High-Performance Open-Source Archive
New prompt wrap answer_as_dataframe() for extracting
tabular results via structured output, with support for row schemas,
array-of-row schemas, and optional row-count validation.
New prompt wrap answer_as_numeric() for extracting
numeric responses, including optional minimum and maximum value
validation.
send_prompt() can now directly use an ‘ellmer’ chat
object as the llm_provider to evaluate the prompt with
(will build an llm_provider_ellmer() under the
hood)
llm_provider_ellmer() was improved to better
synchronize with the native ‘ellmer’ state, for instance for streaming,
multimodal/image content, and persistent chats, with clearer warnings
when settings need to be configured on the underlying
ellmer chat object.
answer_as_json() and
answer_using_tools() have broader ellmer compatibility,
including ellmer::type_from_schema(), ellmer built-in
tools, and better handling of optional or ignored tool
arguments.
Chat history handling is more robust for tool and ellmer-native
workflows: tool rows are supported, non-replayable native
rows (tool call and thinking rows) are kept for inspection but not
re-sent to the LLM provider, and related metadata is normalized more
reliably.
Update e-mail address of maintainer in DESCRIPTION file (change to a personal e-mail address due to leaving the organization).
llm_provider-class: can now take a
stream_callback function, which can be used to intercept
streamed tokens as they arrive from the LLM provider. This may be used
to build custom streaming behavior, for instance to show a live response
in a Shiny app (see new vignette("streaming_shiny_ipc") for
an example)
’llm_provider_ellmer()`: now supports streaming responses
add_image(): new prompt wrap to add an image to a
prompt, for use with multimodal LLMs
answer_using_r(): fixed error with unsafe conversion
of resulting object to character
Add provider-level prompt wraps
(provider_prompt_wrap()) these are prompt wraps which can
be attached to a LLM provider object. They can be applied to any prompt
which is sent through this LLM provider, either before or after
prompt-specific prompt wraps. This is useful when you want to achieve
certain behavior for various prompts, without having to re-apply the
same prompt wrap to each prompt
answer_as_json(): support ‘ellmer’ definitions of
structured output (e.g., ellmer::type_object()).
answer_as_json() can convert between ellmer definitions and
the previous R list objects which represent JSON schemas; thus, ‘ellmer’
and R list object definitions work with both regular and ‘ellmer’ LLM
providers. When using an llm_provider_ellmer(),
answer_as_json() will ensure the native ‘ellmer’ functions
for obtaining structured output are used
answer_using_tools(): support ‘ellmer’ definitions
of tools (from ellmer::tool()).
answer_using_tools() can convert between ‘ellmer’ tool
definitions and the previous R function objects with documentation from
tools_add_docs(); thus, ‘ellmer’ and
tools_add_docs() definitions work with both regular and
‘ellmer’ LLM providers. When using an
llm_provider_ellmer(), answer_using_tools()
will ensure the native ‘ellmer’ functions for registering tools are
used.
answer_using_tools(): because of the above, and the
fact that package ‘mcptools’ returns ‘ellmer’ tool definitions with
mcptools::mcp_tools(), answer_using_tools()
can now also be used with tools from Model Context Protocol (MCP)
servers
send_prompt() can now return an updated ‘ellmer’
chat object when using an llm_provider_ellmer() (containing
for instance the history of ‘ellmer’ turns and tool calls). Additionally
fixed issues with how turn history is handled in ‘ellmer’ chat
objects
send_prompt()’s clean_chat_history
argument is now defaulted to FALSE, as it may be confusing
for users to see cleaned chat histories without having actively
requested this. If return_mode = "full",
$clean_chat_history is also no longer included when
clean_chat_history = FALSE
llm_provider_openai() now supports (as default) the
OpenAI responses API, which allows setting parameters like
‘reasoning_effort’ and ‘verbosity’ (relevant for gpt-5). The OpenAI chat
completions API is also still supported
llm_provider_google_gemini() has been superseded by
llm_provider_ellmer(ellmer::chat_google_gemini())
Add a json_type & tool_type field
to LLM provider objects; when automatically determining the route
towards structured output (in answer_as_json()) and tool
use (in answer_using_tools()), this can override the type
decided by the api_type field (e.g., user can use this
field to force the text-based type, for instance when using an OpenAI
type LLM provider but with a model which does not support the typical
OpenAI API parameters for structured output)
Update how responses are streamed (with
httr2::req_perform_connection(), since
httr2::req_perform_stream() is being deprecated)
Fix bug where the LLM provider object was not properly passed on
to modify_fn in prompt_wrap(), which could
lead to errors when dynamically constructing prompt text based on the
LLM provider type
New prompt wraps answer_as_category() and
answer_as_multi_category()
New llm_break_soft() interrupts prompt evaluation
without error
New experimental provider llm_provider_ellmer() for
ellmer chat objects
Ollama provider gains num_ctx parameter to control
context window size
set_option() and set_options() are now
available for the Ollama provider to configure options
Error messages are more informative when an LLM provider cannot be reached
Google Gemini provider now works without errors in affected cases
Chat history handling is safer; rows with NA values
no longer cause errors in specific cases
Final-answer extraction in chain-of-thought prompts is more flexible
Printed LLM responses now use message() instead of
cat()
Moved repository to https://github.com/KennispuntTwente/tidyprompt
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