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This document is normally owned by a Tech Lead (TL) and Engineering Manager (EM) in partnership with a Product Manager (PM) and a Data Scientist (DS). Also see the PM artifact around Product Market Fit here
Organizations working in low-bandwidth environments struggle to effectively share and summarize documents with their stakeholders. Current solutions either require high bandwidth or lack the ability to intelligently compress and summarize content. There's a need for a platform that can make documents accessible and digestible in resource-constrained environments while maintaining document organization and accessibility.
- Reduce bandwidth usage by 70% through intelligent compression and summarization
- Decrease time spent by staff managing and sharing documents by 50%
- Increase document accessibility in low-connectivity areas by providing offline capabilities
- Enable organizations to reach 3x more beneficiaries through improved content delivery
- Monthly Active Users (MAU)
- Document processing time
- Average bandwidth saved per document
- User engagement with summaries
- Time saved in document management
- Number of organizations onboarded
- 1,000+ MAU
- 90% of documents processed in <60 seconds
- Average 65% reduction in document size
- 80% of users accessing summaries before full documents
- 100+ organizations onboarded
- Backend: Python (FastAPI)
- Frontend: Next.js
- Database: PostgreSQL
- Document Storage: Integration with Google Drive & Dropbox APIs
- AI/ML: Hugging Face Transformers for summarization
- Deployment: GitHub Actions + Fly.io
- Monitoring: Grafana + Prometheus
# High-level architecture for document processing
class DocumentProcessor:
async def process(self, document):
compressed = await self.compress(document)
summary = await self.summarize(document)
metadata = await self.extract_metadata(document)
return ProcessedDocument(compressed, summary, metadata)- Abstract storage interface supporting multiple providers
- Initial support for Google Drive and Dropbox
- Local cache for frequently accessed documents
/api/v1/
/documents
POST /upload
GET /{id}
GET /{id}/summary
PUT /{id}/metadata
/organizations
POST /
GET /{id}/usage
/auth
POST /login
POST /register
- 100 documents per month
- Maximum document size: 10MB
- Basic summarization features
- Single organization
- 5 team members maximum
- Unlimited documents
- Maximum document size: 50MB
- Advanced summarization with custom training
- Multiple organizations
- Unlimited team members
- Priority processing
- API access
class UsageTracker:
async def track_usage(self, org_id: str, feature: str):
current = await self.get_current_usage(org_id)
if current.exceeds_free_tier():
return PaymentRequired()
await self.increment_usage(org_id, feature)src/
/api # FastAPI routes
/services # Business logic
/models # Data models
/processors # Document processing
/storage # Storage integrations
/auth # Authentication
/utils # Shared utilities
tests/
/unit
/integration
/e2e
- Feature branches from
develop - PR review required
- Automated testing via GitHub Actions
- Staging deployment for review
- Merge to
maintriggers production deployment
- Document processing pipeline
- Storage integration
- API development
- Database management
- Summarization algorithms
- Compression optimization
- Performance monitoring
- ML model management
- Set up project structure
- Implement basic auth
- Create storage abstraction
- Basic document upload/download
- Document processing pipeline
- Basic summarization
- Usage tracking
- API documentation
- Google Drive/Dropbox integration
- Compression optimization
- Frontend development
- Testing & performance optimization
- JWT-based authentication
- Role-based access control
- Document encryption at rest
- Audit logging
- Document caching strategy
- Background processing for large documents
- CDN integration for static assets
- Database indexing strategy
- Error tracking via Sentry
- Performance monitoring via Grafana
- Usage analytics
- Cost tracking per organization
@router.post("/documents/upload")
async def upload_document(
file: UploadFile,
background_tasks: BackgroundTasks,
current_user: User = Depends(get_current_user)
):
"""
Upload a document for processing.
