Changelog

Source: NEWS.md

EndpointR 0.2.3

• Bug fix with error message handling, previously passing in raw error_msg to cli:: functions, which then interpret as glue, so try to handle ‘{ }’ when they appear in the error messages. Fix is to passing “{error_msg}” already string interpolated. Fix added to OpenAI integrations as well as Anthropic Batch Implementation
• Tests added, and request creation for Ant batches now checks against the RegEx Anthropic provide

EndpointR 0.2.2

Anthropic Messages API

• ant_build_messages_request() now automatically enables prompt caching when a system_prompt is provided, structuring it as a content block with cache_control. This benefits ant_complete_chunks() and ant_complete_df() where many requests share the same system prompt — cached reads cost 90% less than uncached.
• Structured outputs is out of BETA and is now generally available, so the header is removed, and output_form –> output_config in the body of the request following Anthropic Docs on Structured Outputs

Anthropic Batch API

Functions for dealing with Anthropic Bathches API, works differently ot the OpenAI API - as we send requests not files.

• ant_batch_create()
• ant_batch_status()
• ant_batch_results()
• ant_batch_list()
• ant_batch_cancel()

See the Sync Async Vignette for more details

EndpointR 0.2.1

OpenAI Batch API

Adds support for OpenAI’s asynchronous Batch API, offering 50% cost savings and higher rate limits compared to synchronous endpoints. Ideal for large-scale embeddings, classifications, and batch inference tasks.

Request preparation:

• oai_batch_build_embed_req() - Build a single embedding request row
• oai_batch_prepare_embeddings() - Prepare an entire data frame for batch embeddings
• oai_batch_build_completions_req() - Build a single chat completions request row
• oai_batch_prepare_completions() - Prepare an entire data frame for batch completions (supports structured outputs via JSON schema)

Job management:

• oai_batch_upload() - Upload prepared JSONL to OpenAI Files API
• oai_batch_start() - Trigger a batch job on an uploaded file
• oai_batch_status() - Check the status of a running batch job
• oai_batch_list() - List all batch jobs associated with your API key
• oai_batch_cancel() - Cancel an in-progress batch job

Results parsing:

• oai_batch_parse_embeddings() - Parse batch embedding results into a tidy data frame
• oai_batch_parse_completions() - Parse batch completion results into a tidy data frame

OpenAI Files API

• oai_file_list() - List files uploaded to the OpenAI Files API
• oai_file_content() - Retrieve the content of a file (e.g., batch results)
• oai_file_delete() - Delete a file from the Files API

EndpointR 0.2.0

• error message and status propagation improvement. Now writes .error, .error_msg (standardised across package), and .status. Main change is preventing httr2 eating the errors before we can deal with them
• adds parquet writing to oai_complete_df and oai_embed_df
• adds chunks func to oai_embed, and re-writes all batch -> chunk logic
• implements the Anthropic messages API with structured outputs (via BETA)
• adds ant_complete_df() and ant_complete_chunks() for batch/chunked processing with the Anthropic API, with parquet writing and metadata tracking
• metadata tracking now includes schema and system_prompt for both OpenAI and Anthropic chunked processing functions
• bug fix: S7 schema objects now correctly serialised to metadata.json (previously caused “No method asJSON S3 class: S7_object” error)
• adds spelling test, sets language to en-GB in DESCRIPTION

EndpointR 0.1.2

• File writing improvements: hf_embed_df() and hf_classify_df() now write intermediate results as .parquet files to output_dir directories, similar to improvements in 0.1.1 for OpenAI functions

• Parameter changes: Moved from batch_size to chunk_size argument across hf_embed_df(), hf_classify_df(), and oai_complete_df() for consistency

• New chunking functions: Introduced hf_embed_chunks() and hf_classify_chunks() for more efficient batch processing with better error handling

• Dependency update: Package now depends on arrow for faster .parquet file writing and reading

• Metadata tracking: Hugging Face functions that write to files (hf_embed_df(), hf_classify_df(), hf_embed_chunks(), hf_classify_chunks()) now write metadata.json to output directories containing:

  • Endpoint URL and API key name used
  • Processing parameters (chunk_size, concurrent_requests, timeout, max_retries)
  • Inference parameters (truncate, max_length)
  • Timestamp and row counts
  • Useful for debugging, reproducibility, and tracking which models/endpoints were used

• max_length parameter: Added max_length parameter to hf_classify_df() and hf_classify_chunks() for text truncation control. Note: hf_embed_df() handles truncation automatically via endpoint configuration (set AUTO_TRUNCATE in endpoint settings)

• New utility functions:

  • hf_get_model_max_length() - Retrieve maximum token length for a Hugging Face model
  • hf_get_endpoint_info() - Retrieve detailed information about a Hugging Face Inference Endpoint

• Improved reporting: Chunked/batch processing functions now report total successes and failures at completion

EndpointR 0.1.1

• oai_complete_chunks() function to better support for chunking/batching in oai_complete_df()
• oai_complete_df() now writes to a file to mitigate the chance of completely lost data

EndpointR 0.1.0

Initial BETA release, ships with:

• Support for embeddings and classification with Hugging Face Inference API & Dedicated Inference Endpoints
• Support for text completion using OpenAI models via the Chat Completions API
• Support for embeddings with the OpenAI Embeddings API
• Structured outputs via JSON schemas and validators