Update README.md

README.md

@@ -1,138 +1,138 @@
----
-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.
-- ignored: Whether the page is ignored.
-- created_at: The creation time of the page.
-- updated_at: The update time of the page.
-
-2. Chunking Table
-
-- id: A unique identifier for the chunk.
-- title: The title of the page.
-- url: The URL 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 '',
-    ignored BOOLEAN NOT NULL DEFAULT FALSE,
-    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    updated_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP
-);
-
-CREATE TABLE IF NOT EXISTS ts_wikipedia_en_embed (
-    id BIGSERIAL PRIMARY KEY,
-    title VARCHAR NOT NULL,
-    url VARCHAR NOT NULL,
-    source_id BIGINT NOT NULL,
-    chunk_index BIGINT NOT NULL,
-    chunk_text VARCHAR 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_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).
+---
+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.
+- ignored: Whether the page is ignored.
+- created_at: The creation time of the page.
+- updated_at: The update time of the page.
+
+2. Chunking Table
+
+- id: A unique identifier for the chunk.
+- title: The title of the page.
+- url: The URL 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,
+    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_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).