useful for exporting the results to an external system or for logging purposes.
#### Returns
- **`str`**: The JSON representation of the swarm's metadata.
#### Example
```python
json_data = swarm.export_to_json()
print(json_data)
```
---
### `data_to_json_file`
```python
def data_to_json_file(self):
```
#### Description
The `data_to_json_file` method saves the swarm's metadata as a JSON file in the specified workspace directory. The file name is generated using the swarm's name and run ID.
#### Example
```python
swarm.data_to_json_file()
```
---
### `_track_output`
```python
def _track_output(self, agent: Agent, task: str, result: str):
```
#### Description
The `_track_output` method is used internally to record the results of tasks executed by the agents. It updates the metadata with the completed tasks and their results.
#### Parameters
- **`agent`** (`Agent`): The agent that executed the task.
- **`task`** (`str`): The task that was executed.
- **`result`** (`str`): The result of the task execution.
#### Example
```python
swarm._track_output(agent1, "Process Data", "Success")
```
---
### `_save_to_csv`
```python
def _save_to_csv(self):
```
#### Description
The `_save_to_csv` method saves the swarm's metadata to a CSV file. It logs each task and its result before writing them to the file. The file is saved in the location specified by `save_file_path`.
#### Example
```python
swarm._save_to_csv()
```
---
## Usage Examples
### Example 1: Basic Swarm Initialization
```python
import os
from swarms import Agent, OpenAIChat
from swarms.prompts.finance_agent_sys_prompt import (
FINANCIAL_AGENT_SYS_PROMPT,
)
from swarms.structs.spreadsheet_swarm import SpreadSheetSwarm
# Example usage:
api_key = os.getenv("OPENAI_API_KEY")
# Model
model = OpenAIChat(
openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
)
# Initialize your agents (assuming the Agent class and model are already defined)
agents = [
Agent(
agent_name=f"Financial-Analysis-Agent-spreesheet-swarm:{i}",
system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
llm=model,
max_loops=1,
dynamic_temperature_enabled=True,
saved_state_path="finance_agent.json",
user_name="swarms_corp",
retry_attempts=1,
)
for i in range(10)
]
# Create a Swarm with the list of agents
swarm = SpreadSheetSwarm(
name="Finance-Spreadsheet-Swarm",
description="A swarm that processes tasks from a queue using multiple agents on different threads.",
agents=agents,
autosave_on=True,
save_file_path="financial_spreed_sheet_swarm_demo.csv",
run_all_agents=False,
repeat_count=1,
)
# Run the swarm
swarm.run(
task="Analyze the states with the least taxes for LLCs. Provide an overview of all tax rates and add them with a comprehensive analysis"
)
```
### Example 2: QR Code Generator
```python
import os
from swarms import Agent, OpenAIChat
from swarms.structs.spreadsheet_swarm import SpreadSheetSwarm
# Define custom system prompts for QR code generation
QR_CODE_AGENT_1_SYS_PROMPT = """
You are a Python coding expert. Your task is to write a Python script to generate a QR code for the link: https://lu.ma/jjc1b2bo. The code should save the QR code as an image file.
"""
QR_CODE_AGENT_2_SYS_PROMPT = """
You are a Python coding expert. Your task is to write a Python script to generate a QR code for the link: https://github.com/The-Swarm-Corporation/Cookbook. The code should save the QR code as an image file.
"""
# Example usage:
api_key = os.getenv("OPENAI_API_KEY")
# Model
model = OpenAIChat(
openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
)
# Initialize your agents for QR code generation
agents = [
Agent(
agent_name="QR-Code-Generator-Agent-Luma",
system_prompt=QR_CODE_AGENT_1_SYS_PROMPT,
llm=model,
max_loops=1,
dynamic_temperature_enabled=True,
saved_state_path="qr_code_agent_luma.json",
user_name="swarms_corp",
retry_attempts=1,
),
Agent(
agent_name="QR-Code-Generator-Agent-Cookbook",
system_prompt=QR_CODE_AGENT_2_SYS_PROMPT,
llm=model,
max_loops=1,
dynamic_temperature_enabled=True,
saved_state_path="qr_code_agent_cookbook.json",
user_name="swarms_corp",
retry_attempts=1,
),
]
# Create a Swarm with the list of agents
swarm = SpreadSheetSwarm(
name="QR-Code-Generation-Swarm",
description="A swarm that generates Python scripts to create QR codes for specific links.",
agents=agents,
autosave_on=True,
save_file_path="qr_code_generation_results.csv",
run_all_agents=False,
repeat_count=1,
)
# Run the swarm
swarm.run(
task="Generate Python scripts to create QR codes for the provided links and save them as image files."
