Implement a new async RPC client module for snek_rpc, including connection pooling, request serialization, and error handling for remote procedure calls.
The profiles table previously lacked an index on the user_id column, causing full table scans during user lookups. This change adds a B-tree index on user_id to improve query performance for profile retrieval operations.
Implement a new monitoring module that periodically checks Docker volume sizes and reports disk usage statistics. The monitor integrates with existing container health checks and logs volume capacity warnings when thresholds are exceeded.
- Add core WordleBot class with methods for generating guesses and processing color-coded feedback
- Implement letter frequency analysis and candidate word filtering based on previous guesses
- Include support for hard mode constraints and optimal guess selection strategy
The new index on the user_id column in the profiles table improves query performance for user-specific lookups, reducing full table scans during authentication and profile retrieval operations.
Introduces a new mouse automation module providing functions for absolute and relative cursor movement, button click simulation (left, right, middle), and scroll wheel control. Includes coordinate validation and platform-agnostic input event handling.
Implement a new shortcuts trainer component that displays key combinations and provides real-time visual feedback on user input. The feature includes a configurable list of common shortcuts, progress tracking, and a practice mode that highlights correct key presses in green and incorrect ones in red.
This new document provides a structured framework for analyzing and interpreting dream content, including symbolic mapping, narrative patterns, and psychological archetypes. It serves as a reference for both automated and manual dream analysis workflows.
Implement a new tokenizer.py file that provides core tokenization functionality including whitespace splitting, punctuation handling, and vocabulary construction from input text.
This commit introduces a new document outlining the costs associated with OpenAI's highest-tier, most expensive AI models, including per-token pricing for GPT-4 Turbo and specialized enterprise offerings. The file details usage-based fees for both input and output tokens, highlighting the premium charged for advanced reasoning and larger context windows.
- Implemented async streaming response handling for real-time token output
- Added configurable temperature, max_tokens, and top_p parameters via environment variables
- Refactored agent initialization to support both streaming and non-streaming modes
This new module provides a Python implementation for encoding and decoding HTTP chunked transfer encoding, including support for chunk extensions and trailer headers.
Updated the OpenAI agent example to demonstrate streaming responses by modifying the chat completion call to include `stream=True` and iterating over response chunks. Added a new `stream_response` function that yields tokens incrementally, showcasing real-time output for interactive use cases. The example now includes a `main` block that calls this function with a sample prompt, improving clarity for developers integrating streaming into their agents.
This commit introduces a new example script that showcases how to build and run an AI agent using the OpenAI API, including setup instructions and basic interaction patterns.
- Include overview of project purpose and key features
- Add step-by-step installation and configuration guide
- Provide usage examples and API documentation references
