In the ever-expanding realm of AI, large language models (LLMs) like ChatGPT are driving innovative research and applications at an unprecedented speed.
One significant development is the emergence of retrieval augmented generation (RAG). This technique combines the power of LLMs with a vector database acting as long-term memory to enhance the accuracy of generated responses.
In Akcio's architecture, domain-specific knowledge is seamlessly integrated into a vector store, such as Milvus or Zilliz (fully managed Milvus), using a data loader. The vector store retrieves the Top-K most relevant results for the user's query and conveys them to the LLM, providing the LLM with context about the user's question.
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