Tests for custom_agent.py

.dockerignore

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+.env
+__pycache__
+.venv

.github/workflows/python-publish.yml

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+
+name: Upload Python Package
+
+on:
+  release:
+    types: [published]
+
+permissions:
+  contents: read
+
+jobs:
+  deploy:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python
+      uses: actions/setup-python@v3
+      with:
+        python-version: '3.x'
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install build
+    - name: Build package
+      run: python -m build
+    - name: Publish package
+      uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
+      with:
+        user: __token__
+        password: ${{ secrets.PYPI_API_TOKEN }}

.github/workflows/test.yml

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+name: test
+
+on:
+  push:
+    branches: [master]
+  pull_request:
+  workflow_dispatch:
+
+env:
+  POETRY_VERSION: "1.4.2"
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version:
+          - "3.8"
+          - "3.9"
+          - "3.10"
+          - "3.11"
+        test_type:
+          - "core"
+          - "extended"
+    name: Python ${{ matrix.python-version }} ${{ matrix.test_type }}
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: ${{ matrix.python-version }}
+          poetry-version: "1.4.2"
+          cache-key: ${{ matrix.test_type }}
+          install-command: |
+              if [ "${{ matrix.test_type }}" == "core" ]; then
+                echo "Running core tests, installing dependencies with poetry..."
+                poetry install
+              else
+                echo "Running extended tests, installing dependencies with poetry..."
+                poetry install -E extended_testing
+              fi
+      - name: Run ${{matrix.test_type}} tests
+        run: |
+          if [ "${{ matrix.test_type }}" == "core" ]; then
+            make test
+          else
+            make extended_tests
+          fi
+        shell: bash

.github/workflows/unit-test.yml

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+name: build
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+
+  build:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Setup Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.10'
+
+    - name: Install dependencies
+      run: pip install -r requirements.txt
+
+    - name: Run Python unit tests
+      run: python3 -m unittest tests/swarms
+
+    - name: Verify that the Docker image for the action builds
+      run: docker build . --file Dockerfile
+
+    - name: Integration test 1
+      uses: ./
+      with:
+        input-one: something
+        input-two: true
+
+    - name: Integration test 2
+      uses: ./
+      with:
+        input-one: something else
+        input-two: false
+
+    - name: Verify integration test results
+      run: python3 -m unittest unittesting/swarms

DOCS/DOCUMENTATION.md

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+# Swarms Documentation
+
+## Overview
+The Swarm module includes the implementation of two classes, `WorkerNode` and `BossNode`, which respectively represent a worker agent and a boss agent. A worker agent is responsible for completing given tasks, while a boss agent is responsible for creating and managing tasks for the worker agent(s).
+
+## Key Classes
+
+### WorkerNode
+```python
+class WorkerNode:
+```
+
+The WorkerNode class represents an autonomous worker agent that can perform a range of tasks.
+
+__Methods__:
+
+- `create_agent(ai_name: str, ai_role: str, human_in_the_loop: bool, search_kwargs: dict) -> None`:
+
+    This method creates a new autonomous agent that can complete tasks. The agent utilizes several tools such as search engines, a file writer/reader, and a multi-modal visual tool. 
+    The agent's configuration is customizable through the method parameters. 
+
+    ```python
+    # Example usage
+    worker_node = WorkerNode(llm, tools, vectorstore)
+    worker_node.create_agent('test_agent', 'test_role', False, {})
+    ```
+
+- `run_agent(prompt: str) -> None`:
+
+    This method runs the agent on a given task, defined by the `prompt` parameter.
+
+    ```python
+    # Example usage
+    worker_node = WorkerNode(llm, tools, vectorstore)
+    worker_node.create_agent('test_agent', 'test_role', False, {})
+    worker_node.run_agent('Calculate the square root of 144.')
+    ```
+
+### BossNode
+```python
+class BossNode:
+```
+
+The BossNode class represents a manager agent that can create tasks and control the execution of these tasks.
+
+__Methods__:
+
+- `create_task(objective: str) -> dict`:
+
+    This method creates a new task based on the provided `objective`. The created task is a dictionary with the objective as its value.
+
+    ```python
+    # Example usage
+    boss_node = BossNode(llm, vectorstore, task_execution_chain, False, 3)
+    task = boss_node.create_task('Find the square root of 144.')
+    ```
+
+- `execute_task(task: dict) -> None`:
+
+    This method triggers the execution of a given task.
+
+    ```python
+    # Example usage
+    boss_node = BossNode(llm, vectorstore, task_execution_chain, False, 3)
+    task = boss_node.create_task('Find the square root of 144.')
+    boss_node.execute_task(task)
+    ```
+
+### Note
+
+Before creating the WorkerNode and BossNode, make sure to initialize the lower level model (llm), tools, and vectorstore which are used as parameters in the constructors of the two classes.
+
+In addition, the WorkerNode class uses the MultiModalVisualAgentTool which is a custom tool that enables the worker agent to run multi-modal visual tasks. Ensure that this tool is correctly initialized before running the WorkerNode.
+
+This documentation provides an overview of the main functionalities of the Swarm module. For additional details and advanced functionalities, please review the source code and the accompanying comments.