rag-service

To build tenant-aware retrieval-augmented generation, you load a per-tenant FAISS vector index and retrieve contextual documents to answer company-specific questions in real time. The system formats retrieved snippets and invokes a shared LLM prompt with timing telemetry.

FAISS enables tenant-aware retrieval by loading a dedicated vector index per tenant. When a query arrives, the system retrieves relevant documents from that specific tenant's index, formats the context window, and passes it to a shared LLM for accurate, company-specific answers.

Yes, this RAG pipeline uses a shared LLM prompt across all tenants while maintaining isolation through per-tenant FAISS indexes. Context formatting ensures each tenant only retrieves and submits their own company documents to the shared LLM prompt.

Real-time company knowledge retrieval uses a FAISS vector index to find relevant document snippets based on user queries. The system formats these snippets into a compact context window for the LLM, returning precise answers like store hours or contact information instantly.

Yes, this retrieval-augmented generation system is specifically designed for a Hungarian voice assistant. It processes spoken company-specific questions in Hungarian, retrieves matching contextual documents, and generates accurate real-time responses using the shared LLM prompt.

Using a per-tenant FAISS index requires loading and managing a separate vector index for each tenant, which increases memory usage and operational overhead. The system is optimized for instant company knowledge retrieval but depends on the quality of tenant-specific indexed documents.