├── .github └── workflows │ └── deploy.yml ├── .gitignore ├── .readthedocs.yaml ├── LICENSE ├── README.md ├── docs ├── api_reference │ ├── api_reference.md │ ├── api_reference2.md │ └── api_reference_.md ├── getting_started │ ├── installation.md │ └── quickstart.md ├── index.md ├── requirements.txt ├── tutorials │ └── tutorial.md └── user_guide │ ├── execution.md │ ├── functions_and_schemas.md │ ├── integration_with_composio_and_langchain.md │ ├── lists_and_iterables.md │ ├── nesting_workflows.md │ ├── user_guide_1.md │ ├── user_guide_2.md │ ├── visualization.md │ └── workflows.md ├── examples ├── agentic_workflows_from_prompts.ipynb ├── gensphere_tutorial.ipynb ├── product_hunt_analyzer.yaml ├── product_hunt_functions.py ├── product_hunt_schemas.py ├── simple_examples.yaml ├── simple_examples_functions.py └── startup_idea_evaluator.yaml ├── gensphere ├── __init__.py ├── genflow.py ├── graph_builder.py ├── hub.py ├── utils.py ├── visualizer.py └── yaml_utils.py ├── gensphere_tutorial.ipynb ├── img ├── gensphere_gif.gif ├── gensphere_workflow_example.png └── globe_with_meridians.png ├── mkdocs.yml ├── pyproject.toml ├── setup.py └── site ├── 404.html ├── api_reference ├── api_reference │ └── index.html ├── api_reference2 │ └── index.html └── api_reference_ │ └── index.html ├── assets ├── images │ └── favicon.png ├── javascripts │ ├── bundle.83f73b43.min.js │ ├── bundle.83f73b43.min.js.map │ ├── lunr │ │ ├── min │ │ │ ├── lunr.ar.min.js │ │ │ ├── lunr.da.min.js │ │ │ ├── lunr.de.min.js │ │ │ ├── lunr.du.min.js │ │ │ ├── lunr.el.min.js │ │ │ ├── lunr.es.min.js │ │ │ ├── lunr.fi.min.js │ │ │ ├── lunr.fr.min.js │ │ │ ├── lunr.he.min.js │ │ │ ├── lunr.hi.min.js │ │ │ ├── lunr.hu.min.js │ │ │ ├── lunr.hy.min.js │ │ │ ├── lunr.it.min.js │ │ │ ├── lunr.ja.min.js │ │ │ ├── lunr.jp.min.js │ │ │ ├── lunr.kn.min.js │ │ │ ├── lunr.ko.min.js │ │ │ ├── lunr.multi.min.js │ │ │ ├── lunr.nl.min.js │ │ │ ├── lunr.no.min.js │ │ │ ├── lunr.pt.min.js │ │ │ ├── lunr.ro.min.js │ │ │ ├── lunr.ru.min.js │ │ │ ├── lunr.sa.min.js │ │ │ ├── lunr.stemmer.support.min.js │ │ │ ├── lunr.sv.min.js │ │ │ ├── lunr.ta.min.js │ │ │ ├── lunr.te.min.js │ │ │ ├── lunr.th.min.js │ │ │ ├── lunr.tr.min.js │ │ │ ├── lunr.vi.min.js │ │ │ └── lunr.zh.min.js │ │ ├── tinyseg.js │ │ └── wordcut.js │ └── workers │ │ ├── search.6ce7567c.min.js │ │ └── search.6ce7567c.min.js.map └── stylesheets │ ├── main.0253249f.min.css │ ├── main.0253249f.min.css.map │ ├── palette.06af60db.min.css │ └── palette.06af60db.min.css.map ├── getting_started ├── installation │ └── index.html └── quickstart │ └── index.html ├── index.html ├── search └── search_index.json ├── sitemap.xml ├── sitemap.xml.gz ├── tutorials └── tutorial │ └── index.html └── user_guide ├── execution └── index.html ├── functions_and_schemas └── index.html ├── integration_with_composio_and_langchain └── index.html ├── lists_and_iterables └── index.html ├── nesting_workflows └── index.html ├── user_guide_1 └── index.html ├── user_guide_2 └── index.html ├── visualization └── index.html └── workflows └── index.html /.github/workflows/deploy.yml: -------------------------------------------------------------------------------- 1 | name: Deploy MkDocs site to GitHub Pages 2 | 3 | on: 4 | push: 5 | branches: 6 | - main # Change to your default branch if different 7 | 8 | jobs: 9 | build-deploy: 10 | runs-on: ubuntu-latest 11 | steps: 12 | - name: Check out repository 13 | uses: actions/checkout@v2 14 | 15 | - name: Set up Python 16 | uses: actions/setup-python@v2 17 | with: 18 | python-version: '3.x' 19 | 20 | - name: Install dependencies 21 | run: | 22 | pip install mkdocs mkdocs-material 23 | 24 | - name: Deploy to GitHub Pages 25 | run: | 26 | mkdocs gh-deploy --force 27 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | 27 | # Poetry specific files 28 | poetry.lock 29 | *.whl 30 | 31 | # PyInstaller 32 | # Usually these files are written by a python script from a template 33 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 34 | *.manifest 35 | *.spec 36 | 37 | # Installer logs 38 | pip-log.txt 39 | pip-delete-this-directory.txt 40 | 41 | # Unit test / coverage reports 42 | htmlcov/ 43 | .tox/ 44 | .nox/ 45 | .coverage 46 | .coverage.* 47 | .cache 48 | nosetests.xml 49 | coverage.xml 50 | *.cover 51 | *.py,cover 52 | .hypothesis/ 53 | .pytest_cache/ 54 | .cache/ 55 | test-reports/ 56 | 57 | # Jupyter Notebook 58 | .ipynb_checkpoints 59 | 60 | # pyenv 61 | .python-version 62 | 63 | # VSCode settings and extensions 64 | .vscode/ 65 | 66 | # macOS files 67 | .DS_Store 68 | *.dmg 69 | .AppleDouble 70 | .LSOverride 71 | 72 | # Windows files 73 | Thumbs.db 74 | ehthumbs.db 75 | Desktop.ini 76 | $RECYCLE.BIN/ 77 | 78 | # IDEs and editors 79 | .idea/ 80 | *.sublime-project 81 | *.sublime-workspace 82 | *.vscode/ 83 | *.swp 84 | *.swo 85 | *.bak 86 | *.tmp 87 | 88 | # FastAPI/Uvicorn log files 89 | *.log 90 | 91 | # Docker 92 | docker-compose.yml 93 | Dockerfile 94 | 95 | # Ignore virtual environments 96 | venv/ 97 | .env/ 98 | *.env 99 | .venv/ 100 | 101 | 102 | # Ignore mypy cache 103 | .mypy_cache/ 104 | .dmypy.json 105 | dmypy.json 106 | 107 | *db 108 | 109 | **/work_dir/ 110 | -------------------------------------------------------------------------------- /.readthedocs.yaml: -------------------------------------------------------------------------------- 1 | # .readthedocs.yaml 2 | 3 | version: 2 4 | 5 | build: 6 | os: ubuntu-22.04 7 | tools: 8 | python: "3.10" 9 | 10 | mkdocs: 11 | configuration: mkdocs.yml 12 | 13 | python: 14 | install: 15 | - requirements: docs/requirements.txt 16 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Copyright 2024 GenSphere 2 | 3 | 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: 4 | 5 | The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 6 | 7 | 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. 8 | 9 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 🌐 GenSphere 2 | [![Documentation Status](https://readthedocs.org/projects/gensphere/badge/?version=latest)](https://.readthedocs.io/en/latest/?badge=latest) 3 | [![PyPI version](https://badge.fury.io/py/gensphere.svg)](https://badge.fury.io/py/gensphere) 4 | 5 | Check the [medium article here](https://medium.com/p/182fd2a70e3e). GenSphere is a **declarative framework** to build LLM applications and an open platform to push and pull them. 6 | 7 | --- 8 | 9 | 10 | 11 | ![gensphere_workflow_example_gif](img/gensphere_gif.gif) 12 | 13 | ## What is GenSphere? 14 | 15 | GenSphere is framework to build LLM applications by declaring tasks and how they connect on YAML files. That unlocks a ton of features, including: 16 | 17 | ### 1. Agents from prompts: 18 | 19 | Because the YAML files are an economical description of workflow logic, which abstract away the execution code, one can use LLMs themselves to generate those files from high-level prompts. The LLM will spend their output tokens and brainpower fully on implementing the workflow logic instead of writing code. This opens the possibility of building full-fledged text-to-agents workflows. [Check this colab notebook](https://github.com/octopus2023-inc/gensphere/blob/main/examples/agentic_workflows_from_prompts.ipynb) 20 | 21 | ### 2. Low-level control 22 | 23 | We break any LLM application down to graphs where each node is either a function call, 24 | an LLM API call or another graph itself. By doing so, you can inspect (and edit) any 25 | application down to its core components. **We want to break the cumbersome pile of 26 | unnecessary abstractions** that some modern frameworks bring, and which have turned LLM application building 27 | quite opaque. 28 | 29 | ### 3. Portability 30 | 31 | Projects are defined by a set of **YAML files and associated python functions and schemas**. 32 | This make it easy to share your application with other developers, and makes it easy 33 | for them to use what you built in more complex workflows. 34 | 35 | ### 4. Community collaboration 36 | 37 | Once you've broken your application down to its core components as described above, you 38 | can **push it to an open platform (no registration required)**. A public ID is generated, 39 | and your project is now publicly accessible to anyone. You can pull any project by 40 | referencing its ID. 41 | 42 | You can also check popularity of published projects, as measured by number of pulls they get. 43 | 44 | ### 5. Composability 45 | 46 | Because you can reference graphs as nodes on parents graphs, GenSphere makes it extremely easy to 47 | **compose complex applications from simpler reusable components**. You can pull projects from the 48 | platform and build with them instead of having to work from scratch. 49 | 50 | --- 51 | 52 | ## Table of Contents 53 | 54 | - [Features](#features) 55 | - [How does it work?](#how-does-it-work) 56 | - [Installation](#installation) 57 | - [Quickstart Guide](#quickstart-guide) 58 | - [Detailed documentation](detailed-documentation) 59 | - [Examples](#examples) 60 | - [How to build agentic systems with GenSphere](#How-to-build-agentic-systems-with-GenSphere) 61 | - [Notes about this version](#notes-about-this-version) 62 | - [Contributing](#contributing) 63 | - [License](#license) 64 | 65 | --- 66 | 67 | ## Features 68 | 69 | - **Define workflows with simple YAML files**: Create complex execution graphs using simple YAML files. 70 | - **Gain low-level control**: GenSphere projects break workflows down to individual function calls and/or AI API calls. By doing so, you know exactly what's going on whenever running your application. 71 | - **Nest LLM applications easily**: You can reference other YAML files as nodes in your workflow, and compose complex systems easily, while maintaining control. 72 | - **Push and pull projects to the open community hub**: Collaborate with others by publishing and pulling projects from the platform (no registration required). 73 | - **Track popularity of your projects**: Check the popularity of your projects as measured by the number of times they were used by others. 74 | - **Visualize workflows**: explore your projects easily with interactive graphical visualization. You can quickly see which functions are attached to which nodes and have complete control over your workflows. 75 | 76 | --- 77 | 78 | 79 | ## How does it work? 80 | 81 | - **1. Define your workflow with yaml files**: Your project is defined by 3 files: 82 | 83 | a) yaml file which represents a graph, where each node is a task to be executed, and which can receive inputs from other nodes. Every node is either a 84 | python function call (see below), an AI API call or another yaml file itself. 85 | 86 | b) a functions.py file, which contains python functions to be executed in the yaml nodes, or to be used as tools for LLMs 87 | 88 | c) a schemas.py file which contains pydantic models (that you can use for structured output schema from openAI) 89 | 90 | - **2. Compose complex workflows by nesting graphs**: A node can also be another graph itself, and the SDK will 91 | take care to resolve dependencies, check consistency and compose a final execution graph. 92 | 93 | - **3. Define your functions and schemas**: Create a .py file with all functions you are planning to use 94 | in your project, be it as individual nodes in the execution graph or as [tools for function calling by LLMs.](https://platform.openai.com/docs/guides/function-calling) 95 | You can also create a separate .py file with pydantic models if you want to use openAI [structured output feature in any given node.](https://platform.openai.com/docs/guides/structured-outputs). 96 | If you are new to pydantic and openAI structured outputs, check this [short tutorial.](https://www.datacamp.com/tutorial/open-ai-structured-outputs) 97 | 98 | - **4. Leverage integration with LangChain and Composio**: when doing function calling, you can leverage tools 99 | available in both [Composio](https://composio.dev/) and [Langchain](https://python.langchain.com/v0.1/docs/modules/tools/). 100 | Check the implementation notebook for further details. 101 | 102 | - **5. Visualize your project**: GenSphere creates an interactive graphical representation of your project. The SDK 103 | renders a graph, and you can inspect every node to see which functions or schemas are associated with it, as well 104 | as its parameters and outputs. 105 | 106 | - **6. Execute the workflow**: the GenSphere SDK resolves dependencies and executes your workflow. 107 | 108 | - **7. Push to the platform**: once ready, your project can be pushed to our open platform. You submit you yaml file 109 | and any associated functions or schemas .py files. An ID is generated, and your project becomes publicly accessible. 110 | 111 | - **8. Pull from the platform**: you can pull yaml files, functions and schemas from the platform by providing its ID 112 | (generated by the creator of the project in the step above). Its then easy to use these components to build more 113 | complex LLM applications. 114 | 115 | - **9. Watch your projects grow**: you can check popularity of your projects by measuring the number of pulls it received. 