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Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 devin-ai-integration[bot]
|
9790ad44ea | ||
|
devin-ai-integration[bot]
|
f23556a04a |
No files matched your search
@@ -1,3 +0,0 @@
|
||||
OPENAI_MODEL_NAME="gpt-4o"
|
||||
OPENAI_API_KEY="Enter your API key"
|
||||
OPENAI_API_BASE="https://api.openai.com/v1"
|
||||
@@ -1,40 +0,0 @@
|
||||
# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
|
||||
# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
|
||||
|
||||
name: Python package
|
||||
|
||||
on:
|
||||
push:
|
||||
branches: [ "develop" ]
|
||||
pull_request:
|
||||
branches: [ "develop" ]
|
||||
|
||||
jobs:
|
||||
build:
|
||||
|
||||
runs-on: ubuntu-latest
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
python-version: ["3.10", "3.11"]
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
- name: Set up Python ${{ matrix.python-version }}
|
||||
uses: actions/setup-python@v3
|
||||
with:
|
||||
python-version: ${{ matrix.python-version }}
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
python -m pip install --upgrade pip
|
||||
python -m pip install flake8 pytest
|
||||
if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
|
||||
# - name: Lint with flake8
|
||||
# run: |
|
||||
# # stop the build if there are Python syntax errors or undefined names
|
||||
# flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
|
||||
# # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
|
||||
# flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
|
||||
# - name: Test with pytest
|
||||
# run: |
|
||||
# pytest
|
||||
@@ -1,39 +0,0 @@
|
||||
# This workflow will upload a Python Package using Twine when a release is created
|
||||
# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries
|
||||
|
||||
# This workflow uses actions that are not certified by GitHub.
|
||||
# They are provided by a third-party and are governed by
|
||||
# separate terms of service, privacy policy, and support
|
||||
# documentation.
|
||||
|
||||
name: Upload Python Package
|
||||
|
||||
on:
|
||||
release:
|
||||
types: [published]
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
deploy:
|
||||
|
||||
runs-on: ubuntu-latest
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v3
|
||||
with:
|
||||
python-version: '3.x'
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
python -m pip install --upgrade pip
|
||||
pip install build
|
||||
- name: Build package
|
||||
run: python -m build
|
||||
- name: Publish package
|
||||
uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
|
||||
with:
|
||||
user: __token__
|
||||
password: ${{ secrets.PYPI_API_TOKEN }}
|
||||
+21
-36
@@ -1,38 +1,23 @@
|
||||
.vscode
|
||||
crewai
|
||||
.cache
|
||||
__pycache__
|
||||
chroma.sqlite3
|
||||
test/
|
||||
dist/
|
||||
.env
|
||||
assets/*
|
||||
.idea
|
||||
# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
|
||||
|
||||
# dependencies
|
||||
/node_modules
|
||||
/.pnp
|
||||
.pnp.js
|
||||
|
||||
# testing
|
||||
/coverage
|
||||
|
||||
# production
|
||||
/build
|
||||
|
||||
# misc
|
||||
.DS_Store
|
||||
.pytest_cache
|
||||
praisonAI.egg-info
|
||||
flagged
|
||||
test.yaml
|
||||
db
|
||||
praisonai_prompt.txt
|
||||
watch.sh
|
||||
docs.sh
|
||||
other
|
||||
.env.local
|
||||
.env.development.local
|
||||
.env.test.local
|
||||
.env.production.local
|
||||
|
||||
.chainlit
|
||||
|
||||
site
|
||||
# Ignore Sphinx build directory
|
||||
docs/_build/
|
||||
docs/source/
|
||||
docs/build/
|
||||
|
||||
# Ignore Sphinx cache
|
||||
docs/_build/doctrees/
|
||||
docs/_build/doctrees/*
|
||||
docs/_build/doctrees/.doctrees
|
||||
|
||||
# Ignore Sphinx logs and local build files
|
||||
docs/_build/.buildinfo
|
||||
docs/_build/.buildinfo/*
|
||||
docs/_build/.buildinfo/.buildinfo
|
||||
npm-debug.log*
|
||||
yarn-debug.log*
|
||||
yarn-error.log*
|
||||
@@ -1,23 +0,0 @@
|
||||
# .readthedocs.yaml
|
||||
# Read the Docs configuration file
|
||||
# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
|
||||
|
||||
# Required
|
||||
version: 2
|
||||
|
||||
# Set the OS, Python version and other tools you might need
|
||||
build:
|
||||
os: ubuntu-22.04
|
||||
tools:
|
||||
python: "3.12"
|
||||
jobs:
|
||||
post_install:
|
||||
- bash docs/generate_docs.sh
|
||||
|
||||
mkdocs:
|
||||
configuration: mkdocs.yml
|
||||
fail_on_warning: true
|
||||
|
||||
python:
|
||||
install:
|
||||
- requirements: docs/requirements.txt
|
||||
@@ -1,6 +0,0 @@
|
||||
FROM python:3.11-slim
|
||||
WORKDIR /app
|
||||
COPY . .
|
||||
RUN pip install flask praisonai==0.0.23 gunicorn markdown
|
||||
EXPOSE 8080
|
||||
CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]
|
||||
@@ -1,20 +0,0 @@
|
||||
MIT License
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining
|
||||
a copy of this software and associated documentation files (the
|
||||
"Software"), to deal in the Software without restriction, including
|
||||
without limitation the rights to use, copy, modify, merge, publish,
|
||||
distribute, sublicense, and/or sell copies of the Software, and to
|
||||
permit persons to whom the Software is furnished to do so, subject to
|
||||
the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be
|
||||
included in all copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
|
||||
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
|
||||
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
|
||||
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
|
||||
LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
|
||||
OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
|
||||
WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
|
||||
@@ -1,291 +1,70 @@
|
||||
# Praison AI
|
||||
# Getting Started with Create React App
|
||||
|
||||
Praison AI, leveraging both AutoGen and CrewAI or any other agent framework, represents a low-code, centralised framework designed to simplify the creation and orchestration of multi-agent systems for various LLM applications, emphasizing ease of use, customization, and human-agent interaction.
|
||||
This project was bootstrapped with [Create React App](https://github.com/facebook/create-react-app).
|
||||
|
||||
## TL;DR
|
||||
```bash
|
||||
pip install praisonai
|
||||
export OPENAI_API_KEY="Enter your API key"
|
||||
praisonai --init create a movie script about dog in moon
|
||||
praisonai
|
||||
```
|
||||
## Available Scripts
|
||||
|
||||
## Installation
|
||||
In the project directory, you can run:
|
||||
|
||||
```bash
|
||||
pip install praisonai
|
||||
```
|
||||
### `npm start`
|
||||
|
||||
## Initialise
|
||||
Runs the app in the development mode.\
|
||||
Open [http://localhost:3000](http://localhost:3000) to view it in your browser.
|
||||
|
||||
```bash
|
||||
export OPENAI_API_KEY="Enter your API key"
|
||||
```
|
||||
The page will reload when you make changes.\
|
||||
You may also see any lint errors in the console.
|
||||
|
||||
Generate your OPENAI API KEY from here: https://platform.openai.com/api-keys
|
||||
### `npm test`
|
||||
|
||||
Note: You can use other providers such as Ollama, Mistral ... etc. Details are provided at the bottom.
|
||||
|
||||
```bash
|
||||
praisonai --init create a movie script about dog in moon
|
||||
```
|
||||
This will automatically create agents.yaml file in the current directory.
|
||||
Launches the test runner in the interactive watch mode.\
|
||||
See the section about [running tests](https://facebook.github.io/create-react-app/docs/running-tests) for more information.
|
||||
|
||||
### To initialse with a specific agent framework (Optional):
|
||||
### `npm run build`
|
||||
|
||||
```bash
|
||||
praisonai --framework autogen --init create movie script about cat in mars
|
||||
```
|
||||
Builds the app for production to the `build` folder.\
|
||||
It correctly bundles React in production mode and optimizes the build for the best performance.
|
||||
|
||||
## Run
|
||||
The build is minified and the filenames include the hashes.\
|
||||
Your app is ready to be deployed!
|
||||
|
||||
```bash
|
||||
praisonai
|
||||
```
|
||||
See the section about [deployment](https://facebook.github.io/create-react-app/docs/deployment) for more information.
|
||||
|
||||
or
|
||||
|
||||
```bash
|
||||
python -m praisonai
|
||||
```
|
||||
### `npm run eject`
|
||||
|
||||
### Specify the agent framework (Optional):
|
||||
**Note: this is a one-way operation. Once you `eject`, you can't go back!**
|
||||
|
||||
```bash
|
||||
praisonai --framework autogen
|
||||
```
|
||||
If you aren't satisfied with the build tool and configuration choices, you can `eject` at any time. This command will remove the single build dependency from your project.
|
||||
|
||||
### Full Automatic Mode
|
||||
Instead, it will copy all the configuration files and the transitive dependencies (webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except `eject` will still work, but they will point to the copied scripts so you can tweak them. At this point you're on your own.
|
||||
|
||||
```bash
|
||||
praisonai --auto create a movie script about Dog in Moon
|
||||
```
|
||||
You don't have to ever use `eject`. The curated feature set is suitable for small and middle deployments, and you shouldn't feel obligated to use this feature. However we understand that this tool wouldn't be useful if you couldn't customize it when you are ready for it.
|
||||
|
||||
## Create Custom Tools
|
||||
## Learn More
|
||||
|
||||
### TL;DR to Create a Custom Tool
|
||||
You can learn more in the [Create React App documentation](https://facebook.github.io/create-react-app/docs/getting-started).
|
||||
|
||||
```bash
|
||||
pip install praisonai duckduckgo-search
|
||||
export OPENAI_API_KEY="Enter your API key"
|
||||
praisonai --init research about the latest AI News and prepare a detailed report
|
||||
```
|
||||
To learn React, check out the [React documentation](https://reactjs.org/).
|
||||
|
||||
- Add `- InternetSearchTool` in the agents.yaml file in the tools section.
|
||||
- Create a file called tools.py and add this code [tools.py](./tools.py)
|
||||
### Code Splitting
|
||||
|
||||
```bash
|
||||
praisonai
|
||||
```
|
||||
This section has moved here: [https://facebook.github.io/create-react-app/docs/code-splitting](https://facebook.github.io/create-react-app/docs/code-splitting)
|
||||
|
||||
### Pre-requisite to Create a Custom Tool
|
||||
`agents.yaml` file should be present in the current directory.
|
||||
### Analyzing the Bundle Size
|
||||
|
||||
If it doesn't exist, create it by running the command `praisonai --init research about the latest AI News and prepare a detailed report`.
|
||||
This section has moved here: [https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size](https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size)
|
||||
|
||||
### Step 1 to Create a Custom Tool
|
||||
### Making a Progressive Web App
|
||||
|
||||
Create a file called tools.py in the same directory as the agents.yaml file.
|
||||
This section has moved here: [https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app](https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app)
|
||||
|
||||
```python
|
||||
# example tools.py
|
||||
from duckduckgo_search import DDGS
|
||||
from praisonai_tools import BaseTool
|
||||
### Advanced Configuration
|
||||
|
||||
class InternetSearchTool(BaseTool):
|
||||
name: str = "InternetSearchTool"
|
||||
description: str = "Search Internet for relevant information based on a query or latest news"
|
||||
This section has moved here: [https://facebook.github.io/create-react-app/docs/advanced-configuration](https://facebook.github.io/create-react-app/docs/advanced-configuration)
|
||||
|
||||
def _run(self, query: str):
|
||||
ddgs = DDGS()
|
||||
results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
|
||||
return results
|
||||
```
|
||||
### Deployment
|
||||
|
||||
### Step 2 to Create a Custom Tool
|
||||
This section has moved here: [https://facebook.github.io/create-react-app/docs/deployment](https://facebook.github.io/create-react-app/docs/deployment)
|
||||
|
||||
Add the tool to the agents.yaml file as show below under the tools section `- InternetSearchTool`.
|
||||
|
||||
```yaml
|
||||
framework: crewai
|
||||
topic: research about the latest AI News and prepare a detailed report
|
||||
roles:
|
||||
research_analyst:
|
||||
backstory: Experienced in gathering and analyzing data related to AI news trends.
|
||||
goal: Analyze AI News trends
|
||||
role: Research Analyst
|
||||
tasks:
|
||||
gather_data:
|
||||
description: Conduct in-depth research on the latest AI News trends from reputable
|
||||
sources.
|
||||
expected_output: Comprehensive report on current AI News trends.
|
||||
tools:
|
||||
- InternetSearchTool
|
||||
```
|
||||
|
||||
## Test
|
||||
|
||||
```bash
|
||||
python -m unittest tests.test
|
||||
```
|
||||
|
||||
## Agents Playbook
|
||||
|
||||
### Simple Playbook Example
|
||||
|
||||
```yaml
|
||||
framework: crewai
|
||||
topic: Artificial Intelligence
|
||||
roles:
|
||||
screenwriter:
|
||||
backstory: 'Skilled in crafting scripts with engaging dialogue about {topic}.'
|
||||
goal: Create scripts from concepts.
|
||||
role: Screenwriter
|
||||
tasks:
|
||||
scriptwriting_task:
|
||||
description: 'Develop scripts with compelling characters and dialogue about {topic}.'
|
||||
expected_output: 'Complete script ready for production.'
|
||||
```
|
||||
|
||||
### Detailed Playbook Example
|
||||
|
||||
```yaml
|
||||
framework: crewai
|
||||
topic: Artificial Intelligence
|
||||
roles:
|
||||
movie_concept_creator:
|
||||
backstory: 'Creative thinker with a deep understanding of cinematic storytelling,
|
||||
capable of using AI-generated storylines to create unique and compelling movie
|
||||
ideas.'
|
||||
goal: Generate engaging movie concepts using AI storylines
|
||||
role: Movie Concept Creator
|
||||
tasks:
|
||||
movie_concept_development:
|
||||
description: 'Develop movie concepts from AI-generated storylines, ensuring
|
||||
they are engaging and have strong narrative arcs.'
|
||||
expected_output: 'Well-structured movie concept document with character
|
||||
bios, settings, and plot outlines.'
|
||||
screenwriter:
|
||||
backstory: 'Expert in writing engaging dialogue and script structure, able to
|
||||
turn movie concepts into production-ready scripts.'
|
||||
goal: Write compelling scripts based on movie concepts
|
||||
role: Screenwriter
|
||||
tasks:
|
||||
scriptwriting_task:
|
||||
description: 'Turn movie concepts into polished scripts with well-developed
|
||||
characters, strong dialogue, and effective scene transitions.'
|
||||
expected_output: 'Production-ready script with a beginning, middle, and
|
||||
end, along with character development and engaging dialogues.'
|
||||
editor:
|
||||
backstory: 'Adept at identifying inconsistencies, improving language usage,
|
||||
and maintaining the overall flow of the script.'
|
||||
goal: Refine the scripts and ensure continuity of the movie storyline
|
||||
role: Editor
|
||||
tasks:
|
||||
editing_task:
|
||||
description: 'Review, edit, and refine the scripts to ensure they are cohesive
|
||||
and follow a well-structured narrative.'
|
||||
expected_output: 'A polished final draft of the script with no inconsistencies,
|
||||
strong character development, and effective dialogue.'
|
||||
dependencies: []
|
||||
```
|
||||
|
||||
## Include praisonai package in your project
|
||||
|
||||
```python
|
||||
from praisonai import PraisonAI
|
||||
|
||||
def basic(): # Basic Mode
|
||||
praison_ai = PraisonAI(agent_file="agents.yaml")
|
||||
praison_ai.main()
|
||||
|
||||
def advanced(): # Advanced Mode with options
|
||||
praison_ai = PraisonAI(
|
||||
agent_file="agents.yaml",
|
||||
framework="autogen",
|
||||
)
|
||||
praison_ai.main()
|
||||
|
||||
def auto(): # Full Automatic Mode
|
||||
praison_ai = PraisonAI(
|
||||
auto="Create a movie script about car in mars",
|
||||
framework="autogen"
|
||||
)
|
||||
print(praison_ai.framework)
|
||||
praison_ai.main()
|
||||
|
||||
if __name__ == "__main__":
|
||||
basic()
|
||||
advanced()
|
||||
auto()
|
||||
```
|
||||
|
||||
## Include CrewAI Tools
|
||||
|
||||
```
|
||||
pip install "praisonai[crewai-tools]"
|
||||
```
|
||||
|
||||
## Deploy
|
||||
|
||||
```bash
|
||||
gcloud init
|
||||
gcloud services enable run.googleapis.com
|
||||
gcloud services enable containerregistry.googleapis.com
|
||||
gcloud services enable cloudbuild.googleapis.com
|
||||
|
||||
export OPENAI_MODEL_NAME="gpt-4o"
|
||||
export OPENAI_API_KEY="Enter your API key"
|
||||
export OPENAI_API_BASE="https://api.openai.com/v1"
|
||||
|
||||
yes | gcloud auth configure-docker us-central1-docker.pkg.dev
|
||||
gcloud artifacts repositories create praisonai-repository --repository-format=docker --location=us-central1
|
||||
|
||||
PROJECT_ID=$(gcloud config get-value project)
|
||||
TAG="latest"
|
||||
docker build --platform linux/amd64 -t gcr.io/${PROJECT_ID}/praisonai-app:${TAG} .
|
||||
docker tag gcr.io/${PROJECT_ID}/praisonai-app:${TAG} us-central1-docker.pkg.dev/${PROJECT_ID}/praisonai-repository/praisonai-app:${TAG}
|
||||
docker push us-central1-docker.pkg.dev/${PROJECT_ID}/praisonai-repository/praisonai-app:${TAG}
|
||||
|
||||
gcloud run deploy praisonai-service \
|
||||
--image us-central1-docker.pkg.dev/${PROJECT_ID}/praisonai-repository/praisonai-app:${TAG} \
|
||||
--platform managed \
|
||||
--region us-central1 \
|
||||
--allow-unauthenticated \
|
||||
--set-env-vars OPENAI_MODEL_NAME=${OPENAI_MODEL_NAME},OPENAI_API_KEY=${OPENAI_API_KEY},OPENAI_API_BASE=${OPENAI_API_BASE}
|
||||
```
|
||||
|
||||
## Other Models
|
||||
|
||||
```bash
|
||||
Ollama
|
||||
OPENAI_API_BASE='http://localhost:11434/v1'
|
||||
OPENAI_MODEL_NAME='mistral'
|
||||
OPENAI_API_KEY='NA'
|
||||
|
||||
FastChat¶
|
||||
OPENAI_API_BASE="http://localhost:8001/v1"
|
||||
OPENAI_MODEL_NAME='oh-2.5m7b-q51'
|
||||
OPENAI_API_KEY=NA
|
||||
|
||||
LM Studio¶
|
||||
OPENAI_API_BASE="http://localhost:8000/v1"
|
||||
OPENAI_MODEL_NAME=NA
|
||||
OPENAI_API_KEY=NA
|
||||
|
||||
Mistral API¶
|
||||
OPENAI_API_BASE=https://api.mistral.ai/v1
|
||||
OPENAI_MODEL_NAME="mistral-small"
|
||||
OPENAI_API_KEY=your-mistral-api-key
|
||||
```
|
||||
|
||||
## Contributing
|
||||
|
||||
- Fork on GitHub: Use the "Fork" button on the repository page.
|
||||
- Clone your fork: `git clone https://github.com/yourusername/praisonAI.git`
|
||||
- Create a branch: `git checkout -b new-feature`
|
||||
- Make changes and commit: `git commit -am "Add some feature"`
|
||||
- Push to your fork: `git push origin new-feature`
|
||||
- Submit a pull request via GitHub's web interface.
|
||||
- Await feedback from project maintainers.
|
||||
### `npm run build` fails to minify
|
||||
|
||||
This section has moved here: [https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify](https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify)
|
||||
@@ -1,42 +0,0 @@
|
||||
framework: crewai
|
||||
topic: create a movie about cat in mars based on https://blog.celtx.com/how-to-write-a-good-story/
|
||||
roles:
|
||||
story_developer:
|
||||
backstory: Experienced in crafting compelling narratives for a diverse audience.
|
||||
goal: Develop an engaging storyline for a movie about cats in Mars
|
||||
role: Story Developer
|
||||
tasks:
|
||||
storyline_creation:
|
||||
description: Create a captivating storyline for a movie about cats in Mars
|
||||
inspired by the blog post at https://blog.celtx.com/how-to-write-a-good-story/
|
||||
expected_output: Detailed storyline with key plot points, character motivations,
|
||||
and setting descriptions.
|
||||
tools:
|
||||
- 'ScrapeWebsiteTool'
|
||||
screenwriter:
|
||||
backstory: Proficient in translating complex narratives into engaging screenplay
|
||||
format.
|
||||
goal: Transform the storyline into a script
|
||||
role: Screenwriter
|
||||
tasks:
|
||||
script_creation:
|
||||
description: Transform the developed storyline into a script for the movie
|
||||
about cats in Mars.
|
||||
expected_output: Complete script with dialogues, scenes, and stage directions
|
||||
ready for production.
|
||||
tools:
|
||||
- ''
|
||||
director:
|
||||
backstory: Experienced in visual storytelling and bringing scripts to life on
|
||||
screen.
|
||||
goal: Oversee the production of the movie
|
||||
role: Director
|
||||
tasks:
|
||||
movie_production:
|
||||
description: Take the script and lead the team to produce the movie 'Cats
|
||||
in Mars' based on the provided content and script.
|
||||
expected_output: Completed movie ready for distribution with compelling visuals
|
||||
and engaging storytelling.
|
||||
tools:
|
||||
- ''
|
||||
dependencies: []
|
||||
-39
@@ -1,39 +0,0 @@
|
||||
framework: autogen
|
||||
topic: research about the causes of lung disease
|
||||
roles:
|
||||
research_analyst:
|
||||
backstory: Experienced in analyzing scientific data related to respiratory health.
|
||||
goal: Analyze data on lung diseases
|
||||
role: Research Analyst
|
||||
tasks:
|
||||
data_analysis:
|
||||
description: Gather and analyze data on the causes and risk factors of lung
|
||||
diseases.
|
||||
expected_output: Report detailing key findings on lung disease causes.
|
||||
tools:
|
||||
- 'InternetSearchTool'
|
||||
medical_writer:
|
||||
backstory: Skilled in translating complex medical information into accessible
|
||||
content.
|
||||
goal: Compile comprehensive content on lung disease causes
|
||||
role: Medical Writer
|
||||
tasks:
|
||||
content_creation:
|
||||
description: Create detailed content summarizing the research findings on
|
||||
lung disease causes.
|
||||
expected_output: Document outlining various causes and risk factors of lung
|
||||
diseases.
|
||||
tools:
|
||||
- ''
|
||||
editor:
|
||||
backstory: Proficient in editing medical content for accuracy and clarity.
|
||||
goal: Review and refine content on lung disease causes
|
||||
role: Editor
|
||||
tasks:
|
||||
content_review:
|
||||
description: Edit and refine the compiled content on lung disease causes for
|
||||
accuracy and coherence.
|
||||
expected_output: Finalized document on lung disease causes ready for dissemination.
|
||||
tools:
|
||||
- ''
|
||||
dependencies: []
|
||||
@@ -1,42 +0,0 @@
|
||||
framework: crewai
|
||||
topic: create a movie about cat in mars based on https://www.grammarly.com/blog/how-to-write-a-story/
|
||||
roles:
|
||||
story_developer:
|
||||
backstory: Experienced in crafting compelling narratives for a diverse audience.
|
||||
goal: Develop an engaging storyline for a movie about cats in Mars
|
||||
role: Story Developer
|
||||
tasks:
|
||||
storyline_creation:
|
||||
description: Create a captivating storyline for a movie about cats in Mars
|
||||
inspired by the blog post at https://www.grammarly.com/blog/how-to-write-a-story/
|
||||
expected_output: Detailed storyline with key plot points, character motivations,
|
||||
and setting descriptions.
|
||||
tools:
|
||||
- 'ScrapeWebsiteTool'
|
||||
screenwriter:
|
||||
backstory: Proficient in translating complex narratives into engaging screenplay
|
||||
format.
|
||||
goal: Transform the storyline into a script
|
||||
role: Screenwriter
|
||||
tasks:
|
||||
script_creation:
|
||||
description: Transform the developed storyline into a script for the movie
|
||||
about cats in Mars.
|
||||
expected_output: Complete script with dialogues, scenes, and stage directions
|
||||
ready for production.
|
||||
tools:
|
||||
- ''
|
||||
director:
|
||||
backstory: Experienced in visual storytelling and bringing scripts to life on
|
||||
screen.
|
||||
goal: Oversee the production of the movie
|
||||
role: Director
|
||||
tasks:
|
||||
movie_production:
|
||||
description: Take the script and lead the team to produce the movie 'Cats
|
||||
in Mars' based on the provided content and script.
|
||||
expected_output: Completed movie ready for distribution with compelling visuals
|
||||
and engaging storytelling.
|
||||
tools:
|
||||
- ''
|
||||
dependencies: []
|
||||
@@ -1,43 +0,0 @@
|
||||
framework: crewai
|
||||
topic: research about nvidia growth
|
||||
roles:
|
||||
data_collector:
|
||||
backstory: An experienced researcher with the ability to efficiently collect and
|
||||
organize vast amounts of data.
|
||||
goal: Gather information on Nvidia's growth
|
||||
role: Data Collector
|
||||
tasks:
|
||||
data_collection_task:
|
||||
description: Collect data on Nvidia's growth from various sources such as
|
||||
financial reports, news articles, and company announcements.
|
||||
expected_output: A comprehensive document detailing data points on Nvidia's
|
||||
growth over the years.
|
||||
tools:
|
||||
- 'YouSearchTool'
|
||||
data_analyst:
|
||||
backstory: Specializes in extracting insights from large datasets, proficient
|
||||
in quantitative and qualitative analysis.
|
||||
goal: Analyze the collected data to identify trends and patterns
|
||||
role: Data Analyst
|
||||
tasks:
|
||||
data_analysis_task:
|
||||
description: Analyze the collected data to identify key trends and patterns
|
||||
in Nvidia's growth.
|
||||
expected_output: An analytical report summarizing trends, patterns, and key
|
||||
growth metrics of Nvidia.
|
||||
tools:
|
||||
- ''
|
||||
report_preparer:
|
||||
backstory: Experienced in creating detailed reports and presentations, turning
|
||||
analytical data into actionable insights.
|
||||
goal: Generate a final report on Nvidia's growth
|
||||
role: Report Preparer
|
||||
tasks:
|
||||
report_preparation_task:
|
||||
description: Create a detailed report based on the analysis, highlighting
|
||||
Nvidia's growth trajectory.
|
||||
expected_output: A polished, comprehensive report summarizing Nvidia's growth
|
||||
with visual aids and key insights.
|
||||
tools:
|
||||
- ''
|
||||
dependencies: []
|
||||
@@ -1,26 +0,0 @@
|
||||
framework: crewai
|
||||
topic: research about the latest AI News and prepare a detailed report
|
||||
roles:
|
||||
research_analyst:
|
||||
backstory: Experienced in gathering and analyzing data related to AI news trends.
|
||||
goal: Analyze AI News trends
|
||||
role: Research Analyst
|
||||
tasks:
|
||||
gather_data:
|
||||
description: Conduct in-depth research on the latest AI News trends from reputable
|
||||
sources.
|
||||
expected_output: Comprehensive report on current AI News trends.
|
||||
tools:
|
||||
- 'InternetSearchTool'
|
||||
content_creator:
|
||||
backstory: Skilled in writing informative and engaging content about AI News.
|
||||
goal: Create detailed reports on AI News
|
||||
role: Content Creator
|
||||
tasks:
|
||||
compile_report:
|
||||
description: Compile all gathered data into a detailed report with analysis
|
||||
and insights.
|
||||
expected_output: Well-structured report on the latest AI News with key insights.
|
||||
tools:
|
||||
- ''
|
||||
dependencies: []
|
||||
@@ -1,39 +0,0 @@
|
||||
framework: crewai
|
||||
topic: research about the causes of lung disease
|
||||
roles:
|
||||
research_analyst:
|
||||
backstory: Experienced in analyzing scientific data related to respiratory health.
|
||||
goal: Analyze data on lung diseases
|
||||
role: Research Analyst
|
||||
tasks:
|
||||
data_analysis:
|
||||
description: Gather and analyze data on the causes and risk factors of lung
|
||||
diseases.
|
||||
expected_output: Report detailing key findings on lung disease causes.
|
||||
tools:
|
||||
- 'PubmedQueryRun'
|
||||
medical_writer:
|
||||
backstory: Skilled in translating complex medical information into accessible
|
||||
content.
|
||||
goal: Compile comprehensive content on lung disease causes
|
||||
role: Medical Writer
|
||||
tasks:
|
||||
content_creation:
|
||||
description: Create detailed content summarizing the research findings on
|
||||
lung disease causes.
|
||||
expected_output: Document outlining various causes and risk factors of lung
|
||||
diseases.
|
||||
tools:
|
||||
- ''
|
||||
editor:
|
||||
backstory: Proficient in editing medical content for accuracy and clarity.
|
||||
goal: Review and refine content on lung disease causes
|
||||
role: Editor
|
||||
tasks:
|
||||
content_review:
|
||||
description: Edit and refine the compiled content on lung disease causes for
|
||||
accuracy and coherence.
|
||||
expected_output: Finalized document on lung disease causes ready for dissemination.
|
||||
tools:
|
||||
- ''
|
||||
dependencies: []
|
||||
@@ -1,18 +0,0 @@
|
||||
from flask import Flask
|
||||
from praisonai import PraisonAI
|
||||
import markdown
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
def basic():
|
||||
praison_ai = PraisonAI(agent_file="agents.yaml")
|
||||
return praison_ai.main()
|
||||
|
||||
@app.route('/')
|
||||
def home():
|
||||
output = basic()
|
||||
html_output = markdown.markdown(output)
|
||||
return f'<html><body>{html_output}</body></html>'
|
||||
|
||||
if __name__ == "__main__":
|
||||
app.run(debug=True)
|
||||
@@ -1 +0,0 @@
|
||||
# Welcome to Praison AI! 🚀🤖
|
||||
@@ -1 +0,0 @@
|
||||
docs.praison.ai
|
||||
@@ -1,20 +0,0 @@
|
||||
# Minimal makefile for Sphinx documentation
|
||||
#
|
||||
|
||||
# You can set these variables from the command line, and also
|
||||
# from the environment for the first two.
|
||||
SPHINXOPTS ?=
|
||||
SPHINXBUILD ?= sphinx-build
|
||||
SOURCEDIR = .
|
||||
BUILDDIR = _build
|
||||
|
||||
# Put it first so that "make" without argument is like "make help".
|
||||
help:
|
||||
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
|
||||
|
||||
.PHONY: help Makefile
|
||||
|
||||
# Catch-all target: route all unknown targets to Sphinx using the new
|
||||
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
|
||||
%: Makefile
|
||||
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
|
||||
@@ -1,60 +0,0 @@
|
||||
# Agents Playbook
|
||||
|
||||
## Simple Playbook Example
|
||||
|
||||
```yaml
|
||||
framework: crewai
|
||||
topic: Artificial Intelligence
|
||||
roles:
|
||||
screenwriter:
|
||||
backstory: 'Skilled in crafting scripts with engaging dialogue about {topic}.'
|
||||
goal: Create scripts from concepts.
|
||||
role: Screenwriter
|
||||
tasks:
|
||||
scriptwriting_task:
|
||||
description: 'Develop scripts with compelling characters and dialogue about {topic}.'
|
||||
expected_output: 'Complete script ready for production.'
|
||||
```
|
||||
|
||||
## Detailed Playbook Example
|
||||
|
||||
```yaml
|
||||
framework: crewai
|
||||
topic: Artificial Intelligence
|
||||
roles:
|
||||
movie_concept_creator:
|
||||
backstory: 'Creative thinker with a deep understanding of cinematic storytelling,
|
||||
capable of using AI-generated storylines to create unique and compelling movie
|
||||
ideas.'
|
||||
goal: Generate engaging movie concepts using AI storylines
|
||||
role: Movie Concept Creator
|
||||
tasks:
|
||||
movie_concept_development:
|
||||
description: 'Develop movie concepts from AI-generated storylines, ensuring
|
||||
they are engaging and have strong narrative arcs.'
|
||||
expected_output: 'Well-structured movie concept document with character
|
||||
bios, settings, and plot outlines.'
|
||||
screenwriter:
|
||||
backstory: 'Expert in writing engaging dialogue and script structure, able to
|
||||
turn movie concepts into production-ready scripts.'
|
||||
goal: Write compelling scripts based on movie concepts
|
||||
role: Screenwriter
|
||||
tasks:
|
||||
scriptwriting_task:
|
||||
description: 'Turn movie concepts into polished scripts with well-developed
|
||||
characters, strong dialogue, and effective scene transitions.'
|
||||
expected_output: 'Production-ready script with a beginning, middle, and
|
||||
end, along with character development and engaging dialogues.'
|
||||
editor:
|
||||
backstory: 'Adept at identifying inconsistencies, improving language usage,
|
||||
and maintaining the overall flow of the script.'
|
||||
goal: Refine the scripts and ensure continuity of the movie storyline
|
||||
role: Editor
|
||||
tasks:
|
||||
editing_task:
|
||||
description: 'Review, edit, and refine the scripts to ensure they are cohesive
|
||||
and follow a well-structured narrative.'
|
||||
expected_output: 'A polished final draft of the script with no inconsistencies,
|
||||
strong character development, and effective dialogue.'
|
||||
dependencies: []
|
||||
```
|
||||
@@ -1,6 +0,0 @@
|
||||
# API Reference
|
||||
::: praisonai
|
||||
::: praisonai.auto
|
||||
::: praisonai.agents_generator
|
||||
::: praisonai.cli
|
||||
::: praisonai.deploy
|
||||
@@ -1,967 +0,0 @@
|
||||
<!doctype html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
|
||||
<meta name="generator" content="pdoc 0.10.0" />
|
||||
<title>praisonai.agents_generator API documentation</title>
|
||||
<meta name="description" content="" />
|
||||
<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/sanitize.min.css" integrity="sha256-PK9q560IAAa6WVRRh76LtCaI8pjTJ2z11v0miyNNjrs=" crossorigin>
|
||||
<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/typography.min.css" integrity="sha256-7l/o7C8jubJiy74VsKTidCy1yBkRtiUGbVkYBylBqUg=" crossorigin>
|
||||
<link rel="stylesheet preload" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/styles/github.min.css" crossorigin>
|
||||
<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
|
||||
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|
||||
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|
||||
<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>praisonai.agents_generator</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># praisonai/agents_generator.py
|
||||
|
||||
import sys
|
||||
from .version import __version__
|
||||
import yaml, os
|
||||
from rich import print
|
||||
from dotenv import load_dotenv
|
||||
from crewai import Agent, Task, Crew
|
||||
from crewai.telemetry import Telemetry
|
||||
load_dotenv()
|
||||
import autogen
|
||||
import gradio as gr
|
||||
import argparse
|
||||
from .auto import AutoGenerator
|
||||
from crewai_tools import (
|
||||
CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, DirectoryReadTool,
|
||||
FileReadTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, RagTool,
|
||||
ScrapeElementFromWebsiteTool, ScrapeWebsiteTool, WebsiteSearchTool, XMLSearchTool, YoutubeChannelSearchTool,
|
||||
YoutubeVideoSearchTool
|
||||
)
|
||||
from .inbuilt_tools import *
|
||||
import inspect
|
||||
from pathlib import Path
|
||||
import importlib
|
||||
import importlib.util
|
||||
from praisonai_tools import BaseTool
|
||||
import os
|
||||
|
||||
os.environ["OTEL_SDK_DISABLED"] = "true"
|
||||
|
||||
def noop(*args, **kwargs):
|
||||
pass
|
||||
|
||||
def disable_crewai_telemetry():
|
||||
for attr in dir(Telemetry):
|
||||
if callable(getattr(Telemetry, attr)) and not attr.startswith("__"):
|
||||
setattr(Telemetry, attr, noop)
|
||||
|
||||
disable_crewai_telemetry()
|
||||
|
||||
class AgentsGenerator:
|
||||
def __init__(self, agent_file, framework, config_list):
|
||||
"""
|
||||
Initialize the AgentsGenerator object.
|
||||
|
||||
Parameters:
|
||||
agent_file (str): The path to the agent file.
|
||||
framework (str): The framework to be used for the agents.
|
||||
config_list (list): A list of configurations for the agents.
|
||||
|
||||
Attributes:
|
||||
agent_file (str): The path to the agent file.
|
||||
framework (str): The framework to be used for the agents.
|
||||
config_list (list): A list of configurations for the agents.
|
||||
"""
|
||||
self.agent_file = agent_file
|
||||
self.framework = framework
|
||||
self.config_list = config_list
|
||||
|
||||
def is_function_or_decorated(self, obj):
|
||||
"""
|
||||
Checks if the given object is a function or has a __call__ method.
|
||||
|
||||
Parameters:
|
||||
obj (object): The object to be checked.
|
||||
|
||||
Returns:
|
||||
bool: True if the object is a function or has a __call__ method, False otherwise.
|
||||
"""
|
||||
return inspect.isfunction(obj) or hasattr(obj, '__call__')
|
||||
|
||||
def load_tools_from_module(self, module_path):
|
||||
"""
|
||||
Loads tools from a specified module path.
|
||||
|
||||
Parameters:
|
||||
module_path (str): The path to the module containing the tools.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary containing the names of the tools as keys and the corresponding functions or objects as values.
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If the specified module path does not exist.
|
||||
"""
|
||||
spec = importlib.util.spec_from_file_location("tools_module", module_path)
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(module)
|
||||
return {name: obj for name, obj in inspect.getmembers(module, self.is_function_or_decorated)}
|
||||
|
||||
def load_tools_from_module_class(self, module_path):
|
||||
"""
|
||||
Loads tools from a specified module path containing classes that inherit from BaseTool or are part of langchain_community.tools package.
|
||||
|
||||
Parameters:
|
||||
module_path (str): The path to the module containing the tools.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If the specified module path does not exist.
|
||||
"""
|
||||
spec = importlib.util.spec_from_file_location("tools_module", module_path)
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(module)
|
||||
return {name: obj() for name, obj in inspect.getmembers(module, lambda x: inspect.isclass(x) and (x.__module__.startswith('langchain_community.tools') or issubclass(x, BaseTool)) and x is not BaseTool)}
|
||||
|
||||
def load_tools_from_package(self, package_path):
|
||||
"""
|
||||
Loads tools from a specified package path containing modules with functions or classes.
|
||||
|
||||
Parameters:
|
||||
package_path (str): The path to the package containing the tools.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If the specified package path does not exist.
|
||||
|
||||
This function iterates through all the .py files in the specified package path, excluding those that start with "__". For each file, it imports the corresponding module and checks if it contains any functions or classes that can be loaded as tools. The function then returns a dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
|
||||
"""
|
||||
tools_dict = {}
|
||||
for module_file in os.listdir(package_path):
|
||||
if module_file.endswith('.py') and not module_file.startswith('__'):
|
||||
module_name = f"{package_path.name}.{module_file[:-3]}" # Remove .py for import
|
||||
module = importlib.import_module(module_name)
|
||||
for name, obj in inspect.getmembers(module, self.is_function_or_decorated):
|
||||
tools_dict[name] = obj
|
||||
return tools_dict
|
||||
|
||||
def generate_crew_and_kickoff(self):
|
||||
"""
|
||||
Generates a crew of agents and initiates tasks based on the provided configuration.
|
||||
|
||||
Parameters:
|
||||
agent_file (str): The path to the agent file.
|
||||
framework (str): The framework to be used for the agents.
|
||||
config_list (list): A list of configurations for the agents.
|
||||
|
||||
Returns:
|
||||
str: The output of the tasks performed by the crew of agents.
