mirror of
https://github.com/crewAIInc/crewAI.git
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remove all references to pipeline and pipeline router (#1661)
* remove all references to pipeline and router * fix linting * drop poetry.lock
This commit is contained in:
Brandon Hancock (bhancock_ai)
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066ad73423
commit
bbea797b0c
@@ -28,20 +28,19 @@ crewai [COMMAND] [OPTIONS] [ARGUMENTS]
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### 1. Create
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Create a new crew or pipeline.
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Create a new crew or flow.
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```shell
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crewai create [OPTIONS] TYPE NAME
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```
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- `TYPE`: Choose between "crew" or "pipeline"
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- `NAME`: Name of the crew or pipeline
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- `--router`: (Optional) Create a pipeline with router functionality
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- `TYPE`: Choose between "crew" or "flow"
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- `NAME`: Name of the crew or flow
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Example:
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```shell
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crewai create crew my_new_crew
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crewai create pipeline my_new_pipeline --router
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crewai create flow my_new_flow
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```
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### 2. Version
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@@ -129,7 +129,6 @@ nav:
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- Processes: 'core-concepts/Processes.md'
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- Crews: 'core-concepts/Crews.md'
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- Collaboration: 'core-concepts/Collaboration.md'
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- Pipeline: 'core-concepts/Pipeline.md'
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- Training: 'core-concepts/Training-Crew.md'
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- Memory: 'core-concepts/Memory.md'
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- Planning: 'core-concepts/Planning.md'
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7507
poetry.lock
generated
7507
poetry.lock
generated
File diff suppressed because it is too large
Load Diff
@@ -5,9 +5,7 @@ from crewai.crew import Crew
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from crewai.flow.flow import Flow
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from crewai.knowledge.knowledge import Knowledge
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from crewai.llm import LLM
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from crewai.pipeline import Pipeline
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from crewai.process import Process
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from crewai.routers import Router
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from crewai.task import Task
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warnings.filterwarnings(
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@@ -22,8 +20,6 @@ __all__ = [
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"Crew",
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"Process",
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"Task",
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"Pipeline",
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"Router",
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"LLM",
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"Flow",
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"Knowledge",
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@@ -6,7 +6,6 @@ import pkg_resources
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from crewai.cli.add_crew_to_flow import add_crew_to_flow
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from crewai.cli.create_crew import create_crew
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from crewai.cli.create_flow import create_flow
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from crewai.cli.create_pipeline import create_pipeline
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from crewai.memory.storage.kickoff_task_outputs_storage import (
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KickoffTaskOutputsSQLiteStorage,
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)
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@@ -31,22 +30,18 @@ def crewai():
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@crewai.command()
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@click.argument("type", type=click.Choice(["crew", "pipeline", "flow"]))
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@click.argument("type", type=click.Choice(["crew", "flow"]))
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@click.argument("name")
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@click.option("--provider", type=str, help="The provider to use for the crew")
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@click.option("--skip_provider", is_flag=True, help="Skip provider validation")
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def create(type, name, provider, skip_provider=False):
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"""Create a new crew, pipeline, or flow."""
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"""Create a new crew, or flow."""
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if type == "crew":
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create_crew(name, provider, skip_provider)
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elif type == "pipeline":
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create_pipeline(name)
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elif type == "flow":
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create_flow(name)
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else:
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click.secho(
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"Error: Invalid type. Must be 'crew', 'pipeline', or 'flow'.", fg="red"
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)
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click.secho("Error: Invalid type. Must be 'crew' or 'flow'.", fg="red")
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@crewai.command()
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@@ -1,107 +0,0 @@
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import shutil
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from pathlib import Path
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import click
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def create_pipeline(name, router=False):
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"""Create a new pipeline project."""
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folder_name = name.replace(" ", "_").replace("-", "_").lower()
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class_name = name.replace("_", " ").replace("-", " ").title().replace(" ", "")
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click.secho(f"Creating pipeline {folder_name}...", fg="green", bold=True)
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project_root = Path(folder_name)
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if project_root.exists():
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click.secho(f"Error: Folder {folder_name} already exists.", fg="red")
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return
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# Create directory structure
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(project_root / "src" / folder_name).mkdir(parents=True)
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(project_root / "src" / folder_name / "pipelines").mkdir(parents=True)
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(project_root / "src" / folder_name / "crews").mkdir(parents=True)
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(project_root / "src" / folder_name / "tools").mkdir(parents=True)
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(project_root / "tests").mkdir(exist_ok=True)
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# Create .env file
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with open(project_root / ".env", "w") as file:
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file.write("OPENAI_API_KEY=YOUR_API_KEY")
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package_dir = Path(__file__).parent
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template_folder = "pipeline_router" if router else "pipeline"
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templates_dir = package_dir / "templates" / template_folder
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# List of template files to copy
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root_template_files = [".gitignore", "pyproject.toml", "README.md"]
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src_template_files = ["__init__.py", "main.py"]
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tools_template_files = ["tools/__init__.py", "tools/custom_tool.py"]
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if router:
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crew_folders = [
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"classifier_crew",
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"normal_crew",
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"urgent_crew",
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]
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pipelines_folders = [
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"pipelines/__init__.py",
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"pipelines/pipeline_classifier.py",
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"pipelines/pipeline_normal.py",
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"pipelines/pipeline_urgent.py",
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]
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else:
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crew_folders = [
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"research_crew",
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"write_linkedin_crew",
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"write_x_crew",
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]
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pipelines_folders = ["pipelines/__init__.py", "pipelines/pipeline.py"]
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def process_file(src_file, dst_file):
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with open(src_file, "r") as file:
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content = file.read()
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content = content.replace("{{name}}", name)
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content = content.replace("{{crew_name}}", class_name)
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content = content.replace("{{folder_name}}", folder_name)
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content = content.replace("{{pipeline_name}}", class_name)
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with open(dst_file, "w") as file:
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file.write(content)
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# Copy and process root template files
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for file_name in root_template_files:
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src_file = templates_dir / file_name
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dst_file = project_root / file_name
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process_file(src_file, dst_file)
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# Copy and process src template files
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for file_name in src_template_files:
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src_file = templates_dir / file_name
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dst_file = project_root / "src" / folder_name / file_name
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process_file(src_file, dst_file)
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# Copy tools files
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for file_name in tools_template_files:
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src_file = templates_dir / file_name
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dst_file = project_root / "src" / folder_name / file_name
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shutil.copy(src_file, dst_file)
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# Copy pipelines folders
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for file_name in pipelines_folders:
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src_file = templates_dir / file_name
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dst_file = project_root / "src" / folder_name / file_name
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process_file(src_file, dst_file)
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# Copy crew folders
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for crew_folder in crew_folders:
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src_crew_folder = templates_dir / "crews" / crew_folder
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dst_crew_folder = project_root / "src" / folder_name / "crews" / crew_folder
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if src_crew_folder.exists():
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shutil.copytree(src_crew_folder, dst_crew_folder)
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else:
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click.secho(
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f"Warning: Crew folder {crew_folder} not found in template.",
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fg="yellow",
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)
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click.secho(f"Pipeline {name} created successfully!", fg="green", bold=True)
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2
src/crewai/cli/templates/pipeline/.gitignore
vendored
2
src/crewai/cli/templates/pipeline/.gitignore
vendored
@@ -1,2 +0,0 @@
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.env
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__pycache__/
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@@ -1,57 +0,0 @@
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# {{crew_name}} Crew
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Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.com). This template is designed to help you set up a multi-agent AI system with ease, leveraging the powerful and flexible framework provided by crewAI. Our goal is to enable your agents to collaborate effectively on complex tasks, maximizing their collective intelligence and capabilities.
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## Installation
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Ensure you have Python >=3.10 <=3.13 installed on your system. This project uses [Poetry](https://python-poetry.org/) for dependency management and package handling, offering a seamless setup and execution experience.
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First, if you haven't already, install Poetry:
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```bash
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pip install poetry
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```
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Next, navigate to your project directory and install the dependencies:
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1. First lock the dependencies and then install them:
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```bash
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crewai install
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```
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### Customizing
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**Add your `OPENAI_API_KEY` into the `.env` file**
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- Modify `src/{{folder_name}}/config/agents.yaml` to define your agents
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- Modify `src/{{folder_name}}/config/tasks.yaml` to define your tasks
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- Modify `src/{{folder_name}}/crew.py` to add your own logic, tools and specific args
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- Modify `src/{{folder_name}}/main.py` to add custom inputs for your agents and tasks
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## Running the Project
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To kickstart your crew of AI agents and begin task execution, run this from the root folder of your project:
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```bash
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crewai run
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```
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This command initializes the {{name}} Crew, assembling the agents and assigning them tasks as defined in your configuration.
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This example, unmodified, will run the create a `report.md` file with the output of a research on LLMs in the root folder.
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## Understanding Your Crew
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The {{name}} Crew is composed of multiple AI agents, each with unique roles, goals, and tools. These agents collaborate on a series of tasks, defined in `config/tasks.yaml`, leveraging their collective skills to achieve complex objectives. The `config/agents.yaml` file outlines the capabilities and configurations of each agent in your crew.
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## Support
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For support, questions, or feedback regarding the {{crew_name}} Crew or crewAI.
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- Visit our [documentation](https://docs.crewai.com)
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- Reach out to us through our [GitHub repository](https://github.com/joaomdmoura/crewai)
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- [Join our Discord](https://discord.com/invite/X4JWnZnxPb)
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- [Chat with our docs](https://chatg.pt/DWjSBZn)
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Let's create wonders together with the power and simplicity of crewAI.
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@@ -1,19 +0,0 @@
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researcher:
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role: >
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{topic} Senior Data Researcher
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goal: >
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Uncover cutting-edge developments in {topic}
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backstory: >
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You're a seasoned researcher with a knack for uncovering the latest
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developments in {topic}. Known for your ability to find the most relevant
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information and present it in a clear and concise manner.
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reporting_analyst:
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role: >
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{topic} Reporting Analyst
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goal: >
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Create detailed reports based on {topic} data analysis and research findings
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backstory: >
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You're a meticulous analyst with a keen eye for detail. You're known for
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your ability to turn complex data into clear and concise reports, making
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it easy for others to understand and act on the information you provide.
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@@ -1,16 +0,0 @@
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research_task:
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description: >
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Conduct a thorough research about {topic}
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Make sure you find any interesting and relevant information given
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the current year is 2024.
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expected_output: >
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A list with 10 bullet points of the most relevant information about {topic}
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agent: researcher
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reporting_task:
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description: >
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Review the context you got and expand each topic into a full section for a report.
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Make sure the report is detailed and contains any and all relevant information.
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expected_output: >
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A fully fledge reports with a title, mains topics, each with a full section of information.
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agent: reporting_analyst
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@@ -1,58 +0,0 @@
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from pydantic import BaseModel
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from crewai import Agent, Crew, Process, Task
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from crewai.project import CrewBase, agent, crew, task
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# Uncomment the following line to use an example of a custom tool
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# from demo_pipeline.tools.custom_tool import MyCustomTool
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# Check our tools documentations for more information on how to use them
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# from crewai_tools import SerperDevTool
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class ResearchReport(BaseModel):
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"""Research Report"""
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title: str
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body: str
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@CrewBase
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class ResearchCrew():
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"""Research Crew"""
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agents_config = 'config/agents.yaml'
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tasks_config = 'config/tasks.yaml'
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@agent
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def researcher(self) -> Agent:
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return Agent(
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config=self.agents_config['researcher'],
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verbose=True
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)
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@agent
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def reporting_analyst(self) -> Agent:
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return Agent(
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config=self.agents_config['reporting_analyst'],
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verbose=True
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)
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@task
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def research_task(self) -> Task:
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return Task(
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config=self.tasks_config['research_task'],
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)
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@task
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def reporting_task(self) -> Task:
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return Task(
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config=self.tasks_config['reporting_task'],
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output_pydantic=ResearchReport
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)
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@crew
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def crew(self) -> Crew:
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"""Creates the Research Crew"""
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return Crew(
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agents=self.agents, # Automatically created by the @agent decorator
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tasks=self.tasks, # Automatically created by the @task decorator
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process=Process.sequential,
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verbose=True,
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)
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@@ -1,51 +0,0 @@
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from crewai import Agent, Crew, Process, Task
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from crewai.project import CrewBase, agent, crew, task
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# Uncomment the following line to use an example of a custom tool
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# from {{folder_name}}.tools.custom_tool import MyCustomTool
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# Check our tools documentations for more information on how to use them
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# from crewai_tools import SerperDevTool
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@CrewBase
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class WriteLinkedInCrew():
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"""Research Crew"""
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agents_config = 'config/agents.yaml'
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tasks_config = 'config/tasks.yaml'
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@agent
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def researcher(self) -> Agent:
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return Agent(
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config=self.agents_config['researcher'],
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verbose=True
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)
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@agent
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def reporting_analyst(self) -> Agent:
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return Agent(
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config=self.agents_config['reporting_analyst'],
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verbose=True
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)
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@task
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def research_task(self) -> Task:
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return Task(
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config=self.tasks_config['research_task'],
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)
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@task
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def reporting_task(self) -> Task:
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return Task(
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config=self.tasks_config['reporting_task'],
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output_file='report.md'
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)
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@crew
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def crew(self) -> Crew:
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"""Creates the {{crew_name}} crew"""
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return Crew(
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agents=self.agents, # Automatically created by the @agent decorator
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tasks=self.tasks, # Automatically created by the @task decorator
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process=Process.sequential,
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verbose=True,
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)
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@@ -1,14 +0,0 @@
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x_writer_agent:
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role: >
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Expert Social Media Content Creator specializing in short form written content
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goal: >
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Create viral-worthy, engaging short form posts that distill complex {topic} information
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into compelling 280-character messages
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backstory: >
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You're a social media virtuoso with a particular talent for short form content. Your posts
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consistently go viral due to your ability to craft hooks that stop users mid-scroll.
