Selecting an appropriate architecture for RAG applications involves balancing data sovereignty with operational efficiency. This section outlines various architectural choices, each with its unique implications for data control and processing capabilities:

• Cloud-Based Data and LLM (e.g., ChatGPT Assistant API):

  • Pros: Benefits from scalability, easy integration, and access to advanced AI models.
  • Cons: Introduces concerns about data privacy in the cloud and dependence on external services.

• Local Data with Cloud LLM (Vector Database and OpenAI API):

  • Pros: Retains local control over data while utilizing the computational power of cloud-based AI.
  • Cons: Challenges include potential latency and data security concerns once the data is processed in the cloud.

• Local Data and LLM (Vector Database and LLaMA 2):

  • Pros: Ensures complete control over both data and AI models, aligning with strict data privacy policies.
  • Cons: The trade-off is that local open models may not match the performance of more advanced cloud-based solutions like GPT-4.