File Upload for AI Chat Applications — System Design
High-level design for file upload: chunked uploads, multimodal processing, security validation, and integration with AI models.
Iteration: v1 — Core File Upload Design Next: Real-time collaborative uploads, advanced RAG pipelines, multi-region CDN optimization
Table of Contents
- Problem Statement
- Requirements
- Capacity Estimations
- Data Model
- API Design
- High-Level Architecture
- Deep Dive: Core Problems
- Critical Tradeoffs
- Failure Modes & Recovery
- Interview Discussion Points
- Extensions for v2
- Real-World Implementations
1. Problem Statement
File upload in AI chat applications enables users to share documents, images, code files, and other media that the AI can analyze, understand, and reference during conversations. Unlike traditional file storage systems, AI chat file uploads require:
- Content extraction — Converting files into formats AI models can process
- Context integration — Making file content available within conversation context
- Real-time processing — Handling uploads without blocking user interaction
- Multimodal support — Processing diverse file types (text, images, PDFs, code)
Why is this challenging?
| Challenge | Description |
|---|---|
| Size limits vs. context windows | AI models have token limits; large files need intelligent chunking |
| Processing latency | Users expect immediate feedback, but extraction takes time |
| Security concerns | Files may contain malware, PII, or sensitive data |
| Format diversity | PDFs, images, spreadsheets, code — each needs different processing |
| Cost management | AI API calls are expensive; inefficient processing burns money |
| Stateful conversations | File context must persist across conversation turns |
The Core Challenge
Without proper file upload design:
- Large files timeout or fail silently
- AI can't access file content effectively
- Security vulnerabilities from unvalidated uploads
- Poor UX from synchronous blocking processing
- Context lost when files exceed token limits
With well-designed file upload:
- Seamless handling of large files via chunked upload
- Intelligent content extraction and summarization
- Secure validation pipeline before processing
- Async processing with real-time status updates
- Smart chunking to fit within context windows2. Requirements
2.1 Functional Requirements
| FR# | Requirement | Description |
|---|---|---|
| FR1 | File Upload | Users can upload files (drag-drop, file picker, paste) |
| FR2 | Multiple File Types | Support PDFs, images, text files, code, spreadsheets, documents |
| FR3 | Large File Support | Handle files up to 100MB with resumable uploads |
| FR4 | Content Extraction | Extract text/data from files for AI consumption |
| FR5 | Conversation Context | AI can reference uploaded files in responses |
| FR6 | File Preview | Users can preview uploaded files in the chat |
| FR7 | Download Original | Users can download the original uploaded file |
| FR8 | Progress Indication | Real-time upload and processing progress |
| FR9 | File Deletion | Users can remove files from conversation context |
2.2 Non-Functional Requirements
| NFR | Target | Why it matters |
|---|---|---|
| Upload Speed | > 5MB/s for users on good connections | User experience; waiting is frustrating |
| Processing Time | < 10s for most files, < 60s for large PDFs | Users need quick AI responses |
| Availability | 99.9% uptime | Core functionality for AI conversations |
| Security | Zero malware reaching AI processing | System integrity and user trust |
| Scalability | 10K concurrent uploads | Support growth without degradation |
| Cost Efficiency | < $0.01 per file processed | Sustainable at scale |
| Data Privacy | No unauthorized data access | Compliance and user trust |
2.3 Out of Scope (v1)
- Real-time collaborative file editing
- Video/audio file transcription
- File versioning and history
- Cross-conversation file sharing
- Advanced OCR for handwritten text
- Encrypted file handling (E2E encrypted uploads)
3. Capacity Estimations
3.1 Scale Parameters
| Parameter | Value | Notes |
|---|---|---|
| Daily active users | 1M DAU | Peak hours: 2-3x average |
| Files per user per day | 2-3 files | Power users upload more |
| Average file size | 2MB | Mix of small images and larger docs |
| Max file size | 100MB | Covers most document types |
| Peak concurrent uploads | 10,000 | During business hours |
| File retention | 90 days | Configurable per account tier |
3.2 Storage Calculations
Daily uploads:
- Users uploading: 1M × 30% = 300K users upload daily
- Files per uploader: 2.5 files average
- Daily files: 300K × 2.5 = 750K files/day
Storage:
- Average size: 2MB per file
- Daily storage: 750K × 2MB = 1.5TB/day
- Monthly storage: 1.5TB × 30 = 45TB/month
- 90-day retention: ~135TB active storage
Extracted content:
- Extraction ratio: ~10% of original size (text extraction)
- Daily extracted: 150GB/day
- 90-day extraction storage: ~13.5TB3.3 Bandwidth Calculations
Upload bandwidth:
- Peak uploads: 10,000 concurrent
- Average upload size: 2MB
- Upload duration: 2-5 seconds
- Peak bandwidth: 10,000 × 2MB / 3s = 6.67 GB/s = ~54 Gbps
Download bandwidth (previews + originals):
- Download requests: 20% of uploads = 150K/day
- Peak downloads: 2,000 concurrent
- Peak bandwidth: 2,000 × 2MB / 3s = 1.3 GB/s = ~10 Gbps3.4 Processing Capacity
Processing queue:
- Files to process: 750K/day = 8.7 files/second average
- Peak processing: 50 files/second
- Processing time: 5-30 seconds average
- Workers needed: 50 × 30 / 1 = 1,500 concurrent workers (worst case)
- With auto-scaling: 100-500 workers typical, burst to 1,500
AI API calls:
- Files needing AI processing: 80% = 600K/day
- Tokens per file: ~2,000 tokens average (after chunking)
- Daily tokens: 600K × 2,000 = 1.2B tokens/day
- Cost (at $0.01/1K tokens): $12,000/day = ~$360K/month3.5 Infrastructure Summary
| Component | Sizing | Notes |
|---|---|---|
| Object Storage (S3) | 150TB active | Plus glacier for old files |
| CDN | 100 Gbps capacity | For preview delivery |
| Processing Workers | 100-1,500 (auto-scale) | Kubernetes pods |
| Message Queue | 100K messages/minute | SQS/Kafka |