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You've studied the techniques of social media masters like Justin Welsh, Dickie Bush,
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Nicolas Cole, and Shaan Puri, incorporating their best practices into your own unique style.
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Your superpower is taking intricate {topic} concepts and transforming them into
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bite-sized, shareable content that resonates with a wide audience. You know exactly
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how to structure a post for maximum impact and engagement.
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@@ -1,22 +0,0 @@
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write_x_task:
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description: >
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Using the research report provided, create an engaging short form post about {topic}.
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Your post should have a great hook, summarize key points, and be structured for easy
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consumption on a digital platform. The post must be under 280 characters.
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Follow these guidelines:
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1. Start with an attention-grabbing hook
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2. Condense the main insights from the research
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3. Use clear, concise language
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4. Include a call-to-action or thought-provoking question if space allows
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5. Ensure the post flows well and is easy to read quickly
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Here is the title of the research report you will be using
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Title: {title}
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Research:
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{body}
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expected_output: >
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A compelling X post under 280 characters that effectively summarizes the key findings
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about {topic}, starts with a strong hook, and is optimized for engagement on the platform.
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agent: x_writer_agent
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@@ -1,36 +0,0 @@
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from crewai import Agent, Crew, Process, Task
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from crewai.project import CrewBase, agent, crew, task
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# Uncomment the following line to use an example of a custom tool
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# from demo_pipeline.tools.custom_tool import MyCustomTool
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# Check our tools documentations for more information on how to use them
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# from crewai_tools import SerperDevTool
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@CrewBase
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class WriteXCrew:
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"""Research Crew"""
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agents_config = "config/agents.yaml"
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tasks_config = "config/tasks.yaml"
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@agent
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def x_writer_agent(self) -> Agent:
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return Agent(config=self.agents_config["x_writer_agent"], verbose=True)
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@task
|
||||
def write_x_task(self) -> Task:
|
||||
return Task(
|
||||
config=self.tasks_config["write_x_task"],
|
||||
)
|
||||
|
||||
@crew
|
||||
def crew(self) -> Crew:
|
||||
"""Creates the Write X Crew"""
|
||||
return Crew(
|
||||
agents=self.agents, # Automatically created by the @agent decorator
|
||||
tasks=self.tasks, # Automatically created by the @task decorator
|
||||
process=Process.sequential,
|
||||
verbose=True,
|
||||
)
|
||||
@@ -1,26 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
import asyncio
|
||||
from {{folder_name}}.pipelines.pipeline import {{pipeline_name}}Pipeline
|
||||
|
||||
async def run():
|
||||
"""
|
||||
Run the pipeline.
|
||||
"""
|
||||
inputs = [
|
||||
{"topic": "AI wearables"},
|
||||
]
|
||||
pipeline = {{pipeline_name}}Pipeline()
|
||||
results = await pipeline.kickoff(inputs)
|
||||
|
||||
# Process and print results
|
||||
for result in results:
|
||||
print(f"Raw output: {result.raw}")
|
||||
if result.json_dict:
|
||||
print(f"JSON output: {result.json_dict}")
|
||||
print("\n")
|
||||
|
||||
def main():
|
||||
asyncio.run(run())
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,87 +0,0 @@
|
||||
"""
|
||||
This pipeline file includes two different examples to demonstrate the flexibility of crewAI pipelines.
|
||||
|
||||
Example 1: Two-Stage Pipeline
|
||||
-----------------------------
|
||||
This pipeline consists of two crews:
|
||||
1. ResearchCrew: Performs research on a given topic.
|
||||
2. WriteXCrew: Generates an X (Twitter) post based on the research findings.
|
||||
|
||||
Key features:
|
||||
- The ResearchCrew's final task uses output_json to store all research findings in a JSON object.
|
||||
- This JSON object is then passed to the WriteXCrew, where tasks can access the research findings.
|
||||
|
||||
Example 2: Two-Stage Pipeline with Parallel Execution
|
||||
-------------------------------------------------------
|
||||
This pipeline consists of three crews:
|
||||
1. ResearchCrew: Performs research on a given topic.
|
||||
2. WriteXCrew and WriteLinkedInCrew: Run in parallel, using the research findings to generate posts for X and LinkedIn, respectively.
|
||||
|
||||
Key features:
|
||||
- Demonstrates the ability to run multiple crews in parallel.
|
||||
- Shows how to structure a pipeline with both sequential and parallel stages.
|
||||
|
||||
Usage:
|
||||
- To switch between examples, comment/uncomment the respective code blocks below.
|
||||
- Ensure that you have implemented all necessary crew classes (ResearchCrew, WriteXCrew, WriteLinkedInCrew) before running.
|
||||
"""
|
||||
|
||||
# Common imports for both examples
|
||||
from crewai import Pipeline
|
||||
|
||||
|
||||
|
||||
# Uncomment the crews you need for your chosen example
|
||||
from ..crews.research_crew.research_crew import ResearchCrew
|
||||
from ..crews.write_x_crew.write_x_crew import WriteXCrew
|
||||
# from .crews.write_linkedin_crew.write_linkedin_crew import WriteLinkedInCrew # Uncomment for Example 2
|
||||
|
||||
# EXAMPLE 1: Two-Stage Pipeline
|
||||
# -----------------------------
|
||||
# Uncomment the following code block to use Example 1
|
||||
|
||||
class {{pipeline_name}}Pipeline:
|
||||
def __init__(self):
|
||||
# Initialize crews
|
||||
self.research_crew = ResearchCrew().crew()
|
||||
self.write_x_crew = WriteXCrew().crew()
|
||||
|
||||
def create_pipeline(self):
|
||||
return Pipeline(
|
||||
stages=[
|
||||
self.research_crew,
|
||||
self.write_x_crew
|
||||
]
|
||||
)
|
||||
|
||||
async def kickoff(self, inputs):
|
||||
pipeline = self.create_pipeline()
|
||||
results = await pipeline.kickoff(inputs)
|
||||
return results
|
||||
|
||||
|
||||
# EXAMPLE 2: Two-Stage Pipeline with Parallel Execution
|
||||
# -------------------------------------------------------
|
||||
# Uncomment the following code block to use Example 2
|
||||
|
||||
# @PipelineBase
|
||||
# class {{pipeline_name}}Pipeline:
|
||||
# def __init__(self):
|
||||
# # Initialize crews
|
||||
# self.research_crew = ResearchCrew().crew()
|
||||
# self.write_x_crew = WriteXCrew().crew()
|
||||
# self.write_linkedin_crew = WriteLinkedInCrew().crew()
|
||||
|
||||
# @pipeline
|
||||
# def create_pipeline(self):
|
||||
# return Pipeline(
|
||||
# stages=[
|
||||
# self.research_crew,
|
||||
# [self.write_x_crew, self.write_linkedin_crew] # Parallel execution
|
||||
# ]
|
||||
# )
|
||||
|
||||
# async def run(self, inputs):
|
||||
# pipeline = self.create_pipeline()
|
||||
# results = await pipeline.kickoff(inputs)
|
||||
# return results
|
||||
@@ -1,17 +0,0 @@
|
||||
[tool.poetry]
|
||||
name = "{{folder_name}}"
|
||||
version = "0.1.0"
|
||||
description = "{{name}} using crewAI"
|
||||
authors = ["Your Name <you@example.com>"]
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.10,<=3.13"
|
||||
crewai = { extras = ["tools"], version = ">=0.85.0,<1.0.0" }
|
||||
asyncio = "*"
|
||||
|
||||
[tool.poetry.scripts]
|
||||
{{folder_name}} = "{{folder_name}}.main:main"
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
@@ -1,19 +0,0 @@
|
||||
from typing import Type
|
||||
from crewai.tools import BaseTool
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class MyCustomToolInput(BaseModel):
|
||||
"""Input schema for MyCustomTool."""
|
||||
argument: str = Field(..., description="Description of the argument.")
|
||||
|
||||
class MyCustomTool(BaseTool):
|
||||
name: str = "Name of my tool"
|
||||
description: str = (
|
||||
"Clear description for what this tool is useful for, you agent will need this information to use it."
|
||||
)
|
||||
args_schema: Type[BaseModel] = MyCustomToolInput
|
||||
|
||||
def _run(self, argument: str) -> str:
|
||||
# Implementation goes here
|
||||
return "this is an example of a tool output, ignore it and move along."
|
||||
@@ -1,2 +0,0 @@
|
||||
.env
|
||||
__pycache__/
|
||||
@@ -1,54 +0,0 @@
|
||||
# {{crew_name}} Crew
|
||||
|
||||
Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.com). This template is designed to help you set up a multi-agent AI system with ease, leveraging the powerful and flexible framework provided by crewAI. Our goal is to enable your agents to collaborate effectively on complex tasks, maximizing their collective intelligence and capabilities.
|
||||
|
||||
## Installation
|
||||
|
||||
Ensure you have Python >=3.10 <=3.13 installed on your system. This project uses [Poetry](https://python-poetry.org/) for dependency management and package handling, offering a seamless setup and execution experience.
|
||||
|
||||
First, if you haven't already, install Poetry:
|
||||
|
||||
```bash
|
||||
pip install poetry
|
||||
```
|
||||
|
||||
Next, navigate to your project directory and install the dependencies:
|
||||
|
||||
1. First lock the dependencies and then install them:
|
||||
```bash
|
||||
crewai install
|
||||
```
|
||||
### Customizing
|
||||
|
||||
**Add your `OPENAI_API_KEY` into the `.env` file**
|
||||
|
||||
- Modify `src/{{folder_name}}/config/agents.yaml` to define your agents
|
||||
- Modify `src/{{folder_name}}/config/tasks.yaml` to define your tasks
|
||||
- Modify `src/{{folder_name}}/crew.py` to add your own logic, tools and specific args
|
||||
- Modify `src/{{folder_name}}/main.py` to add custom inputs for your agents and tasks
|
||||
|
||||
## Running the Project
|
||||
|
||||
To kickstart your crew of AI agents and begin task execution, run this from the root folder of your project:
|
||||
|
||||
```bash
|
||||
crewai run
|
||||
```
|
||||
|
||||
This command initializes the {{name}} Crew, assembling the agents and assigning them tasks as defined in your configuration.
|
||||
|
||||
This example, unmodified, will run the create a `report.md` file with the output of a research on LLMs in the root folder.
|
||||
|
||||
## Understanding Your Crew
|
||||
|
||||
The {{name}} Crew is composed of multiple AI agents, each with unique roles, goals, and tools. These agents collaborate on a series of tasks, defined in `config/tasks.yaml`, leveraging their collective skills to achieve complex objectives. The `config/agents.yaml` file outlines the capabilities and configurations of each agent in your crew.
|
||||
|
||||
## Support
|
||||
|
||||
For support, questions, or feedback regarding the {{crew_name}} Crew or crewAI.
|
||||
- Visit our [documentation](https://docs.crewai.com)
|
||||
- Reach out to us through our [GitHub repository](https://github.com/joaomdmoura/crewai)
|
||||
- [Join our Discord](https://discord.com/invite/X4JWnZnxPb)
|
||||
- [Chat with our docs](https://chatg.pt/DWjSBZn)
|
||||
|
||||
Let's create wonders together with the power and simplicity of crewAI.
|
||||
@@ -1,19 +0,0 @@
|
||||
researcher:
|
||||
role: >
|
||||
{topic} Senior Data Researcher
|
||||
goal: >
|
||||
Uncover cutting-edge developments in {topic}
|
||||
backstory: >
|
||||
You're a seasoned researcher with a knack for uncovering the latest
|
||||
developments in {topic}. Known for your ability to find the most relevant
|
||||
information and present it in a clear and concise manner.
|
||||
|
||||
reporting_analyst:
|
||||
role: >
|
||||
{topic} Reporting Analyst
|
||||
goal: >
|
||||
Create detailed reports based on {topic} data analysis and research findings
|
||||
backstory: >
|
||||
You're a meticulous analyst with a keen eye for detail. You're known for
|
||||
your ability to turn complex data into clear and concise reports, making
|
||||
it easy for others to understand and act on the information you provide.
|
||||
@@ -1,17 +0,0 @@
|
||||
research_task:
|
||||
description: >
|
||||
Conduct a thorough research about {topic}
|
||||
Make sure you find any interesting and relevant information given
|
||||
the current year is 2024.
|
||||
expected_output: >
|
||||
A list with 10 bullet points of the most relevant information about {topic}
|
||||
agent: researcher
|
||||
|
||||
reporting_task:
|
||||
description: >
|
||||
Review the context you got and expand each topic into a full section for a report.
|
||||
Make sure the report is detailed and contains any and all relevant information.
|
||||
expected_output: >
|
||||
A fully fledge reports with the mains topics, each with a full section of information.
