An AI knowledge base keeps documents searchable beyond the conversation in which they were uploaded. To make that reliable, give every source a stable identity, track processing and revisions, enforce access during retrieval, and preserve citations in the answer. A successful upload alone does not prove the document is ready or that a later session will search the correct collection.

Supermemory can provide document ingestion and retrieval. Your application still needs to connect the authenticated user, source document, processing state, and answer evidence. This guide focuses on that document lifecycle rather than a complete file-upload interface.

Keep three identities separate

Use a stable application source ID for the document, a revision ID for its current contents, and the provider's document ID for API operations. The mapping lets you replace an updated source without losing its identity or accidentally creating an unrelated copy.

A content hash can detect identical bytes, but it is not the document's identity. Two customers may upload the same public handbook into different scopes. One customer may replace a handbook while keeping the same source URL. Both cases require more than a hash.

A useful application manifest might contain:

{
  "tenantId": "acme",
  "sourceId": "handbook",
  "revision": 2,
  "providerDocumentId": "doc_example",
  "state": "processing",
  "sourceUrl": "https://example.com/handbook",
  "acceptedForAnswers": false
}

This is your application's bookkeeping, not a payload to send unchanged to Supermemory. Keep authorization and acceptance decisions on the server.

Treat processing as a state transition

The document-operations documentation exposes document status and describes content updates as triggering reprocessing. Wait for readiness before claiming the new revision is searchable; handle failure and timeout explicitly.

This TypeScript helper illustrates bounded polling through an injected status reader. It can be connected to client.documents.get(id). The helper is locally tested with fake status responses; it is not a live-service availability test.

export async function waitForDocument(
  read: () => Promise<{ status: string }>,
  pause: () => Promise<void>,
  attempts = 10,
): Promise<void> {
  if (!Number.isInteger(attempts) || attempts < 1) {
    throw new Error("attempts must be a positive integer");
  }
  for (let i = 0; i < attempts; i++) {
    const doc = await read();
    if (doc.status === "done") return;
    if (doc.status === "failed") throw new Error("Document processing failed");
    if (i + 1 < attempts) await pause();
  }
  throw new Error("Document readiness timed out");
}

Bound individual network requests too. A finite number of attempts does not help if one request can hang indefinitely. Choose retry delays and a total deadline around the user experience, and expose a processing state in the interface instead of quietly answering from a missing document.

Decide what happens during an update

Suppose version 1 says the support window is Monday through Friday, and version 2 adds Saturday. While version 2 is processing, the product can show a refresh notice, temporarily use the accepted previous version with a clear date, or pause answers that require the update. Choose that policy explicitly.

For strict version control, maintain an application-level accepted revision and only include results eligible under it. Do not assume an API update gives your app an atomic switch across every cache and derived memory. Test the behavior of the deployed configuration.

When an older import arrives late, compare source revisions or effective dates. Ingestion time alone cannot tell you which policy is current. The temporal-memory guide explains that distinction.

Search from a fresh session

The returning user needs the same authorized knowledge-base scope, not the previous browser tab's temporary upload state. Store the document-to-scope mapping durably and derive access from the current session.

Supermemory's search documentation distinguishes document chunks from extracted memories and allows scoped queries. For source-grounded questions, inspect the returned document evidence rather than treating an extracted memory as a verbatim quotation.

Build an evidence object for the answer that retains the source ID, revision, relevant passage, and citation locator. Reject citations to records outside the permitted result set. If the system cannot identify a supporting source, say what is missing instead of inventing a source label.

Preserve provenance across research sessions

A research agent also benefits from a ledger of sources considered, findings retained, unresolved contradictions, and questions still open. “Already read” should not mean “never fetch again”: a source can change, and a later task may need a different section.

Keep claims separate from findings that have been verified. If two documents conflict, store both source references and their dates. Do not silently merge them into a single confident statement because their passages are semantically similar.

An example research record might say: “Vendor guide v2 says feature X is supported; migration note v1 describes a limitation; verify current deployment before recommending.” That is more useful than a summary that deletes the disagreement.

Test the lifecycle before expanding the corpus

Use fictional documents to exercise these cases:

A document is uploaded, becomes ready, and is found in another session.
A second user cannot retrieve it.
Repeated delivery of the same revision does not create unwanted copies.
An update changes the current answer and its citation version.
A late old revision does not displace the accepted new revision.
A failed processing job is visible and does not masquerade as missing knowledge.
Removing access excludes the source and any cached answer evidence.
A deleted source does not reappear through the application's own derived records or resync jobs.

The polling helper and a local document-manifest fixture can test application behavior. They cannot establish live ingestion quality, extraction fidelity, or provider deletion guarantees. Run those checks with a small authorized corpus before a production rollout.

For ingestion beyond manual uploads, use the connector workflow. For answer generation and retrieval evaluation, use the RAG chatbot guide. Start with one document that survives a new session, an update, and a permission change before importing thousands more.

For the related implementation, see Team Knowledge and AI Memory: Notes, Sources, and Shared Context.

Put the workflow into practice: open the Supermemory console and connect your application using the document and search APIs linked above. Start with one fictional document and carry it through upload, retrieval, revision, and deletion.

Build an AI Knowledge Base with Persistent Document Context