Refactor README.md to update project details and enhance clarity

.huggingface/Readme.md

@@ -1,38 +0,0 @@
-# Zenith - AI Assistant
-
-## Overview
-Zenith is an advanced conversational AI assistant built with state-of-the-art language models and a modern tech stack. This application leverages the power of LLMs through various integrations to provide intelligent responses, document analysis, and conversational capabilities.
-
-## Features
-- **Interactive Chat Interface**: Engage with AI models through a responsive chat UI
-- **Document Processing**: Upload and analyze documents for information extraction
-- **Multiple Model Support**: Compatible with various LLM providers (OpenAI, Anthropic, etc.)
-- **Context Awareness**: Maintains conversation history for contextual responses
-
-## Technologies
-- **Framework**: Chainlit, FastAPI
-- **AI/ML**: LangChain, Transformers, PyTorch
-- **Data Processing**: Pandas, NumPy, scikit-learn
-- **Document Handling**: PyPDF, Beautiful Soup
-
-## Usage
-1. Type your questions or requests in the chat interface
-2. Upload documents for analysis when needed
-3. Use the available tools and features as indicated in the UI
-
-## Configuration
-The application uses environment variables for configuration. The following can be set in your Hugging Face Space settings under the "Secrets" tab:
-- `OPENAI_API_KEY` - Your OpenAI API key
-- `ANTHROPIC_API_KEY` - Your Anthropic API key (if using Claude)
-- Other API keys as needed for your specific implementation
-
-## Deployment
-This application is deployed on Hugging Face Spaces, providing a scalable and accessible platform for users to interact with the AI assistant without any setup requirements.
-
-## Limitations
-- Processing time may vary based on server load
-- File size limitations apply to document uploads
-- Response quality depends on the underlying AI models
-
-## Feedback
-If you encounter any issues or have suggestions for improvements, please leave a comment on this Space or open an issue in the project repository.

README.md

@@ -1,68 +1,49 @@
-# Chainlit Application
-
-This project is a Chainlit application designed to facilitate interactions with a custom language model for question answering and document retrieval.
-
-## Project Structure
-
-- **src/**: Contains the main application code.
-  - **main.py**: Entry point of the application.
-  - **llm/**: Contains custom language model implementations and prompt templates.
-    - **custom_llm.py**: Defines the `CustomLLM` class for API interactions.
-    - **prompt_templates.py**: Manages prompt templates for generating queries.
-  - **document_processing/**: Handles document loading and chunking.
-    - **chunking.py**: Functions for chunking documents.
-    - **loader.py**: Loads documents from various sources.
-  - **vectorstore/**: Contains functions for generating embeddings.
-    - **embeddings.py**: Functions for creating text embeddings.
-  - **chains/**: Implements the logic for question answering and retrieval.
-    - **qa_chain.py**: Integrates language model and document retrieval.
-    - **retrieval.py**: Functions for retrieving relevant documents.
-  - **utils/**: Utility functions for various tasks.
-    - **helpers.py**: Includes logging and data formatting functions.
-
-- **config/**: Configuration settings for the application.
-  - **settings.py**: Contains API keys and environment variables.
-
-- **data/**: Documentation about the data used in the application.
-  - **README.md**: Information on data formats and sources.
-
-- **vectorstores/**: Documentation about vector stores.
-  - **README.md**: Setup and usage instructions for vector stores.
-
-- **requirements.txt**: Lists dependencies required for the project.
-
-- **chainlit.md**: Documentation specific to the Chainlit framework.
-
-- **.env**: Stores environment variables for sensitive information.
-
-## Setup Instructions
-
-1. Clone the repository:
-   ```
-   git clone <repository-url>
-   cd chainlit-app
-   ```
-
-2. Install the required dependencies:
-   ```
-   pip install -r requirements.txt
-   ```
-
-3. Set up environment variables in the `.env` file.
-
-4. Run the application:
-   ```
-   chainlit run main.py
-   ```
+---
+title: Zenith
+emoji: 🚀
+colorFrom: blue
+colorTo: purple
+sdk: docker
+sdk_version: "latest"
+app_file: main.py
+pinned: false
+---
+
+# Zenith - AI Assistant
+
+## Overview
+Zenith is an advanced conversational AI assistant built with state-of-the-art language models and a modern tech stack. This application leverages the power of LLMs through various integrations to provide intelligent responses, document analysis, and conversational capabilities.
+
+## Features
+- **Interactive Chat Interface**: Engage with AI models through a responsive chat UI
+- **Document Processing**: Upload and analyze documents for information extraction
+- **Multiple Model Support**: Compatible with various LLM providers (OpenAI, Anthropic, etc.)
+- **Context Awareness**: Maintains conversation history for contextual responses
+
+## Technologies
+- **Framework**: Chainlit, FastAPI
+- **AI/ML**: LangChain, Transformers, PyTorch
+- **Data Processing**: Pandas, NumPy, scikit-learn
+- **Document Handling**: PyPDF, Beautiful Soup
 
 ## Usage
-
-Once the application is running, you can interact with the language model through the Chainlit interface. Submit your queries, and the model will provide responses based on the loaded documents and embeddings.
-
-## Contribution Guidelines
-
-Contributions are welcome! Please fork the repository and submit a pull request with your changes. Ensure that your code adheres to the project's coding standards and includes appropriate tests.
-
-## License
-
-This project is licensed under the MIT License. See the LICENSE file for details.
+1. Type your questions or requests in the chat interface
+2. Upload documents for analysis when needed
+3. Use the available tools and features as indicated in the UI
+
+## Configuration
+The application uses environment variables for configuration. The following can be set in your Hugging Face Space settings under the "Secrets" tab:
+- `OPENAI_API_KEY` - Your OpenAI API key
+- `ANTHROPIC_API_KEY` - Your Anthropic API key (if using Claude)
+- Other API keys as needed for your specific implementation
+
+## Deployment
+This application is deployed on Hugging Face Spaces, providing a scalable and accessible platform for users to interact with the AI assistant without any setup requirements.
+
+## Limitations
+- Processing time may vary based on server load
+- File size limitations apply to document uploads
+- Response quality depends on the underlying AI models
+
+## Feedback
+If you encounter any issues or have suggestions for improvements, please leave a comment on this Space or open an issue in the project repository.