Building a Cost-Effective AI Search System with Custom Tokenization

Table of Contents

1. Introduction
2. Azure Cognitive Search and OpenAI Integration
3. Alternative to GPT-4 and OpenAI
4. Overview of Text Embedding Model AIDA-002
5. Retriever Plugin and API Endpoints
6. Utilizing Postgres with PyG Vector Edition
7. Semantic Search with Cosine Similarity
8. Optimization for Domain-Specific Data
9. Building a Custom Tokenizer
10. Generating Vector Representations
11. Creating a Semantic Information Retrieval System

Article

Introduction

In this article, we will explore an alternative approach to using Azure Cognitive Search in combination with the Azure OpenAI set featuring GPT-4. While the previous setup relied on the GPT-4 system, the AIDA-002 model, and the OpenAI retriever plugin, we will now focus on text embedding as a more cost-effective solution.

Azure Cognitive Search and OpenAI Integration

In the previous setup, we utilized Azure Cognitive Search in combination with the OpenAI API endpoints for retrieving and processing data. This involved using the GPT-4 system, the AIDA-002 model, and the PyG Vector Edition for semantic search functionality.

Alternative to GPT-4 and OpenAI

However, in this article, we will explore an alternative approach to achieve similar results without relying heavily on expensive third-party solutions. By leveraging a custom tokenizer, an embedder, and a cosine similarity calculation, we can Create a domain-specific AI system for information retrieval and save valuable resources.

Overview of Text Embedding Model AIDA-002

Before delving into the implementation details, it is important to understand the key components of our alternative solution. The primary focus is on the text embedding model, AIDA-002, which allows us to optimize for domain-specific data. Unlike the GPT-4 system, AIDA-002 provides a higher resolution and specificity when searching for information.

Retriever Plugin and API Endpoints

The OpenAI retriever plugin plays a crucial role in our alternative approach. By utilizing the appropriate API endpoints, we can seamlessly integrate AIDA-002 into our information retrieval system. This plugin allows us to retrieve Relevant information from external knowledge databases, such as Postgres, and calculate the cosine similarity for semantic search.

Utilizing Postgres with PyG Vector Edition

To enhance our information retrieval capabilities, we incorporate the PyG Vector Edition of the Postgres open-source relational database management system. This enables us to calculate cosine similarity and perform semantic searches efficiently. Using Postgres together with AIDA-002 ensures a comprehensive and accurate retrieval process.

Semantic Search with Cosine Similarity

Cosine similarity is a crucial element of our alternative approach. By calculating the semantic similarity between query terms and documents, we can retrieve the most relevant information. Cosine similarity provides a quantitative measure of how similar two vectors are, enabling us to retrieve precise results for our given query.

Optimization for Domain-Specific Data

One of the significant advantages of our alternative solution is the ability to optimize for domain-specific data. Whether You are interested in physics, finance, law, or any other field, you can customize the tokenizer to generate domain-specific tokens. This ensures that the tokenization process is tailored to your specific requirements, resulting in more accurate retrieval and search outcomes.

Building a Custom Tokenizer

To achieve such optimization, we have two options for tokenization. First, we can leverage existing open-sourced tokenizers optimized for specific domains. These tokenizers are freely available and have undergone domain-specific training, making them suitable for various fields such as finance, medicine, or biochemistry. Alternatively, for even greater customization, we can build our own tokenizer specifically designed for our unique domain knowledge.

Generating Vector Representations

Once we have our tokens, the next step is to generate vector representations for each of them. The vector space, created using techniques like the BERT model or SBERT (Sentence-BERT), allows us to calculate the semantic closeness between tokens. The vector representation encapsulates the meaning and Context of each token, enabling efficient retrieval and search operations.

Creating a Semantic Information Retrieval System

With the tokenizer and the embedder in place, we can now create our semantic information retrieval system. This system utilizes the tokenization and vectorization processes to enable efficient querying and retrieval of information. By employing cosine similarity calculations, we can identify the most relevant documents or sentences Based on a given query. This AI-powered retrieval system offers the same functionality as the GPT-4 and OpenAI setup with increased cost-efficiency and domain-specific optimization.

Highlights

• An alternative approach to leveraging Azure Cognitive Search and OpenAI
• Focus on text embedding for cost effectiveness
• Integration of AIDA-002 model and OpenAI's retriever plugin
• Utilizing Postgres with PyG Vector Edition for efficient semantic search
• Optimization for domain-specific data with customized tokenization
• Generating vector representations for accurate retrieval and search
• Creation of a semantic information retrieval system for efficient querying

FAQ

Q: How does this alternative approach compare to the previous Azure Cognitive Search and OpenAI integration?

The alternative approach offers a more cost-effective solution by eliminating the dependence on the GPT-4 system and expensive third-party tools. It focuses on utilizing a custom tokenizer, embedder, and cosine similarity calculations to create a domain-specific AI system for information retrieval.

Q: Can this alternative approach be customized for different domains?

Yes, the alternative approach allows for domain-specific optimization. By choosing the appropriate tokenizer and training it with domain-specific documents, the system can be tailored to various fields such as finance, medicine, law, or biochemistry. This customization ensures more accurate retrieval and search results.

Q: How does the semantic information retrieval system work?

The semantic information retrieval system combines tokenization, vectorization, and cosine similarity calculations to retrieve the most relevant information based on a given query. It employs the OpenAI retriever plugin and Postgres with PyG Vector Edition to efficiently calculate cosine similarity and retrieve documents or sentences that match the query.

Q: Is this alternative approach as intelligent as the GPT-4 and OpenAI setup?

While the alternative approach may not possess the same level of intelligence as GPT-4, it focuses on enhancing the information retrieval process. By leveraging domain-specific optimization and efficient semantic search capabilities, it offers a cost-effective solution without compromising on accuracy and relevance.