Building LLM Applications With Vector Databases

Vector databases are integral to Retrieval-Augmented Generation (RAG) systems, which enhance the accuracy of responses generated by Large Language Models (LLMs) through efficient context retrieval or dynamic few-shot prompting. In building RAG systems, starting with a basic setup and iterating improvements is crucial, as simple implementations often encounter issues like irrelevant data retrieval. Techniques such as parent-document retrieval, hybrid search, and contextual compression can optimize retrieval accuracy and reduce costs. A naive RAG system involves embedding documents into vectors stored in a vector database to facilitate semantic search for relevant context during queries. Challenges arise with semantic search limitations, particularly in domain-specific contexts, which can be mitigated by hybrid search approaches that combine semantic and traditional keyword matching. Re-ranking and contextual compression further enhance LLM response accuracy by filtering and prioritizing the most relevant information. The concept of Retrieval-Augmented Fine-Tuning (RAFT) combines RAG and fine-tuning to improve LLM performance. The future of RAG systems includes advancements in multi-modal workflows and agentic RAG, promising to transform interactions with LLMs.