--- license: mit --- # Responsible-AI-Moderation Model ## Table of Contents - [Introduction](#introduction) - [Features](#features) - [Installation](#installation) - [Set Configuration Variables](#set-configuration-variables) - [Models Required](#models-required) - [Running the Application](#running-the-application) - [License](#license) - [Contact](#contact) ## Introduction The **Moderation Model** module acts as a central hub for machine learning models for prompt injection, toxicity, jailbreak, restricted topic, custom theme and refusal checks. It provides the endpoints to utilize the response generated by these models. ## Features The **Moderation Model** module acts as a wrapper for the traditional AI models we are using for various checks like prompt injection, jailbreak, toxicity etc. ## Installation To run the application, first we need to install Python and the necessary packages: 1. Install Python (version >= 3.9 & <3.12) from the [official website](https://www.python.org/downloads/) and ensure it is added to your system PATH. 2. Clone the repository : responsible-ai-ModerationModel: ```sh git clone ``` 3. Navigate to the `responsible-ai-ModerationModel` directory: ```sh cd responsible-ai-ModerationModel ``` 4. Create a virtual environment: ```sh python -m venv venv ``` 5. Activate the virtual environment: - On Windows: ```sh .\venv\Scripts\activate ``` 6. Go to the `requirements` directory where the `requirement.txt` file is present. In the `requirement.txt` file comment the ```sh lib/torch-2.2.0+cu118-cp39-cp39-linux_x86_64.whl ``` **Note:** Download appropriate torch version supporting python version which is installed [i.e if Python version is 3.10 use torch-2.2.0+cu118-**cp310**-**cp310**-**linux**_x86_64.whl, where cp310 denotes python version 3.10 and linux denotes OS which can be linux/win and **_not applicable for Mac_**] **Note:** If working in windows as this is for linux and replace ```sh lib/ ``` with ```sh ../lib/ ``` **Note:** If working in Mac Os, run the below command after running requirement.txt ```sh pip install --pre torch torchvision torchaudio \--extra-index-url https://download.pytorch.org/whl/nightly/cpu ``` Download and place the en_core_web_lg-3.5.0-py3-none-any.whl inside the lib folder. [en_core_web_lg](https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-3.5.0/en_core_web_lg-3.5.0-py3-none-any.whl) and install the requirements: ```sh pip install -r requirement.txt ``` **Note:** when running requirement.txt, if getting error related to "cuda-python" then comment cuda-python from requirement.txt file and run pip install again Install the fastapi library as well, use the following command: ```sh pip install fastapi ``` ## Set Configuration Variables After installing all the required packages, configure the variables necessary to run the APIs. 1. Navigate to the `src` directory: ```sh cd .. ``` 2. Locate the `.env` file, which contains keys like the following: ```sh workers=1 WORKERS="${workers}" # DB_NAME="${dbname}" # DB_USERNAME="${username}" # DB_PWD="${password}" # DB_IP="${ipaddress}" # DB_PORT="${port}" # MONGO_PATH="mongodb://${DB_USERNAME}:${DB_PWD}@${DB_IP}:${DB_PORT}/" # MONGO_PATH= "mongodb://localhost:27017/" ``` 3. Replace the placeholders with your actual values. ## Models Required The following models are required to run the application. Download all the model files from the links provided, and place it in the folder name provided. 1. [Prompt Injection](https://huggingface.co/deepset/deberta-v3-base-injection/tree/main) Files required to download here are : model.safetensors, config.json, tokenizer_config.json, tokenizer.json, special_tokens_map.json. Name the folder as 'dbertaInjection'. 2. [Restricted Topic](https://huggingface.co/MoritzLaurer/deberta-v3-base-zeroshot-v2.0/tree/main) Files required to download here are : model.safetensors, added_tokens.json, config.json, special_tokens_map.json, spm.model, tokenizer.json, tokenizer_config.json. Name the folder as 'restricted-dberta-base-zeroshot-v2'. 3. [Sentence Transformer Model](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1/tree/main) Files required to download here are : 1_Pooling folder, pytorch_model.bin, vocab.txt, tokenizer.json, tokenizer_config.json, special_tokens_map.json, sentence_bert_config.json, modules.json, config.json, config_sentence_transformers.json. Name the folder as 'multi-qa-mpnet-base-dot-v1'. 4. [Detoxify](https://huggingface.co/FacebookAI/roberta-base/tree/main) Files required to download here are : vocab.json, tokenizer.json, merges.txt, config.json. Now download the model checkpoint file from this url and keep it under this folder - [toxic_model_ckpt_file](https://github.com/unitaryai/detoxify/releases/download/v0.3-alpha/toxic_debiased-c7548aa0.ckpt) Name the folder as 'detoxify'. Place the above folders in a folder named 'models' in the following way: 'responsible-ai-mm-flask/models'. ## Running the Application Once we have completed all the aforementioned steps, we can start the service. 1. Navigate to the `src` directory: 2. Run `main.py` file: ```sh python main.py ``` 3. PORT_NO : Use the Port No that is configured in .env file. Open the following URL in your browser: `http://localhost:/rai/v1/raimoderationmodels/docs` Note:- To address the issue where the Passport Number is not recognized in Privacy, modify the "piiEntitiesToBeRedacted" field in the privacy() under service.py file (line no: 98) from None to an empty list []. This adjustment ensures that the Passport Number is correctly identified. ## License The source code for the project is licensed under the MIT license, which you can find in the [LICENSE.txt](LICENSE.txt) file. ## Contact If you have more questions or need further insights please feel free to connect with us @ Infosysraitoolkit@infosys.com