# ellmer 0.2.1

* When you save a `Chat` object to disk, API keys are automatically redacted. 
  This means that you can no longer easily resume a chat you've saved on disk 
  (we'll figure this out in a future release) but ensures that you never 
  accidentally save your secret key in an RDS file (#534).

* `chat_anthropic()` now defaults to Claude Sonnet 4, and I've added pricing 
  information for the latest generation of Claude models.

* `chat_databricks()` now picks up on Databricks workspace URLs set in the
  configuration file, which should improve compatibility with the Databricks CLI
  (#521, @atheriel).

* `chat_snowflake()` no longer streams answers that include a mysterious
  `list(type = "text", text = "")` trailer (#533, @atheriel). It now parses 
  streaming outputs correctly into turns (#542), supports structured ouputs
  (#544), tool calling (#548), and standard model parameters (#545, @atheriel).

* `chat_snowflake()` and `chat_databricks()` now default to Claude Sonnet 3.7,
  the same default as `chat_anthropic()` (#539 and #546, @atheriel).

* `type_from_schema()` lets you to use pre-existing JSON schemas in structured 
  chats (#133, @hafen)

# ellmer 0.2.0

## Breaking changes

* We have made a number of refinements to the way ellmer converts JSON
  to R data structures. These are breaking changes, although we don't expect
  them to affect much code in the wild. Most importantly, tools are now invoked 
  with their inputs coerced to standard R data structures (#461); opt-out
  by setting `convert = FALSE` in `tool()`.

  Additionally ellmer now converts `NULL` to `NA` for `type_boolean()`, 
  `type_integer()`, `type_number()`, and `type_string()` (#445), and does a 
  better job with arrays when `required = FALSE` (#384).

* `chat_` functions no longer have a `turn` argument. If you need to set the
  turns, you can now use `Chat$set_turns()` (#427). Additionally, 
  `Chat$tokens()` has been renamed to `Chat$get_tokens()` and returns a data 
  frame of tokens, correctly aligned to the individual turn. The print method 
  now uses this to show how many input/output tokens were used by each turn 
  (#354).

## New features

* Two new interfaces help you do multiple chats with a single function call:

  * `batch_chat()` and `batch_chat_structured()` allow you to submit multiple 
    chats to OpenAI and Anthropic's batched interfaces. These only guarantee a 
    response within 24 hours, but are 50% of the price of regular requests 
    (#143).

  * `parallel_chat()` and `parallel_chat_structured()` work with any provider
    and allow you to submit multiple chats in parallel (#143). This doesn't give
    you any cost savings, but it's can be much, much faster.

  This new family of functions is experimental because I'm not 100% sure that 
  the shape of the user interface is correct, particularly as it pertains to 
  handling errors.

* `google_upload()` lets you upload files to Google Gemini or Vertex AI (#310).
  This allows you to work with videos, PDFs, and other large files with Gemini.

* `models_google_gemini()`, `models_anthropic()`, `models_openai()`,
  `models_aws_bedrock()`, `models_ollama()` and `models_vllm()`, list available
  models for Google Gemini, Anthropic, OpenAI, AWS Bedrock, Ollama, and VLLM
  respectively. Different providers return different metadata so they are only
  guaranteed to return a data frame with at least an `id` column (#296).
  Where possible (currently for Gemini, Anthropic, and OpenAI) we include
  known token prices (per million tokens).

* `interpolate()` and friends are now vectorised so you can generate multiple
  prompts for (e.g.) a data frame of inputs. They also now return a specially
  classed object with a custom print method (#445). New `interpolate_package()` 
  makes it easier to interpolate from prompts stored in the `inst/prompts` 
  directory inside a package (#164).

* `chat_anthropic()`, `chat_azure()`, `chat_openai()`, and `chat_gemini()` now 
  take a `params` argument, that coupled with the `params()` helper, makes it 
  easy to specify common model parameters (like `seed` and `temperature`) 
  across providers. Support for other providers will grow as you request it 
  (#280).

* ellmer now tracks the cost of input and output tokens. The cost is displayed
  when you print a `Chat` object, in `tokens_usage()`, and with
  `Chat$get_cost()`. You can also request costs in `parallel_chat_structured()`.
  We do our best to accurately compute the cost, but you should treat it as an
  estimate rather than the exact price. Unfortunately LLM providers currently
  make it very difficult to figure out exactly how much your queries cost (#203).

## Provider updates

* We have support for three new providers:

  * `chat_huggingface()` for models hosted at <https://huggingface.co>
    (#359, @s-spavound).
  * `chat_mistral()` for models hosted at <https://mistral.ai> (#319).
  * `chat_portkey()` and `models_portkey()` for models hosted at
    <https://portkey.ai> (#363, @maciekbanas).

