8 Chatting with your data
This chapter covers
- How bringing your data benefits enterprises
- Installing and using a vector database and vector index
- Planning and retrieving your proprietary data
- Using a vector database to conduct searches
- How to implement an end-to-end chat powered by RAG using a vector database and an LLM
- The benefits of bringing your data and RAG jointly
- How RAG benefits AI safety for enterprises
Utilizing large language models (LLMs) for a chat-with-data implementation is a promising strategy uniquely suitable for enterprises seeking to harness the power of generative artificial intelligence (AI) for their specific business requirements. By synergizing the LLM capabilities with enterprise-specific data sources and tools, businesses can forge intelligent and context-aware chatbots that deliver invaluable insights and recommendations to their clientele and stakeholders.
At a high level, there are two ways to chat with your data using an LLM—one is by employing a retrieval engine as implemented using the retrieval-augmented generation (RAG) pattern, and another is to custom-train the LLM on your data. The latter is more involved and complex and not available to most users.