pg_bestmatch

Overview

Version

Build

You can build the RPM / DEB packages for pg_bestmatch using pig build:

pig build pkg pg_bestmatch         # build RPM / DEB packages

Install

You can install pg_bestmatch directly. First, make sure the PGDG and PIGSTY repositories are added and enabled:

pig repo add pgsql -u          # Add repo and update cache

Install the extension using pig or apt/yum/dnf:

pig install pg_bestmatch;          # Install for current active PG version

pig ext install -y pg_bestmatch -v 18  # PG 18
pig ext install -y pg_bestmatch -v 17  # PG 17
pig ext install -y pg_bestmatch -v 16  # PG 16
pig ext install -y pg_bestmatch -v 15  # PG 15
pig ext install -y pg_bestmatch -v 14  # PG 14

dnf install -y pg_bestmatch_18       # PG 18
dnf install -y pg_bestmatch_17       # PG 17
dnf install -y pg_bestmatch_16       # PG 16
dnf install -y pg_bestmatch_15       # PG 15
dnf install -y pg_bestmatch_14       # PG 14

apt install -y postgresql-18-pg-bestmatch   # PG 18
apt install -y postgresql-17-pg-bestmatch   # PG 17
apt install -y postgresql-16-pg-bestmatch   # PG 16
apt install -y postgresql-15-pg-bestmatch   # PG 15
apt install -y postgresql-14-pg-bestmatch   # PG 14

Preload:

shared_preload_libraries = 'pg_bestmatch';

Create Extension:

CREATE EXTENSION pg_bestmatch;

Usage

• repo: https://github.com/tensorchord/pg_bestmatch.rs
• benchmark: https://hazyresearch.stanford.edu/blog/2024-05-20-m2-bert-retrieval

How does it work?

• Create an BM25 statistics based on your document set by bm25_create(table_name, column_name, statistic_name);. It will create a materilized view to record the stats.
• Generate document sparse vector by bm25_document_to_svector(statistic_name, passage)
• For query, generate query sparse vector bm25_query_to_svector(statistic_name, query)
• Calculate the score by dot product between the query sparse vector and the document sparse vector
• Currently we use huggingface tokenizer with bert-base-uncased vocabulary set to tokenize words. Might support more configuration on tokenizer in the future.

Install

CREATE EXTENSION pg_bestmatch;
SET search_path TO public, bm_catalog;

Example

Here is an example workflow demonstrating the usage of this extension with the example of Stanford LoCo benchmark.

0. Load the dataset. Here is a script for you if you want to experience pg_bestmatch with the dataset.

wget https://huggingface.co/api/datasets/hazyresearch/LoCoV1-Documents/parquet/default/test/0.parquet -O documents.parquet
wget https://huggingface.co/api/datasets/hazyresearch/LoCoV1-Queries/parquet/default/test/0.parquet -O queries.parquet

import pandas as pd
from sqlalchemy import create_engine
import numpy as np
from psycopg2.extensions import register_adapter, AsIs

def adapter_numpy_float64(numpy_float64):
    return AsIs(numpy_float64)

def adapter_numpy_int64(numpy_int64):
    return AsIs(numpy_int64)

def adapter_numpy_float32(numpy_float32):
    return AsIs(numpy_float32)

def adapter_numpy_int32(numpy_int32):
    return AsIs(numpy_int32)

def adapter_numpy_array(numpy_array):
    return AsIs(tuple(numpy_array))

register_adapter(np.float64, adapter_numpy_float64)
register_adapter(np.int64, adapter_numpy_int64)
register_adapter(np.float32, adapter_numpy_float32)
register_adapter(np.int32, adapter_numpy_int32)
register_adapter(np.ndarray, adapter_numpy_array)

db_url = "postgresql://localhost:5432/pg_bestmatch_test"
engine = create_engine(db_url)

def load_documents():
    df = pd.read_parquet("documents.parquet")
    df.to_sql("documents", engine, if_exists='replace', index=False)

def load_queries():
    df = pd.read_parquet("queries.parquet")
    df['answer_pids'] = df['answer_pids'].apply(lambda x: str(x[0]))    
    df.to_sql("queries", engine, if_exists='replace', index=False)

load_documents()
load_queries()

