pgml 1.0.2

Python SDK is designed to facilitate the development of scalable vector search applications on PostgreSQL databases.

Open Source Alternative for Building End-to-End Vector Search Applications without OpenAI & Pinecone

Table of Contents

• Overview
• Quickstart
• Upgrading
• Developer setup
• Roadmap
• Documentation
• Examples

Overview

Python SDK is designed to facilitate the development of scalable vector search applications on PostgreSQL databases. With this SDK, you can seamlessly manage various database tables related to documents, text chunks, text splitters, LLM (Language Model) models, and embeddings. By leveraging the SDK's capabilities, you can efficiently index LLM embeddings using PgVector for fast and accurate queries.

Documentation: PostgresML SDK Docs

Examples Folder: Examples

Key Features

• Automated Database Management: With the SDK, you can easily handle the management of database tables related to documents, text chunks, text splitters, LLM models, and embeddings. This automated management system simplifies the process of setting up and maintaining your vector search application's data structure.

• Embedding Generation from Open Source Models: The Python SDK provides the ability to generate embeddings using hundreds of open source models. These models, trained on vast amounts of data, capture the semantic meaning of text and enable powerful analysis and search capabilities.

• Flexible and Scalable Vector Search: The Python SDK empowers you to build flexible and scalable vector search applications. The Python SDK seamlessly integrates with PgVector, a PostgreSQL extension specifically designed for handling vector-based indexing and querying. By leveraging these indices, you can perform advanced searches, rank results by relevance, and retrieve accurate and meaningful information from your database.

Use Cases

Embeddings, the core concept of the Python SDK, find applications in various scenarios, including:

• Search: Embeddings are commonly used for search functionalities, where results are ranked by relevance to a query string. By comparing the embeddings of query strings and documents, you can retrieve search results in order of their similarity or relevance.

• Clustering: With embeddings, you can group text strings by similarity, enabling clustering of related data. By measuring the similarity between embeddings, you can identify clusters or groups of text strings that share common characteristics.

• Recommendations: Embeddings play a crucial role in recommendation systems. By identifying items with related text strings based on their embeddings, you can provide personalized recommendations to users.

• Anomaly Detection: Anomaly detection involves identifying outliers or anomalies that have little relatedness to the rest of the data. Embeddings can aid in this process by quantifying the similarity between text strings and flagging outliers.

• Classification: Embeddings are utilized in classification tasks, where text strings are classified based on their most similar label. By comparing the embeddings of text strings and labels, you can classify new text strings into predefined categories.

How the Python SDK Works

The Python SDK streamlines the development of vector search applications by abstracting away the complexities of database management and indexing. Here's an overview of how the SDK works:

• Automatic Document and Text Chunk Management: The SDK provides a convenient interface to manage documents and pipelines, automatically handling chunking and embedding for you. You can easily organize and structure your text data within the PostgreSQL database.

• Open Source Model Integration: With the SDK, you can seamlessly incorporate a wide range of open source models to generate high-quality embeddings. These models capture the semantic meaning of text and enable powerful analysis and search capabilities.

• Embedding Indexing: The Python SDK utilizes the PgVector extension to efficiently index the embeddings generated by the open source models. This indexing process optimizes search performance and allows for fast and accurate retrieval of relevant results.

• Querying and Search: Once the embeddings are indexed, you can perform vector-based searches on the documents and text chunks stored in the PostgreSQL database. The SDK provides intuitive methods for executing queries and retrieving search results.

Quickstart

Follow the steps below to quickly get started with the Python SDK for building scalable vector search applications on PostgresML databases.

Prerequisites

Before you begin, make sure you have the following:

• PostgresML Database: Ensure you have a PostgresML database version >= 2.7.7 You can spin up a database using Docker or sign up for a free GPU-powered database.

• Set the DATABASE_URL environment variable to the connection string of your PostgresML database.

• Python version >=3.8.1

Installation

To install the Python SDK, use pip:

pip install pgml

Sample Code

Once you have the Python SDK installed, you can use the following sample code as a starting point for your vector search application:

from pgml import Collection, Model, Splitter, Pipeline
from datasets import load_dataset
from time import time
from dotenv import load_dotenv
from rich.console import Console
import asyncio

async def main():
        load_dotenv()
    console = Console()

    # Initialize collection
    collection = Collection("quora_collection")

Explanation:

• The code imports the necessary modules and packages, including pgml, datasets, time, and rich.
• It creates an instance of the Collection class which we will add pipelines and documents onto

Continuing within async def main():

    # Create a pipeline using the default model and splitter
    model = Model()
    splitter = Splitter()
    pipeline = Pipeline("quorav1", model, splitter)
    await collection.add_pipeline(pipeline)

Explanation

• The code creates an instance of Model and Splitter using their default arguments.
• Finally, the code constructs a pipeline called "quroav1" and add it to the collection we Initialized above. This pipeline automatically generates chunks and embeddings for every upserted document.

