mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-01-09 08:08:32 +00:00
Merge branch 'feature/procedure_v2' into brandon/cre-107-pipeline-conditional-routing
This commit is contained in:
Brandon Hancock
@@ -114,7 +114,7 @@ from langchain.agents import load_tools
|
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langchain_tools = load_tools(["google-serper"], llm=llm)
|
||||
|
||||
agent1 = CustomAgent(
|
||||
role="backstory agent",
|
||||
role="agent role",
|
||||
goal="who is {input}?",
|
||||
backstory="agent backstory",
|
||||
verbose=True,
|
||||
@@ -127,7 +127,7 @@ task1 = Task(
|
||||
)
|
||||
|
||||
agent2 = Agent(
|
||||
role="bio agent",
|
||||
role="agent role",
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||||
goal="summarize the short bio for {input} and if needed do more research",
|
||||
backstory="agent backstory",
|
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verbose=True,
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|
||||
@@ -33,6 +33,7 @@ A crew in crewAI represents a collaborative group of agents working together to
|
||||
| **Manager Callbacks** _(optional)_ | `manager_callbacks` | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used. |
|
||||
| **Prompt File** _(optional)_ | `prompt_file` | Path to the prompt JSON file to be used for the crew. |
|
||||
| **Planning** *(optional)* | `planning` | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.
|
||||
| **Planning LLM** *(optional)* | `planning_llm` | The language model used by the AgentPlanner in a planning process. |
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||||
|
||||
!!! note "Crew Max RPM"
|
||||
The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
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||||
@@ -136,7 +137,7 @@ crew = Crew(
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verbose=2
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||||
)
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result = crew.kickoff()
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crew_output = crew.kickoff()
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|
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# Accessing the crew output
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print(f"Raw Output: {crew_output.raw}")
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|
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@@ -23,6 +23,25 @@ my_crew = Crew(
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|
||||
From this point on, your crew will have planning enabled, and the tasks will be planned before each iteration.
|
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|
||||
#### Planning LLM
|
||||
|
||||
Now you can define the LLM that will be used to plan the tasks. You can use any ChatOpenAI LLM model available.
|
||||
|
||||
```python
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from crewai import Crew, Agent, Task, Process
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from langchain_openai import ChatOpenAI
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|
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# Assemble your crew with planning capabilities and custom LLM
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my_crew = Crew(
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agents=self.agents,
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tasks=self.tasks,
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process=Process.sequential,
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planning=True,
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planning_llm=ChatOpenAI(model="gpt-4o")
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)
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```
|
||||
|
||||
|
||||
### Example
|
||||
|
||||
When running the base case example, you will see something like the following output, which represents the output of the AgentPlanner responsible for creating the step-by-step logic to add to the Agents tasks.
|
||||
|
||||
41
docs/core-concepts/Testing.md
Normal file
41
docs/core-concepts/Testing.md
Normal file
@@ -0,0 +1,41 @@
|
||||
---
|
||||
title: crewAI Testing
|
||||
description: Learn how to test your crewAI Crew and evaluate their performance.
|
||||
---
|
||||
|
||||
## Introduction
|
||||
|
||||
Testing is a crucial part of the development process, and it is essential to ensure that your crew is performing as expected. And with crewAI, you can easily test your crew and evaluate its performance using the built-in testing capabilities.
|
||||
|
||||
### Using the Testing Feature
|
||||
|
||||
We added the CLI command `crewai test` to make it easy to test your crew. This command will run your crew for a specified number of iterations and provide detailed performance metrics.
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||||
The parameters are `n_iterations` and `model` which are optional and default to 2 and `gpt-4o-mini` respectively. For now the only provider available is OpenAI.
|
||||
|
||||
```bash
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crewai test
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||||
```
|
||||
|
||||
If you want to run more iterations or use a different model, you can specify the parameters like this:
|
||||
|
||||
```bash
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||||
crewai test --n_iterations 5 --model gpt-4o
|
||||
```
|
||||
|
||||
What happens when you run the `crewai test` command is that the crew will be executed for the specified number of iterations, and the performance metrics will be displayed at the end of the run.
|
||||
|
||||
A table of scores at the end will show the performance of the crew in terms of the following metrics:
|
||||
```
|
||||
Task Scores
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||||
(1-10 Higher is better)
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||||
┏━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┓
|
||||
┃ Tasks/Crew ┃ Run 1 ┃ Run 2 ┃ Avg. Total ┃
|
||||
┡━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━┩
|
||||
│ Task 1 │ 10.0 │ 9.0 │ 9.5 │
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||||
│ Task 2 │ 9.0 │ 9.0 │ 9.0 │
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||||
│ Crew │ 9.5 │ 9.0 │ 9.2 │
|
||||
└────────────┴───────┴───────┴────────────┘
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||||
```
|
||||
|
||||
The example above shows the test results for two runs of the crew with two tasks, with the average total score for each task and the crew as a whole.
|
||||
|
||||
@@ -18,4 +18,7 @@ pip install crewai
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||||
# Install the main crewAI package and the tools package
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# that includes a series of helpful tools for your agents
|
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pip install 'crewai[tools]'
|
||||
|
||||
# Alternatively, you can also use:
|
||||
pip install crewai crewai-tools
|
||||
```
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||||
@@ -0,0 +1,255 @@
|
||||
---
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||||
title: Starting a New CrewAI Project - Using Template
|
||||
description: A comprehensive guide to starting a new CrewAI project, including the latest updates and project setup methods.
|
||||
---
|
||||
|
||||
# Starting Your CrewAI Project
|
||||
|
||||
Welcome to the ultimate guide for starting a new CrewAI project. This document will walk you through the steps to create, customize, and run your CrewAI project, ensuring you have everything you need to get started.
|
||||
|
||||
Beforre we start there are a couple of things to note:
|
||||
|
||||
1. CrewAI is a Python package and requires Python >=3.10 and <=3.13 to run.
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||||
2. The preferred way of setting up CrewAI is using the `crewai create` command.This will create a new project folder and install a skeleton template for you to work on.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
Before getting started with CrewAI, make sure that you have installed it via pip:
|
||||
|
||||
```shell
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$ pip install crewai crewi-tools
|
||||
```
|
||||
|
||||
### Virtual Environemnts
|
||||
It is highly recommended that you use virtual environments to ensure that your CrewAI project is isolated from other projects and dependencies. Virtual environments provide a clean, separate workspace for each project, preventing conflicts between different versions of packages and libraries. This isolation is crucial for maintaining consistency and reproducibility in your development process. You have multiple options for setting up virtual environments depending on your operating system and Python version:
|
||||
|
||||
1. Use venv (Python's built-in virtual environment tool):
|
||||
venv is included with Python 3.3 and later, making it a convenient choice for many developers. It's lightweight and easy to use, perfect for simple project setups.
|
||||
|
||||
To set up virtual environments with venv, refer to the official [Python documentation](https://docs.python.org/3/tutorial/venv.html).
|
||||
|
||||
2. Use Conda (A Python virtual environment manager):
|
||||
Conda is an open-source package manager and environment management system for Python. It's widely used by data scientists, developers, and researchers to manage dependencies and environments in a reproducible way.
|
||||
|
||||
To set up virtual environments with Conda, refer to the official [Conda documentation](https://docs.conda.io/projects/conda/en/stable/user-guide/getting-started.html).
|
||||
|
||||
3. Use Poetry (A Python package manager and dependency management tool):
|
||||
Poetry is an open-source Python package manager that simplifies the installation of packages and their dependencies. Poetry offers a convenient way to manage virtual environments and dependencies.
|
||||
Poetry is CrewAI's prefered tool for package / dependancy management in CrewAI.
|
||||
|
||||
### Code IDEs
|
||||
|
||||
Most users of CrewAI a Code Editor / Integrated Development Environment (IDE) for building there Crews. You can use any code IDE of your choice. Seee below for some popular options for Code Editors / Integrated Development Environments (IDE):
|
||||
|
||||
- [Visual Studio Code](https://code.visualstudio.com/) - Most popular
|
||||
- [PyCharm](https://www.jetbrains.com/pycharm/)
|
||||
- [Cursor AI](https://cursor.com)
|
||||
|
||||
Pick one that suits your style and needs.
|
||||
|
||||
## Creating a New Project
|
||||
In this example we will be using Venv as our virtual environment manager.
|
||||
|
||||
To setup a virtual environment, run the following CLI command:
|
||||
|
||||
```shell
|
||||
$ python3 -m venv <venv-name>
|
||||
```
|
||||
|
||||
Activate your virtual environment by running the following CLI command:
|
||||
|
||||
```shell
|
||||
$ source <venv-name>/bin/activate
|
||||
```
|
||||
|
||||
Now, to create a new CrewAI project, run the following CLI command:
|
||||
|
||||
```shell
|
||||
$ crewai create <project_name>
|
||||
```
|
||||
|
||||
This command will create a new project folder with the following structure:
|
||||
|
||||
```shell
|
||||
my_project/
|
||||
├── .gitignore
|
||||
├── pyproject.toml
|
||||
├── README.md
|
||||
└── src/
|
||||
└── my_project/
|
||||
├── __init__.py
|
||||
├── main.py
|
||||
├── crew.py
|
||||
├── tools/
|
||||
│ ├── custom_tool.py
|
||||
│ └── __init__.py
|
||||
└── config/
|
||||
├── agents.yaml
|
||||
└── tasks.yaml
|
||||
```
|
||||
|
||||
You can now start developing your project by editing the files in the `src/my_project` folder. The `main.py` file is the entry point of your project, and the `crew.py` file is where you define your agents and tasks.
|
||||
|
||||
## Customizing Your Project
|
||||
|
||||
To customize your project, you can:
|
||||
- Modify `src/my_project/config/agents.yaml` to define your agents.
|
||||
- Modify `src/my_project/config/tasks.yaml` to define your tasks.
|
||||
- Modify `src/my_project/crew.py` to add your own logic, tools, and specific arguments.
|
||||
- Modify `src/my_project/main.py` to add custom inputs for your agents and tasks.
|
||||
- Add your environment variables into the `.env` file.
|
||||
|
||||
### Example: Defining Agents and Tasks
|
||||
|
||||
#### agents.yaml
|
||||
|
||||
```yaml
|
||||
researcher:
|
||||
role: >
|
||||
Job Candidate Researcher
|
||||
goal: >
|
||||
Find potential candidates for the job
|
||||
backstory: >
|
||||
You are adept at finding the right candidates by exploring various online
|
||||
resources. Your skill in identifying suitable candidates ensures the best
|
||||
match for job positions.
|
||||
```
|
||||
|
||||
#### tasks.yaml
|
||||
|
||||
```yaml
|
||||
research_candidates_task:
|
||||
description: >
|
||||
Conduct thorough research to find potential candidates for the specified job.
|
||||
Utilize various online resources and databases to gather a comprehensive list of potential candidates.
|
||||
Ensure that the candidates meet the job requirements provided.
|
||||
|
||||
Job Requirements:
|
||||
{job_requirements}
|
||||
expected_output: >
|
||||
A list of 10 potential candidates with their contact information and brief profiles highlighting their suitability.
|
||||
agent: researcher # THIS NEEDS TO MATCH THE AGENT NAME IN THE AGENTS.YAML FILE AND THE AGENT DEFINED IN THE Crew.PY FILE
|
||||
context: # THESE NEED TO MATCH THE TASK NAMES DEFINED ABOVE AND THE TASKS.YAML FILE AND THE TASK DEFINED IN THE Crew.PY FILE
|
||||
- researcher
|
||||
```
|
||||
|
||||
### Referencing Variables:
|
||||
Your defined functions with the same name will be used. For example, you can reference the agent for specific tasks from task.yaml file. Ensure your annotated agent and function name is the same otherwise your task wont recognize the reference properly.
|
||||
|
||||
#### Example References
|
||||
agent.yaml
|
||||
```yaml
|
||||
email_summarizer:
|
||||
role: >
|
||||
Email Summarizer
|
||||
goal: >
|
||||
Summarize emails into a concise and clear summary
|
||||
backstory: >
|
||||
You will create a 5 bullet point summary of the report
|
||||
llm: mixtal_llm
|
||||
```
|
||||
|
||||
task.yaml
|
||||
```yaml
|
||||
email_summarizer_task:
|
||||
description: >
|
||||
Summarize the email into a 5 bullet point summary
|
||||
expected_output: >
|
||||
A 5 bullet point summary of the email
|
||||
agent: email_summarizer
|
||||
context:
|
||||
- reporting_task
|
||||
- research_task
|
||||
```
|
||||
|
||||
Use the annotations are used to properly reference the agent and task in the crew.py file.
|
||||
|
||||
### Annotations include:
|
||||
* @agent
|
||||
* @task
|
||||
* @crew
|
||||
* @llm
|
||||
* @tool
|
||||
* @callback
|
||||
* @output_json
|
||||
* @output_pydantic
|
||||
* @cache_handler
|
||||
|
||||
|
||||
crew.py
|
||||
```py
|
||||
...
|
||||
@llm
|
||||
def mixtal_llm(self):
|
||||
return ChatGroq(temperature=0, model_name="mixtral-8x7b-32768")
|
||||
|
||||
@agent
|
||||
def email_summarizer(self) -> Agent:
|
||||
return Agent(
|
||||
config=self.agents_config["email_summarizer"],
|
||||
)
|
||||
## ...other tasks defined
|
||||
@task
|
||||
def email_summarizer_task(self) -> Task:
|
||||
return Task(
|
||||
config=self.tasks_config["email_summarizer_task"],
|
||||
)
|
||||
...
|
||||
```
|
||||
|
||||
|
||||
|
||||
## Installing Dependencies
|
||||
|
||||
To install the dependencies for your project, you can use Poetry. First, navigate to your project directory:
|
||||
|
||||
```shell
|
||||
$ cd my_project
|
||||
$ poetry lock
|
||||
$ poetry install
|
||||
```
|
||||
|
||||
This will install the dependencies specified in the `pyproject.toml` file.
|
||||
|
||||
## Interpolating Variables
|
||||
|
||||
Any variable interpolated in your `agents.yaml` and `tasks.yaml` files like `{variable}` will be replaced by the value of the variable in the `main.py` file.
|
||||
|
||||
#### agents.yaml
|
||||
|
||||
```yaml
|
||||
research_task:
|
||||
description: >
|
||||
Conduct a thorough research about the customer and competitors in the context
|
||||
of {customer_domain}.
|
||||
Make sure you find any interesting and relevant information given the
|
||||
current year is 2024.
|
||||
expected_output: >
|
||||
A complete report on the customer and their customers and competitors,
|
||||
including their demographics, preferences, market positioning and audience engagement.
|
||||
```
|
||||
|
||||
#### main.py
|
||||
|
||||
```python
|
||||
# main.py
|
||||
def run():
|
||||
inputs = {
|
||||
"customer_domain": "crewai.com"
|
||||
}
|
||||
MyProjectCrew(inputs).crew().kickoff(inputs=inputs)
|
||||
```
|
||||
|
||||
## Running Your Project
|
||||
|
||||
To run your project, use the following command:
|
||||
|
||||
```shell
|
||||
$ poetry run my_project
|
||||
```
|
||||
|
||||
This will initialize your crew of AI agents and begin task execution as defined in your configuration in the `main.py` file.
|
||||
|
||||
## Deploying Your Project
|
||||
|
||||
The easiest way to deploy your crew is through [CrewAI+](https://www.crewai.com/crewaiplus), where you can deploy your crew in a few clicks.
|
||||
@@ -1,84 +0,0 @@
|
||||
---
|
||||
title: Assembling and Activating Your CrewAI Team
|
||||
description: A comprehensive guide to creating a dynamic CrewAI team for your projects, with updated functionalities including verbose mode, memory capabilities, asynchronous execution, output customization, language model configuration, code execution, integration with third-party agents, and improved task management.
|
||||
---
|
||||
|
||||
## Introduction
|
||||
Embark on your CrewAI journey by setting up your environment and initiating your AI crew with the latest features. This guide ensures a smooth start, incorporating all recent updates for an enhanced experience, including code execution capabilities, integration with third-party agents, and advanced task management.
|
||||
|
||||
## Step 0: Installation
|
||||
Install CrewAI and any necessary packages for your project. CrewAI is compatible with Python >=3.10,<=3.13.
|
||||
|
||||
```shell
|
||||
pip install crewai
|
||||
pip install 'crewai[tools]'
|
||||
```
|
||||
|
||||
## Step 1: Assemble Your Agents
|
||||
Define your agents with distinct roles, backstories, and enhanced capabilities. The Agent class now supports a wide range of attributes for fine-tuned control over agent behavior and interactions, including code execution and integration with third-party agents.
|
||||
|
||||
```python
|
||||
import os
|
||||
from langchain.llms import OpenAI
|
||||
from crewai import Agent
|
||||
from crewai_tools import SerperDevTool, BrowserbaseLoadTool, EXASearchTool
|
||||
|
||||
os.environ["OPENAI_API_KEY"] = "Your OpenAI Key"
|
||||
os.environ["SERPER_API_KEY"] = "Your Serper Key"
|
||||
os.environ["BROWSERBASE_API_KEY"] = "Your BrowserBase Key"
|
||||
os.environ["BROWSERBASE_PROJECT_ID"] = "Your BrowserBase Project Id"
|
||||
|
||||
search_tool = SerperDevTool()
|
||||
browser_tool = BrowserbaseLoadTool()
|
||||
exa_search_tool = EXASearchTool()
|
||||
|
||||
# Creating a senior researcher agent with advanced configurations
|
||||
researcher = Agent(
|
||||
role='Senior Researcher',
|
||||
goal='Uncover groundbreaking technologies in {topic}',
|
||||
backstory=("Driven by curiosity, you're at the forefront of innovation, "
|
||||
"eager to explore and share knowledge that could change the world."),
|
||||
memory=True,
|
||||
verbose=True,
|
||||
allow_delegation=False,
|
||||
tools=[search_tool, browser_tool],
|
||||
allow_code_execution=False, # New attribute for enabling code execution
|
||||
max_iter=15, # Maximum number of iterations for task execution
|
||||
max_rpm=100, # Maximum requests per minute
|
||||
max_execution_time=3600, # Maximum execution time in seconds
|
||||
system_template="Your custom system template here", # Custom system template
|
||||
prompt_template="Your custom prompt template here", # Custom prompt template
|
||||
response_template="Your custom response template here", # Custom response template
|
||||
)
|
||||
|
||||
# Creating a writer agent with custom tools and specific configurations
|
||||
writer = Agent(
|
||||
role='Writer',
|
||||
goal='Narrate compelling tech stories about {topic}',
|
||||
backstory=("With a flair for simplifying complex topics, you craft engaging "
|
||||
"narratives that captivate and educate, bringing new discoveries to light."),
|
||||
verbose=True,
|
||||
allow_delegation=False,
|
||||
memory=True,
|
||||
tools=[exa_search_tool],
|
||||
function_calling_llm=OpenAI(model_name="gpt-3.5-turbo"), # Separate LLM for function calling
|
||||
)
|
||||
|
||||
# Setting a specific manager agent
|
||||
manager = Agent(
|
||||
role='Manager',
|
||||
goal='Ensure the smooth operation and coordination of the team',
|
||||
verbose=True,
|
||||
backstory=(
|
||||
"As a seasoned project manager, you excel in organizing "
|
||||
"tasks, managing timelines, and ensuring the team stays on track."
|
||||
),
|
||||
allow_code_execution=True, # Enable code execution for the manager
|
||||
)
|
||||
```
|
||||
|
||||
### New Agent Attributes and Features
|
||||
|
||||
1. `allow_code_execution`: Enable or disable code execution capabilities for the agent (default is False).
|
||||
2. `max_execution_time`: Set a maximum execution time (in seconds) for the agent to complete a task.
|
||||
3. `function_calling_llm`: Specify a separate language model for function calling.
|
||||
@@ -7,7 +7,7 @@ description: Learn how to force tool output as the result in of an Agent's task
|
||||
In CrewAI, you can force the output of a tool as the result of an agent's task. This feature is useful when you want to ensure that the tool output is captured and returned as the task result, and avoid the agent modifying the output during the task execution.
|
||||
|
||||
## Forcing Tool Output as Result
|
||||
To force the tool output as the result of an agent's task, you can set the `force_tool_output` parameter to `True` when creating the task. This parameter ensures that the tool output is captured and returned as the task result, without any modifications by the agent.
|
||||
To force the tool output as the result of an agent's task, you can set the `result_as_answer` parameter to `True` when creating the agent. This parameter ensures that the tool output is captured and returned as the task result, without any modifications by the agent.
|
||||
|
||||
Here's an example of how to force the tool output as the result of an agent's task:
|
||||
|
||||
|
||||
@@ -1,137 +0,0 @@
|
||||
---
|
||||
title: Starting a New CrewAI Project
|
||||
description: A comprehensive guide to starting a new CrewAI project, including the latest updates and project setup methods.
|
||||
---
|
||||
|
||||
# Starting Your CrewAI Project
|
||||
|
||||
Welcome to the ultimate guide for starting a new CrewAI project. This document will walk you through the steps to create, customize, and run your CrewAI project, ensuring you have everything you need to get started.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
We assume you have already installed CrewAI. If not, please refer to the [installation guide](https://docs.crewai.com/how-to/Installing-CrewAI/) to install CrewAI and its dependencies.
|
||||
|
||||
## Creating a New Project
|
||||
|
||||
To create a new project, run the following CLI command:
|
||||
|
||||
```shell
|
||||
$ crewai create my_project
|
||||
```
|
||||
|
||||
This command will create a new project folder with the following structure:
|
||||
|
||||
```shell
|
||||
my_project/
|
||||
├── .gitignore
|
||||
├── pyproject.toml
|
||||
├── README.md
|
||||
└── src/
|
||||
└── my_project/
|
||||
├── __init__.py
|
||||
├── main.py
|
||||
├── crew.py
|
||||
├── tools/
|
||||
│ ├── custom_tool.py
|
||||
│ └── __init__.py
|
||||
└── config/
|
||||
├── agents.yaml
|
||||
└── tasks.yaml
|
||||
```
|
||||
|
||||
You can now start developing your project by editing the files in the `src/my_project` folder. The `main.py` file is the entry point of your project, and the `crew.py` file is where you define your agents and tasks.
|
||||
|
||||
## Customizing Your Project
|
||||
|
||||
To customize your project, you can:
|
||||
- Modify `src/my_project/config/agents.yaml` to define your agents.
|
||||
- Modify `src/my_project/config/tasks.yaml` to define your tasks.
|
||||
- Modify `src/my_project/crew.py` to add your own logic, tools, and specific arguments.
|
||||
- Modify `src/my_project/main.py` to add custom inputs for your agents and tasks.
|
||||
- Add your environment variables into the `.env` file.
|
||||
|
||||
### Example: Defining Agents and Tasks
|
||||
|
||||
#### agents.yaml
|
||||
|
||||
```yaml
|
||||
researcher:
|
||||
role: >
|
||||
Job Candidate Researcher
|
||||
goal: >
|
||||
Find potential candidates for the job
|
||||
backstory: >
|
||||
You are adept at finding the right candidates by exploring various online
|
||||
resources. Your skill in identifying suitable candidates ensures the best
|
||||
match for job positions.
|
||||
```
|
||||
|
||||
#### tasks.yaml
|
||||
|
||||
```yaml
|
||||
research_candidates_task:
|
||||
description: >
|
||||
Conduct thorough research to find potential candidates for the specified job.
|
||||
Utilize various online resources and databases to gather a comprehensive list of potential candidates.
|
||||
Ensure that the candidates meet the job requirements provided.
|
||||
|
||||
Job Requirements:
|
||||
{job_requirements}
|
||||
expected_output: >
|
||||
A list of 10 potential candidates with their contact information and brief profiles highlighting their suitability.
|
||||
```
|
||||
|
||||
## Installing Dependencies
|
||||
|
||||
To install the dependencies for your project, you can use Poetry. First, navigate to your project directory:
|
||||
|
||||
```shell
|
||||
$ cd my_project
|
||||
$ poetry lock
|
||||
$ poetry install
|
||||
```
|
||||
|
||||
This will install the dependencies specified in the `pyproject.toml` file.
|
||||
|
||||
## Interpolating Variables
|
||||
|
||||
Any variable interpolated in your `agents.yaml` and `tasks.yaml` files like `{variable}` will be replaced by the value of the variable in the `main.py` file.
|
||||
|
||||
#### agents.yaml
|
||||
|
||||
```yaml
|
||||
research_task:
|
||||
description: >
|
||||
Conduct a thorough research about the customer and competitors in the context
|
||||
of {customer_domain}.
|
||||
Make sure you find any interesting and relevant information given the
|
||||
current year is 2024.
|
||||
expected_output: >
|
||||
A complete report on the customer and their customers and competitors,
|
||||
including their demographics, preferences, market positioning and audience engagement.
|
||||
```
|
||||
|
||||
#### main.py
|
||||
|
||||
```python
|
||||
# main.py
|
||||
def run():
|
||||
inputs = {
|
||||
"customer_domain": "crewai.com"
|
||||
}
|
||||
MyProjectCrew(inputs).crew().kickoff(inputs=inputs)
|
||||
```
|
||||
|
||||
## Running Your Project
|
||||
|
||||
To run your project, use the following command:
|
||||
|
||||
```shell
|
||||
$ poetry run my_project
|
||||
```
|
||||
|
||||
This will initialize your crew of AI agents and begin task execution as defined in your configuration in the `main.py` file.
|
||||
|
||||
## Deploying Your Project
|
||||
|
||||
The easiest way to deploy your crew is through [CrewAI+](https://www.crewai.com/crewaiplus), where you can deploy your crew in a few clicks.