|
||||
Formatted as markdown without '```'
|
||||
agent: reporting_analyst
|
||||
@@ -1,40 +0,0 @@
|
||||
from crewai import Agent, Crew, Process, Task
|
||||
from crewai.project import CrewBase, agent, crew, task
|
||||
from pydantic import BaseModel
|
||||
|
||||
# Uncomment the following line to use an example of a custom tool
|
||||
# from demo_pipeline.tools.custom_tool import MyCustomTool
|
||||
|
||||
# Check our tools documentations for more information on how to use them
|
||||
# from crewai_tools import SerperDevTool
|
||||
|
||||
class UrgencyScore(BaseModel):
|
||||
urgency_score: int
|
||||
|
||||
@CrewBase
|
||||
class ClassifierCrew:
|
||||
"""Email Classifier Crew"""
|
||||
|
||||
agents_config = "config/agents.yaml"
|
||||
tasks_config = "config/tasks.yaml"
|
||||
|
||||
@agent
|
||||
def classifier(self) -> Agent:
|
||||
return Agent(config=self.agents_config["classifier"], verbose=True)
|
||||
|
||||
@task
|
||||
def urgent_task(self) -> Task:
|
||||
return Task(
|
||||
config=self.tasks_config["classify_email"],
|
||||
output_pydantic=UrgencyScore,
|
||||
)
|
||||
|
||||
@crew
|
||||
def crew(self) -> Crew:
|
||||
"""Creates the Email Classifier Crew"""
|
||||
return Crew(
|
||||
agents=self.agents, # Automatically created by the @agent decorator
|
||||
tasks=self.tasks, # Automatically created by the @task decorator
|
||||
process=Process.sequential,
|
||||
verbose=True,
|
||||
)
|
||||
@@ -1,7 +0,0 @@
|
||||
classifier:
|
||||
role: >
|
||||
Email Classifier
|
||||
goal: >
|
||||
Classify the email: {email} as urgent or normal from a score of 1 to 10, where 1 is not urgent and 10 is urgent. Return the urgency score only.`
|
||||
backstory: >
|
||||
You are a highly efficient and experienced email classifier, trained to quickly assess and classify emails. Your ability to remain calm under pressure and provide concise, actionable responses has made you an invaluable asset in managing normal situations and maintaining smooth operations.
|
||||
@@ -1,7 +0,0 @@
|
||||
classify_email:
|
||||
description: >
|
||||
Classify the email: {email}
|
||||
as urgent or normal.
|
||||
expected_output: >
|
||||
Classify the email from a scale of 1 to 10, where 1 is not urgent and 10 is urgent. Return the urgency score only.
|
||||
agent: classifier
|
||||
@@ -1,7 +0,0 @@
|
||||
normal_handler:
|
||||
role: >
|
||||
Normal Email Processor
|
||||
goal: >
|
||||
Process normal emails and create an email to respond to the sender.
|
||||
backstory: >
|
||||
You are a highly efficient and experienced normal email handler, trained to quickly assess and respond to normal communications. Your ability to remain calm under pressure and provide concise, actionable responses has made you an invaluable asset in managing normal situations and maintaining smooth operations.
|
||||
@@ -1,6 +0,0 @@
|
||||
normal_task:
|
||||
description: >
|
||||
Process and respond to normal email quickly.
|
||||
expected_output: >
|
||||
An email response to the normal email.
|
||||
agent: normal_handler
|
||||
@@ -1,36 +0,0 @@
|
||||
from crewai import Agent, Crew, Process, Task
|
||||
from crewai.project import CrewBase, agent, crew, task
|
||||
|
||||
# Uncomment the following line to use an example of a custom tool
|
||||
# from demo_pipeline.tools.custom_tool import MyCustomTool
|
||||
|
||||
# Check our tools documentations for more information on how to use them
|
||||
# from crewai_tools import SerperDevTool
|
||||
|
||||
|
||||
@CrewBase
|
||||
class NormalCrew:
|
||||
"""Normal Email Crew"""
|
||||
|
||||
agents_config = "config/agents.yaml"
|
||||
tasks_config = "config/tasks.yaml"
|
||||
|
||||
@agent
|
||||
def normal_handler(self) -> Agent:
|
||||
return Agent(config=self.agents_config["normal_handler"], verbose=True)
|
||||
|
||||
@task
|
||||
def urgent_task(self) -> Task:
|
||||
return Task(
|
||||
config=self.tasks_config["normal_task"],
|
||||
)
|
||||
|
||||
@crew
|
||||
def crew(self) -> Crew:
|
||||
"""Creates the Normal Email Crew"""
|
||||
return Crew(
|
||||
agents=self.agents, # Automatically created by the @agent decorator
|
||||
tasks=self.tasks, # Automatically created by the @task decorator
|
||||
process=Process.sequential,
|
||||
verbose=True,
|
||||
)
|
||||
@@ -1,7 +0,0 @@
|
||||
urgent_handler:
|
||||
role: >
|
||||
Urgent Email Processor
|
||||
goal: >
|
||||
Process urgent emails and create an email to respond to the sender.
|
||||
backstory: >
|
||||
You are a highly efficient and experienced urgent email handler, trained to quickly assess and respond to time-sensitive communications. Your ability to remain calm under pressure and provide concise, actionable responses has made you an invaluable asset in managing critical situations and maintaining smooth operations.
|
||||
@@ -1,6 +0,0 @@
|
||||
urgent_task:
|
||||
description: >
|
||||
Process and respond to urgent email quickly.
|
||||
expected_output: >
|
||||
An email response to the urgent email.
|
||||
agent: urgent_handler
|
||||
@@ -1,36 +0,0 @@
|
||||
from crewai import Agent, Crew, Process, Task
|
||||
from crewai.project import CrewBase, agent, crew, task
|
||||
|
||||
# Uncomment the following line to use an example of a custom tool
|
||||
# from demo_pipeline.tools.custom_tool import MyCustomTool
|
||||
|
||||
# Check our tools documentations for more information on how to use them
|
||||
# from crewai_tools import SerperDevTool
|
||||
|
||||
|
||||
@CrewBase
|
||||
class UrgentCrew:
|
||||
"""Urgent Email Crew"""
|
||||
|
||||
agents_config = "config/agents.yaml"
|
||||
tasks_config = "config/tasks.yaml"
|
||||
|
||||
@agent
|
||||
def urgent_handler(self) -> Agent:
|
||||
return Agent(config=self.agents_config["urgent_handler"], verbose=True)
|
||||
|
||||
@task
|
||||
def urgent_task(self) -> Task:
|
||||
return Task(
|
||||
config=self.tasks_config["urgent_task"],
|
||||
)
|
||||
|
||||
@crew
|
||||
def crew(self) -> Crew:
|
||||
"""Creates the Urgent Email Crew"""
|
||||
return Crew(
|
||||
agents=self.agents, # Automatically created by the @agent decorator
|
||||
tasks=self.tasks, # Automatically created by the @task decorator
|
||||
process=Process.sequential,
|
||||
verbose=True,
|
||||
)
|
||||
@@ -1,75 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
import asyncio
|
||||
from crewai.routers.router import Route
|
||||
from crewai.routers.router import Router
|
||||
|
||||
from {{folder_name}}.pipelines.pipeline_classifier import EmailClassifierPipeline
|
||||
from {{folder_name}}.pipelines.pipeline_normal import NormalPipeline
|
||||
from {{folder_name}}.pipelines.pipeline_urgent import UrgentPipeline
|
||||
|
||||
async def run():
|
||||
"""
|
||||
Run the pipeline.
|
||||
"""
|
||||
inputs = [
|
||||
{
|
||||
"email": """
|
||||
Subject: URGENT: Marketing Campaign Launch - Immediate Action Required
|
||||
Dear Team,
|
||||
I'm reaching out regarding our upcoming marketing campaign that requires your immediate attention and swift action. We're facing a critical deadline, and our success hinges on our ability to mobilize quickly.
|
||||
Key points:
|
||||
|
||||
Campaign launch: 48 hours from now
|
||||
Target audience: 250,000 potential customers
|
||||
Expected ROI: 35% increase in Q3 sales
|
||||
|
||||
What we need from you NOW:
|
||||
|
||||
Final approval on creative assets (due in 3 hours)
|
||||
Confirmation of media placements (due by end of day)
|
||||
Last-minute budget allocation for paid social media push
|
||||
|
||||
Our competitors are poised to launch similar campaigns, and we must act fast to maintain our market advantage. Delays could result in significant lost opportunities and potential revenue.
|
||||
Please prioritize this campaign above all other tasks. I'll be available for the next 24 hours to address any concerns or roadblocks.
|
||||
Let's make this happen!
|
||||
[Your Name]
|
||||
Marketing Director
|
||||
P.S. I'll be scheduling an emergency team meeting in 1 hour to discuss our action plan. Attendance is mandatory.
|
||||
"""
|
||||
}
|
||||
]
|
||||
|
||||
pipeline_classifier = EmailClassifierPipeline().create_pipeline()
|
||||
pipeline_urgent = UrgentPipeline().create_pipeline()
|
||||
pipeline_normal = NormalPipeline().create_pipeline()
|
||||
|
||||
router = Router(
|
||||
routes={
|
||||
"high_urgency": Route(
|
||||
condition=lambda x: x.get("urgency_score", 0) > 7,
|
||||
pipeline=pipeline_urgent
|
||||
),
|
||||
"low_urgency": Route(
|
||||
condition=lambda x: x.get("urgency_score", 0) <= 7,
|
||||
pipeline=pipeline_normal
|
||||
)
|
||||
},
|
||||
default=pipeline_normal
|
||||
)
|
||||
|
||||
pipeline = pipeline_classifier >> router
|
||||
|
||||
results = await pipeline.kickoff(inputs)
|
||||
|
||||
# Process and print results
|
||||
for result in results:
|
||||
print(f"Raw output: {result.raw}")
|
||||
if result.json_dict:
|
||||
print(f"JSON output: {result.json_dict}")
|
||||
print("\n")
|
||||
|
||||
def main():
|
||||
asyncio.run(run())
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,24 +0,0 @@
|
||||
from crewai import Pipeline
|
||||
from crewai.project import PipelineBase
|
||||
from ..crews.classifier_crew.classifier_crew import ClassifierCrew
|
||||
|
||||
|
||||
@PipelineBase
|
||||
class EmailClassifierPipeline:
|
||||
def __init__(self):
|
||||
# Initialize crews
|
||||
self.classifier_crew = ClassifierCrew().crew()
|
||||
|
||||
def create_pipeline(self):
|
||||
return Pipeline(
|
||||
stages=[
|
||||
self.classifier_crew
|
||||
]
|
||||
)
|
||||
|
||||
async def kickoff(self, inputs):
|
||||
pipeline = self.create_pipeline()
|
||||
results = await pipeline.kickoff(inputs)
|
||||
return results
|
||||
|
||||
|
||||
@@ -1,24 +0,0 @@
|
||||
from crewai import Pipeline
|
||||
from crewai.project import PipelineBase
|
||||
from ..crews.normal_crew.normal_crew import NormalCrew
|
||||
|
||||
|
||||
@PipelineBase
|
||||
class NormalPipeline:
|
||||
def __init__(self):
|
||||
# Initialize crews
|
||||
self.normal_crew = NormalCrew().crew()
|
||||
|
||||
def create_pipeline(self):
|
||||
return Pipeline(
|
||||
stages=[
|
||||
self.normal_crew
|
||||
]
|
||||
)
|
||||
|
||||
async def kickoff(self, inputs):
|
||||
pipeline = self.create_pipeline()
|
||||
results = await pipeline.kickoff(inputs)
|
||||
return results
|
||||
|
||||
|
||||
@@ -1,23 +0,0 @@
|
||||
from crewai import Pipeline
|
||||
from crewai.project import PipelineBase
|
||||
from ..crews.urgent_crew.urgent_crew import UrgentCrew
|
||||
|
||||
@PipelineBase
|
||||
class UrgentPipeline:
|
||||
def __init__(self):
|
||||
# Initialize crews
|
||||
self.urgent_crew = UrgentCrew().crew()
|
||||
|
||||
def create_pipeline(self):
|
||||
return Pipeline(
|
||||
stages=[
|
||||
self.urgent_crew
|
||||
]
|
||||
)
|
||||
|
||||
async def kickoff(self, inputs):
|
||||
pipeline = self.create_pipeline()
|
||||
results = await pipeline.kickoff(inputs)
|
||||
return results
|
||||
|
||||
|
||||
@@ -1,21 +0,0 @@
|
||||
[project]
|
||||
name = "{{folder_name}}"
|
||||
version = "0.1.0"
|
||||
description = "{{name}} using crewAI"
|
||||
authors = ["Your Name <you@example.com>"]
|
||||
requires-python = ">=3.10,<=3.13"
|
||||
dependencies = [
|
||||
"crewai[tools]>=0.85.0,<1.0.0"
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
{{folder_name}} = "{{folder_name}}.main:main"
|
||||
run_crew = "{{folder_name}}.main:main"
|
||||
train = "{{folder_name}}.main:train"
|
||||
replay = "{{folder_name}}.main:replay"
|
||||
test = "{{folder_name}}.main:test"
|
||||
|
||||
[build-system]
|
||||
requires = ["hatchling"]
|
||||
build-backend = "hatchling.build"
|
||||
|
||||
@@ -1,19 +0,0 @@
|
||||
from typing import Type
|
||||
from crewai.tools import BaseTool
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class MyCustomToolInput(BaseModel):
|
||||
"""Input schema for MyCustomTool."""
|
||||
argument: str = Field(..., description="Description of the argument.")
|
||||
|
||||
class MyCustomTool(BaseTool):
|
||||
name: str = "Name of my tool"
|
||||
description: str = (
|
||||
"Clear description for what this tool is useful for, you agent will need this information to use it."
|
||||
)
|
||||
args_schema: Type[BaseModel] = MyCustomToolInput
|
||||
|
||||
def _run(self, argument: str) -> str:
|
||||
# Implementation goes here
|
||||
return "this is an example of a tool output, ignore it and move along."
