Serverless Embeddings Generate & Store - Serverless Framework AWS

This was generated using the Serverless Framework AWS Python template.

This is an example of a way to generate and store embeddings for new documents in an S3 bucket usings Lambda, SQS, Postgres(pgvector) and OpenAI(text-embedding-ada-002).

Plugins

serverless-python-requirements
serverless-lift

Architecture

Requirements

Docker
NPM

Usage

Install the Serverless Framework:

npm install -g serverless

Install the dependencies:

npm install

Add your environment variables to a .env file in the root of the project.

cp .env.local .env.dev
cp .env.local .env

Note: You need to install the pgvector extension in your RDS instance. We are also not indexing the vectors in this example.

CREATE EXTENSION vector;

Deployment

In order to deploy the example, you need to run the following command:

$ serverless deploy

After running deploy, you should see output similar to:

Deploying serverless-embeddings-generator to stage dev (us-east-1)

✔ Service deployed to stack serverless-embeddings-generator-dev (70s)

functions:
  enqueue: serverless-embeddings-generator-dev-enqueue (25 MB)
  testEmbeddings: serverless-embeddings-generator-dev-testEmbeddings (25 MB)
  EmbeddingsQueueWorker: serverless-embeddings-generator-dev-EmbeddingsQueueWorker (25 MB)
SourcesBucket: serverless-embeddings-generator-sourcesbucket
EmbeddingsQueue: https://sqs.us-east-1.amazonaws.com/....

Invocation

After successful deployment, you can invoke the deployed function by using the following command:

sls invoke -f testEmbeddings --data "hello world"

Which should result in response similar to the following:

{
    "results": [
        [
            "hello there friend",
            "source_of_doc",
            0.90
        ],
}

Bundling dependencies

In case you would like to include third-party dependencies, you will need to use a plugin called serverless-python-requirements. You can set it up by running the following command:

serverless plugin install -n serverless-python-requirements

Running the above will automatically add serverless-python-requirements to plugins section in your serverless.yml file and add it as a devDependency to package.json file. The package.json file will be automatically created if it doesn't exist beforehand. Now you will be able to add your dependencies to requirements.txt file (Pipfile and pyproject.toml is also supported but requires additional configuration) and they will be automatically injected to Lambda package during build process. For more details about the plugin's configuration, please refer to official documentation.

rjolaverria/serverless-embeddings-pgvector