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If the specified agent file does not exist.
|
||||
|
||||
This function first loads the agent configuration from the specified file. It then initializes the tools required for the agents based on the specified framework. If the specified framework is "autogen", it loads the LLM configuration dynamically and creates an AssistantAgent for each role in the configuration. It then adds tools to the agents if specified in the configuration. Finally, it prepares tasks for the agents based on the configuration and initiates the tasks using the crew of agents. If the specified framework is not "autogen", it creates a crew of agents and initiates tasks based on the configuration.
|
||||
"""
|
||||
if self.agent_file == '/app/api:app' or self.agent_file == 'api:app':
|
||||
self.agent_file = 'agents.yaml'
|
||||
try:
|
||||
with open(self.agent_file, 'r') as f:
|
||||
config = yaml.safe_load(f)
|
||||
except FileNotFoundError:
|
||||
print(f"File not found: {self.agent_file}")
|
||||
return
|
||||
|
||||
topic = config['topic']
|
||||
tools_dict = {
|
||||
'CodeDocsSearchTool': CodeDocsSearchTool(),
|
||||
'CSVSearchTool': CSVSearchTool(),
|
||||
'DirectorySearchTool': DirectorySearchTool(),
|
||||
'DOCXSearchTool': DOCXSearchTool(),
|
||||
'DirectoryReadTool': DirectoryReadTool(),
|
||||
'FileReadTool': FileReadTool(),
|
||||
# 'GithubSearchTool': GithubSearchTool(),
|
||||
# 'SeperDevTool': SeperDevTool(),
|
||||
'TXTSearchTool': TXTSearchTool(),
|
||||
'JSONSearchTool': JSONSearchTool(),
|
||||
'MDXSearchTool': MDXSearchTool(),
|
||||
'PDFSearchTool': PDFSearchTool(),
|
||||
# 'PGSearchTool': PGSearchTool(),
|
||||
'RagTool': RagTool(),
|
||||
'ScrapeElementFromWebsiteTool': ScrapeElementFromWebsiteTool(),
|
||||
'ScrapeWebsiteTool': ScrapeWebsiteTool(),
|
||||
'WebsiteSearchTool': WebsiteSearchTool(),
|
||||
'XMLSearchTool': XMLSearchTool(),
|
||||
'YoutubeChannelSearchTool': YoutubeChannelSearchTool(),
|
||||
'YoutubeVideoSearchTool': YoutubeVideoSearchTool(),
|
||||
}
|
||||
root_directory = os.getcwd()
|
||||
tools_py_path = os.path.join(root_directory, 'tools.py')
|
||||
tools_dir_path = Path(root_directory) / 'tools'
|
||||
|
||||
if os.path.isfile(tools_py_path):
|
||||
tools_dict.update(self.load_tools_from_module_class(tools_py_path))
|
||||
# print("tools.py exists in the root directory. Loading tools.py and skipping tools folder.")
|
||||
elif tools_dir_path.is_dir():
|
||||
tools_dict.update(self.load_tools_from_module_class(tools_dir_path))
|
||||
# print("tools folder exists in the root directory")
|
||||
|
||||
framework = self.framework or config.get('framework')
|
||||
|
||||
agents = {}
|
||||
tasks = []
|
||||
if framework == "autogen":
|
||||
# Load the LLM configuration dynamically
|
||||
# print(self.config_list)
|
||||
llm_config = {"config_list": self.config_list}
|
||||
|
||||
# Assuming the user proxy agent is set up as per your requirements
|
||||
user_proxy = autogen.UserProxyAgent(
|
||||
name="User",
|
||||
human_input_mode="NEVER",
|
||||
is_termination_msg=lambda x: (x.get("content") or "").rstrip().rstrip(".").lower().endswith("terminate") or "TERMINATE" in (x.get("content") or ""),
|
||||
code_execution_config={
|
||||
"work_dir": "coding",
|
||||
"use_docker": False,
|
||||
},
|
||||
# additional setup for the user proxy agent
|
||||
)
|
||||
|
||||
for role, details in config['roles'].items():
|
||||
agent_name = details['role'].format(topic=topic).replace("{topic}", topic)
|
||||
agent_goal = details['goal'].format(topic=topic)
|
||||
# Creating an AssistantAgent for each role dynamically
|
||||
agents[role] = autogen.AssistantAgent(
|
||||
name=agent_name,
|
||||
llm_config=llm_config,
|
||||
system_message=details['backstory'].format(topic=topic)+". Must Reply \"TERMINATE\" in the end when everything is done.",
|
||||
)
|
||||
for tool in details.get('tools', []):
|
||||
if tool in tools_dict:
|
||||
try:
|
||||
tool_class = globals()[f'autogen_{type(tools_dict[tool]).__name__}']
|
||||
print(f"Found {tool_class.__name__} for {tool}")
|
||||
except KeyError:
|
||||
print(f"Warning: autogen_{type(tools_dict[tool]).__name__} function not found. Skipping this tool.")
|
||||
continue
|
||||
tool_class(agents[role], user_proxy)
|
||||
|
||||
# Preparing tasks for initiate_chats
|
||||
for task_name, task_details in details.get('tasks', {}).items():
|
||||
description_filled = task_details['description'].format(topic=topic)
|
||||
expected_output_filled = task_details['expected_output'].format(topic=topic)
|
||||
|
||||
chat_task = {
|
||||
"recipient": agents[role],
|
||||
"message": description_filled,
|
||||
"summary_method": "last_msg",
|
||||
# Additional fields like carryover can be added based on dependencies
|
||||
}
|
||||
tasks.append(chat_task)
|
||||
|
||||
response = user_proxy.initiate_chats(tasks)
|
||||
result = "### Output ###\n"+response[-1].summary if hasattr(response[-1], 'summary') else ""
|
||||
else:
|
||||
for role, details in config['roles'].items():
|
||||
role_filled = details['role'].format(topic=topic)
|
||||
goal_filled = details['goal'].format(topic=topic)
|
||||
backstory_filled = details['backstory'].format(topic=topic)
|
||||
|
||||
# Adding tools to the agent if exists
|
||||
agent_tools = [tools_dict[tool] for tool in details.get('tools', []) if tool in tools_dict]
|
||||
agent = Agent(role=role_filled, goal=goal_filled, backstory=backstory_filled, tools=agent_tools, allow_delegation=False)
|
||||
agents[role] = agent
|
||||
|
||||
for task_name, task_details in details.get('tasks', {}).items():
|
||||
description_filled = task_details['description'].format(topic=topic)
|
||||
expected_output_filled = task_details['expected_output'].format(topic=topic)
|
||||
|
||||
task = Task(description=description_filled, expected_output=expected_output_filled, agent=agent)
|
||||
tasks.append(task)
|
||||
crew = Crew(
|
||||
agents=list(agents.values()),
|
||||
tasks=tasks,
|
||||
verbose=2
|
||||
)
|
||||
|
||||
response = crew.kickoff()
|
||||
result = f"### Task Output ###\n{response}"
|
||||
return result</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="praisonai.agents_generator.disable_crewai_telemetry"><code class="name flex">
|
||||
<span>def <span class="ident">disable_crewai_telemetry</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def disable_crewai_telemetry():
|
||||
for attr in dir(Telemetry):
|
||||
if callable(getattr(Telemetry, attr)) and not attr.startswith("__"):
|
||||
setattr(Telemetry, attr, noop)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.agents_generator.noop"><code class="name flex">
|
||||
<span>def <span class="ident">noop</span></span>(<span>*args, **kwargs)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def noop(*args, **kwargs):
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="praisonai.agents_generator.AgentsGenerator"><code class="flex name class">
|
||||
<span>class <span class="ident">AgentsGenerator</span></span>
|
||||
<span>(</span><span>agent_file, framework, config_list)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>Initialize the AgentsGenerator object.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<p>agent_file (str): The path to the agent file.
|
||||
framework (str): The framework to be used for the agents.
|
||||
config_list (list): A list of configurations for the agents.</p>
|
||||
<h2 id="attributes">Attributes</h2>
|
||||
<dl>
|
||||
<dt><strong><code>agent_file</code></strong> : <code>str</code></dt>
|
||||
<dd>The path to the agent file.</dd>
|
||||
<dt><strong><code>framework</code></strong> : <code>str</code></dt>
|
||||
<dd>The framework to be used for the agents.</dd>
|
||||
<dt><strong><code>config_list</code></strong> : <code>list</code></dt>
|
||||
<dd>A list of configurations for the agents.</dd>
|
||||
</dl></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class AgentsGenerator:
|
||||
def __init__(self, agent_file, framework, config_list):
|
||||
"""
|
||||
Initialize the AgentsGenerator object.
|
||||
|
||||
Parameters:
|
||||
agent_file (str): The path to the agent file.
|
||||
framework (str): The framework to be used for the agents.
|
||||
config_list (list): A list of configurations for the agents.
|
||||
|
||||
Attributes:
|
||||
agent_file (str): The path to the agent file.
|
||||
framework (str): The framework to be used for the agents.
|
||||
config_list (list): A list of configurations for the agents.
|
||||
"""
|
||||
self.agent_file = agent_file
|
||||
self.framework = framework
|
||||
self.config_list = config_list
|
||||
|
||||
def is_function_or_decorated(self, obj):
|
||||
"""
|
||||
Checks if the given object is a function or has a __call__ method.
|
||||
|
||||
Parameters:
|
||||
obj (object): The object to be checked.
|
||||
|
||||
Returns:
|
||||
bool: True if the object is a function or has a __call__ method, False otherwise.
|
||||
"""
|
||||
return inspect.isfunction(obj) or hasattr(obj, '__call__')
|
||||
|
||||
def load_tools_from_module(self, module_path):
|
||||
"""
|
||||
Loads tools from a specified module path.
|
||||
|
||||
Parameters:
|
||||
module_path (str): The path to the module containing the tools.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary containing the names of the tools as keys and the corresponding functions or objects as values.
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If the specified module path does not exist.
|
||||
"""
|
||||
spec = importlib.util.spec_from_file_location("tools_module", module_path)
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(module)
|
||||
return {name: obj for name, obj in inspect.getmembers(module, self.is_function_or_decorated)}
|
||||
|
||||
def load_tools_from_module_class(self, module_path):
|
||||
"""
|
||||
Loads tools from a specified module path containing classes that inherit from BaseTool or are part of langchain_community.tools package.
|
||||
|
||||
Parameters:
|
||||
module_path (str): The path to the module containing the tools.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If the specified module path does not exist.
|
||||
"""
|
||||
spec = importlib.util.spec_from_file_location("tools_module", module_path)
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(module)
|
||||
return {name: obj() for name, obj in inspect.getmembers(module, lambda x: inspect.isclass(x) and (x.__module__.startswith('langchain_community.tools') or issubclass(x, BaseTool)) and x is not BaseTool)}
|
||||
|
||||
def load_tools_from_package(self, package_path):
|
||||
"""
|
||||
Loads tools from a specified package path containing modules with functions or classes.
|
||||
|
||||
Parameters:
|
||||
package_path (str): The path to the package containing the tools.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If the specified package path does not exist.
|
||||
|
||||
This function iterates through all the .py files in the specified package path, excluding those that start with "__". For each file, it imports the corresponding module and checks if it contains any functions or classes that can be loaded as tools. The function then returns a dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
|
||||
"""
|
||||
tools_dict = {}
|
||||
for module_file in os.listdir(package_path):
|
||||
if module_file.endswith('.py') and not module_file.startswith('__'):
|
||||
module_name = f"{package_path.name}.{module_file[:-3]}" # Remove .py for import
|
||||
module = importlib.import_module(module_name)
|
||||
for name, obj in inspect.getmembers(module, self.is_function_or_decorated):
|
||||
tools_dict[name] = obj
|
||||
return tools_dict
|
||||
|
||||
def generate_crew_and_kickoff(self):
|
||||
"""
|
||||
Generates a crew of agents and initiates tasks based on the provided configuration.
|
||||
|
||||
Parameters:
|
||||
agent_file (str): The path to the agent file.
|
||||
framework (str): The framework to be used for the agents.
|
||||
config_list (list): A list of configurations for the agents.
|
||||
|
||||
Returns:
|
||||
str: The output of the tasks performed by the crew of agents.
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If the specified agent file does not exist.
|
||||
|
||||
This function first loads the agent configuration from the specified file. It then initializes the tools required for the agents based on the specified framework. If the specified framework is "autogen", it loads the LLM configuration dynamically and creates an AssistantAgent for each role in the configuration. It then adds tools to the agents if specified in the configuration. Finally, it prepares tasks for the agents based on the configuration and initiates the tasks using the crew of agents. If the specified framework is not "autogen", it creates a crew of agents and initiates tasks based on the configuration.
|
||||
"""
|
||||
if self.agent_file == '/app/api:app' or self.agent_file == 'api:app':
|
||||
self.agent_file = 'agents.yaml'
|
||||
try:
|
||||
with open(self.agent_file, 'r') as f:
|
||||
config = yaml.safe_load(f)
|
||||
except FileNotFoundError:
|
||||
print(f"File not found: {self.agent_file}")
|
||||
return
|
||||
|
||||
topic = config['topic']
|
||||
tools_dict = {
|
||||
'CodeDocsSearchTool': CodeDocsSearchTool(),
|
||||
'CSVSearchTool': CSVSearchTool(),
|
||||
'DirectorySearchTool': DirectorySearchTool(),
|
||||
'DOCXSearchTool': DOCXSearchTool(),
|
||||
'DirectoryReadTool': DirectoryReadTool(),
|
||||
'FileReadTool': FileReadTool(),
|
||||
# 'GithubSearchTool': GithubSearchTool(),
|
||||
# 'SeperDevTool': SeperDevTool(),
|
||||
'TXTSearchTool': TXTSearchTool(),
|
||||
'JSONSearchTool': JSONSearchTool(),
|
||||
'MDXSearchTool': MDXSearchTool(),
|
||||
'PDFSearchTool': PDFSearchTool(),
|
||||
# 'PGSearchTool': PGSearchTool(),
|
||||
'RagTool': RagTool(),
|
||||
'ScrapeElementFromWebsiteTool': ScrapeElementFromWebsiteTool(),
|
||||
'ScrapeWebsiteTool': ScrapeWebsiteTool(),
|
||||
'WebsiteSearchTool': WebsiteSearchTool(),
|
||||
'XMLSearchTool': XMLSearchTool(),
|
||||
'YoutubeChannelSearchTool': YoutubeChannelSearchTool(),
|
||||
'YoutubeVideoSearchTool': YoutubeVideoSearchTool(),
|
||||
}
|
||||
root_directory = os.getcwd()
|
||||
tools_py_path = os.path.join(root_directory, 'tools.py')
|
||||
tools_dir_path = Path(root_directory) / 'tools'
|
||||
|
||||
if os.path.isfile(tools_py_path):
|
||||
tools_dict.update(self.load_tools_from_module_class(tools_py_path))
|
||||
# print("tools.py exists in the root directory. Loading tools.py and skipping tools folder.")
|
||||
elif tools_dir_path.is_dir():
|
||||
tools_dict.update(self.load_tools_from_module_class(tools_dir_path))
|
||||
# print("tools folder exists in the root directory")
|
||||
|
||||
framework = self.framework or config.get('framework')
|
||||
|
||||
agents = {}
|
||||
tasks = []
|
||||
if framework == "autogen":
|
||||
# Load the LLM configuration dynamically
|
||||
# print(self.config_list)
|
||||
llm_config = {"config_list": self.config_list}
|
||||
|
||||
# Assuming the user proxy agent is set up as per your requirements
|
||||
user_proxy = autogen.UserProxyAgent(
|
||||
name="User",
|
||||
human_input_mode="NEVER",
|
||||
is_termination_msg=lambda x: (x.get("content") or "").rstrip().rstrip(".").lower().endswith("terminate") or "TERMINATE" in (x.get("content") or ""),
|
||||
code_execution_config={
|
||||
"work_dir": "coding",
|
||||
"use_docker": False,
|
||||
},
|
||||
# additional setup for the user proxy agent
|
||||
)
|
||||
|
||||
for role, details in config['roles'].items():
|
||||
agent_name = details['role'].format(topic=topic).replace("{topic}", topic)
|
||||
agent_goal = details['goal'].format(topic=topic)
|
||||
# Creating an AssistantAgent for each role dynamically
|
||||
agents[role] = autogen.AssistantAgent(
|
||||
name=agent_name,
|
||||
llm_config=llm_config,
|
||||
system_message=details['backstory'].format(topic=topic)+". Must Reply \"TERMINATE\" in the end when everything is done.",
|
||||
)
|
||||
for tool in details.get('tools', []):
|
||||
if tool in tools_dict:
|
||||
try:
|
||||
tool_class = globals()[f'autogen_{type(tools_dict[tool]).__name__}']
|
||||
print(f"Found {tool_class.__name__} for {tool}")
|
||||
except KeyError:
|
||||
print(f"Warning: autogen_{type(tools_dict[tool]).__name__} function not found. Skipping this tool.")
|
||||
continue
|
||||
tool_class(agents[role], user_proxy)
|
||||
|
||||
# Preparing tasks for initiate_chats
|
||||
for task_name, task_details in details.get('tasks', {}).items():
|
||||
description_filled = task_details['description'].format(topic=topic)
|
||||
expected_output_filled = task_details['expected_output'].format(topic=topic)
|
||||
|
||||
chat_task = {
|
||||
"recipient": agents[role],
|
||||
"message": description_filled,
|
||||
"summary_method": "last_msg",
|
||||
# Additional fields like carryover can be added based on dependencies
|
||||
}
|
||||
tasks.append(chat_task)
|
||||
|
||||
response = user_proxy.initiate_chats(tasks)
|
||||
result = "### Output ###\n"+response[-1].summary if hasattr(response[-1], 'summary') else ""
|
||||
else:
|
||||
for role, details in config['roles'].items():
|
||||
role_filled = details['role'].format(topic=topic)
|
||||
goal_filled = details['goal'].format(topic=topic)
|
||||
backstory_filled = details['backstory'].format(topic=topic)
|
||||
|
||||
# Adding tools to the agent if exists
|
||||
agent_tools = [tools_dict[tool] for tool in details.get('tools', []) if tool in tools_dict]
|
||||
agent = Agent(role=role_filled, goal=goal_filled, backstory=backstory_filled, tools=agent_tools, allow_delegation=False)
|
||||
agents[role] = agent
|
||||
|
||||
for task_name, task_details in details.get('tasks', {}).items():
|
||||
description_filled = task_details['description'].format(topic=topic)
|
||||
expected_output_filled = task_details['expected_output'].format(topic=topic)
|
||||
|
||||
task = Task(description=description_filled, expected_output=expected_output_filled, agent=agent)
|
||||
tasks.append(task)
|
||||
crew = Crew(
|
||||
agents=list(agents.values()),
|
||||
tasks=tasks,
|
||||
verbose=2
|
||||
)
|
||||
|
||||
response = crew.kickoff()
|
||||
result = f"### Task Output ###\n{response}"
|
||||
return result</code></pre>
|
||||
</details>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="praisonai.agents_generator.AgentsGenerator.generate_crew_and_kickoff"><code class="name flex">
|
||||
<span>def <span class="ident">generate_crew_and_kickoff</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>Generates a crew of agents and initiates tasks based on the provided configuration.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<p>agent_file (str): The path to the agent file.
|
||||
framework (str): The framework to be used for the agents.
|
||||
config_list (list): A list of configurations for the agents.</p>
|
||||
<h2 id="returns">Returns</h2>
|
||||
<dl>
|
||||
<dt><code>str</code></dt>
|
||||
<dd>The output of the tasks performed by the crew of agents.</dd>
|
||||
</dl>
|
||||
<h2 id="raises">Raises</h2>
|
||||
<dl>
|
||||
<dt><code>FileNotFoundError</code></dt>
|
||||
<dd>If the specified agent file does not exist.</dd>
|
||||
</dl>
|
||||
<p>This function first loads the agent configuration from the specified file. It then initializes the tools required for the agents based on the specified framework. If the specified framework is "autogen", it loads the LLM configuration dynamically and creates an AssistantAgent for each role in the configuration. It then adds tools to the agents if specified in the configuration. Finally, it prepares tasks for the agents based on the configuration and initiates the tasks using the crew of agents. If the specified framework is not "autogen", it creates a crew of agents and initiates tasks based on the configuration.</p></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def generate_crew_and_kickoff(self):
|
||||
"""
|
||||
Generates a crew of agents and initiates tasks based on the provided configuration.
|
||||
|
||||
Parameters:
|
||||
agent_file (str): The path to the agent file.
|
||||
framework (str): The framework to be used for the agents.
|
||||
config_list (list): A list of configurations for the agents.
|
||||
|
||||
Returns:
|
||||
str: The output of the tasks performed by the crew of agents.
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If the specified agent file does not exist.
|
||||
|
||||
This function first loads the agent configuration from the specified file. It then initializes the tools required for the agents based on the specified framework. If the specified framework is "autogen", it loads the LLM configuration dynamically and creates an AssistantAgent for each role in the configuration. It then adds tools to the agents if specified in the configuration. Finally, it prepares tasks for the agents based on the configuration and initiates the tasks using the crew of agents. If the specified framework is not "autogen", it creates a crew of agents and initiates tasks based on the configuration.
|
||||
"""
|
||||
if self.agent_file == '/app/api:app' or self.agent_file == 'api:app':
|
||||
self.agent_file = 'agents.yaml'
|
||||
try:
|
||||
with open(self.agent_file, 'r') as f:
|
||||
config = yaml.safe_load(f)
|
||||
except FileNotFoundError:
|
||||
print(f"File not found: {self.agent_file}")
|
||||
return
|
||||
|
||||
topic = config['topic']
|
||||
tools_dict = {
|
||||
'CodeDocsSearchTool': CodeDocsSearchTool(),
|
||||
'CSVSearchTool': CSVSearchTool(),
|
||||
'DirectorySearchTool': DirectorySearchTool(),
|
||||
'DOCXSearchTool': DOCXSearchTool(),
|
||||
'DirectoryReadTool': DirectoryReadTool(),
|
||||
'FileReadTool': FileReadTool(),
|
||||
# 'GithubSearchTool': GithubSearchTool(),
|
||||
# 'SeperDevTool': SeperDevTool(),
|
||||
'TXTSearchTool': TXTSearchTool(),
|
||||
'JSONSearchTool': JSONSearchTool(),
|
||||
'MDXSearchTool': MDXSearchTool(),
|
||||
'PDFSearchTool': PDFSearchTool(),
|
||||
# 'PGSearchTool': PGSearchTool(),
|
||||
'RagTool': RagTool(),
|
||||
'ScrapeElementFromWebsiteTool': ScrapeElementFromWebsiteTool(),
|
||||
'ScrapeWebsiteTool': ScrapeWebsiteTool(),
|
||||
'WebsiteSearchTool': WebsiteSearchTool(),
|
||||
'XMLSearchTool': XMLSearchTool(),
|
||||
'YoutubeChannelSearchTool': YoutubeChannelSearchTool(),
|
||||
'YoutubeVideoSearchTool': YoutubeVideoSearchTool(),
|
||||
}
|
||||
root_directory = os.getcwd()
|
||||
tools_py_path = os.path.join(root_directory, 'tools.py')
|
||||
tools_dir_path = Path(root_directory) / 'tools'
|
||||
|
||||
if os.path.isfile(tools_py_path):
|
||||
tools_dict.update(self.load_tools_from_module_class(tools_py_path))
|
||||
# print("tools.py exists in the root directory. Loading tools.py and skipping tools folder.")
|
||||
elif tools_dir_path.is_dir():
|
||||
tools_dict.update(self.load_tools_from_module_class(tools_dir_path))
|
||||
# print("tools folder exists in the root directory")
|
||||
|
||||
framework = self.framework or config.get('framework')
|
||||
|
||||
agents = {}
|
||||
tasks = []
|
||||
if framework == "autogen":
|
||||
# Load the LLM configuration dynamically
|
||||
# print(self.config_list)
|
||||
llm_config = {"config_list": self.config_list}
|
||||
|
||||
# Assuming the user proxy agent is set up as per your requirements
|
||||
user_proxy = autogen.UserProxyAgent(
|
||||
name="User",
|
||||
human_input_mode="NEVER",
|
||||
is_termination_msg=lambda x: (x.get("content") or "").rstrip().rstrip(".").lower().endswith("terminate") or "TERMINATE" in (x.get("content") or ""),
|
||||
code_execution_config={
|
||||
"work_dir": "coding",
|
||||
"use_docker": False,
|
||||
},
|
||||
# additional setup for the user proxy agent
|
||||
)
|
||||
|
||||
for role, details in config['roles'].items():
|
||||
agent_name = details['role'].format(topic=topic).replace("{topic}", topic)
|
||||
agent_goal = details['goal'].format(topic=topic)
|
||||
# Creating an AssistantAgent for each role dynamically
|
||||
agents[role] = autogen.AssistantAgent(
|
||||
name=agent_name,
|
||||
llm_config=llm_config,
|
||||
system_message=details['backstory'].format(topic=topic)+". Must Reply \"TERMINATE\" in the end when everything is done.",
|
||||
)
|
||||
for tool in details.get('tools', []):
|
||||
if tool in tools_dict:
|
||||
try:
|
||||
tool_class = globals()[f'autogen_{type(tools_dict[tool]).__name__}']
|
||||
print(f"Found {tool_class.__name__} for {tool}")
|
||||
except KeyError:
|
||||
print(f"Warning: autogen_{type(tools_dict[tool]).__name__} function not found. Skipping this tool.")
|
||||
continue
|
||||
tool_class(agents[role], user_proxy)
|
||||
|
||||
# Preparing tasks for initiate_chats
|
||||
for task_name, task_details in details.get('tasks', {}).items():
|
||||
description_filled = task_details['description'].format(topic=topic)
|
||||
expected_output_filled = task_details['expected_output'].format(topic=topic)
|
||||
|
||||
chat_task = {
|
||||
"recipient": agents[role],
|
||||
"message": description_filled,
|
||||
"summary_method": "last_msg",
|
||||
# Additional fields like carryover can be added based on dependencies
|
||||
}
|
||||
tasks.append(chat_task)
|
||||
|
||||
response = user_proxy.initiate_chats(tasks)
|
||||
result = "### Output ###\n"+response[-1].summary if hasattr(response[-1], 'summary') else ""
|
||||
else:
|
||||
for role, details in config['roles'].items():
|
||||
role_filled = details['role'].format(topic=topic)
|
||||
goal_filled = details['goal'].format(topic=topic)
|
||||
backstory_filled = details['backstory'].format(topic=topic)
|
||||
|
||||
# Adding tools to the agent if exists
|
||||
agent_tools = [tools_dict[tool] for tool in details.get('tools', []) if tool in tools_dict]
|
||||
agent = Agent(role=role_filled, goal=goal_filled, backstory=backstory_filled, tools=agent_tools, allow_delegation=False)
|
||||
agents[role] = agent
|
||||
|
||||
for task_name, task_details in details.get('tasks', {}).items():
|
||||
description_filled = task_details['description'].format(topic=topic)
|
||||
expected_output_filled = task_details['expected_output'].format(topic=topic)
|
||||
|
||||
task = Task(description=description_filled, expected_output=expected_output_filled, agent=agent)
|
||||
tasks.append(task)
|
||||
crew = Crew(
|
||||
agents=list(agents.values()),
|
||||
tasks=tasks,
|
||||
verbose=2
|
||||
)
|
||||
|
||||
response = crew.kickoff()
|
||||
result = f"### Task Output ###\n{response}"
|
||||
return result</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.agents_generator.AgentsGenerator.is_function_or_decorated"><code class="name flex">
|
||||
<span>def <span class="ident">is_function_or_decorated</span></span>(<span>self, obj)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>Checks if the given object is a function or has a <strong>call</strong> method.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<p>obj (object): The object to be checked.</p>
|
||||
<h2 id="returns">Returns</h2>
|
||||
<dl>
|
||||
<dt><code>bool</code></dt>
|
||||
<dd>True if the object is a function or has a <strong>call</strong> method, False otherwise.</dd>
|
||||
</dl></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def is_function_or_decorated(self, obj):
|
||||
"""
|
||||
Checks if the given object is a function or has a __call__ method.
|
||||
|
||||
Parameters:
|
||||
obj (object): The object to be checked.
|
||||
|
||||
Returns:
|
||||
bool: True if the object is a function or has a __call__ method, False otherwise.
|
||||
"""
|
||||
return inspect.isfunction(obj) or hasattr(obj, '__call__')</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.agents_generator.AgentsGenerator.load_tools_from_module"><code class="name flex">
|
||||
<span>def <span class="ident">load_tools_from_module</span></span>(<span>self, module_path)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>Loads tools from a specified module path.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<p>module_path (str): The path to the module containing the tools.</p>
|
||||
<h2 id="returns">Returns</h2>
|
||||
<dl>
|
||||
<dt><code>dict</code></dt>
|
||||
<dd>A dictionary containing the names of the tools as keys and the corresponding functions or objects as values.</dd>
|
||||
</dl>
|
||||
<h2 id="raises">Raises</h2>
|
||||
<dl>
|
||||
<dt><code>FileNotFoundError</code></dt>
|
||||
<dd>If the specified module path does not exist.</dd>
|
||||
</dl></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def load_tools_from_module(self, module_path):
|
||||
"""
|
||||
Loads tools from a specified module path.
|
||||
|
||||
Parameters:
|
||||
module_path (str): The path to the module containing the tools.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary containing the names of the tools as keys and the corresponding functions or objects as values.
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If the specified module path does not exist.
|
||||
"""
|
||||
spec = importlib.util.spec_from_file_location("tools_module", module_path)
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(module)
|
||||
return {name: obj for name, obj in inspect.getmembers(module, self.is_function_or_decorated)}</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.agents_generator.AgentsGenerator.load_tools_from_module_class"><code class="name flex">
|
||||
<span>def <span class="ident">load_tools_from_module_class</span></span>(<span>self, module_path)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>Loads tools from a specified module path containing classes that inherit from BaseTool or are part of langchain_community.tools package.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<p>module_path (str): The path to the module containing the tools.</p>
|
||||
<h2 id="returns">Returns</h2>
|
||||
<dl>
|
||||
<dt><code>dict</code></dt>
|
||||
<dd>A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.</dd>
|
||||
</dl>
|
||||
<h2 id="raises">Raises</h2>
|
||||
<dl>
|
||||
<dt><code>FileNotFoundError</code></dt>
|
||||
<dd>If the specified module path does not exist.</dd>
|
||||
</dl></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def load_tools_from_module_class(self, module_path):
|
||||
"""
|
||||
Loads tools from a specified module path containing classes that inherit from BaseTool or are part of langchain_community.tools package.
|
||||
|
||||
Parameters:
|
||||
module_path (str): The path to the module containing the tools.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If the specified module path does not exist.
|
||||
"""
|
||||
spec = importlib.util.spec_from_file_location("tools_module", module_path)
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(module)
|
||||
return {name: obj() for name, obj in inspect.getmembers(module, lambda x: inspect.isclass(x) and (x.__module__.startswith('langchain_community.tools') or issubclass(x, BaseTool)) and x is not BaseTool)}</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.agents_generator.AgentsGenerator.load_tools_from_package"><code class="name flex">
|
||||
<span>def <span class="ident">load_tools_from_package</span></span>(<span>self, package_path)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>Loads tools from a specified package path containing modules with functions or classes.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<p>package_path (str): The path to the package containing the tools.</p>
|
||||
<h2 id="returns">Returns</h2>
|
||||
<dl>
|
||||
<dt><code>dict</code></dt>
|
||||
<dd>A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.</dd>
|
||||
</dl>
|
||||
<h2 id="raises">Raises</h2>
|
||||
<dl>
|
||||
<dt><code>FileNotFoundError</code></dt>
|
||||
<dd>If the specified package path does not exist.</dd>
|
||||
</dl>
|
||||
<p>This function iterates through all the .py files in the specified package path, excluding those that start with "__". For each file, it imports the corresponding module and checks if it contains any functions or classes that can be loaded as tools. The function then returns a dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.</p></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def load_tools_from_package(self, package_path):
|
||||
"""
|
||||
Loads tools from a specified package path containing modules with functions or classes.
|
||||
|
||||
Parameters:
|
||||
package_path (str): The path to the package containing the tools.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If the specified package path does not exist.
|
||||
|
||||
This function iterates through all the .py files in the specified package path, excluding those that start with "__". For each file, it imports the corresponding module and checks if it contains any functions or classes that can be loaded as tools. The function then returns a dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
|
||||
"""
|
||||
tools_dict = {}
|
||||
for module_file in os.listdir(package_path):
|
||||
if module_file.endswith('.py') and not module_file.startswith('__'):
|
||||
module_name = f"{package_path.name}.{module_file[:-3]}" # Remove .py for import
|
||||
module = importlib.import_module(module_name)
|
||||
for name, obj in inspect.getmembers(module, self.is_function_or_decorated):
|
||||
tools_dict[name] = obj
|
||||
return tools_dict</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="praisonai.agents_generator.disable_crewai_telemetry" href="#praisonai.agents_generator.disable_crewai_telemetry">disable_crewai_telemetry</a></code></li>
|
||||
<li><code><a title="praisonai.agents_generator.noop" href="#praisonai.agents_generator.noop">noop</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="praisonai.agents_generator.AgentsGenerator" href="#praisonai.agents_generator.AgentsGenerator">AgentsGenerator</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="praisonai.agents_generator.AgentsGenerator.generate_crew_and_kickoff" href="#praisonai.agents_generator.AgentsGenerator.generate_crew_and_kickoff">generate_crew_and_kickoff</a></code></li>
|
||||
<li><code><a title="praisonai.agents_generator.AgentsGenerator.is_function_or_decorated" href="#praisonai.agents_generator.AgentsGenerator.is_function_or_decorated">is_function_or_decorated</a></code></li>
|
||||
<li><code><a title="praisonai.agents_generator.AgentsGenerator.load_tools_from_module" href="#praisonai.agents_generator.AgentsGenerator.load_tools_from_module">load_tools_from_module</a></code></li>
|
||||
<li><code><a title="praisonai.agents_generator.AgentsGenerator.load_tools_from_module_class" href="#praisonai.agents_generator.AgentsGenerator.load_tools_from_module_class">load_tools_from_module_class</a></code></li>
|
||||
<li><code><a title="praisonai.agents_generator.AgentsGenerator.load_tools_from_package" href="#praisonai.agents_generator.AgentsGenerator.load_tools_from_package">load_tools_from_package</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
||||
</main>
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<footer id="footer">
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<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
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</footer>
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</body>
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</html>
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||||
@@ -1,942 +0,0 @@
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<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
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<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
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<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
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</head>
|
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<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>praisonai.auto</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">from openai import OpenAI
|
||||
from pydantic import BaseModel
|
||||
from typing import Dict, List
|
||||
import instructor
|
||||
import os
|
||||
import json
|
||||
import yaml
|
||||
from rich import print
|
||||
|
||||
# Define Pydantic models outside of the generate method
|
||||
class TaskDetails(BaseModel):
|
||||
description: str
|
||||
expected_output: str
|
||||
|
||||
class RoleDetails(BaseModel):
|
||||
role: str
|
||||
goal: str
|
||||
backstory: str
|
||||
tasks: Dict[str, TaskDetails]
|
||||
tools: List[str]
|
||||
|
||||
class TeamStructure(BaseModel):
|
||||
roles: Dict[str, RoleDetails]
|
||||
|
||||
class AutoGenerator:
|
||||
def __init__(self, topic="Movie Story writing about AI", agent_file="test.yaml", framework="crewai"):
|
||||
"""
|
||||
Initialize the AutoGenerator class with the specified topic, agent file, and framework.
|
||||
note: autogen framework is different from this AutoGenerator class.
|
||||
|
||||
Args:
|
||||
topic (str, optional): The topic for the generated team structure. Defaults to "Movie Story writing about AI".
|
||||
agent_file (str, optional): The name of the YAML file to save the generated team structure. Defaults to "test.yaml".
|
||||
framework (str, optional): The framework for the generated team structure. Defaults to "crewai".
|
||||
|
||||
Attributes:
|
||||
config_list (list): A list containing the configuration details for the OpenAI API.
|
||||
topic (str): The specified topic for the generated team structure.
|
||||
agent_file (str): The specified name of the YAML file to save the generated team structure.
|
||||
framework (str): The specified framework for the generated team structure.
|
||||
client (instructor.Client): An instance of the instructor.Client class initialized with the specified OpenAI API configuration.
|
||||
"""
|
||||
self.config_list = [
|
||||
{
|
||||
'model': os.environ.get("OPENAI_MODEL_NAME", "gpt-4o"),
|
||||
'base_url': os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
|
||||
}
|
||||
]
|
||||
self.topic = topic
|
||||
self.agent_file = agent_file
|
||||
self.framework = framework or "crewai"
|
||||
self.client = instructor.patch(
|
||||
OpenAI(
|
||||
base_url=self.config_list[0]['base_url'],
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
),
|
||||
mode=instructor.Mode.JSON,
|
||||
)
|
||||
|
||||
def generate(self):
|
||||
"""
|
||||
Generates a team structure for the specified topic.
|
||||
|
||||
Args:
|
||||
None
|
||||
|
||||
Returns:
|
||||
str: The full path of the YAML file containing the generated team structure.
|
||||
|
||||
Raises:
|
||||
Exception: If the generation process fails.
|
||||
|
||||
Usage:
|
||||
generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
|
||||
path = generator.generate()
|
||||
print(path)
|
||||
"""
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.config_list[0]['model'],
|
||||
response_model=TeamStructure,
|
||||
max_retries=10,
|
||||
messages=[
|
||||
{"role": "system", "content": "You are a helpful assistant designed to output complex team structures."},
|
||||
{"role": "user", "content": self.get_user_content()}
|
||||
]
|
||||
)
|
||||
json_data = json.loads(response.model_dump_json())
|
||||
self.convert_and_save(json_data)
|
||||
full_path = os.path.abspath(self.agent_file)
|
||||
return full_path
|
||||
|
||||
def convert_and_save(self, json_data):
|
||||
"""Converts the provided JSON data into the desired YAML format and saves it to a file.
|
||||
|
||||
Args:
|
||||
json_data (dict): The JSON data representing the team structure.
|
||||
topic (str, optional): The topic to be inserted into the YAML. Defaults to "Artificial Intelligence".
|
||||
agent_file (str, optional): The name of the YAML file to save. Defaults to "test.yaml".
|
||||
"""
|
||||
|
||||
yaml_data = {
|
||||
"framework": self.framework,
|
||||
"topic": self.topic,
|
||||
"roles": {},
|
||||
"dependencies": []
|
||||
}
|
||||
|
||||
for role_id, role_details in json_data['roles'].items():
|
||||
yaml_data['roles'][role_id] = {
|
||||
"backstory": "" + role_details['backstory'],
|
||||
"goal": role_details['goal'],
|
||||
"role": role_details['role'],
|
||||
"tasks": {},
|
||||
# "tools": role_details.get('tools', []),
|
||||
"tools": ['']
|
||||
}
|
||||
|
||||
for task_id, task_details in role_details['tasks'].items():
|
||||
yaml_data['roles'][role_id]['tasks'][task_id] = {
|
||||
"description": "" + task_details['description'],
|
||||
"expected_output": "" + task_details['expected_output']
|
||||
}
|
||||
|
||||
# Save to YAML file, maintaining the order
|
||||
with open(self.agent_file, 'w') as f:
|
||||
yaml.dump(yaml_data, f, allow_unicode=True, sort_keys=False)
|
||||
|
||||
def get_user_content(self):
|
||||
"""
|
||||
Generates a prompt for the OpenAI API to generate a team structure.
|
||||
|
||||
Args:
|
||||
None
|
||||
|
||||
Returns:
|
||||
str: The prompt for the OpenAI API.
|
||||
|
||||
Usage:
|
||||
generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
|
||||
prompt = generator.get_user_content()
|
||||
print(prompt)
|
||||
"""
|
||||
user_content = """Generate a team structure for \"""" + self.topic + """\" task.
|
||||
No Input data will be provided to the team.
|
||||
The team will work in sequence. First role will pass the output to the next role, and so on.
|
||||
The last role will generate the final output.
|
||||
Think step by step.
|
||||
With maximum 3 roles, each with 1 task. Include role goals, backstories, task descriptions, and expected outputs.
|
||||
List of Available Tools: CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, DirectoryReadTool, FileReadTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, RagTool, ScrapeElementFromWebsiteTool, ScrapeWebsiteTool, WebsiteSearchTool, XMLSearchTool, YoutubeChannelSearchTool, YoutubeVideoSearchTool.
|
||||
Only use Available Tools. Do Not use any other tools.
|
||||
Example Below:
|
||||
Use below example to understand the structure of the output.
|
||||
The final role you create should satisfy the provided task: """ + self.topic + """.
|
||||
{
|
||||
"roles": {
|
||||
"narrative_designer": {
|
||||
"role": "Narrative Designer",
|
||||
"goal": "Create AI storylines",
|
||||
"backstory": "Skilled in narrative development for AI, with a focus on story resonance.",
|
||||
"tools": ["ScrapeWebsiteTool"],
|
||||
"tasks": {
|
||||
"story_concept_development": {
|
||||
"description": "Craft a unique AI story concept with depth and engagement using concept from this page the content https://www.asthebirdfliesblog.com/posts/how-to-write-book-story-development .",
|
||||
"expected_output": "Document with narrative arcs, character bios, and settings."
|
||||
}
|
||||
}
|
||||
},
|
||||
"scriptwriter": {
|
||||
"role": "Scriptwriter",
|
||||
"goal": "Write scripts from AI concepts",
|
||||
"backstory": "Expert in dialogue and script structure, translating concepts into scripts.",
|
||||
"tasks": {
|
||||
"scriptwriting_task": {
|
||||
"description": "Turn narrative concepts into scripts, including dialogue and scenes.",
|
||||
"expected_output": "Production-ready script with dialogue and scene details."
