User wants to search for a specific text in patent literature stored in AlloyDB, meaning-driven, not just keyword-driven. The app data is stored in AlloyDB, uses pgvector extension for embeddings and Cosine Similarity for Vector Search. The app is deployed as a Cloud Function endpoint.

AlloyDB is a fully managed, PostgreSQL-compatible database service that offers enterprise-grade performance, availability, and scale. AlloyDB offers a number of features that make it a good choice for demanding transactional and analytical workloads, including:

AlloyDB is designed to be highly performant for both transactional and analytical workloads. It uses a number of techniques to achieve this, including a shared-nothing architecture, columnar storage, and vectorized execution.

AlloyDB offers a 99.99% uptime SLA, inclusive of maintenance. It uses a number of techniques to achieve this, including synchronous replication, automatic failover, and hot backups.

AlloyDB can scale to meet the needs of even the most demanding applications. It can be scaled up to support up to 128 vCPUs and 1 TB of memory per instance, and it can be scaled out to support up to 100 instances per cluster.

AlloyDB is designed with security in mind. It uses a number of features to protect your data, including encryption, auditing, and access control.

AlloyDB is designed to meet the needs of organisations with demanding compliance requirements. It supports a number of compliance standards, including HIPAA, PCI DSS, and SOC 2.

Cloud Function is a lightweight compute solution for developers to create single-purpose, stand-alone functions that respond to Cloud events without needing to manage a server or runtime environment.

1. Create the AlloyDB cluster, instance, database and table

CREATE TABLE patents_data ( id VARCHAR(25), type VARCHAR(25), number VARCHAR(20), country VARCHAR(2), date VARCHAR(20), abstract VARCHAR(300000), title VARCHAR(100000), kind VARCHAR(5), num_claims BIGINT, filename VARCHAR(100), withdrawn BIGINT) ;

2. Create Extensions:

   CREATE EXTENSION vector;

   CREATE EXTENSION google_ml_integration;

3. Grant Permission:

GRANT EXECUTE ON FUNCTION embedding TO postgres;

4. Alter the table to add a Vector column for storing the Embeddings:

   alter table patents_data ADD column abstract_embeddings vector(768);

5. Insert DML:

   Refer to the file "insert_scripts.sql" in this repo

6. Grant Vertex AI User ROLE to the AlloyDB service account PRINCIPAL:

   You can do this from IAM on Google Cloud Console

7. Test if embedding function works on a test string:

SELECT embedding( 'textembedding-gecko@001', 'AlloyDB is a managed, cloud-hosted SQL database service.');

8. Update the abstract_embeddings Vector field:

   update patents_data set abstract_embeddings = embedding( 'textembedding-gecko@001', abstract);

9. Query to check if Cosine Similarity works (Vector Search by Text):

SELECT * FROM patents_data ORDER BY abstract_embeddings <=> embedding('textembedding-gecko@001', 'A new Natural Language Processing related Machine Learning Model')::vector LIMIT 10;

10. Create Cloud Functions, deploy and test it on the web!