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

说明与用途

GhostInTheMachine 在 FastAPI 上实现本地 RAG 微服务，实现即时向量搜索，无需云端数据传输。

问题

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.

结果

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

工作流

• 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

证据

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.

来源

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

来源

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

来源