* We also renamed (with deprecation) a few functions to make the naming 
  scheme more consistent (#382, @gadenbuie):

  * `chat_azure_openai()` replaces `chat_azure()`.
  * `chat_aws_bedrock()` replaces `chat_bedrock()`.
  * `chat_anthropic()` replaces `chat_anthropic()`.
  * `chat_google_gemini()` replaces `chat_gemini()`.

* We have updated the default model for a couple of providers:
  * `chat_anthropic()` uses Sonnet 3.7 (which it also now displays) (#336).
  * `chat_openai()` uses GPT-4.1 (#512)

## Developer tooling

* New `Chat$get_provider()` lets you access the underlying provider object 
  (#202).

* `Chat$chat_async()` and `Chat$stream_async()` gain a `tool_mode` argument to 
  decide between `"sequential"` and `"concurrent"` tool calling. This is an 
  advanced feature that primarily affects asynchronous tools (#488, @gadenbuie).

* `Chat$stream()` and `Chat$stream_async()` gain support for streaming the 
  additional content types generated during a tool call with a new `stream` 
  argument. When `stream = "content"` is set, the streaming response yields 
  `Content` objects, including the `ContentToolRequest` and `ContentToolResult` 
  objects used to request and return tool calls (#400, @gadenbuie).

* New `Chat$on_tool_request()` and `$on_tool_result()` methods allow you to
  register callbacks to run on a tool request or tool result. These callbacks
  can be used to implement custom logging or other actions when tools are
  called, without modifying the tool function (#493, @gadenbuie).

* `Chat$chat(echo = "output")` replaces the now-deprecated `echo = "text"` 
  option. When using `echo = "output"`, additional output, such as tool
  requests and results, are shown as they occur. When `echo = "none"`, tool
  call failures are emitted as warnings (#366, @gadenbuie).

* `ContentToolResult` objects can now be returned directly from the `tool()`
  function and now includes additional information (#398 #399, @gadenbuie):

  * `extra`: A list of additional data associated with the tool result that is
    not shown to the chatbot.
  * `request`: The `ContentToolRequest` that triggered the tool call.
    `ContentToolResult` no longer has an `id` property, instead the tool call
    ID can be retrieved from `request@id`.

  They also include the error condition in the `error` property when a tool call 
  fails (#421, @gadenbuie).

* `ContentToolRequest` gains a `tool` property that includes the `tool()`
  definition when a request is matched to a tool by ellmer (#423, @gadenbuie).

* `tool()` gains an `.annotations` argument that can be created with the
  `tool_annotations()` helper. Tool annotations are described in the
  [Model Context Protocol](https://modelcontextprotocol.io/introduction) and can
  be used to describe the tool to clients. (#402, @gadenbuie)

* New `tool_reject()` function can be used to reject a tool request with an
  explanation for the rejection reason. `tool_reject()` can be called within a
  tool function or in a `Chat$on_tool_request()` callback. In the latter case,
  rejecting a tool call will ensure that the tool function is not evaluated
  (#490, #493, @gadenbuie).

## Minor improvements and bug fixes

* All requests now set a custom User-Agent that identifies that the requests
  come from ellmer (#341). The default timeout has been increased to 
  5 minutes (#451, #321).

* `chat_anthropic()` now supports the thinking content type (#396), and 
  `content_image_url()` (#347). It gains a `beta_header` argument to opt-in 
  to beta features (#339). It (along with `chat_bedrock()`) no longer chokes 
  after receiving an output that consists only of whitespace (#376).
  Finally, `chat_anthropic(max_tokens =)` is now deprecated in favour of
  `chat_anthropic(params = )` (#280).

* `chat_google_gemini()` and `chat_google_vertex()` gain more ways to 
  authenticate. They can use `GEMINI_API_KEY` if set (@t-kalinowski, #513), 
  authenticate with Google default application credentials (including service 
  accounts, etc) (#317, @atheriel) and use viewer-based credentials when 
  running on Posit Connect (#320, @atheriel). Authentication with default 
  application credentials requires the {gargle} package. They now also can now 
  handle responses that include citation metadata (#358).

* `chat_ollama()` now works with `tool()` definitions with optional arguments 
  or empty properties (#342, #348, @gadenbuie), and now accepts `api_key` and 
  consults the `OLLAMA_API_KEY` environment variable. This is not needed for 
  local usage, but enables bearer-token authentication when Ollama is running 
  behind a reverse proxy (#501, @gadenbuie).

* `chat_openai(seed =)` is now deprecated in favour of `chat_openai(params = )` 
  (#280).

* `create_tool_def()` can now use any Chat instance (#118, @pedrobtz).

* `live_browser()` now requires {shinychat} v0.2.0 or later which provides
  access to the app that powers `live_browser()` via `shinychat::chat_app()`,
  as well as a Shiny module for easily including a chat interface for an ellmer
  `Chat` object in your Shiny apps (#397, @gadenbuie). It now initializes the
  UI with the messages from the chat turns, rather than replaying the turns
  server-side (#381).

* `Provider` gains `name` and `model` fields (#406). These are now reported when
  you print a chat object and are used in `token_usage()`.