| Metadata DB | 10TB (PostgreSQL) | File metadata, extraction results |
| Vector DB | 50TB (Pinecone/Weaviate) | For RAG embeddings |
| Cache (Redis) | 100GB | Upload sessions, rate limits |
4. Data Model
4.1 Core Entities
File Upload
FileUpload {
id: UUID // Primary identifier
conversation_id: UUID // Parent conversation
user_id: UUID // Uploader
// Original file info
original_filename: String // User's filename
content_type: String // MIME type
size_bytes: Long // Original size
checksum: String // SHA-256 hash
// Storage
storage_key: String // S3 key for original
cdn_url: String // CDN URL for previews (nullable)
thumbnail_key: String // Thumbnail S3 key (nullable)
// Processing status
status: Enum // UPLOADING, PROCESSING, READY, FAILED
processing_started_at: Timestamp // When processing began
processing_completed_at: Timestamp // When processing finished
error_message: String // If FAILED, why
// Metadata
created_at: Timestamp
expires_at: Timestamp // For retention policy
deleted_at: Timestamp // Soft delete
}Chunked Upload Session
ChunkedUploadSession {
id: UUID // Session identifier
file_upload_id: UUID // Target file
user_id: UUID // Owner
// Chunking config
total_size: Long // Expected total bytes
chunk_size: Integer // Size per chunk (e.g., 5MB)
total_chunks: Integer // Total expected chunks
// Progress
chunks_received: Set<Integer> // Chunk numbers received
bytes_received: Long // Total bytes so far
// Session management
status: Enum // ACTIVE, COMPLETED, EXPIRED, ABORTED
created_at: Timestamp
expires_at: Timestamp // Session timeout (24h)
last_activity: Timestamp
}Extracted Content
ExtractedContent {
id: UUID
file_upload_id: UUID // Source file
// Content type
extraction_type: Enum // TEXT, TABLE, IMAGE_DESCRIPTION, CODE
// Extracted data
content: Text // Extracted text/data
page_number: Integer // For paginated docs (nullable)
section: String // Section identifier (nullable)
// Token estimation
token_count: Integer // Estimated tokens
// For images
image_description: Text // AI-generated description
detected_objects: JSON // Object detection results
// Metadata
extraction_method: String // "pdfplumber", "tesseract", "gpt-4-vision"
confidence_score: Float // Extraction confidence
created_at: Timestamp
}File Chunk (for RAG)
FileChunk {
id: UUID
file_upload_id: UUID
extracted_content_id: UUID
// Chunk info
chunk_index: Integer // Order within file
content: Text // Chunk text
token_count: Integer // Tokens in chunk
// Embedding
embedding_vector: Vector[1536] // OpenAI ada-002 or similar
embedding_model: String // Model used
// Context
metadata: JSON // Page, section, headers for context
created_at: Timestamp
}4.2 Relationships
┌─────────────────┐
│ Conversation │
└────────┬────────┘
│ 1:N
▼
┌─────────────────┐ 1:1 ┌──────────────────────┐
│ FileUpload │◄────────────────►│ ChunkedUploadSession │
└────────┬────────┘ └──────────────────────┘
│ 1:N
▼
┌─────────────────┐
│ ExtractedContent│
└────────┬────────┘
│ 1:N
▼
┌─────────────────┐
│ FileChunk │ ──────► Vector DB (for RAG retrieval)
└─────────────────┘5. API Design
5.1 Upload APIs
Initiate Upload (for chunked uploads)
POST /api/v1/conversations/{conversation_id}/uploads/initiate
Request:
{
"filename": "quarterly-report.pdf",
"content_type": "application/pdf",
"size_bytes": 52428800,
"checksum": "sha256:abc123..."
}
Response: 201 Created
{
"upload_id": "uuid",
"session_id": "uuid",
"chunk_size": 5242880,
"total_chunks": 10,
"upload_urls": [
{
"chunk_number": 0,
"upload_url": "https://presigned-s3-url...",
"expires_at": "2024-01-01T00:15:00Z"
},
// ... more chunks
],
"expires_at": "2024-01-01T12:00:00Z"
}Upload Chunk
PUT /api/v1/uploads/{session_id}/chunks/{chunk_number}
Headers:
Content-Type: application/octet-stream
Content-Length: 5242880
X-Chunk-Checksum: sha256:def456...
Body: <binary chunk data>
Response: 200 OK
{
"chunk_number": 0,
"received_bytes": 5242880,
"chunks_completed": 1,
"chunks_remaining": 9
}Complete Upload
POST /api/v1/uploads/{session_id}/complete
Request:
{
"chunk_checksums": [
{"chunk_number": 0, "checksum": "sha256:..."},
// ...
]
}
Response: 202 Accepted
{
"upload_id": "uuid",
"status": "PROCESSING",
"estimated_completion_seconds": 15,
"status_url": "/api/v1/uploads/{upload_id}/status"
}Simple Upload (for small files < 10MB)
POST /api/v1/conversations/{conversation_id}/uploads
Headers:
Content-Type: multipart/form-data
Body:
file: <file binary>
Response: 202 Accepted
{
"upload_id": "uuid",
"status": "PROCESSING",
"original_filename": "image.png",
"size_bytes": 1048576
}5.2 Status & Retrieval APIs
Get Upload Status
GET /api/v1/uploads/{upload_id}/status
Response: 200 OK
{
"upload_id": "uuid",
"status": "READY", // UPLOADING | PROCESSING | READY | FAILED
"original_filename": "quarterly-report.pdf",
"content_type": "application/pdf",
"size_bytes": 52428800,
"processing_progress": 100,
"preview_url": "https://cdn.example.com/previews/...",
"download_url": "https://cdn.example.com/files/...",
"extracted_summary": "Q3 financial report showing...",
"page_count": 45,
"token_count": 28500,
"created_at": "2024-01-01T10:00:00Z",
"expires_at": "2024-04-01T10:00:00Z"
}Get File Content (for AI context)
GET /api/v1/uploads/{upload_id}/content
Query params:
format: "full" | "summary" | "chunks"
max_tokens: 4000
page: 1 (for paginated access)
Response: 200 OK
{
"upload_id": "uuid",
"format": "chunks",
"total_chunks": 12,
"chunks": [
{
"chunk_id": "uuid",
"content": "...",
"token_count": 350,
"metadata": {
"page": 1,
"section": "Executive Summary"
}
}
],
"has_more": true,
"next_page": 2
}5.3 WebSocket Events (Real-time Updates)
// Client subscribes to upload events
ws://api.example.com/ws/uploads/{conversation_id}
// Server pushes events:
// Upload progress
{
"event": "upload_progress",
"upload_id": "uuid",
"chunks_received": 5,
"chunks_total": 10,
"bytes_received": 26214400,
"bytes_total": 52428800
}
// Processing progress
{
"event": "processing_progress",
"upload_id": "uuid",
"stage": "extracting", // validating | extracting | embedding | complete
"progress_percent": 65,
"current_page": 30,
"total_pages": 45
}
// Processing complete
{
"event": "processing_complete",
"upload_id": "uuid",
"status": "READY",
"preview_url": "...",
"summary": "..."
