Create README.md

README.md

@@ -0,0 +1,43 @@
+# Redactable-LLM
+The high-level overview for integrating multiple Open Source Large Language Models within the AutoGen Framework is as follows:
+
+### Development of Custom Agents
+- **Agent Design**: Tasks include NLP/NER/PII identification, interpreting natural language commands, executing document redaction, and final verification.
+- **Customization**: Custom agents trained on specific tasks related to each aspect of the redaction process.  
+- **Human Interaction**: Implement features to facilitate seamless human-agent interaction, allowing users to input commands and queries naturally (Optional)
+
+### LLM & VLLM AutoGen Integration  
+
+- **Model Selection**: Automatic, task-dependent agent selection.
+- **Enhanced Inference**: Enhanced LLM inference features for optimal performance, including tuning, caching, error handling, and templating.
+- **Quality Control**: Vision agents analyze redacted documents using Set-of-Mark (SoM) prompting. Rejected documents are reprocessed and reviewed.
+- 
+![AutoGen Agents](https://i.imgur.com/aFgV7yd.png)
+
+### System Optimization  
+- **Workflow Automation**: Automate the redaction workflow using a blend of LLMs, custom agents, and human inputs for efficient detection and redaction of sensitive information.  
+- **Performance Maximization**: Optimize the system for both efficiency and accuracy, utilizing AutoGen's complex workflow management features.  
+
+### User Interface Development
+- **Interface Design**: Develop a user-friendly interface that enables non-technical users to interact with the system via natural language prompts.  
+- **Feedback Integration**: Implement a feedback loop to continuously refine the system's accuracy and user-friendliness based on user inputs.
+- **User Knowledgebase**: (Optional) User account, profile, and domain knowledge will be accessible by the `Research` agent, for personalized interaction and results.  
+
+### Training, Testing and Validation  
+- **Model Training**: Develop new datasets, focused on document understanding related to redaction.   
+- **Unit Testing**: Conduct extensive unit tests to ensure individual system components function correctly. 
+- **System Testing**: Perform comprehensive end-to-end testing to validate the entire redaction process, from user input to output.  
+- **User Trials**: Facilitate user trials to gather feedback and make necessary system adjustments.
+---
+
+- #### Mistral AI (LLM)
+  [Paper](https://mistral.ai/news/mixtral-of-experts/) | [Model](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
+
+- #### QwenLM (VLLM)
+  [Paper](https://arxiv.org/abs/2308.12966) | [Code](https://github.com/QwenLM/Qwen-VL?tab=readme-ov-file) | [Paper: Set-of-Mark Prompting](https://arxiv.org/abs/2310.11441)
+
+- #### AutoGen
+  [Paper](https://arxiv.org/abs/2308.08155) | [Code](https://github.com/microsoft/autogen/tree/main)
+
+- #### Gretel AI (Synthetic Dataset Generation)
+  [Model Page](https://gretel.ai/solutions/public-sector) | [Code](https://github.com/gretelai) | [Paper: Textbooks Are All You Need II](https://arxiv.org/abs/2309.05463)