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
"""
|
||||
return user_content
|
||||
|
||||
|
||||
# generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
|
||||
# print(generator.generate())</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="praisonai.auto.AutoGenerator"><code class="flex name class">
|
||||
<span>class <span class="ident">AutoGenerator</span></span>
|
||||
<span>(</span><span>topic='Movie Story writing about AI', agent_file='test.yaml', framework='crewai')</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>Initialize the AutoGenerator class with the specified topic, agent file, and framework.
|
||||
note: autogen framework is different from this AutoGenerator class.</p>
|
||||
<h2 id="args">Args</h2>
|
||||
<dl>
|
||||
<dt><strong><code>topic</code></strong> : <code>str</code>, optional</dt>
|
||||
<dd>The topic for the generated team structure. Defaults to "Movie Story writing about AI".</dd>
|
||||
<dt><strong><code>agent_file</code></strong> : <code>str</code>, optional</dt>
|
||||
<dd>The name of the YAML file to save the generated team structure. Defaults to "test.yaml".</dd>
|
||||
<dt><strong><code>framework</code></strong> : <code>str</code>, optional</dt>
|
||||
<dd>The framework for the generated team structure. Defaults to "crewai".</dd>
|
||||
</dl>
|
||||
<h2 id="attributes">Attributes</h2>
|
||||
<dl>
|
||||
<dt><strong><code>config_list</code></strong> : <code>list</code></dt>
|
||||
<dd>A list containing the configuration details for the OpenAI API.</dd>
|
||||
<dt><strong><code>topic</code></strong> : <code>str</code></dt>
|
||||
<dd>The specified topic for the generated team structure.</dd>
|
||||
<dt><strong><code>agent_file</code></strong> : <code>str</code></dt>
|
||||
<dd>The specified name of the YAML file to save the generated team structure.</dd>
|
||||
<dt><strong><code>framework</code></strong> : <code>str</code></dt>
|
||||
<dd>The specified framework for the generated team structure.</dd>
|
||||
<dt><strong><code>client</code></strong> : <code>instructor.Client</code></dt>
|
||||
<dd>An instance of the instructor.Client class initialized with the specified OpenAI API configuration.</dd>
|
||||
</dl></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class AutoGenerator:
|
||||
def __init__(self, topic="Movie Story writing about AI", agent_file="test.yaml", framework="crewai"):
|
||||
"""
|
||||
Initialize the AutoGenerator class with the specified topic, agent file, and framework.
|
||||
note: autogen framework is different from this AutoGenerator class.
|
||||
|
||||
Args:
|
||||
topic (str, optional): The topic for the generated team structure. Defaults to "Movie Story writing about AI".
|
||||
agent_file (str, optional): The name of the YAML file to save the generated team structure. Defaults to "test.yaml".
|
||||
framework (str, optional): The framework for the generated team structure. Defaults to "crewai".
|
||||
|
||||
Attributes:
|
||||
config_list (list): A list containing the configuration details for the OpenAI API.
|
||||
topic (str): The specified topic for the generated team structure.
|
||||
agent_file (str): The specified name of the YAML file to save the generated team structure.
|
||||
framework (str): The specified framework for the generated team structure.
|
||||
client (instructor.Client): An instance of the instructor.Client class initialized with the specified OpenAI API configuration.
|
||||
"""
|
||||
self.config_list = [
|
||||
{
|
||||
'model': os.environ.get("OPENAI_MODEL_NAME", "gpt-4o"),
|
||||
'base_url': os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
|
||||
}
|
||||
]
|
||||
self.topic = topic
|
||||
self.agent_file = agent_file
|
||||
self.framework = framework or "crewai"
|
||||
self.client = instructor.patch(
|
||||
OpenAI(
|
||||
base_url=self.config_list[0]['base_url'],
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
),
|
||||
mode=instructor.Mode.JSON,
|
||||
)
|
||||
|
||||
def generate(self):
|
||||
"""
|
||||
Generates a team structure for the specified topic.
|
||||
|
||||
Args:
|
||||
None
|
||||
|
||||
Returns:
|
||||
str: The full path of the YAML file containing the generated team structure.
|
||||
|
||||
Raises:
|
||||
Exception: If the generation process fails.
|
||||
|
||||
Usage:
|
||||
generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
|
||||
path = generator.generate()
|
||||
print(path)
|
||||
"""
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.config_list[0]['model'],
|
||||
response_model=TeamStructure,
|
||||
max_retries=10,
|
||||
messages=[
|
||||
{"role": "system", "content": "You are a helpful assistant designed to output complex team structures."},
|
||||
{"role": "user", "content": self.get_user_content()}
|
||||
]
|
||||
)
|
||||
json_data = json.loads(response.model_dump_json())
|
||||
self.convert_and_save(json_data)
|
||||
full_path = os.path.abspath(self.agent_file)
|
||||
return full_path
|
||||
|
||||
def convert_and_save(self, json_data):
|
||||
"""Converts the provided JSON data into the desired YAML format and saves it to a file.
|
||||
|
||||
Args:
|
||||
json_data (dict): The JSON data representing the team structure.
|
||||
topic (str, optional): The topic to be inserted into the YAML. Defaults to "Artificial Intelligence".
|
||||
agent_file (str, optional): The name of the YAML file to save. Defaults to "test.yaml".
|
||||
"""
|
||||
|
||||
yaml_data = {
|
||||
"framework": self.framework,
|
||||
"topic": self.topic,
|
||||
"roles": {},
|
||||
"dependencies": []
|
||||
}
|
||||
|
||||
for role_id, role_details in json_data['roles'].items():
|
||||
yaml_data['roles'][role_id] = {
|
||||
"backstory": "" + role_details['backstory'],
|
||||
"goal": role_details['goal'],
|
||||
"role": role_details['role'],
|
||||
"tasks": {},
|
||||
# "tools": role_details.get('tools', []),
|
||||
"tools": ['']
|
||||
}
|
||||
|
||||
for task_id, task_details in role_details['tasks'].items():
|
||||
yaml_data['roles'][role_id]['tasks'][task_id] = {
|
||||
"description": "" + task_details['description'],
|
||||
"expected_output": "" + task_details['expected_output']
|
||||
}
|
||||
|
||||
# Save to YAML file, maintaining the order
|
||||
with open(self.agent_file, 'w') as f:
|
||||
yaml.dump(yaml_data, f, allow_unicode=True, sort_keys=False)
|
||||
|
||||
def get_user_content(self):
|
||||
"""
|
||||
Generates a prompt for the OpenAI API to generate a team structure.
|
||||
|
||||
Args:
|
||||
None
|
||||
|
||||
Returns:
|
||||
str: The prompt for the OpenAI API.
|
||||
|
||||
Usage:
|
||||
generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
|
||||
prompt = generator.get_user_content()
|
||||
print(prompt)
|
||||
"""
|
||||
user_content = """Generate a team structure for \"""" + self.topic + """\" task.
|
||||
No Input data will be provided to the team.
|
||||
The team will work in sequence. First role will pass the output to the next role, and so on.
|
||||
The last role will generate the final output.
|
||||
Think step by step.
|
||||
With maximum 3 roles, each with 1 task. Include role goals, backstories, task descriptions, and expected outputs.
|
||||
List of Available Tools: CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, DirectoryReadTool, FileReadTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, RagTool, ScrapeElementFromWebsiteTool, ScrapeWebsiteTool, WebsiteSearchTool, XMLSearchTool, YoutubeChannelSearchTool, YoutubeVideoSearchTool.
|
||||
Only use Available Tools. Do Not use any other tools.
|
||||
Example Below:
|
||||
Use below example to understand the structure of the output.
|
||||
The final role you create should satisfy the provided task: """ + self.topic + """.
|
||||
{
|
||||
"roles": {
|
||||
"narrative_designer": {
|
||||
"role": "Narrative Designer",
|
||||
"goal": "Create AI storylines",
|
||||
"backstory": "Skilled in narrative development for AI, with a focus on story resonance.",
|
||||
"tools": ["ScrapeWebsiteTool"],
|
||||
"tasks": {
|
||||
"story_concept_development": {
|
||||
"description": "Craft a unique AI story concept with depth and engagement using concept from this page the content https://www.asthebirdfliesblog.com/posts/how-to-write-book-story-development .",
|
||||
"expected_output": "Document with narrative arcs, character bios, and settings."
|
||||
}
|
||||
}
|
||||
},
|
||||
"scriptwriter": {
|
||||
"role": "Scriptwriter",
|
||||
"goal": "Write scripts from AI concepts",
|
||||
"backstory": "Expert in dialogue and script structure, translating concepts into scripts.",
|
||||
"tasks": {
|
||||
"scriptwriting_task": {
|
||||
"description": "Turn narrative concepts into scripts, including dialogue and scenes.",
|
||||
"expected_output": "Production-ready script with dialogue and scene details."
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
"""
|
||||
return user_content</code></pre>
|
||||
</details>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="praisonai.auto.AutoGenerator.convert_and_save"><code class="name flex">
|
||||
<span>def <span class="ident">convert_and_save</span></span>(<span>self, json_data)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>Converts the provided JSON data into the desired YAML format and saves it to a file.</p>
|
||||
<h2 id="args">Args</h2>
|
||||
<dl>
|
||||
<dt><strong><code>json_data</code></strong> : <code>dict</code></dt>
|
||||
<dd>The JSON data representing the team structure.</dd>
|
||||
<dt><strong><code>topic</code></strong> : <code>str</code>, optional</dt>
|
||||
<dd>The topic to be inserted into the YAML. Defaults to "Artificial Intelligence".</dd>
|
||||
<dt><strong><code>agent_file</code></strong> : <code>str</code>, optional</dt>
|
||||
<dd>The name of the YAML file to save. Defaults to "test.yaml".</dd>
|
||||
</dl></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def convert_and_save(self, json_data):
|
||||
"""Converts the provided JSON data into the desired YAML format and saves it to a file.
|
||||
|
||||
Args:
|
||||
json_data (dict): The JSON data representing the team structure.
|
||||
topic (str, optional): The topic to be inserted into the YAML. Defaults to "Artificial Intelligence".
|
||||
agent_file (str, optional): The name of the YAML file to save. Defaults to "test.yaml".
|
||||
"""
|
||||
|
||||
yaml_data = {
|
||||
"framework": self.framework,
|
||||
"topic": self.topic,
|
||||
"roles": {},
|
||||
"dependencies": []
|
||||
}
|
||||
|
||||
for role_id, role_details in json_data['roles'].items():
|
||||
yaml_data['roles'][role_id] = {
|
||||
"backstory": "" + role_details['backstory'],
|
||||
"goal": role_details['goal'],
|
||||
"role": role_details['role'],
|
||||
"tasks": {},
|
||||
# "tools": role_details.get('tools', []),
|
||||
"tools": ['']
|
||||
}
|
||||
|
||||
for task_id, task_details in role_details['tasks'].items():
|
||||
yaml_data['roles'][role_id]['tasks'][task_id] = {
|
||||
"description": "" + task_details['description'],
|
||||
"expected_output": "" + task_details['expected_output']
|
||||
}
|
||||
|
||||
# Save to YAML file, maintaining the order
|
||||
with open(self.agent_file, 'w') as f:
|
||||
yaml.dump(yaml_data, f, allow_unicode=True, sort_keys=False)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.auto.AutoGenerator.generate"><code class="name flex">
|
||||
<span>def <span class="ident">generate</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>Generates a team structure for the specified topic.</p>
|
||||
<h2 id="args">Args</h2>
|
||||
<p>None</p>
|
||||
<h2 id="returns">Returns</h2>
|
||||
<dl>
|
||||
<dt><code>str</code></dt>
|
||||
<dd>The full path of the YAML file containing the generated team structure.</dd>
|
||||
</dl>
|
||||
<h2 id="raises">Raises</h2>
|
||||
<dl>
|
||||
<dt><code>Exception</code></dt>
|
||||
<dd>If the generation process fails.</dd>
|
||||
</dl>
|
||||
<h2 id="usage">Usage</h2>
|
||||
<p>generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
|
||||
path = generator.generate()
|
||||
print(path)</p></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def generate(self):
|
||||
"""
|
||||
Generates a team structure for the specified topic.
|
||||
|
||||
Args:
|
||||
None
|
||||
|
||||
Returns:
|
||||
str: The full path of the YAML file containing the generated team structure.
|
||||
|
||||
Raises:
|
||||
Exception: If the generation process fails.
|
||||
|
||||
Usage:
|
||||
generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
|
||||
path = generator.generate()
|
||||
print(path)
|
||||
"""
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.config_list[0]['model'],
|
||||
response_model=TeamStructure,
|
||||
max_retries=10,
|
||||
messages=[
|
||||
{"role": "system", "content": "You are a helpful assistant designed to output complex team structures."},
|
||||
{"role": "user", "content": self.get_user_content()}
|
||||
]
|
||||
)
|
||||
json_data = json.loads(response.model_dump_json())
|
||||
self.convert_and_save(json_data)
|
||||
full_path = os.path.abspath(self.agent_file)
|
||||
return full_path</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.auto.AutoGenerator.get_user_content"><code class="name flex">
|
||||
<span>def <span class="ident">get_user_content</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>Generates a prompt for the OpenAI API to generate a team structure.</p>
|
||||
<h2 id="args">Args</h2>
|
||||
<p>None</p>
|
||||
<h2 id="returns">Returns</h2>
|
||||
<dl>
|
||||
<dt><code>str</code></dt>
|
||||
<dd>The prompt for the OpenAI API.</dd>
|
||||
</dl>
|
||||
<h2 id="usage">Usage</h2>
|
||||
<p>generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
|
||||
prompt = generator.get_user_content()
|
||||
print(prompt)</p></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"> def get_user_content(self):
|
||||
"""
|
||||
Generates a prompt for the OpenAI API to generate a team structure.
|
||||
|
||||
Args:
|
||||
None
|
||||
|
||||
Returns:
|
||||
str: The prompt for the OpenAI API.
|
||||
|
||||
Usage:
|
||||
generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
|
||||
prompt = generator.get_user_content()
|
||||
print(prompt)
|
||||
"""
|
||||
user_content = """Generate a team structure for \"""" + self.topic + """\" task.
|
||||
No Input data will be provided to the team.
|
||||
The team will work in sequence. First role will pass the output to the next role, and so on.
|
||||
The last role will generate the final output.
|
||||
Think step by step.
|
||||
With maximum 3 roles, each with 1 task. Include role goals, backstories, task descriptions, and expected outputs.
|
||||
List of Available Tools: CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, DirectoryReadTool, FileReadTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, RagTool, ScrapeElementFromWebsiteTool, ScrapeWebsiteTool, WebsiteSearchTool, XMLSearchTool, YoutubeChannelSearchTool, YoutubeVideoSearchTool.
|
||||
Only use Available Tools. Do Not use any other tools.
|
||||
Example Below:
|
||||
Use below example to understand the structure of the output.
|
||||
The final role you create should satisfy the provided task: """ + self.topic + """.
|
||||
{
|
||||
"roles": {
|
||||
"narrative_designer": {
|
||||
"role": "Narrative Designer",
|
||||
"goal": "Create AI storylines",
|
||||
"backstory": "Skilled in narrative development for AI, with a focus on story resonance.",
|
||||
"tools": ["ScrapeWebsiteTool"],
|
||||
"tasks": {
|
||||
"story_concept_development": {
|
||||
"description": "Craft a unique AI story concept with depth and engagement using concept from this page the content https://www.asthebirdfliesblog.com/posts/how-to-write-book-story-development .",
|
||||
"expected_output": "Document with narrative arcs, character bios, and settings."
|
||||
}
|
||||
}
|
||||
},
|
||||
"scriptwriter": {
|
||||
"role": "Scriptwriter",
|
||||
"goal": "Write scripts from AI concepts",
|
||||
"backstory": "Expert in dialogue and script structure, translating concepts into scripts.",
|
||||
"tasks": {
|
||||
"scriptwriting_task": {
|
||||
"description": "Turn narrative concepts into scripts, including dialogue and scenes.",
|
||||
"expected_output": "Production-ready script with dialogue and scene details."
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
"""
|
||||
return user_content</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
<dt id="praisonai.auto.RoleDetails"><code class="flex name class">
|
||||
<span>class <span class="ident">RoleDetails</span></span>
|
||||
<span>(</span><span>**data: Any)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>Usage docs: <a href="https://docs.pydantic.dev/2.7/concepts/models/">https://docs.pydantic.dev/2.7/concepts/models/</a></p>
|
||||
<p>A base class for creating Pydantic models.</p>
|
||||
<h2 id="attributes">Attributes</h2>
|
||||
<dl>
|
||||
<dt><strong><code>__class_vars__</code></strong></dt>
|
||||
<dd>The names of classvars defined on the model.</dd>
|
||||
<dt><strong><code>__private_attributes__</code></strong></dt>
|
||||
<dd>Metadata about the private attributes of the model.</dd>
|
||||
<dt><strong><code>__signature__</code></strong></dt>
|
||||
<dd>The signature for instantiating the model.</dd>
|
||||
<dt><strong><code>__pydantic_complete__</code></strong></dt>
|
||||
<dd>Whether model building is completed, or if there are still undefined fields.</dd>
|
||||
<dt><strong><code>__pydantic_core_schema__</code></strong></dt>
|
||||
<dd>The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.</dd>
|
||||
<dt><strong><code>__pydantic_custom_init__</code></strong></dt>
|
||||
<dd>Whether the model has a custom <code>__init__</code> function.</dd>
|
||||
<dt><strong><code>__pydantic_decorators__</code></strong></dt>
|
||||
<dd>Metadata containing the decorators defined on the model.
|
||||
This replaces <code>Model.__validators__</code> and <code>Model.__root_validators__</code> from Pydantic V1.</dd>
|
||||
<dt><strong><code>__pydantic_generic_metadata__</code></strong></dt>
|
||||
<dd>Metadata for generic models; contains data used for a similar purpose to
|
||||
<strong>args</strong>, <strong>origin</strong>, <strong>parameters</strong> in typing-module generics. May eventually be replaced by these.</dd>
|
||||
<dt><strong><code>__pydantic_parent_namespace__</code></strong></dt>
|
||||
<dd>Parent namespace of the model, used for automatic rebuilding of models.</dd>
|
||||
<dt><strong><code>__pydantic_post_init__</code></strong></dt>
|
||||
<dd>The name of the post-init method for the model, if defined.</dd>
|
||||
<dt><strong><code>__pydantic_root_model__</code></strong></dt>
|
||||
<dd>Whether the model is a <code>RootModel</code>.</dd>
|
||||
<dt><strong><code>__pydantic_serializer__</code></strong></dt>
|
||||
<dd>The pydantic-core SchemaSerializer used to dump instances of the model.</dd>
|
||||
<dt><strong><code>__pydantic_validator__</code></strong></dt>
|
||||
<dd>The pydantic-core SchemaValidator used to validate instances of the model.</dd>
|
||||
<dt><strong><code>__pydantic_extra__</code></strong></dt>
|
||||
<dd>An instance attribute with the values of extra fields from validation when
|
||||
<code>model_config['extra'] == 'allow'</code>.</dd>
|
||||
<dt><strong><code>__pydantic_fields_set__</code></strong></dt>
|
||||
<dd>An instance attribute with the names of fields explicitly set.</dd>
|
||||
<dt><strong><code>__pydantic_private__</code></strong></dt>
|
||||
<dd>Instance attribute with the values of private attributes set on the model instance.</dd>
|
||||
</dl>
|
||||
<p>Create a new model by parsing and validating input data from keyword arguments.</p>
|
||||
<p>Raises [<code>ValidationError</code>][pydantic_core.ValidationError] if the input data cannot be
|
||||
validated to form a valid model.</p>
|
||||
<p><code>self</code> is explicitly positional-only to allow <code>self</code> as a field name.</p></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class RoleDetails(BaseModel):
|
||||
role: str
|
||||
goal: str
|
||||
backstory: str
|
||||
tasks: Dict[str, TaskDetails]
|
||||
tools: List[str]</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li>pydantic.main.BaseModel</li>
|
||||
</ul>
|
||||
<h3>Class variables</h3>
|
||||
<dl>
|
||||
<dt id="praisonai.auto.RoleDetails.backstory"><code class="name">var <span class="ident">backstory</span> : str</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
<dt id="praisonai.auto.RoleDetails.goal"><code class="name">var <span class="ident">goal</span> : str</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
<dt id="praisonai.auto.RoleDetails.model_computed_fields"><code class="name">var <span class="ident">model_computed_fields</span></code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
<dt id="praisonai.auto.RoleDetails.model_config"><code class="name">var <span class="ident">model_config</span></code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
<dt id="praisonai.auto.RoleDetails.model_fields"><code class="name">var <span class="ident">model_fields</span></code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
<dt id="praisonai.auto.RoleDetails.role"><code class="name">var <span class="ident">role</span> : str</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
<dt id="praisonai.auto.RoleDetails.tasks"><code class="name">var <span class="ident">tasks</span> : Dict[str, <a title="praisonai.auto.TaskDetails" href="#praisonai.auto.TaskDetails">TaskDetails</a>]</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
<dt id="praisonai.auto.RoleDetails.tools"><code class="name">var <span class="ident">tools</span> : List[str]</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
<dt id="praisonai.auto.TaskDetails"><code class="flex name class">
|
||||
<span>class <span class="ident">TaskDetails</span></span>
|
||||
<span>(</span><span>**data: Any)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>Usage docs: <a href="https://docs.pydantic.dev/2.7/concepts/models/">https://docs.pydantic.dev/2.7/concepts/models/</a></p>
|
||||
<p>A base class for creating Pydantic models.</p>
|
||||
<h2 id="attributes">Attributes</h2>
|
||||
<dl>
|
||||
<dt><strong><code>__class_vars__</code></strong></dt>
|
||||
<dd>The names of classvars defined on the model.</dd>
|
||||
<dt><strong><code>__private_attributes__</code></strong></dt>
|
||||
<dd>Metadata about the private attributes of the model.</dd>
|
||||
<dt><strong><code>__signature__</code></strong></dt>
|
||||
<dd>The signature for instantiating the model.</dd>
|
||||
<dt><strong><code>__pydantic_complete__</code></strong></dt>
|
||||
<dd>Whether model building is completed, or if there are still undefined fields.</dd>
|
||||
<dt><strong><code>__pydantic_core_schema__</code></strong></dt>
|
||||
<dd>The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.</dd>
|
||||
<dt><strong><code>__pydantic_custom_init__</code></strong></dt>
|
||||
<dd>Whether the model has a custom <code>__init__</code> function.</dd>
|
||||
<dt><strong><code>__pydantic_decorators__</code></strong></dt>
|
||||
<dd>Metadata containing the decorators defined on the model.
|
||||
This replaces <code>Model.__validators__</code> and <code>Model.__root_validators__</code> from Pydantic V1.</dd>
|
||||
<dt><strong><code>__pydantic_generic_metadata__</code></strong></dt>
|
||||
<dd>Metadata for generic models; contains data used for a similar purpose to
|
||||
<strong>args</strong>, <strong>origin</strong>, <strong>parameters</strong> in typing-module generics. May eventually be replaced by these.</dd>
|
||||
<dt><strong><code>__pydantic_parent_namespace__</code></strong></dt>
|
||||
<dd>Parent namespace of the model, used for automatic rebuilding of models.</dd>
|
||||
<dt><strong><code>__pydantic_post_init__</code></strong></dt>
|
||||
<dd>The name of the post-init method for the model, if defined.</dd>
|
||||
<dt><strong><code>__pydantic_root_model__</code></strong></dt>
|
||||
<dd>Whether the model is a <code>RootModel</code>.</dd>
|
||||
<dt><strong><code>__pydantic_serializer__</code></strong></dt>
|
||||
<dd>The pydantic-core SchemaSerializer used to dump instances of the model.</dd>
|
||||
<dt><strong><code>__pydantic_validator__</code></strong></dt>
|
||||
<dd>The pydantic-core SchemaValidator used to validate instances of the model.</dd>
|
||||
<dt><strong><code>__pydantic_extra__</code></strong></dt>
|
||||
<dd>An instance attribute with the values of extra fields from validation when
|
||||
<code>model_config['extra'] == 'allow'</code>.</dd>
|
||||
<dt><strong><code>__pydantic_fields_set__</code></strong></dt>
|
||||
<dd>An instance attribute with the names of fields explicitly set.</dd>
|
||||
<dt><strong><code>__pydantic_private__</code></strong></dt>
|
||||
<dd>Instance attribute with the values of private attributes set on the model instance.</dd>
|
||||
</dl>
|
||||
<p>Create a new model by parsing and validating input data from keyword arguments.</p>
|
||||
<p>Raises [<code>ValidationError</code>][pydantic_core.ValidationError] if the input data cannot be
|
||||
validated to form a valid model.</p>
|
||||
<p><code>self</code> is explicitly positional-only to allow <code>self</code> as a field name.</p></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class TaskDetails(BaseModel):
|
||||
description: str
|
||||
expected_output: str</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li>pydantic.main.BaseModel</li>
|
||||
</ul>
|
||||
<h3>Class variables</h3>
|
||||
<dl>
|
||||
<dt id="praisonai.auto.TaskDetails.description"><code class="name">var <span class="ident">description</span> : str</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
<dt id="praisonai.auto.TaskDetails.expected_output"><code class="name">var <span class="ident">expected_output</span> : str</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
<dt id="praisonai.auto.TaskDetails.model_computed_fields"><code class="name">var <span class="ident">model_computed_fields</span></code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
<dt id="praisonai.auto.TaskDetails.model_config"><code class="name">var <span class="ident">model_config</span></code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
<dt id="praisonai.auto.TaskDetails.model_fields"><code class="name">var <span class="ident">model_fields</span></code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
<dt id="praisonai.auto.TeamStructure"><code class="flex name class">
|
||||
<span>class <span class="ident">TeamStructure</span></span>
|
||||
<span>(</span><span>**data: Any)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>Usage docs: <a href="https://docs.pydantic.dev/2.7/concepts/models/">https://docs.pydantic.dev/2.7/concepts/models/</a></p>
|
||||
<p>A base class for creating Pydantic models.</p>
|
||||
<h2 id="attributes">Attributes</h2>
|
||||
<dl>
|
||||
<dt><strong><code>__class_vars__</code></strong></dt>
|
||||
<dd>The names of classvars defined on the model.</dd>
|
||||
<dt><strong><code>__private_attributes__</code></strong></dt>
|
||||
<dd>Metadata about the private attributes of the model.</dd>
|
||||
<dt><strong><code>__signature__</code></strong></dt>
|
||||
<dd>The signature for instantiating the model.</dd>
|
||||
<dt><strong><code>__pydantic_complete__</code></strong></dt>
|
||||
<dd>Whether model building is completed, or if there are still undefined fields.</dd>
|
||||
<dt><strong><code>__pydantic_core_schema__</code></strong></dt>
|
||||
<dd>The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.</dd>
|
||||
<dt><strong><code>__pydantic_custom_init__</code></strong></dt>
|
||||
<dd>Whether the model has a custom <code>__init__</code> function.</dd>
|
||||
<dt><strong><code>__pydantic_decorators__</code></strong></dt>
|
||||
<dd>Metadata containing the decorators defined on the model.
|
||||
This replaces <code>Model.__validators__</code> and <code>Model.__root_validators__</code> from Pydantic V1.</dd>
|
||||
<dt><strong><code>__pydantic_generic_metadata__</code></strong></dt>
|
||||
<dd>Metadata for generic models; contains data used for a similar purpose to
|
||||
<strong>args</strong>, <strong>origin</strong>, <strong>parameters</strong> in typing-module generics. May eventually be replaced by these.</dd>
|
||||
<dt><strong><code>__pydantic_parent_namespace__</code></strong></dt>
|
||||
<dd>Parent namespace of the model, used for automatic rebuilding of models.</dd>
|
||||
<dt><strong><code>__pydantic_post_init__</code></strong></dt>
|
||||
<dd>The name of the post-init method for the model, if defined.</dd>
|
||||
<dt><strong><code>__pydantic_root_model__</code></strong></dt>
|
||||
<dd>Whether the model is a <code>RootModel</code>.</dd>
|
||||
<dt><strong><code>__pydantic_serializer__</code></strong></dt>
|
||||
<dd>The pydantic-core SchemaSerializer used to dump instances of the model.</dd>
|
||||
<dt><strong><code>__pydantic_validator__</code></strong></dt>
|
||||
<dd>The pydantic-core SchemaValidator used to validate instances of the model.</dd>
|
||||
<dt><strong><code>__pydantic_extra__</code></strong></dt>
|
||||
<dd>An instance attribute with the values of extra fields from validation when
|
||||
<code>model_config['extra'] == 'allow'</code>.</dd>
|
||||
<dt><strong><code>__pydantic_fields_set__</code></strong></dt>
|
||||
<dd>An instance attribute with the names of fields explicitly set.</dd>
|
||||
<dt><strong><code>__pydantic_private__</code></strong></dt>
|
||||
<dd>Instance attribute with the values of private attributes set on the model instance.</dd>
|
||||
</dl>
|
||||
<p>Create a new model by parsing and validating input data from keyword arguments.</p>
|
||||
<p>Raises [<code>ValidationError</code>][pydantic_core.ValidationError] if the input data cannot be
|
||||
validated to form a valid model.</p>
|
||||
<p><code>self</code> is explicitly positional-only to allow <code>self</code> as a field name.</p></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class TeamStructure(BaseModel):
|
||||
roles: Dict[str, RoleDetails]</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li>pydantic.main.BaseModel</li>
|
||||
</ul>
|
||||
<h3>Class variables</h3>
|
||||
<dl>
|
||||
<dt id="praisonai.auto.TeamStructure.model_computed_fields"><code class="name">var <span class="ident">model_computed_fields</span></code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
<dt id="praisonai.auto.TeamStructure.model_config"><code class="name">var <span class="ident">model_config</span></code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
<dt id="praisonai.auto.TeamStructure.model_fields"><code class="name">var <span class="ident">model_fields</span></code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
<dt id="praisonai.auto.TeamStructure.roles"><code class="name">var <span class="ident">roles</span> : Dict[str, <a title="praisonai.auto.RoleDetails" href="#praisonai.auto.RoleDetails">RoleDetails</a>]</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="praisonai.auto.AutoGenerator" href="#praisonai.auto.AutoGenerator">AutoGenerator</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="praisonai.auto.AutoGenerator.convert_and_save" href="#praisonai.auto.AutoGenerator.convert_and_save">convert_and_save</a></code></li>
|
||||
<li><code><a title="praisonai.auto.AutoGenerator.generate" href="#praisonai.auto.AutoGenerator.generate">generate</a></code></li>
|
||||
<li><code><a title="praisonai.auto.AutoGenerator.get_user_content" href="#praisonai.auto.AutoGenerator.get_user_content">get_user_content</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="praisonai.auto.RoleDetails" href="#praisonai.auto.RoleDetails">RoleDetails</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="praisonai.auto.RoleDetails.backstory" href="#praisonai.auto.RoleDetails.backstory">backstory</a></code></li>
|
||||
<li><code><a title="praisonai.auto.RoleDetails.goal" href="#praisonai.auto.RoleDetails.goal">goal</a></code></li>
|
||||
<li><code><a title="praisonai.auto.RoleDetails.model_computed_fields" href="#praisonai.auto.RoleDetails.model_computed_fields">model_computed_fields</a></code></li>
|
||||
<li><code><a title="praisonai.auto.RoleDetails.model_config" href="#praisonai.auto.RoleDetails.model_config">model_config</a></code></li>
|
||||
<li><code><a title="praisonai.auto.RoleDetails.model_fields" href="#praisonai.auto.RoleDetails.model_fields">model_fields</a></code></li>
|
||||
<li><code><a title="praisonai.auto.RoleDetails.role" href="#praisonai.auto.RoleDetails.role">role</a></code></li>
|
||||
<li><code><a title="praisonai.auto.RoleDetails.tasks" href="#praisonai.auto.RoleDetails.tasks">tasks</a></code></li>
|
||||
<li><code><a title="praisonai.auto.RoleDetails.tools" href="#praisonai.auto.RoleDetails.tools">tools</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="praisonai.auto.TaskDetails" href="#praisonai.auto.TaskDetails">TaskDetails</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="praisonai.auto.TaskDetails.description" href="#praisonai.auto.TaskDetails.description">description</a></code></li>
|
||||
<li><code><a title="praisonai.auto.TaskDetails.expected_output" href="#praisonai.auto.TaskDetails.expected_output">expected_output</a></code></li>
|
||||
<li><code><a title="praisonai.auto.TaskDetails.model_computed_fields" href="#praisonai.auto.TaskDetails.model_computed_fields">model_computed_fields</a></code></li>
|
||||
<li><code><a title="praisonai.auto.TaskDetails.model_config" href="#praisonai.auto.TaskDetails.model_config">model_config</a></code></li>
|
||||
<li><code><a title="praisonai.auto.TaskDetails.model_fields" href="#praisonai.auto.TaskDetails.model_fields">model_fields</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="praisonai.auto.TeamStructure" href="#praisonai.auto.TeamStructure">TeamStructure</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="praisonai.auto.TeamStructure.model_computed_fields" href="#praisonai.auto.TeamStructure.model_computed_fields">model_computed_fields</a></code></li>
|
||||
<li><code><a title="praisonai.auto.TeamStructure.model_config" href="#praisonai.auto.TeamStructure.model_config">model_config</a></code></li>
|
||||
<li><code><a title="praisonai.auto.TeamStructure.model_fields" href="#praisonai.auto.TeamStructure.model_fields">model_fields</a></code></li>
|
||||
<li><code><a title="praisonai.auto.TeamStructure.roles" href="#praisonai.auto.TeamStructure.roles">roles</a></code></li>
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<main>
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<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>praisonai.cli</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># praisonai/cli.py
|
||||
|
||||
import sys
|
||||
from .version import __version__
|
||||
import yaml, os
|
||||
from rich import print
|
||||
from dotenv import load_dotenv
|
||||
from crewai import Agent, Task, Crew
|
||||
load_dotenv()
|
||||
import autogen
|
||||
import gradio as gr
|
||||
import argparse
|
||||
from .auto import AutoGenerator
|
||||
from .agents_generator import AgentsGenerator
|
||||
from .inbuilt_tools import *
|
||||
|
||||
class PraisonAI:
|
||||
def __init__(self, agent_file="agents.yaml", framework="", auto=False, init=False):
|
||||
"""
|
||||
Initialize the PraisonAI object with default parameters.
|
||||
|
||||
Parameters:
|
||||
agent_file (str): The default agent file to use. Defaults to "agents.yaml".
|
||||
framework (str): The default framework to use. Defaults to "crewai".
|
||||
auto (bool): A flag indicating whether to enable auto mode. Defaults to False.
|
||||
init (bool): A flag indicating whether to enable initialization mode. Defaults to False.
|
||||
|
||||
Attributes:
|
||||
config_list (list): A list of configuration dictionaries for the OpenAI API.
|
||||
agent_file (str): The agent file to use.
|
||||
framework (str): The framework to use.
|
||||
auto (bool): A flag indicating whether to enable auto mode.
|
||||
init (bool): A flag indicating whether to enable initialization mode.
|
||||
"""
|
||||
self.config_list = [
|
||||
{
|
||||
'model': os.environ.get("OPENAI_MODEL_NAME", "gpt-4o"),
|
||||
'base_url': os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
|
||||
}
|
||||
]
|
||||
self.agent_file = agent_file
|
||||
self.framework = framework
|
||||
self.auto = auto
|
||||
self.init = init
|
||||
|
||||
def main(self):
|
||||
"""
|
||||
The main function of the PraisonAI object. It parses the command-line arguments,
|
||||
initializes the necessary attributes, and then calls the appropriate methods based on the
|
||||
provided arguments.
|
||||
|
||||
Args:
|
||||
self (PraisonAI): An instance of the PraisonAI class.
|
||||
|
||||
Returns:
|
||||
Any: Depending on the arguments provided, the function may return a result from the
|
||||
AgentsGenerator, a deployment result from the CloudDeployer, or a message indicating
|
||||
the successful creation of a file.
|
||||
"""
|
||||
args = self.parse_args()
|
||||
if args is None:
|
||||
agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
|
||||
result = agents_generator.generate_crew_and_kickoff()
|
||||
return result
|
||||
if args.deploy:
|
||||
from .deploy import CloudDeployer
|
||||
deployer = CloudDeployer()
|
||||
deployer.run_commands()
|
||||
return
|
||||
invocation_cmd = "praisonai"
|
||||
version_string = f"PraisonAI version {__version__}"
|
||||
|
||||
self.framework = args.framework or self.framework
|
||||
|
||||
ui = args.ui
|
||||
if args.agent_file:
|
||||
if args.agent_file.startswith("tests.test"): # Argument used for testing purposes
|
||||
print("test")
|
||||
else:
|
||||
self.agent_file = args.agent_file
|
||||
|
||||
|
||||
if args.auto or args.init:
|
||||
temp_topic = ' '.join(args.auto) if args.auto else ' '.join(args.init)
|
||||
self.topic = temp_topic
|
||||
elif self.auto or self.init: # Use the auto attribute if args.auto is not provided
|
||||
self.topic = self.auto
|
||||
|
||||
if args.auto or self.auto:
|
||||
self.agent_file = "test.yaml"
|
||||
generator = AutoGenerator(topic=self.topic , framework=self.framework, agent_file=self.agent_file)
|
||||
self.agent_file = generator.generate()
|
||||
agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
|
||||
result = agents_generator.generate_crew_and_kickoff()
|
||||
return result
|
||||
elif args.init or self.init:
|
||||
self.agent_file = "agents.yaml"
|
||||
generator = AutoGenerator(topic=self.topic , framework=self.framework, agent_file=self.agent_file)
|
||||
self.agent_file = generator.generate()
|
||||
print("File {} created successfully".format(self.agent_file))
|
||||
return "File {} created successfully".format(self.agent_file)
|
||||
|
||||
if ui:
|
||||
self.create_gradio_interface()
|
||||
else:
|
||||
agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
|
||||
result = agents_generator.generate_crew_and_kickoff()
|
||||
return result
|
||||
|
||||
def parse_args(self):
|
||||
"""
|
||||
Parse the command-line arguments for the PraisonAI CLI.
|
||||
|
||||
Args:
|
||||
self (PraisonAI): An instance of the PraisonAI class.
|
||||
|
||||
Returns:
|
||||
argparse.Namespace: An object containing the parsed command-line arguments.
|
||||
|
||||
Raises:
|
||||
argparse.ArgumentError: If the arguments provided are invalid.
