Feature 46/add model params (#58)

* oauth refactored and added to all routes

* Added auth to query service

* Initial auths added

* Autorization moved to the request level as return objects are not needed for many functions

* addedd params model_name

* List all downloaded ollama models

* feat ✨: Extend llm_utils module with OLLAMA model list

- This commit extends the `llm_utils` module to provide a function for listing all available OLLAMA models.

- Improved the query service and added model list functionality.

* feat ✨: Improved user interface

- Improved the user interface by adding a select dropdown to filter models.

- Addthe ability to send a question along with the model name when pressing enter.

* feat ✨: Enhanced data modeling and documentation

- Implemented enhanced `pydantic` models for increased data modeling and clarity.

- Implemented added status field and timestamps for Documents model objects.

- The service file has been updated to incorporate document progress tracking and returns the validated DocumentPydantic model object for each document.

- Improved document processing for better query response clarity and structure.

* docs 📝:: Error handling for document retrieval updated.

- Replaced error handling for document retrieval with a default empty list.

* feat ✨: Consistent datetime format for timestamps

- The codebase now uses a consistent datetime for both `created_at` and `updated_at`.

* feat ✨: UI Document upload feature implementation

- Implemented a document upload feature with a popup and modal.

* feat ✨: UI Store Document management functionality added

- Added document management functionality to the main store.

* feat ✨: User authentication token clearing

- Functionality for clearing the user's authentication token has been added.

- The code implements a mechanism to handle unauthorized access attempts and redirect the user to the login page.

* feat ✨: New response template implementation

- The code implements a new response template for user queries based on the provided context.

* feat ✨: Support for different document types

- Added support for accepting different file types for document upload.

* feat ✨: Improved vectorstore and AI assistant

- The code updates the vectorstore initialization and embedding function to improve efficiency and accuracy.

- Implemented an improved prompt template for AI assistant responses.

* feat ✨: Monitoring system for document updates

- Implemented a monitoring system to check for changes in the uploaded documents and update their status.

* feat ✨: Update watchdog with new vector database

- The code updates the `watchdog` to use a new vector database and update document status in the background.

* fix 🐛: Simplified database configuration

- Simplified database configuration for improved consistency and reduced complexity.

* feat ✨: Use 'aora.db' for SQLite persistence

- Changed database connection settings to use the 'aora.db' file for SQLite persistence.

* feat ✨: Build and store vector embeddings for PDF documents

- The code rewrites the logic to build and store vector embeddings of PDF documents into a persistent vector database.

* feat ✨: Document saving and hashing method

- The code defines a method to save documents into the designated directory and hash them for persistence in the database.

* feat ✨: Implement environment variable loading

- The code implements environment variables loading for the application, then sets up a context manager.

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Co-authored-by: raikarn <nikhil.raikar@apl-landau.de>
Co-authored-by: tmeftah <sudtechnics@gmail.com>
Co-authored-by: tmeftah <46863341+tmeftah@users.noreply.github.com>