|
||||
@@ -5,6 +5,19 @@
|
||||
Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
|
||||
|
||||
<div style="display:flex; margin:0 auto; justify-content: center;">
|
||||
<div style="width:25%">
|
||||
<h2>Getting Started</h2>
|
||||
<ul>
|
||||
<li><a href='./getting-started/Installing-CrewAI'>
|
||||
Installing CrewAI
|
||||
</a>
|
||||
</li>
|
||||
<li><a href='./getting-started/Start-a-New-CrewAI-Project-Template-Method'>
|
||||
Start a New CrewAI Project: Template Method
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
<div style="width:25%">
|
||||
<h2>Core Concepts</h2>
|
||||
<ul>
|
||||
@@ -58,21 +71,6 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
|
||||
<div style="width:30%">
|
||||
<h2>How-To Guides</h2>
|
||||
<ul>
|
||||
<li>
|
||||
<a href="./how-to/Start-a-New-CrewAI-Project">
|
||||
Starting Your crewAI Project
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="./how-to/Installing-CrewAI">
|
||||
Installing crewAI
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="./how-to/Creating-a-Crew-and-kick-it-off">
|
||||
Getting Started
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="./how-to/Create-Custom-Tools">
|
||||
Create Custom Tools
|
||||
|
||||
@@ -29,5 +29,70 @@ To effectively use the `SerperDevTool`, follow these steps:
|
||||
2. **API Key Acquisition**: Acquire a `serper.dev` API key by registering for a free account at `serper.dev`.
|
||||
3. **Environment Configuration**: Store your obtained API key in an environment variable named `SERPER_API_KEY` to facilitate its use by the tool.
|
||||
|
||||
## Parameters
|
||||
|
||||
The `SerperDevTool` comes with several parameters that will be passed to the API :
|
||||
|
||||
- **search_url**: The URL endpoint for the search API. (Default is `https://google.serper.dev/search`)
|
||||
|
||||
- **country**: Optional. Specify the country for the search results.
|
||||
- **location**: Optional. Specify the location for the search results.
|
||||
- **locale**: Optional. Specify the locale for the search results.
|
||||
- **n_results**: Number of search results to return. Default is `10`.
|
||||
|
||||
The values for `country`, `location`, `lovale` and `search_url` can be found on the [Serper Playground](https://serper.dev/playground).
|
||||
|
||||
## Example with Parameters
|
||||
|
||||
Here is an example demonstrating how to use the tool with additional parameters:
|
||||
|
||||
```python
|
||||
from crewai_tools import SerperDevTool
|
||||
|
||||
tool = SerperDevTool(
|
||||
search_url="https://google.serper.dev/scholar",
|
||||
n_results=2,
|
||||
)
|
||||
|
||||
print(tool.run(search_query="ChatGPT"))
|
||||
|
||||
# Using Tool: Search the internet
|
||||
|
||||
# Search results: Title: Role of chat gpt in public health
|
||||
# Link: https://link.springer.com/article/10.1007/s10439-023-03172-7
|
||||
# Snippet: … ChatGPT in public health. In this overview, we will examine the potential uses of ChatGPT in
|
||||
# ---
|
||||
# Title: Potential use of chat gpt in global warming
|
||||
# Link: https://link.springer.com/article/10.1007/s10439-023-03171-8
|
||||
# Snippet: … as ChatGPT, have the potential to play a critical role in advancing our understanding of climate
|
||||
# ---
|
||||
|
||||
```
|
||||
|
||||
```python
|
||||
from crewai_tools import SerperDevTool
|
||||
|
||||
tool = SerperDevTool(
|
||||
country="fr",
|
||||
locale="fr",
|
||||
location="Paris, Paris, Ile-de-France, France",
|
||||
n_results=2,
|
||||
)
|
||||
|
||||
print(tool.run(search_query="Jeux Olympiques"))
|
||||
|
||||
# Using Tool: Search the internet
|
||||
|
||||
# Search results: Title: Jeux Olympiques de Paris 2024 - Actualités, calendriers, résultats
|
||||
# Link: https://olympics.com/fr/paris-2024
|
||||
# Snippet: Quels sont les sports présents aux Jeux Olympiques de Paris 2024 ? · Athlétisme · Aviron · Badminton · Basketball · Basketball 3x3 · Boxe · Breaking · Canoë ...
|
||||
# ---
|
||||
# Title: Billetterie Officielle de Paris 2024 - Jeux Olympiques et Paralympiques
|
||||
# Link: https://tickets.paris2024.org/
|
||||
# Snippet: Achetez vos billets exclusivement sur le site officiel de la billetterie de Paris 2024 pour participer au plus grand événement sportif au monde.
|
||||
# ---
|
||||
|
||||
```
|
||||
|
||||
## Conclusion
|
||||
By integrating the `SerperDevTool` into Python projects, users gain the ability to conduct real-time, relevant searches across the internet directly from their applications. By adhering to the setup and usage guidelines provided, incorporating this tool into projects is streamlined and straightforward.
|
||||
By integrating the `SerperDevTool` into Python projects, users gain the ability to conduct real-time, relevant searches across the internet directly from their applications. The updated parameters allow for more customized and localized search results. By adhering to the setup and usage guidelines provided, incorporating this tool into projects is streamlined and straightforward.
|
||||
|
||||
@@ -119,6 +119,9 @@ theme:
|
||||
|
||||
nav:
|
||||
- Home: '/'
|
||||
- Getting Started:
|
||||
- Installing CrewAI: 'getting-started/Installing-CrewAI.md'
|
||||
- Starting a new CrewAI project: 'getting-started/Start-a-New-CrewAI-Project-Template-Method.md'
|
||||
- Core Concepts:
|
||||
- Agents: 'core-concepts/Agents.md'
|
||||
- Tasks: 'core-concepts/Tasks.md'
|
||||
@@ -129,6 +132,7 @@ nav:
|
||||
- Training: 'core-concepts/Training-Crew.md'
|
||||
- Memory: 'core-concepts/Memory.md'
|
||||
- Planning: 'core-concepts/Planning.md'
|
||||
- Testing: 'core-concepts/Testing.md'
|
||||
- Using LangChain Tools: 'core-concepts/Using-LangChain-Tools.md'
|
||||
- Using LlamaIndex Tools: 'core-concepts/Using-LlamaIndex-Tools.md'
|
||||
- How to Guides:
|
||||
|
||||
414
poetry.lock
generated
414
poetry.lock
generated
@@ -2,13 +2,13 @@
|
||||
|
||||
[[package]]
|
||||
name = "agentops"
|
||||
version = "0.3.2"
|
||||
version = "0.3.4"
|
||||
description = "Python SDK for developing AI agent evals and observability"
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "agentops-0.3.2-py3-none-any.whl", hash = "sha256:b35988e04378624204572bb3d7a454094f879ea573f05b57d4e75ab0bfbb82af"},
|
||||
{file = "agentops-0.3.2.tar.gz", hash = "sha256:55559ac4a43634831dfa8937c2597c28e332809dc7c6bb3bc3c8b233442e224c"},
|
||||
{file = "agentops-0.3.4-py3-none-any.whl", hash = "sha256:126f7aed4ba43c1399b5488d67a03d10cb4c531e619c650776f826ca00c1aa24"},
|
||||
{file = "agentops-0.3.4.tar.gz", hash = "sha256:a92c9cb7c511197f0ecb8cb5aca15d35022c15a3d2fd2aaaa34cd7e5dc59393f"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -294,38 +294,38 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "bcrypt"
|
||||
version = "4.1.3"
|
||||
version = "4.2.0"
|
||||
description = "Modern password hashing for your software and your servers"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "bcrypt-4.1.3-cp37-abi3-macosx_10_12_universal2.whl", hash = "sha256:48429c83292b57bf4af6ab75809f8f4daf52aa5d480632e53707805cc1ce9b74"},
|
||||
{file = "bcrypt-4.1.3-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4a8bea4c152b91fd8319fef4c6a790da5c07840421c2b785084989bf8bbb7455"},
|
||||
{file = "bcrypt-4.1.3-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3d3b317050a9a711a5c7214bf04e28333cf528e0ed0ec9a4e55ba628d0f07c1a"},
|
||||
{file = "bcrypt-4.1.3-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:094fd31e08c2b102a14880ee5b3d09913ecf334cd604af27e1013c76831f7b05"},
|
||||
{file = "bcrypt-4.1.3-cp37-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:4fb253d65da30d9269e0a6f4b0de32bd657a0208a6f4e43d3e645774fb5457f3"},
|
||||
{file = "bcrypt-4.1.3-cp37-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:193bb49eeeb9c1e2db9ba65d09dc6384edd5608d9d672b4125e9320af9153a15"},
|
||||
{file = "bcrypt-4.1.3-cp37-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:8cbb119267068c2581ae38790e0d1fbae65d0725247a930fc9900c285d95725d"},
|
||||
{file = "bcrypt-4.1.3-cp37-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:6cac78a8d42f9d120b3987f82252bdbeb7e6e900a5e1ba37f6be6fe4e3848286"},
|
||||
{file = "bcrypt-4.1.3-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:01746eb2c4299dd0ae1670234bf77704f581dd72cc180f444bfe74eb80495b64"},
|
||||
{file = "bcrypt-4.1.3-cp37-abi3-win32.whl", hash = "sha256:037c5bf7c196a63dcce75545c8874610c600809d5d82c305dd327cd4969995bf"},
|
||||
{file = "bcrypt-4.1.3-cp37-abi3-win_amd64.whl", hash = "sha256:8a893d192dfb7c8e883c4576813bf18bb9d59e2cfd88b68b725990f033f1b978"},
|
||||
{file = "bcrypt-4.1.3-cp39-abi3-macosx_10_12_universal2.whl", hash = "sha256:0d4cf6ef1525f79255ef048b3489602868c47aea61f375377f0d00514fe4a78c"},
|
||||
{file = "bcrypt-4.1.3-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f5698ce5292a4e4b9e5861f7e53b1d89242ad39d54c3da451a93cac17b61921a"},
|
||||
{file = "bcrypt-4.1.3-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ec3c2e1ca3e5c4b9edb94290b356d082b721f3f50758bce7cce11d8a7c89ce84"},
|
||||
{file = "bcrypt-4.1.3-cp39-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:3a5be252fef513363fe281bafc596c31b552cf81d04c5085bc5dac29670faa08"},
|
||||
{file = "bcrypt-4.1.3-cp39-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:5f7cd3399fbc4ec290378b541b0cf3d4398e4737a65d0f938c7c0f9d5e686611"},
|
||||
{file = "bcrypt-4.1.3-cp39-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:c4c8d9b3e97209dd7111bf726e79f638ad9224b4691d1c7cfefa571a09b1b2d6"},
|
||||
{file = "bcrypt-4.1.3-cp39-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:31adb9cbb8737a581a843e13df22ffb7c84638342de3708a98d5c986770f2834"},
|
||||
{file = "bcrypt-4.1.3-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:551b320396e1d05e49cc18dd77d970accd52b322441628aca04801bbd1d52a73"},
|
||||
{file = "bcrypt-4.1.3-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:6717543d2c110a155e6821ce5670c1f512f602eabb77dba95717ca76af79867d"},
|
||||
{file = "bcrypt-4.1.3-cp39-abi3-win32.whl", hash = "sha256:6004f5229b50f8493c49232b8e75726b568535fd300e5039e255d919fc3a07f2"},
|
||||
{file = "bcrypt-4.1.3-cp39-abi3-win_amd64.whl", hash = "sha256:2505b54afb074627111b5a8dc9b6ae69d0f01fea65c2fcaea403448c503d3991"},
|
||||
{file = "bcrypt-4.1.3-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:cb9c707c10bddaf9e5ba7cdb769f3e889e60b7d4fea22834b261f51ca2b89fed"},
|
||||
{file = "bcrypt-4.1.3-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:9f8ea645eb94fb6e7bea0cf4ba121c07a3a182ac52876493870033141aa687bc"},
|
||||
{file = "bcrypt-4.1.3-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:f44a97780677e7ac0ca393bd7982b19dbbd8d7228c1afe10b128fd9550eef5f1"},
|
||||
{file = "bcrypt-4.1.3-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:d84702adb8f2798d813b17d8187d27076cca3cd52fe3686bb07a9083930ce650"},
|
||||
{file = "bcrypt-4.1.3.tar.gz", hash = "sha256:2ee15dd749f5952fe3f0430d0ff6b74082e159c50332a1413d51b5689cf06623"},
|
||||
{file = "bcrypt-4.2.0-cp37-abi3-macosx_10_12_universal2.whl", hash = "sha256:096a15d26ed6ce37a14c1ac1e48119660f21b24cba457f160a4b830f3fe6b5cb"},
|
||||
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{file = "bcrypt-4.2.0-cp39-abi3-macosx_10_12_universal2.whl", hash = "sha256:c52aac18ea1f4a4f65963ea4f9530c306b56ccd0c6f8c8da0c06976e34a6e841"},
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{file = "bcrypt-4.2.0-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3bbbfb2734f0e4f37c5136130405332640a1e46e6b23e000eeff2ba8d005da68"},
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{file = "bcrypt-4.2.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3413bd60460f76097ee2e0a493ccebe4a7601918219c02f503984f0a7ee0aebe"},
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{file = "bcrypt-4.2.0-cp39-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:3d3a6d28cb2305b43feac298774b997e372e56c7c7afd90a12b3dc49b189151c"},
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{file = "bcrypt-4.2.0-cp39-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:9c1c4ad86351339c5f320ca372dfba6cb6beb25e8efc659bedd918d921956bae"},
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||||
{file = "bcrypt-4.2.0-cp39-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:27fe0f57bb5573104b5a6de5e4153c60814c711b29364c10a75a54bb6d7ff48d"},
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||||
{file = "bcrypt-4.2.0-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:8ac68872c82f1add6a20bd489870c71b00ebacd2e9134a8aa3f98a0052ab4b0e"},
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||||
{file = "bcrypt-4.2.0-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:cb2a8ec2bc07d3553ccebf0746bbf3d19426d1c6d1adbd4fa48925f66af7b9e8"},
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||||
{file = "bcrypt-4.2.0-cp39-abi3-win32.whl", hash = "sha256:77800b7147c9dc905db1cba26abe31e504d8247ac73580b4aa179f98e6608f34"},
|
||||
{file = "bcrypt-4.2.0-cp39-abi3-win_amd64.whl", hash = "sha256:61ed14326ee023917ecd093ee6ef422a72f3aec6f07e21ea5f10622b735538a9"},
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||||
{file = "bcrypt-4.2.0-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:39e1d30c7233cfc54f5c3f2c825156fe044efdd3e0b9d309512cc514a263ec2a"},
|
||||
{file = "bcrypt-4.2.0-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:f4f4acf526fcd1c34e7ce851147deedd4e26e6402369304220250598b26448db"},
|
||||
{file = "bcrypt-4.2.0-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:1ff39b78a52cf03fdf902635e4c81e544714861ba3f0efc56558979dd4f09170"},
|
||||
{file = "bcrypt-4.2.0-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:373db9abe198e8e2c70d12b479464e0d5092cc122b20ec504097b5f2297ed184"},
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||||
{file = "bcrypt-4.2.0.tar.gz", hash = "sha256:cf69eaf5185fd58f268f805b505ce31f9b9fc2d64b376642164e9244540c1221"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
@@ -355,17 +355,17 @@ lxml = ["lxml"]
|
||||
|
||||
[[package]]
|
||||
name = "boto3"
|
||||
version = "1.34.145"
|
||||
version = "1.34.149"
|
||||
description = "The AWS SDK for Python"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "boto3-1.34.145-py3-none-any.whl", hash = "sha256:69d5afb7a017d07dd6bdfb680d2912d5d369b3fafa0a45161207d9f393b14d7e"},
|
||||
{file = "boto3-1.34.145.tar.gz", hash = "sha256:ac770fb53dde1743aec56bd8e56b7ee2e2f5ad42a37825968ec4ff8428822640"},
|
||||
{file = "boto3-1.34.149-py3-none-any.whl", hash = "sha256:11edeeacdd517bda3b7615b754d8440820cdc9ddd66794cc995a9693ddeaa3be"},
|
||||
{file = "boto3-1.34.149.tar.gz", hash = "sha256:f4e6489ba9dc7fb37d53e0e82dbc97f2cb0a4969ef3970e2c88b8f94023ae81a"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
botocore = ">=1.34.145,<1.35.0"
|
||||
botocore = ">=1.34.149,<1.35.0"
|
||||
jmespath = ">=0.7.1,<2.0.0"
|
||||
s3transfer = ">=0.10.0,<0.11.0"
|
||||
|
||||
@@ -374,13 +374,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
|
||||
|
||||
[[package]]
|
||||
name = "botocore"
|
||||
version = "1.34.145"
|
||||
version = "1.34.149"
|
||||
description = "Low-level, data-driven core of boto 3."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "botocore-1.34.145-py3-none-any.whl", hash = "sha256:2e72e262de02adcb0264ac2bac159a28f55dbba8d9e52aa0308773a42950dff5"},
|
||||
{file = "botocore-1.34.145.tar.gz", hash = "sha256:edf0fb4c02186ae29b76263ac5fda18b0a085d334a310551c9984407cf1079e6"},
|
||||
{file = "botocore-1.34.149-py3-none-any.whl", hash = "sha256:ae6c4be52eeee96f68c116b27d252bab069cd046d61a17cfe8e9da411cf22906"},
|
||||
{file = "botocore-1.34.149.tar.gz", hash = "sha256:2e1eb5ef40102a3d796bb3dd05f2ac5e8fb43fe1ff114b4f6d33153437f5a372"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -747,13 +747,13 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
|
||||
|
||||
[[package]]
|
||||
name = "cohere"
|
||||
version = "5.6.1"
|
||||
version = "5.6.2"
|
||||
description = ""
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8"
|
||||
files = [
|
||||
{file = "cohere-5.6.1-py3-none-any.whl", hash = "sha256:1c8bcd39a54622d64b83cafb865f102cd2565ce091b0856fd5ce11bf7169109a"},
|
||||
{file = "cohere-5.6.1.tar.gz", hash = "sha256:5d7efda64f0e512d4cc35aa04b17a6f74b3d8c175a99f2797991a7f31dfac349"},
|
||||
{file = "cohere-5.6.2-py3-none-any.whl", hash = "sha256:cfecf1343bcaa4091266c5a231fbcb3ccbd80cad05ea093ef80024a117aa3a2f"},
|
||||
{file = "cohere-5.6.2.tar.gz", hash = "sha256:6bb901afdfb02f62ad8ed2d82f12d8ea87a6869710f5f880cb89190c4e994805"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -1012,13 +1012,13 @@ idna = ">=2.0.0"
|
||||
|
||||
[[package]]
|
||||
name = "embedchain"
|
||||
version = "0.1.118"
|
||||
version = "0.1.119"
|
||||
description = "Simplest open source retrieval (RAG) framework"
|
||||
optional = false
|
||||
python-versions = "<=3.13,>=3.9"
|
||||
files = [
|
||||
{file = "embedchain-0.1.118-py3-none-any.whl", hash = "sha256:38ead471df9d9234bf42e6f7a32cab26431d50d6f2f894f18a6cabc0b02bf31a"},
|
||||
{file = "embedchain-0.1.118.tar.gz", hash = "sha256:1fa1e799882a1dc4e63af344595b043f1c1f30fbd59461b6660b1934b85a1e4b"},
|
||||
{file = "embedchain-0.1.119-py3-none-any.whl", hash = "sha256:8ec3e7f139939fa1dc8fda898f8d8d9d31a5abfe08e184b607e38733d863d606"},
|
||||
{file = "embedchain-0.1.119.tar.gz", hash = "sha256:0f4f45e092b7f3192ea6fe82575726532573b1231d7af6c22edc695b701b4223"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -1032,7 +1032,7 @@ langchain = ">0.2,<=0.3"
|
||||
langchain-cohere = ">=0.1.4,<0.2.0"
|
||||
langchain-community = ">=0.2.6,<0.3.0"
|
||||
langchain-openai = ">=0.1.7,<0.2.0"
|
||||
mem0ai = ">=0.0.5,<0.0.6"
|
||||
mem0ai = ">=0.0.9,<0.0.10"
|
||||
openai = ">=1.1.1"
|
||||
posthog = ">=3.0.2,<4.0.0"
|
||||
pypdf = ">=4.0.1,<5.0.0"
|
||||
@@ -1061,20 +1061,6 @@ together = ["together (>=1.2.1,<2.0.0)"]
|
||||
vertexai = ["langchain-google-vertexai (>=1.0.6,<2.0.0)"]
|
||||
weaviate = ["weaviate-client (>=3.24.1,<4.0.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "eval-type-backport"
|
||||
version = "0.2.0"
|
||||
description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "eval_type_backport-0.2.0-py3-none-any.whl", hash = "sha256:ac2f73d30d40c5a30a80b8739a789d6bb5e49fdffa66d7912667e2015d9c9933"},
|
||||
{file = "eval_type_backport-0.2.0.tar.gz", hash = "sha256:68796cfbc7371ebf923f03bdf7bef415f3ec098aeced24e054b253a0e78f7b37"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
tests = ["pytest"]
|
||||
|
||||
[[package]]
|
||||
name = "exceptiongroup"
|
||||
version = "1.2.2"
|
||||
@@ -1402,13 +1388,13 @@ requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
|
||||
|
||||
[[package]]
|
||||
name = "google-cloud-aiplatform"
|
||||
version = "1.59.0"
|
||||
version = "1.60.0"
|
||||
description = "Vertex AI API client library"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "google-cloud-aiplatform-1.59.0.tar.gz", hash = "sha256:2bebb59c0ba3e3b4b568305418ca1b021977988adbee8691a5bed09b037e7e63"},
|
||||
{file = "google_cloud_aiplatform-1.59.0-py2.py3-none-any.whl", hash = "sha256:549e6eb1844b0f853043309138ebe2db00de4bbd8197b3bde26804ac163ef52a"},
|
||||
{file = "google-cloud-aiplatform-1.60.0.tar.gz", hash = "sha256:782c7f1ec0e77a7c7daabef3b65bfd506ed2b4b1dc2186753c43cd6faf8dd04e"},
|
||||
{file = "google_cloud_aiplatform-1.60.0-py2.py3-none-any.whl", hash = "sha256:5f14159c9575f4b46335027e3ceb8fa57bd5eaa76a07f858105b8c6c034ec0d6"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -1430,8 +1416,8 @@ cloud-profiler = ["tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow
|
||||
datasets = ["pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)"]
|
||||
endpoint = ["requests (>=2.28.1)"]
|
||||
full = ["cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "tqdm (>=4.23.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)"]
|
||||
langchain = ["langchain (>=0.1.16,<0.3)", "langchain-core (<0.2)", "langchain-google-vertexai (<2)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "tenacity (<=8.3)"]
|
||||
langchain-testing = ["absl-py", "cloudpickle (>=3.0,<4.0)", "langchain (>=0.1.16,<0.3)", "langchain-core (<0.2)", "langchain-google-vertexai (<2)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<3)", "pytest-xdist", "tenacity (<=8.3)"]
|
||||
langchain = ["langchain (>=0.1.16,<0.3)", "langchain-core (<0.3)", "langchain-google-vertexai (<2)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "tenacity (<=8.3)"]
|
||||
langchain-testing = ["absl-py", "cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "langchain (>=0.1.16,<0.3)", "langchain-core (<0.3)", "langchain-google-vertexai (<2)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<3)", "pytest-xdist", "tenacity (<=8.3)"]
|
||||
lit = ["explainable-ai-sdk (>=1.0.0)", "lit-nlp (==0.4.0)", "pandas (>=1.0.0)", "tensorflow (>=2.3.0,<3.0.0dev)"]
|
||||
metadata = ["numpy (>=1.15.0)", "pandas (>=1.0.0)"]
|
||||
pipelines = ["pyyaml (>=5.3.1,<7)"]
|
||||
@@ -1441,7 +1427,7 @@ private-endpoints = ["requests (>=2.28.1)", "urllib3 (>=1.21.1,<1.27)"]
|
||||
rapid-evaluation = ["pandas (>=1.0.0,<2.2.0)", "tqdm (>=4.23.0)"]
|
||||
ray = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "setuptools (<70.0.0)"]
|
||||
ray-testing = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pytest-xdist", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "ray[train] (==2.9.3)", "scikit-learn", "setuptools (<70.0.0)", "tensorflow", "torch (>=2.0.0,<2.1.0)", "xgboost", "xgboost-ray"]
|
||||
reasoningengine = ["cloudpickle (>=3.0,<4.0)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<3)"]
|
||||
reasoningengine = ["cloudpickle (>=3.0,<4.0)", "google-cloud-trace (<2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<3)"]
|
||||
tensorboard = ["tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "werkzeug (>=2.0.0,<2.1.0dev)"]
|
||||
testing = ["bigframes", "cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-api-core (>=2.11,<3.0.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "grpcio-testing", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "ipython", "kfp (>=2.6.0,<3.0.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nltk", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyfakefs", "pytest-asyncio", "pytest-xdist", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "requests-toolbelt (<1.0.0)", "scikit-learn", "sentencepiece (>=0.2.0)", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (==2.13.0)", "tensorflow (==2.16.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "torch (>=2.0.0,<2.1.0)", "torch (>=2.2.0)", "tqdm (>=4.23.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)", "xgboost"]
|
||||
tokenization = ["sentencepiece (>=0.2.0)"]
|
||||
@@ -1517,13 +1503,13 @@ protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4
|
||||
|
||||
[[package]]
|
||||
name = "google-cloud-storage"
|
||||
version = "2.17.0"
|
||||
version = "2.18.0"
|
||||
description = "Google Cloud Storage API client library"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "google-cloud-storage-2.17.0.tar.gz", hash = "sha256:49378abff54ef656b52dca5ef0f2eba9aa83dc2b2c72c78714b03a1a95fe9388"},
|
||||
{file = "google_cloud_storage-2.17.0-py2.py3-none-any.whl", hash = "sha256:5b393bc766b7a3bc6f5407b9e665b2450d36282614b7945e570b3480a456d1e1"},
|
||||
{file = "google_cloud_storage-2.18.0-py2.py3-none-any.whl", hash = "sha256:e8e1a9577952143c3fca8163005ecfadd2d70ec080fa158a8b305000e2c22fbb"},
|
||||
{file = "google_cloud_storage-2.18.0.tar.gz", hash = "sha256:0aa3f7c57f3632f81b455d91558d2b27ada96eee2de3aaa17f689db1470d9578"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -1535,7 +1521,8 @@ google-resumable-media = ">=2.6.0"
|
||||
requests = ">=2.18.0,<3.0.0dev"
|
||||
|
||||
[package.extras]
|
||||
protobuf = ["protobuf (<5.0.0dev)"]
|
||||
protobuf = ["protobuf (<6.0.0dev)"]
|
||||
tracing = ["opentelemetry-api (>=1.1.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "google-crc32c"
|
||||
@@ -1755,25 +1742,6 @@ files = [
|
||||
backports-strenum = {version = ">=1.3", markers = "python_version < \"3.11\""}
|
||||
colorama = ">=0.4"
|
||||
|
||||
[[package]]
|
||||
name = "groq"
|
||||
version = "0.9.0"
|
||||
description = "The official Python library for the groq API"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "groq-0.9.0-py3-none-any.whl", hash = "sha256:d0e46f4ad645504672bb09c8100af3ced3a7db0d5119dc13e4aca535fc455874"},
|
||||
{file = "groq-0.9.0.tar.gz", hash = "sha256:130ed5e35d3acfaab46b9e7a078eeaebf91052f4a9d71f86f87fb319b5fec332"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
anyio = ">=3.5.0,<5"
|
||||
distro = ">=1.7.0,<2"
|
||||
httpx = ">=0.23.0,<1"
|
||||
pydantic = ">=1.9.0,<3"
|
||||
sniffio = "*"
|
||||
typing-extensions = ">=4.7,<5"
|
||||
|
||||
[[package]]
|
||||
name = "grpc-google-iam-v1"
|
||||
version = "0.13.1"
|
||||
@@ -2076,13 +2044,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "huggingface-hub"
|
||||
version = "0.24.0"
|
||||
version = "0.24.3"
|
||||
description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
|
||||
optional = false
|
||||
python-versions = ">=3.8.0"
|
||||
files = [
|
||||
{file = "huggingface_hub-0.24.0-py3-none-any.whl", hash = "sha256:7ad92edefb93d8145c061f6df8d99df2ff85f8379ba5fac8a95aca0642afa5d7"},
|
||||
{file = "huggingface_hub-0.24.0.tar.gz", hash = "sha256:6c7092736b577d89d57b3cdfea026f1b0dc2234ae783fa0d59caf1bf7d52dfa7"},
|
||||
{file = "huggingface_hub-0.24.3-py3-none-any.whl", hash = "sha256:69ecce486dd6cdad69937ba76779e893c224a670a9d947636c1d5cbd049e44d8"},
|
||||
{file = "huggingface_hub-0.24.3.tar.gz", hash = "sha256:bfdc05cc9b64a0e24e8614a44222698799183268f6b68be209aa2df70cff2cde"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -2160,22 +2128,22 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "importlib-metadata"
|
||||
version = "7.1.0"
|
||||
version = "8.0.0"
|
||||
description = "Read metadata from Python packages"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "importlib_metadata-7.1.0-py3-none-any.whl", hash = "sha256:30962b96c0c223483ed6cc7280e7f0199feb01a0e40cfae4d4450fc6fab1f570"},
|
||||
{file = "importlib_metadata-7.1.0.tar.gz", hash = "sha256:b78938b926ee8d5f020fc4772d487045805a55ddbad2ecf21c6d60938dc7fcd2"},
|
||||
{file = "importlib_metadata-8.0.0-py3-none-any.whl", hash = "sha256:15584cf2b1bf449d98ff8a6ff1abef57bf20f3ac6454f431736cd3e660921b2f"},
|
||||
{file = "importlib_metadata-8.0.0.tar.gz", hash = "sha256:188bd24e4c346d3f0a933f275c2fec67050326a856b9a359881d7c2a697e8812"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
zipp = ">=0.5"
|
||||
|
||||
[package.extras]
|
||||
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
|
||||
doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
|
||||
perf = ["ipython"]
|
||||
testing = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
|
||||
test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "importlib-resources"
|
||||
@@ -2455,19 +2423,19 @@ tests = ["aiohttp", "duckdb", "pandas (>=1.4)", "polars (>=0.19)", "pytest", "py
|
||||
|
||||
[[package]]
|
||||
name = "langchain"
|
||||
version = "0.2.10"
|
||||
version = "0.2.11"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langchain-0.2.10-py3-none-any.whl", hash = "sha256:b4fb58c7faf4f4999cfe3325474979a7121a1737dd101655a723a1d957ef0617"},
|
||||
{file = "langchain-0.2.10.tar.gz", hash = "sha256:1f861c1b59ac9c91b02bb0fa58d3adad1c1d0686636872b5b357bbce3ce41d06"},
|
||||
{file = "langchain-0.2.11-py3-none-any.whl", hash = "sha256:5a7a8b4918f3d3bebce9b4f23b92d050699e6f7fb97591e8941177cf07a260a2"},
|
||||
{file = "langchain-0.2.11.tar.gz", hash = "sha256:d7a9e4165f02dca0bd78addbc2319d5b9286b5d37c51d784124102b57e9fd297"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
aiohttp = ">=3.8.3,<4.0.0"
|
||||
async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
|
||||
langchain-core = ">=0.2.22,<0.3.0"
|
||||
langchain-core = ">=0.2.23,<0.3.0"
|
||||
langchain-text-splitters = ">=0.2.0,<0.3.0"
|
||||
langsmith = ">=0.1.17,<0.2.0"
|
||||
numpy = [
|
||||
@@ -2503,20 +2471,20 @@ langchain-community = ["langchain-community (>=0.2.4)"]
|
||||
|
||||
[[package]]
|
||||
name = "langchain-community"
|
||||
version = "0.2.9"
|
||||
version = "0.2.10"
|
||||
description = "Community contributed LangChain integrations."
