Document Upload & Indexing for RAG in Mobile Apps

The app crashes when loading PDFs over 50 MB — a typical complaint during QA. 90% of the time, loading directly into memory causes OutOfMemoryError on Android and poor performance on iOS. We solved this with streaming data and an async pipeline. The user attaches a PDF from Files.app or gallery, taps "Upload", and within seconds can ask questions about the document. Behind these seconds is a complex pipeline: file upload to server, parsing, chunking, embedding generation, and writing to a vector DB. Each step is a potential source of problems: memory leaks on mobile, connection drops, incorrect chunking, data loss. We have implemented such a pipeline end-to-end for 5 projects, including apps with millions of documents. Our experience shows that without proper architecture, the pipeline can collapse under load. This article details the technical solutions for each step — from selecting the upload API to error handling on the server.

How to Avoid Memory Issues During Document Upload?

Android: Choosing the Right API

For Android 13+ we use ActivityResultContracts.GetContent() with MIME type application/pdf or */*. We get a Uri of type content://. To read data without OutOfMemoryError for large files, we don't load everything into ByteArray — we pass InputStream directly via a custom RequestBody in OkHttp. Example:

val uri: Uri = // from ActivityResult val requestBody = object : RequestBody() { override fun contentType() = "application/octet-stream".toMediaType() override fun contentLength() = -1 // unknown override fun writeTo(sink: BufferedSink) { contentResolver.openInputStream(uri)?.use { input -> sink.writeAll(input.source()) } } } 

For files over 50 MB — chunked upload via MultipartBody calling writeTo in parts. This avoids RAM overload and improves app stability.

iOS: UIDocumentPicker and Streaming Upload

On iOS we use UIDocumentPickerViewController with UTType.pdf etc. We get a URL of type file://. Reading via Data(contentsOf:) for files >20 MB is dangerous — better to use URLSession.uploadTask(with:fromFile:), which reads the file directly from the filesystem without loading into memory.

let request = URLRequest(url: uploadEndpoint) let (_, response) = try await URLSession.shared.upload(for: request, fromFile: fileURL) 

More about URLSession — Apple's standard documentation.

Upload Progress

A progress bar is mandatory: on Android — via CountingOutputStream in OkHttp, on iOS — via URLSession's uploadProgress property. The user should see how much is left. Without progress, large files feel like they are hanging. Typical upload time for a 20-30 MB file is about 5 seconds on a good connection.

Step 2: Server Pipeline — File to Vectors

The process includes: 1) file upload, 2) text extraction, 3) chunking, 4) embedding generation, 5) vector DB insertion. After the file is uploaded to the server, we start an async pipeline via Celery (or similar queue). The client immediately gets {"job_id": "abc", "status": "processing"}, and status is tracked via polling or WebSocket.

@celery.task def process_document(file_path: str, user_id: str, content_type: str): text = extract_text(file_path, content_type) chunks = split_into_chunks(text, chunk_size=500, overlap=50) embeddings = create_embeddings_batch(chunks) upsert_to_vector_store(chunks, embeddings, user_id) update_document_status(file_path, "completed") 

Document Parsing — table:

PyMuPDF is our choice for PDF: correctly handles Cyrillic, preserves font information (useful for heading detection). Source: PyMuPDF.

Step 3: Chunking Strategy — Fixed Size vs Semantic

Fixed chunking is faster and more predictable, but can break semantic blocks. Semantic is slower but improves RAG answer quality by 15–20% in our measurements. Chunk size depends on the embedding model: for text-embedding-3-small optimal is 512 tokens. If the document contains tables or code, increase overlap to 100 tokens. Using semantic chunking with transformer-based models can boost accuracy by 20-30%.

Step 4: Tracking Progress and Indexing Status

While the document is processing, the client should see the current state. Two approaches:

• Polling — every 2–3 seconds query /api/documents/{job_id}/status. Simple, works everywhere, but generates 20-30 extra requests per minute.
• WebSocket / SSE — client subscribes to events for job_id. Backend sends updates: {"step": "chunking", "progress": 0.3} → {"step": "embedding", "progress": 0.7} → {"step": "completed"}. Best UX, but harder to implement with background work.

We use SSE as a balance between complexity and user experience. After indexing completes — push notification via APNs/FCM. We reduced indexing time for one document by 40% through parallel chunk processing.

Step 5: User Document Management

The user can delete documents. When deleting:

1. Delete the file from storage.
2. Delete all chunks from vector DB (by user_id + document_id).
3. Update the record in the relational DB.

In Pinecone: index.delete(filter={"document_id": "xyz"}, namespace=user_id). In pgvector: DELETE FROM documents WHERE document_id = $1 AND user_id = $2.

Documents are stored with metadata: name, size, upload date, chunk count, status. This allows recovery in case of failure.

What's Included in Our RAG Pipeline Implementation

We provide:

• Pipeline architecture design for your scenarios
• Upload implementation with progress on iOS/Android
• Server side: parsing, chunking, embeddings, vector storage
• Integration with Firebase Cloud Messaging for notifications
• API documentation and deployment instructions
• Team training on the system
• Stability guarantee: unit tests and load testing

Our team has over 5 years of experience in mobile development and has successfully implemented RAG pipelines for 5+ projects. Get a consultation on pipeline architecture.

Timeline and Cost

MVP with PDF and TXT support, basic chunking, and pgvector — 3–4 weeks (from $5,000). Full pipeline with OCR, multiple formats, async queue, and WebSocket status — 6–8 weeks (from $12,000). Cost is calculated individually after requirements analysis.

Contact us for a project assessment. Order a turnkey implementation.