--- title: "How to backfill historical data into Supermemory" sidebarTitle: "Backfill historical data" description: "Backfill historical documents into Supermemory with documentDate, stable custom IDs, and the batch ingestion API." icon: "history" --- Use `POST /v3/documents/batch` to backfill exports, emails, messages, or other dated records. Sort the source data oldest to newest, add `documentDate` to every document. ## Backfill in batches Backfill dated content by setting `documentDate` on each document, sorting the source records oldest to newest, and sending them in batches. Each request can contain up to 600 documents. **Endpoint:** [`POST /v3/documents/batch`](/api-reference/ingest/batch-add-documents) ```typescript TypeScript import Supermemory from "supermemory"; type SourceDocument = { id: string; content: string; createdAt: string; }; const client = new Supermemory(); const batchSize = 100; async function backfillHistoricalData(sourceDocuments: SourceDocument[]) { const documents = sourceDocuments .map((document) => ({ content: document.content, customId: document.id, documentDate: new Date(document.createdAt).toISOString() })) .sort((a, b) => a.documentDate.localeCompare(b.documentDate)); for (let offset = 0; offset < documents.length; offset += batchSize) { const result = await client.documents.batchAdd({ containerTag: "historical_import", documents: documents.slice(offset, offset + batchSize) }); if (result.failed > 0) { throw new Error(`${result.failed} documents failed to ingest`); } } } ``` ```python Python from datetime import datetime, timezone from supermemory import Supermemory client = Supermemory() batch_size = 100 def to_utc(value: str) -> str: parsed = datetime.fromisoformat(value.replace("Z", "+00:00")) if parsed.tzinfo is None: raise ValueError("created_at must include a timezone") return parsed.astimezone(timezone.utc).isoformat().replace("+00:00", "Z") def backfill_historical_data(source_documents: list[dict[str, str]]) -> None: documents = sorted( [ { "content": document["content"], "custom_id": document["id"], "document_date": to_utc(document["created_at"]), } for document in source_documents ], key=lambda document: document["document_date"], ) for offset in range(0, len(documents), batch_size): result = client.documents.batch_add( container_tag="historical_import", documents=documents[offset : offset + batch_size], ) if result.failed > 0: raise RuntimeError(f"{result.failed} documents failed to ingest") ``` ## Optional: wait for processing to finish **Endpoint:** [`GET /v3/documents/{id}`](/api-reference/documents/get-document) The batch endpoint returns after accepting the documents. If a later step depends on completed memory generation, poll the returned document IDs until both `status` and `dreamingStatus` are `done`. ```typescript TypeScript async function waitUntilDone(ids: string[]) { while (true) { const documents = await Promise.all( ids.map((id) => client.documents.get(id)) ); if (documents.some((document) => document.status === "failed")) { throw new Error("A document failed to process"); } if ( documents.every( (document) => document.status === "done" && document.dreamingStatus === "done" ) ) { return; } await new Promise((resolve) => setTimeout(resolve, 10_000)); } } ``` ```python Python import time def wait_until_done(ids: list[str]) -> None: while True: documents = [client.documents.get(document_id) for document_id in ids] if any(document.status == "failed" for document in documents): raise RuntimeError("A document failed to process") if all( document.status == "done" and document.dreaming_status == "done" for document in documents ): return time.sleep(10) ```