BIS Compliance Engine

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Step 6: Architecture & System Design ​

High-Level System Architecture ​

The 21-BIS-Compliance-Engine follows a decoupled, two-tier architecture:

  1. Frontend Presentation Tier: A lightweight Single Page Application (SPA) built with React 19 and Vite 8 that manages local component state, renders audit cards, and handles client-side CSV blob downloads.
  2. Inference & Regulatory API Tier: A high-performance Python 3.11 ASGI microservice powered by FastAPI and Uvicorn. It houses the entire machine learning inference pipeline (FastEmbed ONNX, FAISS vector index, Scikit-Learn classifier) and statutory business logic (domain guardrail, weighted gap engine) inside a single process address space.

Request Lifecycles (End-to-End) ​

1. Statutory Compliance Audit Lifecycle (POST /chat) ​

2. Statutory Audit Pack CSV Export Lifecycle (POST /audit/export) ​

Data Schema & In-Memory Entity Relationships ​

Because the platform operates in-memory without an external relational or NoSQL database, entities are structured as pre-indexed JSON documents in [model2_corpus.json](file:///Users/avishjain/avishjain9-repos/21-bis-compliance-engine/model2_corpus.json) and mapped to Python dictionaries.

Caching Strategy & Error Propagation ​

In-Memory Pre-Loaded Cache ​

  1. Cold-Start Preloading: Upon process boot, app.py loads TextEmbedding, joblib.load(), faiss.read_index(), and model2_corpus.json into resident memory ([app.py:L24-L29](file:///Users/avishjain/avishjain9-repos/21-bis-compliance-engine/app.py#L24-L29)).
  2. Subsequent Query Speed: Every request reads directly from pre-warmed RAM pointers with zero disk I/O.
  3. Invalidation: Updates to model2_corpus.json trigger [rebuild_faiss_index.py](file:///Users/avishjain/avishjain9-repos/21-bis-compliance-engine/rebuild_faiss_index.py) offline; restarting Uvicorn hot-reloads the new index.

Error Propagation Across Layers ​

Scaling to 100x Traffic & Bottleneck Analysis ​

Currently, a single Uvicorn process handles ~60 to 75 requests per second (RPS) on a standard 4-core CPU, with an average latency of 16ms. At 100x traffic (~7,500 RPS), the following architectural evolutions occur:

The First Three Bottlenecks at Scale ​

  1. CPU Saturation during ONNX Embedding Generation:
    • Bottleneck: Calculating 384-dimensional dense vectors via ONNX Runtime consumes ~12ms of CPU time per query. At 7,500 RPS, this requires 90 dedicated CPU cores.
    • Mitigation: Introduce an in-memory Redis vector cache caching embeddings for the top 5,000 common queries. For cache misses, deploy GPU-accelerated ONNX embedding sidecars using TensorRT.
  2. Python GIL Contention in Single Process:
    • Bottleneck: Python's Global Interpreter Lock prevents parallel execution of CPU loops across threads in a single process.
    • Mitigation: Run Uvicorn in multi-worker mode (--workers 4 per container) orchestrated by Kubernetes with horizontal pod autoscaling based on CPU utilization ($\ge 70%$).
  3. Frontend Connection Limit to Port 8000:
    • Bottleneck: The frontend hardcodes API_URL = "http://127.0.0.1:8000".
    • Mitigation: Externalize API URL to environment configuration (VITE_API_URL) and place behind an Nginx reverse proxy with connection pooling and HTTP/2 multiplexing.