System Architecture Design Document
Part 3: Component Design
Context
The current situation requires a decision because:
- Requirement 1
- Constraint 2
- Need 3
Status
Accepted | YYYY-MM-DD
1. Document Processing Component
1.1 Document Processor
Responsibility: Manages the core document processing pipeline.
Boundaries:
Input: Raw document files, processing configuration
Output: Processed document chunks, metadata
Key Interfaces:
- DocumentProcessorService
- ChunkManager
- VectorGenerator
- RelationshipBuilder
Dependencies:
- Storage Service
- Vector Service
- Background Queue
Implementation Details
class DocumentProcessor:
"""
Core document processing orchestrator
"""
def __init__(
self,
chunk_manager: ChunkManager,
vector_service: VectorService,
storage_service: StorageService
):
self.chunk_manager = chunk_manager
self.vector_service = vector_service
self.storage_service = storage_service
async def process_document(
self,
document: Document,
options: ProcessingOptions
) -> ProcessingResult:
# Processing pipeline implementation
chunks = await self.chunk_manager.create_chunks(document, options)
vectors = await self.vector_service.generate_vectors(chunks)
relationships = await self.build_relationships(chunks, vectors)
return ProcessingResult(chunks, vectors, relationships)
1.2 Chunk Manager
Responsibility: Handles document chunking with overlap and context preservation.
Boundaries:
Input: Document content, chunking configuration
Output: Document chunks with metadata
Key Interfaces:
- ChunkCreator
- OverlapManager
- ChunkValidator
Dependencies:
- Storage Service
Implementation Details
class ChunkManager:
"""
Manages document chunking with overlap
"""
def create_chunks(
self,
content: str,
options: ChunkOptions
) -> List[Chunk]:
chunks = []
start = 0
while start < len(content):
# Calculate chunk boundaries with overlap
end = self._calculate_chunk_end(
content,
start,
options.chunk_size,
options.overlap
)
# Create chunk with metadata
chunk = Chunk(
content=content[start:end],
start_pos=start,
end_pos=end,
metadata=self._generate_metadata()
)
chunks.append(chunk)
start = end - options.overlap
return chunks
2. Vector Processing Component
2.1 Vector Service
Responsibility: Manages vector embeddings generation and storage.
Boundaries:
Input: Text chunks
Output: Vector embeddings
Key Interfaces:
- VectorGenerator
- VectorStorage
- EmbeddingModel
Dependencies:
- ML Model Service
- Vector Database
Implementation Details
class VectorService:
"""
Vector embedding generation and management
"""
def __init__(
self,
embedding_model: EmbeddingModel,
vector_store: VectorStore
):
self.embedding_model = embedding_model
self.vector_store = vector_store
async def generate_vectors(
self,
chunks: List[Chunk]
) -> List[Vector]:
embeddings = await self.embedding_model.embed_batch(
[chunk.content for chunk in chunks]
)
vectors = [
Vector(
embedding=embedding,
chunk_id=chunk.id,
metadata=chunk.metadata
)
for chunk, embedding in zip(chunks, embeddings)
]
await self.vector_store.store_batch(vectors)
return vectors
3. Search Component
3.1 Search Service
Responsibility: Manages search operations across vectors and relationships.
Boundaries:
Input: Search query, search parameters
Output: Ranked search results
Key Interfaces:
- VectorSearcher
- GraphTraversal
- ResultRanker
Dependencies:
- Vector Service
- Graph Service
- Ranking Service
Implementation Details
class SearchService:
"""
Search orchestration and result management
"""
def __init__(
self,
vector_searcher: VectorSearcher,
graph_traversal: GraphTraversal,
result_ranker: ResultRanker
):
self.vector_searcher = vector_searcher
self.graph_traversal = graph_traversal
self.result_ranker = result_ranker
async def search(
self,
query: SearchQuery
) -> SearchResults:
# Vector search
vector_results = await self.vector_searcher.search(
query.text,
query.limit
)
# Graph expansion
expanded_results = await self.graph_traversal.expand_results(
vector_results,
query.depth
)
# Result ranking
ranked_results = self.result_ranker.rank_results(
expanded_results,
query.ranking_params
)
return SearchResults(ranked_results)
4. Monitoring Component
4.1 Metrics Service
Responsibility: Collects and manages system metrics.