}
// Error
{
"event": "processing_error",
"upload_id": "uuid",
"error_code": "EXTRACTION_FAILED",
"error_message": "Unable to parse PDF structure"
}6. High-Level Architecture
6.1 System Overview
┌─────────────────────────────────────────────────────────────────────────────┐
│ CLIENT LAYER │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Web App │ │ Mobile App │ │ Desktop │ │ CLI │ │
│ │ (React) │ │ (React N.) │ │ (Electron) │ │ │ │
│ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │
└─────────┼────────────────┼────────────────┼────────────────┼────────────────┘
│ │ │ │
└────────────────┴────────────────┴────────────────┘
│
┌───────▼───────┐
│ CDN / Edge │
│ (CloudFront) │
└───────┬───────┘
│
┌───────────────────────────────────┼─────────────────────────────────────────┐
│ API GATEWAY │
│ ┌────────────────────────────────┼────────────────────────────────────┐ │
│ │ Load Balancer (ALB) + WAF + Rate Limiting │ │
│ └────────────────────────────────┼────────────────────────────────────┘ │
└───────────────────────────────────┼─────────────────────────────────────────┘
│
┌───────────────────────────────────┼─────────────────────────────────────────┐
│ APPLICATION LAYER │
│ │ │
│ ┌────────────────────────────────┼────────────────────────────────────┐ │
│ │ Upload Service │ │
│ │ ┌──────────────┐ ┌───────────────┐ ┌─────────────────┐ │ │
│ │ │ Upload API │ │ Chunk Manager │ │ Presigned URL │ │ │
│ │ │ Controller │ │ │ │ Generator │ │ │
│ │ └──────────────┘ └───────────────┘ └─────────────────┘ │ │
│ └─────────┬────────────────┬─────────────────────┬────────────────────┘ │
│ │ │ │ │
│ ┌─────────▼────────┐ ┌─────▼──────┐ ┌──────────▼───────────┐ │
│ │ Session Store │ │ File Meta │ │ WebSocket Service │ │
│ │ (Redis) │ │ (Postgres) │ │ (Real-time updates) │ │
│ └──────────────────┘ └────────────┘ └──────────────────────┘ │
└───────────────────────────────────┬─────────────────────────────────────────┘
│
┌────────▼────────┐
│ Message Queue │
│ (SQS / Kafka) │
└────────┬────────┘
│
┌───────────────────────────────────┼─────────────────────────────────────────┐
│ PROCESSING LAYER │
│ │ │
│ ┌────────────────────────────────┼────────────────────────────────────┐ │
│ │ Processing Workers (K8s Pods) │ │
│ │ │ │ │
│ │ ┌─────────────────────────────┼─────────────────────────────────┐ │ │
│ │ │ Processing Pipeline │ │ │
│ │ │ │ │ │ │
│ │ │ ┌───────────┐ ┌──────────▼─────────┐ ┌──────────────────┐ │ │ │
│ │ │ │ Security │ │ Content Extractor │ │ AI Processor │ │ │ │
│ │ │ │ Scanner │──► ┌─────────────┐ │──► ┌────────────┐ │ │ │ │
│ │ │ │ │ │ │ PDF Parser │ │ │ │ Embeddings │ │ │ │ │
│ │ │ │ • Malware │ │ │ Image OCR │ │ │ │ Summarizer │ │ │ │ │
│ │ │ │ • MIME │ │ │ Doc Parser │ │ │ │ Chunker │ │ │ │ │
│ │ │ │ • Size │ │ │ Code Parse │ │ │ └────────────┘ │ │ │ │
│ │ │ └───────────┘ │ └─────────────┘ │ └──────────────────┘ │ │ │
│ │ │ └────────────────────┘ │ │ │
│ │ └───────────────────────────────────────────────────────────────┘ │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │ │
└───────────────────────────────────┼─────────────────────────────────────────┘
│
┌───────────────────────────────────┼─────────────────────────────────────────┐
│ STORAGE LAYER │
│ │ │
│ ┌────────────────┐ ┌───────────▼────────┐ ┌──────────────────┐ │
│ │ Object Store │ │ Vector Database │ │ Metadata Store │ │
│ │ (S3) │ │ (Pinecone/Weaviate│ │ (PostgreSQL) │ │
│ │ │ │ │ │ │ │
│ │ • Originals │ │ • Embeddings │ │ • File metadata │ │
│ │ • Thumbnails │ │ • Chunk vectors │ │ • User data │ │
│ │ • Previews │ │ • Similarity idx │ │ • Conversations │ │
│ └────────────────┘ └────────────────────┘ └──────────────────┘ │
│ │
└──────────────────────────────────────────────────────────────────────────────┘6.2 Upload Flow (Chunked)
┌────────┐ ┌────────┐ ┌─────────┐ ┌────┐ ┌─────────┐
│ Client │ │ Upload │ │ Redis │ │ S3 │ │ Queue │
│ │ │ Service│ │ │ │ │ │ │
└───┬────┘ └───┬────┘ └────┬────┘ └─┬──┘ └────┬────┘
│ │ │ │ │
│ 1. Initiate │ │ │ │
│ ─────────────────►│ │ │ │
│ │ 2. Create session │ │ │
│ │ ─────────────────►│ │ │
│ │ │ │ │
│ │ 3. Generate presigned URLs │ │
│ │ ─────────────────────────────────►│ │
│ │ │ │ │
│ 4. Return URLs │ │ │ │
│ ◄─────────────────│ │ │ │
│ │ │ │ │
│ 5. Upload chunk directly to S3 │ │ │
│ ─────────────────────────────────────────────────────►│ │
│ │ │ │ │
│ 6. Notify chunk complete │ │ │
│ ─────────────────►│ │ │ │
│ │ 7. Update session │ │ │
│ │ ─────────────────►│ │ │
│ │ │ │ │
│ (repeat 5-7 for all chunks) │ │ │
│ │ │ │ │
│ 8. Complete upload│ │ │ │
│ ─────────────────►│ │ │ │
│ │ 9. Verify all chunks │ │
│ │ ─────────────────────────────────►│ │