|
||||
@@ -5,7 +5,7 @@ import uuid
|
||||
import warnings
|
||||
from concurrent.futures import Future
|
||||
from hashlib import md5
|
||||
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
|
||||
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
|
||||
|
||||
from pydantic import (
|
||||
UUID4,
|
||||
@@ -23,12 +23,12 @@ from crewai.agent import Agent
|
||||
from crewai.agents.agent_builder.base_agent import BaseAgent
|
||||
from crewai.agents.cache import CacheHandler
|
||||
from crewai.crews.crew_output import CrewOutput
|
||||
from crewai.knowledge.knowledge import Knowledge
|
||||
from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
|
||||
from crewai.llm import LLM
|
||||
from crewai.memory.entity.entity_memory import EntityMemory
|
||||
from crewai.memory.long_term.long_term_memory import LongTermMemory
|
||||
from crewai.memory.short_term.short_term_memory import ShortTermMemory
|
||||
from crewai.knowledge.knowledge import Knowledge
|
||||
from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
|
||||
from crewai.memory.user.user_memory import UserMemory
|
||||
from crewai.process import Process
|
||||
from crewai.task import Task
|
||||
@@ -56,8 +56,6 @@ if os.environ.get("AGENTOPS_API_KEY"):
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from crewai.pipeline.pipeline import Pipeline
|
||||
|
||||
warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
|
||||
|
||||
@@ -1073,17 +1071,5 @@ class Crew(BaseModel):
|
||||
|
||||
evaluator.print_crew_evaluation_result()
|
||||
|
||||
def __rshift__(self, other: "Crew") -> "Pipeline":
|
||||
"""
|
||||
Implements the >> operator to add another Crew to an existing Pipeline.
|
||||
"""
|
||||
from crewai.pipeline.pipeline import Pipeline
|
||||
|
||||
if not isinstance(other, Crew):
|
||||
raise TypeError(
|
||||
f"Unsupported operand type for >>: '{type(self).__name__}' and '{type(other).__name__}'"
|
||||
)
|
||||
return Pipeline(stages=[self, other])
|
||||
|
||||
def __repr__(self):
|
||||
return f"Crew(id={self.id}, process={self.process}, number_of_agents={len(self.agents)}, number_of_tasks={len(self.tasks)})"
|
||||
|
||||
@@ -1,5 +0,0 @@
|
||||
from crewai.pipeline.pipeline import Pipeline
|
||||
from crewai.pipeline.pipeline_kickoff_result import PipelineKickoffResult
|
||||
from crewai.pipeline.pipeline_output import PipelineOutput
|
||||
|
||||
__all__ = ["Pipeline", "PipelineKickoffResult", "PipelineOutput"]
|
||||
@@ -1,405 +0,0 @@
|
||||
import asyncio
|
||||
import copy
|
||||
from typing import Any, Dict, List, Tuple, Union
|
||||
|
||||
from pydantic import BaseModel, Field, model_validator
|
||||
|
||||
from crewai.crew import Crew
|
||||
from crewai.crews.crew_output import CrewOutput
|
||||
from crewai.pipeline.pipeline_kickoff_result import PipelineKickoffResult
|
||||
from crewai.routers.router import Router
|
||||
from crewai.types.usage_metrics import UsageMetrics
|
||||
|
||||
Trace = Union[Union[str, Dict[str, Any]], List[Union[str, Dict[str, Any]]]]
|
||||
PipelineStage = Union[Crew, List[Crew], Router]
|
||||
|
||||
"""
|
||||
Developer Notes:
|
||||
|
||||
This module defines a Pipeline class that represents a sequence of operations (stages)
|
||||
to process inputs. Each stage can be either sequential or parallel, and the pipeline
|
||||
can process multiple kickoffs concurrently.
|
||||
|
||||
Core Loop Explanation:
|
||||
1. The `process_kickoffs` method processes multiple kickoffs in parallel, each going through
|
||||
all pipeline stages.
|
||||
2. The `process_single_kickoff` method handles the processing of a single kickouff through
|
||||
all stages, updating metrics and input data along the way.
|
||||
3. The `_process_stage` method determines whether a stage is sequential or parallel
|
||||
and processes it accordingly.
|
||||
4. The `_process_single_crew` and `_process_parallel_crews` methods handle the
|
||||
execution of single and parallel crew stages.
|
||||
5. The `_update_metrics_and_input` method updates usage metrics and the current input
|
||||
with the outputs from a stage.
|
||||
6. The `_build_pipeline_kickoff_results` method constructs the final results of the
|
||||
pipeline kickoff, including traces and outputs.
|
||||
|
||||
Handling Traces and Crew Outputs:
|
||||
- During the processing of stages, we handle the results (traces and crew outputs)
|
||||
for all stages except the last one differently from the final stage.
|
||||
- For intermediate stages, the primary focus is on passing the input data between stages.
|
||||
This involves merging the output dictionaries from all crews in a stage into a single
|
||||
dictionary and passing it to the next stage. This merged dictionary allows for smooth
|
||||
data flow between stages.
|
||||
- For the final stage, in addition to passing the input data, we also need to prepare
|
||||
the final outputs and traces to be returned as the overall result of the pipeline kickoff.
|
||||
In this case, we do not merge the results, as each result needs to be included
|
||||
separately in its own pipeline kickoff result.
|
||||
|
||||
Pipeline Terminology:
|
||||
- Pipeline: The overall structure that defines a sequence of operations.
|
||||
- Stage: A distinct part of the pipeline, which can be either sequential or parallel.
|
||||
- Kickoff: A specific execution of the pipeline for a given set of inputs, representing a single instance of processing through the pipeline.
|
||||
- Branch: Parallel executions within a stage (e.g., concurrent crew operations).
|
||||
- Trace: The journey of an individual input through the entire pipeline.
|
||||
|
||||
Example pipeline structure:
|
||||
crew1 >> crew2 >> crew3
|
||||
|
||||
This represents a pipeline with three sequential stages:
|
||||
1. crew1 is the first stage, which processes the input and passes its output to crew2.
|
||||
2. crew2 is the second stage, which takes the output from crew1 as its input, processes it, and passes its output to crew3.
|
||||
3. crew3 is the final stage, which takes the output from crew2 as its input and produces the final output of the pipeline.
|
||||
|
||||
Each input creates its own kickoff, flowing through all stages of the pipeline.
|
||||
Multiple kickoffss can be processed concurrently, each following the defined pipeline structure.
|
||||
|
||||
Another example pipeline structure:
|
||||
crew1 >> [crew2, crew3] >> crew4
|
||||
|
||||
This represents a pipeline with three stages:
|
||||
1. A sequential stage (crew1)
|
||||
2. A parallel stage with two branches (crew2 and crew3 executing concurrently)
|
||||
3. Another sequential stage (crew4)
|
||||
|
||||
Each input creates its own kickoff, flowing through all stages of the pipeline.
|
||||
Multiple kickoffs can be processed concurrently, each following the defined pipeline structure.
|
||||
"""
|
||||
|
||||
|
||||
class Pipeline(BaseModel):
|
||||
stages: List[PipelineStage] = Field(
|
||||
..., description="List of crews representing stages to be executed in sequence"
|
||||
)
|
||||
|
||||
@model_validator(mode="before")
|
||||
@classmethod
|
||||
def validate_stages(cls, values):
|
||||
stages = values.get("stages", [])
|
||||
|
||||
def check_nesting_and_type(item, depth=0):
|
||||
if depth > 1:
|
||||
raise ValueError("Double nesting is not allowed in pipeline stages")
|
||||
if isinstance(item, list):
|
||||
for sub_item in item:
|
||||
check_nesting_and_type(sub_item, depth + 1)
|
||||
elif not isinstance(item, (Crew, Router)):
|
||||
raise ValueError(
|
||||
f"Expected Crew instance, Router instance, or list of Crews, got {type(item)}"
|
||||
)
|
||||
|
||||
for stage in stages:
|
||||
check_nesting_and_type(stage)
|
||||
return values
|
||||
|
||||
async def kickoff(
|
||||
self, inputs: List[Dict[str, Any]]
|
||||
) -> List[PipelineKickoffResult]:
|
||||
"""
|
||||
Processes multiple runs in parallel, each going through all pipeline stages.
|
||||
|
||||
Args:
|
||||
inputs (List[Dict[str, Any]]): List of inputs for each run.
|
||||
|
||||
Returns:
|
||||
List[PipelineKickoffResult]: List of results from each run.
|
||||
"""
|
||||
pipeline_results: List[PipelineKickoffResult] = []
|
||||
|
||||
# Process all runs in parallel
|
||||
all_run_results = await asyncio.gather(
|
||||
*(self.process_single_kickoff(input_data) for input_data in inputs)
|
||||
)
|
||||
|
||||
# Flatten the list of lists into a single list of results
|
||||
pipeline_results.extend(
|
||||
result for run_result in all_run_results for result in run_result
|
||||
)
|
||||
|
||||
return pipeline_results
|
||||
|
||||
async def process_single_kickoff(
|
||||
self, kickoff_input: Dict[str, Any]
|
||||
) -> List[PipelineKickoffResult]:
|
||||
"""
|
||||
Processes a single run through all pipeline stages.
|
||||
|
||||
Args:
|
||||
input (Dict[str, Any]): The input for the run.
|
||||
|
||||
Returns:
|
||||
List[PipelineKickoffResult]: The results of processing the run.
|
||||
"""
|
||||
initial_input = copy.deepcopy(kickoff_input)
|
||||
current_input = copy.deepcopy(kickoff_input)
|
||||
stages = self._copy_stages()
|
||||
pipeline_usage_metrics: Dict[str, UsageMetrics] = {}
|
||||
all_stage_outputs: List[List[CrewOutput]] = []
|
||||
traces: List[List[Union[str, Dict[str, Any]]]] = [[initial_input]]
|
||||
|
||||
stage_index = 0
|
||||
while stage_index < len(stages):
|
||||
stage = stages[stage_index]
|
||||
stage_input = copy.deepcopy(current_input)
|
||||
|
||||
if isinstance(stage, Router):
|
||||
next_pipeline, route_taken = stage.route(stage_input)
|
||||
stages = (
|
||||
stages[: stage_index + 1]
|
||||
+ list(next_pipeline.stages)
|
||||
+ stages[stage_index + 1 :]
|
||||
)
|
||||
traces.append([{"route_taken": route_taken}])
|
||||
stage_index += 1
|
||||
continue
|
||||
|
||||
stage_outputs, stage_trace = await self._process_stage(stage, stage_input)
|
||||
|
||||
self._update_metrics_and_input(
|
||||
pipeline_usage_metrics, current_input, stage, stage_outputs
|
||||
)
|
||||
traces.append(stage_trace)
|
||||
all_stage_outputs.append(stage_outputs)
|
||||
stage_index += 1
|
||||
|
||||
return self._build_pipeline_kickoff_results(
|
||||
all_stage_outputs, traces, pipeline_usage_metrics
|
||||
)
|
||||
|
||||
async def _process_stage(
|
||||
self, stage: PipelineStage, current_input: Dict[str, Any]
|
||||
) -> Tuple[List[CrewOutput], List[Union[str, Dict[str, Any]]]]:
|
||||
"""
|
||||
Processes a single stage of the pipeline, which can be either sequential or parallel.
|
||||
|
||||
Args:
|
||||
stage (Union[Crew, List[Crew]]): The stage to process.
|
||||
current_input (Dict[str, Any]): The input for the stage.
|
||||
|
||||
Returns:
|
||||
Tuple[List[CrewOutput], List[Union[str, Dict[str, Any]]]]: The outputs and trace of the stage.
|
||||
"""
|
||||
if isinstance(stage, Crew):
|
||||
return await self._process_single_crew(stage, current_input)
|
||||
elif isinstance(stage, list) and all(isinstance(crew, Crew) for crew in stage):
|
||||
return await self._process_parallel_crews(stage, current_input)
|
||||
else:
|
||||
raise ValueError(f"Unsupported stage type: {type(stage)}")
|
||||
|
||||
async def _process_single_crew(
|
||||
self, crew: Crew, current_input: Dict[str, Any]
|
||||
) -> Tuple[List[CrewOutput], List[Union[str, Dict[str, Any]]]]:
|
||||
"""
|
||||
Processes a single crew.
|
||||
|
||||
Args:
|
||||
crew (Crew): The crew to process.
|
||||
current_input (Dict[str, Any]): The input for the crew.
|
||||
|
||||
Returns:
|
||||
Tuple[List[CrewOutput], List[Union[str, Dict[str, Any]]]]: The output and trace of the crew.
|
||||
"""
|
||||
output = await crew.kickoff_async(inputs=current_input)
|
||||
return [output], [crew.name or str(crew.id)]
|
||||
|
||||
async def _process_parallel_crews(
|
||||
self, crews: List[Crew], current_input: Dict[str, Any]
|
||||
) -> Tuple[List[CrewOutput], List[Union[str, Dict[str, Any]]]]:
|
||||
"""
|
||||
Processes multiple crews in parallel.
|
||||
|
||||
Args:
|
||||
crews (List[Crew]): The list of crews to process in parallel.
|
||||
current_input (Dict[str, Any]): The input for the crews.
|
||||
|
||||
Returns:
|
||||
Tuple[List[CrewOutput], List[Union[str, Dict[str, Any]]]]: The outputs and traces of the crews.
|
||||
"""
|
||||
parallel_outputs = await asyncio.gather(
|
||||
*[crew.kickoff_async(inputs=current_input) for crew in crews]
|
||||
)
|
||||
return parallel_outputs, [crew.name or str(crew.id) for crew in crews]
|
||||
|
||||
def _update_metrics_and_input(
|
||||
self,
|
||||
usage_metrics: Dict[str, UsageMetrics],
|
||||
current_input: Dict[str, Any],
|
||||
stage: PipelineStage,
|
||||
outputs: List[CrewOutput],
|
||||
) -> None:
|
||||
"""
|
||||
Updates metrics and current input with the outputs of a stage.
|
||||
|
||||
Args:
|
||||
usage_metrics (Dict[str, Any]): The usage metrics to update.
|
||||
current_input (Dict[str, Any]): The current input to update.
|
||||
stage (Union[Crew, List[Crew]]): The stage that was processed.
|
||||
outputs (List[CrewOutput]): The outputs of the stage.