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langchain_community-0.2.9-py3-none-any.whl", hash = "sha256:b51d3adf9346a1161c1098917585b9e303cf24e2f5c71f5d232a0504edada5f2"},
|
||||
{file = "langchain_community-0.2.9.tar.gz", hash = "sha256:1e7c180232916cbe35fe00509680dd1f805e32d7c87b5e80b3a9ec8754ecae37"},
|
||||
{file = "langchain_community-0.2.10-py3-none-any.whl", hash = "sha256:9f4d1b5ab7f0b0a704f538e26e50fce45a461da6d2bf6b7b636d24f22fbc088a"},
|
||||
{file = "langchain_community-0.2.10.tar.gz", hash = "sha256:3a0404bad4bd07d6f86affdb62fb3d080a456c66191754d586a409d9d6024d62"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
aiohttp = ">=3.8.3,<4.0.0"
|
||||
dataclasses-json = ">=0.5.7,<0.7"
|
||||
langchain = ">=0.2.9,<0.3.0"
|
||||
langchain-core = ">=0.2.22,<0.3.0"
|
||||
langchain-core = ">=0.2.23,<0.3.0"
|
||||
langsmith = ">=0.1.0,<0.2.0"
|
||||
numpy = [
|
||||
{version = ">=1,<2", markers = "python_version < \"3.12\""},
|
||||
@@ -2529,13 +2497,13 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
|
||||
|
||||
[[package]]
|
||||
name = "langchain-core"
|
||||
version = "0.2.22"
|
||||
version = "0.2.24"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langchain_core-0.2.22-py3-none-any.whl", hash = "sha256:7731a86440c0958b3186c003fb9b26b2d5a682a6344bda7bfb9174e2898f8b43"},
|
||||
{file = "langchain_core-0.2.22.tar.gz", hash = "sha256:582d6f929a43b830139444e4124123cd415331ad62f25757b1406252958cdcac"},
|
||||
{file = "langchain_core-0.2.24-py3-none-any.whl", hash = "sha256:9444fc082d21ef075d925590a684a73fe1f9688a3d90087580ec929751be55e7"},
|
||||
{file = "langchain_core-0.2.24.tar.gz", hash = "sha256:f2e3fa200b124e8c45d270da9bf836bed9c09532612c96ff3225e59b9a232f5a"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -2551,13 +2519,13 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
|
||||
|
||||
[[package]]
|
||||
name = "langchain-experimental"
|
||||
version = "0.0.62"
|
||||
version = "0.0.63"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langchain_experimental-0.0.62-py3-none-any.whl", hash = "sha256:9240f9e3490e819976f20a37863970036e7baacb7104b9eb6833d19ab6d518c9"},
|
||||
{file = "langchain_experimental-0.0.62.tar.gz", hash = "sha256:9737fbc8429d24457ea4d368e3c9ba9ed1cace0564fb5f1a96a3027a588bd0ac"},
|
||||
{file = "langchain_experimental-0.0.63-py3-none-any.whl", hash = "sha256:cb4ae7a685bb3c077d138b4533ed02e8df1f5f784333c3e52dcae8c80f031ca2"},
|
||||
{file = "langchain_experimental-0.0.63.tar.gz", hash = "sha256:fc894599bfac43445004a9ff60d9a28751426b2fea1979e4b2fa453c847850c4"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -2566,17 +2534,17 @@ langchain-core = ">=0.2.10,<0.3.0"
|
||||
|
||||
[[package]]
|
||||
name = "langchain-openai"
|
||||
version = "0.1.17"
|
||||
version = "0.1.19"
|
||||
description = "An integration package connecting OpenAI and LangChain"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langchain_openai-0.1.17-py3-none-any.whl", hash = "sha256:30bef5574ecbbbb91b8025b2dc5a1bd81fd62157d3ad1a35d820141f31c5b443"},
|
||||
{file = "langchain_openai-0.1.17.tar.gz", hash = "sha256:c5d70ddecdcb93e146f376bdbadbb6ec69de9ac0f402cd5b83de50b655ba85ee"},
|
||||
{file = "langchain_openai-0.1.19-py3-none-any.whl", hash = "sha256:a7a739f1469d54cd988865420e7fc21b50fb93727b2e6da5ad30273fc61ecf19"},
|
||||
{file = "langchain_openai-0.1.19.tar.gz", hash = "sha256:3bf342bb302d1444f4abafdf01c467dbd9b248497e1133808c4bae70396c79b3"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
langchain-core = ">=0.2.20,<0.3.0"
|
||||
langchain-core = ">=0.2.24,<0.3.0"
|
||||
openai = ">=1.32.0,<2.0.0"
|
||||
tiktoken = ">=0.7,<1"
|
||||
|
||||
@@ -2772,23 +2740,20 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "mem0ai"
|
||||
version = "0.0.5"
|
||||
version = "0.0.9"
|
||||
description = "Long-term memory for AI Agents"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8"
|
||||
files = [
|
||||
{file = "mem0ai-0.0.5-py3-none-any.whl", hash = "sha256:6f6e5356fd522adf0510322cd581476ea456fd7ccefca11b5ac050e9a6f00f36"},
|
||||
{file = "mem0ai-0.0.5.tar.gz", hash = "sha256:f2ac35d15e4e620becb8d06b8ebeb1ffa85fac0b7cb2d3138056babec48dd5dd"},
|
||||
{file = "mem0ai-0.0.9-py3-none-any.whl", hash = "sha256:d4de435729af4fd3d597d022ffb2af89a0630d6c3b4769792bbe27d2ce816858"},
|
||||
{file = "mem0ai-0.0.9.tar.gz", hash = "sha256:e4374d5d04aa3f543cd3325f700e4b62f5358ae1c6fa5c44b2ff790c10c4e5f1"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
boto3 = ">=1.34.144,<2.0.0"
|
||||
groq = ">=0.9.0,<0.10.0"
|
||||
openai = ">=1.33.0,<2.0.0"
|
||||
posthog = ">=3.5.0,<4.0.0"
|
||||
pydantic = ">=2.7.3,<3.0.0"
|
||||
qdrant-client = ">=1.9.1,<2.0.0"
|
||||
together = ">=1.2.1,<2.0.0"
|
||||
|
||||
[[package]]
|
||||
name = "mergedeep"
|
||||
@@ -2865,13 +2830,13 @@ pyyaml = ">=5.1"
|
||||
|
||||
[[package]]
|
||||
name = "mkdocs-material"
|
||||
version = "9.5.29"
|
||||
version = "9.5.30"
|
||||
description = "Documentation that simply works"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "mkdocs_material-9.5.29-py3-none-any.whl", hash = "sha256:afc1f508e2662ded95f0a35a329e8a5acd73ee88ca07ba73836eb6fcdae5d8b4"},
|
||||
{file = "mkdocs_material-9.5.29.tar.gz", hash = "sha256:3e977598ec15a4ddad5c4dfc9e08edab6023edb51e88f0729bd27be77e3d322a"},
|
||||
{file = "mkdocs_material-9.5.30-py3-none-any.whl", hash = "sha256:fc070689c5250a180e9b9d79d8491ef9a3a7acb240db0728728d6c31eeb131d4"},
|
||||
{file = "mkdocs_material-9.5.30.tar.gz", hash = "sha256:3fd417dd42d679e3ba08b9e2d72cd8b8af142cc4a3969676ad6b00993dd182ec"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -3337,13 +3302,13 @@ sympy = "*"
|
||||
|
||||
[[package]]
|
||||
name = "openai"
|
||||
version = "1.36.1"
|
||||
version = "1.37.1"
|
||||
description = "The official Python library for the openai API"
|
||||
optional = false
|
||||
python-versions = ">=3.7.1"
|
||||
files = [
|
||||
{file = "openai-1.36.1-py3-none-any.whl", hash = "sha256:d399b9d476dbbc167aceaac6bc6ed0b2e2bb6c9e189c7f7047f822743ae62e64"},
|
||||
{file = "openai-1.36.1.tar.gz", hash = "sha256:41be9e0302e95dba8a9374b885c5cb1cec2202816a70b98736fee25a2cadd1f2"},
|
||||
{file = "openai-1.37.1-py3-none-any.whl", hash = "sha256:9a6adda0d6ae8fce02d235c5671c399cfa40d6a281b3628914c7ebf244888ee3"},
|
||||
{file = "openai-1.37.1.tar.gz", hash = "sha256:faf87206785a6b5d9e34555d6a3242482a6852bc802e453e2a891f68ee04ce55"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -3360,42 +3325,42 @@ datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
|
||||
|
||||
[[package]]
|
||||
name = "opentelemetry-api"
|
||||
version = "1.25.0"
|
||||
version = "1.26.0"
|
||||
description = "OpenTelemetry Python API"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "opentelemetry_api-1.25.0-py3-none-any.whl", hash = "sha256:757fa1aa020a0f8fa139f8959e53dec2051cc26b832e76fa839a6d76ecefd737"},
|
||||
{file = "opentelemetry_api-1.25.0.tar.gz", hash = "sha256:77c4985f62f2614e42ce77ee4c9da5fa5f0bc1e1821085e9a47533a9323ae869"},
|
||||
{file = "opentelemetry_api-1.26.0-py3-none-any.whl", hash = "sha256:7d7ea33adf2ceda2dd680b18b1677e4152000b37ca76e679da71ff103b943064"},
|
||||
{file = "opentelemetry_api-1.26.0.tar.gz", hash = "sha256:2bd639e4bed5b18486fef0b5a520aaffde5a18fc225e808a1ac4df363f43a1ce"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
deprecated = ">=1.2.6"
|
||||
importlib-metadata = ">=6.0,<=7.1"
|
||||
importlib-metadata = ">=6.0,<=8.0.0"
|
||||
|
||||
[[package]]
|
||||
name = "opentelemetry-exporter-otlp-proto-common"
|
||||
version = "1.25.0"
|
||||
version = "1.26.0"
|
||||
description = "OpenTelemetry Protobuf encoding"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "opentelemetry_exporter_otlp_proto_common-1.25.0-py3-none-any.whl", hash = "sha256:15637b7d580c2675f70246563363775b4e6de947871e01d0f4e3881d1848d693"},
|
||||
{file = "opentelemetry_exporter_otlp_proto_common-1.25.0.tar.gz", hash = "sha256:c93f4e30da4eee02bacd1e004eb82ce4da143a2f8e15b987a9f603e0a85407d3"},
|
||||
{file = "opentelemetry_exporter_otlp_proto_common-1.26.0-py3-none-any.whl", hash = "sha256:ee4d8f8891a1b9c372abf8d109409e5b81947cf66423fd998e56880057afbc71"},
|
||||
{file = "opentelemetry_exporter_otlp_proto_common-1.26.0.tar.gz", hash = "sha256:bdbe50e2e22a1c71acaa0c8ba6efaadd58882e5a5978737a44a4c4b10d304c92"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
opentelemetry-proto = "1.25.0"
|
||||
opentelemetry-proto = "1.26.0"
|
||||
|
||||
[[package]]
|
||||
name = "opentelemetry-exporter-otlp-proto-grpc"
|
||||
version = "1.25.0"
|
||||
version = "1.26.0"
|
||||
description = "OpenTelemetry Collector Protobuf over gRPC Exporter"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "opentelemetry_exporter_otlp_proto_grpc-1.25.0-py3-none-any.whl", hash = "sha256:3131028f0c0a155a64c430ca600fd658e8e37043cb13209f0109db5c1a3e4eb4"},
|
||||
{file = "opentelemetry_exporter_otlp_proto_grpc-1.25.0.tar.gz", hash = "sha256:c0b1661415acec5af87625587efa1ccab68b873745ca0ee96b69bb1042087eac"},
|
||||
{file = "opentelemetry_exporter_otlp_proto_grpc-1.26.0-py3-none-any.whl", hash = "sha256:e2be5eff72ebcb010675b818e8d7c2e7d61ec451755b8de67a140bc49b9b0280"},
|
||||
{file = "opentelemetry_exporter_otlp_proto_grpc-1.26.0.tar.gz", hash = "sha256:a65b67a9a6b06ba1ec406114568e21afe88c1cdb29c464f2507d529eb906d8ae"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -3403,39 +3368,39 @@ deprecated = ">=1.2.6"
|
||||
googleapis-common-protos = ">=1.52,<2.0"
|
||||
grpcio = ">=1.0.0,<2.0.0"
|
||||
opentelemetry-api = ">=1.15,<2.0"
|
||||
opentelemetry-exporter-otlp-proto-common = "1.25.0"
|
||||
opentelemetry-proto = "1.25.0"
|
||||
opentelemetry-sdk = ">=1.25.0,<1.26.0"
|
||||
opentelemetry-exporter-otlp-proto-common = "1.26.0"
|
||||
opentelemetry-proto = "1.26.0"
|
||||
opentelemetry-sdk = ">=1.26.0,<1.27.0"
|
||||
|
||||
[[package]]
|
||||
name = "opentelemetry-exporter-otlp-proto-http"
|
||||
version = "1.25.0"
|
||||
version = "1.26.0"
|
||||
description = "OpenTelemetry Collector Protobuf over HTTP Exporter"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "opentelemetry_exporter_otlp_proto_http-1.25.0-py3-none-any.whl", hash = "sha256:2eca686ee11b27acd28198b3ea5e5863a53d1266b91cda47c839d95d5e0541a6"},
|
||||
{file = "opentelemetry_exporter_otlp_proto_http-1.25.0.tar.gz", hash = "sha256:9f8723859e37c75183ea7afa73a3542f01d0fd274a5b97487ea24cb683d7d684"},
|
||||
{file = "opentelemetry_exporter_otlp_proto_http-1.26.0-py3-none-any.whl", hash = "sha256:ee72a87c48ec977421b02f16c52ea8d884122470e0be573905237b540f4ee562"},
|
||||
{file = "opentelemetry_exporter_otlp_proto_http-1.26.0.tar.gz", hash = "sha256:5801ebbcf7b527377883e6cbbdda35ee712dc55114fff1e93dfee210be56c908"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
deprecated = ">=1.2.6"
|
||||
googleapis-common-protos = ">=1.52,<2.0"
|
||||
opentelemetry-api = ">=1.15,<2.0"
|
||||
opentelemetry-exporter-otlp-proto-common = "1.25.0"
|
||||
opentelemetry-proto = "1.25.0"
|
||||
opentelemetry-sdk = ">=1.25.0,<1.26.0"
|
||||
opentelemetry-exporter-otlp-proto-common = "1.26.0"
|
||||
opentelemetry-proto = "1.26.0"
|
||||
opentelemetry-sdk = ">=1.26.0,<1.27.0"
|
||||
requests = ">=2.7,<3.0"
|
||||
|
||||
[[package]]
|
||||
name = "opentelemetry-instrumentation"
|
||||
version = "0.46b0"
|
||||
version = "0.47b0"
|
||||
description = "Instrumentation Tools & Auto Instrumentation for OpenTelemetry Python"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "opentelemetry_instrumentation-0.46b0-py3-none-any.whl", hash = "sha256:89cd721b9c18c014ca848ccd11181e6b3fd3f6c7669e35d59c48dc527408c18b"},
|
||||
{file = "opentelemetry_instrumentation-0.46b0.tar.gz", hash = "sha256:974e0888fb2a1e01c38fbacc9483d024bb1132aad92d6d24e2e5543887a7adda"},
|
||||
{file = "opentelemetry_instrumentation-0.47b0-py3-none-any.whl", hash = "sha256:88974ee52b1db08fc298334b51c19d47e53099c33740e48c4f084bd1afd052d5"},
|
||||
{file = "opentelemetry_instrumentation-0.47b0.tar.gz", hash = "sha256:96f9885e450c35e3f16a4f33145f2ebf620aea910c9fd74a392bbc0f807a350f"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -3445,55 +3410,55 @@ wrapt = ">=1.0.0,<2.0.0"
|
||||
|
||||
[[package]]
|
||||
name = "opentelemetry-instrumentation-asgi"
|
||||
version = "0.46b0"
|
||||
version = "0.47b0"
|
||||
description = "ASGI instrumentation for OpenTelemetry"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "opentelemetry_instrumentation_asgi-0.46b0-py3-none-any.whl", hash = "sha256:f13c55c852689573057837a9500aeeffc010c4ba59933c322e8f866573374759"},
|
||||
{file = "opentelemetry_instrumentation_asgi-0.46b0.tar.gz", hash = "sha256:02559f30cf4b7e2a737ab17eb52aa0779bcf4cc06573064f3e2cb4dcc7d3040a"},
|
||||
{file = "opentelemetry_instrumentation_asgi-0.47b0-py3-none-any.whl", hash = "sha256:b798dc4957b3edc9dfecb47a4c05809036a4b762234c5071212fda39ead80ade"},
|
||||
{file = "opentelemetry_instrumentation_asgi-0.47b0.tar.gz", hash = "sha256:e78b7822c1bca0511e5e9610ec484b8994a81670375e570c76f06f69af7c506a"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
asgiref = ">=3.0,<4.0"
|
||||
opentelemetry-api = ">=1.12,<2.0"
|
||||
opentelemetry-instrumentation = "0.46b0"
|
||||
opentelemetry-semantic-conventions = "0.46b0"
|
||||
opentelemetry-util-http = "0.46b0"
|
||||
opentelemetry-instrumentation = "0.47b0"
|
||||
opentelemetry-semantic-conventions = "0.47b0"
|
||||
opentelemetry-util-http = "0.47b0"
|
||||
|
||||
[package.extras]
|
||||
instruments = ["asgiref (>=3.0,<4.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "opentelemetry-instrumentation-fastapi"
|
||||
version = "0.46b0"
|
||||
version = "0.47b0"
|
||||
description = "OpenTelemetry FastAPI Instrumentation"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "opentelemetry_instrumentation_fastapi-0.46b0-py3-none-any.whl", hash = "sha256:e0f5d150c6c36833dd011f0e6ef5ede6d7406c1aed0c7c98b2d3b38a018d1b33"},
|
||||
{file = "opentelemetry_instrumentation_fastapi-0.46b0.tar.gz", hash = "sha256:928a883a36fc89f9702f15edce43d1a7104da93d740281e32d50ffd03dbb4365"},
|
||||
{file = "opentelemetry_instrumentation_fastapi-0.47b0-py3-none-any.whl", hash = "sha256:5ac28dd401160b02e4f544a85a9e4f61a8cbe5b077ea0379d411615376a2bd21"},
|
||||
{file = "opentelemetry_instrumentation_fastapi-0.47b0.tar.gz", hash = "sha256:0c7c10b5d971e99a420678ffd16c5b1ea4f0db3b31b62faf305fbb03b4ebee36"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
opentelemetry-api = ">=1.12,<2.0"
|
||||
opentelemetry-instrumentation = "0.46b0"
|
||||
opentelemetry-instrumentation-asgi = "0.46b0"
|
||||
opentelemetry-semantic-conventions = "0.46b0"
|
||||
opentelemetry-util-http = "0.46b0"
|
||||
opentelemetry-instrumentation = "0.47b0"
|
||||
opentelemetry-instrumentation-asgi = "0.47b0"
|
||||
opentelemetry-semantic-conventions = "0.47b0"
|
||||
opentelemetry-util-http = "0.47b0"
|
||||
|
||||
[package.extras]
|
||||
instruments = ["fastapi (>=0.58,<1.0)"]
|
||||
instruments = ["fastapi (>=0.58,<1.0)", "fastapi-slim (>=0.111.0,<0.112.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "opentelemetry-proto"
|
||||
version = "1.25.0"
|
||||
version = "1.26.0"
|
||||
description = "OpenTelemetry Python Proto"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "opentelemetry_proto-1.25.0-py3-none-any.whl", hash = "sha256:f07e3341c78d835d9b86665903b199893befa5e98866f63d22b00d0b7ca4972f"},
|
||||
{file = "opentelemetry_proto-1.25.0.tar.gz", hash = "sha256:35b6ef9dc4a9f7853ecc5006738ad40443701e52c26099e197895cbda8b815a3"},
|
||||
{file = "opentelemetry_proto-1.26.0-py3-none-any.whl", hash = "sha256:6c4d7b4d4d9c88543bcf8c28ae3f8f0448a753dc291c18c5390444c90b76a725"},
|
||||
{file = "opentelemetry_proto-1.26.0.tar.gz", hash = "sha256:c5c18796c0cab3751fc3b98dee53855835e90c0422924b484432ac852d93dc1e"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -3501,43 +3466,44 @@ protobuf = ">=3.19,<5.0"
|
||||
|
||||
[[package]]
|
||||
name = "opentelemetry-sdk"
|
||||
version = "1.25.0"
|
||||
version = "1.26.0"
|
||||
description = "OpenTelemetry Python SDK"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "opentelemetry_sdk-1.25.0-py3-none-any.whl", hash = "sha256:d97ff7ec4b351692e9d5a15af570c693b8715ad78b8aafbec5c7100fe966b4c9"},
|
||||
{file = "opentelemetry_sdk-1.25.0.tar.gz", hash = "sha256:ce7fc319c57707ef5bf8b74fb9f8ebdb8bfafbe11898410e0d2a761d08a98ec7"},
|
||||
{file = "opentelemetry_sdk-1.26.0-py3-none-any.whl", hash = "sha256:feb5056a84a88670c041ea0ded9921fca559efec03905dddeb3885525e0af897"},
|
||||
{file = "opentelemetry_sdk-1.26.0.tar.gz", hash = "sha256:c90d2868f8805619535c05562d699e2f4fb1f00dbd55a86dcefca4da6fa02f85"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
opentelemetry-api = "1.25.0"
|
||||
opentelemetry-semantic-conventions = "0.46b0"
|
||||
opentelemetry-api = "1.26.0"
|
||||
opentelemetry-semantic-conventions = "0.47b0"
|
||||
typing-extensions = ">=3.7.4"
|
||||
|
||||
[[package]]
|
||||
name = "opentelemetry-semantic-conventions"
|
||||
version = "0.46b0"
|
||||
version = "0.47b0"
|
||||
description = "OpenTelemetry Semantic Conventions"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "opentelemetry_semantic_conventions-0.46b0-py3-none-any.whl", hash = "sha256:6daef4ef9fa51d51855d9f8e0ccd3a1bd59e0e545abe99ac6203804e36ab3e07"},
|
||||
{file = "opentelemetry_semantic_conventions-0.46b0.tar.gz", hash = "sha256:fbc982ecbb6a6e90869b15c1673be90bd18c8a56ff1cffc0864e38e2edffaefa"},
|
||||
{file = "opentelemetry_semantic_conventions-0.47b0-py3-none-any.whl", hash = "sha256:4ff9d595b85a59c1c1413f02bba320ce7ea6bf9e2ead2b0913c4395c7bbc1063"},
|
||||
{file = "opentelemetry_semantic_conventions-0.47b0.tar.gz", hash = "sha256:a8d57999bbe3495ffd4d510de26a97dadc1dace53e0275001b2c1b2f67992a7e"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
opentelemetry-api = "1.25.0"
|
||||
deprecated = ">=1.2.6"
|
||||
opentelemetry-api = "1.26.0"
|
||||
|
||||
[[package]]
|
||||
name = "opentelemetry-util-http"
|
||||
version = "0.46b0"
|
||||
version = "0.47b0"
|
||||
description = "Web util for OpenTelemetry"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "opentelemetry_util_http-0.46b0-py3-none-any.whl", hash = "sha256:8dc1949ce63caef08db84ae977fdc1848fe6dc38e6bbaad0ae3e6ecd0d451629"},
|
||||
{file = "opentelemetry_util_http-0.46b0.tar.gz", hash = "sha256:03b6e222642f9c7eae58d9132343e045b50aca9761fcb53709bd2b663571fdf6"},
|
||||
{file = "opentelemetry_util_http-0.47b0-py3-none-any.whl", hash = "sha256:3d3215e09c4a723b12da6d0233a31395aeb2bb33a64d7b15a1500690ba250f19"},
|
||||
{file = "opentelemetry_util_http-0.47b0.tar.gz", hash = "sha256:352a07664c18eef827eb8ddcbd64c64a7284a39dd1655e2f16f577eb046ccb32"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -3916,13 +3882,13 @@ test = ["coverage", "flake8", "freezegun (==0.3.15)", "mock (>=2.0.0)", "pylint"
|
||||
|
||||
[[package]]
|
||||
name = "pre-commit"
|
||||
version = "3.7.1"
|
||||
version = "3.8.0"
|
||||
description = "A framework for managing and maintaining multi-language pre-commit hooks."