│ │ │ │ │
│ │ 10. Assemble multipart │ │
│ │ ─────────────────────────────────►│ │
│ │ │ │ │
│ │ 11. Queue for processing │ │
│ │ ─────────────────────────────────────────────────►│
│ │ │ │ │
│ 12. Accepted │ │ │ │
│ ◄─────────────────│ │ │ │
│ │ │ │ │6.3 Processing Pipeline
┌─────────────────────────────────────────────────────────────────────────────┐
│ File Processing Pipeline │
│ │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌───────┐ │
│ │ Validate │───►│ Sanitize │───►│ Extract │───►│ Chunk │───►│ Embed │ │
│ └──────────┘ └──────────┘ └──────────┘ └──────────┘ └───────┘ │
│ │ │ │ │ │ │
│ ▼ ▼ ▼ ▼ ▼ │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌───────┐ │
│ │ MIME │ │ Strip │ │ PDF/Doc/ │ │ Semantic │ │OpenAI │ │
│ │ Check │ │ Metadata │ │ Image │ │ Chunking │ │ ada │ │
│ │ Size │ │ Resize │ │ Code │ │ ~512 tok │ │ │ │
│ │ Malware │ │ Convert │ │ Parser │ │ overlap │ │ │ │
│ └──────────┘ └──────────┘ └──────────┘ └──────────┘ └───────┘ │
│ │
│ ┌──────────────────────────────────────────────────────────────────────┐ │
│ │ Processing Results │ │
│ │ • Original stored in S3 │ │
│ │ • Thumbnail/preview generated │ │
│ │ • Extracted text stored in PostgreSQL │ │
│ │ • Chunks with embeddings stored in Vector DB │ │
│ │ • Summary generated for quick AI context │ │
│ └──────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────┘7. Deep Dive: Core Problems
7.1 Problem: Large File Uploads
Uploading large files (>10MB) over HTTP is unreliable due to network interruptions, timeouts, and browser limitations.
Challenge Analysis
| Issue | Impact |
|---|---|
| Connection drops | Upload fails, user must restart |
| Browser memory | Large files consume client memory |
| Server timeout | Long uploads exceed request limits |
| Progress visibility | Users don’t know if upload is working |
| Bandwidth waste | Failed uploads waste already-transmitted data |
Solution: Chunked Resumable Uploads
┌─────────────────────────────────────────────────────────────────┐
│ Chunked Upload Strategy │
│ │
│ Original File: 50MB │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │█████████████████████████████████████████████████████████│ │
│ └─────────────────────────────────────────────────────────┘ │
│ ▼ │
│ Chunked (5MB each): │
│ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ ... ┌─────┐ │
│ │ 1 │ │ 2 │ │ 3 │ │ 4 │ │ 5 │ │ 6 │ │ 10 │ │
│ └─────┘ └─────┘ └─────┘ └─────┘ └─────┘ └─────┘ └─────┘ │
│ ✓ ✓ ✓ ✗ - - - │
│ │ │
│ Network failure at chunk 4 │
│ ▼ │
│ Resume: Only re-upload from chunk 4 │
│ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ │
│ │ 4 │ │ 5 │ │ 6 │ ... │ 10 │ │
│ └─────┘ └─────┘ └─────┘ └─────┘ │
└─────────────────────────────────────────────────────────────────┘Implementation Components
Client-side chunking:
// Pseudocode for client-side chunking
async function uploadFile(file, sessionInfo) {
const chunkSize = sessionInfo.chunk_size;
const totalChunks = Math.ceil(file.size / chunkSize);
// Track progress locally (for resume)
const completedChunks = loadCompletedChunks(sessionInfo.session_id);
for (let i = 0; i < totalChunks; i++) {
if (completedChunks.has(i)) continue; // Skip completed
const start = i * chunkSize;
const end = Math.min(start + chunkSize, file.size);
const chunk = file.slice(start, end);
// Upload with retry
await uploadChunkWithRetry(chunk, i, sessionInfo);
// Save progress
saveChunkProgress(sessionInfo.session_id, i);
// Report progress
onProgress((i + 1) / totalChunks * 100);
}
// Complete the upload
await completeUpload(sessionInfo.session_id);
}Server-side assembly:
// Pseudocode for server-side chunk assembly
public void completeMultipartUpload(String sessionId, List<ChunkChecksum> checksums) {
ChunkedUploadSession session = sessionStore.get(sessionId);
// Verify all chunks received
if (session.getChunksReceived().size() != session.getTotalChunks()) {
throw new IncompleteUploadException("Missing chunks");
}
// Verify checksums
for (ChunkChecksum cs : checksums) {
String storedChecksum = s3Client.getObjectChecksum(
getChunkKey(sessionId, cs.getChunkNumber())
);
if (!storedChecksum.equals(cs.getChecksum())) {
throw new ChecksumMismatchException(cs.getChunkNumber());
}
}
// Assemble in S3 (server-side, no download needed)
s3Client.completeMultipartUpload(
session.getMultipartUploadId(),
session.getCompletedParts()
);
// Queue for processing
messageQueue.send(new ProcessFileMessage(session.getFileUploadId()));
}7.2 Problem: Content Extraction at Scale
Different file types require different extraction strategies. Extraction must be fast, accurate, and cost-efficient.
