- Added Appshell component with responsive navbar and main content area
- Integrated ColorSchemeToggle for light/dark mode switching
- Created Welcome component with styled title and introductory text
- Developed ChatPage for LLM interaction with WebSocket support
- Implemented Biblioteca for managing notes with rich text editor
- Added LoginPage for user authentication with error handling
- Introduced MessageList and MessageBubble components for chat messages
- Styled components with CSS modules for consistent design
- Added WebSocket endpoint for real-time chat interactions.
- Refactored ChatPage component to utilize WebSocket for sending and receiving messages.
- Updated chat service to handle streaming responses from the LLM agent.
- Introduced error handling for WebSocket connections and message processing.
- Modified Editor_Test to include AppShellWithMenu for better layout.
- Adjusted file path in generar_tree.py for correct directory structure.
- Created llm_chat_endpoint_v1.py and llm_chat_srvc.py for handling chat requests and responses.
- Established logging for WebSocket interactions and errors.
- Updated LoggerDB to remove all active sinks on initialization.
- Added a new PostgresCredencial setup in notas_mmr.py for database connection.
- Replaced print statements with logger calls for better logging in notas_mmr.py.
- Introduced new FastAPI endpoints for various chart types (bar, line, pie, scatter).
- Created Editor_biblioteca.css for styling the rich text editor.
- Implemented Editor_Test.tsx to test the rich text editor functionality.
- Added `text_manager.py` to handle the creation of text libraries via FastAPI.
- Introduced database connection management in `conexion.py` using PostgreSQL credentials from environment variables.
- Created abstract base class `EmbedderABC` in `Base_Embedder.py` for embedding models.
- Developed `OpenAIEmbedder` class to generate embeddings using OpenAI's API.
- Implemented `OpenAIEmbedderModel` and repository pattern for managing OpenAI embedders in `Openai_embedder_mmr.py`.
- Established `Biblioteca` class for managing text libraries and their associated notes in `biblioteca.py`.
- Created SQLAlchemy models and mappers for `Biblioteca` and `Nota` in `biblioteca_mmr.py` and `notas_biblioteca_mmr.py`.
- Added functionality for dynamic table generation for notes associated with libraries.
- Included comprehensive methods for adding, retrieving, and managing notes and libraries in their respective repositories.
- Removed unused security module and updated import paths.
- Enhanced OpenAI client with streaming capabilities for chat completions.
- Added new backend API endpoints for health check (ping).
- Established a new FastAPI application with CORS configuration.
- Created a new Appshell component for the frontend with navigation links.
- Integrated SVG icons and improved styling for the Appshell component.
- Implemented memory management for conversation history using PostgreSQL.
- Developed abstract classes for AI agents and models, with OpenAI integration.
- Added encryption utilities for secure data handling.
- Implemented OpenAICredencial class for managing OpenAI API keys.
- Created OpenAICredencialModel and OpenAICredencialMapper for SQLAlchemy integration.
- Developed OpenAICredencialRepo for CRUD operations on OpenAI credentials.
- Established OpenAICliente class for interacting with OpenAI API.
- Introduced PostgresCredencial class for managing PostgreSQL connection details.
- Created PostgresCredencialModel and PostgresCredencialMapper for SQLAlchemy integration.
- Developed PostgresCredencialRepo for CRUD operations on PostgreSQL credentials.
- Added base connection class and PostgreSQL connection implementation.
- Included environment variable loading for sensitive data management.
egutierrez