Building a Powerful RAG Pipeline with PostgreSQL 🧠

Table of Contents

Introduction

This guide explores how to build an advanced Retrieval Augmented Generation (RAG) pipeline using only PostgreSQL. We’ll dive into hybrid search, reranking, and how these techniques can elevate your RAG pipeline’s performance.

Why Hybrid Search? 🤔

Imagine searching for specific information within a vast sea of data. Traditional semantic search might miss crucial details, while keyword-based search can be too restrictive. Hybrid search bridges this gap, combining the strengths of both approaches for more comprehensive results.

1. Setting the Stage 🧰

Before we begin, ensure you have the following:

Docker installed
Python installed
An OpenAI API key
A PostgreSQL GUI client (e.g., TablePlus)
(Optional) A Cohere API key for reranking

Setting up the Environment:

Clone the repository: https://github.com/daveebbelaar/pgvectorscale-rag-solution/tree/hybrid-search
Navigate to the ‘docker’ folder and run docker-compose up -d to start the PostgreSQL database.
Connect to the database using your PostgreSQL GUI client.
Create a new Python environment and install the required libraries listed in requirements.txt.
Create an .env file based on example.env and input your OpenAI API key.

2. Embeddings and Data Ingestion 📥

We’ll use the CNN Daily Mail dataset for this example.

Preparing the Data:

Load the dataset using the datasets library in Python.
Select a subset of articles (e.g., 1,000) to work with.
Create embeddings for each article using OpenAI’s embedding model.

Populating the Database:

Create a table named ‘documents’ in your PostgreSQL database with columns for ID, metadata, content, and embedding.
Create indexes on the ’embedding’ column for semantic search and the ‘content’ column for keyword-based search.
Insert the data with embeddings into the ‘documents’ table.

3. Unleashing the Power of Hybrid Search ⚡

Now, let’s explore the different search methods:

3.1 Semantic Search:

Leverages vector embeddings to find semantically similar documents.
Uses cosine similarity to measure the distance between vectors.

3.2 Keyword-Based Search:

Utilizes PostgreSQL’s built-in full-text search capabilities.
Matches documents containing specific keywords or phrases.

3.3 Hybrid Search:

Performs both keyword-based and semantic searches.
Combines the results, prioritizing keyword matches.
Removes duplicate entries to present a concise list.

4. Refining Results with Reranking 🏆

Reranking adds an extra layer of intelligence by using a large language model (LLM) to reorder the search results based on relevance to the query.

How it Works:

The LLM analyzes the query and the retrieved documents.
It assigns a relevance score to each document based on its understanding of the query’s intent.
The results are reordered based on these scores, pushing the most relevant documents to the top.

5. Putting it All Together: Building the RAG Pipeline 🏗️

Our RAG pipeline now consists of the following steps:

Query Processing: The user inputs a query.
Hybrid Search: The system performs both semantic and keyword-based searches.
Reranking: An LLM reorders the results based on relevance.
Response Synthesis: The most relevant information is extracted and synthesized into a coherent response.

Conclusion 🎉

By combining hybrid search and reranking techniques, we’ve built a powerful RAG pipeline within PostgreSQL. This approach allows for more accurate and relevant information retrieval, enhancing the user experience.