rtemis.llm NEWS

0.8.7

• Added config_Apple() and create_Apple() for Apple Foundation Models: Apple’s on-device model, served over the OpenAI Chat Completions wire by the rtemis-afm bridge on http://127.0.0.1:1977. AppleConfig is an OpenAIConfig, so chat, structured output, tool calling, agents, and batch generation work unchanged; no API key is resolved or sent. config_Apple() reads the bridge’s /health endpoint by default and stops with instructions when the bridge is not running or the model is unavailable (validate_model = FALSE skips the check). apple_health() returns the served model, its availability, and its context window; apple_check_available() performs the check on its own.

0.8.6

• Added items to field(): an "array" field now says what it contains, as a type name (items = "string"), a field() where the elements carry their own description or enum, or a schema() for an array of objects. Emitted as the JSON Schema items key and passed through by all three backends, so a list of values can be requested as a list instead of as one delimited string the caller splits back apart.
• An "array" field declared without items is now an error rather than a schema that fails at request time: OpenAI’s strict mode rejects an array with no element type, and a constrained-decoding backend has nothing to constrain. items on any other type is likewise refused.
• field() refuses type = "object": a field carries no properties, which OpenAI and Anthropic require of an object schema. Declare an array of objects with type = "array" and items = schema(...). tool_param() still accepts "object".
• validate_output() checks array elements against items, where before an array field constrained only its outer type.

0.8.5

• Structured responses are validated locally by default with a cached Ajv validator. validate_output = FALSE skips validation while still requesting the schema. on_validation_failure = "warn" retains invalid output and emits an informational rtemis.core message, not an R warning; "collect" is silent and "abort" raises an error carrying the response and diagnostics. Batches validate per item and summarize mismatches once at completion.
• Added standalone validate_output() and the validation_results() accessor. Reports preserve original text, input positions/names, statuses, and diagnostics, including when batch responses are extracted. Validation does not repair output.
• Agent final answers are validated before memory commit. responses() recognizes histories returned by generate(agent, ...) and extracts their final answer.
• Anthropic synthetic structured-output tool inputs are returned as answer JSON, retaining raw content metadata and excluding the synthetic tool from execution.
• Added enum to field(): restrict a field to a fixed set of permitted values. Emitted as the JSON Schema enum key and passed through unchanged by all three backends, so a backend with constrained decoding (Ollama) makes an invalid value impossible rather than merely detectable.
• Added on_error to llmapply(), agentapply(), and map(), defaulting to "na": a failed call now warns and yields NA rather than discarding every result in the batch, and the result carries an errors attribute (a data.frame of index and message) for retrying just the failures. Pass on_error = "abort" for the previous behavior of propagating the first error.
• responses() and reasoning() now accept NULL elements, mapping them to NA_character_.
• Added num_ctx and keep_alive as per-call Ollama options in generate().
• Added logprobs and top_logprobs per-call options for the Ollama and OpenAI-compatible backends, with new logprobs() and token_probs() accessors. token_probs() reads the probability of each candidate answer straight off the model’s token distribution, which is better calibrated than asking a model to emit a number. Anthropic does not return log probabilities; its messages yield NULL rather than an error.
• Per-call options passed to generate() (temperature, top_p, max_tokens, stop, top_k, seed, num_ctx, keep_alive, logprobs, top_logprobs) are validated against each backend’s documented bounds before a request is built, so an out-of-range or wrong-typed value fails locally instead of on the server. Bounds follow the backend: temperature accepts up to 2 on Ollama and OpenAI-compatible backends and up to 1 on Anthropic, and top_logprobs up to 20 on OpenAI. top_logprobs requires logprobs = TRUE, which every backend that returns alternatives needs in order to return them.

0.8.4

• Added zero_data_retention to config_OpenAI() and create_OpenAI() for per-request OpenRouter ZDR routing.
• Added think to config_Ollama(), create_Ollama(), and generate() for Ollama thinking control.

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