Unlocking ChatGPT RAG Potential with Vector Database
Table of Contents
- Introduction
- What is a Large Language Model?
- Retrieval Augmented Generation (RAG)
- The RAG Pattern
- The Benefits of Vector Search
- The Challenges of Vector Databases
- Using SQL Server for Vector Search
- Obtaining Vectors for Vector Search
- Setting Up the Sample Application
- Exploring the Code
- Performing a Vector Search
- Creating the Prompt
- Conclusion
Introduction
In this article, we will explore the use of a poor developer's Vector database to implement the RAG (Retrieval Augmented Generation) pattern. We will discuss the concept of a Large Language Model (LLM), the benefits and challenges of vector search, and the use of SQL Server for vector search. Additionally, we will provide step-by-step instructions for setting up the sample application and explore the Relevant code. By the end of this article, You will have a comprehensive understanding of the RAG pattern and how to implement it using a vector database.
What is a Large Language Model?
A Large Language Model (LLM) is an artificial intelligence system that can generate natural language text Based on a given input. It has the capability to complete various kinds of Prompts, such as questions, sentences, paragraphs, or stories. LLMs use advanced algorithms and complex models to analyze and understand the input and generate coherent and contextually relevant responses.
Retrieval Augmented Generation (RAG)
Retrieval Augmented Generation (RAG) is a technique for building natural language generation systems that can retrieve and use relevant information from external sources. The RAG pattern consists of three main steps:
- Retrieve a set of passages related to the search query.
- Use the retrieved passages to provide grounding to the prompt.
- Generate a natural language response that incorporates the retrieved information.
The RAG pattern requires accurate and granular chunks of information. However, the number of chunks that can be used is limited due to the constraints of AI completions, which are determined by the size of the prompt. To address this, a vector search can be used instead of a traditional keyword search.
The RAG Pattern
The RAG pattern combines the power of retrieval-based search with generative models to Create more accurate and contextually grounded responses. By retrieving relevant information from external sources and integrating it into the prompt, the generated response becomes more informed and coherent. This pattern allows LLMs to provide well-grounded answers that incorporate real-world knowledge.
The Benefits of Vector Search
A vector search, as opposed to a usual keyword search, is able to return results that are semantically related to the search query. This is crucial for properly grounding the model and generating accurate responses. To perform a vector search, a vector database is required. However, setting up and operating a vector database can be complex and expensive.
The Challenges of Vector Databases
Vector databases play a crucial role in vector search, but they can be challenging to set up and maintain. They require specialized knowledge and expertise to ensure optimal performance and accuracy. Additionally, the cost associated with operating a vector database can be a limiting factor for many developers.
Using SQL Server for Vector Search
To overcome the challenges associated with vector databases, a poor developer's solution is to use SQL Server for vector search. SQL Server provides a reliable and cost-effective alternative to setting up and maintaining a dedicated vector database. By leveraging SQL Server's capabilities, developers can implement vector search functionality without the complexity and high costs.
Obtaining Vectors for Vector Search
To perform a vector search, the first step is to obtain the vectors needed for comparison. This is achieved by processing the text through an open AI model that produces embeddings. Embeddings are mathematical representations of words or phrases that capture their meaning in a high-dimensional space. These embeddings are then used to calculate the Cosine similarity between the search query and the vectors stored in the text chunks in the database.
Setting Up the Sample Application
To run the sample application, follow these steps:
- Download the application from the downloads page on blazerhelpwebsite.com.
- Open the application in Visual Studio 2022 or higher.
- Open the SQL script located in the SQL directory.
- Create a database in your Microsoft SQL Server called "Blazer poor person vector" and run the script. This will create the required database objects.
- If you don't have an open AI API key, sign up on OpenAI.com and navigate to the org settings page to copy your organization ID.
- If you don't have an API key, navigate to the API Keys page and create a new one. Save the API key for later use.
- Open the app settings.json file and enter your open AI API key and organization ID.
Exploring the Code
The code for the sample application is organized into different sections. The first step is to navigate to the data page and click the "Load Data" button. This will open a dialog where you can enter a title and article Contents. When you click the submit button, the code will create chunks of 200 words each, call open AI to get the embeddings for each chunk, and insert the vectors into the database. The progress of creating the chunks will be displayed on the screen.
Performing a Vector Search
On the home page of the application, users can enter a search query. When they click the search button, the code will create an embedding of the search query and call the SQL function to calculate the cosine similarity with the other documents in the database. The results of the vector search will be displayed on the search results tab.
Creating the Prompt
The prompt is an essential part of the RAG pattern. Constructing an effective prompt involves explaining what you want the model to do, what you don't want, and providing examples. In the sample application, the code constructs the prompt by providing past knowledge, explaining how to use the knowledge in generating a response, and providing examples for the desired answer and what to do if the model cannot answer the query.
Conclusion
In this article, we have explored the RAG pattern and its implementation using a poor developer's Vector database. We have learned about the benefits of vector search and the challenges of setting up and operating a vector database. By leveraging SQL Server, developers can overcome these challenges and implement vector search functionality in a cost-effective manner. The sample application provides a practical demonstration of how to utilize the RAG pattern and deliver well-grounded and coherent responses generated by an LLM.