---
title: ACL Anthology Search API
emoji: 🔎
sdk: docker
app_port: 7860
pinned: false
---

# ACL Anthology Search API

FastAPI service providing keyword (SQLite FTS5) and embedding-similarity (FAISS)
search over the full ACL Anthology corpus (~128k papers). Runs entirely on free
infrastructure (HF Spaces + HF Hub dataset repo + GitHub Actions). See
`docs/superpowers/specs/2026-07-03-acl-anthology-search-api-design.md` for the
full design.

## API reference

All endpoints return JSON. The service answers `503` on every route except
`/health` until the index/DB snapshot has finished loading on startup.

### `GET /health`

Liveness + readiness probe.

**Query:** none.

**Response:** `200`
```json
{
  "status": "ok",
  "index_size": 128618,
  "last_synced_at": "2026-07-04T15:06:00.654970+00:00"
}
```
Returns `503` when the snapshot is not yet loaded. `last_synced_at` is `null`
until the first sync has run.

### `GET /search/keyword?q=<query>&limit=<n>`

Keyword search over paper titles and abstracts via SQLite FTS5 (Porter-stemmed,
ranked by BM25).

**Query parameters:**

| Param   | Type | Default | Notes                                                                 |
|---------|------|---------|-----------------------------------------------------------------------|
| `q`     | str  | —       | Required. Raw FTS5 query (see below).                                 |
| `limit` | int  | `10`    | Max number of results.                                                |

**FTS5 query format:** space-separated terms are ANDed. Supports `"exact phrase"`,
`prefix*`, `term1 OR term2`, `term1 NOT term2`, and parentheses. Examples:
`q=transformer attention`, `q="neural machine"`, `q=translat*`, `q=(attention OR transformer) NOT survey`.

**Response:** `200` — array of `PaperResult`:
```json
[
  {
    "id": "1993.tc-1.6",
    "title": "Terminology and the computer - attention shifts to the micro",
    "abstract": "…",
    "authors": "Warren Weaver",
    "venue": "TC",
    "year": 1993,
    "url": "https://aclanthology.org/1993.tc-1.6/",
    "bibtex": "@inproceedings{weaver-1993-terminology,\n    title = \"Terminology and the computer - attention shifts to the micro\",\n    author = \"Weaver, Warren\",\n    booktitle = \"Proceedings of the …\",\n    year = \"1993\",\n    url = \"https://aclanthology.org/1993.tc-1.6/\"\n}",
    "pdf_url": "https://aclanthology.org/1993.tc-1.6.pdf"
  }
]
```
`year` is `null` when the anthology has no year for a paper. `bibtex` is the
canonical ACL Anthology BibTeX entry for the paper (empty when the anthology
has no citable entry, e.g. some frontmatter). `pdf_url` is the direct link to
the paper's PDF (empty when the anthology has no PDF reference). Inactive
(retracted) papers are excluded.

### `GET /search/similarity?q=<query>&k=<n>`

Embedding-similarity search. The query is embedded via the external embedding
API, then matched against the FAISS index (cosine similarity over normalized
vectors). Results are sorted by descending similarity.

**Query parameters:**

| Param | Type | Default | Notes                                            |
|-------|------|---------|--------------------------------------------------|
| `q`   | str  | —       | Required. Free-text query, no FTS5 syntax.        |
| `k`   | int  | `10`    | Number of nearest neighbors to return.           |

**Response:** `200` — array of `SimilarityResult` (a `PaperResult` plus a
similarity `score`):
```json
[
  {
    "id": "N18-3011",
    "title": "…",
    "abstract": "…",
    "authors": "…",
    "venue": "…",
    "year": 2018,
    "url": "https://aclanthology.org/N18-3011/",
    "bibtex": "@inproceedings{…}",
    "pdf_url": "https://aclanthology.org/N18-3011.pdf",
    "score": 0.8123
  }
]
```
`score` is cosine similarity in `[-1, 1]` (higher is more similar).

### `GET /paper/{paper_id}`

Metadata lookup by ACL Anthology paper id (e.g. `N18-3011`, `2023.acl-long.123`).

**Response:** `200` — a single `PaperResult` (shape as in `/search/keyword`),
including the canonical `bibtex` entry for citing the paper and its `pdf_url`.
Returns `404` if the id is unknown or the paper is inactive.

### Examples

```bash
# health
curl "http://localhost:8000/health"

# keyword search
curl "http://localhost:8000/search/keyword?q=transformer%20attention&limit=5"

# similarity search
curl "http://localhost:8000/search/similarity?q=attention%20mechanism&k=5"

# paper lookup
curl "http://localhost:8000/paper/1993.tc-1.6"
```

## Configuration

Settings are read from the environment (and, for local dev, from a `.env` file
in the working directory). Required:

- `HF_REPO_ID` — dataset repo holding `index.faiss` / `papers.db` / `state.json`
- `HF_TOKEN` — token with read access to that dataset repo
- `EMBEDDING_BASE_URL` — university embedding API base URL
- `EMBEDDING_API_KEY` — university embedding API key

Optional:

- `data_dir` — local directory for the downloaded snapshot (default `./data`)

## Local development

```bash
pip install -e ".[dev]"
python -m app          # serves on http://0.0.0.0:8000 with reload
```

On startup the service downloads the snapshot from the HF Hub dataset repo into
`data_dir` and loads it into memory (~1.3 GB index + ~300 MB DB). The only
outbound call at request time is to the embedding API, to embed
similarity-search queries.

## Required Space secrets

- `HF_REPO_ID` — dataset repo holding `index.faiss` / `papers.db` / `state.json`
- `HF_TOKEN` — token with read access to that dataset repo
- `EMBEDDING_BASE_URL` — university embedding API base URL
- `EMBEDDING_API_KEY` — university embedding API key

## Required GitHub Actions secrets

- `HF_REPO_ID`, `HF_TOKEN`, `EMBEDDING_BASE_URL`, `EMBEDDING_API_KEY` — same as the Space secrets above
- `HF_SPACE_ID` — e.g. `your-org/acl-anthology-search-api`, used to trigger a restart after sync