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+# RAG with SQL Router
+
+We are developing a system that will guide you in creating a custom agent. This agent can query either your Vector DB index for RAG-based retrieval or a separate SQL query engine. 
+
+## 🔍 **The Critical Component: Response Validation**
+
+**While everyone is trying to build agents, no one tells you how to ensure their outputs are reliable.**
+
+**[Cleanlab Codex](https://help.cleanlab.ai/codex/)**, developed by researchers from MIT, offers a platform to evaluate and monitor any RAG or agentic app you're building. This system integrates Cleanlab Codex for automatic response validation, ensuring your AI outputs are trustworthy and continuously improving.
+
+### **Why Cleanlab Codex is Essential:**
+
+- **🔍 Automatic Detection**: Detects inaccurate/unhelpful responses from your AI automatically
+- **📈 Continuous Improvement**: Allows Subject Matter Experts to directly improve responses without engineering intervention  
+- **🎯 Trust Scoring**: Provides reliability metrics for every response
+- **🔄 Real-time Validation**: Validates queries and responses in real-time
+- **📊 Analytics**: Track improvement rates and response quality over time
+
+### **How It Works in This System:**
+
+1. **Query Processing**: Your queries are automatically validated by Cleanlab Codex
+2. **Response Validation**: AI responses are scored for reliability and accuracy
+3. **SME Intervention**: Subject Matter Experts can improve responses through the Codex interface
+4. **Continuous Learning**: The system learns from validated responses for future queries
+
+We use:
+
+- [Llama_Index](https://docs.llamaindex.ai/en/stable/) for orchestration
+- [Docling](https://docling-project.github.io/docling) for simplifying document processing
+- [Milvus](https://milvus.io/) to self-host a VectorDB
+- **[Cleanlab Codex](https://help.cleanlab.ai/codex/)** for **response validation and reliability assurance** ⭐
+- [OpenRouterAI](https://openrouter.ai/docs/quick-start) to access Alibaba's Qwen model
+
+> **💡 Key Insight**: While most tutorials focus on building agents, **[Cleanlab Codex](https://help.cleanlab.ai/codex/)** addresses the critical gap of ensuring those agents produce reliable, trustworthy outputs.
+
+## Set Up
+
+Follow these steps one by one:
+
+### Setup Milvus VectorDB
+
+Milvus provides an installation script to install it as a docker container.
+
+To install Milvus in Docker, you can use the following command:
+
+```bash
+curl -sfL https://raw.githubusercontent.com/milvus-io/milvus/master/scripts/standalone_embed.sh -o standalone_embed.sh
+
+bash standalone_embed.sh start
+```
+
+### Install Dependencies
+
+```bash
+uv sync
+```
+
+## Run the Notebook
+
+You can run the `notebook.ipynb` file to test the functionality of the code in a Jupyter Notebook environment. This notebook will help you understand routing, tool calling, and validating responses.
+
+## Run the Application
+
+To run the Streamlit app, use the following command:
+
+```bash
+streamlit run app.py
+```
+
+Open your browser and navigate to `http://localhost:8501` to access the app.
+
+## 📬 Stay Updated with Our Newsletter!
+
+**Get a FREE Data Science eBook** 📖 with 150+ essential lessons in Data Science when you subscribe to our newsletter! Stay in the loop with the latest tutorials, insights, and exclusive resources. [Subscribe now!](https://join.dailydoseofds.com)
+
+[![Daily Dose of Data Science Newsletter](https://github.com/patchy631/ai-engineering/blob/main/resources/join_ddods.png)](https://join.dailydoseofds.com)
+
+## Contribution
+
+Contributions are welcome! Feel free to fork this repository and submit pull requests with your improvements.