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "pre_commit-3.7.1-py2.py3-none-any.whl", hash = "sha256:fae36fd1d7ad7d6a5a1c0b0d5adb2ed1a3bda5a21bf6c3e5372073d7a11cd4c5"},
|
||||
{file = "pre_commit-3.7.1.tar.gz", hash = "sha256:8ca3ad567bc78a4972a3f1a477e94a79d4597e8140a6e0b651c5e33899c3654a"},
|
||||
{file = "pre_commit-3.8.0-py2.py3-none-any.whl", hash = "sha256:9a90a53bf82fdd8778d58085faf8d83df56e40dfe18f45b19446e26bf1b3a63f"},
|
||||
{file = "pre_commit-3.8.0.tar.gz", hash = "sha256:8bb6494d4a20423842e198980c9ecf9f96607a07ea29549e180eef9ae80fe7af"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -3951,22 +3917,22 @@ testing = ["google-api-core (>=1.31.5)"]
|
||||
|
||||
[[package]]
|
||||
name = "protobuf"
|
||||
version = "4.25.3"
|
||||
version = "4.25.4"
|
||||
description = ""
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "protobuf-4.25.3-cp310-abi3-win32.whl", hash = "sha256:d4198877797a83cbfe9bffa3803602bbe1625dc30d8a097365dbc762e5790faa"},
|
||||
{file = "protobuf-4.25.3-cp310-abi3-win_amd64.whl", hash = "sha256:209ba4cc916bab46f64e56b85b090607a676f66b473e6b762e6f1d9d591eb2e8"},
|
||||
{file = "protobuf-4.25.3-cp37-abi3-macosx_10_9_universal2.whl", hash = "sha256:f1279ab38ecbfae7e456a108c5c0681e4956d5b1090027c1de0f934dfdb4b35c"},
|
||||
{file = "protobuf-4.25.3-cp37-abi3-manylinux2014_aarch64.whl", hash = "sha256:e7cb0ae90dd83727f0c0718634ed56837bfeeee29a5f82a7514c03ee1364c019"},
|
||||
{file = "protobuf-4.25.3-cp37-abi3-manylinux2014_x86_64.whl", hash = "sha256:7c8daa26095f82482307bc717364e7c13f4f1c99659be82890dcfc215194554d"},
|
||||
{file = "protobuf-4.25.3-cp38-cp38-win32.whl", hash = "sha256:f4f118245c4a087776e0a8408be33cf09f6c547442c00395fbfb116fac2f8ac2"},
|
||||
{file = "protobuf-4.25.3-cp38-cp38-win_amd64.whl", hash = "sha256:c053062984e61144385022e53678fbded7aea14ebb3e0305ae3592fb219ccfa4"},
|
||||
{file = "protobuf-4.25.3-cp39-cp39-win32.whl", hash = "sha256:19b270aeaa0099f16d3ca02628546b8baefe2955bbe23224aaf856134eccf1e4"},
|
||||
{file = "protobuf-4.25.3-cp39-cp39-win_amd64.whl", hash = "sha256:e3c97a1555fd6388f857770ff8b9703083de6bf1f9274a002a332d65fbb56c8c"},
|
||||
{file = "protobuf-4.25.3-py3-none-any.whl", hash = "sha256:f0700d54bcf45424477e46a9f0944155b46fb0639d69728739c0e47bab83f2b9"},
|
||||
{file = "protobuf-4.25.3.tar.gz", hash = "sha256:25b5d0b42fd000320bd7830b349e3b696435f3b329810427a6bcce6a5492cc5c"},
|
||||
{file = "protobuf-4.25.4-cp310-abi3-win32.whl", hash = "sha256:db9fd45183e1a67722cafa5c1da3e85c6492a5383f127c86c4c4aa4845867dc4"},
|
||||
{file = "protobuf-4.25.4-cp310-abi3-win_amd64.whl", hash = "sha256:ba3d8504116a921af46499471c63a85260c1a5fc23333154a427a310e015d26d"},
|
||||
{file = "protobuf-4.25.4-cp37-abi3-macosx_10_9_universal2.whl", hash = "sha256:eecd41bfc0e4b1bd3fa7909ed93dd14dd5567b98c941d6c1ad08fdcab3d6884b"},
|
||||
{file = "protobuf-4.25.4-cp37-abi3-manylinux2014_aarch64.whl", hash = "sha256:4c8a70fdcb995dcf6c8966cfa3a29101916f7225e9afe3ced4395359955d3835"},
|
||||
{file = "protobuf-4.25.4-cp37-abi3-manylinux2014_x86_64.whl", hash = "sha256:3319e073562e2515c6ddc643eb92ce20809f5d8f10fead3332f71c63be6a7040"},
|
||||
{file = "protobuf-4.25.4-cp38-cp38-win32.whl", hash = "sha256:7e372cbbda66a63ebca18f8ffaa6948455dfecc4e9c1029312f6c2edcd86c4e1"},
|
||||
{file = "protobuf-4.25.4-cp38-cp38-win_amd64.whl", hash = "sha256:051e97ce9fa6067a4546e75cb14f90cf0232dcb3e3d508c448b8d0e4265b61c1"},
|
||||
{file = "protobuf-4.25.4-cp39-cp39-win32.whl", hash = "sha256:90bf6fd378494eb698805bbbe7afe6c5d12c8e17fca817a646cd6a1818c696ca"},
|
||||
{file = "protobuf-4.25.4-cp39-cp39-win_amd64.whl", hash = "sha256:ac79a48d6b99dfed2729ccccee547b34a1d3d63289c71cef056653a846a2240f"},
|
||||
{file = "protobuf-4.25.4-py3-none-any.whl", hash = "sha256:bfbebc1c8e4793cfd58589acfb8a1026be0003e852b9da7db5a4285bde996978"},
|
||||
{file = "protobuf-4.25.4.tar.gz", hash = "sha256:0dc4a62cc4052a036ee2204d26fe4d835c62827c855c8a03f29fe6da146b380d"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -4316,13 +4282,13 @@ torch = ["torch"]
|
||||
|
||||
[[package]]
|
||||
name = "pymdown-extensions"
|
||||
version = "10.8.1"
|
||||
version = "10.9"
|
||||
description = "Extension pack for Python Markdown."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "pymdown_extensions-10.8.1-py3-none-any.whl", hash = "sha256:f938326115884f48c6059c67377c46cf631c733ef3629b6eed1349989d1b30cb"},
|
||||
{file = "pymdown_extensions-10.8.1.tar.gz", hash = "sha256:3ab1db5c9e21728dabf75192d71471f8e50f216627e9a1fa9535ecb0231b9940"},
|
||||
{file = "pymdown_extensions-10.9-py3-none-any.whl", hash = "sha256:d323f7e90d83c86113ee78f3fe62fc9dee5f56b54d912660703ea1816fed5626"},
|
||||
{file = "pymdown_extensions-10.9.tar.gz", hash = "sha256:6ff740bcd99ec4172a938970d42b96128bdc9d4b9bcad72494f29921dc69b753"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -4387,13 +4353,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "pyright"
|
||||
version = "1.1.372"
|
||||
version = "1.1.373"
|
||||
description = "Command line wrapper for pyright"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "pyright-1.1.372-py3-none-any.whl", hash = "sha256:25b15fb8967740f0949fd35b963777187f0a0404c0bd753cc966ec139f3eaa0b"},
|
||||
{file = "pyright-1.1.372.tar.gz", hash = "sha256:a9f5e0daa955daaa17e3d1ef76d3623e75f8afd5e37b437d3ff84d5b38c15420"},
|
||||
{file = "pyright-1.1.373-py3-none-any.whl", hash = "sha256:b805413227f2c209f27b14b55da27fe5e9fb84129c9f1eb27708a5d12f6f000e"},
|
||||
{file = "pyright-1.1.373.tar.gz", hash = "sha256:f41bcfc8b9d1802b09921a394d6ae1ce19694957b628bc657629688daf8a83ff"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -4427,13 +4393,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "pytest"
|
||||
version = "8.3.1"
|
||||
version = "8.3.2"
|
||||
description = "pytest: simple powerful testing with Python"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "pytest-8.3.1-py3-none-any.whl", hash = "sha256:e9600ccf4f563976e2c99fa02c7624ab938296551f280835ee6516df8bc4ae8c"},
|
||||
{file = "pytest-8.3.1.tar.gz", hash = "sha256:7e8e5c5abd6e93cb1cc151f23e57adc31fcf8cfd2a3ff2da63e23f732de35db6"},
|
||||
{file = "pytest-8.3.2-py3-none-any.whl", hash = "sha256:4ba08f9ae7dcf84ded419494d229b48d0903ea6407b030eaec46df5e6a73bba5"},
|
||||
{file = "pytest-8.3.2.tar.gz", hash = "sha256:c132345d12ce551242c87269de812483f5bcc87cdbb4722e48487ba194f9fdce"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -4900,22 +4866,22 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "selenium"
|
||||
version = "4.22.0"
|
||||
version = "4.23.1"
|
||||
description = "Official Python bindings for Selenium WebDriver"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "selenium-4.22.0-py3-none-any.whl", hash = "sha256:e424991196e9857e19bf04fe5c1c0a4aac076794ff5e74615b1124e729d93104"},
|
||||
{file = "selenium-4.22.0.tar.gz", hash = "sha256:903c8c9d61b3eea6fcc9809dc7d9377e04e2ac87709876542cc8f863e482c4ce"},
|
||||
{file = "selenium-4.23.1-py3-none-any.whl", hash = "sha256:3a8d9f23dc636bd3840dd56f00c2739e32ec0c1e34a821dd553e15babef24477"},
|
||||
{file = "selenium-4.23.1.tar.gz", hash = "sha256:128d099e66284437e7128d2279176ec7a06e6ec7426e167f5d34987166bd8f46"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
certifi = ">=2021.10.8"
|
||||
trio = ">=0.17,<1.0"
|
||||
trio-websocket = ">=0.9,<1.0"
|
||||
typing_extensions = ">=4.9.0"
|
||||
typing_extensions = ">=4.9,<5.0"
|
||||
urllib3 = {version = ">=1.26,<3", extras = ["socks"]}
|
||||
websocket-client = ">=1.8.0"
|
||||
websocket-client = ">=1.8,<2.0"
|
||||
|
||||
[[package]]
|
||||
name = "semver"
|
||||
@@ -4930,13 +4896,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "setuptools"
|
||||
version = "71.1.0"
|
||||
version = "72.1.0"
|
||||
description = "Easily download, build, install, upgrade, and uninstall Python packages"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "setuptools-71.1.0-py3-none-any.whl", hash = "sha256:33874fdc59b3188304b2e7c80d9029097ea31627180896fb549c578ceb8a0855"},
|
||||
{file = "setuptools-71.1.0.tar.gz", hash = "sha256:032d42ee9fb536e33087fb66cac5f840eb9391ed05637b3f2a76a7c8fb477936"},
|
||||
{file = "setuptools-72.1.0-py3-none-any.whl", hash = "sha256:5a03e1860cf56bb6ef48ce186b0e557fdba433237481a9a625176c2831be15d1"},
|
||||
{file = "setuptools-72.1.0.tar.gz", hash = "sha256:8d243eff56d095e5817f796ede6ae32941278f542e0f941867cc05ae52b162ec"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
@@ -5285,34 +5251,6 @@ webencodings = ">=0.4"
|
||||
doc = ["sphinx", "sphinx_rtd_theme"]
|
||||
test = ["pytest", "ruff"]
|
||||
|
||||
[[package]]
|
||||
name = "together"
|
||||
version = "1.2.2"
|
||||
description = "Python client for Together's Cloud Platform!"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8"
|
||||
files = [
|
||||
{file = "together-1.2.2-py3-none-any.whl", hash = "sha256:7ce89f902dbaca67e46e693d90182514494f510f3bc16cb89d816a5031ab0433"},
|
||||
{file = "together-1.2.2.tar.gz", hash = "sha256:fd026f4a604e1fb3ee2fa5803f31e5e36ad31b3d182ef47f611326de66907d13"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
aiohttp = ">=3.9.3,<4.0.0"
|
||||
click = ">=8.1.7,<9.0.0"
|
||||
eval-type-backport = ">=0.1.3,<0.3.0"
|
||||
filelock = ">=3.13.1,<4.0.0"
|
||||
numpy = [
|
||||
{version = ">=1.23.5", markers = "python_version < \"3.12\""},
|
||||
{version = ">=1.26.0", markers = "python_version >= \"3.12\""},
|
||||
]
|
||||
pillow = ">=10.3.0,<11.0.0"
|
||||
pyarrow = ">=10.0.1"
|
||||
pydantic = ">=2.6.3,<3.0.0"
|
||||
requests = ">=2.31.0,<3.0.0"
|
||||
tabulate = ">=0.9.0,<0.10.0"
|
||||
tqdm = ">=4.66.2,<5.0.0"
|
||||
typer = ">=0.9,<0.13"
|
||||
|
||||
[[package]]
|
||||
name = "tokenizers"
|
||||
version = "0.19.1"
|
||||
|
||||
@@ -55,8 +55,6 @@ class Agent(BaseAgent):
|
||||
tools: Tools at agents disposal
|
||||
step_callback: Callback to be executed after each step of the agent execution.
|
||||
callbacks: A list of callback functions from the langchain library that are triggered during the agent's execution process
|
||||
allow_code_execution: Enable code execution for the agent.
|
||||
max_retry_limit: Maximum number of retries for an agent to execute a task when an error occurs.
|
||||
"""
|
||||
|
||||
_times_executed: int = PrivateAttr(default=0)
|
||||
@@ -199,9 +197,7 @@ class Agent(BaseAgent):
|
||||
"tools": self.agent_executor.tools_description,
|
||||
}
|
||||
)["output"]
|
||||
print("Result when things went well:", result)
|
||||
except Exception as e:
|
||||
print("FAILED TO EXECUTE TASK", e)
|
||||
self._times_executed += 1
|
||||
if self._times_executed > self.max_retry_limit:
|
||||
raise e
|
||||
@@ -217,7 +213,6 @@ class Agent(BaseAgent):
|
||||
if tool_result.get("result_as_answer", False):
|
||||
result = tool_result["result"]
|
||||
|
||||
print("RESULT TO RETURN", result)
|
||||
return result
|
||||
|
||||
def format_log_to_str(
|
||||
@@ -265,6 +260,7 @@ class Agent(BaseAgent):
|
||||
"tools_handler": self.tools_handler,
|
||||
"function_calling_llm": self.function_calling_llm,
|
||||
"callbacks": self.callbacks,
|
||||
"max_tokens": self.max_tokens,
|
||||
}
|
||||
|
||||
if self._rpm_controller:
|
||||
|
||||
@@ -45,6 +45,7 @@ class BaseAgent(ABC, BaseModel):
|
||||
i18n (I18N): Internationalization settings.
|
||||
cache_handler (InstanceOf[CacheHandler]): An instance of the CacheHandler class.
|
||||
tools_handler (InstanceOf[ToolsHandler]): An instance of the ToolsHandler class.
|
||||
max_tokens: Maximum number of tokens for the agent to generate in a response.
|
||||
|
||||
|
||||
Methods:
|
||||
@@ -118,6 +119,9 @@ class BaseAgent(ABC, BaseModel):
|
||||
tools_handler: InstanceOf[ToolsHandler] = Field(
|
||||
default=None, description="An instance of the ToolsHandler class."
|
||||
)
|
||||
max_tokens: Optional[int] = Field(
|
||||
default=None, description="Maximum number of tokens for the agent's execution."
|
||||
)
|
||||
|
||||
_original_role: str | None = None
|
||||
_original_goal: str | None = None
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
from typing import Any, Dict
|
||||
from crewai.types.usage_metrics import UsageMetrics
|
||||
|
||||
|
||||
class TokenProcess:
|
||||
@@ -18,10 +18,10 @@ class TokenProcess:
|
||||
def sum_successful_requests(self, requests: int):
|
||||
self.successful_requests = self.successful_requests + requests
|
||||
|
||||
def get_summary(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"total_tokens": self.total_tokens,
|
||||
"prompt_tokens": self.prompt_tokens,
|
||||
"completion_tokens": self.completion_tokens,
|
||||
"successful_requests": self.successful_requests,
|
||||
}
|
||||
def get_summary(self) -> UsageMetrics:
|
||||
return UsageMetrics(
|
||||
total_tokens=self.total_tokens,
|
||||
prompt_tokens=self.prompt_tokens,
|
||||
completion_tokens=self.completion_tokens,
|
||||
successful_requests=self.successful_requests,
|
||||
)
|
||||
|
||||
@@ -5,11 +5,11 @@ from crewai.memory.storage.kickoff_task_outputs_storage import (
|
||||
KickoffTaskOutputsSQLiteStorage,
|
||||
)
|
||||
|
||||
|
||||
from .create_crew import create_crew
|
||||
from .train_crew import train_crew
|
||||
from .replay_from_task import replay_task_command
|
||||
from .reset_memories_command import reset_memories_command
|
||||
from .test_crew import test_crew
|
||||
from .train_crew import train_crew
|
||||
|
||||
|
||||
@click.group()
|
||||
@@ -126,5 +126,26 @@ def reset_memories(long, short, entities, kickoff_outputs, all):
|
||||
click.echo(f"An error occurred while resetting memories: {e}", err=True)
|
||||
|
||||
|
||||
@crewai.command()
|
||||
@click.option(
|
||||
"-n",
|
||||
"--n_iterations",
|
||||
type=int,
|
||||
default=3,
|
||||
help="Number of iterations to Test the crew",
|
||||
)
|
||||
@click.option(
|
||||
"-m",
|
||||
"--model",
|
||||
type=str,
|
||||
default="gpt-4o-mini",
|
||||
help="LLM Model to run the tests on the Crew. For now only accepting only OpenAI models.",
|
||||
)
|
||||
def test(n_iterations: int, model: str):
|
||||
"""Test the crew and evaluate the results."""
|
||||
click.echo(f"Testing the crew for {n_iterations} iterations with model {model}")
|
||||
test_crew(n_iterations, model)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
crewai()
|
||||
|
||||
@@ -9,10 +9,14 @@ from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandle
|
||||
|
||||
def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
|
||||
"""
|
||||
Replay the crew execution from a specific task.
|
||||
Reset the crew memories.
|
||||
|
||||
Args:
|
||||
task_id (str): The ID of the task to replay from.
|
||||
long (bool): Whether to reset the long-term memory.
|
||||
short (bool): Whether to reset the short-term memory.
|
||||
entity (bool): Whether to reset the entity memory.
|
||||
kickoff_outputs (bool): Whether to reset the latest kickoff task outputs.
|
||||
all (bool): Whether to reset all memories.
|
||||
"""
|
||||
|
||||
try:
|
||||
|
||||
@@ -5,6 +5,7 @@ research_task:
|
||||
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: >
|
||||
@@ -13,3 +14,4 @@ reporting_task:
|
||||
expected_output: >
|
||||
A fully fledge reports with the mains topics, each with a full section of information.
|
||||
Formatted as markdown without '```'
|
||||
agent: reporting_analyst
|
||||
|
||||
@@ -32,14 +32,12 @@ class {{crew_name}}Crew():
|
||||
def research_task(self) -> Task:
|
||||
return Task(
|
||||
config=self.tasks_config['research_task'],
|
||||
agent=self.researcher()
|
||||
)
|
||||
|
||||
@task
|
||||
def reporting_task(self) -> Task:
|
||||
return Task(
|
||||
config=self.tasks_config['reporting_task'],
|
||||
agent=self.reporting_analyst(),
|
||||
output_file='report.md'
|
||||
)
|
||||
|
||||
|
||||
@@ -39,3 +39,16 @@ def replay():
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(f"An error occurred while replaying the crew: {e}")
|
||||
|
||||
def test():
|
||||
"""
|
||||
Test the crew execution and returns the results.
|
||||
"""
|
||||
inputs = {
|
||||
"topic": "AI LLMs"
|
||||
}
|
||||
try:
|
||||
{{crew_name}}Crew().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(f"An error occurred while replaying the crew: {e}")
|
||||
|
||||
@@ -12,6 +12,7 @@ crewai = { extras = ["tools"], version = "^0.41.1" }
|
||||
{{folder_name}} = "{{folder_name}}.main:run"
|
||||
train = "{{folder_name}}.main:train"
|
||||
replay = "{{folder_name}}.main:replay"
|
||||
test = "{{folder_name}}.main:test"
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core"]
|
||||
|
||||
32
src/crewai/cli/test_crew.py
Normal file
32
src/crewai/cli/test_crew.py
Normal file
@@ -0,0 +1,32 @@
|
||||
import subprocess
|
||||
import click
|
||||
import pytest
|
||||
|
||||
pytest.skip(allow_module_level=True)
|
||||
|
||||
|
||||
def test_crew(n_iterations: int, model: str) -> None:
|
||||
"""
|
||||
Test the crew by running a command in the Poetry environment.
|
||||
|
||||
Args:
|
||||
n_iterations (int): The number of iterations to test the crew.
|
||||
model (str): The model to test the crew with.
|
||||
"""
|
||||
command = ["poetry", "run", "test", str(n_iterations), model]
|
||||
|
||||
try:
|
||||
if n_iterations <= 0:
|
||||
raise ValueError("The number of iterations must be a positive integer.")
