Introduce AI medical extraction service with new API, agents, comprehensive testing, and documentation, while reorganizing existing scripts.

Enhance AI service by updating token limits for Phi-3.5 models, improving model loading logic, and refining prompt formatting in patient summary documentation.

feat: Add utilities for robust EHR data parsing, visit delta computation, and JSON serialization.

Enhance patient data processing by adding robust serialization and flattening utilities. The conversion function now captures all fields from EHR payloads, ensuring no data is lost during preprocessing. Additional logging improvements and error handling for plain text processing have been implemented.

Add robust JSON parsing utilities for medical data with flexible key matching and normalization capabilities.

Revert " Add robust JSON parsing utility for medical data with flexible key matching and data normalization."

Add robust JSON parsing utility for medical data with flexible key matching and data normalization.

Merge branch 'FT-DEV-26-11-2025'

feat: Add Colab patient summary script, AI service utilities for performance, and related documentation.

Merge branch 'FT-DEV-26-11-2025'

h immediate queue slot release and delayed job cleanup

Merge branch 'FT-DEV-26-11-2025'

feat: Add JobManager for asynchronous task tracking with auto-cleanup and a verification test.

add automatic cleanup for completed and error jobs, along with verification and demonstration tests.

Revert " implement `JobManager` for thread-safe asynchronous job tracking, including creation, updates, and cleanup, with supporting demo and test files."

Revert " implement `JobManager` for thread-safe asynchronous job tracking, including creation, updates, and cleanup, with supporting demo and test files."

implement `JobManager` for thread-safe asynchronous job tracking, including creation, updates, and cleanup, with supporting demo and test files.

Revert "feat: Introduce JobManager service for tracking asynchronous tasks with automatic cleanup and add dedicated tests."

feat: Introduce JobManager service for tracking asynchronous tasks with automatic cleanup and add dedicated tests.

Implement comprehensive AI model configuration, including T4 optimizations, token limits, and unified model management.

feat: Add prompt and token diagnostics to error responses for enhanced debugging.

feat: Implement unified model management, centralized constants, and error handling for AI medical extraction services.

1200 seconds from 600 secs timeout

Revert "feat: Establish AI medical extraction service with performance optimizations, unified model management, and detailed Hugging Face Spaces deployment guides."

Revert "feat: Add patient data deduplication and plain text conversion utility with EHR parsing and unit tests."

Revert "feat: Add T4-optimized unified model manager for seamless loading and generation across various AI model types, updating model configurations."

Revert "feat: Add T4-optimized unified model manager for seamless loading and generation across various AI model types."

feat: Add T4-optimized unified model manager for seamless loading and generation across various AI model types.

feat: Add T4-optimized unified model manager for seamless loading and generation across various AI model types, updating model configurations.

feat: Add patient data deduplication and plain text conversion utility with EHR parsing and unit tests.

feat: Establish AI medical extraction service with performance optimizations, unified model management, and detailed Hugging Face Spaces deployment guides.

max ques size 6

Increase maximum concurrent requests in `request_queue.py` from 2 to 5 to enhance request handling capacity and improve performance in the AI service.

Update maximum token limits to 8192 in `fallback_pipeline.py` and `unified_model_manager.py` for improved handling of longer inputs and enhanced performance in summary generation.

Remove redundant clinical assistant prompt from text generation in `routes_fastapi.py` to streamline the prompt structure and improve clarity.

Update maximum token limits in `patient_summary_agent.py` and `summarizer.py` to 8192 for enhanced summary generation capabilities, allowing for better handling of longer inputs and fuller outputs.

Adjust temperature setting in `GenerationConfig` to 0.3 for improved determinism in medical summary generation.

Increase `max_new_tokens` limit to 8192 in `unified_model_manager.py` for improved summary generation, allowing for fuller outputs and better handling of longer inputs.

Revert "documentation removed"

documentation removed

Remove obsolete documentation files related to HF Spaces fixes, including summaries and troubleshooting guides, to streamline the repository and reduce clutter. This cleanup enhances maintainability and focuses on the most relevant resources for users.

Update model configuration to increase maximum token limits for improved summary generation. Adjusted `max_length`, `max_new_tokens`, and context window settings from 2048 to 8192 in `model_config.py` and `unified_model_manager.py` for enhanced performance and better handling of longer inputs.

Enhance model loading and result handling in `async_patient_summary` by adding fallback tracking. Update `build_result_dict` to include fallback status and reason, improving error reporting and user feedback. Refactor model loading logic to propagate fallback information throughout the summary generation process.

Revert "Enhance patient summary generation by adding model information tracking in `build_result_dict` and related functions. Update `async_patient_summary` and `process_patient_summary_background` to accept and propagate model info, improving error handling and logging for model loading. Refactor model configuration in `async_patient_summary_optimized` to dynamically handle requested models and fallback scenarios, ensuring better user feedback and performance."

Enhance patient summary generation by adding model information tracking in `build_result_dict` and related functions. Update `async_patient_summary` and `process_patient_summary_background` to accept and propagate model info, improving error handling and logging for model loading. Refactor model configuration in `async_patient_summary_optimized` to dynamically handle requested models and fallback scenarios, ensuring better user feedback and performance.

Implement T4 Medium optimizations in model handling and logging. Enhance PatientSummarizerAgent for dynamic model configuration, allowing flexible model loading based on user input. Update environment variable checks for improved performance on Hugging Face Spaces. Refactor model generation settings for T4 compatibility, including memory management and generation parameters. Improve error handling and logging throughout the application for better user support.

Update binary cache files and enhance error handling in recommendations to include detailed error information for user support.

Update error handling in recommendations to include error details in support contact message for better user guidance.

Enhance PatientSummarizerAgent and user_models_config with improved environment variable handling for Hugging Face Spaces. Introduce async support for clinical summary generation and refine model loading error handling. Update model type definitions for clarity and adjust model retrieval functions to ignore active status.

Enhance model loading in PatientSummarizerAgent with improved error handling and fallback mechanism. Introduce environment variable check for HF Spaces, update logging for better clarity, and refine fallback summary generation to include extracted patient information and error details.