|
||||
"""
|
||||
if isinstance(stage, Crew):
|
||||
usage_metrics[stage.name or str(stage.id)] = outputs[0].token_usage
|
||||
current_input.update(outputs[0].to_dict())
|
||||
elif isinstance(stage, list) and all(isinstance(crew, Crew) for crew in stage):
|
||||
for crew, output in zip(stage, outputs):
|
||||
usage_metrics[crew.name or str(crew.id)] = output.token_usage
|
||||
current_input.update(output.to_dict())
|
||||
else:
|
||||
raise ValueError(f"Unsupported stage type: {type(stage)}")
|
||||
|
||||
def _build_pipeline_kickoff_results(
|
||||
self,
|
||||
all_stage_outputs: List[List[CrewOutput]],
|
||||
traces: List[List[Union[str, Dict[str, Any]]]],
|
||||
token_usage: Dict[str, UsageMetrics],
|
||||
) -> List[PipelineKickoffResult]:
|
||||
"""
|
||||
Builds the results of a pipeline run.
|
||||
|
||||
Args:
|
||||
all_stage_outputs (List[List[CrewOutput]]): All stage outputs.
|
||||
traces (List[List[Union[str, Dict[str, Any]]]]): All traces.
|
||||
token_usage (Dict[str, Any]): Token usage metrics.
|
||||
|
||||
Returns:
|
||||
List[PipelineKickoffResult]: The results of the pipeline run.
|
||||
"""
|
||||
formatted_traces = self._format_traces(traces)
|
||||
formatted_crew_outputs = self._format_crew_outputs(all_stage_outputs)
|
||||
|
||||
return [
|
||||
PipelineKickoffResult(
|
||||
token_usage=token_usage,
|
||||
trace=formatted_trace,
|
||||
raw=crews_outputs[-1].raw,
|
||||
pydantic=crews_outputs[-1].pydantic,
|
||||
json_dict=crews_outputs[-1].json_dict,
|
||||
crews_outputs=crews_outputs,
|
||||
)
|
||||
for crews_outputs, formatted_trace in zip(
|
||||
formatted_crew_outputs, formatted_traces
|
||||
)
|
||||
]
|
||||
|
||||
def _format_traces(
|
||||
self, traces: List[List[Union[str, Dict[str, Any]]]]
|
||||
) -> List[List[Trace]]:
|
||||
"""
|
||||
Formats the traces of a pipeline run.
|
||||
|
||||
Args:
|
||||
traces (List[List[Union[str, Dict[str, Any]]]]): The traces to format.
|
||||
|
||||
Returns:
|
||||
List[List[Trace]]: The formatted traces.
|
||||
"""
|
||||
formatted_traces: List[Trace] = self._format_single_trace(traces[:-1])
|
||||
return self._format_multiple_traces(formatted_traces, traces[-1])
|
||||
|
||||
def _format_single_trace(
|
||||
self, traces: List[List[Union[str, Dict[str, Any]]]]
|
||||
) -> List[Trace]:
|
||||
"""
|
||||
Formats single traces.
|
||||
|
||||
Args:
|
||||
traces (List[List[Union[str, Dict[str, Any]]]]): The traces to format.
|
||||
|
||||
Returns:
|
||||
List[Trace]: The formatted single traces.
|
||||
"""
|
||||
formatted_traces: List[Trace] = []
|
||||
for trace in traces:
|
||||
formatted_traces.append(trace[0] if len(trace) == 1 else trace)
|
||||
return formatted_traces
|
||||
|
||||
def _format_multiple_traces(
|
||||
self,
|
||||
formatted_traces: List[Trace],
|
||||
final_trace: List[Union[str, Dict[str, Any]]],
|
||||
) -> List[List[Trace]]:
|
||||
"""
|
||||
Formats multiple traces.
|
||||
|
||||
Args:
|
||||
formatted_traces (List[Trace]): The formatted single traces.
|
||||
final_trace (List[Union[str, Dict[str, Any]]]): The final trace to format.
|
||||
|
||||
Returns:
|
||||
List[List[Trace]]: The formatted multiple traces.
|
||||
"""
|
||||
traces_to_return: List[List[Trace]] = []
|
||||
if len(final_trace) == 1:
|
||||
formatted_traces.append(final_trace[0])
|
||||
traces_to_return.append(formatted_traces)
|
||||
else:
|
||||
for trace in final_trace:
|
||||
copied_traces = formatted_traces.copy()
|
||||
copied_traces.append(trace)
|
||||
traces_to_return.append(copied_traces)
|
||||
return traces_to_return
|
||||
|
||||
def _format_crew_outputs(
|
||||
self, all_stage_outputs: List[List[CrewOutput]]
|
||||
) -> List[List[CrewOutput]]:
|
||||
"""
|
||||
Formats the outputs of all stages into a list of crew outputs.
|
||||
|
||||
Args:
|
||||
all_stage_outputs (List[List[CrewOutput]]): All stage outputs.
|
||||
|
||||
Returns:
|
||||
List[List[CrewOutput]]: Formatted crew outputs.
|
||||
"""
|
||||
crew_outputs: List[CrewOutput] = [
|
||||
output
|
||||
for stage_outputs in all_stage_outputs[:-1]
|
||||
for output in stage_outputs
|
||||
]
|
||||
return [crew_outputs + [output] for output in all_stage_outputs[-1]]
|
||||
|
||||
def _copy_stages(self):
|
||||
"""Create a deep copy of the Pipeline's stages."""
|
||||
new_stages = []
|
||||
for stage in self.stages:
|
||||
if isinstance(stage, list):
|
||||
new_stages.append(
|
||||
[
|
||||
crew.copy() if hasattr(crew, "copy") else copy.deepcopy(crew)
|
||||
for crew in stage
|
||||
]
|
||||
)
|
||||
elif hasattr(stage, "copy"):
|
||||
new_stages.append(stage.copy())
|
||||
else:
|
||||
new_stages.append(copy.deepcopy(stage))
|
||||
|
||||
return new_stages
|
||||
|
||||
def __rshift__(self, other: PipelineStage) -> "Pipeline":
|
||||
"""
|
||||
Implements the >> operator to add another Stage (Crew or List[Crew]) to an existing Pipeline.
|
||||
|
||||
Args:
|
||||
other (Any): The stage to add.
|
||||
|
||||
Returns:
|
||||
Pipeline: A new pipeline with the added stage.
|
||||
"""
|
||||
if isinstance(other, (Crew, Router)) or (
|
||||
isinstance(other, list) and all(isinstance(item, Crew) for item in other)
|
||||
):
|
||||
return type(self)(stages=self.stages + [other])
|
||||
else:
|
||||
raise TypeError(
|
||||
f"Unsupported operand type for >>: '{type(self).__name__}' and '{type(other).__name__}'"
|
||||
)
|
||||
@@ -1,61 +0,0 @@
|
||||
import json
|
||||
import uuid
|
||||
from typing import Any, Dict, List, Optional, Union
|
||||
|
||||
from pydantic import UUID4, BaseModel, Field
|
||||
|
||||
from crewai.crews.crew_output import CrewOutput
|
||||
from crewai.types.usage_metrics import UsageMetrics
|
||||
|
||||
|
||||
class PipelineKickoffResult(BaseModel):
|
||||
"""Class that represents the result of a pipeline run."""
|
||||
|
||||
id: UUID4 = Field(
|
||||
default_factory=uuid.uuid4,
|
||||
frozen=True,
|
||||
description="Unique identifier for the object, not set by user.",
|
||||
)
|
||||
raw: str = Field(description="Raw output of the pipeline run", default="")
|
||||
pydantic: Any = Field(
|
||||
description="Pydantic output of the pipeline run", default=None
|
||||
)
|
||||
json_dict: Union[Dict[str, Any], None] = Field(
|
||||
description="JSON dict output of the pipeline run", default={}
|
||||
)
|
||||
|
||||
token_usage: Dict[str, UsageMetrics] = Field(
|
||||
description="Token usage for each crew in the run"
|
||||
)
|
||||
trace: List[Any] = Field(
|
||||
description="Trace of the journey of inputs through the run"
|
||||
)
|
||||
crews_outputs: List[CrewOutput] = Field(
|
||||
description="Output from each crew in the run",
|
||||
default=[],
|
||||
)
|
||||
|
||||
@property
|
||||
def json(self) -> Optional[str]:
|
||||
if self.crews_outputs[-1].tasks_output[-1].output_format != "json":
|
||||
raise ValueError(
|
||||
"No JSON output found in the final task of the final crew. Please make sure to set the output_json property in the final task in your crew."
|
||||
)
|
||||
|
||||
return json.dumps(self.json_dict)
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
"""Convert json_output and pydantic_output to a dictionary."""
|
||||
output_dict = {}
|
||||
if self.json_dict:
|
||||
output_dict.update(self.json_dict)
|
||||
elif self.pydantic:
|
||||
output_dict.update(self.pydantic.model_dump())
|
||||
return output_dict
|
||||
|
||||
def __str__(self):
|
||||
if self.pydantic:
|
||||
return str(self.pydantic)
|
||||
if self.json_dict:
|
||||
return str(self.json_dict)
|
||||
return self.raw
|
||||
@@ -1,20 +0,0 @@
|
||||
import uuid
|
||||
from typing import List
|
||||
|
||||
from pydantic import UUID4, BaseModel, Field
|
||||
|
||||
from crewai.pipeline.pipeline_kickoff_result import PipelineKickoffResult
|
||||
|
||||
|
||||
class PipelineOutput(BaseModel):
|
||||
id: UUID4 = Field(
|
||||
default_factory=uuid.uuid4,
|
||||
frozen=True,
|
||||
description="Unique identifier for the object, not set by user.",
|
||||
)
|
||||
run_results: List[PipelineKickoffResult] = Field(
|
||||
description="List of results for each run through the pipeline", default=[]
|
||||
)
|
||||
|
||||
def add_run_result(self, result: PipelineKickoffResult):
|
||||
self.run_results.append(result)
|
||||
@@ -8,12 +8,10 @@ from .annotations import (
|
||||
llm,
|
||||
output_json,
|
||||
output_pydantic,
|
||||
pipeline,
|
||||
task,
|
||||
tool,
|
||||
)
|
||||
from .crew_base import CrewBase
|
||||
from .pipeline_base import PipelineBase
|
||||
|
||||
__all__ = [
|
||||
"agent",
|
||||
@@ -24,10 +22,8 @@ __all__ = [
|
||||
"tool",
|
||||
"callback",
|
||||
"CrewBase",
|
||||
"PipelineBase",
|
||||
"llm",
|
||||
"cache_handler",
|
||||
"pipeline",
|
||||
"before_kickoff",
|
||||
"after_kickoff",
|
||||
]
|
||||
|
||||
@@ -65,21 +65,6 @@ def cache_handler(func):
|
||||
return memoize(func)
|
||||
|
||||
|
||||
def stage(func):
|
||||
func.is_stage = True
|
||||
return memoize(func)
|
||||
|
||||
|
||||
def router(func):
|
||||
func.is_router = True
|
||||
return memoize(func)
|
||||
|
||||
|
||||
def pipeline(func):
|
||||
func.is_pipeline = True
|
||||
return memoize(func)
|
||||
|
||||
|
||||
def crew(func) -> Callable[..., Crew]:
|
||||
def wrapper(self, *args, **kwargs) -> Crew:
|
||||
instantiated_tasks = []
|
||||
|
||||
@@ -1,58 +0,0 @@
|
||||
from typing import Any, Callable, Dict, List, Type, Union
|
||||
|
||||
from crewai.crew import Crew
|
||||
from crewai.pipeline.pipeline import Pipeline
|
||||
from crewai.routers.router import Router
|
||||
|
||||
PipelineStage = Union[Crew, List[Crew], Router]
|
||||
|
||||
|
||||
# TODO: Could potentially remove. Need to check with @joao and @gui if this is needed for CrewAI+
|
||||
def PipelineBase(cls: Type[Any]) -> Type[Any]:
|
||||
class WrappedClass(cls):
|
||||
is_pipeline_class: bool = True # type: ignore
|
||||
stages: List[PipelineStage]
|
||||
|
||||
def __init__(self, *args: Any, **kwargs: Any) -> None:
|
||||
super().__init__(*args, **kwargs)
|
||||
self.stages = []
|
||||
self._map_pipeline_components()
|
||||
|
||||
def _get_all_functions(self) -> Dict[str, Callable[..., Any]]:
|
||||
return {
|
||||
name: getattr(self, name)
|
||||
for name in dir(self)
|
||||
if callable(getattr(self, name))
|
||||
}
|
||||
|
||||
def _filter_functions(
|
||||
self, functions: Dict[str, Callable[..., Any]], attribute: str
|
||||
) -> Dict[str, Callable[..., Any]]:
|
||||
return {
|
||||
name: func
|
||||
for name, func in functions.items()
|
||||
if hasattr(func, attribute)
|
||||
}
|
||||
|
||||
def _map_pipeline_components(self) -> None:
|
||||
all_functions = self._get_all_functions()
|
||||
crew_functions = self._filter_functions(all_functions, "is_crew")
|
||||
router_functions = self._filter_functions(all_functions, "is_router")
|
||||
|
||||
for stage_attr in dir(self):
|
||||
stage = getattr(self, stage_attr)
|
||||
if isinstance(stage, (Crew, Router)):
|
||||
self.stages.append(stage)
|
||||
elif callable(stage) and hasattr(stage, "is_crew"):
|
||||
self.stages.append(crew_functions[stage_attr]())
|
||||
elif callable(stage) and hasattr(stage, "is_router"):
|
||||
self.stages.append(router_functions[stage_attr]())
|
||||
elif isinstance(stage, list) and all(
|
||||
isinstance(item, Crew) for item in stage
|
||||
):
|
||||
self.stages.append(stage)
|
||||
|
||||
def build_pipeline(self) -> Pipeline:
|
||||
return Pipeline(stages=self.stages)
|
||||
|
||||
return WrappedClass
|
||||
@@ -1,3 +0,0 @@
|
||||
from crewai.routers.router import Router
|
||||
|
||||
__all__ = ["Router"]
|
||||
@@ -1,84 +0,0 @@
|
||||
from copy import deepcopy
|
||||
from typing import Any, Callable, Dict, Tuple
|
||||
|
||||
from pydantic import BaseModel, Field, PrivateAttr
|
||||
|
||||
|
||||
class Route(BaseModel):
|
||||
condition: Callable[[Dict[str, Any]], bool]
|
||||
pipeline: Any
|
||||
|
||||
|
||||
class Router(BaseModel):
|
||||
routes: Dict[str, Route] = Field(
|
||||
default_factory=dict,
|
||||
description="Dictionary of route names to (condition, pipeline) tuples",
|
||||
)
|
||||
default: Any = Field(..., description="Default pipeline if no conditions are met")
|
||||
_route_types: Dict[str, type] = PrivateAttr(default_factory=dict)
|
||||
|
||||
class Config:
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
def __init__(self, routes: Dict[str, Route], default: Any, **data):
|
||||
super().__init__(routes=routes, default=default, **data)
|
||||
self._check_copyable(default)
|
||||
for name, route in routes.items():
|
||||
self._check_copyable(route.pipeline)
|
||||
self._route_types[name] = type(route.pipeline)
|
||||
|
||||
@staticmethod
|
||||
def _check_copyable(obj: Any) -> None:
|
||||
if not hasattr(obj, "copy") or not callable(getattr(obj, "copy")):
|
||||
raise ValueError(f"Object of type {type(obj)} must have a 'copy' method")
|
||||
|
||||
def add_route(
|
||||
self,
|
||||
name: str,
|
||||
condition: Callable[[Dict[str, Any]], bool],
|
||||
pipeline: Any,
|
||||
) -> "Router":
|
||||
"""
|
||||
Add a named route with its condition and corresponding pipeline to the router.