|
||||
|
||||
Example:
|
||||
>>> args = praison_ai.parse_args()
|
||||
>>> print(args.agent_file) # Output: 'agents.yaml'
|
||||
"""
|
||||
parser = argparse.ArgumentParser(prog="praisonai", description="praisonAI command-line interface")
|
||||
parser.add_argument("--framework", choices=["crewai", "autogen"], help="Specify the framework")
|
||||
parser.add_argument("--ui", action="store_true", help="Enable UI mode")
|
||||
parser.add_argument("--auto", nargs=argparse.REMAINDER, help="Enable auto mode and pass arguments for it")
|
||||
parser.add_argument("--init", nargs=argparse.REMAINDER, help="Enable auto mode and pass arguments for it")
|
||||
parser.add_argument("agent_file", nargs="?", help="Specify the agent file")
|
||||
parser.add_argument("--deploy", action="store_true", help="Deploy the application") # New argument
|
||||
|
||||
args, unknown_args = parser.parse_known_args()
|
||||
|
||||
if unknown_args and unknown_args[0] == '-b' and unknown_args[1] == 'api:app':
|
||||
args.agent_file = 'agents.yaml'
|
||||
if args.agent_file == 'api:app' or args.agent_file == '/app/api:app':
|
||||
args.agent_file = 'agents.yaml'
|
||||
|
||||
return args
|
||||
|
||||
def create_gradio_interface(self):
|
||||
"""
|
||||
Create a Gradio interface for generating agents and performing tasks.
|
||||
|
||||
Args:
|
||||
self (PraisonAI): An instance of the PraisonAI class.
|
||||
|
||||
Returns:
|
||||
None: This method does not return any value. It launches the Gradio interface.
|
||||
|
||||
Raises:
|
||||
None: This method does not raise any exceptions.
|
||||
|
||||
Example:
|
||||
>>> praison_ai.create_gradio_interface()
|
||||
"""
|
||||
def generate_crew_and_kickoff_interface(auto_args, framework):
|
||||
"""
|
||||
Generate a crew and kick off tasks based on the provided auto arguments and framework.
|
||||
|
||||
Args:
|
||||
auto_args (list): Topic.
|
||||
framework (str): The framework to use for generating agents.
|
||||
|
||||
Returns:
|
||||
str: A string representing the result of generating the crew and kicking off tasks.
|
||||
|
||||
Raises:
|
||||
None: This method does not raise any exceptions.
|
||||
|
||||
Example:
|
||||
>>> result = generate_crew_and_kickoff_interface("Create a movie about Cat in Mars", "crewai")
|
||||
>>> print(result)
|
||||
"""
|
||||
self.framework = framework
|
||||
self.agent_file = "test.yaml"
|
||||
generator = AutoGenerator(topic=auto_args , framework=self.framework)
|
||||
self.agent_file = generator.generate()
|
||||
agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
|
||||
result = agents_generator.generate_crew_and_kickoff()
|
||||
return result
|
||||
|
||||
gr.Interface(
|
||||
fn=generate_crew_and_kickoff_interface,
|
||||
inputs=[gr.Textbox(lines=2, label="Auto Args"), gr.Dropdown(choices=["crewai", "autogen"], label="Framework")],
|
||||
outputs="textbox",
|
||||
title="Praison AI Studio",
|
||||
description="Create Agents and perform tasks",
|
||||
theme="default"
|
||||
).launch()
|
||||
|
||||
if __name__ == "__main__":
|
||||
praison_ai = PraisonAI()
|
||||
praison_ai.main()</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="praisonai.cli.PraisonAI"><code class="flex name class">
|
||||
<span>class <span class="ident">PraisonAI</span></span>
|
||||
<span>(</span><span>agent_file='agents.yaml', framework='', auto=False, init=False)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>Initialize the PraisonAI object with default parameters.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<p>agent_file (str): The default agent file to use. Defaults to "agents.yaml".
|
||||
framework (str): The default framework to use. Defaults to "crewai".
|
||||
auto (bool): A flag indicating whether to enable auto mode. Defaults to False.
|
||||
init (bool): A flag indicating whether to enable initialization mode. Defaults to False.</p>
|
||||
<h2 id="attributes">Attributes</h2>
|
||||
<dl>
|
||||
<dt><strong><code>config_list</code></strong> : <code>list</code></dt>
|
||||
<dd>A list of configuration dictionaries for the OpenAI API.</dd>
|
||||
<dt><strong><code>agent_file</code></strong> : <code>str</code></dt>
|
||||
<dd>The agent file to use.</dd>
|
||||
<dt><strong><code>framework</code></strong> : <code>str</code></dt>
|
||||
<dd>The framework to use.</dd>
|
||||
<dt><strong><code>auto</code></strong> : <code>bool</code></dt>
|
||||
<dd>A flag indicating whether to enable auto mode.</dd>
|
||||
<dt><strong><code>init</code></strong> : <code>bool</code></dt>
|
||||
<dd>A flag indicating whether to enable initialization mode.</dd>
|
||||
</dl></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class PraisonAI:
|
||||
def __init__(self, agent_file="agents.yaml", framework="", auto=False, init=False):
|
||||
"""
|
||||
Initialize the PraisonAI object with default parameters.
|
||||
|
||||
Parameters:
|
||||
agent_file (str): The default agent file to use. Defaults to "agents.yaml".
|
||||
framework (str): The default framework to use. Defaults to "crewai".
|
||||
auto (bool): A flag indicating whether to enable auto mode. Defaults to False.
|
||||
init (bool): A flag indicating whether to enable initialization mode. Defaults to False.
|
||||
|
||||
Attributes:
|
||||
config_list (list): A list of configuration dictionaries for the OpenAI API.
|
||||
agent_file (str): The agent file to use.
|
||||
framework (str): The framework to use.
|
||||
auto (bool): A flag indicating whether to enable auto mode.
|
||||
init (bool): A flag indicating whether to enable initialization mode.
|
||||
"""
|
||||
self.config_list = [
|
||||
{
|
||||
'model': os.environ.get("OPENAI_MODEL_NAME", "gpt-4o"),
|
||||
'base_url': os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
|
||||
}
|
||||
]
|
||||
self.agent_file = agent_file
|
||||
self.framework = framework
|
||||
self.auto = auto
|
||||
self.init = init
|
||||
|
||||
def main(self):
|
||||
"""
|
||||
The main function of the PraisonAI object. It parses the command-line arguments,
|
||||
initializes the necessary attributes, and then calls the appropriate methods based on the
|
||||
provided arguments.
|
||||
|
||||
Args:
|
||||
self (PraisonAI): An instance of the PraisonAI class.
|
||||
|
||||
Returns:
|
||||
Any: Depending on the arguments provided, the function may return a result from the
|
||||
AgentsGenerator, a deployment result from the CloudDeployer, or a message indicating
|
||||
the successful creation of a file.
|
||||
"""
|
||||
args = self.parse_args()
|
||||
if args is None:
|
||||
agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
|
||||
result = agents_generator.generate_crew_and_kickoff()
|
||||
return result
|
||||
if args.deploy:
|
||||
from .deploy import CloudDeployer
|
||||
deployer = CloudDeployer()
|
||||
deployer.run_commands()
|
||||
return
|
||||
invocation_cmd = "praisonai"
|
||||
version_string = f"PraisonAI version {__version__}"
|
||||
|
||||
self.framework = args.framework or self.framework
|
||||
|
||||
ui = args.ui
|
||||
if args.agent_file:
|
||||
if args.agent_file.startswith("tests.test"): # Argument used for testing purposes
|
||||
print("test")
|
||||
else:
|
||||
self.agent_file = args.agent_file
|
||||
|
||||
|
||||
if args.auto or args.init:
|
||||
temp_topic = ' '.join(args.auto) if args.auto else ' '.join(args.init)
|
||||
self.topic = temp_topic
|
||||
elif self.auto or self.init: # Use the auto attribute if args.auto is not provided
|
||||
self.topic = self.auto
|
||||
|
||||
if args.auto or self.auto:
|
||||
self.agent_file = "test.yaml"
|
||||
generator = AutoGenerator(topic=self.topic , framework=self.framework, agent_file=self.agent_file)
|
||||
self.agent_file = generator.generate()
|
||||
agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
|
||||
result = agents_generator.generate_crew_and_kickoff()
|
||||
return result
|
||||
elif args.init or self.init:
|
||||
self.agent_file = "agents.yaml"
|
||||
generator = AutoGenerator(topic=self.topic , framework=self.framework, agent_file=self.agent_file)
|
||||
self.agent_file = generator.generate()
|
||||
print("File {} created successfully".format(self.agent_file))
|
||||
return "File {} created successfully".format(self.agent_file)
|
||||
|
||||
if ui:
|
||||
self.create_gradio_interface()
|
||||
else:
|
||||
agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
|
||||
result = agents_generator.generate_crew_and_kickoff()
|
||||
return result
|
||||
|
||||
def parse_args(self):
|
||||
"""
|
||||
Parse the command-line arguments for the PraisonAI CLI.
|
||||
|
||||
Args:
|
||||
self (PraisonAI): An instance of the PraisonAI class.
|
||||
|
||||
Returns:
|
||||
argparse.Namespace: An object containing the parsed command-line arguments.
|
||||
|
||||
Raises:
|
||||
argparse.ArgumentError: If the arguments provided are invalid.
|
||||
|
||||
Example:
|
||||
>>> args = praison_ai.parse_args()
|
||||
>>> print(args.agent_file) # Output: 'agents.yaml'
|
||||
"""
|
||||
parser = argparse.ArgumentParser(prog="praisonai", description="praisonAI command-line interface")
|
||||
parser.add_argument("--framework", choices=["crewai", "autogen"], help="Specify the framework")
|
||||
parser.add_argument("--ui", action="store_true", help="Enable UI mode")
|
||||
parser.add_argument("--auto", nargs=argparse.REMAINDER, help="Enable auto mode and pass arguments for it")
|
||||
parser.add_argument("--init", nargs=argparse.REMAINDER, help="Enable auto mode and pass arguments for it")
|
||||
parser.add_argument("agent_file", nargs="?", help="Specify the agent file")
|
||||
parser.add_argument("--deploy", action="store_true", help="Deploy the application") # New argument
|
||||
|
||||
args, unknown_args = parser.parse_known_args()
|
||||
|
||||
if unknown_args and unknown_args[0] == '-b' and unknown_args[1] == 'api:app':
|
||||
args.agent_file = 'agents.yaml'
|
||||
if args.agent_file == 'api:app' or args.agent_file == '/app/api:app':
|
||||
args.agent_file = 'agents.yaml'
|
||||
|
||||
return args
|
||||
|
||||
def create_gradio_interface(self):
|
||||
"""
|
||||
Create a Gradio interface for generating agents and performing tasks.
|
||||
|
||||
Args:
|
||||
self (PraisonAI): An instance of the PraisonAI class.
|
||||
|
||||
Returns:
|
||||
None: This method does not return any value. It launches the Gradio interface.
|
||||
|
||||
Raises:
|
||||
None: This method does not raise any exceptions.
|
||||
|
||||
Example:
|
||||
>>> praison_ai.create_gradio_interface()
|
||||
"""
|
||||
def generate_crew_and_kickoff_interface(auto_args, framework):
|
||||
"""
|
||||
Generate a crew and kick off tasks based on the provided auto arguments and framework.
|
||||
|
||||
Args:
|
||||
auto_args (list): Topic.
|
||||
framework (str): The framework to use for generating agents.
|
||||
|
||||
Returns:
|
||||
str: A string representing the result of generating the crew and kicking off tasks.
|
||||
|
||||
Raises:
|
||||
None: This method does not raise any exceptions.
|
||||
|
||||
Example:
|
||||
>>> result = generate_crew_and_kickoff_interface("Create a movie about Cat in Mars", "crewai")
|
||||
>>> print(result)
|
||||
"""
|
||||
self.framework = framework
|
||||
self.agent_file = "test.yaml"
|
||||
generator = AutoGenerator(topic=auto_args , framework=self.framework)
|
||||
self.agent_file = generator.generate()
|
||||
agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
|
||||
result = agents_generator.generate_crew_and_kickoff()
|
||||
return result
|
||||
|
||||
gr.Interface(
|
||||
fn=generate_crew_and_kickoff_interface,
|
||||
inputs=[gr.Textbox(lines=2, label="Auto Args"), gr.Dropdown(choices=["crewai", "autogen"], label="Framework")],
|
||||
outputs="textbox",
|
||||
title="Praison AI Studio",
|
||||
description="Create Agents and perform tasks",
|
||||
theme="default"
|
||||
).launch()</code></pre>
|
||||
</details>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="praisonai.cli.PraisonAI.create_gradio_interface"><code class="name flex">
|
||||
<span>def <span class="ident">create_gradio_interface</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>Create a Gradio interface for generating agents and performing tasks.</p>
|
||||
<h2 id="args">Args</h2>
|
||||
<dl>
|
||||
<dt><strong><code>self</code></strong> : <code><a title="praisonai.cli.PraisonAI" href="#praisonai.cli.PraisonAI">PraisonAI</a></code></dt>
|
||||
<dd>An instance of the PraisonAI class.</dd>
|
||||
</dl>
|
||||
<h2 id="returns">Returns</h2>
|
||||
<dl>
|
||||
<dt><code>None</code></dt>
|
||||
<dd>This method does not return any value. It launches the Gradio interface.</dd>
|
||||
</dl>
|
||||
<h2 id="raises">Raises</h2>
|
||||
<dl>
|
||||
<dt><code>None</code></dt>
|
||||
<dd>This method does not raise any exceptions.</dd>
|
||||
</dl>
|
||||
<h2 id="example">Example</h2>
|
||||
<pre><code class="language-python-repl">>>> praison_ai.create_gradio_interface()
|
||||
</code></pre></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def create_gradio_interface(self):
|
||||
"""
|
||||
Create a Gradio interface for generating agents and performing tasks.
|
||||
|
||||
Args:
|
||||
self (PraisonAI): An instance of the PraisonAI class.
|
||||
|
||||
Returns:
|
||||
None: This method does not return any value. It launches the Gradio interface.
|
||||
|
||||
Raises:
|
||||
None: This method does not raise any exceptions.
|
||||
|
||||
Example:
|
||||
>>> praison_ai.create_gradio_interface()
|
||||
"""
|
||||
def generate_crew_and_kickoff_interface(auto_args, framework):
|
||||
"""
|
||||
Generate a crew and kick off tasks based on the provided auto arguments and framework.
|
||||
|
||||
Args:
|
||||
auto_args (list): Topic.
|
||||
framework (str): The framework to use for generating agents.
|
||||
|
||||
Returns:
|
||||
str: A string representing the result of generating the crew and kicking off tasks.
|
||||
|
||||
Raises:
|
||||
None: This method does not raise any exceptions.
|
||||
|
||||
Example:
|
||||
>>> result = generate_crew_and_kickoff_interface("Create a movie about Cat in Mars", "crewai")
|
||||
>>> print(result)
|
||||
"""
|
||||
self.framework = framework
|
||||
self.agent_file = "test.yaml"
|
||||
generator = AutoGenerator(topic=auto_args , framework=self.framework)
|
||||
self.agent_file = generator.generate()
|
||||
agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
|
||||
result = agents_generator.generate_crew_and_kickoff()
|
||||
return result
|
||||
|
||||
gr.Interface(
|
||||
fn=generate_crew_and_kickoff_interface,
|
||||
inputs=[gr.Textbox(lines=2, label="Auto Args"), gr.Dropdown(choices=["crewai", "autogen"], label="Framework")],
|
||||
outputs="textbox",
|
||||
title="Praison AI Studio",
|
||||
description="Create Agents and perform tasks",
|
||||
theme="default"
|
||||
).launch()</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.cli.PraisonAI.main"><code class="name flex">
|
||||
<span>def <span class="ident">main</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>The main function of the PraisonAI object. It parses the command-line arguments,
|
||||
initializes the necessary attributes, and then calls the appropriate methods based on the
|
||||
provided arguments.</p>
|
||||
<h2 id="args">Args</h2>
|
||||
<dl>
|
||||
<dt><strong><code>self</code></strong> : <code><a title="praisonai.cli.PraisonAI" href="#praisonai.cli.PraisonAI">PraisonAI</a></code></dt>
|
||||
<dd>An instance of the PraisonAI class.</dd>
|
||||
</dl>
|
||||
<h2 id="returns">Returns</h2>
|
||||
<dl>
|
||||
<dt><code>Any</code></dt>
|
||||
<dd>Depending on the arguments provided, the function may return a result from the</dd>
|
||||
</dl>
|
||||
<p>AgentsGenerator, a deployment result from the CloudDeployer, or a message indicating
|
||||
the successful creation of a file.</p></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def main(self):
|
||||
"""
|
||||
The main function of the PraisonAI object. It parses the command-line arguments,
|
||||
initializes the necessary attributes, and then calls the appropriate methods based on the
|
||||
provided arguments.
|
||||
|
||||
Args:
|
||||
self (PraisonAI): An instance of the PraisonAI class.
|
||||
|
||||
Returns:
|
||||
Any: Depending on the arguments provided, the function may return a result from the
|
||||
AgentsGenerator, a deployment result from the CloudDeployer, or a message indicating
|
||||
the successful creation of a file.
|
||||
"""
|
||||
args = self.parse_args()
|
||||
if args is None:
|
||||
agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
|
||||
result = agents_generator.generate_crew_and_kickoff()
|
||||
return result
|
||||
if args.deploy:
|
||||
from .deploy import CloudDeployer
|
||||
deployer = CloudDeployer()
|
||||
deployer.run_commands()
|
||||
return
|
||||
invocation_cmd = "praisonai"
|
||||
version_string = f"PraisonAI version {__version__}"
|
||||
|
||||
self.framework = args.framework or self.framework
|
||||
|
||||
ui = args.ui
|
||||
if args.agent_file:
|
||||
if args.agent_file.startswith("tests.test"): # Argument used for testing purposes
|
||||
print("test")
|
||||
else:
|
||||
self.agent_file = args.agent_file
|
||||
|
||||
|
||||
if args.auto or args.init:
|
||||
temp_topic = ' '.join(args.auto) if args.auto else ' '.join(args.init)
|
||||
self.topic = temp_topic
|
||||
elif self.auto or self.init: # Use the auto attribute if args.auto is not provided
|
||||
self.topic = self.auto
|
||||
|
||||
if args.auto or self.auto:
|
||||
self.agent_file = "test.yaml"
|
||||
generator = AutoGenerator(topic=self.topic , framework=self.framework, agent_file=self.agent_file)
|
||||
self.agent_file = generator.generate()
|
||||
agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
|
||||
result = agents_generator.generate_crew_and_kickoff()
|
||||
return result
|
||||
elif args.init or self.init:
|
||||
self.agent_file = "agents.yaml"
|
||||
generator = AutoGenerator(topic=self.topic , framework=self.framework, agent_file=self.agent_file)
|
||||
self.agent_file = generator.generate()
|
||||
print("File {} created successfully".format(self.agent_file))
|
||||
return "File {} created successfully".format(self.agent_file)
|
||||
|
||||
if ui:
|
||||
self.create_gradio_interface()
|
||||
else:
|
||||
agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
|
||||
result = agents_generator.generate_crew_and_kickoff()
|
||||
return result</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.cli.PraisonAI.parse_args"><code class="name flex">
|
||||
<span>def <span class="ident">parse_args</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>Parse the command-line arguments for the PraisonAI CLI.</p>
|
||||
<h2 id="args">Args</h2>
|
||||
<dl>
|
||||
<dt><strong><code>self</code></strong> : <code><a title="praisonai.cli.PraisonAI" href="#praisonai.cli.PraisonAI">PraisonAI</a></code></dt>
|
||||
<dd>An instance of the PraisonAI class.</dd>
|
||||
</dl>
|
||||
<h2 id="returns">Returns</h2>
|
||||
<dl>
|
||||
<dt><code>argparse.Namespace</code></dt>
|
||||
<dd>An object containing the parsed command-line arguments.</dd>
|
||||
</dl>
|
||||
<h2 id="raises">Raises</h2>
|
||||
<dl>
|
||||
<dt><code>argparse.ArgumentError</code></dt>
|
||||
<dd>If the arguments provided are invalid.</dd>
|
||||
</dl>
|
||||
<h2 id="example">Example</h2>
|
||||
<pre><code class="language-python-repl">>>> args = praison_ai.parse_args()
|
||||
>>> print(args.agent_file) # Output: 'agents.yaml'
|
||||
</code></pre></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def parse_args(self):
|
||||
"""
|
||||
Parse the command-line arguments for the PraisonAI CLI.
|
||||
|
||||
Args:
|
||||
self (PraisonAI): An instance of the PraisonAI class.
|
||||
|
||||
Returns:
|
||||
argparse.Namespace: An object containing the parsed command-line arguments.
|
||||
|
||||
Raises:
|
||||
argparse.ArgumentError: If the arguments provided are invalid.
|
||||
|
||||
Example:
|
||||
>>> args = praison_ai.parse_args()
|
||||
>>> print(args.agent_file) # Output: 'agents.yaml'
|
||||
"""
|
||||
parser = argparse.ArgumentParser(prog="praisonai", description="praisonAI command-line interface")
|
||||
parser.add_argument("--framework", choices=["crewai", "autogen"], help="Specify the framework")
|
||||
parser.add_argument("--ui", action="store_true", help="Enable UI mode")
|
||||
parser.add_argument("--auto", nargs=argparse.REMAINDER, help="Enable auto mode and pass arguments for it")
|
||||
parser.add_argument("--init", nargs=argparse.REMAINDER, help="Enable auto mode and pass arguments for it")
|
||||
parser.add_argument("agent_file", nargs="?", help="Specify the agent file")
|
||||
parser.add_argument("--deploy", action="store_true", help="Deploy the application") # New argument
|
||||
|
||||
args, unknown_args = parser.parse_known_args()
|
||||
|
||||
if unknown_args and unknown_args[0] == '-b' and unknown_args[1] == 'api:app':
|
||||
args.agent_file = 'agents.yaml'
|
||||
if args.agent_file == 'api:app' or args.agent_file == '/app/api:app':
|
||||
args.agent_file = 'agents.yaml'
|
||||
|
||||
return args</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="praisonai.cli.PraisonAI" href="#praisonai.cli.PraisonAI">PraisonAI</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="praisonai.cli.PraisonAI.create_gradio_interface" href="#praisonai.cli.PraisonAI.create_gradio_interface">create_gradio_interface</a></code></li>
|
||||
<li><code><a title="praisonai.cli.PraisonAI.main" href="#praisonai.cli.PraisonAI.main">main</a></code></li>
|
||||
<li><code><a title="praisonai.cli.PraisonAI.parse_args" href="#praisonai.cli.PraisonAI.parse_args">parse_args</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
||||
</main>
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||||
<footer id="footer">
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<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
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||||
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|
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|
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|
||||
<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
|
||||
<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>praisonai.deploy</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">import subprocess
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
|
||||
class CloudDeployer:
|
||||
"""
|
||||
A class for deploying a cloud-based application.
|
||||
|
||||
Attributes:
|
||||
None
|
||||
|
||||
Methods:
|
||||
__init__(self):
|
||||
Loads environment variables from .env file or system and sets them.
|
||||
|
||||
"""
|
||||
def __init__(self):
|
||||
"""
|
||||
Loads environment variables from .env file or system and sets them.
|
||||
|
||||
Parameters:
|
||||
self: An instance of the CloudDeployer class.
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Raises:
|
||||
None
|
||||
|
||||
"""
|
||||
# Load environment variables from .env file or system
|
||||
load_dotenv()
|
||||
self.set_environment_variables()
|
||||
|
||||
def create_dockerfile(self):
|
||||
"""
|
||||
Creates a Dockerfile for the application.
|
||||
|
||||
Parameters:
|
||||
self: An instance of the CloudDeployer class.
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Raises:
|
||||
None
|
||||
|
||||
This method creates a Dockerfile in the current directory with the specified content.
|
||||
The Dockerfile is used to build a Docker image for the application.
|
||||
The content of the Dockerfile includes instructions to use the Python 3.11-slim base image,
|
||||
set the working directory to /app, copy the current directory contents into the container,
|
||||
install the required Python packages (flask, praisonai, gunicorn, and markdown),
|
||||
expose port 8080, and run the application using Gunicorn.
|
||||
"""
|
||||
with open("Dockerfile", "w") as file:
|
||||
file.write("FROM python:3.11-slim\n")
|
||||
file.write("WORKDIR /app\n")
|
||||
file.write("COPY . .\n")
|
||||
file.write("RUN pip install flask praisonai==0.0.21 gunicorn markdown\n")
|
||||
file.write("EXPOSE 8080\n")
|
||||
file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
|
||||
|
||||
def create_api_file(self):
|
||||
"""
|
||||
Creates an API file for the application.
|
||||
|
||||
Parameters:
|
||||
self (CloudDeployer): An instance of the CloudDeployer class.
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
This method creates an API file named "api.py" in the current directory. The file contains a basic Flask application that uses the PraisonAI library to run a simple agent and returns the output as an HTML page. The application listens on the root path ("/") and uses the Markdown library to format the output.
|
||||
"""
|
||||
with open("api.py", "w") as file:
|
||||
file.write("from flask import Flask\n")
|
||||
file.write("from praisonai import PraisonAI\n")
|
||||
file.write("import markdown\n\n")
|
||||
file.write("app = Flask(__name__)\n\n")
|
||||
file.write("def basic():\n")
|
||||
file.write(" praison_ai = PraisonAI(agent_file=\"agents.yaml\")\n")
|
||||
file.write(" return praison_ai.main()\n\n")
|
||||
file.write("@app.route('/')\n")
|
||||
file.write("def home():\n")
|
||||
file.write(" output = basic()\n")
|
||||
file.write(" html_output = markdown.markdown(output)\n")
|
||||
file.write(" return f'<html><body>{html_output}</body></html>'\n\n")
|
||||
file.write("if __name__ == \"__main__\":\n")
|
||||
file.write(" app.run(debug=True)\n")
|
||||
|
||||
def set_environment_variables(self):
|
||||
"""Sets environment variables with fallback to .env values or defaults."""
|
||||
os.environ["OPENAI_MODEL_NAME"] = os.getenv("OPENAI_MODEL_NAME", "gpt-4o")
|
||||
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "Enter your API key")
|
||||
os.environ["OPENAI_API_BASE"] = os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1")
|
||||
|
||||
def run_commands(self):
|
||||
"""
|
||||
Sets environment variables with fallback to .env values or defaults.
|
||||
|
||||
Parameters:
|
||||
None
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Raises:
|
||||
None
|
||||
|
||||
This method sets environment variables for the application. It uses the `os.environ` dictionary to set the following environment variables:
|
||||
|
||||
- `OPENAI_MODEL_NAME`: The name of the OpenAI model to use. If not specified in the .env file, it defaults to "gpt-4o".
|
||||
- `OPENAI_API_KEY`: The API key for accessing the OpenAI API. If not specified in the .env file, it defaults to "Enter your API key".
|
||||
- `OPENAI_API_BASE`: The base URL for the OpenAI API. If not specified in the .env file, it defaults to "https://api.openai.com/v1".
|
||||
"""
|
||||
self.create_api_file()
|
||||
self.create_dockerfile()
|
||||
"""Runs a sequence of shell commands for deployment, continues on error."""
|
||||
commands = [
|
||||
"yes | gcloud auth configure-docker us-central1-docker.pkg.dev",
|
||||
"gcloud artifacts repositories create praisonai-repository --repository-format=docker --location=us-central1",
|
||||
"docker build --platform linux/amd64 -t gcr.io/$(gcloud config get-value project)/praisonai-app:latest .",
|
||||
"docker tag gcr.io/$(gcloud config get-value project)/praisonai-app:latest us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest",
|
||||
"docker push us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest",
|
||||
"gcloud run deploy praisonai-service --image us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest --platform managed --region us-central1 --allow-unauthenticated --set-env-vars OPENAI_MODEL_NAME=${OPENAI_MODEL_NAME},OPENAI_API_KEY=${OPENAI_API_KEY},OPENAI_API_BASE=${OPENAI_API_BASE}"
|
||||
]
|
||||
|
||||
for cmd in commands:
|
||||
try:
|
||||
subprocess.run(cmd, shell=True, check=True)
|
||||
except subprocess.CalledProcessError as e:
|
||||
print(f"ERROR: Command '{e.cmd}' failed with exit status {e.returncode}")
|
||||
print(f"Continuing with the next command...")
|
||||
|
||||
# Usage
|
||||
if __name__ == "__main__":
|
||||
deployer = CloudDeployer()
|
||||
deployer.run_commands()</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="praisonai.deploy.CloudDeployer"><code class="flex name class">
|
||||
<span>class <span class="ident">CloudDeployer</span></span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>A class for deploying a cloud-based application.</p>
|
||||
<h2 id="attributes">Attributes</h2>
|
||||
<p>None</p>
|
||||
<h2 id="methods">Methods</h2>
|
||||
<p><strong>init</strong>(self):
|
||||
Loads environment variables from .env file or system and sets them.</p>
|
||||
<p>Loads environment variables from .env file or system and sets them.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<p>self: An instance of the CloudDeployer class.</p>
|
||||
<h2 id="returns">Returns</h2>
|
||||
<p>None</p>
|
||||
<h2 id="raises">Raises</h2>
|
||||
<p>None</p></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class CloudDeployer:
|
||||
"""
|
||||
A class for deploying a cloud-based application.
|
||||
|
||||
Attributes:
|
||||
None
|
||||
|
||||
Methods:
|
||||
__init__(self):
|
||||
Loads environment variables from .env file or system and sets them.
|
||||
|
||||
"""
|
||||
def __init__(self):
|
||||
"""
|
||||
Loads environment variables from .env file or system and sets them.
|
||||
|
||||
Parameters:
|
||||
self: An instance of the CloudDeployer class.
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Raises:
|
||||
None
|
||||
|
||||
"""
|
||||
# Load environment variables from .env file or system
|
||||
load_dotenv()
|
||||
self.set_environment_variables()
|
||||
|
||||
def create_dockerfile(self):
|
||||
"""
|
||||
Creates a Dockerfile for the application.
|
||||
|
||||
Parameters:
|
||||
self: An instance of the CloudDeployer class.
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Raises:
|
||||
None
|
||||
|
||||
This method creates a Dockerfile in the current directory with the specified content.
|
||||
The Dockerfile is used to build a Docker image for the application.
|
||||
The content of the Dockerfile includes instructions to use the Python 3.11-slim base image,
|
||||
set the working directory to /app, copy the current directory contents into the container,
|
||||
install the required Python packages (flask, praisonai, gunicorn, and markdown),
|
||||
expose port 8080, and run the application using Gunicorn.
|
||||
"""
|
||||
with open("Dockerfile", "w") as file:
|
||||
file.write("FROM python:3.11-slim\n")
|
||||
file.write("WORKDIR /app\n")
|
||||
file.write("COPY . .\n")
|
||||
file.write("RUN pip install flask praisonai==0.0.21 gunicorn markdown\n")
|
||||
file.write("EXPOSE 8080\n")
|
||||
file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
|
||||
|
||||
def create_api_file(self):
|
||||
"""
|
||||
Creates an API file for the application.
|
||||
|
||||
Parameters:
|
||||
self (CloudDeployer): An instance of the CloudDeployer class.
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
This method creates an API file named "api.py" in the current directory. The file contains a basic Flask application that uses the PraisonAI library to run a simple agent and returns the output as an HTML page. The application listens on the root path ("/") and uses the Markdown library to format the output.
|
||||
"""
|
||||
with open("api.py", "w") as file:
|
||||
file.write("from flask import Flask\n")
|
||||
file.write("from praisonai import PraisonAI\n")
|
||||
file.write("import markdown\n\n")
|
||||
file.write("app = Flask(__name__)\n\n")
|
||||
file.write("def basic():\n")
|
||||
file.write(" praison_ai = PraisonAI(agent_file=\"agents.yaml\")\n")
|
||||
file.write(" return praison_ai.main()\n\n")
|
||||
file.write("@app.route('/')\n")
|
||||
file.write("def home():\n")
|
||||
file.write(" output = basic()\n")
|
||||
file.write(" html_output = markdown.markdown(output)\n")
|
||||
file.write(" return f'<html><body>{html_output}</body></html>'\n\n")
|
||||
file.write("if __name__ == \"__main__\":\n")
|
||||
file.write(" app.run(debug=True)\n")
|
||||
|
||||
def set_environment_variables(self):
|
||||
"""Sets environment variables with fallback to .env values or defaults."""
|
||||
os.environ["OPENAI_MODEL_NAME"] = os.getenv("OPENAI_MODEL_NAME", "gpt-4o")
|
||||
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "Enter your API key")
|
||||
os.environ["OPENAI_API_BASE"] = os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1")
|
||||
|
||||
def run_commands(self):
|
||||
"""
|
||||
Sets environment variables with fallback to .env values or defaults.
|
||||
|
||||
Parameters:
|
||||
None
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Raises:
|
||||
None
|
||||
|
||||
This method sets environment variables for the application. It uses the `os.environ` dictionary to set the following environment variables:
|
||||
|
||||
- `OPENAI_MODEL_NAME`: The name of the OpenAI model to use. If not specified in the .env file, it defaults to "gpt-4o".
|
||||
- `OPENAI_API_KEY`: The API key for accessing the OpenAI API. If not specified in the .env file, it defaults to "Enter your API key".
|
||||
- `OPENAI_API_BASE`: The base URL for the OpenAI API. If not specified in the .env file, it defaults to "https://api.openai.com/v1".
|
||||
"""
|
||||
self.create_api_file()
|
||||
self.create_dockerfile()
|
||||
"""Runs a sequence of shell commands for deployment, continues on error."""
|
||||
commands = [
|
||||
"yes | gcloud auth configure-docker us-central1-docker.pkg.dev",
|
||||
"gcloud artifacts repositories create praisonai-repository --repository-format=docker --location=us-central1",
|
||||
"docker build --platform linux/amd64 -t gcr.io/$(gcloud config get-value project)/praisonai-app:latest .",
|
||||
"docker tag gcr.io/$(gcloud config get-value project)/praisonai-app:latest us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest",
|
||||
"docker push us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest",
|
||||
"gcloud run deploy praisonai-service --image us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest --platform managed --region us-central1 --allow-unauthenticated --set-env-vars OPENAI_MODEL_NAME=${OPENAI_MODEL_NAME},OPENAI_API_KEY=${OPENAI_API_KEY},OPENAI_API_BASE=${OPENAI_API_BASE}"
|
||||
]
|
||||
|
||||
for cmd in commands:
|
||||
try:
|
||||
subprocess.run(cmd, shell=True, check=True)
|
||||
except subprocess.CalledProcessError as e:
|
||||
print(f"ERROR: Command '{e.cmd}' failed with exit status {e.returncode}")
|
||||
print(f"Continuing with the next command...")</code></pre>
|
||||
</details>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="praisonai.deploy.CloudDeployer.create_api_file"><code class="name flex">
|
||||
<span>def <span class="ident">create_api_file</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>Creates an API file for the application.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<p>self (CloudDeployer): An instance of the CloudDeployer class.</p>
|
||||
<h2 id="returns">Returns</h2>
|
||||
<p>None
|
||||
This method creates an API file named "api.py" in the current directory. The file contains a basic Flask application that uses the PraisonAI library to run a simple agent and returns the output as an HTML page. The application listens on the root path ("/") and uses the Markdown library to format the output.</p></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def create_api_file(self):
|
||||
"""
|
||||
Creates an API file for the application.
|
||||
|
||||
Parameters:
|
||||
self (CloudDeployer): An instance of the CloudDeployer class.
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
This method creates an API file named "api.py" in the current directory. The file contains a basic Flask application that uses the PraisonAI library to run a simple agent and returns the output as an HTML page. The application listens on the root path ("/") and uses the Markdown library to format the output.
|
||||
"""
|
||||
with open("api.py", "w") as file:
|
||||
file.write("from flask import Flask\n")
|
||||
file.write("from praisonai import PraisonAI\n")
|
||||
file.write("import markdown\n\n")
|
||||
file.write("app = Flask(__name__)\n\n")
|
||||
file.write("def basic():\n")
|
||||
file.write(" praison_ai = PraisonAI(agent_file=\"agents.yaml\")\n")
|
||||
file.write(" return praison_ai.main()\n\n")
|
||||
file.write("@app.route('/')\n")
|
||||
file.write("def home():\n")
|
||||
file.write(" output = basic()\n")
|
||||
file.write(" html_output = markdown.markdown(output)\n")
|
||||
file.write(" return f'<html><body>{html_output}</body></html>'\n\n")
|
||||
file.write("if __name__ == \"__main__\":\n")
|
||||
file.write(" app.run(debug=True)\n")</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.deploy.CloudDeployer.create_dockerfile"><code class="name flex">
|
||||
<span>def <span class="ident">create_dockerfile</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>Creates a Dockerfile for the application.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<p>self: An instance of the CloudDeployer class.</p>
|
||||
<h2 id="returns">Returns</h2>
|
||||
<p>None</p>
|
||||
<h2 id="raises">Raises</h2>
|
||||
<p>None
|
||||
This method creates a Dockerfile in the current directory with the specified content.
|
||||
The Dockerfile is used to build a Docker image for the application.
|
||||
The content of the Dockerfile includes instructions to use the Python 3.11-slim base image,
|
||||
set the working directory to /app, copy the current directory contents into the container,
|
||||
install the required Python packages (flask, praisonai, gunicorn, and markdown),
|
||||
expose port 8080, and run the application using Gunicorn.</p></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def create_dockerfile(self):
|
||||
"""
|
||||
Creates a Dockerfile for the application.
|
||||
|
||||
Parameters:
|
||||
self: An instance of the CloudDeployer class.