|
||||
|
||||
result = subprocess.run(command, capture_output=False, text=True, check=True)
|
||||
|
||||
if result.stderr:
|
||||
click.echo(result.stderr, err=True)
|
||||
|
||||
except subprocess.CalledProcessError as e:
|
||||
click.echo(f"An error occurred while testing the crew: {e}", err=True)
|
||||
click.echo(e.output, err=True)
|
||||
|
||||
except Exception as e:
|
||||
click.echo(f"An unexpected error occurred: {e}", err=True)
|
||||
@@ -32,8 +32,10 @@ from crewai.tasks.conditional_task import ConditionalTask
|
||||
from crewai.tasks.task_output import TaskOutput
|
||||
from crewai.telemetry import Telemetry
|
||||
from crewai.tools.agent_tools import AgentTools
|
||||
from crewai.types.usage_metrics import UsageMetrics
|
||||
from crewai.utilities import I18N, FileHandler, Logger, RPMController
|
||||
from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
|
||||
from crewai.utilities.evaluators.crew_evaluator_handler import CrewEvaluator
|
||||
from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
|
||||
from crewai.utilities.formatter import (
|
||||
aggregate_raw_outputs_from_task_outputs,
|
||||
@@ -96,7 +98,7 @@ class Crew(BaseModel):
|
||||
default_factory=TaskOutputStorageHandler
|
||||
)
|
||||
|
||||
name: Optional[str] = Field(default="")
|
||||
name: Optional[str] = Field(default=None)
|
||||
cache: bool = Field(default=True)
|
||||
model_config = ConfigDict(arbitrary_types_allowed=True)
|
||||
tasks: List[Task] = Field(default_factory=list)
|
||||
@@ -111,7 +113,7 @@ class Crew(BaseModel):
|
||||
default={"provider": "openai"},
|
||||
description="Configuration for the embedder to be used for the crew.",
|
||||
)
|
||||
usage_metrics: Optional[dict] = Field(
|
||||
usage_metrics: Optional[UsageMetrics] = Field(
|
||||
default=None,
|
||||
description="Metrics for the LLM usage during all tasks execution.",
|
||||
)
|
||||
@@ -148,13 +150,17 @@ class Crew(BaseModel):
|
||||
description="Path to the prompt json file to be used for the crew.",
|
||||
)
|
||||
output_log_file: Optional[str] = Field(
|
||||
default="",
|
||||
default=None,
|
||||
description="output_log_file",
|
||||
)
|
||||
planning: Optional[bool] = Field(
|
||||
default=False,
|
||||
description="Plan the crew execution and add the plan to the crew.",
|
||||
)
|
||||
planning_llm: Optional[Any] = Field(
|
||||
default=None,
|
||||
description="Language model that will run the AgentPlanner if planning is True.",
|
||||
)
|
||||
task_execution_output_json_files: Optional[List[str]] = Field(
|
||||
default=None,
|
||||
description="List of file paths for task execution JSON files.",
|
||||
@@ -267,20 +273,6 @@ class Crew(BaseModel):
|
||||
|
||||
return self
|
||||
|
||||
@model_validator(mode="after")
|
||||
def check_tasks_in_hierarchical_process_not_async(self):
|
||||
"""Validates that the tasks in hierarchical process are not flagged with async_execution."""
|
||||
if self.process == Process.hierarchical:
|
||||
for task in self.tasks:
|
||||
if task.async_execution:
|
||||
raise PydanticCustomError(
|
||||
"async_execution_in_hierarchical_process",
|
||||
"Hierarchical process error: Tasks cannot be flagged with async_execution.",
|
||||
{},
|
||||
)
|
||||
|
||||
return self
|
||||
|
||||
@model_validator(mode="after")
|
||||
def validate_end_with_at_most_one_async_task(self):
|
||||
"""Validates that the crew ends with at most one asynchronous task."""
|
||||
@@ -463,7 +455,7 @@ class Crew(BaseModel):
|
||||
if self.planning:
|
||||
self._handle_crew_planning()
|
||||
|
||||
metrics = []
|
||||
metrics: List[UsageMetrics] = []
|
||||
|
||||
if self.process == Process.sequential:
|
||||
result = self._run_sequential_process()
|
||||
@@ -473,11 +465,12 @@ class Crew(BaseModel):
|
||||
raise NotImplementedError(
|
||||
f"The process '{self.process}' is not implemented yet."
|
||||
)
|
||||
|
||||
metrics += [agent._token_process.get_summary() for agent in self.agents]
|
||||
|
||||
self.usage_metrics = {
|
||||
key: sum([m[key] for m in metrics if m is not None]) for key in metrics[0]
|
||||
}
|
||||
self.usage_metrics = UsageMetrics()
|
||||
for metric in metrics:
|
||||
self.usage_metrics.add_usage_metrics(metric)
|
||||
|
||||
return result
|
||||
|
||||
@@ -486,12 +479,7 @@ class Crew(BaseModel):
|
||||
results: List[CrewOutput] = []
|
||||
|
||||
# Initialize the parent crew's usage metrics
|
||||
total_usage_metrics = {
|
||||
"total_tokens": 0,
|
||||
"prompt_tokens": 0,
|
||||
"completion_tokens": 0,
|
||||
"successful_requests": 0,
|
||||
}
|
||||
total_usage_metrics = UsageMetrics()
|
||||
|
||||
for input_data in inputs:
|
||||
crew = self.copy()
|
||||
@@ -499,8 +487,7 @@ class Crew(BaseModel):
|
||||
output = crew.kickoff(inputs=input_data)
|
||||
|
||||
if crew.usage_metrics:
|
||||
for key in total_usage_metrics:
|
||||
total_usage_metrics[key] += crew.usage_metrics.get(key, 0)
|
||||
total_usage_metrics.add_usage_metrics(crew.usage_metrics)
|
||||
|
||||
results.append(output)
|
||||
|
||||
@@ -529,29 +516,10 @@ class Crew(BaseModel):
|
||||
|
||||
results = await asyncio.gather(*tasks)
|
||||
|
||||
total_usage_metrics = {
|
||||
"total_tokens": 0,
|
||||
"prompt_tokens": 0,
|
||||
"completion_tokens": 0,
|
||||
"successful_requests": 0,
|
||||
}
|
||||
total_usage_metrics = UsageMetrics()
|
||||
for crew in crew_copies:
|
||||
if crew.usage_metrics:
|
||||
for key in total_usage_metrics:
|
||||
total_usage_metrics[key] += crew.usage_metrics.get(key, 0)
|
||||
|
||||
self.usage_metrics = total_usage_metrics
|
||||
|
||||
total_usage_metrics = {
|
||||
"total_tokens": 0,
|
||||
"prompt_tokens": 0,
|
||||
"completion_tokens": 0,
|
||||
"successful_requests": 0,
|
||||
}
|
||||
for crew in crew_copies:
|
||||
if crew.usage_metrics:
|
||||
for key in total_usage_metrics:
|
||||
total_usage_metrics[key] += crew.usage_metrics.get(key, 0)
|
||||
total_usage_metrics.add_usage_metrics(crew.usage_metrics)
|
||||
|
||||
self.usage_metrics = total_usage_metrics
|
||||
self._task_output_handler.reset()
|
||||
@@ -560,15 +528,12 @@ class Crew(BaseModel):
|
||||
def _handle_crew_planning(self):
|
||||
"""Handles the Crew planning."""
|
||||
self._logger.log("info", "Planning the crew execution")
|
||||
result = CrewPlanner(self.tasks)._handle_crew_planning()
|
||||
result = CrewPlanner(
|
||||
tasks=self.tasks, planning_agent_llm=self.planning_llm
|
||||
)._handle_crew_planning()
|
||||
|
||||
if result is not None and hasattr(result, "list_of_plans_per_task"):
|
||||
for task, step_plan in zip(self.tasks, result.list_of_plans_per_task):
|
||||
task.description += step_plan
|
||||
else:
|
||||
self._logger.log(
|
||||
"info", "Something went wrong with the planning process of the Crew"
|
||||
)
|
||||
for task, step_plan in zip(self.tasks, result.list_of_plans_per_task):
|
||||
task.description += step_plan
|
||||
|
||||
def _store_execution_log(
|
||||
self,
|
||||
@@ -606,7 +571,7 @@ class Crew(BaseModel):
|
||||
def _run_hierarchical_process(self) -> CrewOutput:
|
||||
"""Creates and assigns a manager agent to make sure the crew completes the tasks."""
|
||||
self._create_manager_agent()
|
||||
return self._execute_tasks(self.tasks, self.manager_agent)
|
||||
return self._execute_tasks(self.tasks)
|
||||
|
||||
def _create_manager_agent(self):
|
||||
i18n = I18N(prompt_file=self.prompt_file)
|
||||
@@ -630,7 +595,6 @@ class Crew(BaseModel):
|
||||
def _execute_tasks(
|
||||
self,
|
||||
tasks: List[Task],
|
||||
manager: Optional[BaseAgent] = None,
|
||||
start_index: Optional[int] = 0,
|
||||
was_replayed: bool = False,
|
||||
) -> CrewOutput:
|
||||
@@ -658,13 +622,13 @@ class Crew(BaseModel):
|
||||
last_sync_output = task.output
|
||||
continue
|
||||
|
||||
agent_to_use = self._get_agent_to_use(task, manager)
|
||||
agent_to_use = self._get_agent_to_use(task)
|
||||
if agent_to_use is None:
|
||||
raise ValueError(
|
||||
f"No agent available for task: {task.description}. Ensure that either the task has an assigned agent or a manager agent is provided."
|
||||
)
|
||||
|
||||
self._prepare_agent_tools(task, manager)
|
||||
self._prepare_agent_tools(task)
|
||||
self._log_task_start(task, agent_to_use.role)
|
||||
|
||||
if isinstance(task, ConditionalTask):
|
||||
@@ -730,20 +694,18 @@ class Crew(BaseModel):
|
||||
return skipped_task_output
|
||||
return None
|
||||
|
||||
def _prepare_agent_tools(self, task: Task, manager: Optional[BaseAgent]):
|
||||
def _prepare_agent_tools(self, task: Task):
|
||||
if self.process == Process.hierarchical:
|
||||
if manager:
|
||||
self._update_manager_tools(task, manager)
|
||||
if self.manager_agent:
|
||||
self._update_manager_tools(task)
|
||||
else:
|
||||
raise ValueError("Manager agent is required for hierarchical process.")
|
||||
elif task.agent and task.agent.allow_delegation:
|
||||
self._add_delegation_tools(task)
|
||||
|
||||
def _get_agent_to_use(
|
||||
self, task: Task, manager: Optional[BaseAgent]
|
||||
) -> Optional[BaseAgent]:
|
||||
def _get_agent_to_use(self, task: Task) -> Optional[BaseAgent]:
|
||||
if self.process == Process.hierarchical:
|
||||
return manager
|
||||
return self.manager_agent
|
||||
return task.agent
|
||||
|
||||
def _add_delegation_tools(self, task: Task):
|
||||
@@ -779,11 +741,14 @@ class Crew(BaseModel):
|
||||
if self.output_log_file:
|
||||
self._file_handler.log(agent=role, task=task.description, status="started")
|
||||
|
||||
def _update_manager_tools(self, task: Task, manager: BaseAgent):
|
||||
if task.agent:
|
||||
manager.tools = task.agent.get_delegation_tools([task.agent])
|
||||
else:
|
||||
manager.tools = manager.get_delegation_tools(self.agents)
|
||||
def _update_manager_tools(self, task: Task):
|
||||
if self.manager_agent:
|
||||
if task.agent:
|
||||
self.manager_agent.tools = task.agent.get_delegation_tools([task.agent])
|
||||
else:
|
||||
self.manager_agent.tools = self.manager_agent.get_delegation_tools(
|
||||
self.agents
|
||||
)
|
||||
|
||||
def _get_context(self, task: Task, task_outputs: List[TaskOutput]):
|
||||
context = (
|
||||
@@ -882,7 +847,7 @@ class Crew(BaseModel):
|
||||
self.tasks[i].output = task_output
|
||||
|
||||
self._logging_color = "bold_blue"
|
||||
result = self._execute_tasks(self.tasks, self.manager_agent, start_index, True)
|
||||
result = self._execute_tasks(self.tasks, start_index, True)
|
||||
return result
|
||||
|
||||
def copy(self):
|
||||
@@ -945,28 +910,36 @@ class Crew(BaseModel):
|
||||
)
|
||||
self._telemetry.end_crew(self, final_string_output)
|
||||
|
||||
def calculate_usage_metrics(self) -> Dict[str, int]:
|
||||
def calculate_usage_metrics(self) -> UsageMetrics:
|
||||
"""Calculates and returns the usage metrics."""
|
||||
total_usage_metrics = {
|
||||
"total_tokens": 0,
|
||||
"prompt_tokens": 0,
|
||||
"completion_tokens": 0,
|
||||
"successful_requests": 0,
|
||||
}
|
||||
total_usage_metrics = UsageMetrics()
|
||||
|
||||
for agent in self.agents:
|
||||
if hasattr(agent, "_token_process"):
|
||||
token_sum = agent._token_process.get_summary()
|
||||
for key in total_usage_metrics:
|
||||
total_usage_metrics[key] += token_sum.get(key, 0)
|
||||
total_usage_metrics.add_usage_metrics(token_sum)
|
||||
|
||||
if self.manager_agent and hasattr(self.manager_agent, "_token_process"):
|
||||
token_sum = self.manager_agent._token_process.get_summary()
|
||||
for key in total_usage_metrics:
|
||||
total_usage_metrics[key] += token_sum.get(key, 0)
|
||||
total_usage_metrics.add_usage_metrics(token_sum)
|
||||
|
||||
return total_usage_metrics
|
||||
|
||||
def test(
|
||||
self,
|
||||
n_iterations: int,
|
||||
openai_model_name: str,
|
||||
inputs: Optional[Dict[str, Any]] = None,
|
||||
) -> None:
|
||||
"""Test and evaluate the Crew with the given inputs for n iterations."""
|
||||
evaluator = CrewEvaluator(self, openai_model_name)
|
||||
|
||||
for i in range(1, n_iterations + 1):
|
||||
evaluator.set_iteration(i)
|
||||
self.kickoff(inputs=inputs)
|
||||
|
||||
evaluator.print_crew_evaluation_result()
|
||||
|
||||
def __rshift__(self, other: "Crew") -> "Pipeline":
|
||||
"""
|
||||
Implements the >> operator to add another Crew to an existing Pipeline.
|
||||
|
||||
@@ -5,6 +5,7 @@ from pydantic import BaseModel, Field
|
||||
|
||||
from crewai.tasks.output_format import OutputFormat
|
||||
from crewai.tasks.task_output import TaskOutput
|
||||
from crewai.types.usage_metrics import UsageMetrics
|
||||
|
||||
|
||||
class CrewOutput(BaseModel):
|
||||
@@ -20,9 +21,7 @@ class CrewOutput(BaseModel):
|
||||
tasks_output: list[TaskOutput] = Field(
|
||||
description="Output of each task", default=[]
|
||||
)
|
||||
token_usage: Dict[str, Any] = Field(
|
||||
description="Processed token summary", default={}
|
||||
)
|
||||
token_usage: UsageMetrics = Field(description="Processed token summary", default={})
|
||||
|
||||
@property
|
||||
def json(self) -> Optional[str]:
|
||||
|
||||
@@ -19,7 +19,7 @@ class ShortTermMemory(Memory):
|
||||
super().__init__(storage)
|
||||
|
||||
def save(self, item: ShortTermMemoryItem) -> None:
|
||||
super().save(item.data, item.metadata, item.agent)
|
||||
super().save(value=item.data, metadata=item.metadata, agent=item.agent)
|
||||
|
||||
def search(self, query: str, score_threshold: float = 0.35):
|
||||
return self.storage.search(query=query, score_threshold=score_threshold) # type: ignore # BUG? The reference is to the parent class, but the parent class does not have this parameters
|
||||
|
||||
@@ -10,19 +10,53 @@ from crewai.crew import Crew
|
||||
from crewai.crews.crew_output import CrewOutput
|
||||
from crewai.pipeline.pipeline_run_result import PipelineRunResult
|
||||
from crewai.types.pipeline_stage import PipelineStage
|
||||
from crewai.types.usage_metrics import UsageMetrics
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from crewai.routers.pipeline_router import PipelineRouter
|
||||
|
||||
Trace = Union[Union[str, Dict[str, Any]], List[Union[str, Dict[str, Any]]]]
|
||||
|
||||
|
||||
"""
|
||||
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 runs concurrently.
|
||||
|
||||
Core Loop Explanation:
|
||||
1. The `process_runs` method processes multiple runs in parallel, each going through
|
||||
all pipeline stages.
|
||||
2. The `process_single_run` method handles the processing of a single run 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_run_results` method constructs the final results of the
|
||||
pipeline run, 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 run.
|
||||
In this case, we do not merge the results, as each result needs to be included
|
||||
separately in its own pipeline run 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.
|
||||
Run: 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.
|
||||
- Pipeline: The overall structure that defines a sequence of operations.
|
||||
- Stage: A distinct part of the pipeline, which can be either sequential or parallel.
|
||||
- Run: 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
|
||||
@@ -56,6 +90,15 @@ class Pipeline(BaseModel):
|
||||
@model_validator(mode="before")
|
||||
@classmethod
|
||||
def validate_stages(cls, values):
|
||||
"""
|
||||
Validates the stages to ensure correct nesting and types.
|
||||
|
||||
Args:
|
||||
values (dict): Dictionary containing the pipeline stages.
|
||||
|
||||
Returns:
|
||||
dict: Validated stages.
|
||||
"""
|
||||
stages = values.get("stages", [])
|
||||
|
||||
def check_nesting_and_type(item, depth=0):
|
||||
@@ -77,9 +120,15 @@ class Pipeline(BaseModel):
|
||||
self, run_inputs: List[Dict[str, Any]]
|
||||
) -> List[PipelineRunResult]:
|
||||
"""
|
||||
Process multiple runs in parallel, with each run going through all stages.
|
||||
Processes multiple runs in parallel, each going through all pipeline stages.
|
||||
|
||||
Args:
|
||||
run_inputs (List[Dict[str, Any]]): List of inputs for each run.
|
||||
|
||||
Returns:
|
||||
List[PipelineRunResult]: List of results from each run.
|
||||
"""
|
||||
pipeline_results = []
|
||||
pipeline_results: List[PipelineRunResult] = []
|
||||
|
||||
# Process all runs in parallel
|
||||
all_run_results = await asyncio.gather(
|
||||
@@ -96,9 +145,18 @@ class Pipeline(BaseModel):
|
||||
async def process_single_run(
|
||||
self, run_input: Dict[str, Any]
|
||||
) -> List[PipelineRunResult]:
|
||||
"""
|
||||
Processes a single run through all pipeline stages.
|
||||
|
||||
Args:
|
||||
run_input (Dict[str, Any]): The input for the run.
|
||||
|
||||
Returns:
|
||||
List[PipelineRunResult]: The results of processing the run.
|
||||
"""
|
||||
initial_input = copy.deepcopy(run_input)
|
||||
current_input = copy.deepcopy(run_input)
|
||||
usage_metrics = {}
|
||||
pipeline_usage_metrics: Dict[str, UsageMetrics] = {}
|
||||
all_stage_outputs: List[List[CrewOutput]] = []
|
||||
traces: List[List[Union[str, Dict[str, Any]]]] = [[initial_input]]
|
||||
|
||||
@@ -121,19 +179,29 @@ class Pipeline(BaseModel):
|
||||
stage_outputs, stage_trace = await self._process_stage(stage, stage_input)
|
||||
|
||||
self._update_metrics_and_input(
|
||||
usage_metrics, current_input, stage, stage_outputs
|
||||
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_run_results(
|
||||
all_stage_outputs, traces, usage_metrics
|
||||
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):
|
||||
@@ -154,12 +222,32 @@ class Pipeline(BaseModel):
|
||||
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]
|
||||
)
|
||||
@@ -167,11 +255,20 @@ class Pipeline(BaseModel):
|
||||
|
||||
def _update_metrics_and_input(
|
||||
self,
|
||||
usage_metrics: Dict[str, Any],
|
||||
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())
|
||||
@@ -186,8 +283,19 @@ class Pipeline(BaseModel):
|
||||
self,
|
||||
all_stage_outputs: List[List[CrewOutput]],
|
||||
traces: List[List[Union[str, Dict[str, Any]]]],
|
||||
token_usage: Dict[str, Any],
|
||||
token_usage: Dict[str, UsageMetrics],
|
||||
) -> List[PipelineRunResult]:
|
||||
"""
|
||||
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[PipelineRunResult]: The results of the pipeline run.
|
||||
"""
|
||||
formatted_traces = self._format_traces(traces)
|
||||
formatted_crew_outputs = self._format_crew_outputs(all_stage_outputs)
|
||||
|
||||
@@ -208,12 +316,51 @@ class Pipeline(BaseModel):
|
||||
def _format_traces(
|
||||
self, traces: List[List[Union[str, Dict[str, Any]]]]
|
||||
) -> List[List[Trace]]:
|
||||
formatted_traces: List[Trace] = []
|
||||
for trace in traces[:-1]:
|
||||
formatted_traces.append(trace[0] if len(trace) == 1 else 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]] = []
|
||||
final_trace = traces[-1]
|
||||
if len(final_trace) == 1:
|
||||
formatted_traces.append(final_trace[0])
|
||||
traces_to_return.append(formatted_traces)
|
||||
@@ -222,12 +369,20 @@ class Pipeline(BaseModel):
|
||||
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]
|
||||
|
||||
@@ -5,6 +5,7 @@ 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 PipelineRunResult(BaseModel):
|
||||
@@ -23,7 +24,7 @@ class PipelineRunResult(BaseModel):
|
||||
description="JSON dict output of the pipeline run", default={}
|
||||
)
|
||||
|
||||
token_usage: Dict[str, Any] = Field(
|
||||
token_usage: Dict[str, UsageMetrics] = Field(
|
||||
description="Token usage for each crew in the run"
|
||||
)
|
||||
trace: List[Any] = Field(
|
||||
|
||||
@@ -1,2 +1,25 @@
|
||||
from .annotations import agent, crew, task
|
||||
from .annotations import (
|
||||
agent,
|
||||
crew,
|
||||
task,
|
||||
output_json,
|
||||
output_pydantic,
|
||||
tool,
|
||||
callback,
|
||||
llm,
|
||||
cache_handler,
|
||||
)
|
||||
from .crew_base import CrewBase
|
||||
|
||||
__all__ = [
|
||||
"agent",
|
||||
"crew",
|
||||
"task",
|
||||
"output_json",
|
||||
"output_pydantic",
|
||||
"tool",
|
||||
"callback",
|
||||
"CrewBase",
|
||||
"llm",
|
||||
"cache_handler",
|
||||
]
|
||||
|
||||
@@ -30,6 +30,37 @@ def agent(func):
|
||||
return func
|
||||
|
||||
|
||||
def llm(func):
|
||||
func.is_llm = True
|
||||
func = memoize(func)
|
||||
return func
|
||||
|
||||
|
||||
def output_json(cls):
|
||||
cls.is_output_json = True
|
||||
return cls
|
||||
|
||||
|
||||
def output_pydantic(cls):
|
||||
cls.is_output_pydantic = True
|
||||
return cls
|
||||
|
||||
|
||||
def tool(func):
|
||||
func.is_tool = True
|
||||
return memoize(func)
|
||||
|
||||
|
||||
def callback(func):
|
||||
func.is_callback = True
|
||||
return memoize(func)
|
||||
|
||||
|
||||
def cache_handler(func):
|
||||
func.is_cache_handler = True
|
||||
return memoize(func)
|
||||
|
||||
|
||||
def crew(func):
|
||||
def wrapper(self, *args, **kwargs):
|
||||
instantiated_tasks = []
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
import inspect
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import Any, Callable, Dict
|
||||
|
||||
import yaml
|
||||
from dotenv import load_dotenv
|
||||
@@ -20,11 +21,6 @@ def CrewBase(cls):
|
||||
base_directory = Path(frame_info.filename).parent.resolve()
|
||||
break
|
||||
|
||||
if base_directory is None:
|
||||
raise Exception(
|
||||
"Unable to dynamically determine the project's base directory, you must run it from the project's root directory."
|
||||
)
|
||||
|
||||
original_agents_config_path = getattr(
|
||||
cls, "agents_config", "config/agents.yaml"
|
||||
)
|
||||
@@ -32,12 +28,20 @@ def CrewBase(cls):
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
if self.base_directory is None:
|
||||
raise Exception(
|
||||
"Unable to dynamically determine the project's base directory, you must run it from the project's root directory."
