openalexVectorComp

Embedding of Corpora, implemented in R.

• Embeddings served by TEI (Text Embeddings Inference; Hugging Face).
• Embeddings via a backend-neutral interface (hf, openai, tei).
• Scoring: prototype cosine-distance + reference-area ridge score (distance_ridge() + score_ridge()) + threshold calibration.
• Works great with DuckDB/Arrow pipelines.

The badges show the released version (r-universe, built from main) and the development version (dev); what changed in each release is in NEWS.md.

Development Continuity

See DEVELOPMENT_CONTINUITY.md for design principles, architectural decisions, and the required pre-commit update checklist that keeps development context continuous for both humans and AI agents.

Installation

Released version, from the openalexPro r-universe:

install.packages(
  "openalexVectorComp",
  repos = c("https://openalexpro.r-universe.dev", "https://cloud.r-project.org")
)

Development version (the dev branch), from GitHub:

# install.packages("remotes")
remotes::install_github("openalexPro/openalexVectorComp", ref = "dev")

Runtime dependencies

• For provider = "tei":

  text-embeddings-router --model BAAI/bge-small-en-v1.5 --port 8080
  

• For hosted embedding backends (provider = "hf" or "openai"), set OVC_API_TOKEN in your environment.

Vignettes

Start with vignettes/simplestart.qmd, then see:

• vignettes/package-overview.qmd
• vignettes/openai-batch-async.qmd
• vignettes/abstract-cleaning.qmd
• vignettes/specter2-setup.qmd (academic-paper embeddings via TEI)

Run a Local Demo Project

Create a full demo in getwd()/demos/openalex (fixtures + Quarto analysis):

run_demo_openalex(
  demo_dir = file.path(getwd(), "demos", "openalex"),
  render = FALSE
)

Set render = TRUE to run quarto render directly. For hosted backends (provider = "hf" or "openai"), set OVC_API_TOKEN first. The Quarto file and backend YAML are placed in demo_dir, while all pipeline artifacts are written under demo_dir/project/.

OpenAI-specific demo (same structure, explicit API key argument):

run_demo_openai(
  api_key = Sys.getenv("OVC_API_TOKEN"),
  demo_dir = file.path(getwd(), "demos", "openai"),
  render = FALSE
)

The OpenAI demo now follows a two-phase async flow:

1. run_demo_openai(..., render = TRUE) submits batch and continues.
2. If batch is still pending, finalize later:

demo_finalize_openai_batch(
  demo_dir = file.path(getwd(), "demos", "openai"),
  api_key = Sys.getenv("OVC_API_TOKEN"),
  label = "corpus_batch"
)

This writes comparison outputs to: project/openai_batch_comparison/label=corpus_batch/.

OpenAI Batch Workflow (Async, Pure R)

For long-running OpenAI embedding jobs, use the async batch helpers:

backend <- backend_config(
  provider = "openai",
  model = "text-embedding-3-small"
)

# 1) submit and return immediately
batch_submit_openai(
  project_dir = "my_project",
  backend = backend,
  label = "corpus"
)

# 2) check job status
batch_status_openai(
  project_dir = "my_project",
  label = "corpus"
)

# 3) collect completed jobs and write canonical embeddings parquet
batch_collect_openai(
  project_dir = "my_project",
  backend = backend,
  label = "corpus"
)

Preflight checks run before submission. Jobs are auto-split by size/count when needed. A single oversized request line stops with a clear error.

Prototype distance output

distance_reference_cosine() writes one parquet file:

• distance_reference_cosine/model_id=<...>/corpus_label=<...>/reference_label=<...>/pairwise-cosine.parquet

This is a distance-only matrix with centroid axes:

• first column id: corpus ids plus one row "centroid" (corpus centroid)
• remaining columns: reference ids plus one column "centroid" (reference centroid)
• all values are cosine distances (1 - cosine)

To convert this full distance matrix to scores:

score_reference_cosine(
  distance_parquet = "my_project/distance_reference_cosine/model_id=.../corpus_label=.../reference_label=...",
  method = "linear" # or "exponential"
)