File Type Processing Matrix
| File Type | Extraction Method | Processing Time | Complexity |
|---|---|---|---|
| Plain text (.txt, .md) | Direct read | < 1s | Low |
| Code files (.py, .java) | Syntax-aware parsing | 1-2s | Medium |
| PDF (text-based) | pdfplumber / PyMuPDF | 2-10s | Medium |
| PDF (scanned/image) | OCR (Tesseract/Cloud Vision) | 10-60s | High |
| Images | GPT-4 Vision / BLIP-2 | 2-5s | Medium |
| Word docs (.docx) | python-docx | 2-5s | Medium |
| Spreadsheets (.xlsx) | openpyxl with structure detection | 5-15s | High |
| Presentations (.pptx) | python-pptx + image extraction | 10-30s | High |
Extraction Pipeline Architecture
┌─────────────────────────────────────────────────────────────────────────┐
│ Extraction Router │
│ │
│ Input: File + MIME type │
│ │ │
│ ▼ │
│ ┌──────────────────────────────────────────────────────────────────┐ │
│ │ Route by MIME Type │ │
│ └──────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ├──────────┬──────────┬──────────┬──────────┬──────────┐ │
│ ▼ ▼ ▼ ▼ ▼ ▼ │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ Text │ │ Code │ │ PDF │ │ Image │ │ Office │ │
│ │ Extractor│ │ Extractor│ │ Extractor│ │ Extractor│ │ Extractor│ │
│ └────┬─────┘ └────┬─────┘ └────┬─────┘ └────┬─────┘ └────┬─────┘ │
│ │ │ │ │ │ │
│ ▼ ▼ │ ▼ ▼ │
│ ┌──────────┐ ┌──────────┐ │ ┌──────────┐ ┌──────────┐ │
│ │ Preserve │ │ AST + │ │ │ Vision │ │ Structure │ │
│ │ Structure│ │ Comments │ │ │ Analysis │ │ Detection │ │
│ └──────────┘ └──────────┘ │ └──────────┘ └──────────┘ │
│ │ │
│ ┌───────────┴───────────┐ │
│ ▼ ▼ │
│ ┌──────────┐ ┌──────────┐ │
│ │Text-based│ │ Scanned │ │
│ │ PDF │ │ PDF │ │
│ └────┬─────┘ └────┬─────┘ │
│ ▼ ▼ │
│ ┌──────────┐ ┌──────────┐ │
│ │pdfplumber│ │ OCR │ │
│ │ PyMuPDF │ │ Pipeline │ │
│ └──────────┘ └──────────┘ │
│ │
│ All paths converge to: │
│ ┌──────────────────────────────────────────────────────────────────┐ │
│ │ Unified Content Output │ │
│ │ { │ │
│ │ "text": "...", │ │
│ │ "structure": { "pages": [...], "sections": [...] }, │ │
│ │ "tables": [...], │ │
│ │ "images": [{ "description": "..." }], │ │
│ │ "code_blocks": [...] │ │
│ │ } │ │
│ └──────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘OCR Decision Tree (PDF Processing)
PDF Received
│
▼
┌─────────────────┐
│ Extract text │
│ with pdfplumber │
└────────┬────────┘
│
▼
┌────────────┐ Yes ┌───────────────────┐
│ Text found │─────────►│ Use extracted text│
│ > 100 chars│ │ (fast path) │
└────────────┘ └───────────────────┘
│ No
▼
┌─────────────────┐
│ Check if scanned│
│ (image-based) │
└────────┬────────┘
│
▼
┌────────────┐ No ┌───────────────────┐
│ Has images │─────────►│ Mark as empty/ │
│ per page? │ │ unextractable │
└────────────┘ └───────────────────┘
│ Yes
▼
┌─────────────────┐
│ Quality check: │
│ DPI > 150? │
└────────┬────────┘
│
┌────┴────┐
Yes No
│ │
▼ ▼
┌───────┐ ┌──────────┐
│ OCR │ │ Upscale │
│Direct │ │ then OCR │
└───────┘ └──────────┘7.3 Problem: Fitting Files into AI Context Windows
AI models have token limits (e.g., 128K for GPT-4, 200K for Claude). Large documents exceed these limits.