|
||||
|
||||
Args:
|
||||
name: A unique name for this route
|
||||
condition: A function that takes a dictionary input and returns a boolean
|
||||
pipeline: The Pipeline to execute if the condition is met
|
||||
|
||||
Returns:
|
||||
The Router instance for method chaining
|
||||
"""
|
||||
self._check_copyable(pipeline)
|
||||
self.routes[name] = Route(condition=condition, pipeline=pipeline)
|
||||
self._route_types[name] = type(pipeline)
|
||||
return self
|
||||
|
||||
def route(self, input_data: Dict[str, Any]) -> Tuple[Any, str]:
|
||||
"""
|
||||
Evaluate the input against the conditions and return the appropriate pipeline.
|
||||
|
||||
Args:
|
||||
input_data: The input dictionary to be evaluated
|
||||
|
||||
Returns:
|
||||
A tuple containing the next Pipeline to be executed and the name of the route taken
|
||||
"""
|
||||
for name, route in self.routes.items():
|
||||
if route.condition(input_data):
|
||||
return route.pipeline, name
|
||||
|
||||
return self.default, "default"
|
||||
|
||||
def copy(self) -> "Router":
|
||||
"""Create a deep copy of the Router."""
|
||||
new_routes = {
|
||||
name: Route(
|
||||
condition=deepcopy(route.condition),
|
||||
pipeline=route.pipeline.copy(),
|
||||
)
|
||||
for name, route in self.routes.items()
|
||||
}
|
||||
new_default = self.default.copy()
|
||||
|
||||
return Router(routes=new_routes, default=new_default)
|
||||
@@ -1,921 +0,0 @@
|
||||
import json
|
||||
from unittest.mock import AsyncMock, MagicMock, patch
|
||||
|
||||
import pytest
|
||||
from crewai.agent import Agent
|
||||
from crewai.crew import Crew
|
||||
from crewai.crews.crew_output import CrewOutput
|
||||
from crewai.pipeline.pipeline import Pipeline
|
||||
from crewai.pipeline.pipeline_kickoff_result import PipelineKickoffResult
|
||||
from crewai.process import Process
|
||||
from crewai.routers.router import Route, Router
|
||||
from crewai.task import Task
|
||||
from crewai.tasks.task_output import TaskOutput
|
||||
from crewai.types.usage_metrics import UsageMetrics
|
||||
from pydantic import BaseModel, ValidationError
|
||||
|
||||
DEFAULT_TOKEN_USAGE = UsageMetrics(
|
||||
total_tokens=100, prompt_tokens=50, completion_tokens=50, successful_requests=3
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_crew_factory():
|
||||
def _create_mock_crew(name: str, output_json_dict=None, pydantic_output=None):
|
||||
MockCrewClass = type("MockCrew", (MagicMock, Crew), {})
|
||||
|
||||
class MockCrew(MockCrewClass):
|
||||
def __deepcopy__(self):
|
||||
result = MockCrewClass()
|
||||
result.kickoff_async = self.kickoff_async
|
||||
result.name = self.name
|
||||
return result
|
||||
|
||||
def copy(
|
||||
self,
|
||||
):
|
||||
return self
|
||||
|
||||
crew = MockCrew()
|
||||
crew.name = name
|
||||
crew.knowledge = None
|
||||
|
||||
task_output = TaskOutput(
|
||||
description="Test task", raw="Task output", agent="Test Agent"
|
||||
)
|
||||
crew_output = CrewOutput(
|
||||
raw="Test output",
|
||||
tasks_output=[task_output],
|
||||
token_usage=DEFAULT_TOKEN_USAGE,
|
||||
json_dict=output_json_dict if output_json_dict else None,
|
||||
pydantic=pydantic_output,
|
||||
)
|
||||
|
||||
async def kickoff_async(inputs=None):
|
||||
return crew_output
|
||||
|
||||
# Create an AsyncMock for kickoff_async
|
||||
crew.kickoff_async = AsyncMock(side_effect=kickoff_async)
|
||||
|
||||
# Mock the synchronous kickoff method
|
||||
crew.kickoff = MagicMock(return_value=crew_output)
|
||||
|
||||
# Add more attributes that Procedure might be expecting
|
||||
crew.verbose = False
|
||||
crew.output_log_file = None
|
||||
crew.max_rpm = None
|
||||
crew.memory = False
|
||||
crew.process = Process.sequential
|
||||
crew.config = None
|
||||
crew.cache = True
|
||||
crew.embedder = None
|
||||
|
||||
# Add non-empty agents and tasks
|
||||
mock_agent = MagicMock(spec=Agent)
|
||||
mock_task = MagicMock(spec=Task)
|
||||
mock_task.agent = mock_agent
|
||||
mock_task.async_execution = False
|
||||
mock_task.context = None
|
||||
|
||||
crew.agents = [mock_agent]
|
||||
crew.tasks = [mock_task]
|
||||
|
||||
return crew
|
||||
|
||||
return _create_mock_crew
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_router_factory(mock_crew_factory):
|
||||
def _create_mock_router():
|
||||
crew1 = mock_crew_factory(name="Crew 1", output_json_dict={"output": "crew1"})
|
||||
crew2 = mock_crew_factory(name="Crew 2", output_json_dict={"output": "crew2"})
|
||||
crew3 = mock_crew_factory(name="Crew 3", output_json_dict={"output": "crew3"})
|
||||
|
||||
MockRouterClass = type("MockRouter", (MagicMock, Router), {})
|
||||
|
||||
class MockRouter(MockRouterClass):
|
||||
def __deepcopy__(self, memo):
|
||||
result = MockRouterClass()
|
||||
result.route = self.route
|
||||
return result
|
||||
|
||||
mock_router = MockRouter()
|
||||
mock_router.route = MagicMock(
|
||||
side_effect=lambda x: (
|
||||
(
|
||||
Pipeline(stages=[crew1])
|
||||
if x.get("score", 0) > 80
|
||||
else (
|
||||
Pipeline(stages=[crew2])
|
||||
if x.get("score", 0) > 50
|
||||
else Pipeline(stages=[crew3])
|
||||
)
|
||||
),
|
||||
(
|
||||
"route1"
|
||||
if x.get("score", 0) > 80
|
||||
else "route2"
|
||||
if x.get("score", 0) > 50
|
||||
else "default"
|
||||
),
|
||||
)
|
||||
)
|
||||
|
||||
return mock_router
|
||||
|
||||
return _create_mock_router
|
||||
|
||||
|
||||
def test_pipeline_initialization(mock_crew_factory):
|
||||
"""
|
||||
Test that a Pipeline is correctly initialized with the given stages.
|
||||
"""
|
||||
crew1 = mock_crew_factory(name="Crew 1")
|
||||
crew2 = mock_crew_factory(name="Crew 2")
|
||||
|
||||
pipeline = Pipeline(stages=[crew1, crew2])
|
||||
assert len(pipeline.stages) == 2
|
||||
assert pipeline.stages[0] == crew1
|
||||
assert pipeline.stages[1] == crew2
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_pipeline_with_empty_input(mock_crew_factory):
|
||||
"""
|
||||
Ensure the pipeline handles an empty input list correctly.
|
||||
"""
|
||||
crew = mock_crew_factory(name="Test Crew")
|
||||
pipeline = Pipeline(stages=[crew])
|
||||
|
||||
input_data = []
|
||||
pipeline_results = await pipeline.kickoff(input_data)
|
||||
|
||||
assert (
|
||||
len(pipeline_results) == 0
|
||||
), "Pipeline should return empty results for empty input"
|
||||
|
||||
|
||||
agent = Agent(
|
||||
role="Test Role",
|
||||
goal="Test Goal",
|
||||
backstory="Test Backstory",
|
||||
allow_delegation=False,
|
||||
verbose=False,
|
||||
)
|
||||
task = Task(
|
||||
description="Return: Test output",
|
||||
expected_output="Test output",
|
||||
agent=agent,
|
||||
async_execution=False,
|
||||
context=None,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_pipeline_process_streams_single_input():
|
||||
"""
|
||||
Test that Pipeline.process_streams() correctly processes a single input
|
||||
and returns the expected CrewOutput.
|
||||
"""
|
||||
crew_name = "Test Crew"
|
||||
mock_crew = Crew(
|
||||
agents=[agent],
|
||||
tasks=[task],
|
||||
process=Process.sequential,
|
||||
)
|
||||
mock_crew.name = crew_name
|
||||
pipeline = Pipeline(stages=[mock_crew])
|
||||
input_data = [{"key": "value"}]
|
||||
with patch.object(Crew, "kickoff_async") as mock_kickoff:
|
||||
task_output = TaskOutput(
|
||||
description="Test task", raw="Task output", agent="Test Agent"
|
||||
)
|
||||
mock_kickoff.return_value = CrewOutput(
|
||||
raw="Test output",
|
||||
tasks_output=[task_output],
|
||||
token_usage=DEFAULT_TOKEN_USAGE,
|
||||
json_dict=None,
|
||||
pydantic=None,
|
||||
)
|
||||
pipeline_results = await pipeline.kickoff(input_data)
|
||||
mock_crew.kickoff_async.assert_called_once_with(inputs={"key": "value"})
|
||||
|
||||
for pipeline_result in pipeline_results:
|
||||
assert isinstance(pipeline_result, PipelineKickoffResult)
|
||||
assert pipeline_result.raw == "Test output"
|
||||
assert len(pipeline_result.crews_outputs) == 1
|
||||
assert pipeline_result.token_usage == {crew_name: DEFAULT_TOKEN_USAGE}
|
||||
assert pipeline_result.trace == [input_data[0], "Test Crew"]
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_pipeline_result_ordering():
|
||||
"""
|
||||
Ensure that results are returned in the same order as the inputs, especially with parallel processing.
