llama-index-cloud-sql-pg 0.1.0

LlamaIndex integrations for Google Cloud SQL for PostgreSQL

• Product Documentation

The Cloud SQL for PostgreSQL for LlamaIndex package provides a first class experience for connecting to Cloud SQL instances from the LlamaIndex ecosystem while providing the following benefits:

• Simplified & Secure Connections: easily and securely create shared connection pools to connect to Google Cloud databases utilizing IAM for authorization and database authentication without needing to manage SSL certificates, configure firewall rules, or enable authorized networks.

• Improved metadata handling: store metadata in columns instead of JSON, resulting in significant performance improvements.

• Clear separation: clearly separate table and extension creation, allowing for distinct permissions and streamlined workflows.

Quick Start

In order to use this library, you first need to go through the following steps:

1. Select or create a Cloud Platform project.

2. Enable billing for your project.

3. Enable the Cloud SQL Admin API.

4. Setup Authentication.

Installation

Install this library in a virtualenv using pip. virtualenv is a tool to create isolated Python environments. The basic problem it addresses is one of dependencies and versions, and indirectly permissions.

With virtualenv, it’s possible to install this library without needing system install permissions, and without clashing with the installed system dependencies.

Supported Python Versions

Python >= 3.9

Mac/Linux

pip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install llama-index-cloud-sql-pg

Windows

pip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install llama-index-cloud-sql-pg

Example Usage

Code samples and snippets live in the samples/ folder.

Vector Store Usage

Use a vector store to store embedded data and perform vector search.

import google.auth
from llama_index.core import Settings
from llama_index.embeddings.vertex import VertexTextEmbedding
from llama_index_cloud_sql_pg import PostgresEngine, PostgresVectorStore


credentials, project_id = google.auth.default()
engine = await PostgresEngine.afrom_instance(
   "project-id", "region", "my-cluster", "my-instance", "my-database"
)
Settings.embed_model = VertexTextEmbedding(
   model_name="textembedding-gecko@003",
   project="project-id",
   credentials=credentials,
)

vector_store = await PostgresVectorStore.create(
   engine=engine, table_name="vector_store"
)

Document Store Usage

Use a document store to make storage and maintenance of data easier.

from llama_index_cloud_sql_pg import PostgresEngine, PostgresDocumentStore


engine = await PostgresEngine.afrom_instance(
   "project-id", "region", "my-cluster", "my-instance", "my-database"
)
doc_store = await PostgresDocumentStore.create(
   engine=engine, table_name="doc_store"
)

Index Store Usage

Use an index store to keep track of indexes built on documents.

from llama_index_cloud_sql_pg import PostgresIndexStore, PostgresEngine


engine = await PostgresEngine.from_instance(
   "project-id", "region", "my-cluster", "my-instance", "my-database"
)
index_store = await PostgresIndexStore.create(
   engine=engine, table_name="index_store"
)

Contributions

Contributions to this library are always welcome and highly encouraged.

See CONTRIBUTING for more information how to get started.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. See Code of Conduct for more information.

License

Apache 2.0 - See LICENSE for more information.

Disclaimer

This is not an officially supported Google product.