Returns:
document_id: str
status: ProcessingStatus
estimated_time: int
"""
# Implementation- Offline mode support
- Mobile app development
- Custom ML model training
- Batch processing
- Integration with popular LMS platforms
- Horizontal scaling of processing workers
- Caching layer implementation
- Database sharding strategy
- Multi-region deployment
class DocumentVectorizer:
def __init__(self):
# Using sentence-transformers for document embedding
self.model = SentenceTransformer('all-MiniLM-L6-v2')
self.file_processors = {
'pdf': PDFProcessor(),
'ppt': PPTProcessor(),
'jpg': ImageProcessor(), # Uses OCR for text extraction
}
async def process_document(self, file_path: str, file_type: str) -> DocumentVector:
# Extract text based on file type
text = await self.file_processors[file_type].extract_text(file_path)
# Generate embedding
embedding = self.model.encode(text)
return DocumentVector(
file_path=file_path,
embedding=embedding,
metadata=await self.extract_metadata(file_path)
)class DocumentClusterer:
def __init__(self):
self.index = faiss.IndexFlatL2(384) # Vector dimension
self.document_map = {}
async def add_document(self, doc_vector: DocumentVector):
# Add to FAISS index
self.index.add(doc_vector.embedding.reshape(1, -1))
# Store mapping
self.document_map[len(self.document_map)] = doc_vector.file_path
# Trigger re-clustering if needed
await self.maybe_recluster()
async def find_similar(self, query_vector: np.array, k: int = 5):
distances, indices = self.index.search(query_vector.reshape(1, -1), k)
return [
{
'file_path': self.document_map[idx],
'similarity_score': float(dist)
}
for idx, dist in zip(indices[0], distances[0])
]- Automatically creates and maintains virtual folders based on document similarity
- Updates in real-time as new documents are added
- Configurable similarity thresholds
class SmartFolder:
def __init__(self, name: str, similarity_threshold: float = 0.85):
self.name = name
self.threshold = similarity_threshold
self.centroid = None
self.documents = []
async def maybe_add_document(self, doc_vector: DocumentVector) -> bool:
if not self.centroid:
self.centroid = doc_vector.embedding
self.documents.append(doc_vector)
return True
similarity = cosine_similarity(self.centroid, doc_vector.embedding)
if similarity >= self.threshold:
self.update_centroid(doc_vector)
self.documents.append(doc_vector)
return True
return Falseclass TopicModeler:
def __init__(self):
self.model = BERTopic()
async def suggest_folder_names(self, documents: List[str]) -> List[str]:
topics, _ = self.model.fit_transform(documents)
return [self.model.get_topic_info(topic).Name for topic in set(topics)]class GoogleDriveOrganizer:
async def create_smart_folder(self, folder_name: str, file_ids: List[str]):
# Create a new folder
folder_metadata = {
'name': folder_name,
'mimeType': 'application/vnd.google-apps.folder',
'properties': {'isSmartFolder': 'true'}
}
folder = self.service.files().create(
body=folder_metadata,
fields='id'
).execute()
# Create shortcuts to files in the smart folder
for file_id in file_ids:
self.service.files().create(
body={
'mimeType': 'application/vnd.google-apps.shortcut',
'shortcutDetails': {'targetId': file_id},
'parents': [folder['id']]
}
).execute()class DropboxOrganizer:
async def create_smart_folder(self, folder_name: str, file_paths: List[str]):
# Create a new folder
self.dbx.files_create_folder_v2(f"/{folder_name}")
# Create symlinks in Dropbox
for file_path in file_paths:
self.dbx.files_copy_v2(
from_path=file_path,
to_path=f"/{folder_name}/{os.path.basename(file_path)}"
)- Interactive force-directed graph showing document relationships
- Drag-and-drop interface for manual organization
- Color coding by document type and similarity strength
interface SmartFolderConfig {
name: string;
similarityThreshold: number;
autoUpdateEnabled: boolean;
includedFileTypes: string[];
excludedTerms: string[];
parentFolderId?: string;
}- Process multiple documents in parallel
- Update similarity indexes in batches
- Cached similarity scores for frequently accessed documents
class SimilarityIndexManager:
async def optimize_index(self):
# Periodically rebuild index for optimal performance
if self.index.ntotal > 1000:
new_index = faiss.IndexIVFFlat(
self.index,
num_clusters=int(math.sqrt(self.index.ntotal))
)
self.index = new_index