116 | 117 | --- 118 | 119 | 120 | ## Installation 121 | 122 | ```bash 123 | pip install gensphere 124 | ``` 125 | --- 126 | 127 | ## Quickstart guide 128 | 129 | Import necessary modules 130 | 131 | ``` 132 | from gensphere.genflow import GenFlow 133 | from gensphere.hub import Hub 134 | import os 135 | ``` 136 | Set your OpenAI API key. 137 | 138 | ``` 139 | os.environ['OPENAI_API_KEY']="PLACE_YOUR_OPENAI_API_KEY" 140 | ``` 141 | 142 | Pull a project from the platform 143 | 144 | ``` 145 | hub=Hub() 146 | hub.pull(push_id='2c03079c-0e33-489e-bbbe-777da744d56f', 147 | yaml_filename='simple_examples.yaml', 148 | functions_filename='simple_examples_functions.py', 149 | save_to_disk=True) 150 | ``` 151 | 152 | This saves simple_examples.yaml and simple_examples_functions.py to your current working diretory. Now you can execute the flow with. 153 | 154 | ``` 155 | flow=GenFlow('simple_examples.yaml','simple_examples_functions.py') 156 | flow.parse_yaml() 157 | flow.run() 158 | ``` 159 | 160 | You can access nodes outputs with 161 | 162 | ``` 163 | flow.outputs 164 | ``` 165 | 166 | ### Go over our 5-min tutorial notebook 167 | This [notebook](https://github.com/octopus2023-inc/gensphere/blob/main/gensphere_tutorial.ipynb) contains everything you need to know about GenSphere, including yaml syntax, visualizastion, yaml parsing, nesting workflows, etc. 168 | 169 | --- 170 | ## Detailed documentation 171 | 172 | Check our [detailed documentation here](https://gensphere.readthedocs.io/en/latest/) 173 | 174 | --- 175 | ## Examples 176 | 177 | [coming soon] 178 | 179 | --- 180 | ## How to build agentic systems with GenSphere 181 | 182 | [coming soon] 183 | 184 | --- 185 | 186 | ## Notes about this version 187 | 188 | - In this alpha version, only openAI is supported for AI API calls. We are planning to 189 | integrate with other major providers. 190 | 191 | - Graphs in yaml files need to be [DAGs](https://en.wikipedia.org/wiki/Directed_acyclic_graph). We are 192 | planning to allow for cycles, which will unlock full agentic system building using only GenSphere. 193 | 194 | - Feedback is welcome! Please [reach out by email](mailto:octopus2023.contact@gmail.com) or join our 195 | [discord server](https://discord.gg/qvgEaR3E). 196 | 197 | --- 198 | ## Contributing 199 | 200 | We welcome contributions! Please [reach out by email](mailto:octopus2023.contact@gmail.com) and join our [Discord server](https://discord.gg/qvgEaR3E). 201 | 202 | --- 203 | ## Feedback 204 | Leave your [anonymous feedback here](https://docs.google.com/forms/d/e/1FAIpQLSeGjy0YAlh2-qdgIUplern67rCt-nC59JeJi6NkRtt7DkCVTQ/viewform?pli=1) 205 | 206 | --- 207 | ## License 208 | 209 | This project is licensed under the MIT License - see the LICENSE file for details. 210 | 211 | --- 212 | 213 | ## Star History 214 | 215 | [![Star History Chart](https://api.star-history.com/svg?repos=octopus2023-inc/gensphere&type=Date)](https://star-history.com/#octopus2023-inc/gensphere&Date) 216 | -------------------------------------------------------------------------------- /docs/getting_started/installation.md: -------------------------------------------------------------------------------- 1 | # Installation 2 | 3 | Welcome to the GenSphere documentation! This section will guide you through the installation process of GenSphere and its necessary dependencies. 4 | 5 | ## Prerequisites 6 | 7 | Before installing GenSphere, ensure that your system meets the following prerequisites: 8 | 9 | ### 1. Python 10 | 11 | - **Version**: Python 3.10 or higher is required. 12 | - **Installation**: 13 | - **Windows**: Download and install Python from the [official website](https://www.python.org/downloads/windows/). 14 | - **macOS**: Python is pre-installed on macOS. However, it's recommended to install the latest version using [Homebrew](https://brew.sh/): 15 | ```bash 16 | brew install python 17 | ``` 18 | - **Linux**: Use your distribution's package manager. For example, on Ubuntu: 19 | ```bash 20 | sudo apt update 21 | sudo apt install python3 python3-pip 22 | ``` 23 | 24 | ### 2. API Keys 25 | 26 | GenSphere integrates with various external services that require API keys. Ensure you have the following: 27 | 28 | - **OpenAI API Key**: Sign up and obtain an API key from [OpenAI](https://platform.openai.com/account/api-keys). 29 | - **Composio API Key** (optional): If you plan to use Composio tools, obtain an API key from [Composio](https://composio.dev/). Follow the Composio documentation to understand how to add services to your account. 30 | 31 | ## Installation Steps 32 | 33 | Follow these steps to install GenSphere and its dependencies: 34 | 35 | ### 1. Create a Virtual Environment (Recommended) 36 | 37 | It's recommended to create a virtual environment to manage your project's dependencies. Open a terminal command window and type: 38 | 39 | ```bash 40 | # Create a virtual environment named 'venv' 41 | python3 -m venv venv 42 | 43 | # Activate the virtual environment 44 | # On Windows 45 | venv\Scripts\activate 46 | 47 | # On macOS and Linux 48 | source venv/bin/activate 49 | ``` 50 | 51 | ### 2. Upgrade pip 52 | 53 | Ensure that you have the latest version of `pip`. 54 | 55 | ```bash 56 | pip install --upgrade pip 57 | ``` 58 | 59 | ### 3. Install GenSphere 60 | 61 | Install GenSphere using `pip`. 62 | 63 | ```bash 64 | pip install gensphere 65 | ``` 66 | 67 | **Note**: If you encounter permission issues, you may need to use `pip` with `--user` or consider using a virtual environment as shown above. 68 | 69 | ### 4. Verify the Installation 70 | 71 | To verify that GenSphere has been installed correctly, you can perform a simple test by importing GenSphere in Python. 72 | 73 | ```python 74 | python -c "import gensphere; print('GenSphere installed successfully!')" 75 | ``` 76 | 77 | If the installation was successful, you should see the following output: 78 | 79 | ``` 80 | GenSphere installed successfully! 81 | ``` 82 | 83 | ## Additional Dependencies 84 | 85 | Depending on your project's requirements, you might need to install additional dependencies. 86 | 87 | ## Troubleshooting 88 | 89 | If you encounter any issues during installation, consider the following steps: 90 | 91 | 1. **Check Python Version**: Ensure you're using Python 3.10 or higher. 92 | 2. **Upgrade pip**: Make sure `pip` is up to date. 93 | 3. **Virtual Environment**: Use a virtual environment to avoid dependency conflicts. 94 | 4. **Contact Support**: If you're still having trouble,join our [Discord server](https://discord.gg/DZFWMXJv). 95 | 96 | --- 97 | 98 | Proceed to the [Quickstart Guide](quickstart.md) to begin creating and running your first GenSphere workflow! -------------------------------------------------------------------------------- /docs/getting_started/quickstart.md: -------------------------------------------------------------------------------- 1 | # Quickstart Guide 2 | 3 | Welcome to the GenSphere Quickstart Guide! This guide will walk you through creating and executing your first GenSphere workflow. By the end of this guide, you will have a functional workflow that interacts with the GenSphere platform. 4 | 5 | ## Table of Contents 6 | 7 | 1. [Setting Up Environment Variables](#setting-up-environment-variables) 8 | 2. [Importing GenSphere Modules](#importing-gensphere-modules) 9 | 3. [Running Your First Flow](#running-your-first-flow) 10 | - [Pulling a Project from the Platform](#pulling-a-project-from-the-platform) 11 | - [Executing the Flow](#executing-the-flow) 12 | - [Accessing Outputs](#accessing-outputs) 13 | 4. [Next Steps](#next-steps) 14 | 15 | ## Setting Up Environment Variables 16 | 17 | Before you can run GenSphere workflows, you need to set up the necessary environment variables, including your API keys. 18 | 19 | ### 1. Obtain API Keys 20 | 21 | Ensure you have the following API keys: 22 | 23 | - **OpenAI API Key**: [Obtain here](https://platform.openai.com/account/api-keys). 24 | - **Composio API Key** (optional): [Obtain here](https://composio.dev/). 25 | 26 | 27 | ### 2. Define Environment Variables 28 | 29 | Set your environment variables in your terminal or include them in a `.env` file. Using a `.env` file is recommended for convenience and security. 30 | 31 | #### Using a `.env` File 32 | 33 | Create a file named `.env` in your project directory and add the following lines: 34 | 35 | ```env 36 | OPENAI_API_KEY="YOUR_OPENAI_API_KEY" 37 | ``` 38 | Substitute "YOUR_API_KEY" by your actual api key. 39 | 40 | #### Loading Environment Variables 41 | 42 | Ensure that your Python environment loads the `.env` file. GenSphere uses the `python-dotenv` package to load environment variables from a `.env` file automatically. 43 | 44 | If you haven't installed `python-dotenv`, install it using `pip`: 45 | 46 | ```bash 47 | pip install python-dotenv 48 | ``` 49 | 50 | Alternatively, you can manually set environment variables in your terminal session: 51 | 52 | ```bash 53 | # On Windows 54 | set OPENAI_API_KEY=YOUR_OPENAI_API_KEY 55 | 56 | # On macOS and Linux 57 | export OPENAI_API_KEY=YOUR_OPENAI_API_KEY 58 | ``` 59 | 60 | ## Importing GenSphere Modules 61 | 62 | Begin by importing the necessary GenSphere modules in your Python script or Jupyter Notebook. 63 | 64 | ```python 65 | import logging 66 | import traceback 67 | 68 | 69 | # Set up logging configuration before importing other modules 70 | logging.basicConfig( 71 | level=logging.DEBUG, 72 | format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', 73 | handlers=[ 74 | logging.FileHandler("app.log", mode='w'), 75 | logging.StreamHandler() 76 | ] 77 | ) 78 | 79 | from gensphere import genflow, yaml_utils 80 | from gensphere.genflow import GenFlow 81 | from gensphere.yaml_utils import YamlCompose 82 | from gensphere.visualizer import Visualizer 83 | from gensphere.hub import Hub 84 | ``` 85 | 86 | ### Explanation of Imports 87 | 88 | - **logging & traceback**: For logging and debugging. 89 | - **gensphere modules**: Core classes and utilities for defining and running workflows. 90 | 91 | ## Running Your First Flow 92 | 93 | This section guides you through pulling a pre-built workflow from the GenSphere platform, executing it, and accessing its outputs. 94 | 95 | ### Pulling a Project from the Platform 96 | 97 | GenSphere allows you to pull pre-built workflows from its open platform using a unique `push_id`. This enables you to quickly start with existing workflows. 98 | 99 | ```python 100 | # Initialize the Hub 101 | hub = Hub() 102 | 103 | # Define the push_id and filenames to save the pulled files 104 | push_id = '2c03079c-0e33-489e-bbbe-777da744d56f' 105 | yaml_filename = 'simple_examples.yaml' #this will be the path of your YAML file after downloading it 106 | functions_filename = 'simple_examples_functions.py' #this will be the path of your functions file after downloading it 107 | 108 | # Pull the project from the platform 109 | hub.pull( 110 | push_id=push_id, 111 | yaml_filename=yaml_filename, 112 | functions_filename=functions_filename, 113 | save_to_disk=True 114 | ) 115 | ``` 116 | 117 | **Explanation:** 118 | 119 | - **Hub**: The `Hub` class manages interactions with the GenSphere platform. 120 | - **push_id**: Unique identifier for the project you want to pull. 121 | - **yaml_filename & functions_filename**: Names of the files to save locally. 122 | 123 | **Result:** 124 | 125 | This command downloads the specified YAML workflow and associated Python functions, saving them to your current working directory. 126 | 127 | ### Executing the Flow 128 | 129 | Once you've pulled the workflow files, you can execute the workflow using the `GenFlow` class. 130 | 131 | ```python 132 | # Initialize GenFlow with the pulled YAML and functions files 133 | flow = GenFlow('simple_examples.yaml', 'simple_examples_functions.py') 134 | 135 | # Parse the YAML file to construct the execution graph 136 | flow.parse_yaml() 137 | 138 | # Run the workflow 139 | flow.run() 140 | ``` 141 | 142 | **Explanation:** 143 | 144 | - **GenFlow**: Core class responsible for parsing, building, and executing workflows. 145 | - **parse_yaml()**: Parses the YAML file and constructs the execution graph. 146 | - **run()**: Executes the workflow based on the constructed graph. 147 | 148 | ### Accessing Outputs 149 | 150 | After running the workflow, you can access the outputs generated by each node. 151 | 152 | ```python 153 | # Access the outputs of the workflow 154 | outputs = flow.outputs 155 | 156 | # Print the outputs 157 | for node_name, output in outputs.items(): 158 | print(f"Outputs from node '{node_name}': {output}") 159 | ``` 160 | 161 | **Explanation:** 162 | 163 | - **flow.outputs**: A dictionary containing the outputs from each node in the workflow. 164 | - **Iterating through outputs**: Print or utilize the outputs as needed. 165 | 166 | **Example Output:** 167 | 168 | ``` 169 | Outputs from node 'get_current_date': {'current_date': '2024-04-27'} 170 | Outputs from node 'get_timewindow': {'time_window': 'last month'} 171 | Outputs from node 'product_hunt_scrape': {'product_hunt_scrape_results': ...