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https://github.com/robinmillford/cortex-ai-multi-model-insights-hub

Cortex AI: Multi-Model Insights Hub is an advanced platform that leverages cutting-edge AI to empower your research, analysis, and data exploration. By integrating multiple Large Language Models (LLMs) with a sophisticated Retrieve-and-Generate (RAG) system
https://github.com/robinmillford/cortex-ai-multi-model-insights-hub

article-extractor chatbot data-analysis data-visualization deepseek-chat deepseek-r1 llama3 llm pdf-document-processor rag streamlit-webapp summarizer vector-database

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Cortex AI: Multi-Model Insights Hub is an advanced platform that leverages cutting-edge AI to empower your research, analysis, and data exploration. By integrating multiple Large Language Models (LLMs) with a sophisticated Retrieve-and-Generate (RAG) system

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# Cortex AI: Multi-Model Insights Hub



Cortex AI: Multi-Model Insights Hub is an advanced platform that leverages cutting-edge AI to empower your research, analysis, and data exploration. By integrating multiple Large Language Models (LLMs) with a sophisticated Retrieve-and-Generate (RAG) system, Cortex AI Hub enables you to extract insights from documents and data with ease and precision.



Deployed on **Streamlit Cloud**, this platform offers an intuitive interface for interactive document analysis, dynamic data exploration, and multi-model comparisons—all in one place.



---



## 📌 **Features**



- **📂 Document Upload**: Upload PDFs or enter URLs containing articles or documents.

- **📖 Intelligent Text Extraction**: Automatically extract and process text from uploaded documents or web pages.

- **📑 Dynamic Chunking & Embedding**: Split the extracted text into manageable chunks and generate embeddings for efficient search and retrieval.

- **⚡ Robust Vector Database**: Store embeddings in a high-performance vector database for rapid, context-driven searches.

- **🧠 Multi-LLM Integration**: Leverage a suite of LLMs (including DeepSeek, Qwen, Gemma, Llama, Mistral, and Mixtral) to generate context-aware, accurate responses.

- **💬 Persistent Conversation History**: Maintain conversation continuity with stored dialogue history.

- **🔄 Flexible Model Selection**: Easily switch between different LLMs or compare outputs by selecting multiple models simultaneously.

- **📊 Data Analysis Chatbot**: Interactively visualize and analyze your data with AI-driven assistance on a dedicated page.

- **🚀 Scalable Streamlit Cloud Deployment**: Access the platform through a responsive, cloud-hosted interface for global reach.



---



## 🚀 **Supported Models**



| Model                         | RPM  | RPD    | Tokens/Min | Tokens/Day | Advantages                                                            | Disadvantages                        |

| ----------------------------- | ---- | ------ | ---------- | ---------- | --------------------------------------------------------------------- | ------------------------------------ |

| deepseek-r1-distill-llama-70b | 30   | 1,000  | 6,000      | Unlimited  | Highly optimized for low latency with unlimited token capacity.       | Limited daily requests.              |

| qwen-2.5-32b                  | 30   | 14,400 | 10,000     | 500,000    | Powerful 32B model optimized for long-context comprehension.          | Requires more computational resources. |

| gemma2-9b-it                  | 30   | 14,400 | 15,000     | 500,000    | High token throughput, suitable for fast inference on large-scale tasks.| Limited versatility compared to larger models. |

| llama-3.1-8b-instant          | 30   | 14,400 | 20,000     | 500,000    | High-speed processing with large token capacity, ideal for real-time apps. | Less accurate for complex reasoning. |

| llama-3.3-70b-versatile       | 30   | 1,000  | 6,000      | 100,000    | Versatile model optimized for high accuracy in diverse scenarios.       | Lower throughput compared to smaller models. |

| llama3-70b-8192               | 30   | 14,400 | 6,000      | 500,000    | Long-context capabilities, perfect for detailed research articles.      | Moderate speed and accuracy for shorter tasks. |

| llama3-8b-8192                | 30   | 14,400 | 20,000     | 500,000    | High-speed inference with extensive long-context support.             | Slightly less accurate for complex reasoning. |

| mistral-saba-24b              | 30   | 7,000  | 7,000      | 250,000    | Excellent for multi-turn conversations and effective retrieval augmentation. | Limited token capacity compared to larger models. |

| mixtral-8x7b-32768            | 30   | 14,400 | 5,000      | 500,000    | Optimized for processing long documents with superior contextual understanding. | Lower token throughput.              |



---



## How It Works



### Article Chatbot

1. **User Input**: Upload a PDF file or provide a URL to an article or document.