|
||||
)
|
||||
|
||||
self.agents_config = self.load_yaml(
|
||||
os.path.join(self.base_directory, self.original_agents_config_path)
|
||||
)
|
||||
self.tasks_config = self.load_yaml(
|
||||
os.path.join(self.base_directory, self.original_tasks_config_path)
|
||||
)
|
||||
self.map_all_agent_variables()
|
||||
self.map_all_task_variables()
|
||||
|
||||
@staticmethod
|
||||
def load_yaml(config_path: str):
|
||||
@@ -45,4 +49,138 @@ def CrewBase(cls):
|
||||
# parsedContent = YamlParser.parse(file) # type: ignore # Argument 1 to "parse" has incompatible type "TextIOWrapper"; expected "YamlParser"
|
||||
return yaml.safe_load(file)
|
||||
|
||||
def _get_all_functions(self):
|
||||
return {
|
||||
name: getattr(self, name)
|
||||
for name in dir(self)
|
||||
if callable(getattr(self, name))
|
||||
}
|
||||
|
||||
def _filter_functions(
|
||||
self, functions: Dict[str, Callable], attribute: str
|
||||
) -> Dict[str, Callable]:
|
||||
return {
|
||||
name: func
|
||||
for name, func in functions.items()
|
||||
if hasattr(func, attribute)
|
||||
}
|
||||
|
||||
def map_all_agent_variables(self) -> None:
|
||||
all_functions = self._get_all_functions()
|
||||
llms = self._filter_functions(all_functions, "is_llm")
|
||||
tool_functions = self._filter_functions(all_functions, "is_tool")
|
||||
cache_handler_functions = self._filter_functions(
|
||||
all_functions, "is_cache_handler"
|
||||
)
|
||||
callbacks = self._filter_functions(all_functions, "is_callback")
|
||||
agents = self._filter_functions(all_functions, "is_agent")
|
||||
|
||||
for agent_name, agent_info in self.agents_config.items():
|
||||
self._map_agent_variables(
|
||||
agent_name,
|
||||
agent_info,
|
||||
agents,
|
||||
llms,
|
||||
tool_functions,
|
||||
cache_handler_functions,
|
||||
callbacks,
|
||||
)
|
||||
|
||||
def _map_agent_variables(
|
||||
self,
|
||||
agent_name: str,
|
||||
agent_info: Dict[str, Any],
|
||||
agents: Dict[str, Callable],
|
||||
llms: Dict[str, Callable],
|
||||
tool_functions: Dict[str, Callable],
|
||||
cache_handler_functions: Dict[str, Callable],
|
||||
callbacks: Dict[str, Callable],
|
||||
) -> None:
|
||||
if llm := agent_info.get("llm"):
|
||||
self.agents_config[agent_name]["llm"] = llms[llm]()
|
||||
|
||||
if tools := agent_info.get("tools"):
|
||||
self.agents_config[agent_name]["tools"] = [
|
||||
tool_functions[tool]() for tool in tools
|
||||
]
|
||||
|
||||
if function_calling_llm := agent_info.get("function_calling_llm"):
|
||||
self.agents_config[agent_name]["function_calling_llm"] = agents[
|
||||
function_calling_llm
|
||||
]()
|
||||
|
||||
if step_callback := agent_info.get("step_callback"):
|
||||
self.agents_config[agent_name]["step_callback"] = callbacks[
|
||||
step_callback
|
||||
]()
|
||||
|
||||
if cache_handler := agent_info.get("cache_handler"):
|
||||
self.agents_config[agent_name]["cache_handler"] = (
|
||||
cache_handler_functions[cache_handler]()
|
||||
)
|
||||
|
||||
def map_all_task_variables(self) -> None:
|
||||
all_functions = self._get_all_functions()
|
||||
agents = self._filter_functions(all_functions, "is_agent")
|
||||
tasks = self._filter_functions(all_functions, "is_task")
|
||||
output_json_functions = self._filter_functions(
|
||||
all_functions, "is_output_json"
|
||||
)
|
||||
tool_functions = self._filter_functions(all_functions, "is_tool")
|
||||
callback_functions = self._filter_functions(all_functions, "is_callback")
|
||||
output_pydantic_functions = self._filter_functions(
|
||||
all_functions, "is_output_pydantic"
|
||||
)
|
||||
|
||||
for task_name, task_info in self.tasks_config.items():
|
||||
self._map_task_variables(
|
||||
task_name,
|
||||
task_info,
|
||||
agents,
|
||||
tasks,
|
||||
output_json_functions,
|
||||
tool_functions,
|
||||
callback_functions,
|
||||
output_pydantic_functions,
|
||||
)
|
||||
|
||||
def _map_task_variables(
|
||||
self,
|
||||
task_name: str,
|
||||
task_info: Dict[str, Any],
|
||||
agents: Dict[str, Callable],
|
||||
tasks: Dict[str, Callable],
|
||||
output_json_functions: Dict[str, Callable],
|
||||
tool_functions: Dict[str, Callable],
|
||||
callback_functions: Dict[str, Callable],
|
||||
output_pydantic_functions: Dict[str, Callable],
|
||||
) -> None:
|
||||
if context_list := task_info.get("context"):
|
||||
self.tasks_config[task_name]["context"] = [
|
||||
tasks[context_task_name]() for context_task_name in context_list
|
||||
]
|
||||
|
||||
if tools := task_info.get("tools"):
|
||||
self.tasks_config[task_name]["tools"] = [
|
||||
tool_functions[tool]() for tool in tools
|
||||
]
|
||||
|
||||
if agent_name := task_info.get("agent"):
|
||||
self.tasks_config[task_name]["agent"] = agents[agent_name]()
|
||||
|
||||
if output_json := task_info.get("output_json"):
|
||||
self.tasks_config[task_name]["output_json"] = output_json_functions[
|
||||
output_json
|
||||
]
|
||||
|
||||
if output_pydantic := task_info.get("output_pydantic"):
|
||||
self.tasks_config[task_name]["output_pydantic"] = (
|
||||
output_pydantic_functions[output_pydantic]
|
||||
)
|
||||
|
||||
if callbacks := task_info.get("callbacks"):
|
||||
self.tasks_config[task_name]["callbacks"] = [
|
||||
callback_functions[callback]() for callback in callbacks
|
||||
]
|
||||
|
||||
return WrappedClass
|
||||
|
||||
@@ -1,6 +1,5 @@
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import threading
|
||||
import uuid
|
||||
from concurrent.futures import Future
|
||||
@@ -8,7 +7,6 @@ from copy import copy
|
||||
from hashlib import md5
|
||||
from typing import Any, Dict, List, Optional, Tuple, Type, Union
|
||||
|
||||
from langchain_openai import ChatOpenAI
|
||||
from opentelemetry.trace import Span
|
||||
from pydantic import UUID4, BaseModel, Field, field_validator, model_validator
|
||||
from pydantic_core import PydanticCustomError
|
||||
@@ -17,10 +15,8 @@ from crewai.agents.agent_builder.base_agent import BaseAgent
|
||||
from crewai.tasks.output_format import OutputFormat
|
||||
from crewai.tasks.task_output import TaskOutput
|
||||
from crewai.telemetry.telemetry import Telemetry
|
||||
from crewai.utilities.converter import Converter, ConverterError
|
||||
from crewai.utilities.converter import Converter, convert_to_model
|
||||
from crewai.utilities.i18n import I18N
|
||||
from crewai.utilities.printer import Printer
|
||||
from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
|
||||
|
||||
|
||||
class Task(BaseModel):
|
||||
@@ -50,6 +46,7 @@ class Task(BaseModel):
|
||||
tools_errors: int = 0
|
||||
delegations: int = 0
|
||||
i18n: I18N = I18N()
|
||||
name: Optional[str] = Field(default=None)
|
||||
prompt_context: Optional[str] = None
|
||||
description: str = Field(description="Description of the actual task.")
|
||||
expected_output: str = Field(
|
||||
@@ -126,7 +123,7 @@ class Task(BaseModel):
|
||||
|
||||
@field_validator("output_file")
|
||||
@classmethod
|
||||
def output_file_validattion(cls, value: str) -> str:
|
||||
def output_file_validation(cls, value: str) -> str:
|
||||
"""Validate the output file path by removing the / from the beginning of the path."""
|
||||
if value.startswith("/"):
|
||||
return value[1:]
|
||||
@@ -254,9 +251,7 @@ class Task(BaseModel):
|
||||
content = (
|
||||
json_output
|
||||
if json_output
|
||||
else pydantic_output.model_dump_json()
|
||||
if pydantic_output
|
||||
else result
|
||||
else pydantic_output.model_dump_json() if pydantic_output else result
|
||||
)
|
||||
self._save_file(content)
|
||||
|
||||
@@ -326,18 +321,6 @@ class Task(BaseModel):
|
||||
|
||||
return copied_task
|
||||
|
||||
def _create_converter(self, *args, **kwargs) -> Converter:
|
||||
"""Create a converter instance."""
|
||||
if self.agent and not self.converter_cls:
|
||||
converter = self.agent.get_output_converter(*args, **kwargs)
|
||||
elif self.converter_cls:
|
||||
converter = self.converter_cls(*args, **kwargs)
|
||||
|
||||
if not converter:
|
||||
raise Exception("No output converter found or set.")
|
||||
|
||||
return converter
|
||||
|
||||
def _export_output(
|
||||
self, result: str
|
||||
) -> Tuple[Optional[BaseModel], Optional[Dict[str, Any]]]:
|
||||
@@ -345,75 +328,26 @@ class Task(BaseModel):
|
||||
json_output: Optional[Dict[str, Any]] = None
|
||||
|
||||
if self.output_pydantic or self.output_json:
|
||||
model_output = self._convert_to_model(result)
|
||||
pydantic_output = (
|
||||
model_output if isinstance(model_output, BaseModel) else None
|
||||
model_output = convert_to_model(
|
||||
result,
|
||||
self.output_pydantic,
|
||||
self.output_json,
|
||||
self.agent,
|
||||
self.converter_cls,
|
||||
)
|
||||
if isinstance(model_output, str):
|
||||
|
||||
if isinstance(model_output, BaseModel):
|
||||
pydantic_output = model_output
|
||||
elif isinstance(model_output, dict):
|
||||
json_output = model_output
|
||||
elif isinstance(model_output, str):
|
||||
try:
|
||||
json_output = json.loads(model_output)
|
||||
except json.JSONDecodeError:
|
||||
json_output = None
|
||||
else:
|
||||
json_output = model_output if isinstance(model_output, dict) else None
|
||||
|
||||
return pydantic_output, json_output
|
||||
|
||||
def _convert_to_model(self, result: str) -> Union[dict, BaseModel, str]:
|
||||
model = self.output_pydantic or self.output_json
|
||||
if model is None:
|
||||
return result
|
||||
|
||||
try:
|
||||
return self._validate_model(result, model)
|
||||
except Exception:
|
||||
return self._handle_partial_json(result, model)
|
||||
|
||||
def _validate_model(
|
||||
self, result: str, model: Type[BaseModel]
|
||||
) -> Union[dict, BaseModel]:
|
||||
exported_result = model.model_validate_json(result)
|
||||
if self.output_json:
|
||||
return exported_result.model_dump()
|
||||
return exported_result
|
||||
|
||||
def _handle_partial_json(
|
||||
self, result: str, model: Type[BaseModel]
|
||||
) -> Union[dict, BaseModel, str]:
|
||||
match = re.search(r"({.*})", result, re.DOTALL)
|
||||
if match:
|
||||
try:
|
||||
exported_result = model.model_validate_json(match.group(0))
|
||||
if self.output_json:
|
||||
return exported_result.model_dump()
|
||||
return exported_result
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return self._convert_with_instructions(result, model)
|
||||
|
||||
def _convert_with_instructions(
|
||||
self, result: str, model: Type[BaseModel]
|
||||
) -> Union[dict, BaseModel, str]:
|
||||
llm = self.agent.function_calling_llm or self.agent.llm # type: ignore # Item "None" of "BaseAgent | None" has no attribute "function_calling_llm"
|
||||
instructions = self._get_conversion_instructions(model, llm)
|
||||
|
||||
converter = self._create_converter(
|
||||
llm=llm, text=result, model=model, instructions=instructions
|
||||
)
|
||||
exported_result = (
|
||||
converter.to_pydantic() if self.output_pydantic else converter.to_json()
|
||||
)
|
||||
|
||||
if isinstance(exported_result, ConverterError):
|
||||
Printer().print(
|
||||
content=f"{exported_result.message} Using raw output instead.",
|
||||
color="red",
|
||||
)
|
||||
return result
|
||||
|
||||
return exported_result
|
||||
|
||||
def _get_output_format(self) -> OutputFormat:
|
||||
if self.output_json:
|
||||
return OutputFormat.JSON
|
||||
@@ -421,34 +355,22 @@ class Task(BaseModel):
|
||||
return OutputFormat.PYDANTIC
|
||||
return OutputFormat.RAW
|
||||
|
||||
def _get_conversion_instructions(self, model: Type[BaseModel], llm: Any) -> str:
|
||||
instructions = "I'm gonna convert this raw text into valid JSON."
|
||||
if not self._is_gpt(llm):
|
||||
model_schema = PydanticSchemaParser(model=model).get_schema()
|
||||
instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
|
||||
return instructions
|
||||
|
||||
def _save_output(self, content: str) -> None:
|
||||
if not self.output_file:
|
||||
raise Exception("Output file path is not set.")
|
||||
|
||||
directory = os.path.dirname(self.output_file)
|
||||
if directory and not os.path.exists(directory):
|
||||
os.makedirs(directory)
|
||||
with open(self.output_file, "w", encoding="utf-8") as file:
|
||||
file.write(content)
|
||||
|
||||
def _is_gpt(self, llm) -> bool:
|
||||
return isinstance(llm, ChatOpenAI) and llm.openai_api_base is None
|
||||
|
||||
def _save_file(self, result: Any) -> None:
|
||||
if self.output_file is None:
|
||||
raise ValueError("output_file is not set.")
|
||||
|
||||
directory = os.path.dirname(self.output_file) # type: ignore # Value of type variable "AnyOrLiteralStr" of "dirname" cannot be "str | None"
|
||||
|
||||
if directory and not os.path.exists(directory):
|
||||
os.makedirs(directory)
|
||||
|
||||
with open(self.output_file, "w", encoding="utf-8") as file: # type: ignore # Argument 1 to "open" has incompatible type "str | None"; expected "int | str | bytes | PathLike[str] | PathLike[bytes]"
|
||||
file.write(result)
|
||||
with open(self.output_file, "w", encoding="utf-8") as file:
|
||||
if isinstance(result, dict):
|
||||
import json
|
||||
|
||||
json.dump(result, file, ensure_ascii=False, indent=2)
|
||||
else:
|
||||
file.write(str(result))
|
||||
return None
|
||||
|
||||
def __repr__(self):
|
||||
|
||||
@@ -86,7 +86,8 @@ class ToolUsage:
|
||||
) -> str:
|
||||
if isinstance(calling, ToolUsageErrorException):
|
||||
error = calling.message
|
||||
self._printer.print(content=f"\n\n{error}\n", color="red")
|
||||
if self.agent.verbose:
|
||||
self._printer.print(content=f"\n\n{error}\n", color="red")
|
||||
self.task.increment_tools_errors()
|
||||
return error
|
||||
|
||||
@@ -96,7 +97,8 @@ class ToolUsage:
|
||||
except Exception as e:
|
||||
error = getattr(e, "message", str(e))
|
||||
self.task.increment_tools_errors()
|
||||
self._printer.print(content=f"\n\n{error}\n", color="red")
|
||||
if self.agent.verbose:
|
||||
self._printer.print(content=f"\n\n{error}\n", color="red")
|
||||
return error
|
||||
return f"{self._use(tool_string=tool_string, tool=tool, calling=calling)}" # type: ignore # BUG?: "_use" of "ToolUsage" does not return a value (it only ever returns None)
|
||||
|
||||
@@ -112,7 +114,8 @@ class ToolUsage:
|
||||
result = self._i18n.errors("task_repeated_usage").format(
|
||||
tool_names=self.tools_names
|
||||
)
|
||||
self._printer.print(content=f"\n\n{result}\n", color="purple")
|
||||
if self.agent.verbose:
|
||||
self._printer.print(content=f"\n\n{result}\n", color="purple")
|
||||
self._telemetry.tool_repeated_usage(
|
||||
llm=self.function_calling_llm,
|
||||
tool_name=tool.name,
|
||||
@@ -168,7 +171,10 @@ class ToolUsage:
|
||||
f'\n{error_message}.\nMoving on then. {self._i18n.slice("format").format(tool_names=self.tools_names)}'
|
||||
).message
|
||||
self.task.increment_tools_errors()
|
||||
self._printer.print(content=f"\n\n{error_message}\n", color="red")
|
||||
if self.agent.verbose:
|
||||
self._printer.print(
|
||||
content=f"\n\n{error_message}\n", color="red"
|
||||
)
|
||||
return error # type: ignore # No return value expected
|
||||
|
||||
self.task.increment_tools_errors()
|
||||
@@ -192,7 +198,8 @@ class ToolUsage:
|
||||
calling=calling, output=result, should_cache=should_cache
|
||||
)
|
||||
|
||||
self._printer.print(content=f"\n\n{result}\n", color="purple")
|
||||
if self.agent.verbose:
|
||||
self._printer.print(content=f"\n\n{result}\n", color="purple")
|
||||
if agentops:
|
||||
agentops.record(tool_event)
|
||||
self._telemetry.tool_usage(
|
||||
@@ -346,7 +353,8 @@ class ToolUsage:
|
||||
if self._run_attempts > self._max_parsing_attempts:
|
||||
self._telemetry.tool_usage_error(llm=self.function_calling_llm)
|
||||
self.task.increment_tools_errors()
|
||||
self._printer.print(content=f"\n\n{e}\n", color="red")
|
||||
if self.agent.verbose:
|
||||
self._printer.print(content=f"\n\n{e}\n", color="red")
|
||||
return ToolUsageErrorException( # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
|
||||
f'{self._i18n.errors("tool_usage_error").format(error=e)}\nMoving on then. {self._i18n.slice("format").format(tool_names=self.tools_names)}'
|
||||
)
|
||||
|
||||
36
src/crewai/types/usage_metrics.py
Normal file
36
src/crewai/types/usage_metrics.py
Normal file
@@ -0,0 +1,36 @@
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class UsageMetrics(BaseModel):
|
||||
"""
|
||||
Model to track usage metrics for the crew's execution.
|
||||
|
||||
Attributes:
|
||||
total_tokens: Total number of tokens used.
|
||||
prompt_tokens: Number of tokens used in prompts.
|
||||
completion_tokens: Number of tokens used in completions.
|
||||
successful_requests: Number of successful requests made.
|
||||
"""
|
||||
|
||||
total_tokens: int = Field(default=0, description="Total number of tokens used.")
|
||||
prompt_tokens: int = Field(
|
||||
default=0, description="Number of tokens used in prompts."
|
||||
)
|
||||
completion_tokens: int = Field(
|
||||
default=0, description="Number of tokens used in completions."
|
||||
)
|
||||
successful_requests: int = Field(
|
||||
default=0, description="Number of successful requests made."
|
||||
)
|
||||
|
||||
def add_usage_metrics(self, usage_metrics: "UsageMetrics"):
|
||||
"""
|
||||
Add the usage metrics from another UsageMetrics object.
|
||||
|
||||
Args:
|
||||
usage_metrics (UsageMetrics): The usage metrics to add.
|
||||
"""
|
||||
self.total_tokens += usage_metrics.total_tokens
|
||||
self.prompt_tokens += usage_metrics.prompt_tokens
|
||||
self.completion_tokens += usage_metrics.completion_tokens
|
||||
self.successful_requests += usage_metrics.successful_requests
|
||||
@@ -1,9 +1,14 @@
|
||||
import json
|
||||
import re
|
||||
from typing import Any, Optional, Type, Union
|
||||
|
||||
from langchain.schema import HumanMessage, SystemMessage
|
||||
from langchain_openai import ChatOpenAI
|
||||
from pydantic import BaseModel, ValidationError
|
||||
|
||||
from crewai.agents.agent_builder.utilities.base_output_converter import OutputConverter
|
||||
from crewai.utilities.printer import Printer
|
||||
from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
|
||||
|
||||
|
||||
class ConverterError(Exception):
|
||||
@@ -72,3 +77,153 @@ class Converter(OutputConverter):
|
||||
def is_gpt(self) -> bool:
|
||||
"""Return if llm provided is of gpt from openai."""
|
||||
return isinstance(self.llm, ChatOpenAI) and self.llm.openai_api_base is None
|
||||
|
||||
|
||||
def convert_to_model(
|
||||
result: str,
|
||||
output_pydantic: Optional[Type[BaseModel]],
|
||||
output_json: Optional[Type[BaseModel]],
|
||||
agent: Any,
|
||||
converter_cls: Optional[Type[Converter]] = None,
|
||||
) -> Union[dict, BaseModel, str]:
|
||||
model = output_pydantic or output_json
|
||||
if model is None:
|
||||
return result
|
||||
|
||||
try:
|
||||
escaped_result = json.dumps(json.loads(result, strict=False))
|
||||
return validate_model(escaped_result, model, bool(output_json))
|
||||
except json.JSONDecodeError as e:
|
||||
Printer().print(
|
||||
content=f"Error parsing JSON: {e}. Attempting to handle partial JSON.",
|
||||
color="yellow",
|
||||
)
|
||||
return handle_partial_json(
|
||||
result, model, bool(output_json), agent, converter_cls
|
||||
)
|
||||
except ValidationError as e:
|
||||
Printer().print(
|
||||
content=f"Pydantic validation error: {e}. Attempting to handle partial JSON.",
|
||||
color="yellow",
|
||||
)
|
||||
return handle_partial_json(
|
||||
result, model, bool(output_json), agent, converter_cls
|
||||
)
|
||||
except Exception as e:
|
||||
Printer().print(
|
||||
content=f"Unexpected error during model conversion: {type(e).__name__}: {e}. Returning original result.",
|
||||
color="red",
|
||||
)
|
||||
return result
|
||||
|
||||
|
||||
def validate_model(
|
||||
result: str, model: Type[BaseModel], is_json_output: bool
|
||||
) -> Union[dict, BaseModel]:
|
||||
exported_result = model.model_validate_json(result)
|
||||
if is_json_output:
|
||||
return exported_result.model_dump()
|
||||
return exported_result
|
||||
|
||||
|
||||
def handle_partial_json(
|
||||
result: str,
|
||||
model: Type[BaseModel],
|
||||
is_json_output: bool,
|
||||
agent: Any,
|
||||
converter_cls: Optional[Type[Converter]] = None,
|
||||
) -> Union[dict, BaseModel, str]:
|
||||
match = re.search(r"({.*})", result, re.DOTALL)
|
||||
if match:
|
||||
try:
|
||||
exported_result = model.model_validate_json(match.group(0))
|
||||
if is_json_output:
|
||||
return exported_result.model_dump()
|
||||
return exported_result
|
||||
except json.JSONDecodeError as e:
|
||||
Printer().print(
|
||||
content=f"Error parsing JSON: {e}. The extracted JSON-like string is not valid JSON. Attempting alternative conversion method.",
|
||||
color="yellow",
|
||||
)
|
||||
except ValidationError as e:
|
||||
Printer().print(
|
||||
content=f"Pydantic validation error: {e}. The JSON structure doesn't match the expected model. Attempting alternative conversion method.",
|
||||
color="yellow",
|
||||
)
|
||||
except Exception as e:
|
||||
Printer().print(
|
||||
content=f"Unexpected error during partial JSON handling: {type(e).__name__}: {e}. Attempting alternative conversion method.",
|
||||
color="red",
|
||||
)
|
||||
|
||||
return convert_with_instructions(
|
||||
result, model, is_json_output, agent, converter_cls
|
||||
)
|
||||
|
||||
|
||||
def convert_with_instructions(
|
||||
result: str,
|
||||
model: Type[BaseModel],
|
||||
is_json_output: bool,
|
||||
agent: Any,
|
||||
converter_cls: Optional[Type[Converter]] = None,
|
||||
) -> Union[dict, BaseModel, str]:
|
||||
llm = agent.function_calling_llm or agent.llm
|
||||
instructions = get_conversion_instructions(model, llm)
|
||||
|
||||
converter = create_converter(
|
||||
agent=agent,
|
||||
converter_cls=converter_cls,
|
||||
llm=llm,
|
||||
text=result,
|
||||
model=model,
|
||||
instructions=instructions,
|
||||
)
|
||||
exported_result = (
|
||||
converter.to_pydantic() if not is_json_output else converter.to_json()
|
||||
)
|
||||
|
||||
if isinstance(exported_result, ConverterError):
|
||||
Printer().print(
|
||||
content=f"{exported_result.message} Using raw output instead.",
|
||||
color="red",
|
||||
)
|
||||
return result
|
||||
|
||||
return exported_result
|
||||
|
||||
|
||||
def get_conversion_instructions(model: Type[BaseModel], llm: Any) -> str:
|
||||
instructions = "I'm gonna convert this raw text into valid JSON."
|
||||
if not is_gpt(llm):
|
||||
model_schema = PydanticSchemaParser(model=model).get_schema()
|
||||
instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
|
||||
return instructions
|
||||
|
||||
|
||||
def is_gpt(llm: Any) -> bool:
|
||||
from langchain_openai import ChatOpenAI
|
||||
|
||||
return isinstance(llm, ChatOpenAI) and llm.openai_api_base is None
|
||||
|
||||
|
||||
def create_converter(
|
||||
agent: Optional[Any] = None,
|
||||
converter_cls: Optional[Type[Converter]] = None,
|
||||
*args,
|
||||
**kwargs,
|
||||
) -> Converter:
|
||||
if agent and not converter_cls:
|
||||
if hasattr(agent, "get_output_converter"):
|
||||
converter = agent.get_output_converter(*args, **kwargs)
|
||||
else:
|
||||
raise AttributeError("Agent does not have a 'get_output_converter' method")
|
||||
elif converter_cls:
|
||||
converter = converter_cls(*args, **kwargs)
|
||||
else:
|
||||
raise ValueError("Either agent or converter_cls must be provided")
|
||||
|
||||
if not converter:
|
||||
raise Exception("No output converter found or set.")
|
||||
|
||||
return converter
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import json
|
||||
from typing import Any, List, Type, Union
|
||||
from typing import Any, List, Type
|
||||
|
||||
import regex
|
||||
from langchain.output_parsers import PydanticOutputParser
|
||||
@@ -7,29 +7,24 @@ from langchain_core.exceptions import OutputParserException
|
||||
from langchain_core.outputs import Generation
|
||||
from langchain_core.pydantic_v1 import ValidationError
|
||||
from pydantic import BaseModel
|
||||
from pydantic.v1 import BaseModel as V1BaseModel
|
||||
|
||||
|
||||
class CrewPydanticOutputParser(PydanticOutputParser):
|
||||
"""Parses the text into pydantic models"""
|
||||
|
||||
pydantic_object: Union[Type[BaseModel], Type[V1BaseModel]]
|
||||
pydantic_object: Type[BaseModel]
|
||||
|
||||
def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
|
||||
def parse_result(self, result: List[Generation]) -> Any:
|
||||
result[0].text = self._transform_in_valid_json(result[0].text)
|
||||
|
||||
# Treating edge case of function calling llm returning the name instead of tool_name
|
||||
json_object = json.loads(result[0].text)
|
||||
json_object["tool_name"] = (
|
||||
json_object["name"]
|
||||
if "tool_name" not in json_object
|
||||
else json_object["tool_name"]
|
||||
)
|
||||
if "tool_name" not in json_object:
|
||||
json_object["tool_name"] = json_object.get("name", "")
|
||||
result[0].text = json.dumps(json_object)
|
||||
|
||||
json_object = super().parse_result(result)
|
||||
try:
|
||||
return self.pydantic_object.parse_obj(json_object)
|
||||
return self.pydantic_object.model_validate(json_object)
|
||||
except ValidationError as e:
|
||||
name = self.pydantic_object.__name__
|
||||
msg = f"Failed to parse {name} from completion {json_object}. Got: {e}"
|
||||
|
||||
149
src/crewai/utilities/evaluators/crew_evaluator_handler.py
Normal file
149
src/crewai/utilities/evaluators/crew_evaluator_handler.py
Normal file
@@ -0,0 +1,149 @@
|
||||
from collections import defaultdict
|
||||
|
||||
from langchain_openai import ChatOpenAI
|
||||
from pydantic import BaseModel, Field
|
||||
from rich.console import Console
|
||||
from rich.table import Table
|
||||
|
||||
from crewai.agent import Agent
|
||||
from crewai.task import Task
|
||||
from crewai.tasks.task_output import TaskOutput
|
||||
|
||||
|
||||
class TaskEvaluationPydanticOutput(BaseModel):
|
||||
quality: float = Field(
|
||||
description="A score from 1 to 10 evaluating on completion, quality, and overall performance from the task_description and task_expected_output to the actual Task Output."