} 172 | ``` 173 | 174 | ## Next Steps 175 | 176 | Congratulations! You've successfully installed GenSphere, pulled a workflow from the platform, executed it, and accessed its outputs. Here are some suggested next steps to further enhance your understanding and usage of GenSphere: 177 | 178 | 1. **Explore the Tutorials**: Dive into the [Quickstart Tutorial](../tutorials/tutorial.md) for more detailed guidance. 179 | 2. **Define Your Own Workflows**: Start creating your own workflows by defining YAML files, custom functions, and schemas. 180 | 3. **Integrate with Tools**: Leverage integrations with LangChain and Composio to enhance your workflows. 181 | 4. **Visualize Your Workflows**: Use the `Visualizer` class to get a graphical representation of your workflows. 182 | 5. **Contribute to GenSphere**: Share your workflows and contribute to the GenSphere community by pushing your projects to the platform. 183 | 184 | --- 185 | 186 | Continue to the [Tutorials](../tutorials/tutorial.md) section to deepen your understanding of GenSphere's capabilities! 187 | ``` -------------------------------------------------------------------------------- /docs/index.md: -------------------------------------------------------------------------------- 1 | # 🌐 GenSphere Documentation 2 | 3 | Welcome to the official documentation for **GenSphere**, a declarative framework to build LLM applications and an open platform to push and pull them. Think of GenSphere as **Docker for LLM applications**. 4 | 5 | --- 6 | 7 | ## Introduction 8 | 9 | ### What is GenSphere? 10 | 11 | GenSphere is a framework that allows you to build Large Language Model (LLM) applications by declaring tasks and how they connect using YAML files. It breaks down any LLM application into graphs where each node is either a function call, an LLM API call, or another graph itself. This approach provides: 12 | 13 | - **Low-Level Control**: Inspect and edit applications down to their core components, avoiding unnecessary abstractions. 14 | - **Portability**: Projects are defined by YAML files and associated Python functions and schemas, making sharing easy. 15 | - **Community Collaboration**: Push your application to an open platform (no registration required) and generate a public ID to make it accessible to anyone. 16 | - **Composability**: Easily compose complex applications from simpler, reusable components. 17 | 18 | ### Key Features 19 | 20 | - **Define Workflows with Simple YAML Files**: Create complex execution graphs using simple YAML files. 21 | - **Gain Low-Level Control**: Break workflows down to individual function calls and/or AI API calls. 22 | - **Nest LLM Applications Easily**: Reference other YAML files as nodes in your workflow. 23 | - **Push and Pull Projects to the Open Community Hub**: Collaborate by publishing and pulling projects from the platform. 24 | - **Track Popularity of Your Projects**: Check how popular your projects are by the number of times they are used by others. 25 | - **Visualize Workflows**: Explore your projects with interactive graphical visualization. 26 | 27 | ### Why Use GenSphere? 28 | 29 | GenSphere provides a transparent and flexible way to build LLM applications without the cumbersome abstractions that some modern frameworks introduce. It promotes collaboration and reuse by enabling developers to share and compose workflows easily. 30 | 31 | ### How Does GenSphere Work? 32 | 33 | 1. **Define Your Workflow with YAML Files**: Your project is defined by YAML files representing graphs where each node is a task to be executed. 34 | 2. **Compose Complex Workflows by Nesting Graphs**: Reference other YAML files as nodes to build upon existing workflows. 35 | 3. **Define Your Functions and Schemas**: Create Python files with functions and Pydantic models for structured outputs. 36 | 4. **Leverage Integration with LangChain and Composio**: Utilize tools available in [Composio](https://composio.dev/) and [LangChain](https://python.langchain.com/). 37 | 5. **Visualize Your Project**: Generate interactive graphical representations of your workflows. 38 | 6. **Execute the Workflow**: GenSphere resolves dependencies and executes your workflow. 39 | 7. **Push to the Platform**: Share your project on the open platform with a generated ID. 40 | 8. **Pull from the Platform**: Use projects from others to build more complex applications. 41 | 9. **Watch Your Projects Grow**: Monitor the popularity of your projects by tracking the number of pulls. 42 | 43 | --- 44 | 45 | ## Getting Started 46 | 47 | - **[Installation](getting_started/installation.md)** 48 | - **[Quickstart Guide](getting_started/quickstart.md)** 49 | 50 | ## Tutorials 51 | 52 | - **[Quickstart Tutorial](tutorials/tutorial.md)** 53 | 54 | ## User Guide 55 | 56 | - **[Workflows](user_guide/workflows.md)** 57 | - **[Functions and schemas](user_guide/functions_and_schemas.md)** 58 | - **[Nesting workflows](user_guide/nesting_workflows.md)** 59 | - **[Integration with Composio and Langchain](user_guide/integration_with_composion_and_langchain.md)** 60 | - **[Visualization](user_guide/visualization.md)** 61 | - **[Execution](user_guide/execution.md)** 62 | 63 | ## API reference 64 | 65 | - **[API reference](api_reference/api_reference.md)** 66 | 67 | ## Contributing 68 | 69 | We welcome contributions! Please join our [Discord server](https://discord.gg/DZFWMXJv). 70 | 71 | --- 72 | 73 | ![Star History Chart](https://api.star-history.com/svg?repos=octopus2023-inc/gensphere&type=Date) 74 | 75 | -------------------------------------------------------------------------------- /docs/requirements.txt: -------------------------------------------------------------------------------- 1 | mkdocs 2 | mkdocs-material 3 | mkdocs-git-revision-date-localized-plugin -------------------------------------------------------------------------------- /docs/user_guide/execution.md: -------------------------------------------------------------------------------- 1 | ## 7. Execution 2 | 3 | Executing workflows in GenSphere is straightforward using the `GenFlow` class. This section explains how to execute your workflow and handle outputs. 4 | 5 | ### 7.1 Setting Up the Environment 6 | 7 | Ensure that: 8 | 9 | - All required environment variables (e.g., API keys) are set. 10 | - All necessary files (`yaml`, `functions.py`, `schemas.py`) are available. 11 | 12 | ### 7.2 Executing the Workflow 13 | 14 | Use the `GenFlow` class to execute your workflow. 15 | 16 | **Example:** 17 | 18 | ```python 19 | from gensphere.genflow import GenFlow 20 | 21 | flow = GenFlow( 22 | yaml_file='combined.yaml', 23 | functions_filepath='functions.py', 24 | structured_output_schema_filepath='schemas.py' 25 | ) 26 | flow.parse_yaml() 27 | flow.run() 28 | ``` 29 | 30 | ### 7.3 Accessing Outputs 31 | 32 | After execution, outputs from each node are accessible via the `outputs` attribute. 33 | 34 | **Example:** 35 | 36 | ```python 37 | # Access all outputs 38 | outputs = flow.outputs 39 | 40 | # Access specific node outputs 41 | data_collection_output = outputs.get('data_collection').get('collected_data') 42 | 43 | # Print the outputs 44 | print("Collected Data:") 45 | print(data_collection_output) 46 | ``` 47 | 48 | ### 7.4 Handling Exceptions 49 | 50 | If an error occurs during execution: 51 | 52 | - Check the log files for detailed error messages. 53 | - Ensure that all referenced nodes, functions, and schemas exist. 54 | - Verify that all parameters are correctly specified. 55 | 56 | ### 7.5 Logging 57 | 58 | GenSphere uses Python's logging module to log execution details. 59 | 60 | - **Logging Levels**: Adjust logging levels as needed. 61 | ```python 62 | import logging 63 | logging.getLogger('gensphere').setLevel(logging.DEBUG) 64 | ``` 65 | - **Log File**: By default, logs may be written to `app.log` or the console. 66 | 67 | ### 7.6 Execution Flow 68 | 69 | The execution order of nodes is determined by their dependencies: 70 | 71 | - Nodes with no dependencies are executed first. 72 | - Nodes are executed after all their dependencies have completed. 73 | 74 | --- 75 | -------------------------------------------------------------------------------- /docs/user_guide/functions_and_schemas.md: -------------------------------------------------------------------------------- 1 | ## 2. Functions and Schemas 2 | 3 | In GenSphere, you can define custom functions and schemas to extend the functionality of your workflows. 4 | 5 | ### 2.1 Functions 6 | 7 | Custom functions are Python functions defined in a `.py` file that you specify when running your workflow. These functions can be used in **function_call** nodes or as tools in **llm_service** nodes. 8 | 9 | #### Defining Functions 10 | 11 | - Functions must return a dictionary whose keys match the `outputs` defined in the YAML node. 12 | - All parameters must be type-annotated. 13 | - Include docstrings to describe the function's purpose, parameters, and return values. 14 | 15 | **Example (`functions.py`):** 16 | 17 | ```python 18 | def process_data_function(data: str) -> dict: 19 | """ 20 | Processes the input data. 21 | 22 | Args: 23 | data (str): The data to process. 24 | 25 | Returns: 26 | dict: A dictionary with 'processed_data' as key. 27 | """ 28 | # Your processing logic here 29 | processed_data = data.upper() # Example processing 30 | return {'processed_data': processed_data} 31 | ``` 32 | 33 | #### Using Functions in Nodes 34 | 35 | **Example YAML Node:** 36 | 37 | ```yaml 38 | - name: process_data 39 | type: function_call 40 | function: process_data_function 41 | params: 42 | data: '{{ read_data.raw_data }}' 43 | outputs: 44 | - processed_data 45 | ``` 46 | 47 | ### 2.2 Schemas 48 | 49 | Schemas are used to define structured outputs for language model responses using Pydantic models. 50 | 51 | #### Defining Schemas 52 | 53 | Create a `.py` file (e.g., `schemas.py`) with Pydantic models representing the expected structure of your data. 54 | 55 | **Example (`schemas.py`):** 56 | 57 | ```python 58 | from pydantic import BaseModel, Field 59 | from typing import List 60 | 61 | class Item(BaseModel): 62 | id: int = Field(..., description="The unique identifier of the item.") 63 | name: str = Field(..., description="The name of the item.") 64 | description: str = Field(..., description="A brief description of the item.") 65 | 66 | class ItemList(BaseModel): 67 | items: List[Item] 68 | ``` 69 | 70 | #### Using Schemas in Nodes 71 | 72 | Use the `structured_output_schema` field in an **llm_service** node to specify the schema. 73 | 74 | **Example YAML Node:** 75 | 76 | ```yaml 77 | - name: extract_items 78 | type: llm_service 79 | service: openai 80 | model: "gpt-4" 81 | structured_output_schema: ItemList 82 | params: 83 | prompt: | 84 | Extract item information from the following text: 85 | {{ source_text }} 86 | outputs: 87 | - extracted_items 88 | ``` 89 | 90 | **Key Points:** 91 | 92 | - The `structured_output_schema` value should match the class name in your schema file. 93 | - The language model will attempt to produce output that conforms to the specified schema. 94 | 95 | #### Accessing Structured Outputs 96 | 97 | After execution, the output will be an instance of the schema class. You can access the data using standard attribute access. 98 | 99 | **Example:** 100 | 101 | ```python 102 | items = flow.outputs['extract_items']['extracted_items'] 103 | for item in items.items: 104 | print(f"Item ID: {item.id}, Name: {item.name}") 105 | ``` 106 | 107 | --- 108 | -------------------------------------------------------------------------------- /docs/user_guide/integration_with_composio_and_langchain.md: -------------------------------------------------------------------------------- 1 | ## 4. Integrations with Composio and Langchain 2 | 3 | GenSphere supports integration with external tools and platforms to enhance your workflows. 4 | 5 | ### 4.1 Composio Tools 6 | 7 | [Composio](https://composio.dev/) provides a set of tools that can be used within your GenSphere workflows. 8 | 9 | #### Using Composio Tools 10 | 11 | - Refer to Composio tools in the `tools` field with the syntax `COMPOSIO.tool_name`. 12 | - Ensure you have set your `COMPOSIO_API_KEY` in your environment variables. 13 | 14 | **Example YAML Node:** 15 | 16 | ```yaml 17 | - name: web_scraping 18 | type: llm_service 19 | service: openai 20 | model: "gpt-4" 21 | tools: 22 | - COMPOSIO.FIRECRAWL_SCRAPE 23 | params: 24 | prompt: | 25 | Scrape the content from the following URL: 26 | {{ url_to_scrape }} 27 | outputs: 28 | - scraped_content 29 | ``` 30 | 31 | ### 4.2 LangChain Tools 32 | 33 | [LangChain](https://langchain.com) offers a suite of tools for building applications with language models. 34 | 35 | #### Using LangChain Tools 36 | 37 | - Refer to LangChain tools in the `tools` field with the syntax `LANGCHAIN.tool_name`. 38 | - Ensure the required LangChain packages are installed. 39 | 40 | **Example YAML Node:** 41 | 42 | ```yaml 43 | - name: data_analysis 44 | type: llm_service 45 | service: openai 46 | model: "gpt-4" 47 | tools: 48 | - LANGCHAIN.PandasDataFrameAnalyzer 49 | params: 50 | prompt: | 51 | Analyze the following data: 52 | {{ data_frame }} 53 | outputs: 54 | - analysis_result 55 | ``` 56 | 57 | ### 4.3 Custom Tools 58 | 59 | You can also use custom functions as tools in **llm_service** nodes. 