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
Introduces a new env.py module that centralizes environment variable handling. The module loads configuration from a .env file using python-dotenv and validates required variables like DATABASE_URL and SECRET_KEY, raising clear errors for missing critical settings.
The documentation now includes a comprehensive guide covering data preparation, model architecture selection, hyperparameter tuning, and evaluation metrics for the pattern recognition training pipeline.