|
||||
)
|
||||
|
||||
|
||||
class CrewEvaluator:
|
||||
"""
|
||||
A class to evaluate the performance of the agents in the crew based on the tasks they have performed.
|
||||
|
||||
Attributes:
|
||||
crew (Crew): The crew of agents to evaluate.
|
||||
openai_model_name (str): The model to use for evaluating the performance of the agents (for now ONLY OpenAI accepted).
|
||||
tasks_scores (defaultdict): A dictionary to store the scores of the agents for each task.
|
||||
iteration (int): The current iteration of the evaluation.
|
||||
"""
|
||||
|
||||
tasks_scores: defaultdict = defaultdict(list)
|
||||
iteration: int = 0
|
||||
|
||||
def __init__(self, crew, openai_model_name: str):
|
||||
self.crew = crew
|
||||
self.openai_model_name = openai_model_name
|
||||
self._setup_for_evaluating()
|
||||
|
||||
def _setup_for_evaluating(self) -> None:
|
||||
"""Sets up the crew for evaluating."""
|
||||
for task in self.crew.tasks:
|
||||
task.callback = self.evaluate
|
||||
|
||||
def set_iteration(self, iteration: int) -> None:
|
||||
self.iteration = iteration
|
||||
|
||||
def _evaluator_agent(self):
|
||||
return Agent(
|
||||
role="Task Execution Evaluator",
|
||||
goal=(
|
||||
"Your goal is to evaluate the performance of the agents in the crew based on the tasks they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance."
|
||||
),
|
||||
backstory="Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in the crew based on the tasks they have performed",
|
||||
verbose=False,
|
||||
llm=ChatOpenAI(model=self.openai_model_name),
|
||||
)
|
||||
|
||||
def _evaluation_task(
|
||||
self, evaluator_agent: Agent, task_to_evaluate: Task, task_output: str
|
||||
) -> Task:
|
||||
return Task(
|
||||
description=(
|
||||
"Based on the task description and the expected output, compare and evaluate the performance of the agents in the crew based on the Task Output they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance."
|
||||
f"task_description: {task_to_evaluate.description} "
|
||||
f"task_expected_output: {task_to_evaluate.expected_output} "
|
||||
f"agent: {task_to_evaluate.agent.role if task_to_evaluate.agent else None} "
|
||||
f"agent_goal: {task_to_evaluate.agent.goal if task_to_evaluate.agent else None} "
|
||||
f"Task Output: {task_output}"
|
||||
),
|
||||
expected_output="Evaluation Score from 1 to 10 based on the performance of the agents on the tasks",
|
||||
agent=evaluator_agent,
|
||||
output_pydantic=TaskEvaluationPydanticOutput,
|
||||
)
|
||||
|
||||
def print_crew_evaluation_result(self) -> None:
|
||||
"""
|
||||
Prints the evaluation result of the crew in a table.
|
||||
A Crew with 2 tasks using the command crewai test -n 2
|
||||
will output the following table:
|
||||
|
||||
Task Scores
|
||||
(1-10 Higher is better)
|
||||
┏━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┓
|
||||
┃ Tasks/Crew ┃ Run 1 ┃ Run 2 ┃ Avg. Total ┃
|
||||
┡━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━┩
|
||||
│ Task 1 │ 10.0 │ 9.0 │ 9.5 │
|
||||
│ Task 2 │ 9.0 │ 9.0 │ 9.0 │
|
||||
│ Crew │ 9.5 │ 9.0 │ 9.2 │
|
||||
└────────────┴───────┴───────┴────────────┘
|
||||
"""
|
||||
task_averages = [
|
||||
sum(scores) / len(scores) for scores in zip(*self.tasks_scores.values())
|
||||
]
|
||||
crew_average = sum(task_averages) / len(task_averages)
|
||||
|
||||
# Create a table
|
||||
table = Table(title="Tasks Scores \n (1-10 Higher is better)")
|
||||
|
||||
# Add columns for the table
|
||||
table.add_column("Tasks/Crew")
|
||||
for run in range(1, len(self.tasks_scores) + 1):
|
||||
table.add_column(f"Run {run}")
|
||||
table.add_column("Avg. Total")
|
||||
|
||||
# Add rows for each task
|
||||
for task_index in range(len(task_averages)):
|
||||
task_scores = [
|
||||
self.tasks_scores[run][task_index]
|
||||
for run in range(1, len(self.tasks_scores) + 1)
|
||||
]
|
||||
avg_score = task_averages[task_index]
|
||||
table.add_row(
|
||||
f"Task {task_index + 1}", *map(str, task_scores), f"{avg_score:.1f}"
|
||||
)
|
||||
|
||||
# Add a row for the crew average
|
||||
crew_scores = [
|
||||
sum(self.tasks_scores[run]) / len(self.tasks_scores[run])
|
||||
for run in range(1, len(self.tasks_scores) + 1)
|
||||
]
|
||||
table.add_row("Crew", *map(str, crew_scores), f"{crew_average:.1f}")
|
||||
|
||||
# Display the table in the terminal
|
||||
console = Console()
|
||||
console.print(table)
|
||||
|
||||
def evaluate(self, task_output: TaskOutput):
|
||||
"""Evaluates the performance of the agents in the crew based on the tasks they have performed."""
|
||||
current_task = None
|
||||
for task in self.crew.tasks:
|
||||
if task.description == task_output.description:
|
||||
current_task = task
|
||||
break
|
||||
|
||||
if not current_task or not task_output:
|
||||
raise ValueError(
|
||||
"Task to evaluate and task output are required for evaluation"
|
||||
)
|
||||
|
||||
evaluator_agent = self._evaluator_agent()
|
||||
evaluation_task = self._evaluation_task(
|
||||
evaluator_agent, current_task, task_output.raw
|
||||
)
|
||||
|
||||
evaluation_result = evaluation_task.execute_sync()
|
||||
|
||||
if isinstance(evaluation_result.pydantic, TaskEvaluationPydanticOutput):
|
||||
self.tasks_scores[self.iteration].append(evaluation_result.pydantic.quality)
|
||||
else:
|
||||
raise ValueError("Evaluation result is not in the expected format")
|
||||
@@ -54,23 +54,23 @@ class TaskEvaluator:
|
||||
def __init__(self, original_agent):
|
||||
self.llm = original_agent.llm
|
||||
|
||||
def evaluate(self, task, ouput) -> TaskEvaluation:
|
||||
def evaluate(self, task, output) -> TaskEvaluation:
|
||||
evaluation_query = (
|
||||
f"Assess the quality of the task completed based on the description, expected output, and actual results.\n\n"
|
||||
f"Task Description:\n{task.description}\n\n"
|
||||
f"Expected Output:\n{task.expected_output}\n\n"
|
||||
f"Actual Output:\n{ouput}\n\n"
|
||||
f"Actual Output:\n{output}\n\n"
|
||||
"Please provide:\n"
|
||||
"- Bullet points suggestions to improve future similar tasks\n"
|
||||
"- A score from 0 to 10 evaluating on completion, quality, and overall performance"
|
||||
"- Entities extracted from the task output, if any, their type, description, and relationships"
|
||||
)
|
||||
|
||||
instructions = "I'm gonna convert this raw text into valid JSON."
|
||||
instructions = "Convert all responses into valid JSON output."
|
||||
|
||||
if not self._is_gpt(self.llm):
|
||||
model_schema = PydanticSchemaParser(model=TaskEvaluation).get_schema()
|
||||
instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
|
||||
instructions = f"{instructions}\n\nReturn only valid JSON with the following schema:\n```json\n{model_schema}\n```"
|
||||
|
||||
converter = Converter(
|
||||
llm=self.llm,
|
||||
|
||||
@@ -1,17 +1,28 @@
|
||||
import re
|
||||
|
||||
|
||||
class YamlParser:
|
||||
@staticmethod
|
||||
def parse(file):
|
||||
"""
|
||||
Parses a YAML file, modifies specific patterns, and checks for unsupported 'context' usage.
|
||||
Args:
|
||||
file (file object): The YAML file to parse.
|
||||
Returns:
|
||||
str: The modified content of the YAML file.
|
||||
Raises:
|
||||
ValueError: If 'context:' is used incorrectly.
|
||||
"""
|
||||
content = file.read()
|
||||
|
||||
# Replace single { and } with doubled ones, while leaving already doubled ones intact and the other special characters {# and {%
|
||||
modified_content = re.sub(r"(?<!\{){(?!\{)(?!\#)(?!\%)", "{{", content)
|
||||
modified_content = re.sub(
|
||||
r"(?<!\})(?<!\%)(?<!\#)\}(?!})", "}}", modified_content
|
||||
)
|
||||
modified_content = re.sub(r"(?<!\})(?<!\%)(?<!\#)\}(?!})", "}}", modified_content)
|
||||
|
||||
# Check for 'context:' not followed by '[' and raise an error
|
||||
if re.search(r"context:(?!\s*\[)", modified_content):
|
||||
raise ValueError(
|
||||
"Context is currently only supported in code when creating a task. Please use the 'context' key in the task configuration."
|
||||
"Context is currently only supported in code when creating a task. "
|
||||
"Please use the 'context' key in the task configuration."
|
||||
)
|
||||
|
||||
return modified_content
|
||||
|
||||
@@ -1,5 +1,6 @@
|
||||
from typing import List, Optional
|
||||
from typing import Any, List, Optional
|
||||
|
||||
from langchain_openai import ChatOpenAI
|
||||
from pydantic import BaseModel
|
||||
|
||||
from crewai.agent import Agent
|
||||
@@ -11,17 +12,27 @@ class PlannerTaskPydanticOutput(BaseModel):
|
||||
|
||||
|
||||
class CrewPlanner:
|
||||
def __init__(self, tasks: List[Task]):
|
||||
def __init__(self, tasks: List[Task], planning_agent_llm: Optional[Any] = None):
|
||||
self.tasks = tasks
|
||||
|
||||
def _handle_crew_planning(self) -> Optional[BaseModel]:
|
||||
if planning_agent_llm is None:
|
||||
self.planning_agent_llm = ChatOpenAI(model="gpt-4o-mini")
|
||||
else:
|
||||
self.planning_agent_llm = planning_agent_llm
|
||||
|
||||
def _handle_crew_planning(self) -> PlannerTaskPydanticOutput:
|
||||
"""Handles the Crew planning by creating detailed step-by-step plans for each task."""
|
||||
planning_agent = self._create_planning_agent()
|
||||
tasks_summary = self._create_tasks_summary()
|
||||
|
||||
planner_task = self._create_planner_task(planning_agent, tasks_summary)
|
||||
|
||||
return planner_task.execute_sync().pydantic
|
||||
result = planner_task.execute_sync()
|
||||
|
||||
if isinstance(result.pydantic, PlannerTaskPydanticOutput):
|
||||
return result.pydantic
|
||||
|
||||
raise ValueError("Failed to get the Planning output")
|
||||
|
||||
def _create_planning_agent(self) -> Agent:
|
||||
"""Creates the planning agent for the crew planning."""
|
||||
@@ -32,6 +43,7 @@ class CrewPlanner:
|
||||
"available to each agent so that they can perform the tasks in an exemplary manner"
|
||||
),
|
||||
backstory="Planner agent for crew planning",
|
||||
llm=self.planning_agent_llm,
|
||||
)
|
||||
|
||||
def _create_planner_task(self, planning_agent: Agent, tasks_summary: str) -> Task:
|
||||
|
||||
@@ -16,11 +16,13 @@ class PydanticSchemaParser(BaseModel):
|
||||
return self._get_model_schema(self.model)
|
||||
|
||||
def _get_model_schema(self, model, depth=0) -> str:
|
||||
lines = []
|
||||
indent = " " * depth
|
||||
lines = [f"{indent}{{"]
|
||||
for field_name, field in model.model_fields.items():
|
||||
field_type_str = self._get_field_type(field, depth + 1)
|
||||
lines.append(f"{' ' * 4 * depth}- {field_name}: {field_type_str}")
|
||||
|
||||
lines.append(f"{indent} {field_name}: {field_type_str},")
|
||||
lines[-1] = lines[-1].rstrip(",") # Remove trailing comma from last item
|
||||
lines.append(f"{indent}}}")
|
||||
return "\n".join(lines)
|
||||
|
||||
def _get_field_type(self, field, depth) -> str:
|
||||
@@ -35,6 +37,6 @@ class PydanticSchemaParser(BaseModel):
|
||||
else:
|
||||
return f"List[{list_item_type.__name__}]"
|
||||
elif issubclass(field_type, BaseModel):
|
||||
return f"\n{self._get_model_schema(field_type, depth)}"
|
||||
return self._get_model_schema(field_type, depth)
|
||||
else:
|
||||
return field_type.__name__
|
||||
|
||||
@@ -397,7 +397,7 @@ def test_agent_moved_on_after_max_iterations():
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool over and over until you're told you can give yout final answer.",
|
||||
description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool over and over until you're told you can give your final answer.",
|
||||
expected_output="The final answer",
|
||||
)
|
||||
output = agent.execute_task(
|
||||
@@ -948,7 +948,7 @@ def test_agent_use_trained_data(crew_training_handler):
|
||||
crew_training_handler().load.return_value = {
|
||||
agent.role: {
|
||||
"suggestions": [
|
||||
"The result of the math operatio must be right.",
|
||||
"The result of the math operation must be right.",
|
||||
"Result must be better than 1.",
|
||||
]
|
||||
}
|
||||
@@ -958,7 +958,7 @@ def test_agent_use_trained_data(crew_training_handler):
|
||||
|
||||
assert (
|
||||
result == "What is 1 + 1?You MUST follow these feedbacks: \n "
|
||||
"The result of the math operatio must be right.\n - Result must be better than 1."
|
||||
"The result of the math operation must be right.\n - Result must be better than 1."
|
||||
)
|
||||
crew_training_handler.assert_has_calls(
|
||||
[mock.call(), mock.call("trained_agents_data.pkl"), mock.call().load()]
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -3,7 +3,7 @@ from unittest import mock
|
||||
import pytest
|
||||
from click.testing import CliRunner
|
||||
|
||||
from crewai.cli.cli import train, version, reset_memories
|
||||
from crewai.cli.cli import reset_memories, test, train, version
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
@@ -133,3 +133,33 @@ def test_version_command_with_tools(runner):
|
||||
"crewai tools version:" in result.output
|
||||
or "crewai tools not installed" in result.output
|
||||
)
|
||||
|
||||
|
||||
@mock.patch("crewai.cli.cli.test_crew")
|
||||
def test_test_default_iterations(test_crew, runner):
|
||||
result = runner.invoke(test)
|
||||
|
||||
test_crew.assert_called_once_with(3, "gpt-4o-mini")
|
||||
assert result.exit_code == 0
|
||||
assert "Testing the crew for 3 iterations with model gpt-4o-mini" in result.output
|
||||
|
||||
|
||||
@mock.patch("crewai.cli.cli.test_crew")
|
||||
def test_test_custom_iterations(test_crew, runner):
|
||||
result = runner.invoke(test, ["--n_iterations", "5", "--model", "gpt-4o"])
|
||||
|
||||
test_crew.assert_called_once_with(5, "gpt-4o")
|
||||
assert result.exit_code == 0
|
||||
assert "Testing the crew for 5 iterations with model gpt-4o" in result.output
|
||||
|
||||
|
||||
@mock.patch("crewai.cli.cli.test_crew")
|
||||
def test_test_invalid_string_iterations(test_crew, runner):
|
||||
result = runner.invoke(test, ["--n_iterations", "invalid"])
|
||||
|
||||
test_crew.assert_not_called()
|
||||
assert result.exit_code == 2
|
||||
assert (
|
||||
"Usage: test [OPTIONS]\nTry 'test --help' for help.\n\nError: Invalid value for '-n' / '--n_iterations': 'invalid' is not a valid integer.\n"
|
||||
in result.output
|
||||
)
|
||||
|
||||
97
tests/cli/test_crew_test.py
Normal file
97
tests/cli/test_crew_test.py
Normal file
@@ -0,0 +1,97 @@
|
||||
import subprocess
|
||||
from unittest import mock
|
||||
|
||||
import pytest
|
||||
|
||||
from crewai.cli import test_crew
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"n_iterations,model",
|
||||
[
|
||||
(1, "gpt-4o"),
|
||||
(5, "gpt-3.5-turbo"),
|
||||
(10, "gpt-4"),
|
||||
],
|
||||
)
|
||||
@mock.patch("crewai.cli.test_crew.subprocess.run")
|
||||
def test_crew_success(mock_subprocess_run, n_iterations, model):
|
||||
"""Test the crew function for successful execution."""
|
||||
mock_subprocess_run.return_value = subprocess.CompletedProcess(
|
||||
args=f"poetry run test {n_iterations} {model}", returncode=0
|
||||
)
|
||||
result = test_crew.test_crew(n_iterations, model)
|
||||
|
||||
mock_subprocess_run.assert_called_once_with(
|
||||
["poetry", "run", "test", str(n_iterations), model],
|
||||
capture_output=False,
|
||||
text=True,
|
||||
check=True,
|
||||
)
|
||||
assert result is None
|
||||
|
||||
|
||||
@mock.patch("crewai.cli.test_crew.click")
|
||||
def test_test_crew_zero_iterations(click):
|
||||
test_crew.test_crew(0, "gpt-4o")
|
||||
click.echo.assert_called_once_with(
|
||||
"An unexpected error occurred: The number of iterations must be a positive integer.",
|
||||
err=True,
|
||||
)
|
||||
|
||||
|
||||
@mock.patch("crewai.cli.test_crew.click")
|
||||
def test_test_crew_negative_iterations(click):
|
||||
test_crew.test_crew(-2, "gpt-4o")
|
||||
click.echo.assert_called_once_with(
|
||||
"An unexpected error occurred: The number of iterations must be a positive integer.",
|
||||
err=True,
|
||||
)
|
||||
|
||||
|
||||
@mock.patch("crewai.cli.test_crew.click")
|
||||
@mock.patch("crewai.cli.test_crew.subprocess.run")
|
||||
def test_test_crew_called_process_error(mock_subprocess_run, click):
|
||||
n_iterations = 5
|
||||
mock_subprocess_run.side_effect = subprocess.CalledProcessError(
|
||||
returncode=1,
|
||||
cmd=["poetry", "run", "test", str(n_iterations), "gpt-4o"],
|
||||
output="Error",
|
||||
stderr="Some error occurred",
|
||||
)
|
||||
test_crew.test_crew(n_iterations, "gpt-4o")
|
||||
|
||||
mock_subprocess_run.assert_called_once_with(
|
||||
["poetry", "run", "test", "5", "gpt-4o"],
|
||||
capture_output=False,
|
||||
text=True,
|
||||
check=True,
|
||||
)
|
||||
click.echo.assert_has_calls(
|
||||
[
|
||||
mock.call.echo(
|
||||
"An error occurred while testing the crew: Command '['poetry', 'run', 'test', '5', 'gpt-4o']' returned non-zero exit status 1.",
|
||||
err=True,
|
||||
),
|
||||
mock.call.echo("Error", err=True),
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
@mock.patch("crewai.cli.test_crew.click")
|
||||
@mock.patch("crewai.cli.test_crew.subprocess.run")
|
||||
def test_test_crew_unexpected_exception(mock_subprocess_run, click):
|
||||
# Arrange
|
||||
n_iterations = 5
|
||||
mock_subprocess_run.side_effect = Exception("Unexpected error")
|
||||
test_crew.test_crew(n_iterations, "gpt-4o")
|
||||
|
||||
mock_subprocess_run.assert_called_once_with(
|
||||
["poetry", "run", "test", "5", "gpt-4o"],
|
||||
capture_output=False,
|
||||
text=True,
|
||||
check=True,
|
||||
)
|
||||
click.echo.assert_called_once_with(
|
||||
"An unexpected error occurred: Unexpected error", err=True
|
||||
)
|
||||
@@ -18,6 +18,7 @@ from crewai.task import Task
|
||||
from crewai.tasks.conditional_task import ConditionalTask
|
||||
from crewai.tasks.output_format import OutputFormat
|
||||
from crewai.tasks.task_output import TaskOutput
|
||||
from crewai.types.usage_metrics import UsageMetrics
|
||||
from crewai.utilities import Logger, RPMController
|
||||
from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
|
||||
|
||||
@@ -68,7 +69,7 @@ def test_crew_config_conditional_requirement():
|
||||
"agent": "Senior Researcher",
|
||||
},
|
||||
{
|
||||
"description": "Write a 1 amazing paragraph highlight for each idead that showcases how good an article about this topic could be, check references if necessary or search for more content but make sure it's unique, interesting and well written. Return the list of ideas with their paragraph and your notes.",
|
||||
"description": "Write a 1 amazing paragraph highlight for each idea that showcases how good an article about this topic could be, check references if necessary or search for more content but make sure it's unique, interesting and well written. Return the list of ideas with their paragraph and your notes.",
|
||||
"expected_output": "A 4 paragraph article about AI.",
|
||||
"agent": "Senior Writer",
|
||||
},
|
||||
@@ -597,14 +598,10 @@ def test_crew_kickoff_usage_metrics():
|
||||
assert len(results) == len(inputs)
|
||||
for result in results:
|
||||
# Assert that all required keys are in usage_metrics and their values are not None
|
||||
for key in [
|
||||
"total_tokens",
|
||||
"prompt_tokens",
|
||||
"completion_tokens",
|
||||
"successful_requests",
|
||||
]:
|
||||
assert key in result.token_usage
|
||||
assert result.token_usage[key] > 0
|
||||
assert result.token_usage.total_tokens > 0
|
||||
assert result.token_usage.prompt_tokens > 0
|
||||
assert result.token_usage.completion_tokens > 0
|
||||
assert result.token_usage.successful_requests > 0
|
||||
|
||||
|
||||
def test_agents_rpm_is_never_set_if_crew_max_RPM_is_not_set():
|
||||
@@ -632,18 +629,21 @@ def test_sequential_async_task_execution_completion():
|
||||
list_ideas = Task(
|
||||
description="Give me a list of 5 interesting ideas to explore for an article, what makes them unique and interesting.",
|
||||
expected_output="Bullet point list of 5 important events.",
|
||||
max_retry_limit=3,
|
||||
agent=researcher,
|
||||
async_execution=True,
|
||||
)
|
||||
list_important_history = Task(
|
||||
description="Research the history of AI and give me the 5 most important events that shaped the technology.",
|
||||
expected_output="Bullet point list of 5 important events.",
|
||||
max_retry_limit=3,
|
||||
agent=researcher,
|
||||
async_execution=True,
|
||||
)
|
||||
write_article = Task(
|
||||
description="Write an article about the history of AI and its most important events.",
|
||||
expected_output="A 4 paragraph article about AI.",
|
||||
max_retry_limit=3,
|
||||
agent=writer,
|
||||
context=[list_ideas, list_important_history],
|
||||
)
|
||||
@@ -656,7 +656,7 @@ def test_sequential_async_task_execution_completion():
|
||||
|
||||
sequential_result = sequential_crew.kickoff()
|
||||
assert sequential_result.raw.startswith(
|
||||
"**The Evolution of Artificial Intelligence: A Journey Through Milestones**"
|
||||
"The history of artificial intelligence (AI) is marked by several pivotal events that have shaped its evolution and impact on various sectors."
|
||||
)
|
||||
|
||||
|
||||
@@ -1188,7 +1188,7 @@ def test_task_with_no_arguments():
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Look at the available data nd give me a sense on the total number of sales.",
|
||||
description="Look at the available data and give me a sense on the total number of sales.",
|
||||
expected_output="The total number of sales as an integer",
|
||||
agent=researcher,
|
||||
)
|
||||
@@ -1235,7 +1235,7 @@ def test_delegation_is_not_enabled_if_there_are_only_one_agent():
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Look at the available data nd give me a sense on the total number of sales.",
|
||||
description="Look at the available data and give me a sense on the total number of sales.",
|
||||
expected_output="The total number of sales as an integer",
|
||||
agent=researcher,
|
||||
)
|
||||
@@ -1311,16 +1311,16 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
|
||||
)
|
||||
|
||||
result = crew.kickoff()
|
||||
assert result.raw == '"Howdy!"'
|
||||
assert result.raw == "Howdy!"
|
||||
|
||||
print(crew.usage_metrics)
|
||||
|
||||
assert crew.usage_metrics == {
|
||||
"total_tokens": 311,
|
||||
"prompt_tokens": 224,
|
||||
"completion_tokens": 87,
|
||||
"successful_requests": 1,
|
||||
}
|
||||
assert crew.usage_metrics == UsageMetrics(
|
||||
total_tokens=219,
|
||||
prompt_tokens=201,
|
||||
completion_tokens=18,
|
||||
successful_requests=1,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
@@ -1355,28 +1355,66 @@ def test_hierarchical_crew_creation_tasks_with_agents():
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_hierarchical_crew_creation_tasks_with_async_execution():
|
||||
"""
|
||||
Agents are not required for tasks in a hierarchical process but sometimes they are still added
|
||||
This test makes sure that the manager still delegates the task to the agent even if the agent is passed in the task
|
||||
"""
|
||||
from langchain_openai import ChatOpenAI
|
||||
|
||||
task = Task(
|
||||
description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
|
||||
expected_output="5 bullet points with a paragraph for each idea.",
|
||||
async_execution=True, # should throw an error
|
||||
description="Write one amazing paragraph about AI.",
|
||||
expected_output="A single paragraph with 4 sentences.",
|
||||
agent=writer,
|
||||
async_execution=True,
|
||||
)
|
||||
|
||||
with pytest.raises(pydantic_core._pydantic_core.ValidationError) as exec_info:
|
||||
Crew(
|
||||
tasks=[task],
|
||||
agents=[researcher],
|
||||
process=Process.hierarchical,
|
||||
manager_llm=ChatOpenAI(model="gpt-4o"),
|
||||
)
|
||||
|
||||
assert (
|
||||
exec_info.value.errors()[0]["type"] == "async_execution_in_hierarchical_process"
|
||||
crew = Crew(
|
||||
tasks=[task],
|
||||
agents=[writer, researcher, ceo],
|
||||
process=Process.hierarchical,
|
||||
manager_llm=ChatOpenAI(model="gpt-4o"),
|
||||
)
|
||||
assert (
|
||||
"Hierarchical process error: Tasks cannot be flagged with async_execution."
