Reorganized module directories under `phoenix_technologies` for better namespace clarity and maintainability. Updated the Dockerfile to use an environment variable for the application entry point, enhancing flexibility in deployment. Additionally, revamped the README to reflect the new structure and provide clearer project documentation.
Simplified the descriptions in `deep_research` and `quick_search` docstrings to improve readability and eliminate redundant details. This ensures concise and focused explanations of each function's purpose.
Introduced a CustomLogsHandler class to handle and log JSON data during research or search operations. Updated GPTResearcher initialization to include the CustomLogsHandler for improved logging and debugging.
Updated the `mcp.run` call to include the "sse" mode, ensuring proper handling of server events. This change improves the server's event loop management and prepares it for SSE-specific operations.
Introduced the quick_search function for faster, snippet-based searches and adjusted mcp.run to remove the "sse" argument. Additionally, modified the deep_research docstring to remove the citation requirement note for simpler usage documentation.
Added `RESEARCH_TYPE` environment variable to configure the default research type for GPTResearcher. Updated the initialization of GPTResearcher to use this dynamic configuration, improving flexibility and adaptability.
This update enables specifying the report type during researcher initialization. It introduces "deep" as the default report type, enhancing flexibility for future use cases.
Replaced FastAPI app with an MCP server implementation, enhancing flexibility and modularity for research operations. Deprecated `phoenix_technologies` package, updated server logic, added utility functions, and revised dependencies in `requirements.txt`. Updated Dockerfile and README to align with the new architecture.
Replaced custom log handler and async report generation logic with a simplified fake data streamer for the StreamingResponse. Added uvicorn server startup code for direct script execution.
Simplified the logic for log streaming by consolidating into a single async generator (`log_stream`). Removed redundant tasks and streamlined report generation to improve code readability and maintainability.
Enhanced report generation by integrating `CustomLogsHandler` and separating tasks for generating reports and streaming logs. Replaced the previous implementation with a unified async generator to handle concurrent execution and improve error handling. Updated module exports to include the new `CustomLogsHandler` component.
Replaced `generator.researcher.complete` with the correct `generator.complete`. This resolves a potential logic error and ensures the completion condition is properly evaluated.
Replaced `.is_complete()` method calls with direct `.complete` attribute access to streamline the code. Removed the redundant `is_complete()` method from `deepresearch.py` to reduce unnecessary indirection. This simplifies the logic and improves readability.
The is_complete method was incorrectly referenced without parentheses, causing a potential runtime error. This fix ensures proper method invocation and prevents the loop from behaving incorrectly.
Modified the report generation process to stream logs incrementally during execution. Introduced new methods `init` and `is_complete` to enhance modularity and track completion status. Adjusted generator logic to better manage and yield logs in real-time.
This change introduces status updates to indicate the start and successful completion of the report generation. These progress messages improve user feedback during the asynchronous operation.
Removed the unnecessary `logs` initialization and streamlined the `CustomLogsHandler` setup. This simplifies the code and avoids redundant log storage.
Changed `send_json` to return `None` instead of yielding and updated log retrieval to use `yield` in place of `return`. These modifications align function behavior with intended asynchronous use cases and improve consistency.
Removed unused log yielding in `main.py` to simplify the flow. Updated `send_json` in `deepresearch.py` to use an async generator for more flexible streaming of log data.
The `generate_report` method is now explicitly called within the main process, ensuring the researcher's report generation is initiated properly. Updated its return type annotation for clarity in `deepresearch.py`.
Refactored `CustomLogsHandler` to accept an optional logs list and modified its method to append data instead of yielding. Enhanced `ReportGenerator` with proper asynchronous iterator implementation, enabling smooth log handling and retrieval. Simplified variable naming for better readability.
Introduced `CustomLogsHandler` to manage log handling and WebSocket integration in `ReportGenerator`. Simplified and restructured report generation logic for improved maintainability. Removed obsolete methods and enhanced overall readability with cleaner code structure.
Enhanced docstrings for better clarity and consistency across methods. Added error handling for report generation and ensured default values for research details to avoid null errors. Improved comments and logic flow for readability and maintainability.
Added asynchronous iteration methods (`__aiter__` and `__anext__`) to the `DeepResearch` class to enable streaming of research report chunks. Introduced functionality to generate and split reports into smaller chunks for improved report handling.
Included new environment variable placeholders for better configuration guidance. These updates clarify setup requirements and improve documentation completeness.
Implement a modular FastAPI-based service for generating research reports using `GPTResearcher`. Includes secure API key authentication, a streaming response endpoint, and a Dockerized deployment setup. Also adds documentation, core dependencies, and project structure.