Context Window Challenge
┌─────────────────────────────────────────────────────────────────────────┐
│ Context Window Problem │
│ │
│ Document: 500-page PDF = ~300,000 tokens │
│ Model context window: 128,000 tokens │
│ Conversation history: 20,000 tokens │
│ Available for document: 108,000 tokens │
│ │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │████████████████████████████████████████████████████████████████ │ │
│ │ Document: 300,000 tokens │ │
│ └─────────────────────────────────────────────────────────────────┘ │
│ │
│ ┌──────────────────────────────────────┐ │
│ │████████████████████████████████████ │ Context Window: 128K tokens │
│ └──────────────────────────────────────┘ │
│ │
│ Problem: Document is 2.8x larger than available context! │
└─────────────────────────────────────────────────────────────────────────┘Solution Strategy: Hierarchical Retrieval
┌─────────────────────────────────────────────────────────────────────────┐
│ Hierarchical Content Strategy │
│ │
│ TIER 1: Summary (Always in context) │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ Document summary: 500-1000 tokens │ │
│ │ "This is a quarterly financial report for Q3 2024 containing │ │
│ │ revenue data, expense breakdowns, and future projections..." │ │
│ └─────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ TIER 2: Section Index (On-demand retrieval) │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ Section 1: Executive Summary (pages 1-3, 2000 tokens) │ │
│ │ Section 2: Revenue Analysis (pages 4-15, 8000 tokens) │ │
│ │ Section 3: Expense Breakdown (pages 16-25, 6000 tokens) │ │
│ │ ... │ │
│ └─────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ TIER 3: Semantic Chunks (RAG retrieval) │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ 600 chunks × ~500 tokens each │ │
│ │ Each chunk has embedding vector for semantic search │ │
│ │ Retrieved based on user query similarity │ │
│ └─────────────────────────────────────────────────────────────────┘ │
│ │
│ RUNTIME: Query "What was Q3 revenue?" │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ Context assembled: │ │
│ │ - Document summary (1000 tokens) │ │
│ │ - Retrieved chunks about revenue (3000 tokens) │ │
│ │ - Conversation history (5000 tokens) │ │
│ │ Total: ~9000 tokens (fits easily in context) │ │
│ └─────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘Chunking Strategy
┌─────────────────────────────────────────────────────────────────────────┐
│ Semantic Chunking Algorithm │
│ │
│ Input: Extracted document text │
│ │
│ Step 1: Identify natural boundaries │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ • Paragraph breaks │ │
│ │ • Section headers (H1, H2, H3) │ │
│ │ • Page breaks │ │
│ │ • Sentence boundaries (fallback) │ │
│ └─────────────────────────────────────────────────────────────────┘ │
│ │
│ Step 2: Create chunks with overlap │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ Target chunk size: 512 tokens │ │
│ │ Overlap: 50 tokens (context continuity) │ │
│ │ │ │
│ │ ┌─────────────────────────────────────────┐ │ │
│ │ │ Chunk 1 (512 tokens) │ │ │
│ │ └─────────────────────────────────────────┤ │ │
│ │ ┌─────────────┴─────────────────────┐│ │
│ │ │ Chunk 2 (512 tokens) ││ │
│ │ └───────────────────────────────────┤│ │
│ │ ┌─────────────────────┴┐ │
│ │ │ Chunk 3 (512 tok) │ │
│ │ └─────────────────────┘ │
│ │ ◄───── 50 token overlap ────► │ │
│ └─────────────────────────────────────────────────────────────────┘ │
│ │
│ Step 3: Preserve metadata │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ Each chunk includes: │ │
│ │ • Source page number │ │
│ │ • Section header │ │
│ │ • Table context (if from table) │ │
│ │ • Previous/next chunk IDs (for context expansion) │ │
│ └─────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘7.4 Problem: Security & Validation
Files from users can contain malware, exceed quotas, or violate content policies.
Security Layer Architecture
┌─────────────────────────────────────────────────────────────────────────┐
│ Security Pipeline │
│ │
│ ┌───────────────────────────────────────────────────────────────────┐ │
│ │ LAYER 1: Client-side Validation (Defense in depth, not trusted) │ │
│ │ • File extension check │ │
│ │ • Size limit check │ │
│ │ • Basic MIME type detection │ │
│ └───────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌───────────────────────────────────────────────────────────────────┐ │
│ │ LAYER 2: Upload Gateway Validation │ │
│ │ • Size enforcement (hard limit) │ │
│ │ • Rate limiting per user/IP │ │
│ │ • Content-Type header validation │ │
│ │ • Request signature verification │ │
│ └───────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌───────────────────────────────────────────────────────────────────┐ │
│ │ LAYER 3: Deep Content Validation (Processing Workers) │ │
│ │ │ │
│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────────┐ │ │
│ │ │ Magic Byte │ │ Malware │ │ Content Policy Check │ │ │
│ │ │ Verification│ │ Scanning │ │ (NSFW, PII detection) │ │ │
│ │ └─────────────┘ └─────────────┘ └─────────────────────────┘ │ │
│ │ │ │
│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────────┐ │ │
│ │ │ Archive │ │ Image │ │ Document Structure │ │ │
│ │ │ Bomb Check │ │ Validation │ │ Validation (PDF/DOCX) │ │ │
│ │ └─────────────┘ └─────────────┘ └─────────────────────────┘ │ │
│ └───────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ┌─────────┴─────────┐ │
│ ▼ ▼ │
│ ┌───────────┐ ┌───────────┐ │
│ │ PASSED │ │ REJECTED │ │
│ │ Continue │ │ Quarantine│ │
│ │ Processing│ │ & Alert │ │
│ └───────────┘ └───────────┘ │
└─────────────────────────────────────────────────────────────────────────┘Validation Rules
┌─────────────────────────────────────────────────────────────────────────┐
│ Validation Rules Matrix │
│ │
│ File Type Whitelist (allow list approach): │
│ ┌───────────────────────────────────────────────────────────────────┐ │
│ │ Category │ Extensions │ Max Size │ Notes │ │
│ │───────────────┼──────────────────────────┼──────────┼─────────────│ │
│ │ Documents │ .pdf, .docx, .doc, .txt │ 50MB │ OCR enabled │ │
│ │ Spreadsheets │ .xlsx, .xls, .csv │ 25MB │ 1M cell max │ │
│ │ Presentations │ .pptx, .ppt │ 100MB │ │ │
│ │ Images │ .png, .jpg, .gif, .webp │ 20MB │ 4096px max │ │
│ │ Code │ .py, .js, .java, .go... │ 5MB │ 100K lines │ │
│ │ Markdown │ .md, .mdx │ 2MB │ │ │
│ │ Archives │ BLOCKED │ - │ Security │ │
│ │ Executables │ BLOCKED │ - │ Security │ │
│ └───────────────────────────────────────────────────────────────────┘ │
│ │
│ Rate Limits: │
│ ┌───────────────────────────────────────────────────────────────────┐ │
│ │ Limit Type │ Free Tier │ Pro Tier │ Enterprise │ │
│ │──────────────────────┼──────────────┼──────────────┼─────────────│ │
│ │ Files per hour │ 10 │ 100 │ 1000 │ │
│ │ Total storage │ 100MB │ 5GB │ 100GB │ │
│ │ Max file size │ 10MB │ 50MB │ 100MB │ │
│ │ Concurrent uploads │ 2 │ 10 │ 50 │ │
│ └───────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘8. Critical Tradeoffs
8.1 Storage Strategy
| Option | Pros | Cons | When to Use |
|---|---|---|---|
| S3 Standard | High durability, scalable | Cost at scale | Primary storage for active files |
| S3 Intelligent-Tiering | Auto cost optimization | Monitoring fees | Unknown access patterns |
| S3 Glacier | Very cheap | Retrieval latency | Archival (>90 days) |
| Local/EBS | Low latency | Limited scale, single point of failure | Processing cache only |
Recommendation: S3 Standard for active files, Glacier for archived files with lifecycle policies.
