Guide to Utilizing PostgreSQL as a Vector Database: A Step-by-Step LangChain Tutorial
Hello everyone! Welcome to this blog post where we dive into the fascinating world of utilizing PostgreSQL as a vector database. Have you ever wondered if using PostgreSQL could be a better option than using a dedicated vector database for your language models? Well, today we will explore this topic and discover the potential benefits of using PostgreSQL as a vector database for your AI applications. So, let's get started!
The Journey Begins: Pine Cone as a Factor Database
About a year ago, I embarked on a journey of working with large language models. As I delved deeper into this field, I discovered Pine Cone as my go-to factor database for AI applications. Pine Cone was a new concept for me, and I dived headfirst into building applications using this dedicated vector database.
However, as some of my applications transitioned from proof of concept to production, I encountered some challenges. Managing the data active in the factor database became crucial, and I found myself rethinking my data pipelines and factor databases. This led me to explore alternative ways of working with factors, which introduced me to PG Factor.
The Attraction of PG Factor: Speed and Familiarity
PG Factor is an extension for PostgreSQL databases that allows the use of factors and enables similarity search. Now, I was already familiar with PostgreSQL databases, having used them in my projects. This immediately caught my attention, and I began to question the tradeoffs between using a dedicated vector database like Pine Cone and leveraging the familiarity and speed of a PostgreSQL database.
My curiosity heightened when I came across a fascinating comparison conducted by Superbase, which highlighted that PG Factor is actually faster than Pine Cone. This discovery piqued my interest even further, and I decided to experiment with PG Factor to understand its pros and cons.
Please note that while I share my experiences and insights in this blog post, I want to emphasize that I am not an expert in this field. There might be factors that I am not aware of, so consider this as a beginner's perspective on the topic.
Let's Dive In: Pine Cone vs. PG Factor
Now, let's walk through some examples and see how Pine Cone and PG Factor perform in practice. I will guide you through the setup and share my insights based on running various experiments. You can follow along by checking out the repository linked in the description.
First, we will explore Pine Cone. I will load up an interactive Python session and load some data, specifically an ebook called "A Christmas Carol." By splitting the document into chunks, we can create the Pine Cone index and examine its contents using the Pine Cone Data Explorer. This gives us a basic understanding of how Pine Cone works and how the data is structured.
Next, we'll create a function to run a similarity search and measure the time it takes. This will help us understand the performance of Pine Cone when searching for similarities within the index.
The Speed Dilemma: PG Factor Shines
Now, let's switch gears and explore PG Factor. As with Pine Cone, we will create a collection name and a connection string to our database. By following similar steps to the Pine Cone setup, we can create a PG Factor store using LangChain's PG Factor class.
One of the advantages I immediately noticed when working with PG Factor is the clear and structured data representation within the database. PG Factor automatically creates two tables: a collection table and an embedding table. This offers a traditional SQL database structure combined with the ability to store and query factors. This clear overview of the data enhances control and allows for deeper analysis.
Now, let's run the similarity search query using PG Factor and compare its speed to Pine Cone. You might be surprised! PG Factor consistently outperforms Pine Cone in terms of speed. Its open-source nature and the ability to place the database close to the application reduces latencies and enhances performance.
Furthermore, when working with large language models, managing and updating the dataset becomes crucial. PG Factor shines in this aspect as well. Adding more data to PG Factor is seamless and provides a better overview of the application's data, including the ability to query not only on embeddings but also on documents and collection IDs.
Customizing the Experience: Creating a Powerful PG Factor Surface
To further augment the power of PG Factor, I built a custom PG Factor surface with a custom similarity search that allows querying across multiple collections. This enables efficient data management and continuous improvement of the dataset for your language model applications. By easily adding or removing data in the PG Factor database, you have full control over your application's data.
By using tools like Azure Storage Account and web hooks, you can build user interfaces (GUIs) to manage the data effectively. This flexibility and control allow for a seamless user experience and data management process.
Is PG Factor the Future for Language Model Applications?
Based on my experiments and experiences, I highly recommend exploring PG Factor for large language model applications. If you are currently using a dedicated factor database like Pine Cone or Wv8, give PG Factor a chance and see if it aligns with your requirements and preferences.
Setting up a PostgreSQL database for PG Factor is relatively straightforward. You can start by using Superbase, which offers a managed PostgreSQL database with the ability to enable the PG Factor extension for free. Alternatively, you can set up the database locally or on a server, depending on your deployment preferences. For my client projects, I typically use a managed PostgreSQL database through Microsoft Azure, as it aligns with my deployment practices.
Remember, continuously upgrading and improving your dataset becomes paramount once you move beyond the proof-of-concept stage. While initially, you might have a fixed set of data for testing and validation, the real magic happens when you continuously enhance and fine-tune your dataset. PG Factor provides an effective solution for this continuous improvement process.
I hope this blog post has given you valuable insights into utilizing PostgreSQL as a vector database. If you're interested in learning more about AI and generative AI projects, make sure to subscribe to my YouTube channel, where I share my learnings and experiences working in this exciting field. And if you have any questions about PG Factor or any other AI-related topics, feel free to explore the FAQs below!
FAQs
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1. What is PG Factor?
PG Factor is an extension for PostgreSQL databases that allows the use of factors and enables similarity search.
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2. How does PG Factor compare to dedicated vector databases like Pine Cone?
Based on my experiments, PG Factor has shown faster performance compared to dedicated factor databases like Pine Cone.
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3. Can I manage multiple collections in PG Factor?
Yes, PG Factor allows you to manage multiple collections effectively, providing better control and overview of your language model data.
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4. How can I set up a PostgreSQL database for PG Factor?
You have multiple options for setting up a PostgreSQL database for PG Factor, including using Superbase, setting it up locally, or using a managed PostgreSQL database through cloud providers like Microsoft Azure.
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5. Should I switch to PG Factor for my language model applications?
While the decision ultimately depends on your specific requirements and preferences, I highly recommend exploring PG Factor to see if it aligns with your needs. It offers speed, familiarity, data control, and a seamless user experience.
And that's a wrap! I hope you enjoyed this blog post and found it informative. Remember, as you delve into the world of large language model applications, PG Factor might just be the gamechanger you're looking for. Happy coding and exploring!
