Skip to content

Commit

Permalink
[7.1.7] [cleanup] [no clusterops] [majorityvoting fix]
Browse files Browse the repository at this point in the history
  • Loading branch information
@kyegomez
kyegomez committed Feb 12, 2025
1 parent fb49426 commit 928e1ec
Show file tree
Hide file tree
Showing 36 changed files with 2,343 additions and 1,251 deletions.
2 changes: 0 additions & 2 deletions docs/swarms/models/models_available_overview.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -278,8 +278,6 @@ Use logging to monitor the behavior and performance of your models. The `loguru`
```python
from loguru import logger

logger.add("file.log", rotation="10 MB")

# Log model interactions
logger.info("Running task on Anthropic model")
response = model(task)
Expand Down
314 changes: 157 additions & 157 deletions docs/swarms/structs/majorityvoting.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -2,216 +2,216 @@

The `MajorityVoting` module provides a mechanism for performing majority voting among a group of agents. Majority voting is a decision rule that selects the option which has the majority of votes. This is particularly useful in systems where multiple agents provide responses to a query, and the most common response needs to be identified as the final output.

## Architecture

```mermaid
graph TD
A[MajorityVoting System] --> B[Initialize Agents]
B --> C[Process Task]
C --> D{Execution Mode}
D --> E[Single Task]
D --> F[Batch Tasks]
D --> G[Concurrent Tasks]
D --> H[Async Tasks]
E --> I[Run Agents]
F --> I
G --> I
H --> I
I --> J[Collect Responses]
J --> K[Consensus Analysis]
K --> L{Consensus Agent?}
L -->|Yes| M[Use Consensus Agent]
L -->|No| N[Use Last Agent]
M --> O[Final Output]
N --> O
O --> P[Save Conversation]
```

### Key Concepts

- **Majority Voting**: A method to determine the most common response from a set of answers.
- **Agents**: Entities (e.g., models, algorithms) that provide responses to tasks or queries.
- **Output Parser**: A function that processes the responses from the agents before performing the majority voting.
- **Consensus Agent**: An optional agent that analyzes the responses from all agents to determine the final consensus.
- **Conversation History**: A record of all agent interactions and responses during the voting process.

## Function Definitions

### Function: `majority_voting`

Performs majority voting on a list of answers and returns the most common answer.
## Class Definition: `MajorityVoting`

#### Parameters
### Parameters

| Parameter | Type | Description |
|-----------|----------|------------------------------|
| `answers` | `List[str]` | A list of answers from different agents. |
| Parameter | Type | Description |
|------------------|----------------|-----------------------------------------------------------------------------|
| `name` | `str` | Name of the majority voting system. Default is "MajorityVoting". |
| `description` | `str` | Description of the system. Default is "A majority voting system for agents". |
| `agents` | `List[Agent]` | A list of agents to be used in the majority voting system. |
| `output_parser` | `Callable` | Function to parse agent outputs. Default is `majority_voting` function. |
| `consensus_agent`| `Agent` | Optional agent for analyzing consensus among responses. |
| `autosave` | `bool` | Whether to autosave conversations. Default is `False`. |
| `verbose` | `bool` | Whether to enable verbose logging. Default is `False`. |
| `max_loops` | `int` | Maximum number of voting loops. Default is 1. |

#### Returns
### Methods

| Return Value | Type | Description |
|--------------|-------|----------------------------------------|
| `answer` | `str` | The most common answer in the list. If the list is empty, returns "I don't know". |
#### `run(task: str, correct_answer: str, *args, **kwargs) -> List[Any]`

## Class Definitions
Runs the majority voting system for a single task.

### Class: `MajorityVoting`
**Parameters:**
- `task` (str): The task to be performed by the agents
- `correct_answer` (str): The correct answer for evaluation
- `*args`, `**kwargs`: Additional arguments

Class representing a majority voting system for agents.
**Returns:**
- List[Any]: The conversation history as a string, including the majority vote

#### Parameters
#### `batch_run(tasks: List[str], *args, **kwargs) -> List[Any]`

| Parameter | Type | Description |
|------------------|--------------|-----------------------------------------------------------------------------|
| `agents` | `List[Agent]`| A list of agents to be used in the majority voting system. |
| `output_parser` | `Callable` | A function used to parse the output of the agents. If not provided, the default `majority_voting` function is used. |
| `autosave` | `bool` | A boolean indicating whether to autosave the conversation to a file. Default is `False`. |
| `verbose` | `bool` | A boolean indicating whether to enable verbose logging. Default is `False`. |
Runs multiple tasks in sequence.

### Method: `__init__`
**Parameters:**
- `tasks` (List[str]): List of tasks to be performed
- `*args`, `**kwargs`: Additional arguments

Initializes the `MajorityVoting` system.
**Returns:**
- List[Any]: List of majority votes for each task

#### Parameters
#### `run_concurrently(tasks: List[str], *args, **kwargs) -> List[Any]`

| Parameter | Type | Description |
|------------------|----------------|-----------------------------------------------------------------------------|
| `agents` | `List[Agent]` | A list of agents to be used in the majority voting system. |
| `output_parser` | `Callable` | A function used to parse the output of the agents. Default is the `majority_voting` function. |
| `autosave` | `bool` | A boolean indicating whether to autosave the conversation to a file. Default is `False`. |
| `verbose` | `bool` | A boolean indicating whether to enable verbose logging. Default is `False`. |
| `args` | `tuple` | Additional positional arguments. |
| `kwargs` | `dict` | Additional keyword arguments. |
Runs multiple tasks concurrently using thread pooling.

### Method: `run`
**Parameters:**
- `tasks` (List[str]): List of tasks to be performed
- `*args`, `**kwargs`: Additional arguments

Runs the majority voting system and returns the majority vote.
**Returns:**
- List[Any]: List of majority votes for each task

#### Parameters
#### `run_async(tasks: List[str], *args, **kwargs) -> List[Any]`

| Parameter | Type | Description |
|-----------|------------|------------------------------------------|
| `task` | `str` | The task to be performed by the agents. |
| `args` | `tuple` | Variable length argument list. |
| `kwargs` | `dict` | Arbitrary keyword arguments. |
Runs multiple tasks asynchronously using asyncio.

#### Returns
**Parameters:**
- `tasks` (List[str]): List of tasks to be performed
- `*args`, `**kwargs`: Additional arguments

| Return Value | Type | Description |
|--------------|-----------|--------------------------------------|
| `results` | `List[Any]` | The majority vote. |
**Returns:**
- List[Any]: List of majority votes for each task

## Usage Examples

### Example 1: Basic Majority Voting
### Example 1: Basic Single Task Execution with Modern LLMs

