Version 0.3.0 is currently being prepared for CRAN. This release represents a major milestone for tidyllm
The largest changes compared to 0.2.0 are:
chat(), embed(), send_batch(), check_batch(), and fetch_batch() to interact with APIs. These functions always work with a combination of verbs and providers:
chat(), embed(), send_batch()) define the type of action you want to perform.openai(), claude(), ollama()) are an arguement of verbs and specify the API to handle the action with and take provider-specific argumentsEach verb and provider combination routes the interaction to provider-specific functions like openai_chat() or claude_chat() that do the work in the background. These functions can also be called directly as an alternative more verbose and provider-specific interface.
llm_message("Hello World") |>
openai(.model = "gpt-4o")
# Recommended Verb-Based Approach
llm_message("Hello World") |>
chat(openai(.model = "gpt-4o"))
# Or even configuring a provider outside
my_ollama <- ollama(.model = "llama3.2-vision:90B",
.ollama_server = "https://ollama.example-server.de",
.temperature = 0)
llm_message("Hello World") |>
chat(my_ollama)
# Alternative Approach is to use more verbose specific functions:
llm_message("Hello World") |>
openai_chat(.model = "gpt-4o")
openai(), claude(), etc.) still work if you directly supply an LLMMessage as arguement, but issue deprecation warnings when used directly for chat.R6-based saved LLMMessage objects are no longer compatible with the new version. Saved objects from earlier versions need to be re-createdgemini() and perplexity() as new supported API providers. gemini() brings interesting Video and Audio features as well as search grounding to tidyllm. perplexity() also offers well cited search grounded assitant repliesmistral()get_reply_metadata() to get information on token usage, or on other relevant metadata (like sources used for grounding)R6 to S7 for the main LLMMessage class, improving maintainability, interoperability, and future-proofing..grounding_threshold argument added of the gemini_chat() function allowing you to use Google searches to ground model responses to a search result Gemini models. For example, asking about the maintainer of an obscure R package works with grounding but does only lead to a hallucination without:llm_message("What is tidyllm and who maintains this package?") |>
gemini_chat(.grounding_threshold = 0.3)
perplexity_chat(). The neat feature of perplexity is the up-to-date web search it does with detailed citations. Cited sources are available in the api_specific-list column of get_metadata().json_schema support for ollama() available with Ollama 0.5.0get_metadata() returns a list column with API-specific metadataR6 to S7 for the main LLMMessage classdf_llm_message()APIProvider classesapi_openai.R,api_gemini.R,etc. filesas_tibble() S3 Generic for LLMMessagetrack_rate_limit().onattach() removedR6-based LLMMessage-objects are not compatible with the new version anymore! This also applies to saved objects, like lists of batch files.here::here("local_wip","example.mp3") |> gemini_upload_file()
here::here("local_wip","legrille.mp4") |> gemini_upload_file()
file_tibble <- gemini_list_files()
llm_message("What are these two files about?") |>
gemini_chat(.fileid=file_tibble$name)
Better embedding functions with improved output and error handling and new documentation. New article on using embeddings with tidyllm. Support for embedding models on azure with azure_openai_embedding()
embed() and the related API-specific functions was changed from a matrix to a tibble with an input column and a list column containing one embedding vector and one input per row.One disadvantage of the first iteration of the new interface was that all arguements that needed to be passed to provider-specific functions, were going through the provider function. This feels, unintuitive, because users expect common arguments (e.g., .model, .temperature) to be set directly in main verbs like chat() or send_batch().Moreover, provider functions don't expose arguments for autocomplete, making it harder for users to explore options. Therefore, the main API verbs now directly accept common arguements, and check them against the available arguements for each API.
tidyllm has introduced a verb-based interface overhaul to provide a more intuitive and flexible user experience. Previously, provider-specific functions like claude(), openai(), and others were directly used for chat-based workflows. Now, these functions primarily serve as provider configuration for some general verbs like chat().
chat(), embed(), send_batch(), check_batch(), and fetch_batch() to interact with APIs. These functions always work with a combination of verbs and providers:
chat(), embed(), send_batch()) define the type of action you want to perform.openai(), claude(), ollama()) are an arguement of verbs and specify the API to handle the action with and take provider-specific argumentsEach verb and provider combination routes the interaction to provider-specific functions like openai_chat() or claude_chat() that do the work in the background. These functions can also be called directly as an alternative more verbose and provider-specific interface.
