Use Cases on Vchord

Hybrid Search

Mon, 01 Jan 0001 00:00:00 +0000

We will show you how to use pgvecto.rs to implement hybrid search in this section.

Vector Search and Full Text Search

Postgres provides full text search, based on the match operator @@. It returns true if a tsvector (document) matches a tsquery (query). It doesn’t matter which data type is written first:

SELECT 'a fat cat sat on a mat and ate a fat rat'::tsvector @@ 'cat & rat'::tsquery;
 ?column?
----------
 t

SELECT 'cat & rat'::tsquery @@ 'a fat cat sat on a mat and ate a fat rat'::tsvector;
 ?column?
----------
 t

Tip

As the above example suggests, a tsquery is not just raw text, any more than a tsvector is. A tsquery contains search terms, which must be already-normalized lexemes, and may combine multiple terms using AND, OR, NOT, and FOLLOWED BY operators. In this example, the tsquery 'cat & rat' matches the tsvector 'a fat cat sat on a mat and ate a fat rat' because it contains both the terms 'cat' and 'rat'.

Adaptive retrieval and shortening embeddings

Mon, 01 Jan 0001 00:00:00 +0000

OpenAI latest embedding models support shortening embeddings, where developers could simply “remove some numbers from the end of a sequence” and still maintain a valid representation for text.

This enables very flexible usage. For example, when using a vector data store that only supports embeddings up to 1024 dimensions long, developers can now still use our best embedding model text-embedding-3-large and specify a value of 1024 for the dimensions API parameter, which will shorten the embedding down from 3072 dimensions, trading off some accuracy in exchange for the smaller vector size.

Image Search

Mon, 01 Jan 0001 00:00:00 +0000

Image intelligent search, also known as content-based image retrieval, goes beyond traditional keyword-based search and enables users to search for images based on their visual content. For example, you could search images that contain a red mushroom (image from immich):

The feature looks like a magic because it allows you to search for images that you don’t have keywords for. The search engine understands the content of the images and videos and can find the ones that are similar to the query.

Multi Tenancy

Mon, 01 Jan 0001 00:00:00 +0000

Multi-tenancy is essential for SaaS applications, allowing a single instance to serve multiple users or groups while ensuring data privacy and security. This blog outlines the design and implementation a simple multi-tenant vector search system with pgvecto.rs.

Designing the Database Schema

The success of user-based isolation in multi-tenant systems hinges on a well-structured database schema:

Users and Documents

Each user is identified by a unique user_id, which directly links them to their documents for private access. The documents table contains each user’s documents, each with a distinct id, title, and content. Additional metadata can be included in the documents table to improve search functionality. The user_id is linked to the documents instead of chunk embeddings to replicate real-world scenarios more accurately.

Sparse Vector Search

Mon, 01 Jan 0001 00:00:00 +0000

In the past, hybrid search combined two search methods: traditional keyword-based search and vector-based similarity search. However, sparse vector can act as a substitute for keyword search, unlocking the full potential of the data pipeline with pure vector search.

Tip

For more information about keyword-based hybrid search, please refer to Hybrid Search

This post explores how to use how BGE-M3 and pgvecto.rs to exploit the potential of hybrid search using both sparse and dense vectors.

Generative Question-Answering

Mon, 01 Jan 0001 00:00:00 +0000

Vector search can also be used to provide context to generative models such as OpenAI’s GPT series, improving the quality of the outputs.

This post will explore the use of pgvecto.rs in building and retrieving from a vector knowledge base to bolster the performance of generative models in question-answering tasks. A knowledge base serves as “long-term memory” for generative models, providing them persistent, curated, and accurate information as context to draw from when generating an answer to a question. This results in answers that are more accurate, reduces the possibility of the model producing “hallucinations” (statements with no basis in reality), and increases user trust in the outputs. This technique is known as Retrieval-Augmented Generation, or RAG: see this post or the related paper for more information.