tidyllm Development Version Overview

The development version of tidyllm reflects the ongoing updates in the GitHub repository. Milestone versions are incremented when significant new features, improvements, or breaking changes are introduced.

Versioning Policy

• 0.1.x: Minor updates and feature additions.
• 0.x.0: Releases (will be sent to CRAN)
• 0.1.0: First CRAN release and the beginning of versioning.

Version 0.2.0

New CRAN release. Largest changes compared to 0.1.0:

Major Features: - Batch Request Support: Added support for batch requests with both Anthropic and OpenAI APIs, enabling large-scale request handling. - Schema Support: Improved structured outputs in JSON mode with advanced .json_schema handling in openai(), enhancing support for well-defined JSON responses. - Azure OpenAI Integration: Introduced azure_openai() function for accessing the Azure OpenAI service, with full support for rate-limiting and batch operations tailored to Azure’s API structure. - Embedding Model Support: Added embedding generation functions for the OpenAI, Ollama, and Mistral APIs, supporting message content and media embedding. - Mistral API Integration: New mistral() function provides full support for Mistral models hosted in the EU, including rate-limiting and streaming capabilities. - PDF Batch Processing: Introduced the pdf_page_batch() function, which processes PDFs page by page, allowing users to define page-specific prompts for detailed analysis. - Support for OpenAI-compatible APIs: Introduced a .compatible argument (and flexible url and path) in openai() to allow compatibility with third-party OpenAI-compatible APIs.

Improvements: - API Format Refactoring: Complete refactor of to_api_format() to reduce code duplication, simplify API format generation, and improve maintainability. - Improved Error Handling: Enhanced input validation and error messaging for all API-functions functions, making troubleshooting easier. - Rate-Limiting Enhancements: Updated rate limiting to use httr2::req_retry(), leveraging 429 headers for more accurate request management. - Expanded Testing: Added comprehensive tests for API functions using httptest2, covering rate-limiting, batch processing, error handling, and schema validation.

Breaking Changes: - Redesigned Reply Functions: get_reply() was split into get_reply() for text outputs and get_reply_data() for structured outputs, improving type stability compared to an earlier function that had different outputs based on a .json-arguement. - Deprecation of chatgpt(): The chatgpt() function has been deprecated in favor of openai() for feature alignment and improved consistency.

Minor Updates and Bug Fixes: - Expanded PDF Support in llm_message(): Allows extraction of specific page ranges from PDFs, improving flexibility in document handling. - New ollama_download_model() function to download models from the Ollama API - All sequential chat API functions now support streaming

Version 0.1.11

Major Features

• Support for both the Anthropic and the OpenAI batch request API added
• New .compatible-arguement in openai() to allow working with compatible third party APIs

Improvements

• Complete refactor of to_api_format(): API format generation now has much less code duplication and is more maintainable.

Version 0.1.10

Breaking Changes

• get_reply() was split into two type-stable functions: get_reply() for text and get_reply_data() for structured outputs.

Improvements

• Rate limiting updated to use httr2::req_retry(): Rate limiting now uses the right 429 headers where they come.

Version 0.1.9

Major Features

• Enhanced Input Validation: All API functions now have improved input validation, ensuring better alignment with API documentation

• Improved error handling More human-readable error messages for failed requests from the API

• Advanced JSON Mode in openai(): The openai() function now supports advanced .json_schemas, allowing structured output in JSON mode for more precise responses.

• Reasoning Models Support: Support for O1 reasoning models has been added, with better handling of system prompts in the openai() function.

• Streaming callback functions refactored: Given that the streaming callback format for Open AI, Mistral and Groq is nearly identical the three now rely on the same callback function.

Breaking Changes

• chatgpt() Deprecated: The chatgpt() function has been deprecated in favor of openai(). Users should migrate to openai() to take advantage of the new features and enhancements.

Improvements

• Better Error Handling: The openai(), ollama(), and claude() functions now return more informative error messages when API calls fail, helping with debugging and troubleshooting.

Version 0.1.8

Major Features

• Embedding Models Support: Embedding model support for three APIs:
  • Embedding functions process message histories and combine text from message content and media attachments for embedding models.
  • ollama_embedding() to generate embeddings using the Ollama API.
  • openai_embedding() to generate embeddings using the OpenAI API.
  • mistral_embedding() to generate embeddings using the Mistral API.

Improvements

• PDF Page Support in llm_message(): The llm_message() function now supports specifying a range of pages in a PDF by passing a list with filename, start_page, and end_page. This allows users to extract and process specific pages of a PDF.

Version 0.1.7

Major Features

• PDF Page Batch Processing: Introduced the pdf_page_batch() function, which processes PDF files page by page, extracting text and converting each page into an image, allowing for a general prompt or page-specific prompts. The function generates a list of LLMMessage objects that can be sent to an API and work with the batch-API functions in tidyllm.

Version 0.1.6

Major Features

• Support for the Mistral API: New mistral() function to use Mistral Models on Le Platforme on servers hosted in the EU, with rate-limiting and streaming support.

Version 0.1.5

Major Features

• Message Retrieval Functions: Added functions to retrieve single messages from conversations:
  • last_user_message() pulls the last message the user sent.
  • get_reply() gets the assistant reply at a given index of assistant messages.
  • get_user_message() gets the user message at a given index of user messages.

Improvements

• Easier Troubleshooting in API-function: All API functions now support the .dry_run argument, allowing users to generate an httr2-request for easier debugging and inspection.
• API Function Tests: Implemented httptest2-based tests with mock responses for all API functions, covering both basic functionality and rate-limiting.

Version 0.1.4

Major Features

• New Ollama functions:
  • Model Download: Introduced the ollama_download_model() function to download models from the Ollama API. It supports a streaming mode that provides live progress bar updates on the download progress.

Improvements

• Refactoring of llm_message()

Version 0.1.3

Major Features

• The groq() function now supports images.
• More complete streaming support across API-functions.

Breaking Changes

• Groq Models: System prompts are no longer sent for Groq models, since many models on Groq do not support them and all multimodal models on Groq disallow them.

Version 0.1.2

Improvements

• New unit tests for llm_message().
• Improvements in streaming functions.

Version 0.1.1

Major Features

• JSON Mode: JSON mode is now more widely supported across all API functions, allowing for structured outputs when APIs support them. The .json argument is now passed only to API functions, specifying how the API should respond, and it is not needed anymore in last_reply().

• Improved last_reply() Behavior: The behavior of the last_reply() function has changed. It now automatically handles JSON replies by parsing them into structured data and falling back to raw text in case of errors. You can still force raw text replies even for JSON output using the .raw argument.

Breaking Changes

• last_reply(): The .json argument is no longer used, and JSON replies are automatically parsed. Use .raw to force raw text replies.