Streamlit
Grounding your AI in your own data, with absolute precision
5 followers
Grounding your AI in your own data, with absolute precision
5 followers
Webygraphy RAG is a high-fidelity information retrieval system designed to eliminate AI hallucinations. By bridging the gap between large language models and your proprietary data, our architecture ensures that every generated insight is anchored in your actual business documents. We specialize in building secure, context-aware pipelines that provide verifiable, real-time intelligence for your most complex operational datasets
How does the retrieval actually stay accurate when the source documents get updated frequently, and is reindexing automatic or something I have to trigger manually?
@ensarqp2a We maintain high retrieval precision through a hybrid, dynamic architecture:
Accuracy: We use on-the-fly dynamic sparse indexing, Hybrid Search (RRF), and Cross-Encoder reranking to ensure retrieved context is precise and up-to-date. If local confidence is low, the system automatically triggers a Tavily web search for the latest information.
Maintenance: Document ingestion is manually triggered but triggers a fully automated pipeline (extraction, embedding, and upsert).
Important Note: Because of our 5-tier caching system, you must call the /admin/cache/clear endpoint after any update to ensure the system serves the latest data rather than cached results.
Spent a few minutes testing the retrieval on some internal docs and was impressed by how clean the citation anchors came back. The fact that it doesn’t drift into vague summaries is a nice change.
@adelicay27612 Thank you, we are still working on the UI to make it more user-friendly and easy to understand