60 | 61 | **Example:** 62 | 63 | ```yaml 64 | - name: custom_tool_usage 65 | type: llm_service 66 | service: openai 67 | model: "gpt-4" 68 | tools: 69 | - my_custom_function 70 | params: 71 | prompt: | 72 | Use the custom function to process the data. 73 | outputs: 74 | - custom_tool_output 75 | ``` 76 | 77 | Ensure `my_custom_function` is defined in your functions file. 78 | 79 | --- 80 | 81 | -------------------------------------------------------------------------------- /docs/user_guide/lists_and_iterables.md: -------------------------------------------------------------------------------- 1 | ## 5. Lists and Iterables 2 | 3 | Working with lists and iterables in GenSphere allows you to process collections of data efficiently. 4 | 5 | ### 5.1 Processing Lists 6 | 7 | When a node produces a list output, you can process each element individually in subsequent nodes. 8 | 9 | #### Referencing List Elements 10 | 11 | Use the `[i]` syntax to reference individual elements in a list. 12 | 13 | **Example:** 14 | 15 | ```yaml 16 | - name: process_items 17 | type: function_call 18 | function: process_item_function 19 | params: 20 | item: '{{ item_list.items[i] }}' 21 | outputs: 22 | - processed_items 23 | ``` 24 | 25 | ### 5.2 Iterating Over Lists 26 | 27 | GenSphere automatically detects when you reference a list element and iterates over the list, executing the node for each element. 28 | 29 | #### Example Workflow 30 | 31 | Suppose you have a list of items you want to process individually. 32 | 33 | **Node Producing a List:** 34 | 35 | ```yaml 36 | - name: get_items 37 | type: function_call 38 | function: get_items_function 39 | outputs: 40 | - items # This is a list 41 | ``` 42 | 43 | **Node Processing Each Item:** 44 | 45 | ```yaml 46 | - name: process_each_item 47 | type: function_call 48 | function: process_item_function 49 | params: 50 | item: '{{ get_items.items[i] }}' 51 | outputs: 52 | - processed_items 53 | ``` 54 | 55 | **Key Points:** 56 | 57 | - The node `process_each_item` will be executed for each element in `get_items.items`. 58 | - The outputs will be collected into a list `processed_items`. 59 | 60 | ### 5.3 Collecting Outputs 61 | 62 | After iterating over a list, the outputs are collected into a list corresponding to each input element. 63 | 64 | **Accessing Collected Outputs:** 65 | 66 | ```python 67 | processed_items = flow.outputs['process_each_item']['processed_items'] 68 | for item in processed_items: 69 | print(item) 70 | ``` 71 | 72 | --- 73 | -------------------------------------------------------------------------------- /docs/user_guide/nesting_workflows.md: -------------------------------------------------------------------------------- 1 | ## 3. Nesting Workflows 2 | 3 | GenSphere allows you to create modular and reusable workflows by nesting workflows within each other. This is achieved using the `yml_flow` node type. Nesting workflows can help you organize complex tasks and promote code reuse. 4 | 5 | ### 3.1 Understanding YML Flow Nodes 6 | 7 | A `yml_flow` node refers to another YAML workflow file. When the main workflow is executed, GenSphere will incorporate the nested workflow, resolving dependencies and combining them into a single execution graph. 8 | 9 | **Example:** 10 | 11 | ```yaml 12 | # main_workflow.yaml 13 | 14 | nodes: 15 | - name: data_collection 16 | type: yml_flow 17 | yml_file: data_collection_workflow.yaml 18 | params: 19 | start_date: '2023-01-01' 20 | end_date: '2023-01-31' 21 | outputs: 22 | - collected_data 23 | 24 | - name: data_analysis 25 | type: function_call 26 | function: analyze_data_function 27 | params: 28 | data: '{{ data_collection.collected_data }}' 29 | outputs: 30 | - analysis_results 31 | ``` 32 | 33 | In this example: 34 | 35 | - **`data_collection`**: A `yml_flow` node that references `data_collection_workflow.yaml`. 36 | - **Parameters**: `start_date` and `end_date` are passed to the nested workflow. 37 | - **Outputs**: `collected_data` is an output from the nested workflow used in the main workflow. 38 | 39 | ### 3.2 Creating Nested Workflows 40 | 41 | To create a nested workflow: 42 | 43 | 1. **Define the Sub-Workflow**: Create a separate YAML file (e.g., `data_collection_workflow.yaml`) with its own nodes. 44 | 45 | ```yaml 46 | # data_collection_workflow.yaml 47 | 48 | nodes: 49 | - name: fetch_data 50 | type: function_call 51 | function: fetch_data_function 52 | params: 53 | start_date: '{{ start_date }}' 54 | end_date: '{{ end_date }}' 55 | outputs: 56 | - raw_data 57 | 58 | - name: process_data 59 | type: function_call 60 | function: process_data_function 61 | params: 62 | data: '{{ fetch_data.raw_data }}' 63 | outputs: 64 | - processed_data 65 | ``` 66 | 67 | 2. **Reference the Sub-Workflow**: In your main workflow, use a `yml_flow` node to include the sub-workflow. 68 | 69 | ### 3.3 Passing Parameters to Nested Workflows 70 | 71 | Parameters can be passed to nested workflows using the `params` field in the `yml_flow` node. These parameters can then be used within the nested workflow. 72 | 73 | **Example:** 74 | 75 | ```yaml 76 | # main_workflow.yaml 77 | 78 | nodes: 79 | - name: sub_workflow 80 | type: yml_flow 81 | yml_file: sub_workflow.yaml 82 | params: 83 | param1: '{{ main_param }}' 84 | ``` 85 | 86 | ### 3.4 Accessing Outputs from Nested Workflows 87 | 88 | Outputs specified in the `outputs` field of the `yml_flow` node are made available to the main workflow. 89 | 90 | **Example:** 91 | 92 | ```yaml 93 | - name: data_collection 94 | type: yml_flow 95 | yml_file: data_collection_workflow.yaml 96 | outputs: 97 | - collected_data 98 | ``` 99 | 100 | In subsequent nodes, you can reference `data_collection.collected_data`. 101 | 102 | ### 3.5 Composing the Combined Workflow 103 | 104 | Use the `YamlCompose` class to combine the main workflow and nested workflows into a single executable workflow. 105 | 106 | **Example:** 107 | 108 | ```python 109 | from gensphere.yaml_utils import YamlCompose 110 | 111 | composer = YamlCompose( 112 | yaml_file='main_workflow.yaml', 113 | functions_filepath='functions.py', 114 | structured_output_schema_filepath='schemas.py' 115 | ) 116 | combined_yaml_data = composer.compose(save_combined_yaml=True, output_file='combined.yaml') 117 | ``` 118 | 119 | --- 120 | -------------------------------------------------------------------------------- /docs/user_guide/visualization.md: -------------------------------------------------------------------------------- 1 | ## 6. Visualization 2 | 3 | Visualizing your workflow helps you understand the execution flow and dependencies between nodes. GenSphere provides a visualization tool to render your workflow as an interactive graph. 4 | 5 | ### 6.1 Using the Visualizer 6 | 7 | The `Visualizer` class in GenSphere allows you to visualize your workflow. 8 | 9 | **Example:** 10 | 11 | ```python 12 | from gensphere.visualizer import Visualizer 13 | 14 | viz = Visualizer( 15 | yaml_file='combined.yaml', 16 | functions_file='functions.py', 17 | schema_file='schemas.py', 18 | address='127.0.0.1', 19 | port=8050 20 | ) 21 | viz.start_visualization() 22 | ``` 23 | ### 6.2 Features of the Visualizer 24 | 25 | - **Interactive Graph**: Nodes are displayed as a graph where you can zoom in/out. 26 | - **Node Details**: Click on a node to see details such as inputs, outputs, functions, and schemas. 27 | - **Execution Flow**: Visualize the dependencies and execution order. 28 | 29 | ### 6.3 Running the Visualizer 30 | 31 | When you run `viz.start_visualization()`, the visualizer starts a local web server. Open your browser and navigate to `http://127.0.0.1:8050` (or the specified address and port) to view the visualization. 32 | 33 | **Note:** If you're running in an environment like Google Colab, you might need to set up port forwarding or use a different method to access the visualization. 34 | 35 | --- 36 | -------------------------------------------------------------------------------- /docs/user_guide/workflows.md: -------------------------------------------------------------------------------- 1 | ## 1. Workflows 2 | 3 | GenSphere uses **YAML files** to define workflows that orchestrate the execution of functions, language model services, and nested workflows. Understanding how to structure and write these YAML files is essential to leveraging the full power of GenSphere. 4 | 5 | ### 1.1 Structure of a Workflow 6 | 7 | A GenSphere workflow YAML file consists of a list of **nodes**, each representing a step in the workflow. Here's the basic structure: 8 | 9 | ```yaml 10 | # example_workflow.yaml 11 | 12 | nodes: 13 | - name: node_name 14 | type: node_type # can be either 'function_call','llm_service' of 'yml_flow' 15 | params: 16 | param1: value1 17 | param2: value2 18 | outputs: 19 | - output1 20 | - output2 21 | ``` 22 | There are additional fields needed depending on the 'type' field. 23 | 24 | ### 1.2 Node Types 25 | 26 | There are three primary node types in GenSphere: 27 | 28 | 1. **Function Call Nodes** (`function_call`): Execute Python functions. 29 | 2. **LLM Service Nodes** (`llm_service`): Interact with language model APIs. 30 | 3. **YML Flow Nodes** (`yml_flow`): Nest other YAML workflows. 31 | 32 | #### Function Call Nodes 33 | 34 | Used to execute custom Python functions defined in a separate `.py` file. 35 | 36 | **Example:** 37 | 38 | ```yaml 39 | - name: process_data 40 | type: function_call 41 | function: process_data_function 42 | params: 43 | data: '{{ previous_node.output }}' 44 | outputs: 45 | - processed_data 46 | ``` 47 | 48 | The function `process_data_function` should be defined on a separate `.py` file that contains all functions that you will use. We don't set the path of this `.py` file 49 | directly in the YAML, but pass it as na argument to GenFlow when executing the graph (as explained below). 50 | 51 | #### LLM Service Nodes 52 | 53 | Used to interact with language model services like OpenAI's GPT models. 54 | 55 | **Example:** 56 | 57 | ```yaml 58 | - name: generate_summary 59 | type: llm_service 60 | service: openai 61 | model: "gpt-4" 62 | tools: 63 | - COMPOSIO.composio_tool_name 64 | - LANGCHAIN.langchain_tool_name 65 | - custom_function 66 | params: 67 | prompt: | 68 | Summarize the following data: 69 | {{ process_data.processed_data }} 70 | outputs: 71 | - summary 72 | ``` 73 | In the example above, we are making na API call to the `chat_completions` API from openAI, passing the prompt described. Notice that we can reference others nodes 74 | outputs directly in the prompt (they are turned to strings when executing this node). 75 | 76 | Also, notice that we are passing several tools for function calling (which are optional). 77 | You can use [Composio](https://composio.dev) tools with the syntax COMPOSIO.composio_tool_name. 78 | You can use any tool from [langchain_community.tools](https://python.langchain.com/api_reference/community/tools.html) with the syntax "LANGCHAIN.langchain_tool_name". 79 | 80 | Besides function calling, we can also work with [structured outputs](https://platform.openai.com/docs/guides/structured-outputs). Structured outputs is a great 81 | practical feature that allows you to enforce a predetermined schema from the openAI API call. Using it ensures the model will always generate responses that adhere to your supplied schema. So, if you want the node output to be of the type 82 | 83 | `` 84 | {'field_1':value_1, 'field_2':value_2} 85 | `` 86 | 87 | where the values are strings, you first define a pydantic model that defines the schema on a separate schemas.py file 88 | 89 | 90 | `` 91 | from pydantic import BaseModel 92 | 93 | class ResponseSchema(BaseModel):, 94 | field_1: str = Field(..., description="Field 1 description"), 95 | field_2: str = Field(..., description="Field 2 description") 96 | `` 97 | 98 | and then pass it in the YAML file as a field in the llm_service node 99 | 100 | 101 | ```yaml 102 | - name: generate_summary 103 | type: llm_service 104 | service: openai 105 | model: "gpt-4" 106 | structured_output_schema: ResponseSchema 107 | params: 108 | prompt: | 109 | Summarize the following data: 110 | {{ process_data.processed_data }} 111 | outputs: 112 | - summary 113 | ``` 114 | 115 | The output of this node will then be an instance of the class ResponseSchema that we defined. If we want to transform this to a dictionary or do any other 116 | further post-processing, we should create a separate node for that. 117 | 118 | Like when workinf with functions, we don't pass the path of the schemas.py file directly in the YAML file, but will do it when using `GenFlow` to execute 119 | the workflow. 120 | 121 | 122 | #### YML Flow Nodes 123 | 124 | Used to include and execute another YAML workflow within the current workflow. 125 | 126 | **Example:** 127 | 128 | ```yaml 129 | - name: sub_workflow 130 | type: yml_flow 131 | yml_file: sub_workflow.yaml 132 | params: 133 | input_data: '{{ generate_summary.summary }}' 134 | outputs: 135 | - subflow_output 136 | ``` 137 | 138 | This will trigger the entire execution of the workflow defined on `sub_workflow.yaml`. The path on the `yml_file` field should be relative to where the parente 139 | YAML file is located. 