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Raises:
|
||||
None
|
||||
|
||||
This method creates a Dockerfile in the current directory with the specified content.
|
||||
The Dockerfile is used to build a Docker image for the application.
|
||||
The content of the Dockerfile includes instructions to use the Python 3.11-slim base image,
|
||||
set the working directory to /app, copy the current directory contents into the container,
|
||||
install the required Python packages (flask, praisonai, gunicorn, and markdown),
|
||||
expose port 8080, and run the application using Gunicorn.
|
||||
"""
|
||||
with open("Dockerfile", "w") as file:
|
||||
file.write("FROM python:3.11-slim\n")
|
||||
file.write("WORKDIR /app\n")
|
||||
file.write("COPY . .\n")
|
||||
file.write("RUN pip install flask praisonai==0.0.21 gunicorn markdown\n")
|
||||
file.write("EXPOSE 8080\n")
|
||||
file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.deploy.CloudDeployer.run_commands"><code class="name flex">
|
||||
<span>def <span class="ident">run_commands</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>Sets environment variables with fallback to .env values or defaults.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<p>None</p>
|
||||
<h2 id="returns">Returns</h2>
|
||||
<p>None</p>
|
||||
<h2 id="raises">Raises</h2>
|
||||
<p>None
|
||||
This method sets environment variables for the application. It uses the <code>os.environ</code> dictionary to set the following environment variables:</p>
|
||||
<ul>
|
||||
<li><code>OPENAI_MODEL_NAME</code>: The name of the OpenAI model to use. If not specified in the .env file, it defaults to "gpt-4o".</li>
|
||||
<li><code>OPENAI_API_KEY</code>: The API key for accessing the OpenAI API. If not specified in the .env file, it defaults to "Enter your API key".</li>
|
||||
<li><code>OPENAI_API_BASE</code>: The base URL for the OpenAI API. If not specified in the .env file, it defaults to "https://api.openai.com/v1".</li>
|
||||
</ul></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def run_commands(self):
|
||||
"""
|
||||
Sets environment variables with fallback to .env values or defaults.
|
||||
|
||||
Parameters:
|
||||
None
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Raises:
|
||||
None
|
||||
|
||||
This method sets environment variables for the application. It uses the `os.environ` dictionary to set the following environment variables:
|
||||
|
||||
- `OPENAI_MODEL_NAME`: The name of the OpenAI model to use. If not specified in the .env file, it defaults to "gpt-4o".
|
||||
- `OPENAI_API_KEY`: The API key for accessing the OpenAI API. If not specified in the .env file, it defaults to "Enter your API key".
|
||||
- `OPENAI_API_BASE`: The base URL for the OpenAI API. If not specified in the .env file, it defaults to "https://api.openai.com/v1".
|
||||
"""
|
||||
self.create_api_file()
|
||||
self.create_dockerfile()
|
||||
"""Runs a sequence of shell commands for deployment, continues on error."""
|
||||
commands = [
|
||||
"yes | gcloud auth configure-docker us-central1-docker.pkg.dev",
|
||||
"gcloud artifacts repositories create praisonai-repository --repository-format=docker --location=us-central1",
|
||||
"docker build --platform linux/amd64 -t gcr.io/$(gcloud config get-value project)/praisonai-app:latest .",
|
||||
"docker tag gcr.io/$(gcloud config get-value project)/praisonai-app:latest us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest",
|
||||
"docker push us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest",
|
||||
"gcloud run deploy praisonai-service --image us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest --platform managed --region us-central1 --allow-unauthenticated --set-env-vars OPENAI_MODEL_NAME=${OPENAI_MODEL_NAME},OPENAI_API_KEY=${OPENAI_API_KEY},OPENAI_API_BASE=${OPENAI_API_BASE}"
|
||||
]
|
||||
|
||||
for cmd in commands:
|
||||
try:
|
||||
subprocess.run(cmd, shell=True, check=True)
|
||||
except subprocess.CalledProcessError as e:
|
||||
print(f"ERROR: Command '{e.cmd}' failed with exit status {e.returncode}")
|
||||
print(f"Continuing with the next command...")</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.deploy.CloudDeployer.set_environment_variables"><code class="name flex">
|
||||
<span>def <span class="ident">set_environment_variables</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>Sets environment variables with fallback to .env values or defaults.</p></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def set_environment_variables(self):
|
||||
"""Sets environment variables with fallback to .env values or defaults."""
|
||||
os.environ["OPENAI_MODEL_NAME"] = os.getenv("OPENAI_MODEL_NAME", "gpt-4o")
|
||||
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "Enter your API key")
|
||||
os.environ["OPENAI_API_BASE"] = os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1")</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="praisonai.deploy.CloudDeployer" href="#praisonai.deploy.CloudDeployer">CloudDeployer</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="praisonai.deploy.CloudDeployer.create_api_file" href="#praisonai.deploy.CloudDeployer.create_api_file">create_api_file</a></code></li>
|
||||
<li><code><a title="praisonai.deploy.CloudDeployer.create_dockerfile" href="#praisonai.deploy.CloudDeployer.create_dockerfile">create_dockerfile</a></code></li>
|
||||
<li><code><a title="praisonai.deploy.CloudDeployer.run_commands" href="#praisonai.deploy.CloudDeployer.run_commands">run_commands</a></code></li>
|
||||
<li><code><a title="praisonai.deploy.CloudDeployer.set_environment_variables" href="#praisonai.deploy.CloudDeployer.set_environment_variables">set_environment_variables</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
||||
</main>
|
||||
<footer id="footer">
|
||||
<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
|
||||
</footer>
|
||||
</body>
|
||||
</html>
|
||||
@@ -1,734 +0,0 @@
|
||||
<!doctype html>
|
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|
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<head>
|
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<title>praisonai.inbuilt_tools.autogen_tools API documentation</title>
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<link rel="stylesheet preload" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/styles/github.min.css" crossorigin>
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<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
|
||||
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
|
||||
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
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<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
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||||
<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>praisonai.inbuilt_tools.autogen_tools</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># praisonai/inbuilt_tools/autogen_tools.py
|
||||
|
||||
from crewai_tools import (
|
||||
CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, DirectoryReadTool,
|
||||
FileReadTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, RagTool,
|
||||
ScrapeElementFromWebsiteTool, ScrapeWebsiteTool, WebsiteSearchTool, XMLSearchTool, YoutubeChannelSearchTool,
|
||||
YoutubeVideoSearchTool
|
||||
)
|
||||
from typing import Any
|
||||
from autogen import register_function
|
||||
import os
|
||||
import importlib
|
||||
from pathlib import Path
|
||||
import os
|
||||
import inspect
|
||||
import sys
|
||||
|
||||
def create_autogen_tool_function(tool_name):
|
||||
def autogen_tool(assistant, user_proxy):
|
||||
def register_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(query: str) -> Any:
|
||||
tool_instance = tool_class()
|
||||
return tool_instance.run(query=query)
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
|
||||
root_directory = os.getcwd()
|
||||
tools_py_path = os.path.join(root_directory, 'tools.py')
|
||||
tools_dir_path = Path(root_directory) / 'tools'
|
||||
|
||||
if os.path.isfile(tools_py_path):
|
||||
print(f"{tools_py_path} exists in the root directory. Loading {tools_py_path} and skipping tools folder.")
|
||||
tool_module = importlib.import_module("tools")
|
||||
elif tools_dir_path.is_dir():
|
||||
print(f"tools folder exists in the root directory. Loading {tool_name} from tools/{tool_name}.py.")
|
||||
tool_module = importlib.import_module(f"tools.{tool_name}")
|
||||
else:
|
||||
raise ImportError("Neither tools.py nor tools directory found in the root directory.")
|
||||
|
||||
Tool = getattr(tool_module, tool_name)
|
||||
|
||||
register_tool(Tool, tool_name, f"Description for {tool_name}", assistant, user_proxy)
|
||||
|
||||
return autogen_tool
|
||||
|
||||
# Load tools.py
|
||||
sys.path.insert(0, os.getcwd())
|
||||
root_directory = os.getcwd()
|
||||
tools_py_path = os.path.join(root_directory, 'tools.py')
|
||||
tools_dir_path = Path(root_directory) / 'tools'
|
||||
|
||||
tools_module = None
|
||||
|
||||
if os.path.isfile(tools_py_path):
|
||||
print(f"{tools_py_path} exists in the root directory. Loading {tools_py_path} and skipping tools folder.")
|
||||
tools_module = importlib.import_module("tools")
|
||||
elif tools_dir_path.is_dir():
|
||||
print(f"tools folder exists in the root directory. Loading {tool_name} from tools/{tool_name}.py.")
|
||||
tools_module = importlib.import_module(f"tools.{tool_name}")
|
||||
|
||||
# Create autogen_TOOL_NAME_HERE function for each tool
|
||||
if tools_module is not None:
|
||||
for name, obj in inspect.getmembers(tools_module):
|
||||
if inspect.isclass(obj):
|
||||
globals()[f"autogen_{name}"] = create_autogen_tool_function(name)
|
||||
|
||||
def autogen_CodeDocsSearchTool(assistant, user_proxy):
|
||||
def register_code_docs_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(docs_url: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(docs_url=docs_url, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_code_docs_search_tool(CodeDocsSearchTool, "code_docs_search_tool", "Search a Code Docs content(search_query: 'string', docs_url: 'string') - A tool that can be used to semantic search a query from a Code Docs content.", assistant, user_proxy)
|
||||
|
||||
def autogen_CSVSearchTool(assistant, user_proxy):
|
||||
def register_csv_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(csv: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(csv=csv, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_csv_search_tool(CSVSearchTool, "csv_search_tool", "Search a CSV's content(search_query: 'string', csv: 'string') - A tool that can be used to semantic search a query from a CSV's content.", assistant, user_proxy)
|
||||
|
||||
def autogen_DirectorySearchTool(assistant, user_proxy):
|
||||
def register_directory_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(directory: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(directory=directory, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_directory_search_tool(DirectorySearchTool, "directory_search_tool", "Search a directory's content(search_query: 'string', directory: 'string') - A tool that can be used to semantic search a query from a directory's content.", assistant, user_proxy)
|
||||
|
||||
def autogen_DOCXSearchTool(assistant, user_proxy):
|
||||
def register_docx_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(docx: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(docx=docx, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_docx_search_tool(DOCXSearchTool, "docx_search_tool", "Search a DOCX's content(search_query: 'string', docx: 'string') - A tool that can be used to semantic search a query from a DOCX's content.", assistant, user_proxy)
|
||||
|
||||
# DirectoryReadTool
|
||||
def autogen_DirectoryReadTool(assistant, user_proxy):
|
||||
def register_directory_read_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(directory: str) -> Any:
|
||||
tool_instance = tool_class(directory=directory)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_directory_read_tool(DirectoryReadTool, "directory_read_tool", "List files in directory(directory: 'string') - A tool that can be used to recursively list a directory's content.", assistant, user_proxy)
|
||||
|
||||
# FileReadTool
|
||||
def autogen_FileReadTool(assistant, user_proxy):
|
||||
def register_file_read_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(file_path: str) -> Any:
|
||||
tool_instance = tool_class(file_path=file_path)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_file_read_tool(FileReadTool, "file_read_tool", "Read a file's content(file_path: 'string') - A tool that can be used to read a file's content.", assistant, user_proxy)
|
||||
|
||||
# TXTSearchTool
|
||||
def autogen_TXTSearchTool(assistant, user_proxy):
|
||||
def register_txt_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(txt: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(txt=txt, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_txt_search_tool(TXTSearchTool, "txt_search_tool", "Search a txt's content(search_query: 'string', txt: 'string') - A tool that can be used to semantic search a query from a txt's content.", assistant, user_proxy)
|
||||
|
||||
def autogen_JSONSearchTool(assistant, user_proxy):
|
||||
def register_json_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(json_path: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(json_path=json_path, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_json_search_tool(JSONSearchTool, "json_search_tool", "Search a JSON's content(search_query: 'string', json_path: 'string') - A tool that can be used to semantic search a query from a JSON's content.", assistant, user_proxy)
|
||||
|
||||
def autogen_MDXSearchTool(assistant, user_proxy):
|
||||
def register_mdx_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(mdx: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(mdx=mdx, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_mdx_search_tool(MDXSearchTool, "mdx_search_tool", "Search a MDX's content(search_query: 'string', mdx: 'string') - A tool that can be used to semantic search a query from a MDX's content.", assistant, user_proxy)
|
||||
|
||||
def autogen_PDFSearchTool(assistant, user_proxy):
|
||||
def register_pdf_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(pdf: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(pdf=pdf, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_pdf_search_tool(PDFSearchTool, "pdf_search_tool", "Search a PDF's content(search_query: 'string', pdf: 'string') - A tool that can be used to semantic search a query from a PDF's content.", assistant, user_proxy)
|
||||
|
||||
def autogen_RagTool(assistant, user_proxy):
|
||||
def register_rag_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(query: str, data: Any) -> Any:
|
||||
tool_instance = tool_class(query=query, data=data)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_rag_tool(RagTool, "rag_tool", "Knowledge base(query: 'string', data: Any) - A knowledge base that can be used to answer questions.", assistant, user_proxy)
|
||||
|
||||
def autogen_ScrapeElementFromWebsiteTool(assistant, user_proxy):
|
||||
def register_scrape_element_from_website_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(website_url: str, element_query: str) -> Any:
|
||||
tool_instance = tool_class(website_url=website_url, element_query=element_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_scrape_element_from_website_tool(ScrapeElementFromWebsiteTool, "scrape_element_from_website_tool", "Scrape an element from a website(element_query: 'string', website_url: 'string') - A tool that can be used to scrape an element from a website.", assistant, user_proxy)
|
||||
|
||||
def autogen_ScrapeWebsiteTool(assistant, user_proxy):
|
||||
def register_scrape_website_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(website_url: str) -> Any:
|
||||
tool_instance = tool_class(website_url=website_url)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_scrape_website_tool(ScrapeWebsiteTool, "scrape_website_tool", "Read website content(website_url: 'string') - A tool that can be used to read content from a specified website.", assistant, user_proxy)
|
||||
|
||||
def autogen_WebsiteSearchTool(assistant, user_proxy):
|
||||
def register_website_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(website: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(website=website, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_website_search_tool(WebsiteSearchTool, "website_search_tool", "Search in a specific website(search_query: 'string', website: 'string') - A tool that can be used to semantic search a query from a specific URL content.", assistant, user_proxy)
|
||||
|
||||
def autogen_XMLSearchTool(assistant, user_proxy):
|
||||
def register_xml_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(xml: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(xml=xml, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_xml_search_tool(XMLSearchTool, "xml_search_tool", "Search a XML's content(search_query: 'string', xml: 'string') - A tool that can be used to semantic search a query from a XML's content.", assistant, user_proxy)
|
||||
|
||||
def autogen_YoutubeChannelSearchTool(assistant, user_proxy):
|
||||
def register_youtube_channel_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(youtube_channel_handle: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(youtube_channel_handle=youtube_channel_handle, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_youtube_channel_search_tool(YoutubeChannelSearchTool, "youtube_channel_search_tool", "Search a Youtube Channels content(search_query: 'string', youtube_channel_handle: 'string') - A tool that can be used to semantic search a query from a Youtube Channels content.", assistant, user_proxy)
|
||||
|
||||
def autogen_YoutubeVideoSearchTool(assistant, user_proxy):
|
||||
def register_youtube_video_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(youtube_video_url: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(youtube_video_url=youtube_video_url, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_youtube_video_search_tool(YoutubeVideoSearchTool, "youtube_video_search_tool", "Search a Youtube Video content(search_query: 'string', youtube_video_url: 'string') - A tool that can be used to semantic search a query from a Youtube Video content.", assistant, user_proxy)</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_BaseTool"><code class="name flex">
|
||||
<span>def <span class="ident">autogen_BaseTool</span></span>(<span>assistant, user_proxy)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def autogen_tool(assistant, user_proxy):
|
||||
def register_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(query: str) -> Any:
|
||||
tool_instance = tool_class()
|
||||
return tool_instance.run(query=query)
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
|
||||
root_directory = os.getcwd()
|
||||
tools_py_path = os.path.join(root_directory, 'tools.py')
|
||||
tools_dir_path = Path(root_directory) / 'tools'
|
||||
|
||||
if os.path.isfile(tools_py_path):
|
||||
print(f"{tools_py_path} exists in the root directory. Loading {tools_py_path} and skipping tools folder.")
|
||||
tool_module = importlib.import_module("tools")
|
||||
elif tools_dir_path.is_dir():
|
||||
print(f"tools folder exists in the root directory. Loading {tool_name} from tools/{tool_name}.py.")
|
||||
tool_module = importlib.import_module(f"tools.{tool_name}")
|
||||
else:
|
||||
raise ImportError("Neither tools.py nor tools directory found in the root directory.")
|
||||
|
||||
Tool = getattr(tool_module, tool_name)
|
||||
|
||||
register_tool(Tool, tool_name, f"Description for {tool_name}", assistant, user_proxy)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_CSVSearchTool"><code class="name flex">
|
||||
<span>def <span class="ident">autogen_CSVSearchTool</span></span>(<span>assistant, user_proxy)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def autogen_CSVSearchTool(assistant, user_proxy):
|
||||
def register_csv_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(csv: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(csv=csv, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_csv_search_tool(CSVSearchTool, "csv_search_tool", "Search a CSV's content(search_query: 'string', csv: 'string') - A tool that can be used to semantic search a query from a CSV's content.", assistant, user_proxy)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_CodeDocsSearchTool"><code class="name flex">
|
||||
<span>def <span class="ident">autogen_CodeDocsSearchTool</span></span>(<span>assistant, user_proxy)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def autogen_CodeDocsSearchTool(assistant, user_proxy):
|
||||
def register_code_docs_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(docs_url: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(docs_url=docs_url, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_code_docs_search_tool(CodeDocsSearchTool, "code_docs_search_tool", "Search a Code Docs content(search_query: 'string', docs_url: 'string') - A tool that can be used to semantic search a query from a Code Docs content.", assistant, user_proxy)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_DDGS"><code class="name flex">
|
||||
<span>def <span class="ident">autogen_DDGS</span></span>(<span>assistant, user_proxy)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def autogen_tool(assistant, user_proxy):
|
||||
def register_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(query: str) -> Any:
|
||||
tool_instance = tool_class()
|
||||
return tool_instance.run(query=query)
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
|
||||
root_directory = os.getcwd()
|
||||
tools_py_path = os.path.join(root_directory, 'tools.py')
|
||||
tools_dir_path = Path(root_directory) / 'tools'
|
||||
|
||||
if os.path.isfile(tools_py_path):
|
||||
print(f"{tools_py_path} exists in the root directory. Loading {tools_py_path} and skipping tools folder.")
|
||||
tool_module = importlib.import_module("tools")
|
||||
elif tools_dir_path.is_dir():
|
||||
print(f"tools folder exists in the root directory. Loading {tool_name} from tools/{tool_name}.py.")
|
||||
tool_module = importlib.import_module(f"tools.{tool_name}")
|
||||
else:
|
||||
raise ImportError("Neither tools.py nor tools directory found in the root directory.")
|
||||
|
||||
Tool = getattr(tool_module, tool_name)
|
||||
|
||||
register_tool(Tool, tool_name, f"Description for {tool_name}", assistant, user_proxy)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_DOCXSearchTool"><code class="name flex">
|
||||
<span>def <span class="ident">autogen_DOCXSearchTool</span></span>(<span>assistant, user_proxy)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def autogen_DOCXSearchTool(assistant, user_proxy):
|
||||
def register_docx_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(docx: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(docx=docx, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_docx_search_tool(DOCXSearchTool, "docx_search_tool", "Search a DOCX's content(search_query: 'string', docx: 'string') - A tool that can be used to semantic search a query from a DOCX's content.", assistant, user_proxy)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_DirectoryReadTool"><code class="name flex">
|
||||
<span>def <span class="ident">autogen_DirectoryReadTool</span></span>(<span>assistant, user_proxy)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def autogen_DirectoryReadTool(assistant, user_proxy):
|
||||
def register_directory_read_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(directory: str) -> Any:
|
||||
tool_instance = tool_class(directory=directory)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_directory_read_tool(DirectoryReadTool, "directory_read_tool", "List files in directory(directory: 'string') - A tool that can be used to recursively list a directory's content.", assistant, user_proxy)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_DirectorySearchTool"><code class="name flex">
|
||||
<span>def <span class="ident">autogen_DirectorySearchTool</span></span>(<span>assistant, user_proxy)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def autogen_DirectorySearchTool(assistant, user_proxy):
|
||||
def register_directory_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(directory: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(directory=directory, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_directory_search_tool(DirectorySearchTool, "directory_search_tool", "Search a directory's content(search_query: 'string', directory: 'string') - A tool that can be used to semantic search a query from a directory's content.", assistant, user_proxy)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_FileReadTool"><code class="name flex">
|
||||
<span>def <span class="ident">autogen_FileReadTool</span></span>(<span>assistant, user_proxy)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def autogen_FileReadTool(assistant, user_proxy):
|
||||
def register_file_read_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(file_path: str) -> Any:
|
||||
tool_instance = tool_class(file_path=file_path)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_file_read_tool(FileReadTool, "file_read_tool", "Read a file's content(file_path: 'string') - A tool that can be used to read a file's content.", assistant, user_proxy)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_InternetSearchTool"><code class="name flex">
|
||||
<span>def <span class="ident">autogen_InternetSearchTool</span></span>(<span>assistant, user_proxy)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def autogen_tool(assistant, user_proxy):
|
||||
def register_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(query: str) -> Any:
|
||||
tool_instance = tool_class()
|
||||
return tool_instance.run(query=query)
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
|
||||
root_directory = os.getcwd()
|
||||
tools_py_path = os.path.join(root_directory, 'tools.py')
|
||||
tools_dir_path = Path(root_directory) / 'tools'
|
||||
|
||||
if os.path.isfile(tools_py_path):
|
||||
print(f"{tools_py_path} exists in the root directory. Loading {tools_py_path} and skipping tools folder.")
|
||||
tool_module = importlib.import_module("tools")
|
||||
elif tools_dir_path.is_dir():
|
||||
print(f"tools folder exists in the root directory. Loading {tool_name} from tools/{tool_name}.py.")
|
||||
tool_module = importlib.import_module(f"tools.{tool_name}")
|
||||
else:
|
||||
raise ImportError("Neither tools.py nor tools directory found in the root directory.")
|
||||
|
||||
Tool = getattr(tool_module, tool_name)
|
||||
|
||||
register_tool(Tool, tool_name, f"Description for {tool_name}", assistant, user_proxy)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_JSONSearchTool"><code class="name flex">
|
||||
<span>def <span class="ident">autogen_JSONSearchTool</span></span>(<span>assistant, user_proxy)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def autogen_JSONSearchTool(assistant, user_proxy):
|
||||
def register_json_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(json_path: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(json_path=json_path, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_json_search_tool(JSONSearchTool, "json_search_tool", "Search a JSON's content(search_query: 'string', json_path: 'string') - A tool that can be used to semantic search a query from a JSON's content.", assistant, user_proxy)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_MDXSearchTool"><code class="name flex">
|
||||
<span>def <span class="ident">autogen_MDXSearchTool</span></span>(<span>assistant, user_proxy)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def autogen_MDXSearchTool(assistant, user_proxy):
|
||||
def register_mdx_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(mdx: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(mdx=mdx, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_mdx_search_tool(MDXSearchTool, "mdx_search_tool", "Search a MDX's content(search_query: 'string', mdx: 'string') - A tool that can be used to semantic search a query from a MDX's content.", assistant, user_proxy)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_PDFSearchTool"><code class="name flex">
|
||||
<span>def <span class="ident">autogen_PDFSearchTool</span></span>(<span>assistant, user_proxy)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def autogen_PDFSearchTool(assistant, user_proxy):
|
||||
def register_pdf_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(pdf: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(pdf=pdf, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_pdf_search_tool(PDFSearchTool, "pdf_search_tool", "Search a PDF's content(search_query: 'string', pdf: 'string') - A tool that can be used to semantic search a query from a PDF's content.", assistant, user_proxy)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_RagTool"><code class="name flex">
|
||||
<span>def <span class="ident">autogen_RagTool</span></span>(<span>assistant, user_proxy)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def autogen_RagTool(assistant, user_proxy):
|
||||
def register_rag_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(query: str, data: Any) -> Any:
|
||||
tool_instance = tool_class(query=query, data=data)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_rag_tool(RagTool, "rag_tool", "Knowledge base(query: 'string', data: Any) - A knowledge base that can be used to answer questions.", assistant, user_proxy)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeElementFromWebsiteTool"><code class="name flex">
|
||||
<span>def <span class="ident">autogen_ScrapeElementFromWebsiteTool</span></span>(<span>assistant, user_proxy)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def autogen_ScrapeElementFromWebsiteTool(assistant, user_proxy):
|
||||
def register_scrape_element_from_website_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(website_url: str, element_query: str) -> Any:
|
||||
tool_instance = tool_class(website_url=website_url, element_query=element_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_scrape_element_from_website_tool(ScrapeElementFromWebsiteTool, "scrape_element_from_website_tool", "Scrape an element from a website(element_query: 'string', website_url: 'string') - A tool that can be used to scrape an element from a website.", assistant, user_proxy)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeWebsiteTool"><code class="name flex">
|
||||
<span>def <span class="ident">autogen_ScrapeWebsiteTool</span></span>(<span>assistant, user_proxy)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def autogen_ScrapeWebsiteTool(assistant, user_proxy):
|
||||
def register_scrape_website_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(website_url: str) -> Any:
|
||||
tool_instance = tool_class(website_url=website_url)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_scrape_website_tool(ScrapeWebsiteTool, "scrape_website_tool", "Read website content(website_url: 'string') - A tool that can be used to read content from a specified website.", assistant, user_proxy)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_TXTSearchTool"><code class="name flex">
|
||||
<span>def <span class="ident">autogen_TXTSearchTool</span></span>(<span>assistant, user_proxy)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def autogen_TXTSearchTool(assistant, user_proxy):
|
||||
def register_txt_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(txt: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(txt=txt, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_txt_search_tool(TXTSearchTool, "txt_search_tool", "Search a txt's content(search_query: 'string', txt: 'string') - A tool that can be used to semantic search a query from a txt's content.", assistant, user_proxy)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_WebsiteSearchTool"><code class="name flex">
|
||||
<span>def <span class="ident">autogen_WebsiteSearchTool</span></span>(<span>assistant, user_proxy)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def autogen_WebsiteSearchTool(assistant, user_proxy):
|
||||
def register_website_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(website: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(website=website, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_website_search_tool(WebsiteSearchTool, "website_search_tool", "Search in a specific website(search_query: 'string', website: 'string') - A tool that can be used to semantic search a query from a specific URL content.", assistant, user_proxy)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_XMLSearchTool"><code class="name flex">
|
||||
<span>def <span class="ident">autogen_XMLSearchTool</span></span>(<span>assistant, user_proxy)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def autogen_XMLSearchTool(assistant, user_proxy):
|
||||
def register_xml_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(xml: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(xml=xml, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_xml_search_tool(XMLSearchTool, "xml_search_tool", "Search a XML's content(search_query: 'string', xml: 'string') - A tool that can be used to semantic search a query from a XML's content.", assistant, user_proxy)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeChannelSearchTool"><code class="name flex">
|
||||
<span>def <span class="ident">autogen_YoutubeChannelSearchTool</span></span>(<span>assistant, user_proxy)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def autogen_YoutubeChannelSearchTool(assistant, user_proxy):
|
||||
def register_youtube_channel_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(youtube_channel_handle: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(youtube_channel_handle=youtube_channel_handle, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_youtube_channel_search_tool(YoutubeChannelSearchTool, "youtube_channel_search_tool", "Search a Youtube Channels content(search_query: 'string', youtube_channel_handle: 'string') - A tool that can be used to semantic search a query from a Youtube Channels content.", assistant, user_proxy)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeVideoSearchTool"><code class="name flex">
|
||||
<span>def <span class="ident">autogen_YoutubeVideoSearchTool</span></span>(<span>assistant, user_proxy)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def autogen_YoutubeVideoSearchTool(assistant, user_proxy):
|
||||
def register_youtube_video_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(youtube_video_url: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(youtube_video_url=youtube_video_url, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_youtube_video_search_tool(YoutubeVideoSearchTool, "youtube_video_search_tool", "Search a Youtube Video content(search_query: 'string', youtube_video_url: 'string') - A tool that can be used to semantic search a query from a Youtube Video content.", assistant, user_proxy)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="praisonai.inbuilt_tools.autogen_tools.create_autogen_tool_function"><code class="name flex">
|
||||
<span>def <span class="ident">create_autogen_tool_function</span></span>(<span>tool_name)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def create_autogen_tool_function(tool_name):
|
||||
def autogen_tool(assistant, user_proxy):
|
||||
def register_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(query: str) -> Any:
|
||||
tool_instance = tool_class()
|
||||
return tool_instance.run(query=query)
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
|
||||
root_directory = os.getcwd()
|
||||
tools_py_path = os.path.join(root_directory, 'tools.py')
|
||||
tools_dir_path = Path(root_directory) / 'tools'
|
||||
|
||||
if os.path.isfile(tools_py_path):
|
||||
print(f"{tools_py_path} exists in the root directory. Loading {tools_py_path} and skipping tools folder.")
|
||||
tool_module = importlib.import_module("tools")
|
||||
elif tools_dir_path.is_dir():
|
||||
print(f"tools folder exists in the root directory. Loading {tool_name} from tools/{tool_name}.py.")
|
||||
tool_module = importlib.import_module(f"tools.{tool_name}")
|
||||
else:
|
||||
raise ImportError("Neither tools.py nor tools directory found in the root directory.")
|
||||
|
||||
Tool = getattr(tool_module, tool_name)
|
||||
|
||||
register_tool(Tool, tool_name, f"Description for {tool_name}", assistant, user_proxy)
|
||||
|
||||
return autogen_tool</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="praisonai.inbuilt_tools" href="index.html">praisonai.inbuilt_tools</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_BaseTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_BaseTool">autogen_BaseTool</a></code></li>
|
||||
<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_CSVSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_CSVSearchTool">autogen_CSVSearchTool</a></code></li>
|
||||
<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_CodeDocsSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_CodeDocsSearchTool">autogen_CodeDocsSearchTool</a></code></li>
|
||||
<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_DDGS" href="#praisonai.inbuilt_tools.autogen_tools.autogen_DDGS">autogen_DDGS</a></code></li>
|
||||
<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_DOCXSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_DOCXSearchTool">autogen_DOCXSearchTool</a></code></li>
|
||||
<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_DirectoryReadTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_DirectoryReadTool">autogen_DirectoryReadTool</a></code></li>
|
||||
<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_DirectorySearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_DirectorySearchTool">autogen_DirectorySearchTool</a></code></li>
|
||||
<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_FileReadTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_FileReadTool">autogen_FileReadTool</a></code></li>
|
||||
<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_InternetSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_InternetSearchTool">autogen_InternetSearchTool</a></code></li>
|
||||
<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_JSONSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_JSONSearchTool">autogen_JSONSearchTool</a></code></li>
|
||||
<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_MDXSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_MDXSearchTool">autogen_MDXSearchTool</a></code></li>
|
||||
<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_PDFSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_PDFSearchTool">autogen_PDFSearchTool</a></code></li>
|
||||
<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_RagTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_RagTool">autogen_RagTool</a></code></li>
|
||||
<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeElementFromWebsiteTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeElementFromWebsiteTool">autogen_ScrapeElementFromWebsiteTool</a></code></li>
|
||||
<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeWebsiteTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeWebsiteTool">autogen_ScrapeWebsiteTool</a></code></li>
|
||||
<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_TXTSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_TXTSearchTool">autogen_TXTSearchTool</a></code></li>
|
||||
<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_WebsiteSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_WebsiteSearchTool">autogen_WebsiteSearchTool</a></code></li>
|
||||
<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_XMLSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_XMLSearchTool">autogen_XMLSearchTool</a></code></li>
|
||||
<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeChannelSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeChannelSearchTool">autogen_YoutubeChannelSearchTool</a></code></li>
|
||||
<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeVideoSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeVideoSearchTool">autogen_YoutubeVideoSearchTool</a></code></li>
|
||||
<li><code><a title="praisonai.inbuilt_tools.autogen_tools.create_autogen_tool_function" href="#praisonai.inbuilt_tools.autogen_tools.create_autogen_tool_function">create_autogen_tool_function</a></code></li>
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|
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<body>
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<main>
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<article id="content">
|
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<header>
|
||||
<h1 class="title">Module <code>praisonai.test</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">import yaml
|
||||
import os
|
||||
from rich import print
|
||||
from dotenv import load_dotenv
|
||||
from crewai import Agent, Task, Crew
|
||||
load_dotenv()
|
||||
import autogen
|
||||
config_list = [
|
||||
{
|
||||
'model': os.environ.get("OPENAI_MODEL_NAME", "gpt-3.5-turbo"),
|
||||
'base_url': os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
|
||||
}
|
||||
]
|
||||
|
||||
def generate_crew_and_kickoff(agent_file):
|
||||
"""
|
||||
This function generates a crew of agents and kicks off tasks based on the configuration provided in a YAML file.
|
||||
|
||||
Parameters:
|
||||
agent_file (str): The path to the YAML file containing the configuration for the agents and tasks.
|
||||
|
||||
Returns:
|
||||
str: The result of the last task executed by the crew.
|
||||
"""
|
||||
|
||||
with open(agent_file, 'r') as f:
|
||||
config = yaml.safe_load(f)
|
||||
|
||||
topic = config['topic']
|
||||
framework = config['framework']
|
||||
|
||||
agents = {}
|
||||
tasks = []
|
||||
if framework == "autogen":
|
||||
# Load the LLM configuration dynamically
|
||||
print(config_list)
|
||||
llm_config = {"config_list": config_list}
|
||||
|
||||
for role, details in config['roles'].items():
|
||||
agent_name = details['role'].format(topic=topic).replace("{topic}", topic)
|
||||
agent_goal = details['goal'].format(topic=topic)
|
||||
# Creating an AssistantAgent for each role dynamically
|
||||
agents[role] = autogen.AssistantAgent(
|
||||
name=agent_name,
|
||||
llm_config=llm_config,
|
||||
system_message=details['backstory'].format(topic=topic)+". Reply \"TERMINATE\" in the end when everything is done.",
|
||||
)
|
||||
|
||||
# Preparing tasks for initiate_chats
|
||||
for task_name, task_details in details.get('tasks', {}).items():
|
||||
description_filled = task_details['description'].format(topic=topic)
|
||||
expected_output_filled = task_details['expected_output'].format(topic=topic)
|
||||
|
||||
chat_task = {
|
||||
"recipient": agents[role],
|
||||
"message": description_filled,
|
||||
"summary_method": "last_msg", # Customize as needed
|
||||
# Additional fields like carryover can be added based on dependencies
|
||||
}
|
||||
tasks.append(chat_task)
|
||||
|
||||
# Assuming the user proxy agent is set up as per your requirements
|
||||
user = autogen.UserProxyAgent(
|
||||
name="User",
|
||||
human_input_mode="NEVER",
|
||||
is_termination_msg=lambda x: (x.get("content") or "").rstrip().endswith("TERMINATE"),
|
||||
code_execution_config={
|
||||
"work_dir": "coding",
|
||||
"use_docker": False,
|
||||
},
|
||||
# additional setup for the user proxy agent
|
||||
)
|
||||
response = user.initiate_chats(tasks)
|
||||
result = "### Output ###\n"+response[-1].summary if hasattr(response[-1], 'summary') else ""
|
||||
else:
|
||||
for role, details in config['roles'].items():
|
||||
role_filled = details['role'].format(topic=topic)
|
||||
goal_filled = details['goal'].format(topic=topic)
|
||||
backstory_filled = details['backstory'].format(topic=topic)
|
||||
|
||||
# Assume tools are loaded and handled here as per your requirements
|
||||
agent = Agent(role=role_filled, goal=goal_filled, backstory=backstory_filled)
|
||||
agents[role] = agent
|
||||
|
||||
for task_name, task_details in details.get('tasks', {}).items():
|
||||
description_filled = task_details['description'].format(topic=topic)
|
||||
expected_output_filled = task_details['expected_output'].format(topic=topic)
|
||||
|
||||
task = Task(description=description_filled, expected_output=expected_output_filled, agent=agent)
|
||||
tasks.append(task)
|
||||
|
||||
crew = Crew(
|
||||
agents=list(agents.values()),
|
||||
tasks=tasks,
|
||||
verbose=2
|
||||
)
|
||||
|
||||
result = crew.kickoff()
|
||||
return result
|
||||
|
||||
if __name__ == "__main__":
|
||||
agent_file = "agents.yaml"
|
||||
result = generate_crew_and_kickoff(agent_file)
|
||||
print(result)</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="praisonai.test.generate_crew_and_kickoff"><code class="name flex">
|
||||
<span>def <span class="ident">generate_crew_and_kickoff</span></span>(<span>agent_file)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<div class="desc"><p>This function generates a crew of agents and kicks off tasks based on the configuration provided in a YAML file.</p>
|
||||
<p>Parameters:
|
||||
agent_file (str): The path to the YAML file containing the configuration for the agents and tasks.</p>
|
||||
<p>Returns:
|
||||
str: The result of the last task executed by the crew.</p></div>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def generate_crew_and_kickoff(agent_file):
|
||||
"""
|
||||
This function generates a crew of agents and kicks off tasks based on the configuration provided in a YAML file.
|
||||
|
||||
Parameters:
|
||||
agent_file (str): The path to the YAML file containing the configuration for the agents and tasks.
|
||||
|
||||
Returns:
|
||||
str: The result of the last task executed by the crew.
|
||||
"""
|
||||
|
||||
with open(agent_file, 'r') as f:
|
||||
config = yaml.safe_load(f)
|
||||
|
||||
topic = config['topic']
|
||||
framework = config['framework']
|
||||
|
||||
agents = {}
|
||||
tasks = []
|
||||
if framework == "autogen":
|
||||
# Load the LLM configuration dynamically
|
||||
print(config_list)
|
||||
llm_config = {"config_list": config_list}
|
||||
|
||||
for role, details in config['roles'].items():
|
||||
agent_name = details['role'].format(topic=topic).replace("{topic}", topic)
|
||||
agent_goal = details['goal'].format(topic=topic)
|
||||
# Creating an AssistantAgent for each role dynamically
|
||||
agents[role] = autogen.AssistantAgent(
|
||||
name=agent_name,
|
||||
llm_config=llm_config,
|
||||
system_message=details['backstory'].format(topic=topic)+". Reply \"TERMINATE\" in the end when everything is done.",
|
||||
)
|
||||
|
||||
# Preparing tasks for initiate_chats
|
||||
for task_name, task_details in details.get('tasks', {}).items():
|
||||
description_filled = task_details['description'].format(topic=topic)
|
||||
expected_output_filled = task_details['expected_output'].format(topic=topic)
|
||||
|
||||
chat_task = {
|
||||
"recipient": agents[role],
|
||||
"message": description_filled,
|
||||
"summary_method": "last_msg", # Customize as needed
|
||||
# Additional fields like carryover can be added based on dependencies
|
||||
}
|
||||
tasks.append(chat_task)
|
||||
|
||||
# Assuming the user proxy agent is set up as per your requirements
|
||||
user = autogen.UserProxyAgent(
|
||||
name="User",
|
||||
human_input_mode="NEVER",
|
||||
is_termination_msg=lambda x: (x.get("content") or "").rstrip().endswith("TERMINATE"),
|
||||
code_execution_config={
|
||||
"work_dir": "coding",
|
||||
"use_docker": False,
|
||||
},
|
||||
# additional setup for the user proxy agent
|
||||
)
|
||||
response = user.initiate_chats(tasks)
|
||||
result = "### Output ###\n"+response[-1].summary if hasattr(response[-1], 'summary') else ""
|
||||
else:
|
||||
for role, details in config['roles'].items():
|
||||
role_filled = details['role'].format(topic=topic)
|
||||
goal_filled = details['goal'].format(topic=topic)
|
||||
backstory_filled = details['backstory'].format(topic=topic)
|
||||
|
||||
# Assume tools are loaded and handled here as per your requirements
|
||||
agent = Agent(role=role_filled, goal=goal_filled, backstory=backstory_filled)
|
||||
agents[role] = agent
|
||||
|
||||
for task_name, task_details in details.get('tasks', {}).items():
|
||||
description_filled = task_details['description'].format(topic=topic)
|
||||
expected_output_filled = task_details['expected_output'].format(topic=topic)
|
||||
|
||||
task = Task(description=description_filled, expected_output=expected_output_filled, agent=agent)
|
||||
tasks.append(task)
|
||||
|
||||
crew = Crew(
|
||||
agents=list(agents.values()),
|
||||
tasks=tasks,
|
||||
verbose=2
|
||||
)
|
||||
|
||||
result = crew.kickoff()
|
||||
return result</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="praisonai.test.generate_crew_and_kickoff" href="#praisonai.test.generate_crew_and_kickoff">generate_crew_and_kickoff</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
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<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
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</html>
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@@ -1,59 +0,0 @@
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<title>praisonai.version API documentation</title>
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|
||||
<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
|
||||
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
|
||||
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
|
||||
<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>praisonai.version</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">__version__ = "0.0.1"</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
||||
</main>
|
||||
<footer id="footer">
|
||||
<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
|
||||
</footer>
|
||||
</body>
|
||||
</html>
|
||||
@@ -1,6 +0,0 @@
|
||||
# Auto
|
||||
## Full Automatic Mode
|
||||
|
||||
```bash
|
||||
praisonai --auto create a movie script about Dog in Moon
|
||||
```
|
||||
@@ -1,28 +0,0 @@
|
||||
# Configuration file for the Sphinx documentation builder.
|
||||
#
|
||||
# For the full list of built-in configuration values, see the documentation:
|
||||
# https://www.sphinx-doc.org/en/master/usage/configuration.html
|
||||
|
||||
# -- Project information -----------------------------------------------------
|
||||
# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
|
||||
|
||||
project = 'PraisonAI'
|
||||
copyright = '2024, Mervin Praison'
|
||||
author = 'Mervin Praison'
|
||||
release = '0.0.23'
|
||||
|
||||
# -- General configuration ---------------------------------------------------
|
||||
# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
|
||||
|
||||
extensions = ['sphinx.ext.autodoc', 'sphinx.ext.napoleon', 'sphinx_rtd_theme']
|
||||
|
||||
templates_path = ['_templates']
|
||||
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
|
||||
|
||||
|
||||
|
||||
# -- Options for HTML output -------------------------------------------------
|
||||
# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
|
||||
|
||||
html_theme = 'sphinx_rtd_theme'
|
||||
html_static_path = ['_static']
|
||||
@@ -1,10 +0,0 @@
|
||||
# Contributing
|
||||
|
||||
- Fork on GitHub: Use the "Fork" button on the repository page.
|
||||
- Clone your fork: `git clone https://github.com/yourusername/praisonAI.git`
|
||||
- Create a branch: `git checkout -b new-feature`
|
||||
- Make changes and commit: `git commit -am "Add some feature"`
|
||||
- Push to your fork: `git push origin new-feature`
|
||||
- Submit a pull request via GitHub's web interface.
|
||||
- Await feedback from project maintainers.
