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Postgres & Python Semantic Kernel Examples

Setup

To set up the project, you need to install the required dependencies. You can do this by running the following command in the python directory:

pip install -e .

Configuration

Before running the examples, you need to configure your database and Azure OpenAI settings. Use the .env.example file in the python directory as a template to create a .env file with the proper settings for your database. Update the connection details for your PostgreSQL database and Azure OpenAI instance in the .env file.

This project will work with any PostgreSQL instance that has pgvector installed.

Using Azure DB for PostgreSQL

This project includes connection logic to use Azure DB for PostgreSQL with Entra authentication. If the configured connection string does not contain a user or password, Entra Authentication is used with DefaultAzureCredentials. You'll need to be logged into the Azure CLI to correctly authenticate with your Entra credentials.

Vector Store Retrieval Augmented Generation (RAG)

The vector_store_rag package contains an example of using the Postgres vector store as a memory connector for a RAG example. This example demonstrates how to search the arXiv API for specific papers based on a topic and category, and then use those papers to perform various tasks such as searching and chatting with the model.

Running the Example

To run the example, navigate to the python directory and execute the following commands:

Load Data
pg-sk-examples rag load --num-papers 100 --topic RAG --category cs.AI --env-file-path .env

This will load the data from arXiv papers based on the specified topic and category. The category parameter should be one of the arXiv categories, which you can find here. The abstracts of the papers are then embedded using Azure OpenAI's embedding service, and these embeddings are stored in a PostgreSQL vector store for efficient searching.

Search Data
pg-sk-examples rag search "Your query here" --count 5

This will search the loaded arXiv papers based on the provided query by comparing the query embedding against the stored embeddings in the PostgreSQL vector store.

Chat with the Model
pg-sk-examples rag chat

This will start a chat session with the model using the loaded arXiv papers.

Additional Information

For more details on the project and its usage, refer to the comments and documentation within the codebase.