140 | 141 | ### 1.3 Parameters and Outputs 142 | 143 | - **Parameters (`params`)**: Inputs required for the node to execute. You can reference outputs from previous nodes using the syntax `{{ node_name.output_name }}`. 144 | - **Outputs (`outputs`)**: The data produced by the node, which can be used by subsequent nodes. 145 | 146 | ### 1.4 Referencing Outputs 147 | 148 | Use the Jinja2 templating syntax to reference outputs from other nodes: 149 | 150 | ```yaml 151 | params: 152 | input_data: '{{ previous_node.output_name }}' 153 | ``` 154 | 155 | --- 156 | -------------------------------------------------------------------------------- /examples/product_hunt_analyzer.yaml: -------------------------------------------------------------------------------- 1 | # product_hunt_analyzer.yaml 2 | 3 | nodes: 4 | - name: get_current_date 5 | type: function_call 6 | function: get_current_date_function 7 | outputs: 8 | - current_date 9 | 10 | - name: get_timewindow 11 | type: function_call 12 | function: get_timewindow_function 13 | outputs: 14 | - time_window 15 | 16 | - name: product_hunt_scrape 17 | type: llm_service 18 | service: openai 19 | model: "gpt-4o-2024-08-06" 20 | tools: 21 | - COMPOSIO.FIRECRAWL_SCRAPE 22 | params: 23 | prompt: | 24 | You should visit producthunt at https://www.producthunt.com/leaderboard/monthly/yyyy/mm 25 | Today is {{ get_current_date.current_date }} 26 | You should subsitute yyyy and mm by year and month you want to search. 27 | The search time window should be {{ get_timewindow.time_window }}. 28 | After that, you should extract raw content from the htmls associated, 29 | which will contain information about new product launches, their companies, number of upvotes, etc. 30 | Scroll the page until the end and wait a few miliseconds for it to launch before scraping. 31 | outputs: 32 | - product_hunt_scrape_results 33 | 34 | - name: extract_info_from_search 35 | type: llm_service 36 | service: openai 37 | model: "gpt-4o-2024-08-06" 38 | structured_output_schema: StartupInformationList 39 | params: 40 | prompt: | 41 | You are given reports from a search to https://www.producthunt.com/leaderboard/monthly/, containing 42 | products featured there in the following time window: {{ get_timewindow.time_window }}. Here 43 | is the content of the search: 44 | {{ product_hunt_scrape.product_hunt_scrape_results }}. 45 | We want to extract accurate information about these new product launches. 46 | Structure the information there by the following dimensions: product name, company name, company url, number of upvotes, business model 47 | brief description of it. 48 | outputs: 49 | - structured_search_info 50 | 51 | - name: postprocess_search_results 52 | type: function_call 53 | function: postprocess_search_results_functions 54 | params: 55 | info: '{{ extract_info_from_search.structured_search_info }}' 56 | outputs: 57 | - postprocessed_search_results 58 | 59 | - name: find_extra_info 60 | type: llm_service 61 | service: openai 62 | model: "gpt-4o-2024-08-06" 63 | tools: 64 | - COMPOSIO.TAVILY_TAVILY_SEARCH 65 | params: 66 | prompt: | 67 | You should conduct a comprehensive search on the web about the following entry from producthunt.com: 68 | {{ postprocess_search_results.postprocessed_search_results[i] }}. You should look to find relevant news 69 | about the company, specially related to its revenue, valuation, traction, acquisition if applicable, 70 | number of users, etc. 71 | outputs: 72 | - startup_extra_info -------------------------------------------------------------------------------- /examples/product_hunt_functions.py: -------------------------------------------------------------------------------- 1 | # product_hunt_functions.py 2 | from datetime import datetime 3 | 4 | def read_file_as_string(file_path): 5 | try: 6 | with open(file_path, 'r') as file: 7 | content = file.read() 8 | return {'domains':content} 9 | except FileNotFoundError: 10 | return "Oops! It seems the specified file doesn't exist. Please provide a valid file path." 11 | 12 | def get_current_date_function(): 13 | return {'current_date':datetime.today().strftime('%Y-%m-%d')} 14 | 15 | def get_timewindow_function(): 16 | return {'time_window':'past month'} 17 | 18 | def postprocess_search_results_functions(info): 19 | result=info.model_dump().get('information_list') 20 | return {'postprocessed_search_results':result} -------------------------------------------------------------------------------- /examples/product_hunt_schemas.py: -------------------------------------------------------------------------------- 1 | # product_hunt_schemas.py 2 | 3 | from pydantic import BaseModel, Field 4 | from typing import List 5 | 6 | class StartupInformation(BaseModel): 7 | product_name: str = Field(..., description="The name of the product") 8 | company_name: str = Field(..., description="The name of the company that offers the product. Could be equal to name of the product") 9 | url: str = Field(..., description="URL associated with the product.") 10 | number_upvotes: int = Field(..., description="Number of upvotes associated with the product") 11 | business_model: str = Field(..., description="A brief description about the business model of the product or company") 12 | brief_description: str = Field(..., description="A brief description about the product") 13 | 14 | class StartupInformationList(BaseModel): 15 | information_list:List[StartupInformation] -------------------------------------------------------------------------------- /examples/simple_examples.yaml: -------------------------------------------------------------------------------- 1 | # simple.yaml 2 | 3 | nodes: 4 | - name: get_current_date 5 | type: function_call 6 | function: get_current_date_function 7 | outputs: 8 | - current_date 9 | 10 | - name: tell_fun_facts 11 | type: llm_service 12 | service: openai 13 | model: "gpt-4o-2024-08-06" 14 | params: 15 | prompt: | 16 | Today is {{ get_current_date.current_date }}. Tell us a very short list of fun facts about the month we are in. 17 | outputs: 18 | - fun_facts -------------------------------------------------------------------------------- /examples/simple_examples_functions.py: -------------------------------------------------------------------------------- 1 | # simple_examples_functions.py 2 | from datetime import datetime 3 | 4 | def get_current_date_function(): 5 | return {'current_date':datetime.today().strftime('%Y-%m-%d')} 6 | -------------------------------------------------------------------------------- /examples/startup_idea_evaluator.yaml: -------------------------------------------------------------------------------- 1 | # startup_idea_evaluator.yaml 2 | 3 | nodes: 4 | - name: read_idea 5 | type: function_call 6 | function: read_file_as_string 7 | params: 8 | file_path: "domains_to_search.txt" 9 | outputs: 10 | - domains 11 | 12 | - name: product_hunt_analyzer 13 | type: yml_flow 14 | yml_file: product_hunt_analyzer.yaml 15 | outputs: 16 | - postprocessed_search_results 17 | - startup_extra_info 18 | 19 | - name: generate_report 20 | type: llm_service 21 | service: openai 22 | model: "gpt-4o-2024-08-06" 23 | params: 24 | prompt: | 25 | You are a world class VC analyst. You are currently analyzing the following startup idea: 26 | {{ read_idea.domains }} 27 | Your task is to help analyze this idea in face of recent launches in product hunt. 28 | Some recents launches in producthunt.com are: 29 | {{ product_hunt_analyzer.postprocessed_search_results }} 30 | Besides that, some extra information about these companies is: 31 | {{ product_hunt_analyzer.startup_extra_info }}. 32 | 33 | Given that, you should create a detailed report containing the following: 34 | 1. An overview of recent launches in producthunt.com. What are the main ideas being explored? 35 | 2. A list of companies from producthunt launches that may become direct competitors to the startup idea. 36 | Explain your rational 37 | 3. Create a list of the most promising startups from the producthunt launches, as defined by their 38 | valuation, revenue, traction or other relevant metrics. 39 | 4. A table containing all information you found from producthunt launches. 40 | 41 | Answer in markdown format, and ensure your formatting is correct and that the output will be rendered without issues on a jupyter notebook. 42 | 43 | outputs: 44 | - report -------------------------------------------------------------------------------- /gensphere/__init__.py: -------------------------------------------------------------------------------- 1 | __version__="0.1.0" 2 | __author__="gensphere" 3 | -------------------------------------------------------------------------------- /gensphere/graph_builder.py: -------------------------------------------------------------------------------- 1 | # graph_builder.py 2 | 3 | import yaml 4 | import os 5 | import logging 6 | from typing import Optional, Set, Union, List, Dict 7 | 8 | # Configure module-level logger 9 | logger = logging.getLogger(__name__) 10 | logger.setLevel(logging.INFO) # Change to DEBUG for more detailed logs 11 | handler = logging.StreamHandler() 12 | formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') 13 | handler.setFormatter(formatter) 14 | if not logger.hasHandlers(): 15 | logger.addHandler(handler) 16 | 17 | 18 | def parse_yaml(yaml_file: str) -> dict: 19 | """ 20 | Parses a YAML file and returns its content. 21 | 22 | Args: 23 | yaml_file (str): Path to the YAML file. 24 | 25 | Returns: 26 | dict: Parsed YAML data. 27 | """ 28 | yaml_file = os.path.abspath(yaml_file) 29 | if not os.path.exists(yaml_file): 30 | logger.error(f"YAML file '{yaml_file}' does not exist.") 31 | raise FileNotFoundError(f"YAML file '{yaml_file}' does not exist.") 32 | 33 | with open(yaml_file, 'r') as f: 34 | try: 35 | data = yaml.safe_load(f) 36 | logger.debug(f"Parsed YAML file '{yaml_file}': {data}") 37 | return data 38 | except yaml.YAMLError as e: 39 | logger.error(f"Error parsing YAML file '{yaml_file}': {e}") 40 | raise 41 | 42 | 43 | def extract_referenced_nodes(template_str: str) -> Set[str]: 44 | """ 45 | Extracts all referenced node names from a templated string. 46 | 47 | Args: 48 | template_str (str): The templated string, e.g., "{{ node.output }}". 49 | 50 | Returns: 51 | Set[str]: A set of referenced node names. 52 | """ 53 | import re 54 | pattern = r"\{\{\s*([\w_][\w_\d]*)\.[^\}]*\}\}" 55 | matches = re.findall(pattern, template_str) 56 | logger.debug(f"Extracted referenced nodes from '{template_str}': {matches}") 57 | return set(matches) 58 | 59 | 60 | def traverse_node_fields(node_value: Union[str, Dict, List]) -> Set[str]: 61 | """ 62 | Recursively traverses a node's fields to find all referenced node names. 63 | 64 | Args: 65 | node_value: The node value to traverse. 66 | 67 | Returns: 68 | Set[str]: A set of referenced node names found within the node value. 69 | """ 70 | referenced_nodes = set() 71 | if isinstance(node_value, str): 72 | if '{{' in node_value and '}}' in node_value: 73 | nodes = extract_referenced_nodes(node_value) 74 | referenced_nodes.update(nodes) 75 | elif isinstance(node_value, dict): 76 | for key, value in node_value.items(): 77 | if key not in {'name', 'type', 'outputs'}: 78 | nodes_in_key = extract_referenced_nodes(str(key)) 79 | referenced_nodes.update(nodes_in_key) 80 | nodes_in_value = traverse_node_fields(value) 81 | referenced_nodes.update(nodes_in_value) 82 | elif isinstance(node_value, list): 83 | for item in node_value: 84 | nodes = traverse_node_fields(item) 85 | referenced_nodes.update(nodes) 86 | return referenced_nodes 87 | 88 | 89 | def identify_and_style_entrypoints_outputs(elements: list) -> list: 90 | """ 91 | Identifies entrypoint and output nodes based on incoming and outgoing edges and styles them accordingly. 92 | 93 | Args: 94 | elements (list): List of Cytoscape elements (nodes and edges). 95 | 96 | Returns: 97 | list: Updated list of Cytoscape elements with styled entrypoints and output nodes. 98 | """ 99 | incoming_edges = {} 100 | outgoing_edges = {} 101 | 102 | # Initialize counts 103 | for elem in elements: 104 | if 'source' in elem['data'] and 'target' in elem['data']: 105 | source = elem['data']['source'] 106 | target = elem['data']['target'] 107 | outgoing_edges[source] = outgoing_edges.get(source, 0) + 1 108 | incoming_edges[target] = incoming_edges.get(target, 0) + 1 109 | 110 | # Iterate through nodes to identify entrypoints and outputs 111 | for elem in elements: 112 | if 'label' in elem['data']: 113 | node_id = elem['data']['id'] 114 | node_type = elem['data'].get('type', '') 115 | # Check for entrypoint 116 | if incoming_edges.get(node_id, 0) == 0: 117 | if 'classes' in elem: 118 | elem['classes'] += ' entrypoint-node' 119 | else: 120 | elem['classes'] = 'entrypoint-node' 121 | logger.debug(f"Marked node '{node_id}' as entrypoint.") 122 | # Check for output 123 | if outgoing_edges.get(node_id, 0) == 0: 124 | if 'classes' in elem: 125 | elem['classes'] += ' output-node' 126 | else: 127 | elem['classes'] = 'output-node' 128 | logger.debug(f"Marked node '{node_id}' as output node.") 129 | 130 | return elements 131 | 132 | 133 | def build_graph_data(yaml_file: str) -> list: 134 | """ 135 | Builds graph data compatible with Cytoscape from a YAML workflow definition. 