|
||||
"""
|
||||
crew1 = Crew(
|
||||
name="Crew 1",
|
||||
agents=[agent],
|
||||
tasks=[task],
|
||||
)
|
||||
crew2 = Crew(
|
||||
name="Crew 2",
|
||||
agents=[agent],
|
||||
tasks=[task],
|
||||
)
|
||||
crew3 = Crew(
|
||||
name="Crew 3",
|
||||
agents=[agent],
|
||||
tasks=[task],
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
stages=[crew1, [crew2, crew3]]
|
||||
) # Parallel stage to test ordering
|
||||
input_data = [{"id": 1}, {"id": 2}, {"id": 3}]
|
||||
|
||||
def create_crew_output(crew_name):
|
||||
return CrewOutput(
|
||||
raw=f"Test output from {crew_name}",
|
||||
tasks_output=[
|
||||
TaskOutput(
|
||||
description="Test task",
|
||||
raw=f"Task output from {crew_name}",
|
||||
agent="Test Agent",
|
||||
)
|
||||
],
|
||||
token_usage=DEFAULT_TOKEN_USAGE,
|
||||
json_dict={"output": crew_name.lower().replace(" ", "")},
|
||||
pydantic=None,
|
||||
)
|
||||
|
||||
with patch.object(Crew, "kickoff_async") as mock_kickoff:
|
||||
mock_kickoff.side_effect = [
|
||||
create_crew_output("Crew 1"),
|
||||
create_crew_output("Crew 2"),
|
||||
create_crew_output("Crew 3"),
|
||||
] * 3
|
||||
pipeline_results = await pipeline.kickoff(input_data)
|
||||
mock_kickoff.call_count = 3
|
||||
|
||||
assert (
|
||||
len(pipeline_results) == 6
|
||||
), "Should have 2 results for each input due to the parallel final stage"
|
||||
|
||||
# Group results by their original input id
|
||||
grouped_results = {}
|
||||
for result in pipeline_results:
|
||||
input_id = result.trace[0]["id"]
|
||||
if input_id not in grouped_results:
|
||||
grouped_results[input_id] = []
|
||||
grouped_results[input_id].append(result)
|
||||
|
||||
# Check that we have the correct number of groups and results per group
|
||||
assert len(grouped_results) == 3, "Should have results for each of the 3 inputs"
|
||||
for input_id, results in grouped_results.items():
|
||||
assert (
|
||||
len(results) == 2
|
||||
), f"Each input should have 2 results, but input {input_id} has {len(results)}"
|
||||
|
||||
# Check the ordering and content of the results
|
||||
for input_id in range(1, 4):
|
||||
group = grouped_results[input_id]
|
||||
assert group[0].trace == [
|
||||
{"id": input_id},
|
||||
"Crew 1",
|
||||
"Crew 2",
|
||||
], f"Unexpected trace for first result of input {input_id}"
|
||||
assert group[1].trace == [
|
||||
{"id": input_id},
|
||||
"Crew 1",
|
||||
"Crew 3",
|
||||
], f"Unexpected trace for second result of input {input_id}"
|
||||
|
||||
|
||||
class TestPydanticOutput(BaseModel):
|
||||
key: str
|
||||
value: int
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
async def test_pipeline_process_streams_single_input_pydantic_output():
|
||||
crew_name = "Test Crew"
|
||||
task = Task(
|
||||
description="Return: Key:value",
|
||||
expected_output="Key:Value",
|
||||
agent=agent,
|
||||
async_execution=False,
|
||||
context=None,
|
||||
output_pydantic=TestPydanticOutput,
|
||||
)
|
||||
mock_crew = Crew(
|
||||
name=crew_name,
|
||||
agents=[agent],
|
||||
tasks=[task],
|
||||
)
|
||||
|
||||
pipeline = Pipeline(stages=[mock_crew])
|
||||
input_data = [{"key": "value"}]
|
||||
with patch.object(Crew, "kickoff_async") as mock_kickoff:
|
||||
mock_crew_output = CrewOutput(
|
||||
raw="Test output",
|
||||
tasks_output=[
|
||||
TaskOutput(
|
||||
description="Return: Key:value", raw="Key:Value", agent="Test Agent"
|
||||
)
|
||||
],
|
||||
token_usage=UsageMetrics(
|
||||
total_tokens=171,
|
||||
prompt_tokens=154,
|
||||
completion_tokens=17,
|
||||
successful_requests=1,
|
||||
),
|
||||
pydantic=TestPydanticOutput(key="test", value=42),
|
||||
)
|
||||
mock_kickoff.return_value = mock_crew_output
|
||||
pipeline_results = await pipeline.kickoff(input_data)
|
||||
|
||||
assert len(pipeline_results) == 1
|
||||
pipeline_result = pipeline_results[0]
|
||||
|
||||
assert isinstance(pipeline_result, PipelineKickoffResult)
|
||||
assert pipeline_result.raw == "Test output"
|
||||
assert len(pipeline_result.crews_outputs) == 1
|
||||
assert pipeline_result.token_usage == {
|
||||
crew_name: UsageMetrics(
|
||||
total_tokens=171,
|
||||
prompt_tokens=154,
|
||||
completion_tokens=17,
|
||||
successful_requests=1,
|
||||
)
|
||||
}
|
||||
|
||||
assert pipeline_result.trace == [input_data[0], "Test Crew"]
|
||||
assert isinstance(pipeline_result.pydantic, TestPydanticOutput)
|
||||
assert pipeline_result.pydantic.key == "test"
|
||||
assert pipeline_result.pydantic.value == 42
|
||||
assert pipeline_result.json_dict is None
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_pipeline_preserves_original_input(mock_crew_factory):
|
||||
crew_name = "Test Crew"
|
||||
mock_crew = mock_crew_factory(
|
||||
name=crew_name,
|
||||
output_json_dict={"new_key": "new_value"},
|
||||
)
|
||||
pipeline = Pipeline(stages=[mock_crew])
|
||||
|
||||
# Create a deep copy of the input data to ensure we're not comparing references
|
||||
original_input_data = [{"key": "value", "nested": {"a": 1}}]
|
||||
input_data = json.loads(json.dumps(original_input_data))
|
||||
|
||||
await pipeline.kickoff(input_data)
|
||||
|
||||
# Assert that the original input hasn't been modified
|
||||
assert (
|
||||
input_data == original_input_data
|
||||
), "The original input data should not be modified"
|
||||
|
||||
# Ensure that even nested structures haven't been modified
|
||||
assert (
|
||||
input_data[0]["nested"] == original_input_data[0]["nested"]
|
||||
), "Nested structures should not be modified"
|
||||
|
||||
# Verify that adding new keys to the crew output doesn't affect the original input
|
||||
assert (
|
||||
"new_key" not in input_data[0]
|
||||
), "New keys from crew output should not be added to the original input"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_pipeline_process_streams_multiple_inputs():
|
||||
"""
|
||||
Test that Pipeline.process_streams() correctly processes multiple inputs
|
||||
and returns the expected CrewOutputs.
|
||||
"""
|
||||
mock_crew = Crew(name="Test Crew", tasks=[task], agents=[agent])
|
||||
pipeline = Pipeline(stages=[mock_crew])
|
||||
input_data = [{"key1": "value1"}, {"key2": "value2"}]
|
||||
|
||||
with patch.object(Crew, "kickoff_async") as mock_kickoff:
|
||||
mock_kickoff.return_value = CrewOutput(
|
||||
raw="Test output",
|
||||
tasks_output=[
|
||||
TaskOutput(
|
||||
description="Test task", raw="Task output", agent="Test Agent"
|
||||
)
|
||||
],
|
||||
token_usage=DEFAULT_TOKEN_USAGE,
|
||||
json_dict=None,
|
||||
pydantic=None,
|
||||
)
|
||||
pipeline_results = await pipeline.kickoff(input_data)
|
||||
assert mock_kickoff.call_count == 2
|
||||
assert len(pipeline_results) == 2
|
||||
|
||||
for pipeline_result in pipeline_results:
|
||||
assert all(
|
||||
isinstance(crew_output, CrewOutput)
|
||||
for crew_output in pipeline_result.crews_outputs
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_pipeline_with_parallel_stages():
|
||||
"""
|
||||
Test that Pipeline correctly handles parallel stages.
|
||||
"""
|
||||
crew1 = Crew(name="Crew 1", tasks=[task], agents=[agent])
|
||||
crew2 = Crew(name="Crew 2", tasks=[task], agents=[agent])
|
||||
crew3 = Crew(name="Crew 3", tasks=[task], agents=[agent])
|
||||
|
||||
pipeline = Pipeline(stages=[crew1, [crew2, crew3]])
|
||||
input_data = [{"initial": "data"}]
|
||||
|
||||
with patch.object(Crew, "kickoff_async") as mock_kickoff:
|
||||
mock_kickoff.return_value = CrewOutput(
|
||||
raw="Test output",
|
||||
tasks_output=[
|
||||
TaskOutput(
|
||||
description="Test task", raw="Task output", agent="Test Agent"
|
||||
)
|
||||
],
|
||||
token_usage=DEFAULT_TOKEN_USAGE,
|
||||
json_dict=None,
|
||||
pydantic=None,
|
||||
)
|
||||
pipeline_result = await pipeline.kickoff(input_data)
|
||||
mock_kickoff.assert_called_with(inputs={"initial": "data"})
|
||||
|
||||
assert len(pipeline_result) == 2
|
||||
pipeline_result_1, pipeline_result_2 = pipeline_result
|
||||
|
||||
pipeline_result_1.trace = [
|
||||
"Crew 1",
|
||||
"Crew 2",
|
||||
]
|
||||
pipeline_result_2.trace = [
|
||||
"Crew 1",
|
||||
"Crew 3",
|
||||
]
|
||||
|
||||
expected_token_usage = {
|
||||
"Crew 1": DEFAULT_TOKEN_USAGE,
|
||||
"Crew 2": DEFAULT_TOKEN_USAGE,
|
||||
"Crew 3": DEFAULT_TOKEN_USAGE,
|
||||
}
|
||||
|
||||
assert pipeline_result_1.token_usage == expected_token_usage
|
||||
assert pipeline_result_2.token_usage == expected_token_usage
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_pipeline_with_parallel_stages_end_in_single_stage(mock_crew_factory):
|
||||
"""
|
||||
Test that Pipeline correctly handles parallel stages.
|
||||
"""
|
||||
crew1 = mock_crew_factory(name="Crew 1")
|
||||
crew2 = mock_crew_factory(name="Crew 2")
|
||||
crew3 = mock_crew_factory(name="Crew 3")
|
||||
crew4 = mock_crew_factory(name="Crew 4")
|
||||
|
||||
pipeline = Pipeline(stages=[crew1, [crew2, crew3], crew4])
|
||||
input_data = [{"initial": "data"}]
|
||||
|
||||
pipeline_result = await pipeline.kickoff(input_data)
|
||||
|
||||
crew1.kickoff_async.assert_called_once_with(inputs={"initial": "data"})
|
||||
|
||||
assert len(pipeline_result) == 1
|
||||
pipeline_result_1 = pipeline_result[0]
|
||||
|
||||
pipeline_result_1.trace = [
|
||||
input_data[0],
|
||||
"Crew 1",
|
||||
["Crew 2", "Crew 3"],
|
||||
"Crew 4",
|
||||
]
|
||||
|
||||
expected_token_usage = {
|
||||
"Crew 1": DEFAULT_TOKEN_USAGE,
|
||||
"Crew 2": DEFAULT_TOKEN_USAGE,
|
||||
"Crew 3": DEFAULT_TOKEN_USAGE,
|
||||
"Crew 4": DEFAULT_TOKEN_USAGE,
|
||||
}
|
||||
|
||||
assert pipeline_result_1.token_usage == expected_token_usage
|
||||
|
||||
|
||||
def test_pipeline_rshift_operator(mock_crew_factory):
|
||||
"""
|
||||
Test that the >> operator correctly creates a Pipeline from Crews and lists of Crews.
|
||||
"""
|
||||
crew1 = mock_crew_factory(name="Crew 1")
|
||||
crew2 = mock_crew_factory(name="Crew 2")
|
||||
crew3 = mock_crew_factory(name="Crew 3")
|
||||
|
||||
# Test single crew addition
|
||||
pipeline = Pipeline(stages=[]) >> crew1
|
||||
assert len(pipeline.stages) == 1
|
||||
assert pipeline.stages[0] == crew1
|
||||
|
||||
# Test adding a list of crews
|
||||
pipeline = Pipeline(stages=[crew1])
|
||||
pipeline = pipeline >> [crew2, crew3]
|
||||
assert len(pipeline.stages) == 2
|
||||
assert pipeline.stages[1] == [crew2, crew3]
|
||||
|
||||
# Test error case: trying to shift with non-Crew object
|
||||
with pytest.raises(TypeError):
|
||||
pipeline >> "not a crew"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
async def test_pipeline_parallel_crews_to_parallel_crews():
|
||||
"""
|
||||
Test that feeding parallel crews to parallel crews works correctly.
|
||||
"""
|
||||
crew1 = Crew(name="Crew 1", tasks=[task], agents=[agent])
|
||||
crew2 = Crew(name="Crew 2", tasks=[task], agents=[agent])
|
||||
crew3 = Crew(name="Crew 3", tasks=[task], agents=[agent])
|
||||
crew4 = Crew(name="Crew 4", tasks=[task], agents=[agent])
|
||||
# output_json_dict={"output1": "crew1"}
|
||||
pipeline = Pipeline(stages=[[crew1, crew2], [crew3, crew4]])
|
||||
|
||||
input_data = [{"input": "test"}]
|
||||
|
||||
def create_crew_output(crew_name):
|
||||
return CrewOutput(
|
||||
raw=f"Test output from {crew_name}",
|
||||
tasks_output=[
|
||||
TaskOutput(
|
||||
description="Test task",
|
||||
raw=f"Task output from {crew_name}",
|
||||
agent="Test Agent",
|
||||
)
|
||||
],
|
||||
token_usage=DEFAULT_TOKEN_USAGE,
|
||||
json_dict={"output": crew_name.lower().replace(" ", "")},
|
||||
pydantic=None,
|
||||
)
|
||||
|
||||
with patch.object(Crew, "kickoff_async") as mock_kickoff:
|
||||
mock_kickoff.side_effect = [
|
||||
create_crew_output(crew_name)
|
||||
for crew_name in ["Crew 1", "Crew 2", "Crew 3", "Crew 4"]
|
||||
]
|
||||
pipeline_results = await pipeline.kickoff(input_data)
|
||||
|
||||
assert len(pipeline_results) == 2, "Should have 2 results for final parallel stage"
|
||||
|
||||
pipeline_result_1, pipeline_result_2 = pipeline_results
|
||||
|
||||
# Check the outputs
|
||||
assert pipeline_result_1.json_dict == {"output": "crew3"}
|
||||
assert pipeline_result_2.json_dict == {"output": "crew4"}
|
||||
|
||||
# Check the traces
|
||||
expected_traces = [
|
||||
[{"input": "test"}, ["Crew 1", "Crew 2"], "Crew 3"],
|
||||
[{"input": "test"}, ["Crew 1", "Crew 2"], "Crew 4"],
|
||||
]
|
||||
|
||||
for result, expected_trace in zip(pipeline_results, expected_traces):
|
||||
assert result.trace == expected_trace, f"Unexpected trace: {result.trace}"
|
||||
|
||||
|
||||
def test_pipeline_double_nesting_not_allowed(mock_crew_factory):
|
||||
"""
|
||||
Test that double nesting in pipeline stages is not allowed.
