getting-started.md

Getting Started

Installation

{% tabs %} {% tab title="Python " %} Python > 3.8.1

pip install pgml

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{% tab title="JavaScript " %}

npm i pgml

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Example

Once the SDK is installed, you an use the following example to get started.

Create a collection

{% tabs %} {% tab title="Python" %}

from pgml import Collection, Model, Splitter, Pipeline
import asyncio

async def main():
    # Initialize collection
    collection = Collection("sample_collection")

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{% tab title="JavaScript " %}

const pgml = require("pgml");

const main = async () => {
    collection = pgml.newCollection("sample_collection");

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Explanation:

• The code imports the pgml module.
• It creates an instance of the Collection class which we will add pipelines and documents onto

Create a pipeline

Continuing with main

{% tabs %} {% tab title="Python" %}

    # Create a pipeline using the default model and splitter
    model = Model()
    splitter = Splitter()
    pipeline = Pipeline("sample_pipeline", model, splitter)
    await collection.add_pipeline(pipeline)

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{% tab title="JavaScript" %}

    model = pgml.newModel();
    splitter = pgml.newSplitter();
    pipeline = pgml.Pipeline("sample_pipeline", model, splitter);
    await collection.add_pipeline(pipeline);

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Explanation:

• The code creates an instance of Model and Splitter using their default arguments.
• Finally, the code constructs a pipeline called "sample_pipeline" and add it to the collection we Initialized above. This pipeline automatically generates chunks and embeddings for every upserted document.

Upsert documents

Continuing with main

{% tabs %} {% tab title="Python" %}

    documents = [
            {
              id: "Document One",
              text: "document one contents...",
            },
            {
              id: "Document Two",
              text: "document two contents...",
            },
    ];
    await collection.upsert_documents(documents);

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{% tab title="JavaScript" %}

    const documents = [
            {
              id: "Document One",
              text: "document one contents...",
            },
            {
              id: "Document Two",
              text: "document two contents...",
            },
    ];
    await collection.upsert_documents(documents);

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Explanation

• This code creates and upserts some filler documents.
• As mentioned above, the pipeline added earlier automatically runs and generates chunks and embeddings for each document.

Query documents

Continuing with main

{% tabs %} {% tab title="Python" %}

    # Query
    query = "Some user query that will match document one first"
    results = await collection.query().vector_recall(query, pipeline).limit(2).fetch_all()
    print(results)
    # Archive collection
    await collection.archive()

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{% tab title="JavaScript" %}

const queryResults = await collection
        .query()
        .vector_recall("Some user query that will match document one first", pipeline)
        .limit(2)
        .fetch_all();

    // Convert the results to an array of objects
    const results = queryResults.map((result) => {
      const [similarity, text, metadata] = result;
      return {
        similarity,
        text,
        metadata,
      };
    });
    console.log(results);

    await collection.archive();

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Explanation:

• The query method is called to perform a vector-based search on the collection. The query string is Some user query that will match document one first, and the top 2 results are requested.
• The search results are converted to objects and printed.
• Finally, the archive method is called to archive the collection and free up resources in the PostgresML database.

Call main function.

{% tabs %} {% tab title="Python" %}

if __name__ == "__main__":
    asyncio.run(main())

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{% tab title="JavaScript" %}

main().then(() => {
  console.log("Done with PostgresML demo");
});

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Running the Code

Open a terminal or command prompt and navigate to the directory where the file is saved.

Execute the following command:

{% tabs %} {% tab title="Python" %}

python vector_search.py

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{% tab title="JavaScript" %}

node vector_search.js

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You should see the search results printed in the terminal. As you can see, our vector search engine did match document one first.

[
  {
    similarity: 0.8506832955692104,
    text: 'document one contents...',
    metadata: { id: 'Document One' }
  },
  {
    similarity: 0.8066114609244565,
    text: 'document two contents...',
    metadata: { id: 'Document Two' }
  }
]