136 | 137 | Args: 138 | yaml_file (str): Path to the YAML file. 139 | 140 | Returns: 141 | list: List of Cytoscape elements (nodes and edges). 142 | """ 143 | data = parse_yaml(yaml_file) 144 | elements = [] 145 | node_ids = set() 146 | edges = [] 147 | 148 | nodes = data.get('nodes', []) 149 | if not nodes: 150 | logger.warning(f"No nodes found in YAML file '{yaml_file}'.") 151 | return elements # Return empty list if no nodes 152 | 153 | # Build a mapping from node IDs to node types 154 | node_type_map = {} 155 | 156 | for node in nodes: 157 | node_name = node.get('name') 158 | node_type = node.get('type') 159 | 160 | if not node_name: 161 | logger.error("A node without a 'name' was found.") 162 | raise ValueError("All nodes must have a 'name' field.") 163 | 164 | node_id = node_name 165 | 166 | if node_id in node_ids: 167 | logger.error(f"Duplicate node name '{node_id}' detected.") 168 | raise ValueError(f"Duplicate node name '{node_id}' detected.") 169 | 170 | node_ids.add(node_id) 171 | node_type_map[node_id] = node_type 172 | 173 | # Prepare node data with default fields to prevent null values 174 | node_data = { 175 | 'id': node_id, 176 | 'label': node_name, 177 | 'type': node_type, 178 | 'params': node.get('params', {}), 179 | 'outputs': node.get('outputs', []) 180 | } 181 | 182 | # Include additional fields based on node type 183 | if node_type == 'function_call': 184 | node_data['function'] = node.get('function', '') 185 | node_data['function_call'] = node.get('function_call', '') 186 | elif node_type == 'llm_service': 187 | node_data['tools'] = node.get('tools', []) 188 | node_data['structured_output_schema'] = node.get('structured_output_schema', '') 189 | elif node_type == 'yml_flow': 190 | node_data['yml_file'] = node.get('yml_file', '') 191 | # Add other node types as needed 192 | 193 | # Add node element 194 | element = { 195 | 'data': node_data, 196 | 'classes': node_type # Used for styling in Cytoscape 197 | } 198 | elements.append(element) 199 | logger.debug(f"Added node: {element}") 200 | 201 | # After all nodes are added, create edges 202 | for node in nodes: 203 | node_name = node.get('name') 204 | node_id = node_name 205 | 206 | # Handle dependencies by traversing all node fields 207 | node_fields = node.copy() 208 | # Exclude certain keys that do not contain dependencies 209 | excluded_keys = {'name', 'type', 'outputs'} 210 | for key in excluded_keys: 211 | node_fields.pop(key, None) 212 | referenced_nodes = traverse_node_fields(node_fields) 213 | logger.debug(f"Node '{node_id}' references nodes: {referenced_nodes}") 214 | for referenced_node in referenced_nodes: 215 | referenced_node_id = referenced_node 216 | if referenced_node_id in node_ids: 217 | edge_classes = 'dependency-edge' # Default edge class 218 | # Check if target node is a yml_flow 219 | target_node_type = node_type_map.get(node_id, '') 220 | if target_node_type == 'yml_flow': 221 | edge_classes += ' edge-to-yml-flow' 222 | dependency_edge = { 223 | 'data': {'source': referenced_node_id, 'target': node_id}, 224 | 'classes': edge_classes # Used for styling dependency edges 225 | } 226 | edges.append(dependency_edge) 227 | logger.debug(f"Added dependency edge from '{referenced_node_id}' to '{node_id}': {dependency_edge}") 228 | else: 229 | logger.warning(f"Referenced node '{referenced_node_id}' not found for dependency in node '{node_id}'.") 230 | 231 | # Combine nodes and edges 232 | elements.extend(edges) 233 | 234 | # Identify and style entrypoints and output nodes 235 | elements = identify_and_style_entrypoints_outputs(elements) 236 | 237 | # Log the elements for debugging 238 | logger.info(f"Total elements generated: {len(elements)}") 239 | logger.debug(f"Elements: {elements}") 240 | 241 | return elements 242 | -------------------------------------------------------------------------------- /gensphere/hub.py: -------------------------------------------------------------------------------- 1 | # hub.py 2 | 3 | import requests 4 | import os 5 | import logging 6 | from typing import Optional 7 | import yaml 8 | from gensphere.yaml_utils import validate_yaml 9 | 10 | # Configure logging 11 | logging.basicConfig(level=logging.INFO) 12 | logger = logging.getLogger("hub") 13 | 14 | 15 | class Hub: 16 | def __init__( 17 | self, 18 | yaml_file: Optional[str] = None, 19 | functions_file: Optional[str] = None, 20 | schema_file: Optional[str] = None, 21 | api_base_url: str = 'http://genspherehub.us-east-1.elasticbeanstalk.com/' 22 | ): 23 | self.yaml_file = yaml_file 24 | self.functions_file = functions_file 25 | self.schema_file = schema_file 26 | self.api_base_url = api_base_url 27 | 28 | def push(self, push_name: Optional[str] = None): 29 | if not self.yaml_file: 30 | logger.error("No yaml_file provided for push.") 31 | raise ValueError("You must provide at least a yaml_file to push.") 32 | 33 | validated, error_msgs, node_outputs = validate_yaml( 34 | self.yaml_file, self.functions_file, self.schema_file) 35 | if validated: 36 | logger.info(f"YAML file '{self.yaml_file}' passed all consistency checks.") 37 | files = {} 38 | file_handles = [] 39 | try: 40 | # Open files and add to files dictionary 41 | f_yaml = open(self.yaml_file, 'rb') 42 | files['yaml_file'] = ('yaml_file.yaml', f_yaml, 'application/x-yaml') 43 | file_handles.append(f_yaml) 44 | 45 | if self.functions_file: 46 | if not self.functions_file.endswith('.py'): 47 | logger.error("functions_file must be a .py file.") 48 | raise ValueError("functions_file must be a .py file.") 49 | f_functions = open(self.functions_file, 'rb') 50 | files['functions_file'] = ('functions.py', f_functions, 'text/x-python') 51 | file_handles.append(f_functions) 52 | 53 | if self.schema_file: 54 | if not self.schema_file.endswith('.py'): 55 | logger.error("schema_file must be a .py file.") 56 | raise ValueError("schema_file must be a .py file.") 57 | f_schema = open(self.schema_file, 'rb') 58 | files['schema_file'] = ('structured_output_schema.py', f_schema, 'text/x-python') 59 | file_handles.append(f_schema) 60 | 61 | data = {} 62 | if push_name: 63 | data['push_name'] = push_name 64 | 65 | logger.info("Sending push request to the platform.") 66 | response = requests.post(f"{self.api_base_url}/push", files=files, data=data) 67 | logger.info(f"Push response status code: {response.status_code}") 68 | response.raise_for_status() 69 | result = response.json() 70 | logger.info(f"Push successful. push_id: {result['push_id']}") 71 | logger.info(f"Uploaded files: {result.get('uploaded_files', [])}") 72 | return result 73 | except requests.HTTPError as e: 74 | logger.error(f"HTTP error during push: {e.response.text}") 75 | raise 76 | except Exception as e: 77 | logger.error(f"Error during push: {str(e)}") 78 | raise 79 | finally: 80 | # Close all file handles 81 | for f in file_handles: 82 | f.close() 83 | else: 84 | logger.error(f"YAML file '{self.yaml_file}' did not pass consistency checks.") 85 | logger.error(f"Validation errors: {error_msgs}") 86 | raise Exception( 87 | f"YAML file '{self.yaml_file}' didn't pass consistency checks.\nError messages: {error_msgs}") 88 | 89 | def pull( 90 | self, 91 | push_id: str, 92 | save_to_disk: bool = True, 93 | yaml_filename: Optional[str] = None, 94 | functions_filename: Optional[str] = None, 95 | schema_filename: Optional[str] = None, 96 | download_path: str = "." 97 | ): 98 | try: 99 | logger.info(f"Sending pull request for push_id: {push_id}") 100 | response = requests.get(f"{self.api_base_url}/pull/{push_id}") 101 | logger.info(f"Pull response status code: {response.status_code}") 102 | response.raise_for_status() 103 | files_content = response.json() 104 | 105 | if save_to_disk: 106 | # Map platform filenames to custom filenames 107 | filename_mapping = { 108 | 'yaml_file.yaml': yaml_filename or 'yaml_file.yaml', 109 | 'functions.py': functions_filename or 'functions.py', 110 | 'structured_output_schema.py': schema_filename or 'structured_output_schema.py' 111 | } 112 | for original_filename, content in files_content.items(): 113 | save_filename = filename_mapping.get(original_filename, original_filename) 114 | full_path = os.path.join(download_path, save_filename) 115 | # Check if file exists and rename if necessary 116 | base, extension = os.path.splitext(full_path) 117 | counter = 1 118 | while os.path.exists(full_path): 119 | full_path = f"{base}_{counter}{extension}" 120 | counter += 1 121 | with open(full_path, 'w', encoding='utf-8') as f: 122 | f.write(content) 123 | logger.info(f"Saved {save_filename} to disk at {full_path}") 124 | return files_content 125 | except requests.HTTPError as e: 126 | logger.error(f"HTTP error during pull: {e.response.text}") 127 | raise 128 | except Exception as e: 129 | logger.error(f"Error during pull: {str(e)}") 130 | raise 131 | 132 | def count_pulls(self, push_id: str) -> int: 133 | """ 134 | Retrieves the total number of times a push has been pulled. 135 | 136 | Args: 137 | push_id (str): The ID of the push to count pulls for. 138 | 139 | Returns: 140 | int: The total number of pulls. 141 | 142 | Raises: 143 | HTTPError: If the API request fails. 144 | Exception: For other exceptions. 145 | """ 146 | try: 147 | logger.info(f"Sending count_pulls request for push_id: {push_id}") 148 | response = requests.get(f"{self.api_base_url}/count_pulls/{push_id}") 149 | logger.info(f"count_pulls response status code: {response.status_code}") 150 | response.raise_for_status() 151 | result = response.json() 152 | pull_count = result.get('pull_count', 0) 153 | logger.info(f"Total pulls for push_id '{push_id}': {pull_count}") 154 | return pull_count 155 | except requests.HTTPError as e: 156 | logger.error(f"HTTP error during count_pulls: {e.response.text}") 157 | raise 158 | except Exception as e: 159 | logger.error(f"Error during count_pulls: {str(e)}") 160 | raise 161 | -------------------------------------------------------------------------------- /gensphere/utils.py: -------------------------------------------------------------------------------- 1 | def load_module_from_path(file_path): 2 | import importlib.util 3 | import sys 4 | import os 5 | 6 | module_name = os.path.splitext(os.path.basename(file_path))[0] 7 | spec = importlib.util.spec_from_file_location(module_name, file_path) 8 | if spec is None: 9 | raise ImportError(f"Cannot find spec for module '{module_name}' at '{file_path}'") 10 | module = importlib.util.module_from_spec(spec) 11 | sys.modules[module_name] = module 12 | spec.loader.exec_module(module) 13 | return module -------------------------------------------------------------------------------- /img/gensphere_gif.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/octopus2023-inc/gensphere/c8d156fcf530c9fac2cf2b2484929766b5b7ac31/img/gensphere_gif.gif -------------------------------------------------------------------------------- /img/gensphere_workflow_example.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/octopus2023-inc/gensphere/c8d156fcf530c9fac2cf2b2484929766b5b7ac31/img/gensphere_workflow_example.png -------------------------------------------------------------------------------- /img/globe_with_meridians.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/octopus2023-inc/gensphere/c8d156fcf530c9fac2cf2b2484929766b5b7ac31/img/globe_with_meridians.png -------------------------------------------------------------------------------- /mkdocs.yml: -------------------------------------------------------------------------------- 1 | site_name: GenSphere Documentation 2 | site_description: Official documentation for GenSphere, a declarative framework to build LLM applications. 3 | site_author: Octopus2023 Inc. 4 | repo_url: https://github.com/octopus2023-inc/gensphere 5 | nav: 6 | - Home: index.md 7 | - Getting Started: 8 | - Installation: getting_started/installation.md 9 | - Quickstart Guide: getting_started/quickstart.md 10 | - Tutorials: 11 | - Quickstart Tutorial: tutorials/tutorial.md 12 | - User Guide: 13 | - Workflows: user_guide/workflows.md 14 | - Functions and Schemas: user_guide/functions_and_schemas.md 15 | - Nesting workflows: user_guide/nesting_workflows.md 16 | - Integration with Composio and Langchain tools: user_guide/integration_with_composio_and_langchain.md 17 | - Lists and iterables: user_guide/lists_and_iterables.md 18 | - Visualization: user_guide/visualization.md 19 | - Execution: user_guide/execution.md 20 | - API Reference: 21 | - API reference: api_reference/api_reference.md 22 | theme: 23 | name: material 24 | logo: img/globe_with_meridians.png 25 | favicon: img/globe_with_meridians.png 26 | palette: 27 | - scheme: default 28 | primary: 'indigo' 29 | accent: 'indigo' 30 | features: 31 | - navigation.expand 32 | - navigation.sections 33 | - navigation.top 34 | - navigation.tracking 35 | - toc.integrate 36 | - instant 37 | markdown_extensions: 38 | - admonition 39 | - codehilite 40 | - footnotes 41 | - tables 42 | - toc: 43 | permalink: true 44 | extra_css: 45 | - css/custom.css 46 | extra_javascript: 47 | - js/custom.js 48 | plugins: 49 | - search 50 | - git-revision-date-localized: 51 | locale: en 52 | timezone: UTC 53 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.poetry] 2 | name = "gensphere" 3 | version = "0.1.9" 4 | description = "Build LLM applications from reusable components" 5 | authors = ["Octopus"] 6 | license = "MIT" 7 | readme = "README.md" 8 | 9 | [tool.poetry.dependencies] 10 | python = "^3.10" 11 | Jinja2 = "^3.1.4" 12 | networkx = "^3.4.2" 13 | pydantic = "^2.9.2" 14 | python-dotenv = "^1.0.1" 15 | openai = "^1.52.2" 16 | composio-core = "^0.5.35" 17 | composio-openai = "^0.5.35" 18 | langchain = "^0.3.4" 19 | langchain-community = "^0.3.3" 20 | dash = "^2.18.1" 21 | dash-cytoscape = "^1.0.2" 22 | 23 | 24 | [build-system] 25 | requires = ["poetry-core"] 26 | build-backend = "poetry.core.masonry.api" 27 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup 2 | 3 | setup( 4 | name='gensphere', 5 | version='0.1.9', 6 | description='Reusable components for LLM applications', 7 | url='https://github.com/octopus2023-inc/gensphere/', 8 | author='gensphere', 9 | author_email='octopus2023.contact@gmail.com', 10 | license='MIT', 11 | packages=['gensphere'], 12 | install_requires=['Jinja2', 13 | 'networkx', 14 | 'pydantic', 15 | 'python-dotenv', 16 | 'openai', 17 | 'composio_core', 18 | 'composio_openai', 19 | 'langchain', 20 | 'langchain-community', 21 | 'dash', 22 | 'dash-cytoscape' 23 | ] 24 | 25 | ) 26 | -------------------------------------------------------------------------------- /site/assets/images/favicon.