1. Create BM25 statistics for the documents table.

SELECT bm25_create('documents', 'passage', 'documents_passage_bm25', 0.75, 1.2);

2. Add an embedding column to the documents and queries tables and update the embeddings for documents and queries.

ALTER TABLE documents ADD COLUMN embedding svector; -- for pgvecto.rs users
ALTER TABLE documents ADD COLUMN embedding sparsevec; -- for pgvector users

UPDATE documents SET embedding = bm25_document_to_svector('documents_passage_bm25', passage)::svector; -- for pgvecto.rs users
UPDATE documents SET embedding = bm25_document_to_svector('documents_passage_bm25', passage, 'pgvector')::sparsevec; -- for pgvector users

3. (Optional) Create a vector index on the sparse vector column.

CREATE INDEX ON documents USING vectors (embedding svector_dot_ops); -- for pgvecto.rs users
CREATE INDEX ON documents USING ivfflat (embedding sparsevec_ip_ops); -- for pgvector users

4. Perform a vector search to find the most relevant documents for each query.

ALTER TABLE queries ADD COLUMN embedding svector; -- for pgvecto.rs users
ALTER TABLE queries ADD COLUMN embedding sparsevec; -- for pgvector users

UPDATE queries SET embedding = bm25_query_to_svector('documents_passage_bm25', query)::svector; -- for pgvecto.rs users
UPDATE queries SET embedding = bm25_query_to_svector('documents_passage_bm25', query, 'pgvector')::sparsevec; -- for pgvector users

SELECT sum((array[answer_pids] = array(SELECT pid FROM documents WHERE queries.dataset = documents.dataset ORDER BY queries.embedding <#> documents.embedding LIMIT 1))::int) FROM queries;

This workflow showcases how to leverage BM25 text queries and vector search in PostgreSQL using this extension. The Top 1 recall of BM25 on this dataset is 0.77. If you reproduce the result, your operations are correct.

Comparison with pg_search

• pg_bestmatch.rs only provides methods for generating sparse vectors and does not support index-based search (which can be achieved by pgvecto.rs or pgvector).
• pg_search performs BM25 retrieval via the external tantivy engine, which may have limitations when combined with transactions, filters, or JOIN operations. Since pg_bestmatch.rs is entirely native to Postgres, it offers full compatibility with these operations inside postgres.

Reference

• tokenize
  • Description: Tokenizes an input string into individual tokens.
  • Example:
    SELECT tokenize('i have an apple'); -- result: {i,have,an,apple}

• bm25_create
  • Description: Creates BM25 statistics for a specified table and column.
  • Usage:
    SELECT bm25_create('documents', 'passage', 'documents_passage_bm25');

  • Parameters:
    • table_name: Name of the table.
    • column_name: Name of the column.
    • stat_name: Name of the BM25 statistics.
    • b: BM25 parameter (default 0.75).
    • k: BM25 parameter (default 1.2).
• bm25_refresh
  • Description: Updates the BM25 statistics to reflect any changes in the underlying data.
  • Usage:
    SELECT bm25_refresh('documents_passage_bm25');

  • Parameters:
    • stat_name: Name of the BM25 statistics to update.
• bm25_drop
  • Description: Deletes the BM25 statistics for a specified table and column.
  • Usage:
    SELECT bm25_drop('documents_passage_bm25');

  • Parameters:
    • stat_name: Name of the BM25 statistics to delete.
• bm25_document_to_svector
  • Description: Converts document text into a sparse vector representation.
  • Usage:
    SELECT bm25_document_to_svector('documents_passage_bm25', 'document_text');

  • Parameters:
    • stat_name: Name of the BM25 statistics.
    • document_text: The text of the document.
    • style: Emits pgvecto.rs-style sparse vector or pgvector-style sparse vector.
• bm25_query_to_svector
  • Description: Converts query text into a sparse vector representation.
  • Usage:
    SELECT bm25_query_to_svector('documents_passage_bm25', 'We begin, as always, with the text.');

  • Parameters:
    • stat_name: Name of the BM25 statistics.
    • query_text: The text of the query.
    • style: Emits pgvecto.rs-style sparse vector or pgvector-style sparse vector.