How do you make SharePoint documents searchable with RAG?
RAG lets employees search SharePoint documents with everyday questions. Permissions, synchronization and document structure need attention from the start.
To make SharePoint documents searchable with RAG, retrieve selected files, extract their text and split it into smaller passages. Turn those passages into embeddings and store them in a searchable index. When someone asks a question, the system retrieves relevant passages first and uses them as context for an answer. Access to those passages must follow the user’s existing SharePoint permissions.
You can use Microsoft Graph to retrieve files. A processing pipeline then extracts and indexes their contents and keeps track of changes and deletions. RAG is most useful when employees want to ask questions in everyday language and need an answer that brings together passages from several documents. For basic findability, it may be better to start by improving SharePoint search, metadata or document structure.
Why SharePoint search may not be enough
SharePoint’s built-in search helps people find documents and other content. Microsoft Search can interpret intent in longer queries. Even so, a list of search results is different from an answer that combines information from several files and explains how the relevant passages fit together. This can matter when someone asks, “What steps should we follow when handling a complaint?” and the answer is spread across a procedure, a work instruction and a form.
RAG adds a step between the question and the documents: it retrieves relevant text first, then asks a language model to form an answer using that text. The system can also show its sources so the user can check the passages. Results still depend on the documents, retrieval settings and the way the answer is generated.
The main steps
Start by deciding which SharePoint sites and libraries are in scope. Choose the file types to include and decide how to handle sensitive content. A small, approved document set is usually easier to test and secure than the whole environment.
Next, retrieve files and metadata through Microsoft Graph. In Graph, a document in a SharePoint library is generally available as a driveItem, and its content can be retrieved through the Graph API. The permissions your application needs depend on how it accesses the files and how broadly that access is scoped. Grant only the access the application actually needs.
Extract text from the retrieved files. Remove repeated headers and footers where appropriate, while preserving useful context such as the filename, folder, owner or modification date. Split the text into passages small enough to retrieve precisely, but large enough to retain their meaning. The right passage size and overlap vary by document type, so test them with questions employees actually ask.
Then create an embedding for each passage: a numerical representation that helps find text with a similar meaning. Store each embedding alongside its text, source details and access metadata in an index. When a user asks a question, embed the question, retrieve relevant passages and pass them to the language model as context. Have the model say when the sources do not provide enough information, and show where its answer came from.
Enforcing each user’s permissions
Permissions cannot be an afterthought. If an index makes content searchable by everyone, it can expose sensitive information even when users cannot open the original files. The application must check which documents the signed-in user is allowed to see for every query. An application token that lets the service read files does not, by itself, prove that the end user has access to them.
A common approach is to store each document’s relevant SharePoint permissions, or the corresponding Microsoft Entra user and group IDs, as metadata. At query time, filter results using the user’s identity and group memberships. Account for permissions inherited through a site, library or folder as well as file-level permissions. Sharing links, unique file permissions and group membership changes need particular attention. The exact implementation depends on the search service and may change over time; check the current capabilities of the technology you choose. Microsoft, for example, documents ACL ingestion and access filters for Azure AI Search as preview functionality.
Test with accounts that have different access rights. Check both that permitted files appear and that restricted files do not. Also test what happens when access is revoked. If permissions cannot be enforced reliably, the RAG search is not ready for those documents.
Keeping changes and deletions in sync
An index becomes outdated when documents change. Track a stable source ID and relevant modification details for every file. Microsoft Graph supports delta queries that let an application retrieve new, changed and deleted items since the previous synchronization. The application saves the delta link for the next run and updates the corresponding passages in its index. Deleted items must also be removed from the index.
Permissions can change even when the document text does not. Synchronize access metadata as well as content. Add checks for failed syncs and expired synchronization tokens. The precise coverage of changes depends on the Graph resource and library structure, so verify it for your implementation.
Common pitfalls
Scans are often images rather than searchable text. They need OCR, which can be unreliable with handwriting, poor-quality scans or complex layouts. Flag files that produce little or no extracted text so they do not silently disappear from the index.
Versions can mix old and current content in search results. Decide whether to index only the latest version or make older versions available too. When a new version arrives, remove the old passages or label them clearly as outdated.
Duplicate files in different folders can clutter results. Use source IDs and check whether the same content appears in multiple locations. Keep the original SharePoint link with each passage so users can open the right source.
Excel and PowerPoint need special care during extraction. A spreadsheet may contain tables, multiple worksheets and columns whose meaning depends on their headings. In PowerPoint, key points may be spread across slides, notes and diagrams. Plain text extraction can lose that structure. Test these file types separately and, where possible, preserve worksheet names, slide structure and relevant headings.
When to try something simpler first
Start with SharePoint settings, search filters, columns, metadata, and clear folder and file naming if employees mainly need to find existing files faster. Microsoft Search also offers a Graph API for searching SharePoint content; this may be a useful step if you need search results but do not need custom embeddings or generated answers.
Choose RAG when ordinary search results do not answer questions about document content, or when an answer needs to combine passages from several files. Begin with a limited library, realistic sample questions, and tests for permissions, source links and synchronization. This lets you see whether the extra processing adds value before expanding the system.
Conclusion
RAG makes SharePoint content useful for everyday questions by retrieving, processing and searching documents. Permissions and synchronization need to be part of the design from the start. Test with a defined set of documents first.