2. **Text Extraction**: The system automatically extracts text from your document or webpage.

3. **Chunking & Embeddings**: The extracted text is segmented into smaller chunks, and embeddings are generated using advanced LLM techniques.

4. **Vector Store**: Embeddings are stored in a high-performance vector database (e.g., ChromaDB, FAISS, Pinecone, or DocArrayInMemorySearch from LangChain) for rapid retrieval.

5. **Querying & Response Generation**: Ask questions through the chatbot interface, and the system retrieves relevant context and generates answers using one or more selected LLMs.

6. **Conversation History**: Maintain dialogue continuity with stored conversation history.

7. **Model Switching & Comparison**: Easily switch between LLM models or run them concurrently to compare their outputs.



### Data Analysis Chatbot

1. **Data Upload**: Upload data files in various formats (CSV, Excel, JSON, Parquet) via the dedicated Data Analysis page.

2. **Data Processing**: The app processes and previews your data for further analysis.

3. **Interactive Analysis**: Use the AI-driven chat interface to ask questions, generate visualizations, and analyze your data.

4. **Dynamic Visualizations**: Receive Python code for generating plots and visualizations, ensuring that your data insights are both accurate and engaging.



![System Diagram](https://github.com/RobinMillford/LLM-Based-Text-Summarizer/blob/main/Uml%20Diagram.png)



![System Diagram](https://github.com/RobinMillford/LLM-Based-Text-Summarizer/blob/main/Uml_diagram_2.png)



---



## Getting Started



Follow these steps to set up and run Cortex AI: Multi-Model Insights Hub locally or contribute to its development.



### Prerequisites



- **Python 3.12**

- **pip** for package management

- A **.env** file with your API keys (see below)

- **ChatGroq API Key** for LLM integration



### Clone the Repository



Clone the repository to your local machine:



```bash

git clone https://github.com/RobinMillford/Multi-Model-RAG-Powered-Article-Chatbot.git

cd Multi-Model-RAG-Powered-Article-Chatbot

```



### Install Dependencies



Create a virtual environment and install the required packages:



```bash

python -m venv venv

source venv/bin/activate  # On Windows: venv\Scripts\activate

pip install -r requirements.txt

```



### Configure Environment Variables



Create a `.env` file in the root directory and add your ChatGroq API key:



```

GROQ_API_KEY=your_chatgroq_api_key

```



Replace `your_chatgroq_api_key` with your actual API key.



### Running the App Locally



Start the application locally with:



```bash

streamlit run app.py

```



### Deployed on Streamlit Cloud



Cortex AI Hub is live on Streamlit Cloud. Explore the demo here:



[Streamlit Deployed Demo](https://cortex-ai-multi-model-insights-app.streamlit.app/)



![Demo Image 1](https://github.com/RobinMillford/LLM-Based-Text-Summarizer/blob/main/Llama3-RAG-Chatbot-1.png)

![Demo Image 2](https://github.com/RobinMillford/LLM-Based-Text-Summarizer/blob/main/Llama3-RAG-Chatbot-2.png)

![Demo Image 2](https://github.com/RobinMillford/LLM-Based-Text-Summarizer/blob/main/Data-Analysis-Chatbot-with-Multi-Model-Responses-·-Streamlit.png)



---



## Usage



1. **For Articles**: Upload a PDF file or provide a URL of an article. The system extracts text, processes it into chunks, and allows you to ask questions through the chatbot interface.

2. **For Data Analysis**: Navigate to the Data Analysis page to upload your data files, preview your dataset, and interact with an AI-driven assistant for visualizations and analysis.



---



## Contributing



Contributions are welcome! To contribute:



1. **Fork the Repository** on GitHub.

2. Clone your fork:



   ```bash

   git clone https://github.com/RobinMillford/Multi-Model-RAG-Powered-Article-Chatbot.git

   ```



3. Create a new branch:



   ```bash

   git checkout -b feature-name

   ```



4. Make your changes and commit them:



   ```bash

   git add .

   git commit -m "Description of the changes"

   ```



5. Push your branch:



   ```bash

   git push origin feature-name

   ```



6. Open a **Pull Request** to the `main` branch of the original repository.



---



## License



This project is licensed under the **AGPL-3.0 license**. See the [LICENSE](LICENSE) file for details.