Continuing with async def main():

    # Prep documents for upserting
    data = load_dataset("squad", split="train")
    data = data.to_pandas()
    data = data.drop_duplicates(subset=["context"])
    documents = [
        {"id": r["id"], "text": r["context"], "title": r["title"]}
        for r in data.to_dict(orient="records")
    ]

    # Upsert documents
    await collection.upsert_documents(documents[:200])

Explanation

• The code loads the "squad" dataset, converts it to a pandas DataFrame, and drops any duplicate context values.
• It creates a list of dictionaries representing the documents to be indexed, with each dictionary containing the document's id, text, and title.
• Finally, they are upserted. As mentioned above, the pipeline added earlier automatically runs and generates chunks and embeddings for each document.

Continuing within async def main():

    # Query
    query = "Who won 20 grammy awards?"
    results = await collection.query().vector_recall(query, pipeline).limit(5).fetch_all()
    console.print(results)
    # Archive collection
    await collection.archive()

Explanation:

• The query method is called to perform a vector-based search on the collection. The query string is Who won more than 20 grammy awards?, and the top 5 results are requested.
• The search results are printed.
• Finally, the archive method is called to archive the collection and free up resources in the PostgresML database.

Call main function in an async loop.

asyncio.run(main())

Running the Code

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

Execute the following command:

python vector_search.py

You should see the search results printed in the terminal. As you can see, our vector search engine found the right text chunk with the answer we are looking for.

[
    (
        0.8423336495860181,
        'Beyoncé has won 20 Grammy Awards, both as a solo artist and member of Destiny\'s Child, making her the second most honored female artist by the Grammys, behind Alison Krauss and the most nominated woman in Grammy Award history with 52 nominations. "Single Ladies (Put a Ring on It)" won Song of the Year in 2010 while "Say My Name" and 
"Crazy in Love" had previously won Best R&B Song. Dangerously in Love, B\'Day and I Am... Sasha Fierce have all won Best Contemporary R&B Album. Beyoncé set the record for the most Grammy awards won by a female artist in one night in 2010 when she won six awards, breaking the tie she previously held with Alicia Keys, Norah Jones, Alison Krauss, 
and Amy Winehouse, with Adele equaling this in 2012. Following her role in Dreamgirls she was nominated for Best Original Song for "Listen" and Best Actress at the Golden Globe Awards, and Outstanding Actress in a Motion Picture at the NAACP Image Awards. Beyoncé won two awards at the Broadcast Film Critics Association Awards 2006; Best Song for 
"Listen" and Best Original Soundtrack for Dreamgirls: Music from the Motion Picture.',
        {'id': '56becc903aeaaa14008c949f', 'title': 'Beyoncé'}
    ),
    (
        0.8210567582713351,
        'A self-described "modern-day feminist", Beyoncé creates songs that are often characterized by themes of love, relationships, and monogamy, as well as female sexuality and empowerment. On stage, her dynamic, highly choreographed performances have led to critics hailing her as one of the best entertainers in contemporary popular music. 
Throughout a career spanning 19 years, she has sold over 118 million records as a solo artist, and a further 60 million with Destiny\'s Child, making her one of the best-selling music artists of all time. She has won 20 Grammy Awards and is the most nominated woman in the award\'s history. The Recording Industry Association of America recognized 
her as the Top Certified Artist in America during the 2000s decade. In 2009, Billboard named her the Top Radio Songs Artist of the Decade, the Top Female Artist of the 2000s and their Artist of the Millennium in 2011. Time listed her among the 100 most influential people in the world in 2013 and 2014. Forbes magazine also listed her as the most 
powerful female musician of 2015.',
        {'id': '56be88473aeaaa14008c9080', 'title': 'Beyoncé'}
    )
]

Upgrading

Changes between SDK versions are not necessarily backwards compatible. We provide a migrate function to help transition smoothly.

from pgml import migrate
await migrate()

This will migrate all collections to be compatible with the latest SDK version.

Developer Setup

This Python library is generated from our core rust-sdk. Please check rust-sdk documentation for developer setup.

Roadmap

• Enable filters on document metadata in vector_search. Issue
• text_search functionality on documents using Postgres text search. Issue
• hybrid_search functionality that does a combination of vector_search and text_search. Issue
• Ability to call and manage OpenAI embeddings for comparison purposes. Issue
• Perform chunking on the DB with multiple langchain splitters. Issue
• Save vector_search history for downstream monitoring of model performance. Issue