```python
from swarms.structs.agent import Agent
from swarms.structs.majority_voting import MajorityVoting
from swarms import Agent, MajorityVoting

# Initialize agents
# Initialize multiple agents with different specialties
agents = [
Agent(
agent_name="Devin",
system_prompt=(
"Autonomous agent that can interact with humans and other"
" agents. Be Helpful and Kind. Use the tools provided to"
" assist the user. Return all code in markdown format."
),
llm=llm,
max_loops="auto",
autosave=True,
dashboard=False,
streaming_on=True,
verbose=True,
stopping_token="<DONE>",
interactive=True,
tools=[terminal, browser, file_editor, create_file],
code_interpreter=True,
agent_name="Financial-Analysis-Agent",
agent_description="Personal finance advisor focused on market analysis",
system_prompt="You are a financial advisor specializing in market analysis and investment opportunities.",
max_loops=1,
model_name="gpt-4o"
),
Agent(
agent_name="Codex",
system_prompt=(
"An AI coding assistant capable of writing and understanding"
" code snippets in various programming languages."
),
llm=llm,
max_loops="auto",
autosave=True,
dashboard=False,
streaming_on=True,
verbose=True,
stopping_token="<DONE>",
interactive=True,
tools=[terminal, browser, file_editor, create_file],
code_interpreter=True,
agent_name="Risk-Assessment-Agent",
agent_description="Risk analysis and portfolio management expert",
system_prompt="You are a risk assessment expert focused on evaluating investment risks and portfolio diversification.",
max_loops=1,
model_name="gpt-4o"
),
Agent(
agent_name="Tabnine",
system_prompt=(
"A code completion AI that provides suggestions for code"
" completion and code improvements."
),
llm=llm,
max_loops="auto",
autosave=True,
dashboard=False,
streaming_on=True,
verbose=True,
stopping_token="<DONE>",
interactive=True,
tools=[terminal, browser, file_editor, create_file],
code_interpreter=True,
),
agent_name="Tech-Investment-Agent",
agent_description="Technology sector investment specialist",
system_prompt="You are a technology investment specialist focused on AI, emerging tech, and growth opportunities.",
max_loops=1,
model_name="gpt-4o"
)
]