8.2 Sync vs. Async Processing
┌─────────────────────────────────────────────────────────────────────────┐
│ Sync vs Async Processing │
│ │
│ SYNCHRONOUS (Small files < 5MB) │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ User uploads ──► Process immediately ──► Return result │ │
│ │ │ │
│ │ Pros: │ │
│ │ • Simple implementation │ │
│ │ • Immediate feedback │ │
│ │ • No state management │ │
│ │ │ │
│ │ Cons: │ │
│ │ • Blocks user if processing is slow │ │
│ │ • Timeout risk for larger files │ │
│ │ • Resource contention under load │ │
│ └─────────────────────────────────────────────────────────────────┘ │
│ │
│ ASYNCHRONOUS (Large files > 5MB) │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ User uploads ──► Queue job ──► Return immediately │ │
│ │ │ │ │
│ │ ▼ │ │
│ │ Worker processes ──► Notify via WebSocket │ │
│ │ │ │
│ │ Pros: │ │
│ │ • Non-blocking UX │ │
│ │ • Handles large files gracefully │ │
│ │ • Scalable processing │ │
│ │ │ │
│ │ Cons: │ │
│ │ • Complex state management │ │
│ │ • Requires notification mechanism │ │
│ │ • Eventual consistency │ │
│ └─────────────────────────────────────────────────────────────────┘ │
│ │
│ RECOMMENDATION: Hybrid approach │
│ • Files < 5MB: Sync processing, immediate response │
│ • Files > 5MB: Async with WebSocket status updates │
└─────────────────────────────────────────────────────────────────────────┘8.3 Direct S3 Upload vs. Server Proxy
| Approach | Pros | Cons | Best For |
|---|---|---|---|
| Direct to S3 (Presigned URLs) | No server bandwidth consumed, faster for large files | Requires client-side complexity, CORS setup | Large files, high volume |
| Server Proxy | Simple client, centralized validation | Server becomes bottleneck, doubles bandwidth | Small files, low volume |
| Hybrid | Best of both | More complex architecture | Production systems |
Direct S3 Upload Flow:
┌────────┐ 1. Get presigned URL ┌─────────┐
│ Client │ ─────────────────────────────► │ Server │
│ │ ◄───────────────────────────── │ │
└───┬────┘ 2. Return URL └─────────┘
│
│ 3. Upload directly to S3
│
▼
┌────────┐
│ S3 │
└────────┘
│
│ 4. S3 Event Notification
▼
┌─────────┐
│ Lambda │ ──► Processing Queue
└─────────┘
Benefits:
- Server handles 0 bytes of file data
- Parallel uploads to S3
- S3 handles retries and multipartRecommendation: Direct S3 upload with presigned URLs for files > 1MB.
8.4 RAG vs. Full Context
| Approach | Pros | Cons | Best For |
|---|---|---|---|
| Full Context | Complete document understanding, no retrieval errors | Token limit constraints, expensive | Small documents (<50K tokens) |
| RAG (Retrieval) | Handles any document size, cost-efficient | May miss relevant context, retrieval quality varies | Large documents, knowledge bases |
| Hybrid | Best accuracy for important sections + scale for large docs | Complexity | Production systems |
Decision Matrix:
┌──────────────────────────────────────────────────────────────────────┐
│ │
│ Document Size Strategy │
│ ──────────────────────────────────────────────────────────────── │
│ < 10K tokens Full context (include entire document) │
│ 10K-50K tokens Summary + relevant sections │
│ > 50K tokens Summary + RAG retrieval │
│ │
│ Query Type Context Strategy │
│ ──────────────────────────────────────────────────────────────── │
│ Specific question RAG retrieval (precise chunks) │
│ Summary request Document summary + section summaries │
│ Analysis task Full relevant sections │
│ Comparison Multiple chunk retrieval │
│ │
└──────────────────────────────────────────────────────────────────────┘8.5 Preprocessing Depth vs. Latency
| Level | Processing | Latency | Storage | Use Case |
|---|---|---|---|---|
| Minimal | Store only, extract on-demand | ~1s upload | Low | Infrequent access |
| Standard | Extract text, basic chunking | 5-15s | Medium | Most documents |
| Deep | Extract + embed + summarize | 30-60s | High | Frequently queried docs |
| Premium | All above + multiple model analysis | 2-5min | Very high | Critical documents |
Recommendation: Standard processing by default, with option to trigger deep processing for important documents.
9. Failure Modes & Recovery
9.1 Upload Failures
| Failure | Detection | Recovery | Prevention |
|---|---|---|---|
| Network interruption | Client detects disconnect | Resume from last chunk | Chunked uploads with session persistence |
| Server timeout | 504 Gateway Timeout | Retry with exponential backoff | Async processing, proper timeouts |
| Storage failure | S3 returns 5xx | Retry to different region | Multi-region replication |
| Quota exceeded | 413 Payload Too Large | Inform user, suggest compression | Pre-flight quota check |
9.2 Processing Failures
| Failure | Detection | Recovery | Prevention |
|---|---|---|---|
| Extraction timeout | Worker timeout | Retry with simpler extraction | Timeout per file type, fallback extractors |
| OCR failure | Tesseract error | Try cloud OCR, then mark as image-only | Multiple OCR providers |
| Malformed file | Parser exception | Mark as unprocessable, store original | Validate before processing |
| AI API failure | API returns 5xx | Retry with backoff, use cached embeddings | Multiple API providers, local fallback |
9.3 System Failures
┌─────────────────────────────────────────────────────────────────────────┐
│ Failure Recovery Matrix │
│ │
│ Component Failure Impact Recovery Time │
│ ─────────────────────────────────────────────────────────────────── │
│ Upload Service New uploads fail Auto-heal: 30s-2min │
│ Processing Workers Queue builds up Scale up: 1-5min │
│ Message Queue Processing stops Failover: 30s │
│ S3 Uploads/downloads Region failover: 1-5min │
│ PostgreSQL Metadata unavailable Replica promotion: 30s │
│ Vector DB RAG retrieval fails Fallback to summaries │
│ Redis Sessions lost Clients must re-init │
│ │
│ Graceful Degradation Strategies: │
│ ┌───────────────────────────────────────────────────────────────────┐ │
│ │ 1. Processing backlog: Accept uploads, delay processing │ │
│ │ 2. RAG unavailable: Use document summaries only │ │
│ │ 3. Embedding unavailable: Serve text without semantic search │ │
│ │ 4. CDN unavailable: Serve directly from S3 (slower) │ │
│ └───────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘9.4 Data Recovery
Backup Strategy:
┌─────────────────────────────────────────────────────────────────────────┐
│ │
│ Data Type Backup Frequency Retention RTO RPO │
│ ──────────────────────────────────────────────────────────────────── │
│ Original files (S3) Continuous (CRR) 90 days 1h 0 │
│ File metadata (PG) Hourly snapshots 30 days 30min 1h │
│ Extracted content Daily backup 30 days 2h 24h │
│ Embeddings (Vector) Weekly backup 7 days 4h 7d │
│ Upload sessions No backup (Redis) - - - │
│ │
│ Recovery procedures documented in runbook │
└─────────────────────────────────────────────────────────────────────────┘10. Interview Discussion Points
10.1 Clarifying Questions to Ask
- Scale: How many concurrent users? Expected file sizes?