|
||||
"""
|
||||
crew1 = mock_crew_factory(name="Crew 1")
|
||||
crew2 = mock_crew_factory(name="Crew 2")
|
||||
crew3 = mock_crew_factory(name="Crew 3")
|
||||
crew4 = mock_crew_factory(name="Crew 4")
|
||||
|
||||
with pytest.raises(ValidationError) as exc_info:
|
||||
Pipeline(stages=[crew1, [[crew2, crew3], crew4]])
|
||||
|
||||
error_msg = str(exc_info.value)
|
||||
|
||||
assert (
|
||||
"Double nesting is not allowed in pipeline stages" in error_msg
|
||||
), f"Unexpected error message: {error_msg}"
|
||||
|
||||
|
||||
def test_pipeline_invalid_crew(mock_crew_factory):
|
||||
"""
|
||||
Test that non-Crew objects are not allowed in pipeline stages.
|
||||
"""
|
||||
crew1 = mock_crew_factory(name="Crew 1")
|
||||
not_a_crew = "This is not a crew"
|
||||
|
||||
with pytest.raises(ValidationError) as exc_info:
|
||||
Pipeline(stages=[crew1, not_a_crew])
|
||||
|
||||
error_msg = str(exc_info.value)
|
||||
assert (
|
||||
"Expected Crew instance, Router instance, or list of Crews, got <class 'str'>"
|
||||
in error_msg
|
||||
), f"Unexpected error message: {error_msg}"
|
||||
|
||||
|
||||
"""
|
||||
TODO: Figure out what is the proper output for a pipeline with multiple stages
|
||||
|
||||
Options:
|
||||
- Should the final output only include the last stage's output?
|
||||
- Should the final output include the accumulation of previous stages' outputs?
|
||||
"""
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_pipeline_data_accumulation(mock_crew_factory):
|
||||
crew1 = mock_crew_factory(name="Crew 1", output_json_dict={"key1": "value1"})
|
||||
crew2 = mock_crew_factory(name="Crew 2", output_json_dict={"key2": "value2"})
|
||||
|
||||
pipeline = Pipeline(stages=[crew1, crew2])
|
||||
input_data = [{"initial": "data"}]
|
||||
results = await pipeline.kickoff(input_data)
|
||||
|
||||
# Check that crew1 was called with only the initial input
|
||||
crew1.kickoff_async.assert_called_once_with(inputs={"initial": "data"})
|
||||
|
||||
# Check that crew2 was called with the combined input from the initial data and crew1's output
|
||||
crew2.kickoff_async.assert_called_once_with(
|
||||
inputs={"initial": "data", "key1": "value1"}
|
||||
)
|
||||
|
||||
# Check the final output
|
||||
assert len(results) == 1
|
||||
final_result = results[0]
|
||||
assert final_result.json_dict == {"key2": "value2"}
|
||||
|
||||
# Check that the trace includes all stages
|
||||
assert final_result.trace == [{"initial": "data"}, "Crew 1", "Crew 2"]
|
||||
|
||||
# Check that crews_outputs contain the correct information
|
||||
assert len(final_result.crews_outputs) == 2
|
||||
assert final_result.crews_outputs[0].json_dict == {"key1": "value1"}
|
||||
assert final_result.crews_outputs[1].json_dict == {"key2": "value2"}
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
async def test_pipeline_with_router():
|
||||
crew1 = Crew(name="Crew 1", tasks=[task], agents=[agent])
|
||||
crew2 = Crew(name="Crew 2", tasks=[task], agents=[agent])
|
||||
crew3 = Crew(name="Crew 3", tasks=[task], agents=[agent])
|
||||
routes = {
|
||||
"route1": Route(
|
||||
condition=lambda x: x.get("score", 0) > 80,
|
||||
pipeline=Pipeline(stages=[crew1]),
|
||||
),
|
||||
"route2": Route(
|
||||
condition=lambda x: 50 < x.get("score", 0) <= 80,
|
||||
pipeline=Pipeline(stages=[crew2]),
|
||||
),
|
||||
}
|
||||
router = Router(
|
||||
routes=routes,
|
||||
default=Pipeline(stages=[crew3]),
|
||||
)
|
||||
# Test high score route
|
||||
pipeline = Pipeline(stages=[router])
|
||||
with patch.object(Crew, "kickoff_async") as mock_kickoff:
|
||||
mock_kickoff.return_value = CrewOutput(
|
||||
raw="Test output from Crew 1",
|
||||
tasks_output=[],
|
||||
token_usage=DEFAULT_TOKEN_USAGE,
|
||||
json_dict={"output": "crew1"},
|
||||
pydantic=None,
|
||||
)
|
||||
result_high = await pipeline.kickoff([{"score": 90}])
|
||||
|
||||
assert len(result_high) == 1
|
||||
assert result_high[0].json_dict is not None
|
||||
assert result_high[0].json_dict["output"] == "crew1"
|
||||
assert result_high[0].trace == [
|
||||
{"score": 90},
|
||||
{"route_taken": "route1"},
|
||||
"Crew 1",
|
||||
]
|
||||
with patch.object(Crew, "kickoff_async") as mock_kickoff:
|
||||
mock_kickoff.return_value = CrewOutput(
|
||||
raw="Test output from Crew 2",
|
||||
tasks_output=[],
|
||||
token_usage=DEFAULT_TOKEN_USAGE,
|
||||
json_dict={"output": "crew2"},
|
||||
pydantic=None,
|
||||
)
|
||||
# Test medium score route
|
||||
pipeline = Pipeline(stages=[router])
|
||||
result_medium = await pipeline.kickoff([{"score": 60}])
|
||||
assert len(result_medium) == 1
|
||||
assert result_medium[0].json_dict is not None
|
||||
assert result_medium[0].json_dict["output"] == "crew2"
|
||||
assert result_medium[0].trace == [
|
||||
{"score": 60},
|
||||
{"route_taken": "route2"},
|
||||
"Crew 2",
|
||||
]
|
||||
|
||||
with patch.object(Crew, "kickoff_async") as mock_kickoff:
|
||||
mock_kickoff.return_value = CrewOutput(
|
||||
raw="Test output from Crew 3",
|
||||
tasks_output=[],
|
||||
token_usage=DEFAULT_TOKEN_USAGE,
|
||||
json_dict={"output": "crew3"},
|
||||
pydantic=None,
|
||||
)
|
||||
# Test low score route
|
||||
pipeline = Pipeline(stages=[router])
|
||||
result_low = await pipeline.kickoff([{"score": 30}])
|
||||
assert len(result_low) == 1
|
||||
assert result_low[0].json_dict is not None
|
||||
assert result_low[0].json_dict["output"] == "crew3"
|
||||
assert result_low[0].trace == [
|
||||
{"score": 30},
|
||||
{"route_taken": "default"},
|
||||
"Crew 3",
|
||||
]
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
async def test_router_with_multiple_inputs():
|
||||
crew1 = Crew(name="Crew 1", tasks=[task], agents=[agent])
|
||||
crew2 = Crew(name="Crew 2", tasks=[task], agents=[agent])
|
||||
crew3 = Crew(name="Crew 3", tasks=[task], agents=[agent])
|
||||
router = Router(
|
||||
routes={
|
||||
"route1": Route(
|
||||
condition=lambda x: x.get("score", 0) > 80,
|
||||
pipeline=Pipeline(stages=[crew1]),
|
||||
),
|
||||
"route2": Route(
|
||||
condition=lambda x: 50 < x.get("score", 0) <= 80,
|
||||
pipeline=Pipeline(stages=[crew2]),
|
||||
),
|
||||
},
|
||||
default=Pipeline(stages=[crew3]),
|
||||
)
|
||||
pipeline = Pipeline(stages=[router])
|
||||
|
||||
inputs = [{"score": 90}, {"score": 60}, {"score": 30}]
|
||||
|
||||
with patch.object(Crew, "kickoff_async") as mock_kickoff:
|
||||
mock_kickoff.side_effect = [
|
||||
CrewOutput(
|
||||
raw="Test output from Crew 1",
|
||||
tasks_output=[],
|
||||
token_usage=DEFAULT_TOKEN_USAGE,
|
||||
json_dict={"output": "crew1"},
|
||||
pydantic=None,
|
||||
),
|
||||
CrewOutput(
|
||||
raw="Test output from Crew 2",
|
||||
tasks_output=[],
|
||||
token_usage=DEFAULT_TOKEN_USAGE,
|
||||
json_dict={"output": "crew2"},
|
||||
pydantic=None,
|
||||
),
|
||||
CrewOutput(
|
||||
raw="Test output from Crew 3",
|
||||
tasks_output=[],
|
||||
token_usage=DEFAULT_TOKEN_USAGE,
|
||||
json_dict={"output": "crew3"},
|
||||
pydantic=None,
|
||||
),
|
||||
]
|
||||
results = await pipeline.kickoff(inputs)
|
||||
|
||||
assert len(results) == 3
|
||||
assert results[0].json_dict is not None
|
||||
assert results[0].json_dict["output"] == "crew1"
|
||||
assert results[1].json_dict is not None
|
||||
assert results[1].json_dict["output"] == "crew2"
|
||||
assert results[2].json_dict is not None
|
||||
assert results[2].json_dict["output"] == "crew3"
|
||||
|
||||
assert results[0].trace[1]["route_taken"] == "route1"
|
||||
assert results[1].trace[1]["route_taken"] == "route2"
|
||||
assert results[2].trace[1]["route_taken"] == "default"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
async def test_pipeline_with_multiple_routers():
|
||||
crew1 = Crew(name="Crew 1", tasks=[task], agents=[agent])
|
||||
crew2 = Crew(name="Crew 2", tasks=[task], agents=[agent])
|
||||
router1 = Router(
|
||||
routes={
|
||||
"route1": Route(
|
||||
condition=lambda x: x.get("score", 0) > 80,
|
||||
pipeline=Pipeline(stages=[crew1]),
|
||||
),
|
||||
},
|
||||
default=Pipeline(stages=[crew2]),
|
||||
)
|
||||
router2 = Router(
|
||||
routes={
|
||||
"route2": Route(
|
||||
condition=lambda x: 50 < x.get("score", 0) <= 80,
|
||||
pipeline=Pipeline(stages=[crew2]),
|
||||
),
|
||||
},
|
||||
default=Pipeline(stages=[crew2]),
|
||||
)
|
||||
final_crew = Crew(name="Final Crew", tasks=[task], agents=[agent])
|
||||
|
||||
pipeline = Pipeline(stages=[router1, router2, final_crew])
|
||||
|
||||
with patch.object(Crew, "kickoff_async") as mock_kickoff:
|
||||
mock_kickoff.side_effect = [
|
||||
CrewOutput(
|
||||
raw="Test output from Crew 1",
|
||||
tasks_output=[],
|
||||
token_usage=DEFAULT_TOKEN_USAGE,
|
||||
json_dict={"output": "crew1"},
|
||||
pydantic=None,
|
||||
),
|
||||
CrewOutput(
|
||||
raw="Test output from Crew 2",
|
||||
tasks_output=[],
|
||||
token_usage=DEFAULT_TOKEN_USAGE,
|
||||
json_dict={"output": "crew2"},
|
||||
pydantic=None,
|
||||
),
|
||||
CrewOutput(
|
||||
raw="Test output from Final Crew",
|
||||
tasks_output=[],
|
||||
token_usage=DEFAULT_TOKEN_USAGE,
|
||||
json_dict={"output": "final"},
|
||||
pydantic=None,
|
||||
),
|
||||
]
|
||||
result = await pipeline.kickoff([{"score": 75}])
|
||||
|
||||
assert len(result) == 1
|
||||
assert result[0].json_dict is not None
|
||||
assert result[0].json_dict["output"] == "final"
|
||||
assert (
|
||||
len(result[0].trace) == 6
|
||||
) # Input, Router1, Crew2, Router2, Crew2, Final Crew
|
||||
assert result[0].trace[1]["route_taken"] == "default"
|
||||
assert result[0].trace[3]["route_taken"] == "route2"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_router_default_route(mock_crew_factory):
|
||||
default_crew = mock_crew_factory(
|
||||
name="Default Crew", output_json_dict={"output": "default"}
|
||||
)
|
||||
router = Router(
|
||||
routes={
|
||||
"route1": Route(
|
||||
condition=lambda x: False,
|
||||
pipeline=Pipeline(stages=[mock_crew_factory(name="Never Used")]),
|
||||
),
|
||||
},
|
||||
default=Pipeline(stages=[default_crew]),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(stages=[router])
|
||||
result = await pipeline.kickoff([{"score": 100}])
|
||||
|
||||
assert len(result) == 1
|
||||
assert result[0].json_dict is not None
|
||||
assert result[0].json_dict["output"] == "default"
|
||||
assert result[0].trace[1]["route_taken"] == "default"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
async def test_router_with_empty_input():
|
||||
crew1 = Crew(name="Crew 1", tasks=[task], agents=[agent])
|
||||
crew2 = Crew(name="Crew 2", tasks=[task], agents=[agent])
|
||||
crew3 = Crew(name="Crew 3", tasks=[task], agents=[agent])
|
||||
router = Router(
|
||||
routes={
|
||||
"route1": Route(
|
||||
condition=lambda x: x.get("score", 0) > 80,
|
||||
pipeline=Pipeline(stages=[crew1]),
|
||||
),
|
||||
"route2": Route(
|
||||
condition=lambda x: 50 < x.get("score", 0) <= 80,
|
||||
pipeline=Pipeline(stages=[crew2]),
|
||||
),
|
||||
},
|
||||
default=Pipeline(stages=[crew3]),
|
||||
)
|
||||
pipeline = Pipeline(stages=[router])
|
||||
|
||||
result = await pipeline.kickoff([{}])
|
||||
|
||||
assert len(result) == 1
|
||||
assert result[0].trace[1]["route_taken"] == "default"
|
||||
Reference in New Issue
Block a user