Applied AI · Document search
Internal document assistant with RAG
Query an internal document collection, rank the most relevant documents and return a traceable answer with its sources to speed up access to business information.
The user expresses a need in natural language.
The document collection is searched and documents are ranked by relevance.
Useful passages are retained with their identifier and origin.
An answer grounded in the sources is presented to the user.
Business context
Internal procedures, reference materials and guides are often scattered across several locations. Teams spend time searching for the right information, checking its version and identifying a sufficiently reliable source for decision-making.
Challenge
How can an employee ask a simple question, find the relevant documents and obtain a traceable answer without hiding the origin of the information or inventing an answer when sources are insufficient?
Approach
A RAG system, or Retrieval-Augmented Generation system, first retrieves useful information before producing an answer. This demonstration focuses on the retrieval layer and deliberately replaces generation with a local extractive summary so that every result remains verifiable.
What the demo shows
Enter a question or choose an example. The demonstration ranks documents, displays the best extract and cites the selected sources. The score is a transparent indicator based on matching terms, not a probability calculated by an AI model.
Example questions
Document filters
Front-end demonstration: the JSON corpus, search, ranking and summary run locally. No generative model or remote service is called.
Assisted answer
Local extractive summaryAsk a question to display the most relevant extract and the documents supporting it.
This answer draws directly from the document collection. A production version should evaluate answer quality, manage access rights and explicitly refuse requests that are not sufficiently documented.
Business value
From demo to production
A real architecture could ingest documents from SharePoint or Azure Blob Storage, split them into passages, calculate their embeddings - numerical representations of meaning - and index them in Azure AI Search. Azure OpenAI could then formulate an answer from authorised passages, with citations, access control, logging and quality testing.
Going further
This type of assistant can support an internal help desk, a procedure library, a compliance process or access to Data and BI documentation, with a level of control suited to the information being processed.