Technology
High-Level Concept
DeepStellar AI utilizes a decoupled, multi-tier architecture that separates data ingestion, vector index partitioning, and query execution. Ingestion pipelines process incoming multi-modal data streams, extracting features and converting unstructured content into dense vector embeddings via specialized transformer models.
These embeddings are partitioned into distributed index shards, while a centralized asynchronous state machine manages entity linking and context profiling. During query execution, a lightweight API gateway executes hybrid sparse-dense retrieval against pre-indexed state, returning contextually enriched results to client applications with minimal hop count and latency.
Decoupled Execution Pipeline
System Concept Block
Continuous asynchronous context profiling paired with ultra-low latency query routing
Ingestion
Multi-modal streams (documents, code, structured records, media metadata) are ingested asynchronously via streaming pipelines.
Vector Indexing
Feature extraction & transformer embedding models generate dense representations partitioned across distributed shards.
Context Synthesis
Centralized asynchronous state machine constructs entity linkage graphs and profiles intent prior to query execution.
Query Serving
Lightweight API gateway executes hybrid sparse-dense retrieval against pre-indexed state, returning low-latency results.
AWS Infrastructure Requirements
Cloud Infrastructure Need
Building, benchmarking, and scaling this enterprise neural search platform requires a robust, compute-heavy cloud infrastructure supported by AWS:
Compute Acceleration (AWS EC2 GPU & Graviton)
High-density GPU instances (EC2 G5/P4 instances) are required for batch embedding generation, model benchmarking, and fine-tuning. Low-cost AWS Graviton instances power high-throughput, CPU-bound data parsing and gateway microservices.
Durable Storage & Index Caching (Amazon S3 & EBS)
Amazon S3 provides immutable object storage for raw multi-modal datasets, index checkpoints, and model weights. High-IOPS Amazon EBS volumes back distributed vector database caching nodes for rapid query resolution.
Container Orchestration (AWS Fargate & ECR)
Containerized microservices covering query parsing, ingestion pipelines, and developer API endpoints are managed using AWS Fargate and Elastic Container Registry (ECR) for seamless horizontal scaling.
Isolated Network Topology (AWS VPC & Security Groups)
An isolated Amazon VPC configuration with private subnets, NAT Gateways, and strict security groups ensures secure, minimal-hop communication between storage nodes, database clusters, and inference endpoints.