Calling Hugging Face AI From an Oracle Database Using JavaScript

In this blog, I will show you how to quickly create an entirely free app using a JavaScript program that runs within the free Oracle database and calls Hugging Face AI, the results of which are stored in the database and can then be accessed using SQL, JSON, Rest, or any other language. All of the source code is, of course, available here.

A typical sequence of actions in applications, both in general and particularly in AI applications, involves an application utilizing an AI service. The acquired information is then stored in a database, ready for retrieval, subsequent processing (such as using OML), analysis, and future queries.

When the command originates directly from the database, the data is promptly stored and gains higher reliability. This approach is in-process and eliminates the need for an additional network call for persistence. Additionally, by housing the logic and even code within the database, the remarkable features of the robust Oracle database, including high availability, management, and observability, are naturally harnessed.

This distinct capability is a hallmark of the Oracle database, which encompasses both Java and, as detailed in this blog, JavaScript runtime engines within the database itself.

Hugging Face specializes in crafting tools for constructing applications using machine learning. Particularly notable is its transformers library designed for natural language processing applications, along with its platform facilitating the sharing of machine learning models and datasets. It has garnered significant popularity in recent years.

In short, we’ll…

1. Create a cohere.com or huggingface.co account, create and copy an access token, and select a model.
2. Create the Oracle database.
3. Create the database user and table(s).
4. Run a short JavaScript program in the database to make a call to the Cohere or Hugging Face AI model and store the results in the table.
5. Query these results using SQL, JSON, Rest, etc.

Setup Cohere account and AI model

Go to https://cohere.com and Sign Up, go to your Profile, and click the Settings button.

Click on Dashboard, then API Keys, create a key (or use trial) and copy its value for use later.

Click on API Reference and select the hamburger menu/icon on the left of any page to view the API Reference list and select one. In this case, we will select generate section and Co.Generate.

Select Ianguage and then select the JavaScript option in order to see a sample code snippet to call the model.

Setup Hugging Face account and AI model

Go to https://huggingface.co and Sign Up, go to your Profile, and click the Settings button.

Click on Access Tokens, create a token, and copy its value for use later.

Click on Models and select a model. In this case, we will select a model under the Natural Language Processing section for Question Answering.

Select a model (perhaps one of the more popular ones), notice the information in the Model Card on the left, and then select the Deploy drop-down menu on the right.

Select Inference API and then select the JavaScript option in order to see a sample code snippet to call the model.

Setup Oracle Database

We can use any flavor of the Oracle database 23c. On the cloud, we can use Oracle always free autonomous database later this year when 23c should be available. We can use the Oracle 23c Free version as it’s available now and we can simply install or use a container image locally. Or of course, we could use the local for dev and cloud for production, etc. and either of these options is very quick to set up…

Oracle Database Free…

You can go here to set up the Free Oracle Database.

Using the container image is very simple. You can simply pull the image:

docker pull container-registry.oracle.com/database/free:latest

Then, execute the one line below, replacing -e ORACLE_PASSWORD=Welcome12345 with a password of your choice (you can also add a -v oracle-volume:/somedirectory parameter with a directory location in order to persist changes across restarts). Notice the --add-host docker.for.mac.host.internal:host-gateway param that allows calls out of the container. This is the setting for Macs and will be different if running on a different OS.

docker run -d -p 1521:1521 — add-host docker.for.mac.host.internal:host-gateway -e ORACLE_PASSWORD=Welcome12345 gvenzl/oracle-free

Setup SQLcl (or Database Actions) and connect

You can install SQLcl to manage the database by:

• Downloading and install from this location https://www.oracle.com/database/sqldeveloper/technologies/sqlcl/download/ This will provide an [SQLcl_INSTALL_DIR]/bin/sql executable that we will use to administer the database. For convenience, you may add [SQLcl_INSTALL_DIR]/bin to your PATH.

  
  

• Login replacing [SQLcl_INSTALL_DIR] with the location of your SQLcl and replacing Welcome12345 with the one you provided as ORACLE_PASSWORD when creating the database Here is an example when using a local install (eg Oracle Database Free container image):

  
  

• [SQLcl_INSTALL_DIR]/bin/sql /nolog SQL> connect sys@//localhost:1521/freepdb1 as sysdba Password? (**********?) *************

  Connected.

  
  

Create the users, with appropriate privileges, that will call Cohere and/or Hugging Face via JavaScript.

Run the following SQL files to create the aijs user and its necessary grants and ACLs (note that these can be further tightened for better security).

SQL> @sql/create_aijs_user.sql``SQL> @sql/create_aijs_acl.sql

Now connect the user and create a table to store the results of the call to Hugging Face

SQL> connect aijs/Welcome12345;

SQL> create table huggingfacejson (id json);

SQL> create table coherejson (id json);

Now everything is ready to run!

Run either the Cohere or HuggingFace queries from the JavaScript code in the database

Cohere Example

SQL> @sql/coherequery.sql

PL/SQL procedure successfully completed.

Then, check the JSON results stored in the table by doing a SQL query.

