ellmer 0.4.0

Lifecycle changes

• chat_claude() is no longer deprecated and is an alias for chat_anthropic(), reflecting Anthropic's recent rebranding of developer tools under the Claude name (#758). models_claude() is now an alias for models_anthropic().
• parallel_chat() and batch_chat() are no longer experimental.
• The following deprecated functions/arguments/methods have now been removed:
  • Chat$extract_data() -> chat$chat_structured() (0.2.0)
  • Chat$extract_data_async() -> chat$chat_structured_async() (0.2.0)
  • chat_anthropic(max_tokens) -> chat_anthropic(params) (0.2.0)
  • chat_azure() -> chat_azure_openai() (0.2.0)
  • chat_azure_openai(token) (0.1.1)
  • chat_bedrock() -> chat_aws_bedrock() (0.2.0)
  • chat_claude() -> chat_anthropic() (0.2.0)
  • chat_cortex() -> chat_snowflake() (0.2.0)
  • chat_gemini() -> chat_google_gemini() (0.2.0)
  • chat_openai(seed) -> chat_openai(params) (0.2.0)
  • create_tool_def(model) -> create_tool_def(chat) (0.2.0)

New features

• batch_*() no longer hashes properties of the provider besides the name, model, and base_url. This should provide some protection from accidentally reusing the same .json file with different providers, while still allowing you to use the same batch file across ellmer versions. It also has a new ignore_hash argument that allows you to opt out of the check if you're confident the difference only arises because ellmer itself has changed.
• chat_claude() gains new cache parameter to control caching. By default it is set to "5m". This should (on average) reduce the cost of your chats (#584).
• chat_openai() now uses OpenAI's responses endpoint (#365, #801). This is their recommended endpoint and gives more access to built-in tools.
• chat_openai_compatible() replaces chat_openai() as the interface to use for OpenAI-compatible APIs, and chat_openai() is reserved for the official OpenAI API. Unlike previous versions of chat_openai(), the base_url parameter is now required (#801).
• chat_*() functions now use a credentials function instead of an api_key (#613). This means that API keys are never stored in the chat object (which might be saved to disk), but are instead retrieved on demand as needed. You generally shouldn't need to use the credentials argument, but when you do, you should use it to dynamically retrieve the API key from some other source (i.e. never inline a secret directly into a function call).
• New set of claude_file_() functions for managing file uploads with Claude (@dcomputing, #761).
• ellmer now supports a variety of built-in web search and fetch tools (#578):
  • claude_tool_web_search() and claude_tool_web_fetch() for Claude.
  • google_tool_web_search() and google_tool_web_fetch() for Gemini.
  • openai_tool_web_search() for OpenAI.
    If you want to do web fetch for other providers, you could use btw::btw_tool_web_read_url().
• parallel_chat() and friends now have a more permissive attitude to errors. By default, they will now return when hitting the first error (rather than erroring), and you can control this behaviour with the on_error argument. Or if you interrupt the job, it will finish up current requests and then return all the work done so far. The main downside of this work is that the output of parallel_chat() is more complex: it is now a mix of Chat objects, error objects, and NULL (#628).
• parallel_chat_structured() no longer errors if some results fail to parse. Instead it warns, and the corresponding rows will be filled in with the appropriate missing values (#628).
• New schema_df() to describe the schema of a data frame to an LLM (#744).
• tool()s can now return image or PDF content types, with content_image_file() or content_image_pdf() (#735).
• params() gains new reasoning_effort and reasoning_tokens so you can control the amount of effort a model spends on thinking. Initial support is provided for chat_claude(), chat_google_gemini(), and chat_openai() (#720).
• New type_ignore() allows you to specify that a tool argument should not be provided by the LLM when the R function has a suitable default value (#764).

Minor improvements and bug fixes

• Updated pricing data (#790).
• AssistantTurns now have a @duration slot, containing the total time to complete the request (@simonpcouch, #798).
• batch_chat() logs tokens once, on retrieval (#743).
• batch_chat() now retrieves failed results for chat_openai() (#830) and gracefully handles invalid JSON (#845).
• batch_chat() now works once more for chat_anthropic() (#835).
• batch_chat_*() and parallel_chat_*() now accept a string as the chat object, following the same rules as chat() (#677).
• chat_claude() and chat_aws_bedrock() now default to Claude Sonnet 4.5 (#800).
• chat_databricks() lifts many of its restrictions now that Databricks' API is more OpenAI compatible (#757).
• chat_google_gemini() and chat_openai() support image generation (#368).
• chat_google_gemini() has an experimental fallback interactive OAuth flow, if you're in an interactive session and no other authentication options can be found (#680).
• chat_groq() now defaults to llama-3.1-8b-instant.
• chat_openai() gains a service_tier argument (#712).
• chat_portkey() now requires you to supply a model (#786).
• chat_portkey(virtual_key) no longer needs to be supplied; instead Portkey recommends including the virtual key/provider in the model (#786).
• Chat$chat(), Chat$stream(), and similar methods now add empty tool results when a the chat is interrupted during a tool call loop, allowing the conversation to be resumed without causing an API error (#840).
• Chat$chat_structured() and friends now only warn if multiple JSON payloads found (instead of erroring) (@kbenoit, #732).
• Chat$get_tokens() gives a brief description of the turn contents to make it easier to see which turn tokens are spent on (#618) and also returns the cost (#824). It now returns one row for each assistant turn, better representing the underlying data received from LLM APIs. Similarly, the print() method now reports costs on each assistant turn, rather than trying to parse out individual costs.
• interpolate_package() now provides an informative error if the requested prompt file is not found in the package's prompts/ directory (#763) and now works with in-development packages loaded with devtools (#766).
• models_mistral() lists available models (@rplsmn, #750).
• models_ollama() was fixed to correctly query model capabilities from remote Ollama servers (#746).
• chat_ollama() now uses credentials when checking if Ollama is available and models_ollama() now has a credentials argument. This is useful when accessing Ollama servers that require authentication (@AdaemmerP, #863).
• parallel_chat_structured() now returns a tibble, since this does a better job of printing more complex data frames (#787).