- Introduced a new module `oci_tracer.py` that provides methods to trace Oracle OCI Generative AI LLMs.
- Implemented tracing for both streaming and non-streaming chat completions, capturing metrics such as latency, token usage, and model parameters.
- Added detailed logging for error handling and tracing steps to enhance observability.
- Included comprehensive type annotations and Google-style docstrings for all functions to ensure clarity and maintainability.

- Introduced a comprehensive Jupyter notebook `oci_genai_tracing.ipynb` demonstrating the integration of Oracle OCI Generative AI with Openlayer tracing, covering non-streaming and streaming chat completions, advanced parameter configurations, and error handling.
- Added a simple Python script `simple_oci_example.py` for quick testing of the OCI Generative AI tracer with Openlayer integration.
- Created a README file to provide an overview, prerequisites, usage instructions, and supported models for the OCI tracing examples.
- Enhanced the `__init__.py` file to include the new `trace_oci_genai` function for easier access to the OCI tracing functionality.
- Ensured all new files adhere to coding standards with comprehensive type annotations and Google-style docstrings for clarity and maintainability.

…tion

- Updated the `oci_genai_tracing.ipynb` notebook to include new prerequisites for Openlayer setup, emphasizing the need for an Openlayer account and API key.
- Improved the configuration section with detailed instructions for setting up Openlayer environment variables.
- Refined the tracing logic in the `oci_tracer.py` module to handle streaming and non-streaming chat completions more effectively, including enhanced error handling and metadata extraction.
- Added comprehensive logging for better observability of token usage and response metadata.
- Ensured all changes adhere to coding standards with thorough type annotations and Google-style docstrings for maintainability.

- Added timing measurements around the OCI client chat method to capture latency for both streaming and non-streaming chat completions.
- Introduced a new function `estimate_prompt_tokens_from_chat_details` to estimate prompt tokens when usage information is not provided by OCI.
- Updated `handle_streaming_chat`, `handle_non_streaming_chat`, and `stream_chunks` functions to utilize the new timing parameters for improved performance tracking.
- Ensured all changes are compliant with coding standards, including comprehensive type annotations and Google-style docstrings for maintainability.

…cer.py

- Enhanced code readability by standardizing spacing and formatting throughout the `oci_tracer.py` module.
- Ensured consistent use of double quotes for string literals and improved alignment of code blocks.
- Updated comments and docstrings for clarity and adherence to Google-style guidelines.
- Maintained comprehensive type annotations and logging practices to support maintainability and observability.

… oci_tracer.py

- Simplified the streaming statistics tracking by reducing the number of metrics and focusing on essential timing information.
- Enhanced performance by introducing a new `_extract_chunk_content` function for fast content extraction from OCI chunks, minimizing overhead during processing.
- Removed redundant code related to raw output handling and chunk sampling, streamlining the overall logic for better readability and maintainability.
- Updated comments and docstrings to reflect the changes and ensure compliance with Google-style guidelines.
- Maintained comprehensive type annotations and logging practices to support ongoing maintainability and observability.

- Added support for the new `oci_tracer` in the `INTEGRATION_DEPENDENCIES` dictionary to ensure comprehensive testing of all integration modules.
- Improved code formatting for better readability, including consistent use of double quotes and alignment of code blocks.
- Streamlined the `run_integration_test` function by consolidating command construction for executing test scripts.
- Updated print statements for clarity in test output, ensuring a more informative summary of test results.
- Ensured compliance with Google-style docstrings and maintained comprehensive type annotations throughout the test suite.

…xamples

- Refactored the `oci_genai_tracing.ipynb` notebook to enhance clarity and organization, including a new setup section for Openlayer API key and inference pipeline ID.
- Removed the `README.md` and `simple_oci_example.py` files as they are no longer needed, consolidating documentation within the notebook.
- Improved the structure of the notebook by replacing raw cells with markdown cells for better readability and user experience.
- Ensured all changes comply with coding standards, including comprehensive type annotations and Google-style docstrings for maintainability.

…n options

- Updated the `trace_oci_genai` function to include an optional `estimate_tokens` parameter, allowing users to control token estimation behavior when not provided by OCI responses.
- Enhanced the `oci_genai_tracing.ipynb` notebook to document the new parameter and its implications for token estimation, improving user understanding and experience.
- Modified the `extract_tokens_info` function to handle token estimation more robustly, returning None for token fields when estimation is disabled.
- Ensured all changes comply with coding standards, including comprehensive type annotations and Google-style docstrings for maintainability.

…d code

- Updated the `extract_inputs_from_chat_details` function to convert message roles to lowercase for consistency with OpenAI format.
- Removed commented-out code related to system message extraction to enhance code clarity and maintainability.

- Updated the `__init__.py` file to include the new tracing function in the module's exports, improving accessibility for users.