Beyond AI Vector Database - Neural Search Implementation with GPT-4 and ChatGPT

Learn how to create a domain-specific AI system for information retrieval using a transformer-based approach as an alternative to Azure Cognitive Search and Azure OpenAI services. Explore the implementation of semantic neural search through three key components: a specialized tokenizer for domain-specific text processing, an embedder for creating meaningful vector representations, and similarity calculations for accurate information retrieval. Discover how this cost-effective solution combines with GPT-4 to match the capabilities of more expensive third-party solutions while maintaining high performance. Master the integration of SBERT architecture-based tokenizer and embedder with GPT-4's autoregressive decoder stack to complete a full transformer model architecture, enabling efficient domain-specific information processing and retrieval without relying on Langchain or traditional vector databases.