# ellmer 0.1.1

## Lifecycle changes

* `option(ellmer_verbosity)` is no longer supported; instead use the standard
  httr2 verbosity functions, such as `httr2::with_verbosity()`; these now
  support streaming data.

* `chat_cortex()` has been renamed `chat_cortex_analyst()` to better
  disambiguate it from `chat_snowflake()` (which *also* uses "Cortex")
  (#275, @atheriel).

## New features

* All providers now wait for up to 60s to get the complete response. You can
  increase this with, e.g., `option(ellmer_timeout_s = 120)` (#213, #300).

* `chat_azure()`, `chat_databricks()`, `chat_snowflake()`, and
  `chat_cortex_analyst()` now detect viewer-based credentials when running on
  Posit Connect (#285, @atheriel).

* `chat_deepseek()` provides support for DeepSeek models (#242).

* `chat_openrouter()` provides support for models hosted by OpenRouter (#212).

* `chat_snowflake()` allows chatting with models hosted through Snowflake's
  [Cortex LLM REST API](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-llm-rest-api)
  (#258, @atheriel).

* `content_pdf_file()` and `content_pdf_url()` allow you to upload PDFs to
  supported models. Models that currently support PDFs are Google Gemini and
  Claude Anthropic. With help from @walkerke and @andrie (#265).

## Bug fixes and minor improvements

* `Chat$get_model()` returns the model name (#299).

* `chat_azure()` has greatly improved support for Azure Entra ID. API keys are
  now optional and we can pick up on ambient credentials from Azure service
  principals or attempt to use interactive Entra ID authentication when
  possible. The broken-by-design `token` argument has been deprecated (it
  could not handle refreshing tokens properly), but a new `credentials`
  argument can be used for custom Entra ID support when needed instead
  (for instance, if you're trying to use tokens generated by the `AzureAuth`
  package) (#248, #263, #273, #257, @atheriel).

* `chat_azure()` now reports better error messages when the underlying HTTP
  requests fail (#269, @atheriel). It now also defaults to
  `api_version = "2024-10-21"` which includes data for structured data
  extraction (#271).

* `chat_bedrock()` now handles temporary IAM credentials better
  (#261, @atheriel) and `chat_bedrock()` gains `api_args` argument (@billsanto, #295).

* `chat_databricks()` now handles the `DATABRICKS_HOST` environment variable
  correctly whether it includes an HTTPS prefix or not (#252, @atheriel).
  It also respects the `SPARK_CONNECT_USER_AGENT` environment variable when
  making requests (#254, @atheriel).

* `chat_gemini()` now defaults to using the gemini-2.0-flash model.

* `print(Chat)` no longer wraps long lines, making it easier to read code
  and bulleted lists (#246).

# ellmer 0.1.0

* New `chat_vllm()` to chat with models served by vLLM (#140).

* The default `chat_openai()` model is now GPT-4o.

* New `Chat$set_turns()` to set turns. `Chat$turns()` is now `Chat$get_turns()`. `Chat$system_prompt()` is replaced with `Chat$set_system_prompt()` and `Chat$get_system_prompt()`.

* Async and streaming async chat are now event-driven and use `later::later_fd()` to wait efficiently on curl socket activity (#157).

* New `chat_bedrock()` to chat with AWS bedrock models (#50).

* New `chat$extract_data()` uses the structured data API where available (and tool calling otherwise) to extract data structured according to a known type specification. You can create specs with functions `type_boolean()`, `type_integer()`, `type_number()`, `type_string()`, `type_enum()`, `type_array()`, and `type_object()` (#31).

* The general `ToolArg()` has been replaced by the more specific `type_*()` functions. `ToolDef()` has been renamed to `tool`.

* `content_image_url()` will now create inline images when given a data url (#110).

* Streaming ollama results works once again (#117).

* Streaming OpenAI results now capture more results, including `logprobs` (#115).

* New `interpolate()` and `prompt_file()` make it easier to create prompts that are a mix of static text and dynamic values.

* You can find how many tokens you've used in the current session by calling `token_usage()`.

* `chat_browser()` and `chat_console()` are now `live_browser()` and `live_console()`.

* The `echo` can now be one of three values: "none", "text", or "all". If "all", you'll now see both user and assistant turns, and all content types will be printed, not just text. When running in the global environment, `echo` defaults to "text", and when running inside a function it defaults to "none".

* You can now log low-level JSON request/response info by setting `options(ellmer_verbosity = 2)`.

* `chat$register_tool()` now takes an object created by `Tool()`. This makes it a little easier to reuse tool definitions (#32).

* `new_chat_openai()` is now `chat_openai()`.

* Claude and Gemini are now supported via `chat_claude()` and `chat_gemini()`.

* The Snowflake Cortex Analyst is now supported via `chat_cortex()` (#56).

* Databricks is now supported via `chat_databricks()` (#152).