SQL> select * from coherejson;

[{"id":"1b42f8f9-ea6d-4a65-8f02-bdcf89d9bd79","generations":[{"id":"d6467c0b-4a78-4dd4-b822-c1e2bfa4ecb0","text":"\n LLMs or Large Language Models are artificial intelligence tools that can read, summarize and translate texts and"}],"prompt":"Please explain to me how LLMs work","meta":{"api_version":{"version":"1"}}}

Looking at the code we just executed we can see the JavaScript module created and the function being called:

create or replace mle module cohere_module language javascript as import "mle-js-fetch"; import "mle-js-oracledb";

export async function cohereDemo(apiToken)

{

if (apiToken === undefined) { throw Error("must provide an API token"); }

const modelId = "generate";

const restAPI = `https://api.cohere.ai/v1/${modelId}`;

const headers = { accept: 'application/json', 'content-type': 'application/json', "Authorization": `Bearer ${apiToken}` };

const payload = { max_tokens: 20, return_likelihoods: 'NONE', truncate: 'END', prompt: 'Please explain to me how LLMs work' };

const resp = await fetch( restAPI, { method: "POST", headers: headers, body: JSON.stringify(payload), credentials: "include" } );

const resp_json = await resp.json();

session.execute( `INSERT INTO COHEREJSON (id) VALUES (:resp_json)`, [ JSON.stringify(resp_json) ] );

}

/

create or replace procedure coherequery( p_API_token varchar2 ) as mle module cohere_module signature 'cohereDemo(string)';

/

-- this is how you can test the API call begin utl_http.set_wallet('system:'); coherequery('[yourcoheretokenhere]');

end;

/

Hugging Face Example

SQL> @sql/huggingfacequery.sql

PL/SQL procedure successfully completed.

Then, check the JSON results stored in the table by doing a SQL query.

SQL> select * from huggingfacejson;

[{"score":0.16964051127433777,"token":2053,"token_str":"no","sequence":"the answer to the universe is no."},{"score":0.07344779372215271,"token":2498,"token_str":"nothing","sequence":"the answer to the universe is nothing."},{"score":0.05803246051073074,"token":2748,"token_str":"yes","sequence":"the answer to the universe is yes."},{"score":0.043957971036434174,"token":4242,"token_str":"unknown","sequence":"the answer to the universe is unknown."},{"score":0.04015727713704109,"token":3722,"token_str":"simple","sequence":"the answer to the universe is simple."}]

Looking at the code we just executed we can see the JavaScript module created and the function being called:

create or replace mle module huggingface_module language javascript as import "mle-js-fetch";

import "mle-js-oracledb";

export async function huggingfaceDemo(apiToken)

{

if (apiToken === undefined) { throw Error("must provide an API token"); }

const payload = { inputs: "The answer to the universe is [MASK]." };

const modelId = "bert-base-uncased";

const headers = { "Authorization": `Bearer ${apiToken}` };

const restAPI = `https://api-inference.huggingface.co/models/${modelId}`;

const resp = await fetch( restAPI, { method: "POST", headers: headers, body: JSON.stringify(payload), credentials: "include" } );

const resp_json = await resp.json();

session.execute( `INSERT INTO HUGGINGFACEJSON (id) VALUES (:resp_json)`, [ JSON.stringify(resp_json) ] ); }

/

create or replace procedure huggingfacequery( p_API_token varchar2 ) as mle module huggingface_module signature 'huggingfaceDemo(string)';

/

-- this is how you can test the API call

begin utl_http.set_wallet('system:');

huggingfacequery('[hf_yourhuggingfacetokenhere]');

end;

/

Useful parameters and debug information for Hugging Face can be found here.

The results can now be queried, analyzed, etc. using SQL or JSON (simultaneously thanks to the new JSON duality feature), REST, or even the MongoDB API.Oracle Database is the perfect database for AI for a number of reasons, particularly as it is a vector database with capabilities such as the following already available today:

• Native data types for vector representation and storage: RAW, BLOB, JSON
• In-Memory Column Store to store & search vector embeddings w/ SIMD kernels for amazing performance
• Extensible indexing framework to create data-model specific indices (e.g., Text, Spatial)
• Native Oracle Machine Learning APIs — Modeling, Classification, Scoring, Clustering, etc.
• Plus, many other capabilities which, used together, make Oracle a better Vector Database
• DMLs, Parallel Loading, Partitioning, Advanced Compression, Parallel Query, RAC, Sharding, etc.

It is also possible to call Oracle OCI AI and other services from within the Oracle database.

Conclusion

The blog went into how to call Hugging Face APIs from within the Oracle database using JavaScript thus demonstrating a powerful combination of features perfectly suited to a broad array of AI solutions and friendly to JavaScript developers.

Some other blogs related to JavaScript in the database, in general, the MultiLingual Engine (MLE) that makes it possible, etc. can be found in Martin Bach’s blogs and this post about JavaScript modules. Thanks to Martin and Lucas for all their help.

I look forward to any comments or questions you may have and really appreciate your time reading.