Why SPECTER2 for academic papers | One-time setup | Python environment | Run the merge script | Serving via TEI | Verify | Using from R | Smoke test | Where this fits | Notes

Purpose | File Structure | High-Level Backend Flow | End-to-End Call Order in embed_corpus() | Data Contract | Input dataset expected by embed_corpus() | Canonical text construction | Output columns per embedded row | Shared Backend Rules | Authentication Model | Provider Responsibilities | Hugging Face (provider = "hf") | OpenAI (provider = "openai") | TEI (provider = "tei") | Function Examples | 1) HF backend (default-style) | 2) OpenAI backend | 3) Local TEI backend | How to Add a New Backend | Step 1: add dispatch entry in core | Step 2: create provider file | Step 3: enforce adapter contract | Step 4: docs and validation | Operational Flowchart for embed_corpus() | Troubleshooting | 401 / 403 from provider API | Row mismatch errors | Model metadata unavailable (dim = NA)

Why this workflow exists | OpenAI vs OpenAlex (important) | Lifecycle | Prerequisites | Step 1: Submit jobs | What preflight checks do | Where state is written | Step 2: Check status | Interpreting status values | Step 3: Collect completed jobs | Output compatibility guarantee | Common pitfalls and what to check | 1) Authentication failures (401) | 2) Single oversized records | 3) Duplicate submission risk | 4) Concurrent collectors | 5) Confusing labels | Data retention and expiry (OpenAI) | Recommended operating pattern | Monitoring checklist | References

Purpose | Package Philosophy | Function Groups | 1) Embedding backend abstraction | 2) Corpus embedding orchestration | 3) Text preparation | 4) Similarity and distance | 5) Supervised scoring and calibration | 6) Embedding-space visualization | Canonical Workflow | Data and Storage Model | Corpus input | Embeddings output | Dry-run output | Text-Preparation Contract | Why this contract matters | Decision Model: Geometric + Supervised | Minimal End-to-End Example | Operational Guidance | Related Vignettes | Summary

Introduction | Requirements | Notes on execution | TEI endpoint | Embed a small corpus | Compute prototype distances | Compute ridge distances and scores | Troubleshooting | Reproducibility

Purpose | Start TEI | Local binary | Alternative port | Verify service | Health | Info | Embed smoke test | Use TEI endpoint in package | Process management (shell) | Recommended production pattern | Troubleshooting | Port already in use | Empty/invalid responses | Slow throughput

Purpose | Where Cleaning Happens | Cleaner Contract | Cleaning Pipeline (Step by Step) | Cleaning Flow Diagram | Why These Rules | Mode Behavior (lenient, balanced, strict) | Example: Default Cleaning | Example: Before/After on Individual Cases | Example: Change Missing-Abstract Policy | Example: Use a Stricter Cleaning Configuration | Using Custom Patterns | Integration with embed_corpus() | Preprocessor Validation in embed_corpus() | Output Provenance and Auditability | Recommended Operational Defaults | Summary