|
||||
|
||||
@@ -1,60 +0,0 @@
|
||||
# Create Custom Tools
|
||||
## TL;DR to Create a Custom Tool
|
||||
|
||||
```bash
|
||||
pip install praisonai duckduckgo-search
|
||||
export OPENAI_API_KEY="Enter your API key"
|
||||
praisonai --init research about the latest AI News and prepare a detailed report
|
||||
```
|
||||
|
||||
- Add `- InternetSearchTool` in the agents.yaml file in the tools section.
|
||||
- Create a file called tools.py and add this code [tools.py](./tools.py)
|
||||
|
||||
```bash
|
||||
praisonai
|
||||
```
|
||||
|
||||
## Pre-requisite to Create a Custom Tool
|
||||
`agents.yaml` file should be present in the current directory.
|
||||
|
||||
If it doesn't exist, create it by running the command `praisonai --init research about the latest AI News and prepare a detailed report`.
|
||||
|
||||
### Step 1 to Create a Custom Tool
|
||||
|
||||
Create a file called tools.py in the same directory as the agents.yaml file.
|
||||
|
||||
```python
|
||||
# example tools.py
|
||||
from duckduckgo_search import DDGS
|
||||
from praisonai_tools import BaseTool
|
||||
|
||||
class InternetSearchTool(BaseTool):
|
||||
name: str = "InternetSearchTool"
|
||||
description: str = "Search Internet for relevant information based on a query or latest news"
|
||||
|
||||
def _run(self, query: str):
|
||||
ddgs = DDGS()
|
||||
results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
|
||||
return results
|
||||
```
|
||||
|
||||
### Step 2 to Create a Custom Tool
|
||||
|
||||
Add the tool to the agents.yaml file as show below under the tools section `- InternetSearchTool`.
|
||||
|
||||
```yaml
|
||||
framework: crewai
|
||||
topic: research about the latest AI News and prepare a detailed report
|
||||
roles:
|
||||
research_analyst:
|
||||
backstory: Experienced in gathering and analyzing data related to AI news trends.
|
||||
goal: Analyze AI News trends
|
||||
role: Research Analyst
|
||||
tasks:
|
||||
gather_data:
|
||||
description: Conduct in-depth research on the latest AI News trends from reputable
|
||||
sources.
|
||||
expected_output: Comprehensive report on current AI News trends.
|
||||
tools:
|
||||
- InternetSearchTool
|
||||
```
|
||||
@@ -1,31 +0,0 @@
|
||||
# Deploy
|
||||
|
||||
## Google Cloud
|
||||
|
||||
```bash
|
||||
gcloud init
|
||||
gcloud services enable run.googleapis.com
|
||||
gcloud services enable containerregistry.googleapis.com
|
||||
gcloud services enable cloudbuild.googleapis.com
|
||||
|
||||
export OPENAI_MODEL_NAME="gpt-4o"
|
||||
export OPENAI_API_KEY="Enter your API key"
|
||||
export OPENAI_API_BASE="https://api.openai.com/v1"
|
||||
|
||||
yes | gcloud auth configure-docker us-central1-docker.pkg.dev
|
||||
gcloud artifacts repositories create praisonai-repository --repository-format=docker --location=us-central1
|
||||
|
||||
PROJECT_ID=$(gcloud config get-value project)
|
||||
TAG="latest"
|
||||
docker build --platform linux/amd64 -t gcr.io/${PROJECT_ID}/praisonai-app:${TAG} .
|
||||
docker tag gcr.io/${PROJECT_ID}/praisonai-app:${TAG} us-central1-docker.pkg.dev/${PROJECT_ID}/praisonai-repository/praisonai-app:${TAG}
|
||||
docker push us-central1-docker.pkg.dev/${PROJECT_ID}/praisonai-repository/praisonai-app:${TAG}
|
||||
|
||||
gcloud run deploy praisonai-service \
|
||||
--image us-central1-docker.pkg.dev/${PROJECT_ID}/praisonai-repository/praisonai-app:${TAG} \
|
||||
--platform managed \
|
||||
--region us-central1 \
|
||||
--allow-unauthenticated \
|
||||
--set-env-vars OPENAI_MODEL_NAME=${OPENAI_MODEL_NAME},OPENAI_API_KEY=${OPENAI_API_KEY},OPENAI_API_BASE=${OPENAI_API_BASE}
|
||||
```
|
||||
|
||||
@@ -1,11 +0,0 @@
|
||||
#!/bin/sh
|
||||
set -e # Exit if any command fails
|
||||
|
||||
# Install necessary packages
|
||||
pip install pdoc3 mkdocs mkdocs-material
|
||||
|
||||
# Generate HTML documentation with pdoc
|
||||
pdoc --html --skip-errors -o docs/api praisonai --force
|
||||
|
||||
# Build the MkDocs site
|
||||
mkdocs build
|
||||
@@ -1,64 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Ensure we are in the docs folder
|
||||
cd "$(dirname "$0")"
|
||||
|
||||
# Define the sections and their corresponding filenames
|
||||
sections=(
|
||||
"Praison AI:index.md"
|
||||
"TL;DR:tldr.md"
|
||||
"Installation:installation.md"
|
||||
"Initialise:initialise.md"
|
||||
"Run:run.md"
|
||||
"Create Custom Tools:create_custom_tools.md"
|
||||
"Test:test.md"
|
||||
"Agents Playbook:agents_playbook.md"
|
||||
"Include praisonai package in your project:include_package.md"
|
||||
"Deploy:deploy.md"
|
||||
"Other Models:other_models.md"
|
||||
"Contributing:contributing.md"
|
||||
)
|
||||
|
||||
# Function to extract section content
|
||||
extract_section() {
|
||||
section_name="$1"
|
||||
input_file="$2"
|
||||
output_file="$3"
|
||||
|
||||
# Escape special characters in section name for use in regex
|
||||
section_name_escaped=$(echo "$section_name" | sed 's/[.[\*^$(){}?+|]/\\&/g')
|
||||
|
||||
# Extract content
|
||||
if command -v gsed > /dev/null; then
|
||||
gsed -n "/## $section_name_escaped/,/## /{p;}" "$input_file" | gsed '$d' >> "$output_file"
|
||||
else
|
||||
sed -n "/## $section_name_escaped/,/## /{p;}" "$input_file" | sed '$d' >> "$output_file"
|
||||
fi
|
||||
}
|
||||
|
||||
# Extract content above the first section from README.md
|
||||
if command -v gsed > /dev/null; then
|
||||
gsed -n '1,/^## /{p;}' ../README.md | gsed '$d' > index.md
|
||||
else
|
||||
sed -n '1,/^## /{p;}' ../README.md | sed '$d' > index.md
|
||||
fi
|
||||
|
||||
# Create the files and add content
|
||||
for section in "${sections[@]}"; do
|
||||
section_name="${section%%:*}"
|
||||
filename="${section##*:}"
|
||||
if [[ "$filename" == "index.md" ]]; then
|
||||
extract_section "$section_name" ../README.md index.md
|
||||
elif [[ "$section_name" == "TL;DR" ]]; then
|
||||
extract_section "$section_name" ../README.md index.md
|
||||
else
|
||||
extract_section "$section_name" ../README.md "$filename"
|
||||
fi
|
||||
done
|
||||
|
||||
# Special handling for the last section (Contributing)
|
||||
if command -v gsed > /dev/null; then
|
||||
gsed -n "/## Contributing/,\$p" ../README.md > "contributing.md"
|
||||
else
|
||||
sed -n "/## Contributing/,\$p" ../README.md > "contributing.md"
|
||||
fi
|
||||
@@ -1,26 +0,0 @@
|
||||
# Praison AI
|
||||
|
||||
Praison AI, leveraging both AutoGen and CrewAI or any other agent framework, represents a low-code, centralised framework designed to simplify the creation and orchestration of multi-agent systems for various LLM applications, emphasizing ease of use, customization, and human-agent interaction.
|
||||
|
||||
## Prerequisite:
|
||||
### Export API KEY
|
||||
|
||||
```
|
||||
export OPENAI_API_KEY="Enter your API key"
|
||||
```
|
||||
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
pip install praisonai
|
||||
```
|
||||
|
||||
## Automatically Create Agents to Perform a Task
|
||||
|
||||
```
|
||||
praisonai --init create a movie script about dog in moon
|
||||
```
|
||||
## Run
|
||||
```
|
||||
praisonai
|
||||
```
|
||||
@@ -1,20 +0,0 @@
|
||||
.. PraisonAI documentation master file, created by
|
||||
sphinx-quickstart on Mon Jun 10 12:11:57 2024.
|
||||
You can adapt this file completely to your liking, but it should at least
|
||||
contain the root `toctree` directive.
|
||||
|
||||
Welcome to PraisonAI's documentation!
|
||||
=====================================
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Contents:
|
||||
|
||||
|
||||
|
||||
Indices and tables
|
||||
==================
|
||||
|
||||
* :ref:`genindex`
|
||||
* :ref:`modindex`
|
||||
* :ref:`search`
|
||||
@@ -1,14 +0,0 @@
|
||||
# Initialise
|
||||
|
||||
```bash
|
||||
export OPENAI_API_KEY="Enter your API key"
|
||||
```
|
||||
|
||||
Generate your OPENAI API KEY from here: https://platform.openai.com/api-keys
|
||||
|
||||
Note: You can use other providers such as Ollama, Mistral ... etc. Details are provided at the bottom.
|
||||
|
||||
```bash
|
||||
praisonai --init create a movie script about dog in moon
|
||||
```
|
||||
This will automatically create agents.yaml file in the current directory.
|
||||
@@ -1,5 +0,0 @@
|
||||
# Installation
|
||||
|
||||
```bash
|
||||
pip install praisonai
|
||||
```
|
||||
@@ -1,35 +0,0 @@
|
||||
@ECHO OFF
|
||||
|
||||
pushd %~dp0
|
||||
|
||||
REM Command file for Sphinx documentation
|
||||
|
||||
if "%SPHINXBUILD%" == "" (
|
||||
set SPHINXBUILD=sphinx-build
|
||||
)
|
||||
set SOURCEDIR=.
|
||||
set BUILDDIR=_build
|
||||
|
||||
%SPHINXBUILD% >NUL 2>NUL
|
||||
if errorlevel 9009 (
|
||||
echo.
|
||||
echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
|
||||
echo.installed, then set the SPHINXBUILD environment variable to point
|
||||
echo.to the full path of the 'sphinx-build' executable. Alternatively you
|
||||
echo.may add the Sphinx directory to PATH.
|
||||
echo.
|
||||
echo.If you don't have Sphinx installed, grab it from
|
||||
echo.https://www.sphinx-doc.org/
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
if "%1" == "" goto help
|
||||
|
||||
%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
|
||||
goto end
|
||||
|
||||
:help
|
||||
%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
|
||||
|
||||
:end
|
||||
popd
|
||||
@@ -1,24 +0,0 @@
|
||||
# Other Models
|
||||
|
||||
```bash
|
||||
Ollama
|
||||
OPENAI_API_BASE='http://localhost:11434/v1'
|
||||
OPENAI_MODEL_NAME='mistral'
|
||||
OPENAI_API_KEY='NA'
|
||||
|
||||
FastChat¶
|
||||
OPENAI_API_BASE="http://localhost:8001/v1"
|
||||
OPENAI_MODEL_NAME='oh-2.5m7b-q51'
|
||||
OPENAI_API_KEY=NA
|
||||
|
||||
LM Studio¶
|
||||
OPENAI_API_BASE="http://localhost:8000/v1"
|
||||
OPENAI_MODEL_NAME=NA
|
||||
OPENAI_API_KEY=NA
|
||||
|
||||
Mistral API¶
|
||||
OPENAI_API_BASE=https://api.mistral.ai/v1
|
||||
OPENAI_MODEL_NAME="mistral-small"
|
||||
OPENAI_API_KEY=your-mistral-api-key
|
||||
```
|
||||
|
||||
@@ -1,12 +0,0 @@
|
||||
sphinx
|
||||
sphinx_rtd_theme
|
||||
sphinx-autodoc-typehints
|
||||
mkdocs
|
||||
markdown-include
|
||||
mkdocs-material
|
||||
mkdocs-jupyter
|
||||
pymdown-extensions
|
||||
Pygments
|
||||
pdoc3
|
||||
xmlrunner
|
||||
unittest2
|
||||
@@ -1,24 +0,0 @@
|
||||
sphinx
|
||||
sphinx_rtd_theme
|
||||
sphinx-autodoc-typehints
|
||||
mkdocs
|
||||
markdown-include
|
||||
mkdocs-material
|
||||
mkdocs-jupyter
|
||||
pymdown-extensions
|
||||
Pygments
|
||||
mkdocstrings
|
||||
mkdocs-apidoc
|
||||
mkdocstrings-python
|
||||
mkdocs-jupyter
|
||||
pdoc3
|
||||
rich
|
||||
pyautogen
|
||||
crewai
|
||||
gradio
|
||||
crewai_tools
|
||||
duckduckgo_search
|
||||
praisonai_tools
|
||||
open-interpreter
|
||||
xmlrunner
|
||||
unittest2
|
||||
-23
@@ -1,23 +0,0 @@
|
||||
# Run
|
||||
|
||||
```bash
|
||||
praisonai
|
||||
```
|
||||
|
||||
or
|
||||
|
||||
```bash
|
||||
python -m praisonai
|
||||
```
|
||||
|
||||
## Specify the agent framework (Optional):
|
||||
|
||||
```bash
|
||||
praisonai --framework autogen
|
||||
```
|
||||
|
||||
## Full Automatic Mode
|
||||
|
||||
```bash
|
||||
praisonai --auto create a movie script about Dog in Moon
|
||||
```
|
||||
@@ -1,5 +0,0 @@
|
||||
# Test
|
||||
|
||||
```bash
|
||||
python -m unittest tests.test
|
||||
```
|
||||
@@ -1,7 +0,0 @@
|
||||
# TL;DR
|
||||
```bash
|
||||
pip install praisonai
|
||||
export OPENAI_API_KEY="Enter your API key"
|
||||
praisonai --init create a movie script about dog in moon
|
||||
praisonai
|
||||
```
|
||||
@@ -1,45 +0,0 @@
|
||||
## Include praisonai package in your project
|
||||
|
||||
Note: Please create agents.yaml file before hand.
|
||||
|
||||
```python
|
||||
from praisonai import PraisonAI
|
||||
|
||||
def basic(): # Basic Mode
|
||||
praison_ai = PraisonAI(agent_file="agents.yaml")
|
||||
praison_ai.main()
|
||||
|
||||
if __name__ == "__main__":
|
||||
basic()
|
||||
```
|
||||
|
||||
## All in one
|
||||
|
||||
```python
|
||||
from praisonai import PraisonAI
|
||||
|
||||
def basic(): # Basic Mode
|
||||
praison_ai = PraisonAI(agent_file="agents.yaml")
|
||||
praison_ai.main()
|
||||
|
||||
def advanced(): # Advanced Mode with options
|
||||
praison_ai = PraisonAI(
|
||||
agent_file="agents.yaml",
|
||||
framework="autogen",
|
||||
)
|
||||
praison_ai.main()
|
||||
|
||||
def auto(): # Full Automatic Mode
|
||||
praison_ai = PraisonAI(
|
||||
auto="Create a movie script about car in mars",
|
||||
framework="autogen"
|
||||
)
|
||||
print(praison_ai.framework)
|
||||
praison_ai.main()
|
||||
|
||||
if __name__ == "__main__":
|
||||
basic()
|
||||
advanced()
|
||||
auto()
|
||||
```
|
||||
|
||||
-25
@@ -1,25 +0,0 @@
|
||||
from praisonai import PraisonAI
|
||||
|
||||
def basic():
|
||||
praison_ai = PraisonAI(agent_file="agents.yaml")
|
||||
praison_ai.main()
|
||||
|
||||
def advanced():
|
||||
praison_ai = PraisonAI(
|
||||
agent_file="agents.yaml",
|
||||
framework="autogen",
|
||||
)
|
||||
praison_ai.main()
|
||||
|
||||
def auto():
|
||||
praison_ai = PraisonAI(
|
||||
auto="Create a movie script about car in mars",
|
||||
framework="autogen"
|
||||
)
|
||||
print(praison_ai.framework)
|
||||
praison_ai.main()
|
||||
|
||||
if __name__ == "__main__":
|
||||
basic()
|
||||
advanced()
|
||||
auto()
|
||||
@@ -1,41 +0,0 @@
|
||||
framework: "crewai"
|
||||
topic: "Artificial Intelligence"
|
||||
|
||||
roles:
|
||||
researcher:
|
||||
role: "Senior Data Researcher"
|
||||
goal: "Uncover cutting-edge developments in {topic}"
|
||||
backstory: >
|
||||
You're a seasoned researcher with a knack for uncovering the latest
|
||||
developments in {topic}. Known for your ability to find the most relevant
|
||||
information and present it in a clear and concise manner.
|
||||
tools:
|
||||
- search_tool:
|
||||
engine: google
|
||||
tasks:
|
||||
research_task:
|
||||
description: >
|
||||
Conduct thorough research about {topic}, focusing on the latest
|
||||
trends and technologies.
|
||||
expected_output: >
|
||||
A list of 10 bullet points summarizing key findings with links to
|
||||
relevant sources.
|
||||
|
||||
reporting_analyst:
|
||||
role: "Reporting Analyst"
|
||||
goal: "Create detailed reports based on {topic} data analysis and research findings."
|
||||
backstory: >
|
||||
You're a meticulous analyst with a keen eye for detail. You're known for
|
||||
your ability to turn complex data into clear and concise reports, making
|
||||
it easy for others to understand and act on the information you provide.
|
||||
tasks:
|
||||
reporting_task:
|
||||
description: >
|
||||
Review the research findings and create a comprehensive report on
|
||||
{topic}. Include insights, potential opportunities, and recommendations.
|
||||
expected_output: >
|
||||
A well-structured report in Markdown format, ready for distribution.
|
||||
|
||||
dependencies:
|
||||
- task: reporting_task
|
||||
depends_on: research_task
|
||||
@@ -1,41 +0,0 @@
|
||||
framework: "autogen"
|
||||
topic: "Artificial Intelligence"
|
||||
|
||||
roles:
|
||||
narrative_designer:
|
||||
role: "Narrative Designer"
|
||||
goal: "Craft engaging storylines and narratives for {topic}"
|
||||
backstory: >
|
||||
You're a creative visionary skilled in weaving complex narratives and
|
||||
developing engaging story worlds within {topic}. Your expertise lies in
|
||||
conceptualizing the overarching story and ensuring it resonates with the audience.
|
||||
tasks:
|
||||
story_concept_development:
|
||||
description: >
|
||||
Develop a compelling story concept for {topic}, focusing on originality,
|
||||
thematic depth, and audience engagement. Outline the main narrative arcs,
|
||||
characters, and settings.
|
||||
expected_output: >
|
||||
A comprehensive document outlining the story concept, including key narrative
|
||||
arcs, character bios, and settings descriptions.
|
||||
|
||||
scriptwriter:
|
||||
role: "Scriptwriter"
|
||||
goal: "Transform story concepts into detailed scripts for {topic}"
|
||||
backstory: >
|
||||
With a flair for dialogue and a deep understanding of pacing and structure,
|
||||
you excel at bringing narratives to life through scriptwriting. Your work
|
||||
turns story concepts into blueprints for compelling {topic} experiences.
|
||||
tasks:
|
||||
scriptwriting_task:
|
||||
description: >
|
||||
Based on the narrative concept, write a detailed script for {topic}.
|
||||
Include dialogue, stage directions, and scene descriptions that bring
|
||||
the story to life.
|
||||
expected_output: >
|
||||
A detailed script ready for production, including dialogue, stage
|
||||
directions, and scene descriptions.
|
||||
|
||||
dependencies:
|
||||
- task: scriptwriting_task
|
||||
depends_on: story_concept_development
|
||||
@@ -1,72 +0,0 @@
|
||||
# tools.py
|
||||
from duckduckgo_search import DDGS
|
||||
from praisonai_tools import BaseTool
|
||||
|
||||
class InternetSearchTool(BaseTool):
|
||||
name: str = "InternetSearchTool"
|
||||
description: str = "Search Internet for relevant information based on a query or latest news"
|
||||
|
||||
def _run(self, query: str):
|
||||
ddgs = DDGS()
|
||||
results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
|
||||
return results
|
||||
|
||||
from langchain_community.tools.pubmed.tool import PubmedQueryRun
|
||||
from langchain_community.tools.reddit_search.tool import RedditSearchRun
|
||||
# pip install tavily-python
|
||||
from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper
|
||||
from langchain_community.utilities.you import YouSearchAPIWrapper
|
||||
# pip install youtube_search
|
||||
from langchain_community.tools import YouTubeSearchTool
|
||||
# pip install wikipedia
|
||||
from langchain_community.utilities import WikipediaAPIWrapper
|
||||
|
||||
class TavilyTool(BaseTool):
|
||||
name: str = "TavilyTool"
|
||||
description: str = "Search Tavily for relevant information based on a query."
|
||||
|
||||
def _run(self, query: str):
|
||||
api_wrapper = TavilySearchAPIWrapper()
|
||||
results = api_wrapper.results(query=query, max_results=5)
|
||||
return results
|
||||
|
||||
class YouSearchTool(BaseTool):
|
||||
name: str = "You Search Tool"
|
||||
description: str = "Search You.com for relevant information based on a query."
|
||||
|
||||
def _run(self, query: str):
|
||||
api_wrapper = YouSearchAPIWrapper()
|
||||
results = api_wrapper.results(query=query, max_results=5)
|
||||
return results
|
||||
|
||||
class WikipediaSearchTool(BaseTool):
|
||||
name: str = "WikipediaSearchTool"
|
||||
description: str = "Search Wikipedia for relevant information based on a query."
|
||||
|
||||
def _run(self, query: str):
|
||||
api_wrapper = WikipediaAPIWrapper(top_k_results=4, doc_content_chars_max=100)
|
||||
results = api_wrapper.load(query=query)
|
||||
return results
|
||||
|
||||
# pip install open-interpreter
|
||||
from interpreter import interpreter
|
||||
from praisonai_tools import BaseTool
|
||||
|
||||
class OpenInterpreterTool(BaseTool):
|
||||
name: str = "Open Interpreter Tool"
|
||||
description: str = "Use Open Interpreter configured to create and execute code based on natural language commands."
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
interpreter.auto_run = True
|
||||
|
||||
def _run(self, code: str):
|
||||
result = interpreter.chat(code)
|
||||
return result
|
||||
|
||||
# Example usage
|
||||
if __name__ == "__main__":
|
||||
tool = OpenInterpreterTool()
|
||||
code = "Plot AAPL and META's normalized stock prices"
|
||||
output = tool._run(code)
|
||||
print(output)
|
||||
-52
@@ -1,52 +0,0 @@
|
||||
# Website Info
|
||||
site_name: PraisonAI Documentation
|
||||
site_url: https://docs.praison.ai
|
||||
site_author: MervinPraison
|
||||
|
||||
# Repository
|
||||
repo_name: MervinPraison/PraisonAI
|
||||
repo_url: https://github.com/MervinPraison/PraisonAI
|
||||
|
||||
nav:
|
||||
- Home: index.md
|
||||
- TL;DR: tldr.md
|
||||
- Installation: installation.md
|
||||
- Initialise: initialise.md
|
||||
- Run: run.md
|
||||
- Create Custom Tools: create_custom_tools.md
|
||||
- Test: test.md
|
||||
- Agents Playbook: agents_playbook.md
|
||||
- Wrapper: wrapper.md
|
||||
- Deploy: deploy.md
|
||||
- Other Models: other_models.md
|
||||
- Contributing: contributing.md
|
||||
- API Reference: api.md
|
||||
- API Code: api/praisonai/index.html
|
||||
|
||||
theme:
|
||||
name: material
|
||||
|
||||
markdown_extensions:
|
||||
- admonition
|
||||
- toc:
|
||||
permalink: true
|
||||
- pymdownx.superfences
|
||||
- pymdownx.details
|
||||
|
||||
plugins:
|
||||
- search
|
||||
- mkdocstrings:
|
||||
handlers:
|
||||
python:
|
||||
selection:
|
||||
docstring_style: google
|
||||
# - gen-files:
|
||||
# scripts:
|
||||
# - scripts/gen_ref_pages.py
|
||||
# - mkdocs_apidoc:
|
||||
# function_template: |
|
||||
# ### {{ name }}
|
||||
|
||||
# {{ signature }}
|
||||
|
||||
# {{ docstring }}
|
||||
Generated
+20043
File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,42 @@
|
||||
{
|
||||
"name": "admin-ui",
|
||||
"version": "0.1.0",
|
||||
"private": true,
|
||||
"dependencies": {
|
||||
"@chakra-ui/react": "^2.8.2",
|
||||
"@emotion/react": "^11.11.4",
|
||||
"@emotion/styled": "^11.11.5",
|
||||
"@testing-library/jest-dom": "^5.17.0",
|
||||
"@testing-library/react": "^13.4.0",
|
||||
"@testing-library/user-event": "^13.5.0",
|
||||
"framer-motion": "^11.2.0",
|
||||
"react": "^18.3.1",
|
||||
"react-dom": "^18.3.1",
|
||||
"react-scripts": "5.0.1",
|
||||
"web-vitals": "^2.1.4"
|
||||
},
|
||||
"scripts": {
|
||||
"start": "react-scripts start",
|
||||
"build": "react-scripts build",
|
||||
"test": "react-scripts test",
|
||||
"eject": "react-scripts eject"
|
||||
},
|
||||
"eslintConfig": {
|
||||
"extends": [
|
||||
"react-app",
|
||||
"react-app/jest"
|
||||
]
|
||||
},
|
||||
"browserslist": {
|
||||
"production": [
|
||||
">0.2%",
|
||||
"not dead",
|
||||
"not op_mini all"
|
||||
],
|
||||
"development": [
|
||||
"last 1 chrome version",
|
||||
"last 1 firefox version",
|
||||
"last 1 safari version"
|
||||
]
|
||||
}
|
||||
}
|
||||
Generated
-6351
File diff suppressed because it is too large.
Load diff
@@ -1,2 +0,0 @@
|
||||
from .cli import PraisonAI
|
||||
from .version import __version__
|
||||
@@ -1,10 +0,0 @@
|
||||
# __main__.py
|
||||
|
||||
from .cli import PraisonAI
|
||||
|
||||
def main():
|
||||
praison_ai = PraisonAI()
|
||||
praison_ai.main()
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,272 +0,0 @@
|
||||
# praisonai/agents_generator.py
|
||||
|
||||
import sys
|
||||
from .version import __version__
|
||||
import yaml, os
|
||||
from rich import print
|
||||
from dotenv import load_dotenv
|
||||
from crewai import Agent, Task, Crew
|
||||
from crewai.telemetry import Telemetry
|
||||
load_dotenv()
|
||||
import autogen
|
||||
import gradio as gr
|
||||
import argparse
|
||||
from .auto import AutoGenerator
|
||||
from crewai_tools import (
|
||||
CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, DirectoryReadTool,
|
||||
FileReadTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, RagTool,
|
||||
ScrapeElementFromWebsiteTool, ScrapeWebsiteTool, WebsiteSearchTool, XMLSearchTool, YoutubeChannelSearchTool,
|
||||
YoutubeVideoSearchTool
|
||||
)
|
||||
from .inbuilt_tools import *
|
||||
import inspect
|
||||
from pathlib import Path
|
||||
import importlib
|
||||
import importlib.util
|
||||
from praisonai_tools import BaseTool
|
||||
import os
|
||||
|
||||
os.environ["OTEL_SDK_DISABLED"] = "true"
|
||||
|
||||
def noop(*args, **kwargs):
|
||||
pass
|
||||
|
||||
def disable_crewai_telemetry():
|
||||
for attr in dir(Telemetry):
|
||||
if callable(getattr(Telemetry, attr)) and not attr.startswith("__"):
|
||||
setattr(Telemetry, attr, noop)
|
||||
|
||||
disable_crewai_telemetry()
|
||||
|
||||
class AgentsGenerator:
|
||||
def __init__(self, agent_file, framework, config_list):
|
||||
"""
|
||||
Initialize the AgentsGenerator object.
|
||||
|
||||
Parameters:
|
||||
agent_file (str): The path to the agent file.
|
||||
framework (str): The framework to be used for the agents.
|
||||
config_list (list): A list of configurations for the agents.
|
||||
|
||||
Attributes:
|
||||
agent_file (str): The path to the agent file.
|
||||
framework (str): The framework to be used for the agents.
|
||||
config_list (list): A list of configurations for the agents.
|
||||
"""
|
||||
self.agent_file = agent_file
|
||||
self.framework = framework
|
||||
self.config_list = config_list
|
||||
|
||||
def is_function_or_decorated(self, obj):
|
||||
"""
|
||||
Checks if the given object is a function or has a __call__ method.
|
||||
|
||||
Parameters:
|
||||
obj (object): The object to be checked.
|
||||
|
||||
Returns:
|
||||
bool: True if the object is a function or has a __call__ method, False otherwise.
|
||||
"""
|
||||
return inspect.isfunction(obj) or hasattr(obj, '__call__')
|
||||
|
||||
def load_tools_from_module(self, module_path):
|
||||
"""
|
||||
Loads tools from a specified module path.
|
||||
|
||||
Parameters:
|
||||
module_path (str): The path to the module containing the tools.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary containing the names of the tools as keys and the corresponding functions or objects as values.
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If the specified module path does not exist.
|
||||
"""
|
||||
spec = importlib.util.spec_from_file_location("tools_module", module_path)
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(module)
|
||||
return {name: obj for name, obj in inspect.getmembers(module, self.is_function_or_decorated)}
|
||||
|
||||
def load_tools_from_module_class(self, module_path):
|
||||
"""
|
||||
Loads tools from a specified module path containing classes that inherit from BaseTool or are part of langchain_community.tools package.
|
||||
|
||||
Parameters:
|
||||
module_path (str): The path to the module containing the tools.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If the specified module path does not exist.
|
||||
"""
|
||||
spec = importlib.util.spec_from_file_location("tools_module", module_path)
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(module)
|
||||
return {name: obj() for name, obj in inspect.getmembers(module, lambda x: inspect.isclass(x) and (x.__module__.startswith('langchain_community.tools') or issubclass(x, BaseTool)) and x is not BaseTool)}
|
||||
|
||||
def load_tools_from_package(self, package_path):
|
||||
"""
|
||||
Loads tools from a specified package path containing modules with functions or classes.
|
||||
|
||||
Parameters:
|
||||
package_path (str): The path to the package containing the tools.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If the specified package path does not exist.
|
||||
|
||||
This function iterates through all the .py files in the specified package path, excluding those that start with "__". For each file, it imports the corresponding module and checks if it contains any functions or classes that can be loaded as tools. The function then returns a dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
|
||||
"""
|
||||
tools_dict = {}
|
||||
for module_file in os.listdir(package_path):
|
||||
if module_file.endswith('.py') and not module_file.startswith('__'):
|
||||
module_name = f"{package_path.name}.{module_file[:-3]}" # Remove .py for import
|
||||
module = importlib.import_module(module_name)
|
||||
for name, obj in inspect.getmembers(module, self.is_function_or_decorated):
|
||||
tools_dict[name] = obj
|
||||
return tools_dict
|
||||
|
||||
def generate_crew_and_kickoff(self):
|
||||
"""
|
||||
Generates a crew of agents and initiates tasks based on the provided configuration.
|
||||
|
||||
Parameters:
|
||||
agent_file (str): The path to the agent file.
|
||||
framework (str): The framework to be used for the agents.
|
||||
config_list (list): A list of configurations for the agents.
|
||||
|
||||
Returns:
|
||||
str: The output of the tasks performed by the crew of agents.
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If the specified agent file does not exist.
|
||||
|
||||
This function first loads the agent configuration from the specified file. It then initializes the tools required for the agents based on the specified framework. If the specified framework is "autogen", it loads the LLM configuration dynamically and creates an AssistantAgent for each role in the configuration. It then adds tools to the agents if specified in the configuration. Finally, it prepares tasks for the agents based on the configuration and initiates the tasks using the crew of agents. If the specified framework is not "autogen", it creates a crew of agents and initiates tasks based on the configuration.
|
||||
"""
|
||||
if self.agent_file == '/app/api:app' or self.agent_file == 'api:app':
|
||||
self.agent_file = 'agents.yaml'
|
||||
try:
|
||||
with open(self.agent_file, 'r') as f:
|
||||
config = yaml.safe_load(f)
|
||||
except FileNotFoundError:
|
||||
print(f"File not found: {self.agent_file}")
|
||||
return
|
||||
|
||||
topic = config['topic']
|
||||
tools_dict = {
|
||||
'CodeDocsSearchTool': CodeDocsSearchTool(),
|
||||
'CSVSearchTool': CSVSearchTool(),
|
||||
'DirectorySearchTool': DirectorySearchTool(),
|
||||
'DOCXSearchTool': DOCXSearchTool(),
|
||||
'DirectoryReadTool': DirectoryReadTool(),
|
||||
'FileReadTool': FileReadTool(),
|
||||
# 'GithubSearchTool': GithubSearchTool(),
|
||||
# 'SeperDevTool': SeperDevTool(),
|
||||
'TXTSearchTool': TXTSearchTool(),
|
||||
'JSONSearchTool': JSONSearchTool(),
|
||||
'MDXSearchTool': MDXSearchTool(),
|
||||
'PDFSearchTool': PDFSearchTool(),
|
||||
# 'PGSearchTool': PGSearchTool(),
|
||||
'RagTool': RagTool(),
|
||||
'ScrapeElementFromWebsiteTool': ScrapeElementFromWebsiteTool(),
|
||||
'ScrapeWebsiteTool': ScrapeWebsiteTool(),
|
||||
'WebsiteSearchTool': WebsiteSearchTool(),
|
||||
'XMLSearchTool': XMLSearchTool(),
|
||||
'YoutubeChannelSearchTool': YoutubeChannelSearchTool(),
|
||||
'YoutubeVideoSearchTool': YoutubeVideoSearchTool(),
|
||||
}
|
||||
root_directory = os.getcwd()
|
||||
tools_py_path = os.path.join(root_directory, 'tools.py')
|
||||
tools_dir_path = Path(root_directory) / 'tools'
|
||||
|
||||
if os.path.isfile(tools_py_path):
|
||||
tools_dict.update(self.load_tools_from_module_class(tools_py_path))
|
||||
# print("tools.py exists in the root directory. Loading tools.py and skipping tools folder.")
|
||||
elif tools_dir_path.is_dir():
|
||||
tools_dict.update(self.load_tools_from_module_class(tools_dir_path))
|
||||
# print("tools folder exists in the root directory")
|
||||
|
||||
framework = self.framework or config.get('framework')
|
||||
|
||||
agents = {}
|
||||
tasks = []
|
||||
if framework == "autogen":
|
||||
# Load the LLM configuration dynamically
|
||||
# print(self.config_list)
|
||||
llm_config = {"config_list": self.config_list}
|
||||
|
||||
# Assuming the user proxy agent is set up as per your requirements
|
||||
user_proxy = autogen.UserProxyAgent(
|
||||
name="User",
|
||||
human_input_mode="NEVER",
|
||||
is_termination_msg=lambda x: (x.get("content") or "").rstrip().rstrip(".").lower().endswith("terminate") or "TERMINATE" in (x.get("content") or ""),
|
||||
code_execution_config={
|
||||
"work_dir": "coding",
|
||||
"use_docker": False,
|
||||
},
|
||||
# additional setup for the user proxy agent
|
||||
)
|
||||
|
||||
for role, details in config['roles'].items():
|
||||
agent_name = details['role'].format(topic=topic).replace("{topic}", topic)
|
||||
agent_goal = details['goal'].format(topic=topic)
|
||||
# Creating an AssistantAgent for each role dynamically
|
||||
agents[role] = autogen.AssistantAgent(
|
||||
name=agent_name,
|
||||
llm_config=llm_config,
|
||||
system_message=details['backstory'].format(topic=topic)+". Must Reply \"TERMINATE\" in the end when everything is done.",
|
||||
)
|
||||
for tool in details.get('tools', []):
|
||||
if tool in tools_dict:
|
||||
try:
|
||||
tool_class = globals()[f'autogen_{type(tools_dict[tool]).__name__}']
|
||||
print(f"Found {tool_class.__name__} for {tool}")
|
||||
except KeyError:
|
||||
print(f"Warning: autogen_{type(tools_dict[tool]).__name__} function not found. Skipping this tool.")
|
||||
continue
|
||||
tool_class(agents[role], user_proxy)
|
||||
|
||||
# Preparing tasks for initiate_chats
|
||||
for task_name, task_details in details.get('tasks', {}).items():
|
||||
description_filled = task_details['description'].format(topic=topic)
|
||||
expected_output_filled = task_details['expected_output'].format(topic=topic)
|
||||
|
||||
chat_task = {
|
||||
"recipient": agents[role],
|
||||
"message": description_filled,
|
||||
"summary_method": "last_msg",
|
||||
# Additional fields like carryover can be added based on dependencies
|
||||
}
|
||||
tasks.append(chat_task)
|
||||
|
||||
response = user_proxy.initiate_chats(tasks)
|
||||
result = "### Output ###\n"+response[-1].summary if hasattr(response[-1], 'summary') else ""
|
||||
else:
|
||||
for role, details in config['roles'].items():
|
||||
role_filled = details['role'].format(topic=topic)
|
||||
goal_filled = details['goal'].format(topic=topic)
|
||||
backstory_filled = details['backstory'].format(topic=topic)
|
||||
|
||||
# Adding tools to the agent if exists
|
||||
agent_tools = [tools_dict[tool] for tool in details.get('tools', []) if tool in tools_dict]
|
||||
agent = Agent(role=role_filled, goal=goal_filled, backstory=backstory_filled, tools=agent_tools, allow_delegation=False)
|
||||
agents[role] = agent
|
||||
|
||||
for task_name, task_details in details.get('tasks', {}).items():
|
||||
description_filled = task_details['description'].format(topic=topic)
|
||||
expected_output_filled = task_details['expected_output'].format(topic=topic)
|
||||
|
||||
task = Task(description=description_filled, expected_output=expected_output_filled, agent=agent)
|
||||
tasks.append(task)
|
||||
crew = Crew(
|
||||
agents=list(agents.values()),
|
||||
tasks=tasks,
|
||||
verbose=2
|
||||
)
|
||||
|
||||
response = crew.kickoff()
|
||||
result = f"### Task Output ###\n{response}"
|
||||
return result
|
||||
@@ -1,186 +0,0 @@
|
||||
from openai import OpenAI
|
||||
from pydantic import BaseModel
|
||||
from typing import Dict, List
|
||||
import instructor
|
||||
import os
|
||||
import json
|
||||
import yaml
|
||||
from rich import print
|
||||
|
||||
# Define Pydantic models outside of the generate method
|
||||
class TaskDetails(BaseModel):
|
||||
description: str
|
||||
expected_output: str
|
||||
|
||||
class RoleDetails(BaseModel):
|
||||
role: str
|
||||
goal: str
|
||||
backstory: str
|
||||
tasks: Dict[str, TaskDetails]
|
||||
tools: List[str]
|
||||
|
||||
class TeamStructure(BaseModel):
|
||||
roles: Dict[str, RoleDetails]
|
||||
|
||||
class AutoGenerator:
|
||||
def __init__(self, topic="Movie Story writing about AI", agent_file="test.yaml", framework="crewai"):
|
||||
"""
|
||||
Initialize the AutoGenerator class with the specified topic, agent file, and framework.
|
||||
note: autogen framework is different from this AutoGenerator class.
|
||||
|
||||
Args:
|
||||
topic (str, optional): The topic for the generated team structure. Defaults to "Movie Story writing about AI".
|
||||
agent_file (str, optional): The name of the YAML file to save the generated team structure. Defaults to "test.yaml".
|
||||
framework (str, optional): The framework for the generated team structure. Defaults to "crewai".
|
||||
|
||||
Attributes:
|
||||
config_list (list): A list containing the configuration details for the OpenAI API.
|
||||
topic (str): The specified topic for the generated team structure.
|
||||
agent_file (str): The specified name of the YAML file to save the generated team structure.
|
||||
framework (str): The specified framework for the generated team structure.
|
||||
client (instructor.Client): An instance of the instructor.Client class initialized with the specified OpenAI API configuration.
|
||||
"""
|
||||
self.config_list = [
|
||||
{
|
||||
'model': os.environ.get("OPENAI_MODEL_NAME", "gpt-4o"),
|
||||
'base_url': os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
|
||||
}
|
||||
]
|
||||
self.topic = topic
|
||||
self.agent_file = agent_file
|
||||
self.framework = framework or "crewai"
|
||||
self.client = instructor.patch(
|
||||
OpenAI(
|
||||
base_url=self.config_list[0]['base_url'],
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
),
|
||||
mode=instructor.Mode.JSON,
|
||||
)
|
||||
|
||||
def generate(self):
|
||||
"""
|
||||
Generates a team structure for the specified topic.
|
||||
|
||||
Args:
|
||||
None
|
||||
|
||||
Returns:
|
||||
str: The full path of the YAML file containing the generated team structure.
|
||||
|
||||
Raises:
|
||||
Exception: If the generation process fails.
|
||||
|
||||
Usage:
|
||||
generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
|
||||
path = generator.generate()
|
||||
print(path)
|
||||
"""
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.config_list[0]['model'],
|
||||
response_model=TeamStructure,
|
||||
max_retries=10,
|
||||
messages=[
|
||||
{"role": "system", "content": "You are a helpful assistant designed to output complex team structures."},
|
||||
{"role": "user", "content": self.get_user_content()}
|
||||
]
|
||||
)
|
||||
json_data = json.loads(response.model_dump_json())
|
||||
self.convert_and_save(json_data)
|
||||
full_path = os.path.abspath(self.agent_file)
|
||||
return full_path
|
||||
|
||||
def convert_and_save(self, json_data):
|
||||
"""Converts the provided JSON data into the desired YAML format and saves it to a file.
|
||||
|
||||
Args:
|
||||
json_data (dict): The JSON data representing the team structure.
|
||||
topic (str, optional): The topic to be inserted into the YAML. Defaults to "Artificial Intelligence".
|
||||
agent_file (str, optional): The name of the YAML file to save. Defaults to "test.yaml".
|
||||
"""
|
||||
|
||||
yaml_data = {
|
||||
"framework": self.framework,
|
||||
"topic": self.topic,
|
||||
"roles": {},
|
||||
"dependencies": []
|
||||
}
|
||||
|
||||
for role_id, role_details in json_data['roles'].items():
|
||||
yaml_data['roles'][role_id] = {
|
||||
"backstory": "" + role_details['backstory'],
|
||||
"goal": role_details['goal'],
|
||||
"role": role_details['role'],
|
||||
"tasks": {},
|
||||
# "tools": role_details.get('tools', []),
|
||||
"tools": ['']
|
||||
}
|
||||
|
||||
for task_id, task_details in role_details['tasks'].items():
|
||||
yaml_data['roles'][role_id]['tasks'][task_id] = {
|
||||
"description": "" + task_details['description'],
|
||||
"expected_output": "" + task_details['expected_output']
|
||||
}
|
||||
|
||||
# Save to YAML file, maintaining the order
|
||||
with open(self.agent_file, 'w') as f:
|
||||
yaml.dump(yaml_data, f, allow_unicode=True, sort_keys=False)
|
||||
|
||||
def get_user_content(self):
|
||||
"""
|
||||
Generates a prompt for the OpenAI API to generate a team structure.
|
||||
|
||||
Args:
|
||||
None
|
||||
|
||||
Returns:
|
||||
str: The prompt for the OpenAI API.
|
||||
|
||||
Usage:
|
||||
generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
|
||||
prompt = generator.get_user_content()
|
||||
print(prompt)
|
||||
"""
|
||||
user_content = """Generate a team structure for \"""" + self.topic + """\" task.
|
||||
No Input data will be provided to the team.
|
||||
The team will work in sequence. First role will pass the output to the next role, and so on.
|
||||
The last role will generate the final output.
|
||||
Think step by step.
|
||||
With maximum 3 roles, each with 1 task. Include role goals, backstories, task descriptions, and expected outputs.
|
||||
List of Available Tools: CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, DirectoryReadTool, FileReadTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, RagTool, ScrapeElementFromWebsiteTool, ScrapeWebsiteTool, WebsiteSearchTool, XMLSearchTool, YoutubeChannelSearchTool, YoutubeVideoSearchTool.
|
||||
Only use Available Tools. Do Not use any other tools.
|
||||
Example Below:
|
||||
Use below example to understand the structure of the output.
|
||||
The final role you create should satisfy the provided task: """ + self.topic + """.
|
||||
{
|
||||
"roles": {
|
||||
"narrative_designer": {
|
||||
"role": "Narrative Designer",
|
||||
"goal": "Create AI storylines",
|
||||
"backstory": "Skilled in narrative development for AI, with a focus on story resonance.",
|
||||
"tools": ["ScrapeWebsiteTool"],
|
||||
"tasks": {
|
||||
"story_concept_development": {
|
||||
"description": "Craft a unique AI story concept with depth and engagement using concept from this page the content https://www.asthebirdfliesblog.com/posts/how-to-write-book-story-development .",
|
||||
"expected_output": "Document with narrative arcs, character bios, and settings."
|
||||
}
|
||||
}
|
||||
},
|
||||
"scriptwriter": {
|
||||
"role": "Scriptwriter",
|
||||
"goal": "Write scripts from AI concepts",
|
||||
"backstory": "Expert in dialogue and script structure, translating concepts into scripts.",
|
||||
"tasks": {
|
||||
"scriptwriting_task": {
|
||||
"description": "Turn narrative concepts into scripts, including dialogue and scenes.",
|
||||
"expected_output": "Production-ready script with dialogue and scene details."