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/octopus2023-inc/gensphere/c8d156fcf530c9fac2cf2b2484929766b5b7ac31/site/assets/images/favicon.png -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.da.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * Lunr languages, `Danish` language 3 | * https://github.com/MihaiValentin/lunr-languages 4 | * 5 | * Copyright 2014, Mihai Valentin 6 | * http://www.mozilla.org/MPL/ 7 | */ 8 | /*! 9 | * based on 10 | * Snowball JavaScript Library v0.3 11 | * http://code.google.com/p/urim/ 12 | * http://snowball.tartarus.org/ 13 | * 14 | * Copyright 2010, Oleg Mazko 15 | * http://www.mozilla.org/MPL/ 16 | */ 17 | 18 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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andere anderem anderen anderer anderes anderm andern anderr anders auch auf aus bei bin bis bist da damit dann das dasselbe dazu daß dein deine deinem deinen deiner deines dem demselben den denn denselben der derer derselbe derselben des desselben dessen dich die dies diese dieselbe dieselben diesem diesen dieser dieses dir doch dort du durch ein eine einem einen einer eines einig einige einigem einigen einiger einiges einmal er es etwas euch euer eure eurem euren eurer eures für gegen gewesen hab habe haben hat hatte hatten hier hin hinter ich ihm ihn ihnen ihr ihre ihrem ihren ihrer ihres im in indem ins ist jede jedem jeden jeder jedes jene jenem jenen jener jenes jetzt kann kein keine keinem keinen keiner keines können könnte machen man manche manchem manchen mancher manches mein meine meinem meinen meiner meines mich mir mit muss musste nach nicht nichts noch nun nur ob oder ohne sehr sein seine seinem seinen seiner seines selbst sich sie sind so solche solchem solchen solcher 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Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.el=function(){this.pipeline.reset(),void 0===this.searchPipeline&&this.pipeline.add(e.el.trimmer,e.el.normilizer),this.pipeline.add(e.el.stopWordFilter,e.el.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.el.stemmer))},e.el.wordCharacters="A-Za-zΑαΒβΓγΔδΕεΖζΗηΘθΙιΚκΛλΜμΝνΞξΟοΠπΡρΣσςΤτΥυΦφΧχΨψΩωΆάΈέΉήΊίΌόΎύΏώΪΐΫΰΐΰ",e.el.trimmer=e.trimmerSupport.generateTrimmer(e.el.wordCharacters),e.Pipeline.registerFunction(e.el.trimmer,"trimmer-el"),e.el.stemmer=function(){function e(e){return s.test(e)}function t(e){return/[ΑΕΗΙΟΥΩ]$/.test(e)}function r(e){return/[ΑΕΗΙΟΩ]$/.test(e)}function n(n){var s=n;if(n.length<3)return s;if(!e(n))return s;if(i.indexOf(n)>=0)return s;var u=new RegExp("(.*)("+Object.keys(l).join("|")+")$"),o=u.exec(s);return null!==o&&(s=o[1]+l[o[2]]),null!==(o=/^(.+?)(ΑΔΕΣ|ΑΔΩΝ)$/.exec(s))&&(s=o[1],/(ΟΚ|ΜΑΜ|ΜΑΝ|ΜΠΑΜΠ|ΠΑΤΕΡ|ΓΙΑΓΙ|ΝΤΑΝΤ|ΚΥΡ|ΘΕΙ|ΠΕΘΕΡ|ΜΟΥΣΑΜ|ΚΑΠΛΑΜ|ΠΑΡ|ΨΑΡ|ΤΖΟΥΡ|ΤΑΜΠΟΥΡ|ΓΑΛΑΤ|ΦΑΦΛΑΤ)$/.test(o[1])||(s+="ΑΔ")),null!==(o=/^(.+?)(ΕΔΕΣ|ΕΔΩΝ)$/.exec(s))&&(s=o[1],/(ΟΠ|ΙΠ|ΕΜΠ|ΥΠ|ΓΗΠ|ΔΑΠ|ΚΡΑΣΠ|ΜΙΛ)$/.test(o[1])&&(s+="ΕΔ")),null!==(o=/^(.+?)(ΟΥΔΕΣ|ΟΥΔΩΝ)$/.exec(s))&&(s=o[1],/(ΑΡΚ|ΚΑΛΙΑΚ|ΠΕΤΑΛ|ΛΙΧ|ΠΛΕΞ|ΣΚ|Σ|ΦΛ|ΦΡ|ΒΕΛ|ΛΟΥΛ|ΧΝ|ΣΠ|ΤΡΑΓ|ΦΕ)$/.test(o[1])&&(s+="ΟΥΔ")),null!==(o=/^(.+?)(ΕΩΣ|ΕΩΝ|ΕΑΣ|ΕΑ)$/.exec(s))&&(s=o[1],/^(Θ|Δ|ΕΛ|ΓΑΛ|Ν|Π|ΙΔ|ΠΑΡ|ΣΤΕΡ|ΟΡΦ|ΑΝΔΡ|ΑΝΤΡ)$/.test(o[1])&&(s+="Ε")),null!==(o=/^(.+?)(ΕΙΟ|ΕΙΟΣ|ΕΙΟΙ|ΕΙΑ|ΕΙΑΣ|ΕΙΕΣ|ΕΙΟΥ|ΕΙΟΥΣ|ΕΙΩΝ)$/.exec(s))&&o[1].length>4&&(s=o[1]),null!==(o=/^(.+?)(ΙΟΥΣ|ΙΑΣ|ΙΕΣ|ΙΟΣ|ΙΟΥ|ΙΟΙ|ΙΩΝ|ΙΟΝ|ΙΑ|ΙΟ)$/.exec(s))&&(s=o[1],(t(s)||s.length<2||/^(ΑΓ|ΑΓΓΕΛ|ΑΓΡ|ΑΕΡ|ΑΘΛ|ΑΚΟΥΣ|ΑΞ|ΑΣ|Β|ΒΙΒΛ|ΒΥΤ|Γ|ΓΙΑΓ|ΓΩΝ|Δ|ΔΑΝ|ΔΗΛ|ΔΗΜ|ΔΟΚΙΜ|ΕΛ|ΖΑΧΑΡ|ΗΛ|ΗΠ|ΙΔ|ΙΣΚ|ΙΣΤ|ΙΟΝ|ΙΩΝ|ΚΙΜΩΛ|ΚΟΛΟΝ|ΚΟΡ|ΚΤΗΡ|ΚΥΡ|ΛΑΓ|ΛΟΓ|ΜΑΓ|ΜΠΑΝ|ΜΠΡ|ΝΑΥΤ|ΝΟΤ|ΟΠΑΛ|ΟΞ|ΟΡ|ΟΣ|ΠΑΝΑΓ|ΠΑΤΡ|ΠΗΛ|ΠΗΝ|ΠΛΑΙΣ|ΠΟΝΤ|ΡΑΔ|ΡΟΔ|ΣΚ|ΣΚΟΡΠ|ΣΟΥΝ|ΣΠΑΝ|ΣΤΑΔ|ΣΥΡ|ΤΗΛ|ΤΙΜ|ΤΟΚ|ΤΟΠ|ΤΡΟΧ|ΦΙΛ|ΦΩΤ|Χ|ΧΙΛ|ΧΡΩΜ|ΧΩΡ)$/.test(o[1]))&&(s+="Ι"),/^(ΠΑΛ)$/.test(o[1])&&(s+="ΑΙ")),null!==(o=/^(.+?)(ΙΚΟΣ|ΙΚΟΝ|ΙΚΕΙΣ|ΙΚΟΙ|ΙΚΕΣ|ΙΚΟΥΣ|ΙΚΗ|ΙΚΗΣ|ΙΚΟ|ΙΚΑ|ΙΚΟΥ|ΙΚΩΝ|ΙΚΩΣ)$/.exec(s))&&(s=o[1],(t(s)||/^(ΑΔ|ΑΛ|ΑΜΑΝ|ΑΜΕΡ|ΑΜΜΟΧΑΛ|ΑΝΗΘ|ΑΝΤΙΔ|ΑΠΛ|ΑΤΤ|ΑΦΡ|ΒΑΣ|ΒΡΩΜ|ΓΕΝ|ΓΕΡ|Δ|ΔΙΚΑΝ|ΔΥΤ|ΕΙΔ|ΕΝΔ|ΕΞΩΔ|ΗΘ|ΘΕΤ|ΚΑΛΛΙΝ|ΚΑΛΠ|ΚΑΤΑΔ|ΚΟΥΖΙΝ|ΚΡ|ΚΩΔ|ΛΟΓ|Μ|ΜΕΡ|ΜΟΝΑΔ|ΜΟΥΛ|ΜΟΥΣ|ΜΠΑΓΙΑΤ|ΜΠΑΝ|ΜΠΟΛ|ΜΠΟΣ|ΜΥΣΤ|Ν|ΝΙΤ|ΞΙΚ|ΟΠΤ|ΠΑΝ|ΠΕΤΣ|ΠΙΚΑΝΤ|ΠΙΤΣ|ΠΛΑΣΤ|ΠΛΙΑΤΣ|ΠΟΝΤ|ΠΟΣΤΕΛΝ|ΠΡΩΤΟΔ|ΣΕΡΤ|ΣΗΜΑΝΤ|ΣΤΑΤ|ΣΥΝΑΔ|ΣΥΝΟΜΗΛ|ΤΕΛ|ΤΕΧΝ|ΤΡΟΠ|ΤΣΑΜ|ΥΠΟΔ|Φ|ΦΙΛΟΝ|ΦΥΛΟΔ|ΦΥΣ|ΧΑΣ)$/.test(o[1])||/(ΦΟΙΝ)$/.test(o[1]))&&(s+="ΙΚ")),"ΑΓΑΜΕ"===s&&(s="ΑΓΑΜ"),null!==(o=/^(.+?)(ΑΓΑΜΕ|ΗΣΑΜΕ|ΟΥΣΑΜΕ|ΗΚΑΜΕ|ΗΘΗΚΑΜΕ)$/.exec(s))&&(s=o[1]),null!==(o=/^(.+?)(ΑΜΕ)$/.exec(s))&&(s=o[1],/^(ΑΝΑΠ|ΑΠΟΘ|ΑΠΟΚ|ΑΠΟΣΤ|ΒΟΥΒ|ΞΕΘ|ΟΥΛ|ΠΕΘ|ΠΙΚΡ|ΠΟΤ|ΣΙΧ|Χ)$/.test(o[1])&&(s+="ΑΜ")),null!==(o=/^(.+?)(ΑΓΑΝΕ|ΗΣΑΝΕ|ΟΥΣΑΝΕ|ΙΟΝΤΑΝΕ|ΙΟΤΑΝΕ|ΙΟΥΝΤΑΝΕ|ΟΝΤΑΝΕ|ΟΤΑΝΕ|ΟΥΝΤΑΝΕ|ΗΚΑΝΕ|ΗΘΗΚΑΝΕ)$/.exec(s))&&(s=o[1],/^(ΤΡ|ΤΣ)$/.test(o[1])&&(s+="ΑΓΑΝ")),null!==(o=/^(.+?)(ΑΝΕ)$/.exec(s))&&(s=o[1],(r(s)||/^(ΒΕΤΕΡ|ΒΟΥΛΚ|ΒΡΑΧΜ|Γ|ΔΡΑΔΟΥΜ|Θ|ΚΑΛΠΟΥΖ|ΚΑΣΤΕΛ|ΚΟΡΜΟΡ|ΛΑΟΠΛ|ΜΩΑΜΕΘ|Μ|ΜΟΥΣΟΥΛΜΑΝ|ΟΥΛ|Π|ΠΕΛΕΚ|ΠΛ|ΠΟΛΙΣ|ΠΟΡΤΟΛ|ΣΑΡΑΚΑΤΣ|ΣΟΥΛΤ|ΤΣΑΡΛΑΤ|ΟΡΦ|ΤΣΙΓΓ|ΤΣΟΠ|ΦΩΤΟΣΤΕΦ|Χ|ΨΥΧΟΠΛ|ΑΓ|ΟΡΦ|ΓΑΛ|ΓΕΡ|ΔΕΚ|ΔΙΠΛ|ΑΜΕΡΙΚΑΝ|ΟΥΡ|ΠΙΘ|ΠΟΥΡΙΤ|Σ|ΖΩΝΤ|ΙΚ|ΚΑΣΤ|ΚΟΠ|ΛΙΧ|ΛΟΥΘΗΡ|ΜΑΙΝΤ|ΜΕΛ|ΣΙΓ|ΣΠ|ΣΤΕΓ|ΤΡΑΓ|ΤΣΑΓ|Φ|ΕΡ|ΑΔΑΠ|ΑΘΙΓΓ|ΑΜΗΧ|ΑΝΙΚ|ΑΝΟΡΓ|ΑΠΗΓ|ΑΠΙΘ|ΑΤΣΙΓΓ|ΒΑΣ|ΒΑΣΚ|ΒΑΘΥΓΑΛ|ΒΙΟΜΗΧ|ΒΡΑΧΥΚ|ΔΙΑΤ|ΔΙΑΦ|ΕΝΟΡΓ|ΘΥΣ|ΚΑΠΝΟΒΙΟΜΗΧ|ΚΑΤΑΓΑΛ|ΚΛΙΒ|ΚΟΙΛΑΡΦ|ΛΙΒ|ΜΕΓΛΟΒΙΟΜΗΧ|ΜΙΚΡΟΒΙΟΜΗΧ|ΝΤΑΒ|ΞΗΡΟΚΛΙΒ|ΟΛΙΓΟΔΑΜ|ΟΛΟΓΑΛ|ΠΕΝΤΑΡΦ|ΠΕΡΗΦ|ΠΕΡΙΤΡ|ΠΛΑΤ|ΠΟΛΥΔΑΠ|ΠΟΛΥΜΗΧ|ΣΤΕΦ|ΤΑΒ|ΤΕΤ|ΥΠΕΡΗΦ|ΥΠΟΚΟΠ|ΧΑΜΗΛΟΔΑΠ|ΨΗΛΟΤΑΒ)$/.test(o[1]))&&(s+="ΑΝ")),null!==(o=/^(.+?)(ΗΣΕΤΕ)$/.exec(s))&&(s=o[1]),null!==(o=/^(.+?)(ΕΤΕ)$/.exec(s))&&(s=o[1],(r(s)||/(ΟΔ|ΑΙΡ|ΦΟΡ|ΤΑΘ|ΔΙΑΘ|ΣΧ|ΕΝΔ|ΕΥΡ|ΤΙΘ|ΥΠΕΡΘ|ΡΑΘ|ΕΝΘ|ΡΟΘ|ΣΘ|ΠΥΡ|ΑΙΝ|ΣΥΝΔ|ΣΥΝ|ΣΥΝΘ|ΧΩΡ|ΠΟΝ|ΒΡ|ΚΑΘ|ΕΥΘ|ΕΚΘ|ΝΕΤ|ΡΟΝ|ΑΡΚ|ΒΑΡ|ΒΟΛ|ΩΦΕΛ)$/.test(o[1])||/^(ΑΒΑΡ|ΒΕΝ|ΕΝΑΡ|ΑΒΡ|ΑΔ|ΑΘ|ΑΝ|ΑΠΛ|ΒΑΡΟΝ|ΝΤΡ|ΣΚ|ΚΟΠ|ΜΠΟΡ|ΝΙΦ|ΠΑΓ|ΠΑΡΑΚΑΛ|ΣΕΡΠ|ΣΚΕΛ|ΣΥΡΦ|ΤΟΚ|Υ|Δ|ΕΜ|ΘΑΡΡ|Θ)$/.test(o[1]))&&(s+="ΕΤ")),null!==(o=/^(.+?)(ΟΝΤΑΣ|ΩΝΤΑΣ)$/.exec(s))&&(s=o[1],/^ΑΡΧ$/.test(o[1])&&(s+="ΟΝΤ"),/ΚΡΕ$/.test(o[1])&&(s+="ΩΝΤ")),null!==(o=/^(.+?)(ΟΜΑΣΤΕ|ΙΟΜΑΣΤΕ)$/.exec(s))&&(s=o[1],/^ΟΝ$/.test(o[1])&&(s+="ΟΜΑΣΤ")),null!==(o=/^(.+?)(ΙΕΣΤΕ)$/.exec(s))&&(s=o[1],/^(Π|ΑΠ|ΣΥΜΠ|ΑΣΥΜΠ|ΑΚΑΤΑΠ|ΑΜΕΤΑΜΦ)$/.test(o[1])&&(s+="ΙΕΣΤ")),null!==(o=/^(.+?)(ΕΣΤΕ)$/.exec(s))&&(s=o[1],/^(ΑΛ|ΑΡ|ΕΚΤΕΛ|Ζ|Μ|Ξ|ΠΑΡΑΚΑΛ|ΠΡΟ|ΝΙΣ)$/.test(o[1])&&(s+="ΕΣΤ")),null!==(o=/^(.+?)(ΗΘΗΚΑ|ΗΘΗΚΕΣ|ΗΘΗΚΕ)$/.exec(s))&&(s=o[1]),null!==(o=/^(.+?)(ΗΚΑ|ΗΚΕΣ|ΗΚΕ)$/.exec(s))&&(s=o[1],(/(ΣΚΩΛ|ΣΚΟΥΛ|ΝΑΡΘ|ΣΦ|ΟΘ|ΠΙΘ)$/.test(o[1])||/^(ΔΙΑΘ|Θ|ΠΑΡΑΚΑΤΑΘ|ΠΡΟΣΘ|ΣΥΝΘ)$/.test(o[1]))&&(s+="ΗΚ")),null!==(o=/^(.+?)(ΟΥΣΑ|ΟΥΣΕΣ|ΟΥΣΕ)$/.exec(s))&&(s=o[1],(t(s)||/^(ΦΑΡΜΑΚ|ΧΑΔ|ΑΓΚ|ΑΝΑΡΡ|ΒΡΟΜ|ΕΚΛΙΠ|ΛΑΜΠΙΔ|ΛΕΧ|Μ|ΠΑΤ|Ρ|Λ|ΜΕΔ|ΜΕΣΑΖ|ΥΠΟΤΕΙΝ|ΑΜ|ΑΙΘ|ΑΝΗΚ|ΔΕΣΠΟΖ|ΕΝΔΙΑΦΕΡ)$/.test(o[1])||/(ΠΟΔΑΡ|ΒΛΕΠ|ΠΑΝΤΑΧ|ΦΡΥΔ|ΜΑΝΤΙΛ|ΜΑΛΛ|ΚΥΜΑΤ|ΛΑΧ|ΛΗΓ|ΦΑΓ|ΟΜ|ΠΡΩΤ)$/.test(o[1]))&&(s+="ΟΥΣ")),null!==(o=/^(.+?)(ΑΓΑ|ΑΓΕΣ|ΑΓΕ)$/.exec(s))&&(s=o[1],(/^(ΑΒΑΣΤ|ΠΟΛΥΦ|ΑΔΗΦ|ΠΑΜΦ|Ρ|ΑΣΠ|ΑΦ|ΑΜΑΛ|ΑΜΑΛΛΙ|ΑΝΥΣΤ|ΑΠΕΡ|ΑΣΠΑΡ|ΑΧΑΡ|ΔΕΡΒΕΝ|ΔΡΟΣΟΠ|ΞΕΦ|ΝΕΟΠ|ΝΟΜΟΤ|ΟΛΟΠ|ΟΜΟΤ|ΠΡΟΣΤ|ΠΡΟΣΩΠΟΠ|ΣΥΜΠ|ΣΥΝΤ|Τ|ΥΠΟΤ|ΧΑΡ|ΑΕΙΠ|ΑΙΜΟΣΤ|ΑΝΥΠ|ΑΠΟΤ|ΑΡΤΙΠ|ΔΙΑΤ|ΕΝ|ΕΠΙΤ|ΚΡΟΚΑΛΟΠ|ΣΙΔΗΡΟΠ|Λ|ΝΑΥ|ΟΥΛΑΜ|ΟΥΡ|Π|ΤΡ|Μ)$/.test(o[1])||/(ΟΦ|ΠΕΛ|ΧΟΡΤ|ΛΛ|ΣΦ|ΡΠ|ΦΡ|ΠΡ|ΛΟΧ|ΣΜΗΝ)$/.test(o[1])&&!/^(ΨΟΦ|ΝΑΥΛΟΧ)$/.test(o[1])||/(ΚΟΛΛ)$/.test(o[1]))&&(s+="ΑΓ")),null!==(o=/^(.+?)(ΗΣΕ|ΗΣΟΥ|ΗΣΑ)$/.exec(s))&&(s=o[1],/^(Ν|ΧΕΡΣΟΝ|ΔΩΔΕΚΑΝ|ΕΡΗΜΟΝ|ΜΕΓΑΛΟΝ|ΕΠΤΑΝ|Ι)$/.test(o[1])&&(s+="ΗΣ")),null!==(o=/^(.+?)(ΗΣΤΕ)$/.exec(s))&&(s=o[1],/^(ΑΣΒ|ΣΒ|ΑΧΡ|ΧΡ|ΑΠΛ|ΑΕΙΜΝ|ΔΥΣΧΡ|ΕΥΧΡ|ΚΟΙΝΟΧΡ|ΠΑΛΙΜΨ)$/.test(o[1])&&(s+="ΗΣΤ")),null!==(o=/^(.+?)(ΟΥΝΕ|ΗΣΟΥΝΕ|ΗΘΟΥΝΕ)$/.exec(s))&&(s=o[1],/^(Ν|Ρ|ΣΠΙ|ΣΤΡΑΒΟΜΟΥΤΣ|ΚΑΚΟΜΟΥΤΣ|ΕΞΩΝ)$/.test(o[1])&&(s+="ΟΥΝ")),null!==(o=/^(.+?)(ΟΥΜΕ|ΗΣΟΥΜΕ|ΗΘΟΥΜΕ)$/.exec(s))&&(s=o[1],/^(ΠΑΡΑΣΟΥΣ|Φ|Χ|ΩΡΙΟΠΛ|ΑΖ|ΑΛΛΟΣΟΥΣ|ΑΣΟΥΣ)$/.test(o[1])&&(s+="ΟΥΜ")),null!=(o=/^(.+?)(ΜΑΤΟΙ|ΜΑΤΟΥΣ|ΜΑΤΟ|ΜΑΤΑ|ΜΑΤΩΣ|ΜΑΤΩΝ|ΜΑΤΟΣ|ΜΑΤΕΣ|ΜΑΤΗ|ΜΑΤΗΣ|ΜΑΤΟΥ)$/.exec(s))&&(s=o[1]+"Μ",/^(ΓΡΑΜ)$/.test(o[1])?s+="Α":/^(ΓΕ|ΣΤΑ)$/.test(o[1])&&(s+="ΑΤ")),null!==(o=/^(.+?)(ΟΥΑ)$/.exec(s))&&(s=o[1]+"ΟΥ"),n.length===s.length&&null!==(o=/^(.+?)(Α|ΑΓΑΤΕ|ΑΓΑΝ|ΑΕΙ|ΑΜΑΙ|ΑΝ|ΑΣ|ΑΣΑΙ|ΑΤΑΙ|ΑΩ|Ε|ΕΙ|ΕΙΣ|ΕΙΤΕ|ΕΣΑΙ|ΕΣ|ΕΤΑΙ|Ι|ΙΕΜΑΙ|ΙΕΜΑΣΤΕ|ΙΕΤΑΙ|ΙΕΣΑΙ|ΙΕΣΑΣΤΕ|ΙΟΜΑΣΤΑΝ|ΙΟΜΟΥΝ|ΙΟΜΟΥΝΑ|ΙΟΝΤΑΝ|ΙΟΝΤΟΥΣΑΝ|ΙΟΣΑΣΤΑΝ|ΙΟΣΑΣΤΕ|ΙΟΣΟΥΝ|ΙΟΣΟΥΝΑ|ΙΟΤΑΝ|ΙΟΥΜΑ|ΙΟΥΜΑΣΤΕ|ΙΟΥΝΤΑΙ|ΙΟΥΝΤΑΝ|Η|ΗΔΕΣ|ΗΔΩΝ|ΗΘΕΙ|ΗΘΕΙΣ|ΗΘΕΙΤΕ|ΗΘΗΚΑΤΕ|ΗΘΗΚΑΝ|ΗΘΟΥΝ|ΗΘΩ|ΗΚΑΤΕ|ΗΚΑΝ|ΗΣ|ΗΣΑΝ|ΗΣΑΤΕ|ΗΣΕΙ|ΗΣΕΣ|ΗΣΟΥΝ|ΗΣΩ|Ο|ΟΙ|ΟΜΑΙ|ΟΜΑΣΤΑΝ|ΟΜΟΥΝ|ΟΜΟΥΝΑ|ΟΝΤΑΙ|ΟΝΤΑΝ|ΟΝΤΟΥΣΑΝ|ΟΣ|ΟΣΑΣΤΑΝ|ΟΣΑΣΤΕ|ΟΣΟΥΝ|ΟΣΟΥΝΑ|ΟΤΑΝ|ΟΥ|ΟΥΜΑΙ|ΟΥΜΑΣΤΕ|ΟΥΝ|ΟΥΝΤΑΙ|ΟΥΝΤΑΝ|ΟΥΣ|ΟΥΣΑΝ|ΟΥΣΑΤΕ|Υ||ΥΑ|ΥΣ|Ω|ΩΝ|ΟΙΣ)$/.exec(s))&&(s=o[1]),null!=(o=/^(.+?)(ΕΣΤΕΡ|ΕΣΤΑΤ|ΟΤΕΡ|ΟΤΑΤ|ΥΤΕΡ|ΥΤΑΤ|ΩΤΕΡ|ΩΤΑΤ)$/.exec(s))&&(/^(ΕΞ|ΕΣ|ΑΝ|ΚΑΤ|Κ|ΠΡ)$/.test(o[1])||(s=o[1]),/^(ΚΑ|Μ|ΕΛΕ|ΛΕ|ΔΕ)$/.test(o[1])&&(s+="ΥΤ")),s}var 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")),i.Pipeline.registerFunction(i.fi.stopWordFilter,"stopWordFilter-fi")}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.fr.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * Lunr languages, `French` language 3 | * https://github.com/MihaiValentin/lunr-languages 4 | * 5 | * Copyright 2014, Mihai Valentin 6 | * http://www.mozilla.org/MPL/ 7 | */ 8 | /*! 9 | * based on 10 | * Snowball JavaScript Library v0.3 11 | * http://code.google.com/p/urim/ 12 | * http://snowball.tartarus.org/ 13 | * 14 | * Copyright 2010, Oleg Mazko 15 | * http://www.mozilla.org/MPL/ 16 | */ 17 | 18 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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étiez étions été étée étées étés êtes".split(" ")),e.Pipeline.registerFunction(e.fr.stopWordFilter,"stopWordFilter-fr")}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.he.min.js: -------------------------------------------------------------------------------- 1 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.hi=function(){this.pipeline.reset(),this.pipeline.add(e.hi.trimmer,e.hi.stopWordFilter,e.hi.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.hi.stemmer))},e.hi.wordCharacters="ऀ-ःऄ-एऐ-टठ-यर-िी-ॏॐ-य़ॠ-९॰-ॿa-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.hi.trimmer=e.trimmerSupport.generateTrimmer(e.hi.wordCharacters),e.Pipeline.registerFunction(e.hi.trimmer,"trimmer-hi"),e.hi.stopWordFilter=e.generateStopWordFilter("अत अपना अपनी अपने अभी अंदर आदि आप इत्यादि इन इनका इन्हीं इन्हें इन्हों इस इसका इसकी इसके इसमें इसी इसे उन उनका उनकी उनके उनको उन्हीं उन्हें उन्हों उस उसके उसी उसे एक एवं एस ऐसे और कई कर करता करते करना करने करें कहते कहा का काफ़ी कि कितना किन्हें किन्हों किया किर किस किसी किसे की कुछ कुल के को कोई कौन कौनसा गया घर जब जहाँ जा जितना जिन जिन्हें जिन्हों जिस जिसे जीधर जैसा जैसे जो तक तब तरह तिन तिन्हें तिन्हों तिस तिसे तो था थी थे दबारा दिया दुसरा दूसरे दो द्वारा न नके नहीं ना निहायत नीचे ने पर पहले पूरा पे फिर बनी बही बहुत बाद बाला बिलकुल भी भीतर मगर मानो मे में यदि यह यहाँ यही या यिह ये रखें रहा रहे ऱ्वासा लिए लिये लेकिन व वग़ैरह वर्ग वह वहाँ वहीं वाले वुह वे वो सकता सकते सबसे सभी साथ साबुत साभ सारा से सो संग ही हुआ हुई हुए है हैं हो होता होती होते होना होने".split(" ")),e.hi.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var r=e.wordcut;r.init(),e.hi.tokenizer=function(i){if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(r){return isLunr2?new e.Token(r.toLowerCase()):r.toLowerCase()});var t=i.toString().toLowerCase().replace(/^\s+/,"");return r.cut(t).split("|")},e.Pipeline.registerFunction(e.hi.stemmer,"stemmer-hi"),e.Pipeline.registerFunction(e.hi.stopWordFilter,"stopWordFilter-hi")}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.hu.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * Lunr languages, `Hungarian` language 3 | * https://github.com/MihaiValentin/lunr-languages 4 | * 5 | * Copyright 2014, Mihai Valentin 6 | * http://www.mozilla.org/MPL/ 7 | */ 8 | /*! 9 | * based on 10 | * Snowball JavaScript Library v0.3 11 | * http://code.google.com/p/urim/ 12 | * http://snowball.tartarus.org/ 13 | * 14 | * Copyright 2010, Oleg Mazko 15 | * http://www.mozilla.org/MPL/ 16 | */ 17 | 18 | !function(e,n){"function"==typeof define&&define.amd?define(n):"object"==typeof exports?module.exports=n():n()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.hu=function(){this.pipeline.reset(),this.pipeline.add(e.hu.trimmer,e.hu.stopWordFilter,e.hu.