|
||||
in exec_info.value.errors()[0]["msg"]
|
||||
|
||||
crew.kickoff()
|
||||
assert crew.manager_agent is not None
|
||||
assert crew.manager_agent.tools is not None
|
||||
assert crew.manager_agent.tools[0].description.startswith(
|
||||
"Delegate a specific task to one of the following coworkers: Senior Writer\n"
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_hierarchical_crew_creation_tasks_with_sync_last():
|
||||
"""
|
||||
Agents are not required for tasks in a hierarchical process but sometimes they are still added
|
||||
This test makes sure that the manager still delegates the task to the agent even if the agent is passed in the task
|
||||
"""
|
||||
from langchain_openai import ChatOpenAI
|
||||
|
||||
task = Task(
|
||||
description="Write one amazing paragraph about AI.",
|
||||
expected_output="A single paragraph with 4 sentences.",
|
||||
agent=writer,
|
||||
async_execution=True,
|
||||
)
|
||||
task2 = Task(
|
||||
description="Write one amazing paragraph about AI.",
|
||||
expected_output="A single paragraph with 4 sentences.",
|
||||
async_execution=False,
|
||||
)
|
||||
|
||||
crew = Crew(
|
||||
tasks=[task, task2],
|
||||
agents=[writer, researcher, ceo],
|
||||
process=Process.hierarchical,
|
||||
manager_llm=ChatOpenAI(model="gpt-4o"),
|
||||
)
|
||||
|
||||
crew.kickoff()
|
||||
assert crew.manager_agent is not None
|
||||
assert crew.manager_agent.tools is not None
|
||||
assert crew.manager_agent.tools[0].description.startswith(
|
||||
"Delegate a specific task to one of the following coworkers: Senior Writer, Researcher, CEO\n"
|
||||
)
|
||||
|
||||
|
||||
@@ -1560,16 +1598,16 @@ def test_tools_with_custom_caching():
|
||||
|
||||
writer1 = Agent(
|
||||
role="Writer",
|
||||
goal="You write lesssons of math for kids.",
|
||||
backstory="You're an expert in writting and you love to teach kids but you know nothing of math.",
|
||||
goal="You write lessons of math for kids.",
|
||||
backstory="You're an expert in writing and you love to teach kids but you know nothing of math.",
|
||||
tools=[multiplcation_tool],
|
||||
allow_delegation=False,
|
||||
)
|
||||
|
||||
writer2 = Agent(
|
||||
role="Writer",
|
||||
goal="You write lesssons of math for kids.",
|
||||
backstory="You're an expert in writting and you love to teach kids but you know nothing of math.",
|
||||
goal="You write lessons of math for kids.",
|
||||
backstory="You're an expert in writing and you love to teach kids but you know nothing of math.",
|
||||
tools=[multiplcation_tool],
|
||||
allow_delegation=False,
|
||||
)
|
||||
@@ -2499,3 +2537,34 @@ def test_conditional_should_execute():
|
||||
assert condition_mock.call_count == 1
|
||||
assert condition_mock() is True
|
||||
assert mock_execute_sync.call_count == 2
|
||||
|
||||
|
||||
@mock.patch("crewai.crew.CrewEvaluator")
|
||||
@mock.patch("crewai.crew.Crew.kickoff")
|
||||
def test_crew_testing_function(mock_kickoff, crew_evaluator):
|
||||
task = Task(
|
||||
description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
|
||||
expected_output="5 bullet points with a paragraph for each idea.",
|
||||
agent=researcher,
|
||||
)
|
||||
|
||||
crew = Crew(
|
||||
agents=[researcher],
|
||||
tasks=[task],
|
||||
)
|
||||
n_iterations = 2
|
||||
crew.test(n_iterations, openai_model_name="gpt-4o-mini", inputs={"topic": "AI"})
|
||||
|
||||
assert len(mock_kickoff.mock_calls) == n_iterations
|
||||
mock_kickoff.assert_has_calls(
|
||||
[mock.call(inputs={"topic": "AI"}), mock.call(inputs={"topic": "AI"})]
|
||||
)
|
||||
|
||||
crew_evaluator.assert_has_calls(
|
||||
[
|
||||
mock.call(crew, "gpt-4o-mini"),
|
||||
mock.call().set_iteration(1),
|
||||
mock.call().set_iteration(2),
|
||||
mock.call().print_crew_evaluation_result(),
|
||||
]
|
||||
)
|
||||
|
||||
@@ -11,13 +11,12 @@ from crewai.process import Process
|
||||
from crewai.routers.pipeline_router import PipelineRouter
|
||||
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 = {
|
||||
"total_tokens": 100,
|
||||
"prompt_tokens": 50,
|
||||
"completion_tokens": 50,
|
||||
}
|
||||
DEFAULT_TOKEN_USAGE = UsageMetrics(
|
||||
total_tokens=100, prompt_tokens=50, completion_tokens=50, successful_requests=3
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
||||
@@ -5,13 +5,12 @@ import json
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
import pytest
|
||||
from pydantic import BaseModel
|
||||
from pydantic_core import ValidationError
|
||||
|
||||
from crewai import Agent, Crew, Process, Task
|
||||
from crewai.tasks.conditional_task import ConditionalTask
|
||||
from crewai.tasks.task_output import TaskOutput
|
||||
from crewai.utilities.converter import Converter
|
||||
from pydantic import BaseModel
|
||||
from pydantic_core import ValidationError
|
||||
|
||||
|
||||
def test_task_tool_reflect_agent_tools():
|
||||
@@ -110,7 +109,7 @@ def test_task_callback():
|
||||
task_completed.assert_called_once_with(task.output)
|
||||
|
||||
|
||||
def test_task_callback_returns_task_ouput():
|
||||
def test_task_callback_returns_task_output():
|
||||
from crewai.tasks.output_format import OutputFormat
|
||||
|
||||
researcher = Agent(
|
||||
|
||||
113
tests/utilities/evaluators/test_crew_evaluator_handler.py
Normal file
113
tests/utilities/evaluators/test_crew_evaluator_handler.py
Normal file
@@ -0,0 +1,113 @@
|
||||
from unittest import mock
|
||||
|
||||
import pytest
|
||||
|
||||
from crewai.agent import Agent
|
||||
from crewai.crew import Crew
|
||||
from crewai.task import Task
|
||||
from crewai.tasks.task_output import TaskOutput
|
||||
from crewai.utilities.evaluators.crew_evaluator_handler import (
|
||||
CrewEvaluator,
|
||||
TaskEvaluationPydanticOutput,
|
||||
)
|
||||
|
||||
|
||||
class TestCrewEvaluator:
|
||||
@pytest.fixture
|
||||
def crew_planner(self):
|
||||
agent = Agent(role="Agent 1", goal="Goal 1", backstory="Backstory 1")
|
||||
task = Task(
|
||||
description="Task 1",
|
||||
expected_output="Output 1",
|
||||
agent=agent,
|
||||
)
|
||||
crew = Crew(agents=[agent], tasks=[task])
|
||||
|
||||
return CrewEvaluator(crew, openai_model_name="gpt-4o-mini")
|
||||
|
||||
def test_setup_for_evaluating(self, crew_planner):
|
||||
crew_planner._setup_for_evaluating()
|
||||
assert crew_planner.crew.tasks[0].callback == crew_planner.evaluate
|
||||
|
||||
def test_set_iteration(self, crew_planner):
|
||||
crew_planner.set_iteration(1)
|
||||
assert crew_planner.iteration == 1
|
||||
|
||||
def test_evaluator_agent(self, crew_planner):
|
||||
agent = crew_planner._evaluator_agent()
|
||||
assert agent.role == "Task Execution Evaluator"
|
||||
assert (
|
||||
agent.goal
|
||||
== "Your goal is to evaluate the performance of the agents in the crew based on the tasks they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance."
|
||||
)
|
||||
assert (
|
||||
agent.backstory
|
||||
== "Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in the crew based on the tasks they have performed"
|
||||
)
|
||||
assert agent.verbose is False
|
||||
assert agent.llm.model_name == "gpt-4o-mini"
|
||||
|
||||
def test_evaluation_task(self, crew_planner):
|
||||
evaluator_agent = Agent(
|
||||
role="Evaluator Agent",
|
||||
goal="Evaluate the performance of the agents in the crew",
|
||||
backstory="Master in Evaluation",
|
||||
)
|
||||
task_to_evaluate = Task(
|
||||
description="Task 1",
|
||||
expected_output="Output 1",
|
||||
agent=Agent(role="Agent 1", goal="Goal 1", backstory="Backstory 1"),
|
||||
)
|
||||
task_output = "Task Output 1"
|
||||
task = crew_planner._evaluation_task(
|
||||
evaluator_agent, task_to_evaluate, task_output
|
||||
)
|
||||
|
||||
assert task.description.startswith(
|
||||
"Based on the task description and the expected output, compare and evaluate the performance of the agents in the crew based on the Task Output they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance."
|
||||
)
|
||||
|
||||
assert task.agent == evaluator_agent
|
||||
assert (
|
||||
task.description
|
||||
== "Based on the task description and the expected output, compare and evaluate "
|
||||
"the performance of the agents in the crew based on the Task Output they have "
|
||||
"performed using score from 1 to 10 evaluating on completion, quality, and overall "
|
||||
"performance.task_description: Task 1 task_expected_output: Output 1 "
|
||||
"agent: Agent 1 agent_goal: Goal 1 Task Output: Task Output 1"
|
||||
)
|
||||
|
||||
@mock.patch("crewai.utilities.evaluators.crew_evaluator_handler.Console")
|
||||
@mock.patch("crewai.utilities.evaluators.crew_evaluator_handler.Table")
|
||||
def test_print_crew_evaluation_result(self, table, console, crew_planner):
|
||||
crew_planner.tasks_scores = {
|
||||
1: [10, 9, 8],
|
||||
2: [9, 8, 7],
|
||||
}
|
||||
|
||||
crew_planner.print_crew_evaluation_result()
|
||||
|
||||
table.assert_has_calls(
|
||||
[
|
||||
mock.call(title="Tasks Scores \n (1-10 Higher is better)"),
|
||||
mock.call().add_column("Tasks/Crew"),
|
||||
mock.call().add_column("Run 1"),
|
||||
mock.call().add_column("Run 2"),
|
||||
mock.call().add_column("Avg. Total"),
|
||||
mock.call().add_row("Task 1", "10", "9", "9.5"),
|
||||
mock.call().add_row("Task 2", "9", "8", "8.5"),
|
||||
mock.call().add_row("Task 3", "8", "7", "7.5"),
|
||||
mock.call().add_row("Crew", "9.0", "8.0", "8.5"),
|
||||
]
|
||||
)
|
||||
console.assert_has_calls([mock.call(), mock.call().print(table())])
|
||||
|
||||
def test_evaluate(self, crew_planner):
|
||||
task_output = TaskOutput(
|
||||
description="Task 1", agent=str(crew_planner.crew.agents[0])
|
||||
)
|
||||
|
||||
with mock.patch.object(Task, "execute_sync") as execute:
|
||||
execute().pydantic = TaskEvaluationPydanticOutput(quality=9.5)
|
||||
crew_planner.evaluate(task_output)
|
||||
assert crew_planner.tasks_scores[0] == [9.5]
|
||||
@@ -56,8 +56,7 @@ def test_evaluate_training_data(converter_mock):
|
||||
"based on the human feedback\n",
|
||||
model=TrainingTaskEvaluation,
|
||||
instructions="I'm gonna convert this raw text into valid JSON.\n\nThe json should have the "
|
||||
"following structure, with the following keys:\n- suggestions: List[str]\n- "
|
||||
"quality: float\n- final_summary: str",
|
||||
"following structure, with the following keys:\n{\n suggestions: List[str],\n quality: float,\n final_summary: str\n}",
|
||||
),
|
||||
mock.call().to_pydantic(),
|
||||
]
|
||||
|
||||
266
tests/utilities/test_converter.py
Normal file
266
tests/utilities/test_converter.py
Normal file
@@ -0,0 +1,266 @@
|
||||
import json
|
||||
from unittest.mock import MagicMock, Mock, patch
|
||||
|
||||
import pytest
|
||||
from crewai.utilities.converter import (
|
||||
Converter,
|
||||
ConverterError,
|
||||
convert_to_model,
|
||||
convert_with_instructions,
|
||||
create_converter,
|
||||
get_conversion_instructions,
|
||||
handle_partial_json,
|
||||
is_gpt,
|
||||
validate_model,
|
||||
)
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
||||
# Sample Pydantic models for testing
|
||||
class EmailResponse(BaseModel):
|
||||
previous_message_content: str
|
||||
|
||||
|
||||
class EmailResponses(BaseModel):
|
||||
responses: list[EmailResponse]
|
||||
|
||||
|
||||
class SimpleModel(BaseModel):
|
||||
name: str
|
||||
age: int
|
||||
|
||||
|
||||
class NestedModel(BaseModel):
|
||||
id: int
|
||||
data: SimpleModel
|
||||
|
||||
|
||||
# Fixtures
|
||||
@pytest.fixture
|
||||
def mock_agent():
|
||||
agent = Mock()
|
||||
agent.function_calling_llm = None
|
||||
agent.llm = Mock()
|
||||
return agent
|
||||
|
||||
|
||||
# Tests for convert_to_model
|
||||
def test_convert_to_model_with_valid_json():
|
||||
result = '{"name": "John", "age": 30}'
|
||||
output = convert_to_model(result, SimpleModel, None, None)
|
||||
assert isinstance(output, SimpleModel)
|
||||
assert output.name == "John"
|
||||
assert output.age == 30
|
||||
|
||||
|
||||
def test_convert_to_model_with_invalid_json():
|
||||
result = '{"name": "John", "age": "thirty"}'
|
||||
with patch("crewai.utilities.converter.handle_partial_json") as mock_handle:
|
||||
mock_handle.return_value = "Fallback result"
|
||||
output = convert_to_model(result, SimpleModel, None, None)
|
||||
assert output == "Fallback result"
|
||||
|
||||
|
||||
def test_convert_to_model_with_no_model():
|
||||
result = "Plain text"
|
||||
output = convert_to_model(result, None, None, None)
|
||||
assert output == "Plain text"
|
||||
|
||||
|
||||
def test_convert_to_model_with_special_characters():
|
||||
json_string_test = """
|
||||
{
|
||||
"responses": [
|
||||
{
|
||||
"previous_message_content": "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
|
||||
}
|
||||
]
|
||||
}
|
||||
"""
|
||||
output = convert_to_model(json_string_test, EmailResponses, None, None)
|
||||
assert isinstance(output, EmailResponses)
|
||||
assert len(output.responses) == 1
|
||||
assert (
|
||||
output.responses[0].previous_message_content
|
||||
== "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
|
||||
)
|
||||
|
||||
|
||||
def test_convert_to_model_with_escaped_special_characters():
|
||||
json_string_test = json.dumps(
|
||||
{
|
||||
"responses": [
|
||||
{
|
||||
"previous_message_content": "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
|
||||
}
|
||||
]
|
||||
}
|
||||
)
|
||||
output = convert_to_model(json_string_test, EmailResponses, None, None)
|
||||
assert isinstance(output, EmailResponses)
|
||||
assert len(output.responses) == 1
|
||||
assert (
|
||||
output.responses[0].previous_message_content
|
||||
== "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
|
||||
)
|
||||
|
||||
|
||||
def test_convert_to_model_with_multiple_special_characters():
|
||||
json_string_test = """
|
||||
{
|
||||
"responses": [
|
||||
{
|
||||
"previous_message_content": "Line 1\r\nLine 2\tTabbed\nLine 3\r\n\rEscaped newline"
|
||||
}
|
||||
]
|
||||
}
|
||||
"""
|
||||
output = convert_to_model(json_string_test, EmailResponses, None, None)
|
||||
assert isinstance(output, EmailResponses)
|
||||
assert len(output.responses) == 1
|
||||
assert (
|
||||
output.responses[0].previous_message_content
|
||||
== "Line 1\r\nLine 2\tTabbed\nLine 3\r\n\rEscaped newline"
|
||||
)
|
||||
|
||||
|
||||
# Tests for validate_model
|
||||
def test_validate_model_pydantic_output():
|
||||
result = '{"name": "Alice", "age": 25}'
|
||||
output = validate_model(result, SimpleModel, False)
|
||||
assert isinstance(output, SimpleModel)
|
||||
assert output.name == "Alice"
|
||||
assert output.age == 25
|
||||
|
||||
|
||||
def test_validate_model_json_output():
|
||||
result = '{"name": "Bob", "age": 40}'
|
||||
output = validate_model(result, SimpleModel, True)
|
||||
assert isinstance(output, dict)
|
||||
assert output == {"name": "Bob", "age": 40}
|
||||
|
||||
|
||||
# Tests for handle_partial_json
|
||||
def test_handle_partial_json_with_valid_partial():
|
||||
result = 'Some text {"name": "Charlie", "age": 35} more text'
|
||||
output = handle_partial_json(result, SimpleModel, False, None)
|
||||
assert isinstance(output, SimpleModel)
|
||||
assert output.name == "Charlie"
|
||||
assert output.age == 35
|
||||
|
||||
|
||||
def test_handle_partial_json_with_invalid_partial(mock_agent):
|
||||
result = "No valid JSON here"
|
||||
with patch("crewai.utilities.converter.convert_with_instructions") as mock_convert:
|
||||
mock_convert.return_value = "Converted result"
|
||||
output = handle_partial_json(result, SimpleModel, False, mock_agent)
|
||||
assert output == "Converted result"
|
||||
|
||||
|
||||
# Tests for convert_with_instructions
|
||||
@patch("crewai.utilities.converter.create_converter")
|
||||
@patch("crewai.utilities.converter.get_conversion_instructions")
|
||||
def test_convert_with_instructions_success(
|
||||
mock_get_instructions, mock_create_converter, mock_agent
|
||||
):
|
||||
mock_get_instructions.return_value = "Instructions"
|
||||
mock_converter = Mock()
|
||||
mock_converter.to_pydantic.return_value = SimpleModel(name="David", age=50)
|
||||
mock_create_converter.return_value = mock_converter
|
||||
|
||||
result = "Some text to convert"
|
||||
output = convert_with_instructions(result, SimpleModel, False, mock_agent)
|
||||
|
||||
assert isinstance(output, SimpleModel)
|
||||
assert output.name == "David"
|
||||
assert output.age == 50
|
||||
|
||||
|
||||
@patch("crewai.utilities.converter.create_converter")
|
||||
@patch("crewai.utilities.converter.get_conversion_instructions")
|
||||
def test_convert_with_instructions_failure(
|
||||
mock_get_instructions, mock_create_converter, mock_agent
|
||||
):
|
||||
mock_get_instructions.return_value = "Instructions"
|
||||
mock_converter = Mock()
|
||||
mock_converter.to_pydantic.return_value = ConverterError("Conversion failed")
|
||||
mock_create_converter.return_value = mock_converter
|
||||
|
||||
result = "Some text to convert"
|
||||
with patch("crewai.utilities.converter.Printer") as mock_printer:
|
||||
output = convert_with_instructions(result, SimpleModel, False, mock_agent)
|
||||
assert output == result
|
||||
mock_printer.return_value.print.assert_called_once()
|
||||
|
||||
|
||||
# Tests for get_conversion_instructions
|
||||
def test_get_conversion_instructions_gpt():
|
||||
mock_llm = Mock()
|
||||
mock_llm.openai_api_base = None
|
||||
with patch("crewai.utilities.converter.is_gpt", return_value=True):
|
||||
instructions = get_conversion_instructions(SimpleModel, mock_llm)
|
||||
assert instructions == "I'm gonna convert this raw text into valid JSON."
|
||||
|
||||
|
||||
def test_get_conversion_instructions_non_gpt():
|
||||
mock_llm = Mock()
|
||||
with patch("crewai.utilities.converter.is_gpt", return_value=False):
|
||||
with patch("crewai.utilities.converter.PydanticSchemaParser") as mock_parser:
|
||||
mock_parser.return_value.get_schema.return_value = "Sample schema"
|
||||
instructions = get_conversion_instructions(SimpleModel, mock_llm)
|
||||
assert "Sample schema" in instructions
|
||||
|
||||
|
||||
# Tests for is_gpt
|
||||
def test_is_gpt_true():
|
||||
from langchain_openai import ChatOpenAI
|
||||
|
||||
mock_llm = Mock(spec=ChatOpenAI)
|
||||
mock_llm.openai_api_base = None
|
||||
assert is_gpt(mock_llm) is True
|
||||
|
||||
|
||||
def test_is_gpt_false():
|
||||
mock_llm = Mock()
|
||||
assert is_gpt(mock_llm) is False
|
||||
|
||||
|
||||
class CustomConverter(Converter):
|
||||
pass
|
||||
|
||||
|
||||
def test_create_converter_with_mock_agent():
|
||||
mock_agent = MagicMock()
|
||||
mock_agent.get_output_converter.return_value = MagicMock(spec=Converter)
|
||||
|
||||
converter = create_converter(
|
||||
agent=mock_agent,
|
||||
llm=Mock(),
|
||||
text="Sample",
|
||||
model=SimpleModel,
|
||||
instructions="Convert",
|
||||
)
|
||||
|
||||
assert isinstance(converter, Converter)
|
||||
mock_agent.get_output_converter.assert_called_once()
|
||||
|
||||
|
||||
def test_create_converter_with_custom_converter():
|
||||
converter = create_converter(
|
||||
converter_cls=CustomConverter,
|
||||
llm=Mock(),
|
||||
text="Sample",
|
||||
model=SimpleModel,
|
||||
instructions="Convert",
|
||||
)
|
||||
|
||||
assert isinstance(converter, CustomConverter)
|
||||
|
||||
|
||||
def test_create_converter_fails_without_agent_or_converter_cls():
|
||||
with pytest.raises(
|
||||
ValueError, match="Either agent or converter_cls must be provided"
|
||||
):
|
||||
create_converter(
|
||||
llm=Mock(), text="Sample", model=SimpleModel, instructions="Convert"
|
||||
)
|
||||
@@ -1,10 +1,11 @@
|
||||
from unittest.mock import patch
|
||||
from crewai.tasks.task_output import TaskOutput
|
||||
|
||||
import pytest
|
||||
from langchain_openai import ChatOpenAI
|
||||
|
||||
from crewai.agent import Agent
|
||||
from crewai.task import Task
|
||||
from crewai.tasks.task_output import TaskOutput
|
||||
from crewai.utilities.planning_handler import CrewPlanner, PlannerTaskPydanticOutput
|
||||
|
||||
|
||||
@@ -28,7 +29,19 @@ class TestCrewPlanner:
|
||||
agent=Agent(role="Agent 3", goal="Goal 3", backstory="Backstory 3"),
|
||||
),
|
||||
]
|
||||
return CrewPlanner(tasks)
|
||||
return CrewPlanner(tasks, None)
|
||||
|
||||
@pytest.fixture
|
||||
def crew_planner_different_llm(self):
|
||||
tasks = [
|
||||
Task(
|
||||
description="Task 1",
|
||||
expected_output="Output 1",
|
||||
agent=Agent(role="Agent 1", goal="Goal 1", backstory="Backstory 1"),
|
||||
)
|
||||
]
|
||||
planning_agent_llm = ChatOpenAI(model="gpt-3.5-turbo")
|
||||
return CrewPlanner(tasks, planning_agent_llm)
|
||||
|
||||
def test_handle_crew_planning(self, crew_planner):
|
||||
with patch.object(Task, "execute_sync") as execute:
|
||||
@@ -40,7 +53,7 @@ class TestCrewPlanner:
|
||||
),
|
||||
)
|
||||
result = crew_planner._handle_crew_planning()
|
||||
|
||||
assert crew_planner.planning_agent_llm.model_name == "gpt-4o-mini"
|
||||
assert isinstance(result, PlannerTaskPydanticOutput)
|
||||
assert len(result.list_of_plans_per_task) == len(crew_planner.tasks)
|
||||
execute.assert_called_once()
|
||||
@@ -72,3 +85,22 @@ class TestCrewPlanner:
|
||||
assert isinstance(tasks_summary, str)
|
||||
assert tasks_summary.startswith("\n Task Number 1 - Task 1")
|
||||
assert tasks_summary.endswith('"agent_tools": []\n ')
|
||||
|
||||
def test_handle_crew_planning_different_llm(self, crew_planner_different_llm):
|
||||
with patch.object(Task, "execute_sync") as execute:
|
||||
execute.return_value = TaskOutput(
|
||||
description="Description",
|
||||
agent="agent",
|
||||
pydantic=PlannerTaskPydanticOutput(list_of_plans_per_task=["Plan 1"]),
|
||||
)
|
||||
result = crew_planner_different_llm._handle_crew_planning()
|
||||
|
||||
assert (
|
||||
crew_planner_different_llm.planning_agent_llm.model_name
|
||||
== "gpt-3.5-turbo"
|
||||
)
|
||||
assert isinstance(result, PlannerTaskPydanticOutput)
|
||||
assert len(result.list_of_plans_per_task) == len(
|
||||
crew_planner_different_llm.tasks
|
||||
)
|
||||
execute.assert_called_once()
|
||||
|
||||
Reference in New Issue
Block a user