Ghost in the Machine: Privacy-First Local RAG Microservice with FastAPI

Description and use

GhostInTheMachine implements a local RAG microservice on FastAPI, delivering instant vector search without cloud data transfer.

Problem

Cloud‑based AI APIs introduce latency, costs, rate limits, vendor lock‑in, and privacy risks when sensitive code or documentation is sent to third‑party endpoints.

Outcome

The microservice achieves sub‑millisecond retrieval speeds, retains full data control, and eliminates external API dependencies.

Workflow

• POST /ingest – tokenize, normalize, store text and metadata in memory
• POST /query – convert query to term vector, compute cosine similarity against stored vectors, return top‑k matches
• Aggregate retrieved contexts and synthesize response payload locally

Evidence

Cloud-based AI APIs have simplified adding intelligence to applications, but they come with significant trade-offs: network latency, recurring subscription costs, rate limits, and vendor lock-in.

Source

GhostInTheMachine RAG is an asynchronous Retrieval-Augmented Generation (RAG) microservice built with FastAPI and Python 3.13.

Source

The microservice processes incoming knowledge requests through a lean, three-stage local pipeline: Document Ingestion, Vector Similarity Search, Context Synthesis.

Source