PDFs, emails, spreadsheets and chat logs are split into meaningful chunks; pages become passages.
Your files, turned into something an AI can reason over.
Most of your company's knowledge lives in unstructured files. Hybrid RAG can represent them in two complementary structures: a vector database for meaning and a knowledge graph for facts. On larger screens, scroll through the three stages. On smaller screens, use the step controls. With reduced motion or without WebGL, the same explanation stays visible as a static view.
Each chunk becomes a point in space. Nearby points mean similar meaning: this is your vector database.
Entities and relationships (people, contracts, dates, obligations) link into a knowledge graph. Hybrid RAG queries both.
This static explanation is available now. Supported browsers can add an optional 3D view.
THE INSIGHT
Why both?
Each structure answers a different kind of question. Hybrid RAG can route a query to the appropriate structure, or both.
Finds by meaning
"Find everything similar to this." Great for fuzzy questions, summaries, and discovering related material.
"What do our contracts say about early termination?"
Finds by fact
"Which entities connect to which?" Exact answers across relationships: customers, contracts, dates, obligations.
"Which vendors have net-45 terms AND an active NDA?"
Both, routed by the task
Meaning for recall, facts for precision, with source references when the retrieval path supplies them.
"Summarise our exposure if Vendor X terminates early."
WHAT BREAKS WITHOUT IT
Keyword search can't answer why.
Keyword search can find documents that mention the right terms, but it cannot reliably answer "Which contracts changed after the reorganization?" That question depends on relationships among people, projects, dates, and agreements. A vector database finds related language; a knowledge graph resolves the relationships and gives the answer traceable reasons.