Building a RAG-Based Solution for Contextual Warehouse Data Search

Introduction

In today's fast-paced supply chain environment, warehouses and supplier portals generate a massive amount of data related to customers, invoices, purchase orders, and more. Efficiently querying and analyzing this data is crucial for operational success. We embarked on a journey to build a Retrieval Augmented Generation (RAG)-based solution that allows users to perform context-based searches across their data repositories.

Why RAG for Warehouse Data?

Traditional keyword-based search systems often fall short when dealing with unstructured or semi-structured data. RAG combines retrieval systems with generative models, enabling more nuanced and context-aware searches. This approach significantly enhances the user's ability to find relevant information quickly.

Technology Stack Overview

Our solution leverages:

• Postgres PG Vector: For embedding search capabilities within our Postgres database.
• Kafka: To stream messages and create real-time embeddings.
• Python: For developing the backend services that tie everything together.

Implementing PG Vector for Embedding Search

Postgres PG Vector is an extension that allows for efficient similarity search using vector embeddings. We utilized it to store embeddings of various data entities such as customer records, invoices, and purchase orders.

CREATE TABLE documents (
    id serial PRIMARY KEY,
    content text,
    embedding vector(1536)
);

We generated embeddings using pre-trained language models and stored them in the embedding column. This setup enabled us to perform quick similarity searches using cosine similarity.

Using Kafka for Real-Time Embeddings

To handle the continuous influx of data, we integrated Kafka into our pipeline. Kafka streams messages related to data changes, which we consume to update our embeddings in real-time.

from kafka import KafkaConsumer

consumer = KafkaConsumer('data_topic', bootstrap_servers=['localhost:9092'])

for message in consumer:
    data = process_message(message)
    embedding = generate_embedding(data)
    store_embedding(embedding)

This approach ensures that our search system is always up-to-date with the latest information.

Challenges and Solutions

Data Volume Handling large volumes of data was a significant challenge. We optimized our storage and retrieval processes by indexing embeddings and partitioning the database.

Real-Time Updates Ensuring real-time updates required efficient message processing. We scaled our Kafka consumers and optimized the embedding generation process.

Conclusion

By integrating Postgres PG Vector and Kafka, we built a robust RAG-based solution that empowers users with context-based search capabilities over their warehouse data. This system enhances operational efficiency and provides a solid foundation for future improvements.