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
"""
|
||||
return user_content
|
||||
|
||||
|
||||
# generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
|
||||
# print(generator.generate())
|
||||
@@ -1,59 +0,0 @@
|
||||
from praisonai.agents_generator import AgentsGenerator
|
||||
from praisonai.auto import AutoGenerator
|
||||
import chainlit as cl
|
||||
import os
|
||||
from chainlit.types import ThreadDict
|
||||
from typing import Optional
|
||||
|
||||
framework = "crewai"
|
||||
config_list = [
|
||||
{
|
||||
'model': os.environ.get("OPENAI_MODEL_NAME", "gpt-4o"),
|
||||
'base_url': os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
|
||||
}
|
||||
]
|
||||
agent_file = "test.yaml"
|
||||
|
||||
@cl.on_chat_start
|
||||
async def start_chat():
|
||||
cl.user_session.set(
|
||||
"message_history",
|
||||
[{"role": "system", "content": "You are a helpful assistant."}],
|
||||
)
|
||||
|
||||
@cl.on_chat_resume
|
||||
async def on_chat_resume(thread: ThreadDict):
|
||||
message_history = cl.user_session.get("message_history", [])
|
||||
root_messages = [m for m in thread["steps"] if m["parentId"] is None]
|
||||
for message in root_messages:
|
||||
if message["type"] == "user_message":
|
||||
message_history.append({"role": "user", "content": message["output"]})
|
||||
elif message["type"] == "ai_message":
|
||||
message_history.append({"role": "assistant", "content": message["content"]})
|
||||
cl.user_session.set("message_history", message_history)
|
||||
|
||||
@cl.on_message
|
||||
async def main(message: cl.Message):
|
||||
"""Run PraisonAI with the provided message as the topic."""
|
||||
message_history = cl.user_session.get("message_history")
|
||||
message_history.append({"role": "user", "content": message.content})
|
||||
topic = message.content
|
||||
agent_file = "test.yaml"
|
||||
generator = AutoGenerator(topic=topic, framework=framework)
|
||||
agent_file = generator.generate()
|
||||
agents_generator = AgentsGenerator(agent_file, framework, config_list)
|
||||
result = agents_generator.generate_crew_and_kickoff()
|
||||
msg = cl.Message(content=result)
|
||||
await msg.send()
|
||||
message_history.append({"role": "assistant", "content": message.content})
|
||||
|
||||
@cl.password_auth_callback
|
||||
def auth_callback(username: str, password: str):
|
||||
# Fetch the user matching username from your database
|
||||
# and compare the hashed password with the value stored in the database
|
||||
if (username, password) == ("admin", "admin"):
|
||||
return cl.User(
|
||||
identifier="admin", metadata={"role": "admin", "provider": "credentials"}
|
||||
)
|
||||
else:
|
||||
return None
|
||||
@@ -1,221 +0,0 @@
|
||||
# praisonai/cli.py
|
||||
|
||||
import sys
|
||||
from .version import __version__
|
||||
import yaml, os
|
||||
from rich import print
|
||||
from dotenv import load_dotenv
|
||||
from crewai import Agent, Task, Crew
|
||||
load_dotenv()
|
||||
import autogen
|
||||
import gradio as gr
|
||||
import chainlit as cl
|
||||
import asyncio
|
||||
import uvicorn
|
||||
from chainlit.cli import cli as chainlit_cli
|
||||
from chainlit.server import app
|
||||
import argparse
|
||||
from .auto import AutoGenerator
|
||||
from .agents_generator import AgentsGenerator
|
||||
from .inbuilt_tools import *
|
||||
|
||||
class PraisonAI:
|
||||
def __init__(self, agent_file="agents.yaml", framework="", auto=False, init=False):
|
||||
"""
|
||||
Initialize the PraisonAI object with default parameters.
|
||||
|
||||
Parameters:
|
||||
agent_file (str): The default agent file to use. Defaults to "agents.yaml".
|
||||
framework (str): The default framework to use. Defaults to "crewai".
|
||||
auto (bool): A flag indicating whether to enable auto mode. Defaults to False.
|
||||
init (bool): A flag indicating whether to enable initialization mode. Defaults to False.
|
||||
|
||||
Attributes:
|
||||
config_list (list): A list of configuration dictionaries for the OpenAI API.
|
||||
agent_file (str): The agent file to use.
|
||||
framework (str): The framework to use.
|
||||
auto (bool): A flag indicating whether to enable auto mode.
|
||||
init (bool): A flag indicating whether to enable initialization mode.
|
||||
"""
|
||||
self.config_list = [
|
||||
{
|
||||
'model': os.environ.get("OPENAI_MODEL_NAME", "gpt-4o"),
|
||||
'base_url': os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
|
||||
}
|
||||
]
|
||||
self.agent_file = agent_file
|
||||
self.framework = framework
|
||||
self.auto = auto
|
||||
self.init = init
|
||||
|
||||
def main(self):
|
||||
"""
|
||||
The main function of the PraisonAI object. It parses the command-line arguments,
|
||||
initializes the necessary attributes, and then calls the appropriate methods based on the
|
||||
provided arguments.
|
||||
|
||||
Args:
|
||||
self (PraisonAI): An instance of the PraisonAI class.
|
||||
|
||||
Returns:
|
||||
Any: Depending on the arguments provided, the function may return a result from the
|
||||
AgentsGenerator, a deployment result from the CloudDeployer, or a message indicating
|
||||
the successful creation of a file.
|
||||
"""
|
||||
args = self.parse_args()
|
||||
if args is None:
|
||||
agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
|
||||
result = agents_generator.generate_crew_and_kickoff()
|
||||
return result
|
||||
if args.deploy:
|
||||
from .deploy import CloudDeployer
|
||||
deployer = CloudDeployer()
|
||||
deployer.run_commands()
|
||||
return
|
||||
invocation_cmd = "praisonai"
|
||||
version_string = f"PraisonAI version {__version__}"
|
||||
|
||||
self.framework = args.framework or self.framework
|
||||
|
||||
if args.agent_file:
|
||||
if args.agent_file.startswith("tests.test"): # Argument used for testing purposes. eg: python -m unittest tests.test
|
||||
print("test")
|
||||
else:
|
||||
self.agent_file = args.agent_file
|
||||
|
||||
|
||||
if args.auto or args.init:
|
||||
temp_topic = ' '.join(args.auto) if args.auto else ' '.join(args.init)
|
||||
self.topic = temp_topic
|
||||
elif self.auto or self.init: # Use the auto attribute if args.auto is not provided
|
||||
self.topic = self.auto
|
||||
|
||||
if args.auto or self.auto:
|
||||
self.agent_file = "test.yaml"
|
||||
generator = AutoGenerator(topic=self.topic , framework=self.framework, agent_file=self.agent_file)
|
||||
self.agent_file = generator.generate()
|
||||
agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
|
||||
result = agents_generator.generate_crew_and_kickoff()
|
||||
return result
|
||||
elif args.init or self.init:
|
||||
self.agent_file = "agents.yaml"
|
||||
generator = AutoGenerator(topic=self.topic , framework=self.framework, agent_file=self.agent_file)
|
||||
self.agent_file = generator.generate()
|
||||
print("File {} created successfully".format(self.agent_file))
|
||||
return "File {} created successfully".format(self.agent_file)
|
||||
|
||||
if args.ui:
|
||||
if args.ui == "gradio":
|
||||
self.create_gradio_interface()
|
||||
elif args.ui == "chainlit":
|
||||
self.create_chainlit_interface()
|
||||
else:
|
||||
self.create_chainlit_interface()
|
||||
else:
|
||||
agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
|
||||
result = agents_generator.generate_crew_and_kickoff()
|
||||
return result
|
||||
|
||||
def parse_args(self):
|
||||
"""
|
||||
Parse the command-line arguments for the PraisonAI CLI.
|
||||
|
||||
Args:
|
||||
self (PraisonAI): An instance of the PraisonAI class.
|
||||
|
||||
Returns:
|
||||
argparse.Namespace: An object containing the parsed command-line arguments.
|
||||
|
||||
Raises:
|
||||
argparse.ArgumentError: If the arguments provided are invalid.
|
||||
|
||||
Example:
|
||||
>>> args = praison_ai.parse_args()
|
||||
>>> print(args.agent_file) # Output: 'agents.yaml'
|
||||
"""
|
||||
parser = argparse.ArgumentParser(prog="praisonai", description="praisonAI command-line interface")
|
||||
parser.add_argument("--framework", choices=["crewai", "autogen"], help="Specify the framework")
|
||||
parser.add_argument("--ui", nargs='?', const='chainlit', default="chainlit", help="Specify the UI framework (gradio or chainlit). Default chainlit")
|
||||
parser.add_argument("--auto", nargs=argparse.REMAINDER, help="Enable auto mode and pass arguments for it")
|
||||
parser.add_argument("--init", nargs=argparse.REMAINDER, help="Enable auto mode and pass arguments for it")
|
||||
parser.add_argument("agent_file", nargs="?", help="Specify the agent file")
|
||||
parser.add_argument("--deploy", action="store_true", help="Deploy the application") # New argument
|
||||
args, unknown_args = parser.parse_known_args()
|
||||
|
||||
if unknown_args and unknown_args[0] == '-b' and unknown_args[1] == 'api:app':
|
||||
args.agent_file = 'agents.yaml'
|
||||
if args.agent_file == 'api:app' or args.agent_file == '/app/api:app':
|
||||
args.agent_file = 'agents.yaml'
|
||||
|
||||
return args
|
||||
|
||||
def create_gradio_interface(self):
|
||||
"""
|
||||
Create a Gradio interface for generating agents and performing tasks.
|
||||
|
||||
Args:
|
||||
self (PraisonAI): An instance of the PraisonAI class.
|
||||
|
||||
Returns:
|
||||
None: This method does not return any value. It launches the Gradio interface.
|
||||
|
||||
Raises:
|
||||
None: This method does not raise any exceptions.
|
||||
|
||||
Example:
|
||||
>>> praison_ai.create_gradio_interface()
|
||||
"""
|
||||
def generate_crew_and_kickoff_interface(auto_args, framework):
|
||||
"""
|
||||
Generate a crew and kick off tasks based on the provided auto arguments and framework.
|
||||
|
||||
Args:
|
||||
auto_args (list): Topic.
|
||||
framework (str): The framework to use for generating agents.
|
||||
|
||||
Returns:
|
||||
str: A string representing the result of generating the crew and kicking off tasks.
|
||||
|
||||
Raises:
|
||||
None: This method does not raise any exceptions.
|
||||
|
||||
Example:
|
||||
>>> result = generate_crew_and_kickoff_interface("Create a movie about Cat in Mars", "crewai")
|
||||
>>> print(result)
|
||||
"""
|
||||
self.framework = framework
|
||||
self.agent_file = "test.yaml"
|
||||
generator = AutoGenerator(topic=auto_args , framework=self.framework)
|
||||
self.agent_file = generator.generate()
|
||||
agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
|
||||
result = agents_generator.generate_crew_and_kickoff()
|
||||
return result
|
||||
|
||||
gr.Interface(
|
||||
fn=generate_crew_and_kickoff_interface,
|
||||
inputs=[gr.Textbox(lines=2, label="Auto Args"), gr.Dropdown(choices=["crewai", "autogen"], label="Framework")],
|
||||
outputs="textbox",
|
||||
title="Praison AI Studio",
|
||||
description="Create Agents and perform tasks",
|
||||
theme="default"
|
||||
).launch()
|
||||
|
||||
def create_chainlit_interface(self):
|
||||
"""
|
||||
Create a Chainlit interface for generating agents and performing tasks.
|
||||
|
||||
This function sets up a Chainlit application that listens for messages.
|
||||
When a message is received, it runs PraisonAI with the provided message as the topic.
|
||||
The generated agents are then used to perform tasks.
|
||||
|
||||
Returns:
|
||||
None: This function does not return any value. It starts the Chainlit application.
|
||||
"""
|
||||
from chainlit.cli import chainlit_run # Import chainlit_run
|
||||
os.environ["CHAINLIT_PORT"] = "8082"
|
||||
chainlit_run(["praisonai/chainlit_ui.py"])
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
praison_ai = PraisonAI()
|
||||
praison_ai.main()
|
||||
@@ -1,138 +0,0 @@
|
||||
import subprocess
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
|
||||
class CloudDeployer:
|
||||
"""
|
||||
A class for deploying a cloud-based application.
|
||||
|
||||
Attributes:
|
||||
None
|
||||
|
||||
Methods:
|
||||
__init__(self):
|
||||
Loads environment variables from .env file or system and sets them.
|
||||
|
||||
"""
|
||||
def __init__(self):
|
||||
"""
|
||||
Loads environment variables from .env file or system and sets them.
|
||||
|
||||
Parameters:
|
||||
self: An instance of the CloudDeployer class.
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Raises:
|
||||
None
|
||||
|
||||
"""
|
||||
# Load environment variables from .env file or system
|
||||
load_dotenv()
|
||||
self.set_environment_variables()
|
||||
|
||||
def create_dockerfile(self):
|
||||
"""
|
||||
Creates a Dockerfile for the application.
|
||||
|
||||
Parameters:
|
||||
self: An instance of the CloudDeployer class.
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Raises:
|
||||
None
|
||||
|
||||
This method creates a Dockerfile in the current directory with the specified content.
|
||||
The Dockerfile is used to build a Docker image for the application.
|
||||
The content of the Dockerfile includes instructions to use the Python 3.11-slim base image,
|
||||
set the working directory to /app, copy the current directory contents into the container,
|
||||
install the required Python packages (flask, praisonai, gunicorn, and markdown),
|
||||
expose port 8080, and run the application using Gunicorn.
|
||||
"""
|
||||
with open("Dockerfile", "w") as file:
|
||||
file.write("FROM python:3.11-slim\n")
|
||||
file.write("WORKDIR /app\n")
|
||||
file.write("COPY . .\n")
|
||||
file.write("RUN pip install flask praisonai==0.0.23 gunicorn markdown\n")
|
||||
file.write("EXPOSE 8080\n")
|
||||
file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
|
||||
|
||||
def create_api_file(self):
|
||||
"""
|
||||
Creates an API file for the application.
|
||||
|
||||
Parameters:
|
||||
self (CloudDeployer): An instance of the CloudDeployer class.
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
This method creates an API file named "api.py" in the current directory. The file contains a basic Flask application that uses the PraisonAI library to run a simple agent and returns the output as an HTML page. The application listens on the root path ("/") and uses the Markdown library to format the output.
|
||||
"""
|
||||
with open("api.py", "w") as file:
|
||||
file.write("from flask import Flask\n")
|
||||
file.write("from praisonai import PraisonAI\n")
|
||||
file.write("import markdown\n\n")
|
||||
file.write("app = Flask(__name__)\n\n")
|
||||
file.write("def basic():\n")
|
||||
file.write(" praison_ai = PraisonAI(agent_file=\"agents.yaml\")\n")
|
||||
file.write(" return praison_ai.main()\n\n")
|
||||
file.write("@app.route('/')\n")
|
||||
file.write("def home():\n")
|
||||
file.write(" output = basic()\n")
|
||||
file.write(" html_output = markdown.markdown(output)\n")
|
||||
file.write(" return f'<html><body>{html_output}</body></html>'\n\n")
|
||||
file.write("if __name__ == \"__main__\":\n")
|
||||
file.write(" app.run(debug=True)\n")
|
||||
|
||||
def set_environment_variables(self):
|
||||
"""Sets environment variables with fallback to .env values or defaults."""
|
||||
os.environ["OPENAI_MODEL_NAME"] = os.getenv("OPENAI_MODEL_NAME", "gpt-4o")
|
||||
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "Enter your API key")
|
||||
os.environ["OPENAI_API_BASE"] = os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1")
|
||||
|
||||
def run_commands(self):
|
||||
"""
|
||||
Sets environment variables with fallback to .env values or defaults.
|
||||
|
||||
Parameters:
|
||||
None
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Raises:
|
||||
None
|
||||
|
||||
This method sets environment variables for the application. It uses the `os.environ` dictionary to set the following environment variables:
|
||||
|
||||
- `OPENAI_MODEL_NAME`: The name of the OpenAI model to use. If not specified in the .env file, it defaults to "gpt-4o".
|
||||
- `OPENAI_API_KEY`: The API key for accessing the OpenAI API. If not specified in the .env file, it defaults to "Enter your API key".
|
||||
- `OPENAI_API_BASE`: The base URL for the OpenAI API. If not specified in the .env file, it defaults to "https://api.openai.com/v1".
|
||||
"""
|
||||
self.create_api_file()
|
||||
self.create_dockerfile()
|
||||
"""Runs a sequence of shell commands for deployment, continues on error."""
|
||||
commands = [
|
||||
"yes | gcloud auth configure-docker us-central1-docker.pkg.dev",
|
||||
"gcloud artifacts repositories create praisonai-repository --repository-format=docker --location=us-central1",
|
||||
"docker build --platform linux/amd64 -t gcr.io/$(gcloud config get-value project)/praisonai-app:latest .",
|
||||
"docker tag gcr.io/$(gcloud config get-value project)/praisonai-app:latest us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest",
|
||||
"docker push us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest",
|
||||
"gcloud run deploy praisonai-service --image us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest --platform managed --region us-central1 --allow-unauthenticated --set-env-vars OPENAI_MODEL_NAME=${OPENAI_MODEL_NAME},OPENAI_API_KEY=${OPENAI_API_KEY},OPENAI_API_BASE=${OPENAI_API_BASE}"
|
||||
]
|
||||
|
||||
for cmd in commands:
|
||||
try:
|
||||
subprocess.run(cmd, shell=True, check=True)
|
||||
except subprocess.CalledProcessError as e:
|
||||
print(f"ERROR: Command '{e.cmd}' failed with exit status {e.returncode}")
|
||||
print(f"Continuing with the next command...")
|
||||
|
||||
# Usage
|
||||
if __name__ == "__main__":
|
||||
deployer = CloudDeployer()
|
||||
deployer.run_commands()
|
||||
@@ -1,2 +0,0 @@
|
||||
# from .base_tool import BaseTool, Tool, tool
|
||||
from .autogen_tools import *
|
||||
@@ -1,203 +0,0 @@
|
||||
# praisonai/inbuilt_tools/autogen_tools.py
|
||||
|
||||
from crewai_tools import (
|
||||
CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, DirectoryReadTool,
|
||||
FileReadTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, RagTool,
|
||||
ScrapeElementFromWebsiteTool, ScrapeWebsiteTool, WebsiteSearchTool, XMLSearchTool, YoutubeChannelSearchTool,
|
||||
YoutubeVideoSearchTool
|
||||
)
|
||||
from typing import Any
|
||||
from autogen import register_function
|
||||
import os
|
||||
import importlib
|
||||
from pathlib import Path
|
||||
import os
|
||||
import inspect
|
||||
import sys
|
||||
|
||||
def create_autogen_tool_function(tool_name):
|
||||
def autogen_tool(assistant, user_proxy):
|
||||
def register_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(query: str) -> Any:
|
||||
tool_instance = tool_class()
|
||||
return tool_instance.run(query=query)
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
|
||||
root_directory = os.getcwd()
|
||||
tools_py_path = os.path.join(root_directory, 'tools.py')
|
||||
tools_dir_path = Path(root_directory) / 'tools'
|
||||
|
||||
if os.path.isfile(tools_py_path):
|
||||
print(f"{tools_py_path} exists in the root directory. Loading {tools_py_path} and skipping tools folder.")
|
||||
tool_module = importlib.import_module("tools")
|
||||
elif tools_dir_path.is_dir():
|
||||
print(f"tools folder exists in the root directory. Loading {tool_name} from tools/{tool_name}.py.")
|
||||
tool_module = importlib.import_module(f"tools.{tool_name}")
|
||||
else:
|
||||
raise ImportError("Neither tools.py nor tools directory found in the root directory.")
|
||||
|
||||
Tool = getattr(tool_module, tool_name)
|
||||
|
||||
register_tool(Tool, tool_name, f"Description for {tool_name}", assistant, user_proxy)
|
||||
|
||||
return autogen_tool
|
||||
|
||||
# Load tools.py
|
||||
sys.path.insert(0, os.getcwd())
|
||||
root_directory = os.getcwd()
|
||||
tools_py_path = os.path.join(root_directory, 'tools.py')
|
||||
tools_dir_path = Path(root_directory) / 'tools'
|
||||
|
||||
tools_module = None
|
||||
|
||||
if os.path.isfile(tools_py_path):
|
||||
print(f"{tools_py_path} exists in the root directory. Loading {tools_py_path} and skipping tools folder.")
|
||||
tools_module = importlib.import_module("tools")
|
||||
elif tools_dir_path.is_dir():
|
||||
print(f"tools folder exists in the root directory. Loading {tool_name} from tools/{tool_name}.py.")
|
||||
tools_module = importlib.import_module(f"tools.{tool_name}")
|
||||
|
||||
# Create autogen_TOOL_NAME_HERE function for each tool
|
||||
if tools_module is not None:
|
||||
for name, obj in inspect.getmembers(tools_module):
|
||||
if inspect.isclass(obj):
|
||||
globals()[f"autogen_{name}"] = create_autogen_tool_function(name)
|
||||
|
||||
def autogen_CodeDocsSearchTool(assistant, user_proxy):
|
||||
def register_code_docs_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(docs_url: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(docs_url=docs_url, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_code_docs_search_tool(CodeDocsSearchTool, "code_docs_search_tool", "Search a Code Docs content(search_query: 'string', docs_url: 'string') - A tool that can be used to semantic search a query from a Code Docs content.", assistant, user_proxy)
|
||||
|
||||
def autogen_CSVSearchTool(assistant, user_proxy):
|
||||
def register_csv_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(csv: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(csv=csv, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_csv_search_tool(CSVSearchTool, "csv_search_tool", "Search a CSV's content(search_query: 'string', csv: 'string') - A tool that can be used to semantic search a query from a CSV's content.", assistant, user_proxy)
|
||||
|
||||
def autogen_DirectorySearchTool(assistant, user_proxy):
|
||||
def register_directory_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(directory: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(directory=directory, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_directory_search_tool(DirectorySearchTool, "directory_search_tool", "Search a directory's content(search_query: 'string', directory: 'string') - A tool that can be used to semantic search a query from a directory's content.", assistant, user_proxy)
|
||||
|
||||
def autogen_DOCXSearchTool(assistant, user_proxy):
|
||||
def register_docx_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(docx: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(docx=docx, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_docx_search_tool(DOCXSearchTool, "docx_search_tool", "Search a DOCX's content(search_query: 'string', docx: 'string') - A tool that can be used to semantic search a query from a DOCX's content.", assistant, user_proxy)
|
||||
|
||||
# DirectoryReadTool
|
||||
def autogen_DirectoryReadTool(assistant, user_proxy):
|
||||
def register_directory_read_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(directory: str) -> Any:
|
||||
tool_instance = tool_class(directory=directory)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_directory_read_tool(DirectoryReadTool, "directory_read_tool", "List files in directory(directory: 'string') - A tool that can be used to recursively list a directory's content.", assistant, user_proxy)
|
||||
|
||||
# FileReadTool
|
||||
def autogen_FileReadTool(assistant, user_proxy):
|
||||
def register_file_read_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(file_path: str) -> Any:
|
||||
tool_instance = tool_class(file_path=file_path)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_file_read_tool(FileReadTool, "file_read_tool", "Read a file's content(file_path: 'string') - A tool that can be used to read a file's content.", assistant, user_proxy)
|
||||
|
||||
# TXTSearchTool
|
||||
def autogen_TXTSearchTool(assistant, user_proxy):
|
||||
def register_txt_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(txt: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(txt=txt, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_txt_search_tool(TXTSearchTool, "txt_search_tool", "Search a txt's content(search_query: 'string', txt: 'string') - A tool that can be used to semantic search a query from a txt's content.", assistant, user_proxy)
|
||||
|
||||
def autogen_JSONSearchTool(assistant, user_proxy):
|
||||
def register_json_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(json_path: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(json_path=json_path, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_json_search_tool(JSONSearchTool, "json_search_tool", "Search a JSON's content(search_query: 'string', json_path: 'string') - A tool that can be used to semantic search a query from a JSON's content.", assistant, user_proxy)
|
||||
|
||||
def autogen_MDXSearchTool(assistant, user_proxy):
|
||||
def register_mdx_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(mdx: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(mdx=mdx, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_mdx_search_tool(MDXSearchTool, "mdx_search_tool", "Search a MDX's content(search_query: 'string', mdx: 'string') - A tool that can be used to semantic search a query from a MDX's content.", assistant, user_proxy)
|
||||
|
||||
def autogen_PDFSearchTool(assistant, user_proxy):
|
||||
def register_pdf_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(pdf: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(pdf=pdf, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_pdf_search_tool(PDFSearchTool, "pdf_search_tool", "Search a PDF's content(search_query: 'string', pdf: 'string') - A tool that can be used to semantic search a query from a PDF's content.", assistant, user_proxy)
|
||||
|
||||
def autogen_RagTool(assistant, user_proxy):
|
||||
def register_rag_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(query: str, data: Any) -> Any:
|
||||
tool_instance = tool_class(query=query, data=data)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_rag_tool(RagTool, "rag_tool", "Knowledge base(query: 'string', data: Any) - A knowledge base that can be used to answer questions.", assistant, user_proxy)
|
||||
|
||||
def autogen_ScrapeElementFromWebsiteTool(assistant, user_proxy):
|
||||
def register_scrape_element_from_website_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(website_url: str, element_query: str) -> Any:
|
||||
tool_instance = tool_class(website_url=website_url, element_query=element_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_scrape_element_from_website_tool(ScrapeElementFromWebsiteTool, "scrape_element_from_website_tool", "Scrape an element from a website(element_query: 'string', website_url: 'string') - A tool that can be used to scrape an element from a website.", assistant, user_proxy)
|
||||
|
||||
def autogen_ScrapeWebsiteTool(assistant, user_proxy):
|
||||
def register_scrape_website_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(website_url: str) -> Any:
|
||||
tool_instance = tool_class(website_url=website_url)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_scrape_website_tool(ScrapeWebsiteTool, "scrape_website_tool", "Read website content(website_url: 'string') - A tool that can be used to read content from a specified website.", assistant, user_proxy)
|
||||
|
||||
def autogen_WebsiteSearchTool(assistant, user_proxy):
|
||||
def register_website_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(website: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(website=website, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_website_search_tool(WebsiteSearchTool, "website_search_tool", "Search in a specific website(search_query: 'string', website: 'string') - A tool that can be used to semantic search a query from a specific URL content.", assistant, user_proxy)
|
||||
|
||||
def autogen_XMLSearchTool(assistant, user_proxy):
|
||||
def register_xml_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(xml: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(xml=xml, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_xml_search_tool(XMLSearchTool, "xml_search_tool", "Search a XML's content(search_query: 'string', xml: 'string') - A tool that can be used to semantic search a query from a XML's content.", assistant, user_proxy)
|
||||
|
||||
def autogen_YoutubeChannelSearchTool(assistant, user_proxy):
|
||||
def register_youtube_channel_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(youtube_channel_handle: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(youtube_channel_handle=youtube_channel_handle, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_youtube_channel_search_tool(YoutubeChannelSearchTool, "youtube_channel_search_tool", "Search a Youtube Channels content(search_query: 'string', youtube_channel_handle: 'string') - A tool that can be used to semantic search a query from a Youtube Channels content.", assistant, user_proxy)
|
||||
|
||||
def autogen_YoutubeVideoSearchTool(assistant, user_proxy):
|
||||
def register_youtube_video_search_tool(tool_class, tool_name, tool_description, assistant, user_proxy):
|
||||
def tool_func(youtube_video_url: str, search_query: str) -> Any:
|
||||
tool_instance = tool_class(youtube_video_url=youtube_video_url, search_query=search_query)
|
||||
return tool_instance.run()
|
||||
register_function(tool_func, caller=assistant, executor=user_proxy, name=tool_name, description=tool_description)
|
||||
register_youtube_video_search_tool(YoutubeVideoSearchTool, "youtube_video_search_tool", "Search a Youtube Video content(search_query: 'string', youtube_video_url: 'string') - A tool that can be used to semantic search a query from a Youtube Video content.", assistant, user_proxy)
|
||||
@@ -1,104 +0,0 @@
|
||||
import yaml
|
||||
import os
|
||||
from rich import print
|
||||
from dotenv import load_dotenv
|
||||
from crewai import Agent, Task, Crew
|
||||
load_dotenv()
|
||||
import autogen
|
||||
config_list = [
|
||||
{
|
||||
'model': os.environ.get("OPENAI_MODEL_NAME", "gpt-3.5-turbo"),
|
||||
'base_url': os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
|
||||
}
|
||||
]
|
||||
|
||||
def generate_crew_and_kickoff(agent_file):
|
||||
"""
|
||||
This function generates a crew of agents and kicks off tasks based on the configuration provided in a YAML file.
|
||||
|
||||
Parameters:
|
||||
agent_file (str): The path to the YAML file containing the configuration for the agents and tasks.
|
||||
|
||||
Returns:
|
||||
str: The result of the last task executed by the crew.
|
||||
"""
|
||||
|
||||
with open(agent_file, 'r') as f:
|
||||
config = yaml.safe_load(f)
|
||||
|
||||
topic = config['topic']
|
||||
framework = config['framework']
|
||||
|
||||
agents = {}
|
||||
tasks = []
|
||||
if framework == "autogen":
|
||||
# Load the LLM configuration dynamically
|
||||
print(config_list)
|
||||
llm_config = {"config_list": config_list}
|
||||
|
||||
for role, details in config['roles'].items():
|
||||
agent_name = details['role'].format(topic=topic).replace("{topic}", topic)
|
||||
agent_goal = details['goal'].format(topic=topic)
|
||||
# Creating an AssistantAgent for each role dynamically
|
||||
agents[role] = autogen.AssistantAgent(
|
||||
name=agent_name,
|
||||
llm_config=llm_config,
|
||||
system_message=details['backstory'].format(topic=topic)+". Reply \"TERMINATE\" in the end when everything is done.",
|
||||
)
|
||||
|
||||
# Preparing tasks for initiate_chats
|
||||
for task_name, task_details in details.get('tasks', {}).items():
|
||||
description_filled = task_details['description'].format(topic=topic)
|
||||
expected_output_filled = task_details['expected_output'].format(topic=topic)
|
||||
|
||||
chat_task = {
|
||||
"recipient": agents[role],
|
||||
"message": description_filled,
|
||||
"summary_method": "last_msg", # Customize as needed
|
||||
# Additional fields like carryover can be added based on dependencies
|
||||
}
|
||||
tasks.append(chat_task)
|
||||
|
||||
# Assuming the user proxy agent is set up as per your requirements
|
||||
user = autogen.UserProxyAgent(
|
||||
name="User",
|
||||
human_input_mode="NEVER",
|
||||
is_termination_msg=lambda x: (x.get("content") or "").rstrip().endswith("TERMINATE"),
|
||||
code_execution_config={
|
||||
"work_dir": "coding",
|
||||
"use_docker": False,
|
||||
},
|
||||
# additional setup for the user proxy agent
|
||||
)
|
||||
response = user.initiate_chats(tasks)
|
||||
result = "### Output ###\n"+response[-1].summary if hasattr(response[-1], 'summary') else ""
|
||||
else:
|
||||
for role, details in config['roles'].items():
|
||||
role_filled = details['role'].format(topic=topic)
|
||||
goal_filled = details['goal'].format(topic=topic)
|
||||
backstory_filled = details['backstory'].format(topic=topic)
|
||||
|
||||
# Assume tools are loaded and handled here as per your requirements
|
||||
agent = Agent(role=role_filled, goal=goal_filled, backstory=backstory_filled)
|
||||
agents[role] = agent
|
||||
|
||||
for task_name, task_details in details.get('tasks', {}).items():
|
||||
description_filled = task_details['description'].format(topic=topic)
|
||||
expected_output_filled = task_details['expected_output'].format(topic=topic)
|
||||
|
||||
task = Task(description=description_filled, expected_output=expected_output_filled, agent=agent)
|
||||
tasks.append(task)
|
||||
|
||||
crew = Crew(
|
||||
agents=list(agents.values()),
|
||||
tasks=tasks,
|
||||
verbose=2
|
||||
)
|
||||
|
||||
result = crew.kickoff()
|
||||
return result
|
||||
|
||||
if __name__ == "__main__":
|
||||
agent_file = "agents.yaml"
|
||||
result = generate_crew_and_kickoff(agent_file)
|
||||
print(result)
|
||||
@@ -1 +0,0 @@
|
||||
__version__ = "0.0.1"
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 3.8 KiB |
@@ -0,0 +1,43 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<link rel="icon" href="%PUBLIC_URL%/favicon.ico" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||
<meta name="theme-color" content="#000000" />
|
||||
<meta
|
||||
name="description"
|
||||
content="Web site created using create-react-app"
|
||||
/>
|
||||
<link rel="apple-touch-icon" href="%PUBLIC_URL%/logo192.png" />
|
||||
<!--
|
||||
manifest.json provides metadata used when your web app is installed on a
|
||||
user's mobile device or desktop. See https://developers.google.com/web/fundamentals/web-app-manifest/
|
||||
-->
|
||||
<link rel="manifest" href="%PUBLIC_URL%/manifest.json" />
|
||||
<!--
|
||||
Notice the use of %PUBLIC_URL% in the tags above.
|
||||
It will be replaced with the URL of the `public` folder during the build.
|
||||
Only files inside the `public` folder can be referenced from the HTML.
|
||||
|
||||
Unlike "/favicon.ico" or "favicon.ico", "%PUBLIC_URL%/favicon.ico" will
|
||||
work correctly both with client-side routing and a non-root public URL.
|
||||
Learn how to configure a non-root public URL by running `npm run build`.
|
||||
-->
|
||||
<title>React App</title>
|
||||
</head>
|
||||
<body>
|
||||
<noscript>You need to enable JavaScript to run this app.</noscript>
|
||||
<div id="root"></div>
|
||||
<!--
|
||||
This HTML file is a template.
|
||||
If you open it directly in the browser, you will see an empty page.
|
||||
|
||||
You can add webfonts, meta tags, or analytics to this file.
|
||||
The build step will place the bundled scripts into the <body> tag.
|
||||
|
||||
To begin the development, run `npm start` or `yarn start`.
|
||||
To create a production bundle, use `npm run build` or `yarn build`.