llm_message("Hello World") |>
openai(.model = "gpt-4o")
# Recommended Verb-Based Approach
llm_message("Hello World") |>
chat(openai(.model = "gpt-4o"))
# Or even configuring a provider outside
my_ollama <- ollama(.model = "llama3.2-vision:90B",
.ollama_server = "https://ollama.example-server.de",
.temperature = 0)
llm_message("Hello World") |>
chat(my_ollama)
# Alternative Approach is to use more verbose specific functions:
llm_message("Hello World") |>
openai_chat(.model = "gpt-4o")
openai(), claude(), etc.) still work if you directly supply an LLMMessage as arguement, but issue deprecation warnings when used directly for chat.gemini() main API-function#Upload a file for use with gemini
upload_info <- gemini_upload_file("example.mp3")
#Make the file available during a Gemini API call
llm_message("Summarize this speech") |>
gemini(.fileid = upload_info$name)
#Delte the file from the Google servers
gemini_delete_file(upload_info$name)
tidyllm_schema()gemini()-requests allow for a wide range of file types that can be used for context in messagesgemini() file workflows:
application/pdftext/plaintext/htmltext/csstext/mdtext/csvtext/xmltext/rtfgemini() file workflows:
application/x-javascript, text/javascriptapplication/x-python, text/x-pythongemini() file workflows:
image/pngimage/jpegimage/webpimage/heicimage/heifgemini() file workflows:
video/mp4video/mpegvideo/movvideo/avivideo/x-flvvideo/mpgvideo/webmvideo/wmvvideo/3gppgemini() file workflows:
audio/wavaudio/mp3audio/aiffaudio/aacaudio/oggaudio/flacget_metadata() function to retrieve and format metadata from LLMMessage objects.print method for LLMMessage to support printing metadata, controlled via the new tidyllm_print_metadata option or a new .meta-arguement for the print method.conversation <- llm_message("Write a short poem about software development") |>
claude()
#Get metdata on token usage and model as tibble
get_metadata(conversation)
#or print it with the message
print(conversation,.meta=TRUE)
#Or allways print it
options(tidyllm_print_metadata=TRUE)
send_openai_batch() caused by a missing .json-arguement not being passed for messages without schemaNew CRAN release. Largest changes compared to 0.1.0:
Major Features:
.json_schema handling in openai(), enhancing support for well-defined JSON responses.azure_openai() function for accessing the Azure OpenAI service, with full support for rate-limiting and batch operations tailored to Azure’s API structure.mistral() function provides full support for Mistral models hosted in the EU, including rate-limiting and streaming capabilities.pdf_page_batch() function, which processes PDFs page by page, allowing users to define page-specific prompts for detailed analysis..compatible argument (and flexible url and path) in openai() to allow compatibility with third-party OpenAI-compatible APIs.Improvements:
to_api_format() to reduce code duplication, simplify API format generation, and improve maintainability.httr2::req_retry() in addition to the rate-limit tracking functions in tidyllm, using 429 headers to wait for rate limit resets.httptest2Breaking Changes:
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.chatgpt(): The chatgpt() function has been deprecated in favor of openai() for feature alignment and improved consistency.Minor Updates and Bug Fixes:
llm_message(): Allows extraction of specific page ranges from PDFs, improving flexibility in document handling.ollama_download_model() function to download models from the Ollama API.compatible-arguement in openai() to allow working with compatible third party APIsto_api_format(): API format generation now has much less code duplication and is more maintainable.get_reply() was split into two type-stable functions: get_reply() for text and get_reply_data() for structured outputs.httr2::req_retry(): Rate limiting now uses the right 429 headers where they come.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.
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.openai(), ollama(), and claude() functions now return more informative error messages when API calls fail, helping with debugging and troubleshooting.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.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.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.mistral() function to use Mistral Models on Le Platforme on servers hosted in the EU, with rate-limiting and streaming support.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..dry_run argument, allowing users to generate an httr2-request for easier debugging and inspection.httptest2-based tests with mock responses for all API functions, covering both basic functionality and rate-limiting.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.llm_message()groq() function now supports images.llm_message().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.
last_reply(): The .json argument is no longer used, and JSON replies are automatically parsed. Use .raw to force raw text replies.