- Updated .gitignore to include new model directories.
- Removed version specification from existing docker-compose.yml for llama-cpp-server.
- Created new docker-compose.yml for ai_stack/llamacpp.server with GPU support and environment configurations.
- Added Gemma4_(E2B)_Vision.ipynb notebook for data preparation, training, and inference of the Gemma 4 model.
- Added a new script `generate-ollama-client-simple.sh` for generating a simple TypeScript client using the existing OpenAPI tools.
- Created a comprehensive `generate-ollama-client.sh` script to generate a FastAPI client from the Ollama OpenAPI specification with support for axios.
- Introduced `generate_fastapi_client.py` to support generating FastAPI clients from OpenAPI specifications in multiple languages, including TypeScript and Python.
- Generated TypeScript types for the Ollama API endpoints in `types.gen.ts`.
- Included example usage files for both Python and TypeScript clients to demonstrate how to interact with the Ollama API.
- Implemented WhisperModel from faster_whisper for transcribing audio files.
- Configured model to run on GPU with FP16 for improved performance.
- Added functionality to detect language and print transcription segments with timestamps.
- Added SQLite database to store chat messages.
- Created HTML interface for the chat application using Bootstrap.
- Developed backend logic in FastAPI to handle chat requests and responses.
- Implemented TypeScript for dynamic message rendering and interaction.
- Integrated logging with Logfire for monitoring.
- Configured Ollama model for OpenAI-compatible API usage.
- Established a question graph for evaluating user responses.
- Created a Jupyter notebook to address vector dimension mismatch errors in the RAG database.
- Implemented a solution to reset the database schema to match the current embedding model dimensions.
- Updated rag.py to use the Ollama model with OpenAI-compatible endpoints.
- Added functionality for building the search database and inserting document sections with correct embeddings.
- Included Pydantic models for validation of embedding dimensions.
- Enhanced error handling for asyncpg DataError exceptions during database operations.
- Reset execution counts for cells in `ollama.ipynb` to reflect the correct order.
- Adjusted output tokens in the usage statistics for accuracy.
- Added a new cell to query the top five highest-grossing animated films of 2025 using DuckDuckGo search.
- Updated `pyproject.toml` to include `duckduckgo` as a dependency for the new search functionality.
- Enhanced `uv.lock` to reflect the addition of `duckduckgo-search` and its dependencies.
- Updated the Modelfile path in `pull-deepseek-model.sh` to use an absolute path for model files.
- Simplified the model loading process by removing unnecessary directory changes and directly referencing the mounted Modelfile.
- Added a new Jupyter notebook `ollama.ipynb` that demonstrates the usage of the Ollama API with a sample query and error handling for unsupported tools.
- Created README.md for Ollama Docker setup with instructions for running and using models.
- Added docker-compose.yml for Ollama service configuration.
- Introduced scripts for model setup, including pulling models from Hugging Face and loading them into Ollama.
- Implemented AI launcher script to choose between Ollama and LlamaCPP backends.
- Added GPU support scripts for LlamaCPP with detailed logging and error handling.
- Included simple model pull script for easier model management.
- Established a structured approach for model management and integration with Ollama API.
furyhawk