Boundaries:
Input: Metric events, collection configuration
Output: Aggregated metrics, time series data
Key Interfaces:
- MetricCollector
- MetricAggregator
- TimeSeriesStore
Dependencies:
- Time Series Database
- Event Bus
Implementation Details
class MetricsService:
"""
Metrics collection and aggregation
"""
def __init__(
self,
collector: MetricCollector,
aggregator: MetricAggregator,
storage: TimeSeriesStore
):
self.collector = collector
self.aggregator = aggregator
self.storage = storage
async def collect_metrics(
self,
metric_event: MetricEvent
) -> None:
# Process metric event
processed_metric = await self.collector.process(
metric_event
)
# Aggregate metrics
aggregated = await self.aggregator.aggregate(
processed_metric
)
# Store time series data
await self.storage.store(aggregated)
5. Alert Component
5.1 Alert Service
Responsibility: Manages system alerts and notifications.
Boundaries:
Input: Alert rules, system events
Output: Alert notifications
Key Interfaces:
- AlertManager
- RuleEngine
- NotificationService
Dependencies:
- Metrics Service
- Notification Channels
Implementation Details
class AlertService:
"""
Alert management and notification
"""
def __init__(
self,
rule_engine: RuleEngine,
alert_manager: AlertManager,
notifier: NotificationService
):
self.rule_engine = rule_engine
self.alert_manager = alert_manager
self.notifier = notifier
async def process_event(
self,
event: SystemEvent
) -> None:
# Evaluate rules
triggered_rules = await self.rule_engine.evaluate(
event
)
# Create alerts
alerts = await self.alert_manager.create_alerts(
triggered_rules
)
# Send notifications
await self.notifier.send_notifications(alerts)
6. Background Processing Component
6.1 Task Queue Service
Responsibility: Manages asynchronous task processing.
Boundaries:
Input: Task definitions, execution parameters
Output: Task results, execution status
Key Interfaces:
- TaskScheduler
- WorkerManager
- ResultStore
Dependencies:
- Message Queue
- State Store
Implementation Details
class TaskQueueService:
"""
Background task management
"""
def __init__(
self,
scheduler: TaskScheduler,
worker_manager: WorkerManager,
result_store: ResultStore
):
self.scheduler = scheduler
self.worker_manager = worker_manager
self.result_store = result_store
async def submit_task(
self,
task: Task
) -> TaskResult:
# Schedule task
scheduled_task = await self.scheduler.schedule(task)
# Assign to worker
worker_task = await self.worker_manager.assign_task(
scheduled_task
)
# Store result
result = await self.result_store.store_result(
worker_task
)
return result
7. API Gateway Component
7.1 Gateway Service
Responsibility: Manages API routing and request handling.
Boundaries:
Input: HTTP requests, API configuration
Output: HTTP responses
Key Interfaces:
- RouterManager
- AuthHandler
- RateLimiter
Dependencies:
- Auth Service
- Service Registry
Implementation Details
class GatewayService:
"""
API gateway management
"""
def __init__(
self,
router: RouterManager,
auth_handler: AuthHandler,
rate_limiter: RateLimiter
):
self.router = router
self.auth_handler = auth_handler
self.rate_limiter = rate_limiter
async def handle_request(
self,
request: Request
) -> Response:
# Authenticate request
auth_result = await self.auth_handler.authenticate(
request
)
# Apply rate limiting
await self.rate_limiter.check_limit(request)
# Route request
response = await self.router.route_request(
request,
auth_result
)
return response
Would you like me to:
- Add more detail to any component?
- Create component interaction diagrams?
- Add specific implementation examples?
- Proceed with Part 4: Data Architecture?