- File types: Which formats must be supported? Video/audio?
- Processing requirements: Real-time or batch? Accuracy vs. speed?
- AI model: Which LLM? Context window size?
- Security: Compliance requirements (HIPAA, GDPR)?
- Multi-tenancy: Shared infrastructure or isolated?
10.2 Key Design Decisions to Justify
| Decision | Why | Alternative Considered |
|---|---|---|
| Chunked uploads | Reliability for large files | Simple POST (fails for >10MB) |
| Presigned URLs | Offload bandwidth from servers | Proxy through server (bottleneck) |
| Async processing | Non-blocking UX | Sync (timeout issues) |
| RAG for large docs | Handle unlimited document size | Full context (token limits) |
| S3 + CDN | Scale and global delivery | Local storage (single point of failure) |
10.3 Deep Dive Topics
- Chunking strategies: Semantic vs. fixed-size, overlap handling
- OCR pipeline: When to use local vs. cloud, accuracy tradeoffs
- Security: Defense in depth, malware scanning pipeline
- Cost optimization: Caching strategies, embedding model selection
- Real-time updates: WebSocket vs. polling, connection management
10.4 Red Flags to Avoid
- ❌ Storing files on application servers
- ❌ Synchronous processing for all files
- ❌ No malware scanning
- ❌ Trusting client-side validation
- ❌ No rate limiting or quotas
- ❌ Blocking on AI API calls
11. Extensions for v2
11.1 Planned Enhancements
| Feature | Description | Complexity |
|---|---|---|
| Video/Audio transcription | Whisper API integration for media files | High |
| Collaborative annotations | Multiple users annotating same document | High |
| Version history | Track file versions and changes | Medium |
| Cross-conversation files | Share files across conversations | Medium |
| Advanced OCR | Handwriting recognition, form extraction | High |
| E2E encryption | Client-side encryption for sensitive files | High |
11.2 Multi-Region Architecture
┌─────────────────────────────────────────────────────────────────────────┐
│ Multi-Region File Upload (v2) │
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ US-East │ │ EU-West │ │ AP-South │ │
│ │ Region │ │ Region │ │ Region │ │
│ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │
│ │ │ │ │
│ └────────────────────┼────────────────────┘ │
│ │ │
│ ┌─────────▼─────────┐ │
│ │ Global Router │ │
│ │ (Route53/CF) │ │
│ └───────────────────┘ │
│ │
│ Features: │
│ • Geo-based routing to nearest region │
│ • Cross-region replication for disaster recovery │
│ • Data residency compliance (keep EU data in EU) │
│ • Global CDN for file delivery │
└─────────────────────────────────────────────────────────────────────────┘12. Real-World Implementations
12.1 Reference Architectures
| Product | Approach | Notable Features |
|---|---|---|
| ChatGPT | Integrated file upload | Code interpreter, image analysis |
| Claude | Direct file processing | Large context window (200K) |
| Google Workspace | Chunked uploads | Resumable uploads API |
| Dropbox | Block-level dedup | Delta sync, content hashing |
| Notion AI | Workspace-integrated | Embedded in documents |
12.2 Open Source References
- tus.io: Resumable upload protocol
- Uppy: File uploader with plugins
- Minio: S3-compatible object storage
- Apache Tika: Content extraction
- LangChain: RAG implementation patterns
12.3 Relevant AWS Services
┌─────────────────────────────────────────────────────────────────────────┐
│ AWS Service Mapping │
│ │
│ Component AWS Service Alternative │
│ ──────────────────────────────────────────────────────────────────── │
│ Object Storage S3 GCS, Azure Blob │
│ CDN CloudFront Cloudflare, Akamai │
│ Message Queue SQS Kafka, RabbitMQ │
│ Processing Workers Lambda / ECS Kubernetes │
│ Metadata DB RDS PostgreSQL Aurora, CockroachDB │
│ Vector DB OpenSearch Pinecone, Weaviate │
│ Cache ElastiCache (Redis) Memcached │
│ Malware Scanning GuardDuty + Custom ClamAV │
│ Monitoring CloudWatch Datadog, Prometheus │
└─────────────────────────────────────────────────────────────────────────┘Summary
Designing file upload for AI chat applications requires balancing:
- User experience: Fast uploads, real-time feedback, seamless AI integration
- Scalability: Handling millions of files with varying sizes
- Security: Protecting against malware and enforcing content policies
- Cost efficiency: Optimizing storage, processing, and AI API costs
- AI integration: Making file content accessible within context limits
The key architectural decisions are:
- Chunked resumable uploads for reliability
- Direct-to-S3 with presigned URLs for scale
- Async processing pipeline for non-blocking UX
- Hierarchical RAG for handling large documents
- Defense-in-depth security for protection
This design provides a production-ready foundation that can scale to millions of users while maintaining security and cost efficiency.
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