# Create MajorityVoting instance
majority_voting = MajorityVoting(agents)

# Run the majority voting system
result = majority_voting.run("What is the capital of France?")
print(result) # Output: 'Paris'
consensus_agent = Agent(
agent_name="Consensus-Agent",
agent_description="Consensus agent focused on analyzing investment advice",
system_prompt="You are a consensus agent focused on analyzing investment advice and providing a final answer.",
max_loops=1,
model_name="gpt-4o"
)

# Create majority voting system
majority_voting = MajorityVoting(
name="Investment-Advisory-System",
description="Multi-agent system for investment advice",
agents=agents,
verbose=True,
consensus_agent=consensus_agent
)

# Run the analysis with majority voting
result = majority_voting.run(
task="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.",
correct_answer="" # Optional evaluation metric
)

print(result)

```

### Example 2: Running a Task with Detailed Outputs
## Batch Execution

```python
from swarms.structs.agent import Agent
from swarms.structs.majority_voting import MajorityVoting
from swarms import Agent, MajorityVoting

# Initialize agents
# Initialize multiple agents with different specialties
agents = [
Agent(
agent_name="Devin",
system_prompt=(
"Autonomous agent that can interact with humans and other"
" agents. Be Helpful and Kind. Use the tools provided to"
" assist the user. Return all code in markdown format."
),
llm=llm,
max_loops="auto",
autosave=True,
dashboard=False,
streaming_on=True,
verbose=True,
stopping_token="<DONE>",
interactive=True,
tools=[terminal, browser, file_editor, create_file],
code_interpreter=True,
agent_name="Financial-Analysis-Agent",
agent_description="Personal finance advisor focused on market analysis",
system_prompt="You are a financial advisor specializing in market analysis and investment opportunities.",
max_loops=1,
model_name="gpt-4o"
),
Agent(
agent_name="Codex",
system_prompt=(
"An AI coding assistant capable of writing and understanding"
" code snippets in various programming languages."
),
llm=llm,
max_loops="auto",
autosave=True,
dashboard=False,
streaming_on=True,
verbose=True,
stopping_token="<DONE>",
interactive=True,
tools=[terminal, browser, file_editor, create_file],
code_interpreter=True,
agent_name="Risk-Assessment-Agent",
agent_description="Risk analysis and portfolio management expert",
system_prompt="You are a risk assessment expert focused on evaluating investment risks and portfolio diversification.",
max_loops=1,
model_name="gpt-4o"
),
Agent(
agent_name="Tabnine",
system_prompt=(
"A code completion AI that provides suggestions for code"
" completion and code improvements."
),
llm=llm,
max_loops="auto",
autosave=True,
dashboard=False,
streaming_on=True,
verbose=True,
stopping_token="<DONE>",
interactive=True,
tools=[terminal, browser, file_editor, create_file],
code_interpreter=True,
),
agent_name="Tech-Investment-Agent",
agent_description="Technology sector investment specialist",
system_prompt="You are a technology investment specialist focused on AI, emerging tech, and growth opportunities.",
max_loops=1,
model_name="gpt-4o"
)
]

# Create MajorityVoting instance
majority_voting = MajorityVoting(agents)

# Run the majority voting system with a different task
result = majority_voting.run("Create a new file for a plan to take over the world.")
consensus_agent = Agent(
agent_name="Consensus-Agent",
agent_description="Consensus agent focused on analyzing investment advice",
system_prompt="You are a consensus agent focused on analyzing investment advice and providing a final answer.",
max_loops=1,
model_name="gpt-4o"
)

# Create majority voting system
majority_voting = MajorityVoting(
name="Investment-Advisory-System",
description="Multi-agent system for investment advice",
agents=agents,
verbose=True,
consensus_agent=consensus_agent
)

# Run the analysis with majority voting
result = majority_voting.batch_run(
task="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.",
correct_answer="" # Optional evaluation metric
)

print(result)


```
Loading

0 comments on commit 928e1ec

Please sign in to comment.