How does RAG work technically with a local database of regulatory documents?
sobes.tech AI
Answer from AI
RAG (Retrieval-Augmented Generation) with a local database of regulatory documents works as follows:
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Document indexing: All regulatory documents are uploaded to the local database and go through a vectorization process — converting text into numerical vectors using embedding models.
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Retrieving relevant documents: When a query is received, the system converts it into a vector and searches the database for the most semantically similar documents using vector index search (e.g., with FAISS or other libraries).
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Generating a response: The found documents are passed to a generative model (e.g., an LLM), which forms a response based on them, taking into account the context and details from the regulatory acts.
Thus, the local database serves as a source of reliable information, and the generative model complements it by creating a coherent and accurate answer. Technically, this is a combination of vector search and generative AI.