)
```
## Example 3: Social Media Marketing
```python
import os
from swarms import Agent, OpenAIChat
from swarms.structs.spreadsheet_swarm import SpreadSheetSwarm
# Define custom system prompts for each social media platform
TWITTER_AGENT_SYS_PROMPT = """
You are a Twitter marketing expert. Your task is to create engaging, concise tweets and analyze trends to maximize engagement. Consider hashtags, timing, and content relevance.
"""
INSTAGRAM_AGENT_SYS_PROMPT = """
You are an Instagram marketing expert. Your task is to create visually appealing and engaging content, including captions and hashtags, tailored to a specific audience.
"""
FACEBOOK_AGENT_SYS_PROMPT = """
You are a Facebook marketing expert. Your task is to craft posts that are optimized for engagement and reach on Facebook, including using images, links, and targeted messaging.
"""
EMAIL_AGENT_SYS_PROMPT = """
You are an Email marketing expert. Your task is to write compelling email campaigns that drive conversions, focusing on subject lines, personalization, and call-to-action strategies.
"""
# Example usage:
api_key = os.getenv("OPENAI_API_KEY")
# Model
model = OpenAIChat(
openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
)
# Initialize your agents for different social media platforms
agents = [
Agent(
agent_name="Twitter-Marketing-Agent",
system_prompt=TWITTER_AGENT_SYS_PROMPT,
llm=model,
max_loops=1,
dynamic_temperature_enabled=True,
saved_state_path="twitter_agent.json",
user_name="swarms_corp",
retry_attempts=1,
),
Agent(
agent_name="Instagram-Marketing-Agent",
system_prompt=INSTAGRAM_AGENT_SYS_PROMPT,
llm=model,
max_loops=1,
dynamic_temperature_enabled=True,
saved_state_path="instagram_agent.json",
user_name="swarms_corp",
retry_attempts=1,
),
Agent(
agent_name="Facebook-Marketing-Agent",
system_prompt=FACEBOOK_AGENT_SYS_PROMPT,
llm=model,
max_loops=1,
dynamic_temperature_enabled=True,
saved_state_path="facebook_agent.json",
user_name="swarms_corp",
retry_attempts=1,
),
Agent(
agent_name="Email-Marketing-Agent",
system_prompt=EMAIL_AGENT_SYS_PROMPT,
llm=model,
max_loops=1,
dynamic_temperature_enabled=True,
saved_state_path="email_agent.json",
user_name="swarms_corp",
retry_attempts=1,
),
]
# Create a Swarm with the list of agents
swarm = SpreadSheetSwarm(
name="Social-Media-Marketing-Swarm",
description="A swarm that processes social media marketing tasks using multiple agents on different threads.",
agents=agents,
autosave_on=True,
save_file_path="social_media_marketing_spreadsheet.csv",
run_all_agents=False,
repeat_count=2,
)
# Run the swarm
swarm.run(
task="Create posts to promote hack nights in miami beach for developers, engineers, and tech enthusiasts. Include relevant hashtags, images, and engaging captions."
)
```
---
## Additional Information and Tips
- **Thread Synchronization**: When working with multiple agents in a concurrent environment, it's crucial to ensure that access to shared resources is properly synchronized using locks to avoid race conditions.
- **Autosave Feature**: If you enable the `autosave_on` flag, ensure that the file path provided is correct and writable. This feature is handy for long-running tasks where you want to periodically save the state.
- **Error Handling**
: Implementing proper error handling within your agents can prevent the swarm from crashing during execution. Consider catching exceptions in the `run` method and logging errors appropriately.
- **Custom Agents**: You can extend the `Agent` class to create custom agents that perform specific tasks tailored to your application's needs.
---
## References and Resources
- [Python's `queue` module](https://docs.python.org/3/library/queue.html)
- [Python's `threading` module](https://docs.python.org/3/library/threading.html)
- [CSV File Handling in Python](https://docs.python.org/3/library/csv.html)
- [JSON Handling in Python](https://docs.python.org/3/library/json.html)
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