Principle:PacktPublishing LLM Engineers Handbook REST API Serving

Field	Value
Concept	Exposing ML pipelines as REST APIs
Category	Infrastructure / API Serving
Workflow	RAG_Inference
Repository	PacktPublishing/LLM-Engineers-Handbook
Implemented by	Implementation:PacktPublishing_LLM_Engineers_Handbook_FastAPI_RAG_Endpoint

Overview

API Serving for ML is the practice of wrapping the RAG inference pipeline in a REST API endpoint using FastAPI. This provides a standard HTTP interface for client applications. The endpoint handles request parsing, pipeline orchestration (retrieval followed by generation), error handling, and response formatting. Observability integration (Opik) tracks prompts, latencies, and token counts.

Theory

REST API Design for ML

Serving ML models via REST APIs involves several design considerations:

• Request/Response schema - Using Pydantic models to define typed request and response structures ensures input validation and clear API contracts
• Synchronous vs. asynchronous - FastAPI's async support allows handling concurrent requests efficiently, though the underlying ML computation may be synchronous
• Error handling - ML pipelines can fail at multiple stages (retrieval, generation); proper HTTP error codes and messages help clients handle failures gracefully
• Statelessness - Each request contains all information needed to process it, enabling horizontal scaling

Pipeline Orchestration

The API endpoint orchestrates the full RAG pipeline in a single request:

1. Query reception - Parse the incoming JSON request
2. Context retrieval - Run the retrieval pipeline (self-query, query expansion, vector search, reranking)
3. LLM generation - Assemble context and generate the answer
4. Response formatting - Return the answer in the defined response schema

Observability

Production ML APIs require observability beyond standard web metrics:

• Prompt tracking - Recording the exact prompts sent to the LLM
• Latency breakdown - Measuring time spent in retrieval vs. generation
• Token counts - Tracking input and output token usage for cost monitoring
• Tracing - End-to-end request tracing through all pipeline stages

When to Use

• When exposing the RAG pipeline as a web service for client applications
• When building microservice architectures where the ML pipeline is a standalone service
• When clients need a standard HTTP interface to interact with the inference system
• When observability and monitoring of the inference pipeline are required

Related Concepts

• FastAPI - modern Python web framework with automatic OpenAPI documentation
• Pydantic - data validation and settings management using Python type annotations
• Microservice architecture - decomposing applications into independently deployable services
• API gateway - centralized entry point for API traffic management

See Also

• Implementation:PacktPublishing_LLM_Engineers_Handbook_FastAPI_RAG_Endpoint