|
||||
-->
|
||||
</body>
|
||||
</html>
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 5.2 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 9.4 KiB |
@@ -0,0 +1,25 @@
|
||||
{
|
||||
"short_name": "React App",
|
||||
"name": "Create React App Sample",
|
||||
"icons": [
|
||||
{
|
||||
"src": "favicon.ico",
|
||||
"sizes": "64x64 32x32 24x24 16x16",
|
||||
"type": "image/x-icon"
|
||||
},
|
||||
{
|
||||
"src": "logo192.png",
|
||||
"type": "image/png",
|
||||
"sizes": "192x192"
|
||||
},
|
||||
{
|
||||
"src": "logo512.png",
|
||||
"type": "image/png",
|
||||
"sizes": "512x512"
|
||||
}
|
||||
],
|
||||
"start_url": ".",
|
||||
"display": "standalone",
|
||||
"theme_color": "#000000",
|
||||
"background_color": "#ffffff"
|
||||
}
|
||||
@@ -0,0 +1,3 @@
|
||||
# https://www.robotstxt.org/robotstxt.html
|
||||
User-agent: *
|
||||
Disallow:
|
||||
@@ -1,36 +0,0 @@
|
||||
[tool.poetry]
|
||||
name = "PraisonAI"
|
||||
version = "0.0.23"
|
||||
description = "PraisonAI application combines AutoGen and CrewAI or similar frameworks into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customization, and efficient human-agent collaboration."
|
||||
authors = ["Mervin Praison"]
|
||||
license = ""
|
||||
readme = "README.md"
|
||||
|
||||
[tool.poetry.urls]
|
||||
Homepage = "https://docs.praison.ai"
|
||||
Repository = "https://github.com/mervinpraison/PraisonAI"
|
||||
|
||||
[tool.setuptools]
|
||||
packages = ["PraisonAI"]
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.10,<3.13"
|
||||
rich = ">=13.7"
|
||||
pyautogen = ">=0.2.19"
|
||||
crewai = ">=0.30.4"
|
||||
gradio = ">=4.26.0"
|
||||
Flask = ">=3.0.0"
|
||||
markdown = ">=3.5"
|
||||
crewai-tools = "^0.2.6"
|
||||
praisonai-tools = ">=0.0.4"
|
||||
|
||||
[tool.poetry.dev-dependencies]
|
||||
pytest = "^8.0.0"
|
||||
pre-commit = "^3.7"
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.poetry.scripts]
|
||||
praisonai = "praisonai.__main__:main"
|
||||
@@ -1,7 +0,0 @@
|
||||
rich>=13.7
|
||||
pyautogen>=0.2.19
|
||||
crewai>=0.30.4
|
||||
gradio>=4.20.0
|
||||
crewai_tools
|
||||
duckduckgo_search
|
||||
praisonai_tools
|
||||
+38
@@ -0,0 +1,38 @@
|
||||
.App {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.App-logo {
|
||||
height: 40vmin;
|
||||
pointer-events: none;
|
||||
}
|
||||
|
||||
@media (prefers-reduced-motion: no-preference) {
|
||||
.App-logo {
|
||||
animation: App-logo-spin infinite 20s linear;
|
||||
}
|
||||
}
|
||||
|
||||
.App-header {
|
||||
background-color: #282c34;
|
||||
min-height: 100vh;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
font-size: calc(10px + 2vmin);
|
||||
color: white;
|
||||
}
|
||||
|
||||
.App-link {
|
||||
color: #61dafb;
|
||||
}
|
||||
|
||||
@keyframes App-logo-spin {
|
||||
from {
|
||||
transform: rotate(0deg);
|
||||
}
|
||||
to {
|
||||
transform: rotate(360deg);
|
||||
}
|
||||
}
|
||||
+13
@@ -0,0 +1,13 @@
|
||||
import React from 'react';
|
||||
import './App.css';
|
||||
import SettingsForm from './SettingsForm'; // Import the SettingsForm component
|
||||
|
||||
function App() {
|
||||
return (
|
||||
<div className="App">
|
||||
<SettingsForm /> {/* Render the SettingsForm component */}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
export default App;
|
||||
@@ -0,0 +1,8 @@
|
||||
import { render, screen } from '@testing-library/react';
|
||||
import App from './App';
|
||||
|
||||
test('renders learn react link', () => {
|
||||
render(<App />);
|
||||
const linkElement = screen.getByText(/learn react/i);
|
||||
expect(linkElement).toBeInTheDocument();
|
||||
});
|
||||
@@ -0,0 +1,112 @@
|
||||
import React, { useState } from 'react';
|
||||
import {
|
||||
Container,
|
||||
FormControl,
|
||||
FormLabel,
|
||||
Input,
|
||||
Button,
|
||||
VStack,
|
||||
} from '@chakra-ui/react';
|
||||
|
||||
// Mocking wpApiSettings for development purposes. This should be replaced with the actual object provided by WordPress in production.
|
||||
const wpApiSettings = { nonce: 'dev-nonce' }; // This line is added to mock the wpApiSettings object
|
||||
|
||||
const SettingsForm = () => {
|
||||
const [patreonClientId, setPatreonClientId] = useState('');
|
||||
const [patreonClientSecret, setPatreonClientSecret] = useState('');
|
||||
const [youtubeApiKey, setYoutubeApiKey] = useState('');
|
||||
const [file, setFile] = useState(null);
|
||||
|
||||
const handleSubmit = (event) => {
|
||||
event.preventDefault();
|
||||
// Handle the submission logic here
|
||||
// This will involve saving the API credentials to the WordPress database
|
||||
// For now, we'll log the credentials to the console
|
||||
console.log({
|
||||
patreonClientId,
|
||||
patreonClientSecret,
|
||||
youtubeApiKey,
|
||||
});
|
||||
// TODO: Implement API call to save these settings in WordPress
|
||||
};
|
||||
|
||||
const handleFileChange = (event) => {
|
||||
setFile(event.target.files[0]);
|
||||
};
|
||||
|
||||
const handleFileUpload = async (event) => {
|
||||
event.preventDefault();
|
||||
if (file) {
|
||||
const formData = new FormData();
|
||||
formData.append('file', file);
|
||||
|
||||
try {
|
||||
const response = await fetch('/wp-json/patreon-youtube/v1/upload_media', {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'X-WP-Nonce': wpApiSettings.nonce,
|
||||
},
|
||||
body: formData,
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
throw new Error(`HTTP error! status: ${response.status}`);
|
||||
}
|
||||
|
||||
const result = await response.json();
|
||||
console.log('File uploaded successfully:', result);
|
||||
// TODO: Handle the successful upload (e.g., display a success message, clear the form, etc.)
|
||||
} catch (error) {
|
||||
console.error('File upload error:', error);
|
||||
// TODO: Handle the upload error (e.g., display an error message to the user)
|
||||
}
|
||||
} else {
|
||||
console.error('No file selected');
|
||||
// TODO: Inform the user that no file was selected
|
||||
}
|
||||
};
|
||||
|
||||
return (
|
||||
<Container>
|
||||
<form onSubmit={handleSubmit}>
|
||||
<VStack spacing={4}>
|
||||
<FormControl id="patreon-client-id">
|
||||
<FormLabel>Patreon Client ID</FormLabel>
|
||||
<Input
|
||||
type="text"
|
||||
value={patreonClientId}
|
||||
onChange={(e) => setPatreonClientId(e.target.value)}
|
||||
/>
|
||||
</FormControl>
|
||||
<FormControl id="patreon-client-secret">
|
||||
<FormLabel>Patreon Client Secret</FormLabel>
|
||||
<Input
|
||||
type="text"
|
||||
value={patreonClientSecret}
|
||||
onChange={(e) => setPatreonClientSecret(e.target.value)}
|
||||
/>
|
||||
</FormControl>
|
||||
<FormControl id="youtube-api-key">
|
||||
<FormLabel>YouTube API Key</FormLabel>
|
||||
<Input
|
||||
type="text"
|
||||
value={youtubeApiKey}
|
||||
onChange={(e) => setYoutubeApiKey(e.target.value)}
|
||||
/>
|
||||
</FormControl>
|
||||
<FormControl id="file-upload">
|
||||
<FormLabel>Upload Media</FormLabel>
|
||||
<Input
|
||||
type="file"
|
||||
onChange={handleFileChange}
|
||||
/>
|
||||
<Button onClick={handleFileUpload} colorScheme="blue">Upload</Button>
|
||||
</FormControl>
|
||||
<Button type="submit" colorScheme="blue">Save Settings</Button>
|
||||
</VStack>
|
||||
</form>
|
||||
</Container>
|
||||
);
|
||||
};
|
||||
|
||||
export default SettingsForm;
|
||||
@@ -0,0 +1,13 @@
|
||||
body {
|
||||
margin: 0;
|
||||
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen',
|
||||
'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue',
|
||||
sans-serif;
|
||||
-webkit-font-smoothing: antialiased;
|
||||
-moz-osx-font-smoothing: grayscale;
|
||||
}
|
||||
|
||||
code {
|
||||
font-family: source-code-pro, Menlo, Monaco, Consolas, 'Courier New',
|
||||
monospace;
|
||||
}
|
||||
@@ -0,0 +1,17 @@
|
||||
import React from 'react';
|
||||
import ReactDOM from 'react-dom/client';
|
||||
import './index.css';
|
||||
import App from './App';
|
||||
import reportWebVitals from './reportWebVitals';
|
||||
|
||||
const root = ReactDOM.createRoot(document.getElementById('root'));
|
||||
root.render(
|
||||
<React.StrictMode>
|
||||
<App />
|
||||
</React.StrictMode>
|
||||
);
|
||||
|
||||
// If you want to start measuring performance in your app, pass a function
|
||||
// to log results (for example: reportWebVitals(console.log))
|
||||
// or send to an analytics endpoint. Learn more: https://bit.ly/CRA-vitals
|
||||
reportWebVitals();
|
||||
@@ -0,0 +1 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 841.9 595.3"><g fill="#61DAFB"><path d="M666.3 296.5c0-32.5-40.7-63.3-103.1-82.4 14.4-63.6 8-114.2-20.2-130.4-6.5-3.8-14.1-5.6-22.4-5.6v22.3c4.6 0 8.3.9 11.4 2.6 13.6 7.8 19.5 37.5 14.9 75.7-1.1 9.4-2.9 19.3-5.1 29.4-19.6-4.8-41-8.5-63.5-10.9-13.5-18.5-27.5-35.3-41.6-50 32.6-30.3 63.2-46.9 84-46.9V78c-27.5 0-63.5 19.6-99.9 53.6-36.4-33.8-72.4-53.2-99.9-53.2v22.3c20.7 0 51.4 16.5 84 46.6-14 14.7-28 31.4-41.3 49.9-22.6 2.4-44 6.1-63.6 11-2.3-10-4-19.7-5.2-29-4.7-38.2 1.1-67.9 14.6-75.8 3-1.8 6.9-2.6 11.5-2.6V78.5c-8.4 0-16 1.8-22.6 5.6-28.1 16.2-34.4 66.7-19.9 130.1-62.2 19.2-102.7 49.9-102.7 82.3 0 32.5 40.7 63.3 103.1 82.4-14.4 63.6-8 114.2 20.2 130.4 6.5 3.8 14.1 5.6 22.5 5.6 27.5 0 63.5-19.6 99.9-53.6 36.4 33.8 72.4 53.2 99.9 53.2 8.4 0 16-1.8 22.6-5.6 28.1-16.2 34.4-66.7 19.9-130.1 62-19.1 102.5-49.9 102.5-82.3zm-130.2-66.7c-3.7 12.9-8.3 26.2-13.5 39.5-4.1-8-8.4-16-13.1-24-4.6-8-9.5-15.8-14.4-23.4 14.2 2.1 27.9 4.7 41 7.9zm-45.8 106.5c-7.8 13.5-15.8 26.3-24.1 38.2-14.9 1.3-30 2-45.2 2-15.1 0-30.2-.7-45-1.9-8.3-11.9-16.4-24.6-24.2-38-7.6-13.1-14.5-26.4-20.8-39.8 6.2-13.4 13.2-26.8 20.7-39.9 7.8-13.5 15.8-26.3 24.1-38.2 14.9-1.3 30-2 45.2-2 15.1 0 30.2.7 45 1.9 8.3 11.9 16.4 24.6 24.2 38 7.6 13.1 14.5 26.4 20.8 39.8-6.3 13.4-13.2 26.8-20.7 39.9zm32.3-13c5.4 13.4 10 26.8 13.8 39.8-13.1 3.2-26.9 5.9-41.2 8 4.9-7.7 9.8-15.6 14.4-23.7 4.6-8 8.9-16.1 13-24.1zM421.2 430c-9.3-9.6-18.6-20.3-27.8-32 9 .4 18.2.7 27.5.7 9.4 0 18.7-.2 27.8-.7-9 11.7-18.3 22.4-27.5 32zm-74.4-58.9c-14.2-2.1-27.9-4.7-41-7.9 3.7-12.9 8.3-26.2 13.5-39.5 4.1 8 8.4 16 13.1 24 4.7 8 9.5 15.8 14.4 23.4zM420.7 163c9.3 9.6 18.6 20.3 27.8 32-9-.4-18.2-.7-27.5-.7-9.4 0-18.7.2-27.8.7 9-11.7 18.3-22.4 27.5-32zm-74 58.9c-4.9 7.7-9.8 15.6-14.4 23.7-4.6 8-8.9 16-13 24-5.4-13.4-10-26.8-13.8-39.8 13.1-3.1 26.9-5.8 41.2-7.9zm-90.5 125.2c-35.4-15.1-58.3-34.9-58.3-50.6 0-15.7 22.9-35.6 58.3-50.6 8.6-3.7 18-7 27.7-10.1 5.7 19.6 13.2 40 22.5 60.9-9.2 20.8-16.6 41.1-22.2 60.6-9.9-3.1-19.3-6.5-28-10.2zM310 490c-13.6-7.8-19.5-37.5-14.9-75.7 1.1-9.4 2.9-19.3 5.1-29.4 19.6 4.8 41 8.5 63.5 10.9 13.5 18.5 27.5 35.3 41.6 50-32.6 30.3-63.2 46.9-84 46.9-4.5-.1-8.3-1-11.3-2.7zm237.2-76.2c4.7 38.2-1.1 67.9-14.6 75.8-3 1.8-6.9 2.6-11.5 2.6-20.7 0-51.4-16.5-84-46.6 14-14.7 28-31.4 41.3-49.9 22.6-2.4 44-6.1 63.6-11 2.3 10.1 4.1 19.8 5.2 29.1zm38.5-66.7c-8.6 3.7-18 7-27.7 10.1-5.7-19.6-13.2-40-22.5-60.9 9.2-20.8 16.6-41.1 22.2-60.6 9.9 3.1 19.3 6.5 28.1 10.2 35.4 15.1 58.3 34.9 58.3 50.6-.1 15.7-23 35.6-58.4 50.6zM320.8 78.4z"/><circle cx="420.9" cy="296.5" r="45.7"/><path d="M520.5 78.1z"/></g></svg>
|
||||
|
After Width: | Height: | Size: 2.6 KiB |
@@ -0,0 +1,13 @@
|
||||
const reportWebVitals = onPerfEntry => {
|
||||
if (onPerfEntry && onPerfEntry instanceof Function) {
|
||||
import('web-vitals').then(({ getCLS, getFID, getFCP, getLCP, getTTFB }) => {
|
||||
getCLS(onPerfEntry);
|
||||
getFID(onPerfEntry);
|
||||
getFCP(onPerfEntry);
|
||||
getLCP(onPerfEntry);
|
||||
getTTFB(onPerfEntry);
|
||||
});
|
||||
}
|
||||
};
|
||||
|
||||
export default reportWebVitals;
|
||||
@@ -0,0 +1,5 @@
|
||||
// jest-dom adds custom jest matchers for asserting on DOM nodes.
|
||||
// allows you to do things like:
|
||||
// expect(element).toHaveTextContent(/react/i)
|
||||
// learn more: https://github.com/testing-library/jest-dom
|
||||
import '@testing-library/jest-dom';
|
||||
@@ -1,30 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Check the first argument provided to the script for platform selection
|
||||
if [ "$1" = "groq" ]; then
|
||||
# Check for a second argument for the model name, default to mixtral-8x7b-32768 if not provided
|
||||
MODEL_NAME=${2:-mixtral-8x7b-32768}
|
||||
# Set environment variables for Groq configuration with dynamic model name
|
||||
export OPENAI_MODEL_NAME=$MODEL_NAME
|
||||
export OPENAI_API_BASE=https://api.groq.com/openai/v1
|
||||
export OPENAI_API_KEY=$(echo $GROQ_API_KEY)
|
||||
echo "Groq configuration initiated with model $MODEL_NAME."
|
||||
elif [ "$1" = "openai" ] || [ -z "$1" ]; then
|
||||
# Check for a second argument for the model name, default to gpt-3.5-turbo if not provided
|
||||
MODEL_NAME=${2:-gpt-3.5-turbo}
|
||||
# Set environment variables for OpenAI configuration with dynamic model name
|
||||
export OPENAI_MODEL_NAME=$MODEL_NAME
|
||||
export OPENAI_API_BASE=https://api.openai.com/v1
|
||||
export OPENAI_API_KEY=$(echo $OAI_API_KEY)
|
||||
echo "OpenAI configuration initiated with model $MODEL_NAME."
|
||||
elif [ "$1" = "ollama" ]; then
|
||||
# Check for a second argument for the model name, default to a custom model if not provided
|
||||
MODEL_NAME=${2:-default-ollama-model}
|
||||
# Set environment variables for Ollama local configuration with dynamic model name
|
||||
export OPENAI_MODEL_NAME=$MODEL_NAME
|
||||
export OPENAI_API_BASE=http://127.0.0.1:11434
|
||||
export OPENAI_API_KEY=$(echo $OLLAMA_API_KEY)
|
||||
# Set up SSH public key
|
||||
export SSH_PUBLIC_KEY="ssh-ed25519 AAAA..."
|
||||
echo "Ollama local model configuration initiated with model $MODEL_NAME.
|
||||
fi
|
||||
Whitespace-only changes.
@@ -1,12 +0,0 @@
|
||||
from praisonai import PraisonAI
|
||||
|
||||
def advanced():
|
||||
praison_ai = PraisonAI(
|
||||
agent_file="agents.yaml",
|
||||
framework="autogen",
|
||||
)
|
||||
print(praison_ai)
|
||||
return praison_ai.main()
|
||||
|
||||
if __name__ == "__main__":
|
||||
print(advanced())
|
||||
@@ -1,41 +0,0 @@
|
||||
framework: "crewai"
|
||||
topic: "Artificial Intelligence"
|
||||
|
||||
roles:
|
||||
narrative_designer:
|
||||
role: "Narrative Designer"
|
||||
goal: "Craft engaging storylines and narratives for {topic}"
|
||||
backstory: >
|
||||
You're a creative visionary skilled in weaving complex narratives and
|
||||
developing engaging story worlds within {topic}. Your expertise lies in
|
||||
conceptualizing the overarching story and ensuring it resonates with the audience.
|
||||
tasks:
|
||||
story_concept_development:
|
||||
description: >
|
||||
Develop a compelling story concept for {topic}, focusing on originality,
|
||||
thematic depth, and audience engagement. Outline the main narrative arcs,
|
||||
characters, and settings.
|
||||
expected_output: >
|
||||
A comprehensive document outlining the story concept, including key narrative
|
||||
arcs, character bios, and settings descriptions.
|
||||
|
||||
scriptwriter:
|
||||
role: "Scriptwriter"
|
||||
goal: "Transform story concepts into detailed scripts for {topic}"
|
||||
backstory: >
|
||||
With a flair for dialogue and a deep understanding of pacing and structure,
|
||||
you excel at bringing narratives to life through scriptwriting. Your work
|
||||
turns story concepts into blueprints for compelling {topic} experiences.
|
||||
tasks:
|
||||
scriptwriting_task:
|
||||
description: >
|
||||
Based on the narrative concept, write a detailed script for {topic}.
|
||||
Include dialogue, stage directions, and scene descriptions that bring
|
||||
the story to life.
|
||||
expected_output: >
|
||||
A detailed script ready for production, including dialogue, stage
|
||||
directions, and scene descriptions.
|
||||
|
||||
dependencies:
|
||||
- task: scriptwriting_task
|
||||
depends_on: story_concept_development
|
||||
@@ -1,12 +0,0 @@
|
||||
from praisonai import PraisonAI
|
||||
|
||||
def auto():
|
||||
praison_ai = PraisonAI(
|
||||
auto="Create a movie script about car in mars",
|
||||
framework="autogen"
|
||||
)
|
||||
print(praison_ai.framework)
|
||||
return praison_ai.main()
|
||||
|
||||
if __name__ == "__main__":
|
||||
print(auto())
|
||||
@@ -1,41 +0,0 @@
|
||||
framework: "autogen"
|
||||
topic: "Artificial Intelligence"
|
||||
|
||||
roles:
|
||||
narrative_designer:
|
||||
role: "Narrative Designer"
|
||||
goal: "Craft engaging storylines and narratives for {topic}"
|
||||
backstory: >
|
||||
You're a creative visionary skilled in weaving complex narratives and
|
||||
developing engaging story worlds within {topic}. Your expertise lies in
|
||||
conceptualizing the overarching story and ensuring it resonates with the audience.
|
||||
tasks:
|
||||
story_concept_development:
|
||||
description: >
|
||||
Develop a compelling story concept for {topic}, focusing on originality,
|
||||
thematic depth, and audience engagement. Outline the main narrative arcs,
|
||||
characters, and settings.
|
||||
expected_output: >
|
||||
A comprehensive document outlining the story concept, including key narrative
|
||||
arcs, character bios, and settings descriptions.
|
||||
|
||||
scriptwriter:
|
||||
role: "Scriptwriter"
|
||||
goal: "Transform story concepts into detailed scripts for {topic}"
|
||||
backstory: >
|
||||
With a flair for dialogue and a deep understanding of pacing and structure,
|
||||
you excel at bringing narratives to life through scriptwriting. Your work
|
||||
turns story concepts into blueprints for compelling {topic} experiences.
|
||||
tasks:
|
||||
scriptwriting_task:
|
||||
description: >
|
||||
Based on the narrative concept, write a detailed script for {topic}.
|
||||
Include dialogue, stage directions, and scene descriptions that bring
|
||||
the story to life.
|
||||
expected_output: >
|
||||
A detailed script ready for production, including dialogue, stage
|
||||
directions, and scene descriptions.
|
||||
|
||||
dependencies:
|
||||
- task: scriptwriting_task
|
||||
depends_on: story_concept_development
|
||||
@@ -1,8 +0,0 @@
|
||||
from praisonai import PraisonAI
|
||||
|
||||
def main():
|
||||
praison_ai = PraisonAI(agent_file="agents.yaml")
|
||||
return praison_ai.main()
|
||||
|
||||
if __name__ == "__main__":
|
||||
print(main())
|
||||
@@ -1,41 +0,0 @@
|
||||
framework: "crewai"
|
||||
topic: "Artificial Intelligence"
|
||||
|
||||
roles:
|
||||
narrative_designer:
|
||||
role: "Narrative Designer"
|
||||
goal: "Craft engaging storylines and narratives for {topic}"
|
||||
backstory: >
|
||||
You're a creative visionary skilled in weaving complex narratives and
|
||||
developing engaging story worlds within {topic}. Your expertise lies in
|
||||
conceptualizing the overarching story and ensuring it resonates with the audience.
|
||||
tasks:
|
||||
story_concept_development:
|
||||
description: >
|
||||
Develop a compelling story concept for {topic}, focusing on originality,
|
||||
thematic depth, and audience engagement. Outline the main narrative arcs,
|
||||
characters, and settings.
|
||||
expected_output: >
|
||||
A comprehensive document outlining the story concept, including key narrative
|
||||
arcs, character bios, and settings descriptions.
|
||||
|
||||
scriptwriter:
|
||||
role: "Scriptwriter"
|
||||
goal: "Transform story concepts into detailed scripts for {topic}"
|
||||
backstory: >
|
||||
With a flair for dialogue and a deep understanding of pacing and structure,
|
||||
you excel at bringing narratives to life through scriptwriting. Your work
|
||||
turns story concepts into blueprints for compelling {topic} experiences.
|
||||
tasks:
|
||||
scriptwriting_task:
|
||||
description: >
|
||||
Based on the narrative concept, write a detailed script for {topic}.
|
||||
Include dialogue, stage directions, and scene descriptions that bring
|
||||
the story to life.
|
||||
expected_output: >
|
||||
A detailed script ready for production, including dialogue, stage
|
||||
directions, and scene descriptions.
|
||||
|
||||
dependencies:
|
||||
- task: scriptwriting_task
|
||||
depends_on: story_concept_development
|
||||
@@ -1,21 +0,0 @@
|
||||
# tests/debug.py
|
||||
import tracemalloc
|
||||
import logging
|
||||
import warnings
|
||||
import sys
|
||||
import traceback
|
||||
|
||||
# Enable Tracemalloc
|
||||
tracemalloc.start()
|
||||
|
||||
# Configure Logging
|
||||
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(filename)s - %(funcName)s - %(lineno)d - %(message)s')
|
||||
|
||||
# Function to Log Warnings with Traceback
|
||||
def warn_with_traceback(message, category, filename, lineno, file=None, line=None):
|
||||
log = file if hasattr(file, 'write') else sys.stderr
|
||||
traceback.print_stack(file=log)
|
||||
log.write(warnings.formatwarning(message, category, filename, lineno, line))
|
||||
|
||||
# Override showwarning
|
||||
warnings.showwarning = warn_with_traceback
|
||||
@@ -1,42 +0,0 @@
|
||||
framework: crewai
|
||||
topic: create a movie about cat in mars based on https://blog.celtx.com/how-to-write-a-good-story/
|
||||
roles:
|
||||
story_developer:
|
||||
backstory: Experienced in crafting compelling narratives for a diverse audience.
|
||||
goal: Develop an engaging storyline for a movie about cats in Mars
|
||||
role: Story Developer
|
||||
tasks:
|
||||
storyline_creation:
|
||||
description: Create a captivating storyline for a movie about cats in Mars
|
||||
inspired by the blog post at https://blog.celtx.com/how-to-write-a-good-story/
|
||||
expected_output: Detailed storyline with key plot points, character motivations,
|
||||
and setting descriptions.
|
||||
tools:
|
||||
- ScrapeWebsiteTool
|
||||
screenwriter:
|
||||
backstory: Proficient in translating complex narratives into engaging screenplay
|
||||
format.
|
||||
goal: Transform the storyline into a script
|
||||
role: Screenwriter
|
||||
tasks:
|
||||
script_creation:
|
||||
description: Transform the developed storyline into a script for the movie
|
||||
about cats in Mars.
|
||||
expected_output: Complete script with dialogues, scenes, and stage directions
|
||||
ready for production.
|
||||
tools:
|
||||
- ''
|
||||
director:
|
||||
backstory: Experienced in visual storytelling and bringing scripts to life on
|
||||
screen.
|
||||
goal: Oversee the production of the movie
|
||||
role: Director
|
||||
tasks:
|
||||
movie_production:
|
||||
description: Take the script and lead the team to produce the movie 'Cats
|
||||
in Mars' based on the provided content and script.
|
||||
expected_output: Completed movie ready for distribution with compelling visuals
|
||||
and engaging storytelling.
|
||||
tools:
|
||||
- ''
|
||||
dependencies: []
|
||||
@@ -1,42 +0,0 @@
|
||||
framework: crewai
|
||||
topic: create a movie about cat in mars
|
||||
roles:
|
||||
story_developer:
|
||||
backstory: Experienced in crafting compelling narratives for a diverse audience.
|
||||
goal: Develop an engaging storyline for a movie about cats in Mars
|
||||
role: Story Developer
|
||||
tasks:
|
||||
storyline_creation:
|
||||
description: Create a captivating storyline for a movie about cats in Mars.
|
||||
Use Internet Search Tool to understand the latest story writing ideas.
|
||||
expected_output: Detailed storyline with key plot points, character motivations,
|
||||
and setting descriptions.
|
||||
tools:
|
||||
- internet_search_tool
|
||||
screenwriter:
|
||||
backstory: Proficient in translating complex narratives into engaging screenplay
|
||||
format.
|
||||
goal: Transform the storyline into a script
|
||||
role: Screenwriter
|
||||
tasks:
|
||||
script_creation:
|
||||
description: Transform the developed storyline into a script for the movie
|
||||
about cats in Mars.
|
||||
expected_output: Complete script with dialogues, scenes, and stage directions
|
||||
ready for production.
|
||||
tools:
|
||||
- ''
|
||||
director:
|
||||
backstory: Experienced in visual storytelling and bringing scripts to life on
|
||||
screen.
|
||||
goal: Oversee the production of the movie
|
||||
role: Director
|
||||
tasks:
|
||||
movie_production:
|
||||
description: Take the script and lead the team to produce the movie 'Cats
|
||||
in Mars' based on the provided content and script.
|
||||
expected_output: Completed movie ready for distribution with compelling visuals
|
||||
and engaging storytelling.
|
||||
tools:
|
||||
- ''
|
||||
dependencies: []
|
||||
@@ -1,44 +0,0 @@
|
||||
from typing import Annotated, Literal
|
||||
import os
|
||||
from autogen import ConversableAgent
|
||||
|
||||
Operator = Literal["+", "-", "*", "/"]
|
||||
|
||||
def calculator(a: int, b: int, operator: Annotated[Operator, "operator"]) -> int:
|
||||
if operator == "+":
|
||||
return a + b
|
||||
elif operator == "-":
|
||||
return a - b
|
||||
elif operator == "*":
|
||||
return a * b
|
||||
elif operator == "/":
|
||||
return int(a / b)
|
||||
else:
|
||||
raise ValueError("Invalid operator")
|
||||
|
||||
assistant = ConversableAgent(
|
||||
name="Assistant",
|
||||
system_message="You are a helpful AI assistant. "
|
||||
"You can help with simple calculations. "
|
||||
"Return 'TERMINATE' when the task is done.",
|
||||
llm_config={"config_list": [{"model": "gpt-3.5-turbo", "api_key": os.environ["OPENAI_API_KEY"]}]},
|
||||
)
|
||||
|
||||
user_proxy = ConversableAgent(
|
||||
name="User",
|
||||
llm_config=False,
|
||||
is_termination_msg=lambda msg: msg.get("content") is not None and "TERMINATE" in msg["content"],
|
||||
human_input_mode="NEVER",
|
||||
)
|
||||
|
||||
from autogen import register_function
|
||||
# Register the calculator function to the agent and user proxy to add a calculator tool.
|
||||
register_function(
|
||||
calculator,
|
||||
caller=assistant, # The assistant agent can suggest calls to the calculator.
|
||||
executor=user_proxy, # The user proxy agent can execute the calculator calls.
|
||||
name="calculator", # By default, the function name is used as the tool name.
|
||||
description="A simple calculator", # A description of the tool.
|
||||
)
|
||||
|
||||
chat_result = user_proxy.initiate_chat(assistant, message="What is (44232 + 13312 / (232 - 32)) * 5?")
|
||||
@@ -1,99 +0,0 @@
|
||||
import math
|
||||
import os
|
||||
from typing import Optional, Type
|
||||
|
||||
# Starndard Langchain example
|
||||
from langchain.agents import create_spark_sql_agent
|
||||
from langchain.agents.agent_toolkits import SparkSQLToolkit
|
||||
from langchain.chat_models import ChatOpenAI
|
||||
|
||||
# Import things that are needed generically
|
||||
from langchain.pydantic_v1 import BaseModel, Field
|
||||
from langchain.tools import BaseTool
|
||||
from langchain.tools.file_management.read import ReadFileTool
|
||||
from langchain.utilities.spark_sql import SparkSQL
|
||||
|
||||
import autogen
|
||||
|
||||
class CircumferenceToolInput(BaseModel):
|
||||
radius: float = Field()
|
||||
|
||||
|
||||
class CircumferenceTool(BaseTool):
|
||||
name = "circumference_calculator"
|
||||
description = "Use this tool when you need to calculate a circumference using the radius of a circle"
|
||||
args_schema: Type[BaseModel] = CircumferenceToolInput
|
||||
|
||||
def _run(self, radius: float):
|
||||
return float(radius) * 2.0 * math.pi
|
||||
|
||||
|
||||
def get_file_path_of_example():
|
||||
current_dir = os.path.dirname(__file__)
|
||||
file_path = os.path.join(current_dir, "radius.txt")
|
||||
return file_path
|
||||
|
||||
# Define a function to generate llm_config from a LangChain tool
|
||||
def generate_llm_config(tool):
|
||||
# Define the function schema based on the tool's args_schema
|
||||
function_schema = {
|
||||
"name": tool.name.lower().replace(" ", "_"),
|
||||
"description": tool.description,
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {},
|
||||
"required": [],
|
||||
},
|
||||
}
|
||||
|
||||
if tool.args is not None:
|
||||
function_schema["parameters"]["properties"] = tool.args
|
||||
|
||||
return function_schema
|
||||
|
||||
|
||||
# Instantiate the ReadFileTool
|
||||
read_file_tool = ReadFileTool()
|
||||
custom_tool = CircumferenceTool()
|
||||
|
||||
# Construct the llm_config
|
||||
llm_config = {
|
||||
# Generate functions config for the Tool
|
||||
"functions": [
|
||||
generate_llm_config(custom_tool),
|
||||
generate_llm_config(read_file_tool),
|
||||
],
|
||||
"config_list": [{"model": "gpt-3.5-turbo", "api_key": os.environ["OPENAI_API_KEY"]}],
|
||||
"timeout": 120,
|
||||
}
|
||||
|
||||
user_proxy = autogen.UserProxyAgent(
|
||||
name="user_proxy",
|
||||
is_termination_msg=lambda x: x.get("content", "") and x.get("content", "").rstrip().endswith("TERMINATE"),
|
||||
human_input_mode="NEVER",
|
||||
max_consecutive_auto_reply=10,
|
||||
code_execution_config={
|
||||
"work_dir": "coding",
|
||||
"use_docker": False,
|
||||
}, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.
|
||||
)
|
||||
|
||||
# Register the tool and start the conversation
|
||||
user_proxy.register_function(
|
||||
function_map={
|
||||
custom_tool.name: custom_tool._run,
|
||||
read_file_tool.name: read_file_tool._run,
|
||||
}
|
||||
)
|
||||
|
||||
chatbot = autogen.AssistantAgent(
|
||||
name="chatbot",
|
||||
system_message="For coding tasks, only use the functions you have been provided with. Reply TERMINATE when the task is done.",
|
||||
llm_config=llm_config,
|
||||
)
|
||||
|
||||
user_proxy.initiate_chat(
|
||||
chatbot,
|
||||
message=f"Read the file with the path {get_file_path_of_example()}, then calculate the circumference of a circle that has a radius of that files contents.", # 7.81mm in the file
|
||||
llm_config=llm_config,
|
||||
)
|
||||
@@ -1,71 +0,0 @@
|
||||
import os
|
||||
from crewai import Agent, Task, Crew
|
||||
from duckduckgo_search import DDGS
|
||||
from langchain.tools import tool
|
||||
|
||||
@tool("Internet Search Tool")
|
||||
def internet_search_tool(query: str) -> list:
|
||||
"""Search Internet for relevant information based on a query."""
|
||||
ddgs = DDGS()
|
||||
results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
|
||||
return results
|
||||
|
||||
# Define agents
|
||||
researcher = Agent(
|
||||
role='Researcher',
|
||||
goal='Gather and analyze information on specific topics',
|
||||
verbose=True,
|
||||
backstory=(
|
||||
"As a seasoned researcher, you have a keen eye for detail and a "
|
||||
"deep understanding of your field. You're adept at sifting through "
|
||||
"information to find the most relevant and accurate data."
|
||||
),
|
||||
tools=[internet_search_tool],
|
||||
allow_delegation=True
|
||||
)
|
||||
|
||||
writer = Agent(
|
||||
role='Writer',
|
||||
goal='Compose informative and engaging articles based on research findings',
|
||||
verbose=True,
|
||||
backstory=(
|
||||
"With a talent for storytelling, you translate complex ideas into "
|
||||
"clear, compelling narratives. Your articles are well-researched, "
|
||||
"thought-provoking, and accessible to a broad audience."
|
||||
),
|
||||
tools=[internet_search_tool],
|
||||
allow_delegation=False
|
||||
)
|
||||
|
||||
# Define tasks
|
||||
research_task = Task(
|
||||
description=(
|
||||
"Investigate the latest trends in renewable energy technologies. "
|
||||
"Identify key advancements, challenges, and potential impacts on "
|
||||
"global energy policies."
|
||||
),
|
||||
expected_output='A detailed report summarizing the findings.',
|
||||
tools=[internet_search_tool],
|
||||
agent=researcher,
|
||||
)
|
||||
|
||||
writing_task = Task(
|
||||
description=(
|
||||
"Based on the research findings, write an article that highlights "
|
||||
"the importance of renewable energy innovations. The article should "
|
||||
"educate the public on how these technologies can contribute to "
|
||||
"sustainable development."
|
||||
),
|
||||
expected_output='An engaging and informative article suitable for publication.',
|
||||
tools=[internet_search_tool],
|
||||
agent=writer,
|
||||
)
|
||||
|
||||
# Create and kick off the crew
|
||||
crew = Crew(
|
||||
agents=[researcher, writer],
|
||||
tasks=[research_task, writing_task]
|
||||
)
|
||||
|
||||
result = crew.kickoff(inputs={'topic': 'renewable energy'})
|
||||
print(result)
|
||||
@@ -1 +0,0 @@
|
||||
7.81mm
|
||||
@@ -1,64 +0,0 @@
|
||||
# tests/test.py
|
||||
import unittest
|
||||
import subprocess
|
||||
from praisonai.cli import PraisonAI
|
||||
from .advanced_example import advanced
|
||||
from .basic_example import main
|
||||
from .auto_example import auto
|
||||
from xmlrunner import XMLTestRunner
|
||||
|
||||
class TestPraisonAIFramework(unittest.TestCase):
|
||||
def test_main_with_autogen_framework(self):
|
||||
praison_ai = PraisonAI(agent_file='tests/autogen-agents.yaml')
|
||||
result = praison_ai.main()
|
||||
self.assertIn('### Task Output ###', result)
|
||||
|
||||
def test_main_with_custom_framework(self):
|
||||
praison_ai = PraisonAI(agent_file='tests/crewai-agents.yaml')
|
||||
result = praison_ai.main()
|
||||
self.assertIn('### Task Output ###', result)
|
||||
|
||||
def test_main_with_internet_search_tool(self):
|
||||
praison_ai = PraisonAI(agent_file='tests/search-tool-agents.yaml')
|
||||
result = praison_ai.main()
|
||||
self.assertIn('### Task Output ###', result)
|
||||
|
||||
def test_main_with_built_in_tool(self):
|
||||
praison_ai = PraisonAI(agent_file='tests/inbuilt-tool-agents.yaml')
|
||||
result = praison_ai.main()
|
||||
self.assertIn('### Task Output ###', result)
|
||||
|
||||
class TestPraisonAICommandLine(unittest.TestCase):
|
||||
def run_command(self, command):
|
||||
result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True, text=True)
|
||||
return result.stdout
|
||||
|
||||
def test_praisonai_command(self):
|
||||
command = "praisonai --framework autogen --auto create movie script about cat in mars"
|
||||
result = self.run_command(command)
|
||||
self.assertIn('TERMINATE', result)
|
||||
|
||||
def test_praisonai_init_command(self):
|
||||
command = "praisonai --framework autogen --init create movie script about cat in mars"
|
||||
result = self.run_command(command)
|
||||
self.assertIn('created successfully', result)
|
||||
|
||||
class TestExamples(unittest.TestCase):
|
||||
def test_basic_example(self):
|
||||
# Assuming main() has been adjusted to return a value for assertion
|
||||
result = main()
|
||||
self.assertIsNotNone(result) # Adjust this assertion based on the expected outcome of main()
|
||||
|
||||
def test_advanced_example(self):
|
||||
# Assuming advanced() returns a value suitable for assertion
|
||||
result = advanced()
|
||||
self.assertIsNotNone(result) # Adjust this assertion as needed
|
||||
|
||||
def test_auto_example(self):
|
||||
# Assuming auto() returns a value that can be asserted
|
||||
result = auto()
|
||||
self.assertIsNotNone(result) # Adjust this assertion according to what auto() is expected to do
|
||||
|
||||
if __name__ == '__main__':
|
||||
with open('unittest_report.xml', 'wb') as output:
|
||||
unittest.main(testRunner=XMLTestRunner(output=output))
|
||||
Loaded 100 of 106 files, more files were not shown because too many files have changed in this diff.
Show more
Reference in new issue
Block a user