README.md

---
license: apache-2.0
task_categories:
- feature-extraction
language:
- en
size_categories:
- 10M<n<100M
---

# `wikipedia_en`

This is a curated Wikipedia English dataset for use with the [Chipmunk](https://github.com/Intelligent-Internet/Chipmunk) project.

## Dataset Details

### Dataset Description

This dataset comprises a curated Wikipedia English pages. Data sourced directly from the official English Wikipedia database dump. We extract the pages, chunk them into smaller pieces, and embed them using [Snowflake/snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0). All vector embeddings are 16-bit half-precision vectors optimized for `cosine` indexing with [vectorchord](https://github.com/tensorchord/vectorchord).

### Dataset Sources

Based on the [wikipedia dumps](https://dumps.wikimedia.org/). Please check this page for the [LICENSE](https://dumps.wikimedia.org/legal.html) of the page data.

## Dataset Structure

1. Metadata Table

- id: A unique identifier for the page.
- revid: The revision ID of the page.
- url: The URL of the page.
- title: The title of the page.
- origin_storage_id: The storage ID of the original page.
- created_at: The creation time of the page.
- updated_at: The update time of the page.
- ignored: Whether the page is ignored.

2. Chunking Table

- id: A unique identifier for the chunk.
- title: The title of the page.
- url: The URL of the page.
- snapshot: The snapshot of the page.
- source_id: The source ID of the page.
- chunk_index: The index of the chunk.
- chunk_text: The text of the chunk.
- vector: The vector embedding of the chunk.
- created_at: The creation time of the chunk.
- updated_at: The update time of the chunk.

## Uses

This dataset is available for a wide range of applications.

Here is a demo of how to use the dataset with [Chipmunk](https://github.com/Intelligent-Internet/Chipmunk).

### Create the metadata and chunking tables in PostgreSQL

```sql
CREATE TABLE IF NOT EXISTS ts_wikipedia_en (
    id BIGSERIAL PRIMARY KEY,
    revid BIGINT NOT NULL,
    url VARCHAR NOT NULL,
    title VARCHAR NOT NULL DEFAULT '',
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    ignored BOOLEAN NOT NULL DEFAULT FALSE
);

CREATE TABLE IF NOT EXISTS ts_wikipedia_en_embed (
    id BIGSERIAL PRIMARY KEY,
    title VARCHAR NOT NULL,
    url VARCHAR NOT NULL,
    snapshot VARCHAR NOT NULL,
    chunk_index BIGINT NOT NULL,
    chunk_text VARCHAR NOT NULL,
    source_id BIGINT NOT NULL,
    vector halfvec(768) DEFAULT NULL,
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP
);
```

### Load csv files to database

1. Load the dataset from local file system to a remote PostgreSQL server:

```sql
\copy ts_wikipedia_en FROM 'data/meta/ts_wikipedia_en.csv' CSV HEADER;
\copy ts_wikipedia_en_embed FROM 'data/chunks/0000000.csv' CSV HEADER;
\copy ts_wikipedia_en_embed FROM 'data/chunks/0000001.csv' CSV HEADER;
\copy ts_wikipedia_en_embed FROM 'data/chunks/0000002.csv' CSV HEADER;
...
```

2. Load the dataset from the PostgreSQL server's file system:

```sql
copy ts_wikipedia_en FROM 'data/meta/ts_wikipedia_en.csv' CSV HEADER;
copy ts_wikipedia_en_embed FROM 'data/chunks/0000000.csv' CSV HEADER;
copy ts_wikipedia_en_embed FROM 'data/chunks/0000001.csv' CSV HEADER;
copy ts_wikipedia_en_embed FROM 'data/chunks/0000002.csv' CSV HEADER;
...
```

### Create Indexes

You need to create the following indexes for the best performance.

The `vector` column is a halfvec(768) column, which is a 16-bit half-precision vector optimized for `cosine` indexing with [vectorchord](https://github.com/tensorchord/vectorchord). You can get more information about the vector index from the [vectorchord](https://docs.vectorchord.ai/vectorchord/usage/indexing.html) documentation.

1. Create the metadata table index:

```sql
CREATE INDEX IF NOT EXISTS ts_wikipedia_en_revid_index ON ts_wikipedia_en (revid);
CREATE INDEX IF NOT EXISTS ts_wikipedia_en_url_index ON ts_wikipedia_en (url);
CREATE INDEX IF NOT EXISTS ts_wikipedia_en_title_index ON ts_wikipedia_en (title);
CREATE INDEX IF NOT EXISTS ts_wikipedia_en_origin_storage_id_index ON ts_wikipedia_en (origin_storage_id);
CREATE INDEX IF NOT EXISTS ts_wikipedia_en_ignored_index ON ts_wikipedia_en (ignored);
CREATE INDEX IF NOT EXISTS ts_wikipedia_en_created_at_index ON ts_wikipedia_en (created_at);
CREATE INDEX IF NOT EXISTS ts_wikipedia_en_updated_at_index ON ts_wikipedia_en (updated_at);
```
2. Create the chunking table index:

```sql
CREATE INDEX IF NOT EXISTS ts_wikipedia_en_embed_source_id_index ON ts_wikipedia_en_embed (source_id);
CREATE INDEX IF NOT EXISTS ts_wikipedia_en_embed_chunk_index_index ON ts_wikipedia_en_embed (chunk_index);
CREATE INDEX IF NOT EXISTS ts_wikipedia_en_embed_chunk_text_index ON ts_wikipedia_en_embed USING bm25 (id, title, chunk_text) WITH (key_field='id');
CREATE UNIQUE INDEX IF NOT EXISTS ts_wikipedia_en_embed_source_index ON ts_wikipedia_en_embed (source_id, chunk_index);
CREATE INDEX IF NOT EXISTS ts_wikipedia_en_embed_vector_index ON ts_wikipedia_en_embed USING vchordrq (vector halfvec_cosine_ops) WITH (options = $$
    [build.internal]
    lists = [20000]
    build_threads = 6
    spherical_centroids = true
$$);
CREATE INDEX IF NOT EXISTS ts_wikipedia_en_embed_vector_null_index ON ts_wikipedia_en_embed (vector) WHERE vector IS NULL;
SELECT vchordrq_prewarm('ts_wikipedia_en_embed_vector_index');
```

### Query with Chipmunk

Click this link to learn how to query the dataset with [Chipmunk](https://github.com/Intelligent-Internet/Chipmunk).