- Implemented user profile retrieval and update endpoints in `users.py`.
- Added lightweight dashboard summary endpoint for user statistics.
- Created voice cloning endpoint with Cloudinary integration, currently a stub.
- Introduced new Pydantic schemas for user and voice cloning requests/responses.
- Developed language presets for lyrics generation in `language.py`.
- Established AI service for local lyrics and music generation, avoiding third-party dependencies.
- Integrated Cloudinary service for audio and cover uploads.
- Added utility functions for generating share tokens.
- Included unit tests for local AI configuration settings.
Remove deprecated modules and scripts related to Task A and shared models
- Deleted `shared_models.py`, `task_a_rag.py`, `user_modeling.py`, and `user_modeling_prompt.py` as they are no longer in use.
- Removed associated scripts for building business catalog and Task A review RAG.
- Cleaned up Docker configuration and environment files.
- Eliminated unnecessary data files and ignored patterns.
Enhance agent workflow and integration of Gemini API for text generation
- Updated AGENT_WORKFLOW.md to reflect changes in task workflows, including the integration of Gemini API for text generation in Task A and Task B.
- Modified environment variables in env.example to include `GENERATION_BACKEND`, `GEMINI_API_KEY`, and `SKIP_LOCAL_LLM_HUB_DOWNLOAD` for improved configuration flexibility.
- Introduced `gemini_client.py` for handling Gemini API interactions, enabling seamless switching between Gemini and local models.
- Enhanced recommendation and user modeling services to utilize Gemini for generation, improving response quality and efficiency.
- Updated Dockerfile and docker_build_assets.py to support new environment configurations and optimize model loading during builds.
Enhance user modeling and recommendation services with async support and logging
- Updated user_modeling and recommendation functions to be asynchronous, improving performance and responsiveness.
- Added detailed logging to track the execution time of user modeling and recommendation tasks.
- Adjusted default parameters for top_k_retrieval and top_n_final in recommendation requests to optimize results.
- Introduced a new environment variable TASK_B_LLM_CANDIDATE_CAP to control candidate limits in recommendations.
- Modified Dockerfile to increase the timeout for uvicorn to improve handling of long-running requests.
Update Dockerfile and README.md to set default for DOCKER_BUILD_SKIP_LLM_WARM
- Changed the default value of DOCKER_BUILD_SKIP_LLM_WARM to 1 in the Dockerfile to prevent out-of-memory issues during build.
- Updated README.md to clarify the behavior of the DOCKER_BUILD_SKIP_LLM_WARM variable and its impact on model warming during the build process.
Enhance Dockerfile and application startup process with prewarm functionality
- Added environment variables for model warmup control in Dockerfile and README.md.
- Implemented startup prewarm logic in FastAPI application to load models before serving traffic, with options to skip prewarm.
- Introduced warm methods in user modeling and recommendation services to ensure models are ready for inference.
- Updated docker_build_assets.py to include model warming during the build process, with a flag to skip if necessary.
- Enhanced example environment file to include new configuration options for startup behavior.
Add agent workflow documentation and refactor user modeling and recommendation services
- Introduced AGENT_WORKFLOW.md to outline the agent's workflow for Task A and Task B, detailing shared ideas, user modeling, and recommendation processes.
- Refactored user modeling and recommendation services to remove Azure dependencies, implementing local models and embedding strategies.
- Updated Dockerfile and environment configurations to support local model usage and clarified requirements in README.md.
- Enhanced user modeling prompt to incorporate Nigerian English and added RAG (retrieval-augmented generation) functionality for improved response quality.
- Implemented scripts for building task A review RAG and embedding business catalogs, ensuring reproducibility and ease of use.
- Adjusted error handling and logging for better debugging and user experience.
Enhance Docker configuration by adding HUGGING_FACE_HUB_TOKEN as a build argument in docker-compose.yml and Dockerfile. Update Dockerfile to install CPU-only PyTorch and adjust thread counts for reduced memory usage during builds. Modify docker_build_assets.py to support both HF_TOKEN and HUGGING_FACE_HUB_TOKEN for model caching. Update README.md to clarify token usage and build instructions.
Refactor Docker setup and enhance build process. Updated docker-compose.yml to specify build context and added HF_TOKEN as a build argument. Modified Dockerfile to use Python 3.10, set environment variables, and install additional dependencies. Introduced docker_build_assets.py for prefetching models and building data assets. Updated README.md to reflect changes in port configuration and build instructions.
Update README.md with detailed configuration, deliverables checklist, API endpoints, request bodies, local run instructions, Docker setup, and project layout for the DSN × BCT LLM Agent Challenge.