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.hu.stemmer))},e.hu.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.hu.trimmer=e.trimmerSupport.generateTrimmer(e.hu.wordCharacters),e.Pipeline.registerFunction(e.hu.trimmer,"trimmer-hu"),e.hu.stemmer=function(){var n=e.stemmerSupport.Among,r=e.stemmerSupport.SnowballProgram,i=new function(){function e(){var e,n=L.cursor;if(d=L.limit,L.in_grouping(W,97,252))for(;;){if(e=L.cursor,L.out_grouping(W,97,252))return L.cursor=e,L.find_among(g,8)||(L.cursor=e,e=L.limit)return void(d=e);L.cursor++}if(L.cursor=n,L.out_grouping(W,97,252)){for(;!L.in_grouping(W,97,252);){if(L.cursor>=L.limit)return;L.cursor++}d=L.cursor}}function i(){return d<=L.cursor}function a(){var 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e(),L.limit_backward=n,L.cursor=L.limit,c(),L.cursor=L.limit,o(),L.cursor=L.limit,w(),L.cursor=L.limit,l(),L.cursor=L.limit,u(),L.cursor=L.limit,k(),L.cursor=L.limit,f(),L.cursor=L.limit,b(),L.cursor=L.limit,m(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}}(),e.Pipeline.registerFunction(e.hu.stemmer,"stemmer-hu"),e.hu.stopWordFilter=e.generateStopWordFilter("a abban ahhoz ahogy ahol aki akik akkor alatt amely amelyek amelyekben amelyeket amelyet amelynek ami amikor amit amolyan amíg annak arra arról az azok azon azonban azt aztán azután azzal azért be belül benne bár cikk cikkek cikkeket csak de e ebben eddig egy egyes egyetlen egyik egyre egyéb egész ehhez ekkor el ellen elsõ elég elõ elõször elõtt emilyen ennek erre ez ezek ezen ezt ezzel ezért fel felé hanem hiszen hogy hogyan igen ill ill. illetve ilyen ilyenkor ismét ison itt jobban jó jól kell kellett keressünk keresztül ki kívül között közül legalább legyen lehet lehetett lenne lenni lesz lett maga magát majd majd meg mellett mely melyek mert mi mikor milyen minden mindenki mindent mindig mint mintha mit mivel miért most már más másik még míg nagy nagyobb nagyon ne nekem neki nem nincs néha néhány nélkül olyan ott pedig persze rá s saját sem semmi sok sokat sokkal szemben szerint szinte számára talán tehát teljes tovább továbbá több ugyanis utolsó után utána vagy vagyis vagyok valaki valami valamint való van vannak vele vissza viszont volna volt voltak voltam voltunk által általában át én éppen és így õ õk õket össze úgy új újabb újra".split(" ")),e.Pipeline.registerFunction(e.hu.stopWordFilter,"stopWordFilter-hu")}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.hy.min.js: -------------------------------------------------------------------------------- 1 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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avevo avrai avranno avrebbe avrebbero avrei avremmo avremo avreste avresti avrete avrà avrò avuta avute avuti avuto c che chi ci coi col come con contro cui da dagl dagli dai dal dall dalla dalle dallo degl degli dei del dell della delle dello di dov dove e ebbe ebbero ebbi ed era erano eravamo eravate eri ero essendo faccia facciamo facciano facciate faccio facemmo facendo facesse facessero facessi facessimo faceste facesti faceva facevamo facevano facevate facevi facevo fai fanno farai faranno farebbe farebbero farei faremmo faremo fareste faresti farete farà farò fece fecero feci fosse fossero fossi fossimo foste fosti fu fui fummo furono gli ha hai hanno ho i il in io l la le lei li lo loro lui ma mi mia mie miei mio ne negl negli nei nel nell nella nelle nello noi non nostra nostre nostri nostro o per perché più quale quanta quante quanti quanto quella quelle quelli quello questa queste questi questo sarai saranno sarebbe sarebbero sarei saremmo saremo sareste saresti sarete sarà sarò se sei si sia siamo siano siate siete sono sta stai stando stanno starai staranno starebbe starebbero starei staremmo staremo stareste staresti starete starà starò stava stavamo stavano stavate stavi stavo stemmo stesse stessero stessi stessimo steste stesti stette stettero stetti stia stiamo stiano stiate sto su sua sue sugl sugli sui sul sull sulla sulle sullo suo suoi ti tra tu tua tue tuo tuoi tutti tutto un una uno vi voi vostra vostre vostri vostro è".split(" ")),e.Pipeline.registerFunction(e.it.stopWordFilter,"stopWordFilter-it")}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.ja.min.js: -------------------------------------------------------------------------------- 1 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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n("ость",-1,1)],q=[new n("ейше",-1,1),new n("н",-1,2),new n("ейш",-1,1),new n("ь",-1,3)],S=[33,65,8,232],W=new r;this.setCurrent=function(e){W.setCurrent(e)},this.getCurrent=function(){return W.getCurrent()},this.stem=function(){return w(),W.cursor=W.limit,!(W.cursor=i&&(e-=i,t[e>>3]&1<<(7&e)))return this.cursor++,!0}return!1},in_grouping_b:function(t,i,s){if(this.cursor>this.limit_backward){var e=r.charCodeAt(this.cursor-1);if(e<=s&&e>=i&&(e-=i,t[e>>3]&1<<(7&e)))return this.cursor--,!0}return!1},out_grouping:function(t,i,s){if(this.cursors||e>3]&1<<(7&e)))return this.cursor++,!0}return!1},out_grouping_b:function(t,i,s){if(this.cursor>this.limit_backward){var e=r.charCodeAt(this.cursor-1);if(e>s||e>3]&1<<(7&e)))return this.cursor--,!0}return!1},eq_s:function(t,i){if(this.limit-this.cursor>1),f=0,l=o0||e==s||c)break;c=!0}}for(;;){var _=t[s];if(o>=_.s_size){if(this.cursor=n+_.s_size,!_.method)return _.result;var b=_.method();if(this.cursor=n+_.s_size,b)return 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s=this.replace_s(r,t,i);r<=this.bra&&(this.bra+=s),r<=this.ket&&(this.ket+=s)},slice_to:function(){return this.slice_check(),r.substring(this.bra,this.ket)},eq_v_b:function(r){return this.eq_s_b(r.length,r)}}}},r.trimmerSupport={generateTrimmer:function(r){var t=new RegExp("^[^"+r+"]+"),i=new RegExp("[^"+r+"]+$");return function(r){return"function"==typeof r.update?r.update(function(r){return r.replace(t,"").replace(i,"")}):r.replace(t,"").replace(i,"")}}}}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.sv.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * Lunr languages, `Swedish` language 3 | * https://github.com/MihaiValentin/lunr-languages 4 | * 5 | * Copyright 2014, Mihai Valentin 6 | * http://www.mozilla.org/MPL/ 7 | */ 8 | /*! 9 | * based on 10 | * Snowball JavaScript Library v0.3 11 | * http://code.google.com/p/urim/ 12 | * http://snowball.tartarus.org/ 13 | * 14 | * Copyright 2010, Oleg Mazko 15 | * http://www.mozilla.org/MPL/ 16 | */ 17 | 18 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.sv=function(){this.pipeline.reset(),this.pipeline.add(e.sv.trimmer,e.sv.stopWordFilter,e.sv.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.sv.stemmer))},e.sv.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.sv.trimmer=e.trimmerSupport.generateTrimmer(e.sv.wordCharacters),e.Pipeline.registerFunction(e.sv.trimmer,"trimmer-sv"),e.sv.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,t=new function(){function e(){var e,r=w.cursor+3;if(o=w.limit,0<=r||r<=w.limit){for(a=r;;){if(e=w.cursor,w.in_grouping(l,97,246)){w.cursor=e;break}if(w.cursor=e,w.cursor>=w.limit)return;w.cursor++}for(;!w.out_grouping(l,97,246);){if(w.cursor>=w.limit)return;w.cursor++}o=w.cursor,o=o&&(w.limit_backward=o,w.cursor=w.limit,w.ket=w.cursor,e=w.find_among_b(u,37),w.limit_backward=r,e))switch(w.bra=w.cursor,e){case 1:w.slice_del();break;case 2:w.in_grouping_b(d,98,121)&&w.slice_del()}}function i(){var e=w.limit_backward;w.cursor>=o&&(w.limit_backward=o,w.cursor=w.limit,w.find_among_b(c,7)&&(w.cursor=w.limit,w.ket=w.cursor,w.cursor>w.limit_backward&&(w.bra=--w.cursor,w.slice_del())),w.limit_backward=e)}function s(){var e,r;if(w.cursor>=o){if(r=w.limit_backward,w.limit_backward=o,w.cursor=w.limit,w.ket=w.cursor,e=w.find_among_b(m,5))switch(w.bra=w.cursor,e){case 1:w.slice_del();break;case 2:w.slice_from("lös");break;case 3:w.slice_from("full")}w.limit_backward=r}}var a,o,u=[new r("a",-1,1),new r("arna",0,1),new r("erna",0,1),new r("heterna",2,1),new r("orna",0,1),new r("ad",-1,1),new r("e",-1,1),new r("ade",6,1),new r("ande",6,1),new r("arne",6,1),new r("are",6,1),new r("aste",6,1),new r("en",-1,1),new r("anden",12,1),new r("aren",12,1),new r("heten",12,1),new r("ern",-1,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",18,1),new r("or",-1,1),new r("s",-1,2),new r("as",21,1),new r("arnas",22,1),new r("ernas",22,1),new r("ornas",22,1),new r("es",21,1),new r("ades",26,1),new r("andes",26,1),new r("ens",21,1),new r("arens",29,1),new r("hetens",29,1),new r("erns",21,1),new r("at",-1,1),new r("andet",-1,1),new r("het",-1,1),new r("ast",-1,1)],c=[new r("dd",-1,-1),new r("gd",-1,-1),new r("nn",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1),new r("tt",-1,-1)],m=[new r("ig",-1,1),new r("lig",0,1),new r("els",-1,1),new r("fullt",-1,3),new r("löst",-1,2)],l=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,24,0,32],d=[119,127,149],w=new n;this.setCurrent=function(e){w.setCurrent(e)},this.getCurrent=function(){return w.getCurrent()},this.stem=function(){var r=w.cursor;return e(),w.limit_backward=r,w.cursor=w.limit,t(),w.cursor=w.limit,i(),w.cursor=w.limit,s(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return t.setCurrent(e),t.stem(),t.getCurrent()}):(t.setCurrent(e),t.stem(),t.getCurrent())}}(),e.Pipeline.registerFunction(e.sv.stemmer,"stemmer-sv"),e.sv.stopWordFilter=e.generateStopWordFilter("alla allt att av blev bli blir blivit de dem den denna deras dess dessa det detta dig din dina ditt du där då efter ej eller en er era ert ett från för ha hade han hans har henne hennes hon honom hur här i icke ingen inom inte jag ju kan kunde man med mellan men mig min mina mitt mot mycket ni nu när någon något några och om oss på samma sedan sig sin sina sitta själv skulle som så sådan sådana sådant till under upp ut utan vad var vara varför varit varje vars vart vem vi vid vilka vilkas vilken vilket vår våra vårt än är åt över".split(" ")),e.Pipeline.registerFunction(e.sv.stopWordFilter,"stopWordFilter-sv")}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.ta.min.js: -------------------------------------------------------------------------------- 1 | !function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.ta=function(){this.pipeline.reset(),this.pipeline.add(e.ta.trimmer,e.ta.stopWordFilter,e.ta.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.ta.stemmer))},e.ta.wordCharacters="஀-உஊ-ஏஐ-ஙச-ட஠-னப-யர-ஹ஺-ிீ-௉ொ-௏ௐ-௙௚-௟௠-௩௪-௯௰-௹௺-௿a-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.ta.trimmer=e.trimmerSupport.generateTrimmer(e.ta.wordCharacters),e.Pipeline.registerFunction(e.ta.trimmer,"trimmer-ta"),e.ta.stopWordFilter=e.generateStopWordFilter("அங்கு அங்கே அது அதை அந்த அவர் அவர்கள் அவள் அவன் அவை ஆக ஆகவே ஆகையால் ஆதலால் ஆதலினால் ஆனாலும் ஆனால் இங்கு இங்கே இது இதை இந்த இப்படி இவர் இவர்கள் இவள் இவன் இவை இவ்வளவு உனக்கு உனது உன் உன்னால் எங்கு எங்கே எது எதை எந்த எப்படி எவர் எவர்கள் எவள் எவன் எவை எவ்வளவு எனக்கு எனது எனவே என் என்ன என்னால் ஏது ஏன் தனது தன்னால் தானே தான் நாங்கள் நாம் நான் நீ நீங்கள்".split(" ")),e.ta.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var t=e.wordcut;t.init(),e.ta.tokenizer=function(r){if(!arguments.length||null==r||void 0==r)return[];if(Array.isArray(r))return r.map(function(t){return isLunr2?new e.Token(t.toLowerCase()):t.toLowerCase()});var i=r.toString().toLowerCase().replace(/^\s+/,"");return t.cut(i).split("|")},e.Pipeline.registerFunction(e.ta.stemmer,"stemmer-ta"),e.Pipeline.registerFunction(e.ta.stopWordFilter,"stopWordFilter-ta")}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.te.min.js: -------------------------------------------------------------------------------- 1 | !function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.te=function(){this.pipeline.reset(),this.pipeline.add(e.te.trimmer,e.te.stopWordFilter,e.te.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.te.stemmer))},e.te.wordCharacters="ఀ-ఄఅ-ఔక-హా-ౌౕ-ౖౘ-ౚౠ-ౡౢ-ౣ౦-౯౸-౿఼ఽ్ౝ౷౤౥",e.te.trimmer=e.trimmerSupport.generateTrimmer(e.te.wordCharacters),e.Pipeline.registerFunction(e.te.trimmer,"trimmer-te"),e.te.stopWordFilter=e.generateStopWordFilter("అందరూ అందుబాటులో అడగండి అడగడం అడ్డంగా అనుగుణంగా అనుమతించు అనుమతిస్తుంది అయితే ఇప్పటికే ఉన్నారు ఎక్కడైనా ఎప్పుడు ఎవరైనా ఎవరో ఏ ఏదైనా ఏమైనప్పటికి ఒక ఒకరు కనిపిస్తాయి కాదు కూడా గా గురించి చుట్టూ చేయగలిగింది తగిన తర్వాత దాదాపు దూరంగా నిజంగా పై ప్రకారం ప్రక్కన మధ్య మరియు మరొక మళ్ళీ మాత్రమే మెచ్చుకో వద్ద వెంట వేరుగా వ్యతిరేకంగా సంబంధం".split(" ")),e.te.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var t=e.wordcut;t.init(),e.te.tokenizer=function(r){if(!arguments.length||null==r||void 0==r)return[];if(Array.isArray(r))return r.map(function(t){return isLunr2?new e.Token(t.toLowerCase()):t.toLowerCase()});var i=r.toString().toLowerCase().replace(/^\s+/,"");return t.cut(i).split("|")},e.Pipeline.registerFunction(e.te.stemmer,"stemmer-te"),e.Pipeline.registerFunction(e.te.stopWordFilter,"stopWordFilter-te")}}); -------------------------------------------------------------------------------- /site/assets/javascripts/lunr/min/lunr.th.min.js: -------------------------------------------------------------------------------- 1 | !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. 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